llllll'illlilillllilililli Illilllil

3 1293 01397 9608

This is to certify that the

dissertation entitled

PHYSICAL AND HUMAN DIMENSIONS FOR INTEGRATED wASTAL
MANAGEMENI‘: Assessment of Coastal Cnanges and Resident
Knowledge Base in Coastal Areas of the Yucatan Peninsula .

presented by

Jorge Ivan Euén Avila
has been accepted towards fulﬁllment

of the requirements for

Doctor 0f Phi lOSOPhY degree in mnevelopnent

.%

Major professor

Date 7/16/97

MS U B an Afﬁrmative Action/Equal Opportunity Institution 0-12771

 

LIBRARY

Michigan State
University

 

 

 

PLACE [N RETURN BOX
to remove this checkout from your record.
TO AVOID FINE return on or before date due.

 

um ours we DUE DATE our
. 4;!- \
«Mug: 2

DEC 3 ($1998

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1M CICIWM

PHYSICAL AND HUMAN DIMENSIONS FOR INTEGRATED
COASTAL MANAGEMENT: Assessment of Coastal Changes and
Resident Knowledge Base in Coastal Areas of the Yucatan Peninsula.

by

Jorge Ivan Euan-Avila

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of:

DOCTOR OF PHILOSOPHY

SCHOOL OF AGRICULTURE
DEPARTMENT OF RESOURCE DEVELOPMENT

1997

ABSTRACT

PHYSICAL AND HUMAN DIMENSIONS FOR INTEGRATED
COASTAL MANAGEMENT: Assessment of Coastal Changes and
Resident Knowledge Base in Coastal Areas of the Yucatan Peninsula.

by

Jorge Ivan Euan-Avila

Coastal zones are under severe pressure as a result of multiple human
activities. Environmental degradation, coastal ecosystems' importance and
resource depletion require alternative policy for long-term sustainable development.
This study focuses on physical and human dimensions involved in ecosystem
perturbation and management .

This research analyzed data collected with remote sensors to determine
land cover changes in tropical mangrove wetlands and their spatial relationships
with roads and town developments. It also analyzed data of 139 interviews with
residents in the area to assess their knowledge base regarding: (i) the use and
non-use values of mangrove wetlands, (ii) attitudes in resource use, (iii)
awareness of environmental impacts of urban expansion, solid waste disposal in
wetlands, and coastal road networks, and (iv) acceptance of regulation.

Findings indicated: (i) a signiﬁcant loss of vegetation and soils, (ii)
increasing urban growth and (iii) differences between urban and rural residents

regarding the use, knowledge and attitudes towards coastal ecosystems and

resources. To receive the future beneﬁts from these ecosystems, specially for
those whose daily survival depend upon them requires an integrated management
program that accounts for: (i) the size and location of land cover changes in the
area, and (ii) the heterogeneity between the urban (Progreso and Prochub) and

rural (Chubuma and Chelem) coastal resident knowledge base.

To DIANA, RODRIGO and ANDREA

ACKNOWLEDGMENTS

This work would not have been possible without the assistance and
support of many people. Special gratitude is given to: Dr. Scott Witter for his
invaluable assistance and consideration during my courses and research; to Drs.
Daniel Brown, Carl Page, Frank Fear and Cynthia Fridgen for their useful
suggestions; and to all RD personnel at MSU.

My recognition to the team of interviewers Miguel Cabrera, Claudia
Leonce, Jorge Acosta, Domingo Blanqueto, Juan Zumarraga, Juan Tzec and
Hector Hernandez. Also to Mr. Jorge Luna and Mr. José Ortiz B. who
generously shared with us their coastal experiences, and to Biol. Héctor
Rodriguez for his assistance during wetland ﬁeldtrips. I also wish to thank my
colleagues Drs. Angeles Liceaga, Maria E. Vega, Ernesto Arias and Guadalupe
Mexicano for serving as judges, and to CINVESTAV and CONACYT for their
economic support .

Finally, I would like to give a special thank to Dr. Juan Carlos Seijo and his
family, for their sustainable encouragement during all those years and to Dr.
Manfred Thullen and his wife Dorothy and Drs. Pedro Herrera and Malaquias
Flores and their families for helping us in Lansing. To Joshua Stuart for his
invaluable assistant with the language. Last and most important, to my family —

Diana, Rodrigo and Andrea for all their love and patience.

TABLE OF CONTENTS

LIST OF TABLES .................................................................................................... x

LIST OF FIGURES ............................................................................................... xiv
Chapter 1

INTRODUCTION ........................................................................................................ 1

1.1 Background .................................................................................................... 1

1.1.1 Natural Resources and Coastal Zone Development ................................ 1

1.1.2 Coastal Ecosystems ................................................................................. 3

1.1.3 Sustainable Development in Coastal Areas ............................................ 4

1.1.4 Coastal Zone Management Experiences in OECD Countries ................ 6

1.1.5 Coastal Zone Management in Latin America ......................................... 7

1.1.6 Coastal Zone Management in the State of Yucatan, México .................. 9

1.1.7 Research Needs ..................................................................................... 10

1.2 Purpose of the Study .................... '. ............................................................... 13

1.2.1 Problem Statement ................................................................................ 13

1.2.2 Research Goals ...................................................................................... 14

1.2.3 Hypotheses ............................................................................................ 14

1.2.4 Objectives ............................................................................................. 16

1.2.5 Methodology Summary ........................................................................ 16

1.2.6 Assumptions ........................................................................................ 17
Chapter 2

LITERATURE REVIEW ....................................................................................... 19

2.1 Coastal Zone Management .......................................................................... 19

2.1.1 World Experiences ................................................................................ 19

2.1.2 Comprehensive Approach ..................................................................... 21

2.1.3 Policy Implementation and Integration ................................................. 25

vi

2.2 Coastal Zone Management in Latin America .......................................... 28

2.2.1 Regional Efforts .................................................................................... 28

2.2.2 Mexico's Environmental Policy and Law ............................................. 30

2.2.3 Project of Ecological Order in Mexico ................................................ 33

2.3 Ecosystems and Human Communities in the Coast ................................ 34

2.3.1 Mangrove Wetlands F ragility ............................................................. 34

2.3.2 Changes Affecting Mangroves ........................................................... 37

2.3.3 Coastal Land Use and Control ........................................................... 39

2.4. Data Collection and Analysis ..................................................................... 40

2.4.1 Natural System ...................................................................................... 40

2.4.1.1 Environmental Impact Assessment .............................................. 40

2.4.1.2 Data Type and Methods .............................................................. 44

2.4.1.3 Coastal Classiﬁcation and Detection ........................................... 46

2.4.1.4 Change Detection Procedures ...................................................... 50

2.4.1.5 Field Sampling and Thresholds ................................................... 53

2.4.1.6 Mapping and Precision ................................................................ 55

2.4.2 Social Systems ...................................................................................... 56

2.4.2.1 Knowledge Base .......................................................................... 57

2.4.2.2 Data Collection Methods ............................................................. 60

2.4.2.3 Population and Sampling ............................................................. 62

2.4.2.4 Data Analysis .............................................................................. 63

2.4.3 The study area ...................................................................................... 65

2.4.3.1 Boundaries ................................................................................... 65

2.4.3.2 Opportunities in the Yucatan Peninsula ....................................... 66
Chapter 3

RESEARCH DESIGN ............................................................................................ 69

3.1 General Approach ........................................................................................ 69

3.2 Analysis of the Natural System ................................................................... 70

3.2.1 Boundaries and Location ...................................................................... 70

3.2.2 Land Cover Classiﬁcation .................................................................... 72

3.2.3 Change Detection .................................................................................. 75

3.2.4 Linking Environmental Changes and Development ............................. 78

3.2.5 Criteria for Rejecting of the Null Hypothesis H1 ............................. 79

vii

3.3 Analysis of the Social System ...................................................................... 79

3.3.1 Resource Use and Ecosystem Environmental Services ....................... 79
3.3.1.1 Mangrove Use and Environmental Signiﬁcance
Concepts ....................................................................................... 80
3.3.1.2 Instrument Design for Mangrove Use and
Environmental Signiﬁcance ......................................................... 83
3.3.1.3 Criteria for Rejecting of the Null Hypothesis
H2a, H2b, and H2e ....................................................................... 87
3.3.2 Regulatory Interventions ...................................................................... 89
3.3.2.1 Attitudes Toward Long Term Resource Use ................................ 90
3.3.2.2 Awareness Regarding Threats to mangroves ............................... 90
3.3.2.3 Acceptance of Regulation ........................................................... 91
3.3.2.4 Instrument Design for Long Term Resource Use ........................ 92
3.3.2.5 Criteria for Rejecting of the Null Hypothesis
H2d, H2e and H2f ....................................................................... 95
3.4 Sampling Design .......................................................................................... 98
3.4.1 Population ............................................................................................. 98
3.4.2 Data Collection ................................................................................. 100
Chapter 4
RESULTS .................................................................................................................. 103
4.1 State of the Natural System ....................................................................... 103
4.1.1 Data Preparation .................................................................................. 103
4.1.2 Change Detection ................................................................................ 109
4.1.3 Coastal Land-Cover Changes H1 ........................................................ 116
4.1.4 Environmental Changes and Development ........................................ 122
4.2 State of the Social System .......................................................................... 128
4.2.1 General Information ............................................................................ 128
4.2.2 Use and Environmental Signiﬁcance Hypotheses H2a,
H2b and H2e ....................................................................................... 130
4.2.2.1 Direct Use (HZa) ........................................................................ 130
4.2.2.2 Indirect Use (H2b) ..................................................................... 133
4.2.2.3 Environmental Services (H2e) .................................................. 138
4.2.2.4 Similarities using H2a, H2b and H2e ....................................... 140
4.2.3 Regulatory Interventions Hypotheses H2d, H26 and H2f .................. 142
4.2.3.1 Attitudes Toward Sustainable Resource Use (H2d) ................... 142
4.2.3.2 Awareness Toward Mangrove Threats (H2e) ............................ 147
4.2.3.3 Acceptance of Regulation (H21) ............................................... 149
4.2.3.4 Similarities using H2d and H2e ............................................... 151

viii

4.2.4 Relating Physical Changes to Resident Knowledge

and Attitudes ...................................................................................... 153

Chapter 5
CONCLUSIONS AND RECOMMENDATIONS ....................................... 161
5.1 Assessment of the Natural System ............................................................ 161
5.1.1 Total Land Cover Changes and Road Proximity Map ........................ 161
5.1.2 Towns Land Cover Changes and Town Proximity Maps ................... 163
5.2 Assessment of the Social System ............................................................... 165
5.2.1 Knowledge about Mangrove Direct Use ............................................ 165
5.2.2 Knowledge about Mangrove Indirect Use .......................................... 167
5.2.3 Knowledge about Environmental Services ......................................... 171
5.2.4 Attitudes Toward Resource Use ........................................................ 173
5.2.5 Awareness Regarding Threats to Mangroves ..................................... 174
5.2.6 Acceptance of Regulation ................................................................... 176
5.3 Implications and Recommendations ........................................................ 177
APPENDIX A Instrument Survey Used in this Work ........................................ 185
APPENDIX B Dictionary of Variables .............................................................. 194
BIBLIOGRAPHY ................................................................................................... 198

a

LIST OF TABLES

Table 1 - Examples of major actions on the environment (Schultink 1992) .................. 41
Table 2 - Components of an environmental impact statement (Debuyst 1993) .............. 42
Table 3 - C-CAP coastal land-cover classiﬁcation system (Dobson et a1. 1995) ........... 48
Table 4 - Sample size from Rosenﬁeld et al. (1982) ..................................................... 55
Table 5 - Mangrove direct products ................................................................................ 57
Table 6 - Mangrove indirect products ............................................................................. 58
Table 7 - Mangrove wetland conversions ................................................................. ‘ ...... 5 8
Table 8 - Mangrove environmental services ................................................................... 59

Table 9 - Mangrove direct products included in the survey and the name of
the variables ................................................................................................... 81

Table 10 - Mangrove indirect products included in the survey and the name

of the variables ................................................................................................ 82
Table 11 - Mangrove ecological services included in the survey and the name

of the variables ............................................................................................... 82 \
Table 12 — Mangrove wetland conversions included in the survey and the name

of the variables ................................................................................................ 83
Table 13 - Mangrove direct products variables, explanation, derivation and type ......... 88
Table 14 - Mangrove indirect products variables, explanation, derivation and type ....... 88

Table 15 - Mangrove environmental services variables, explanation, derivation
and type ........................................................................................................... 89

Table 16 — Mangrove potential threats included in the survey ........................................ 91

Table 17 - Long term resource use variables, explanation, derivation and type ............. 96

Table 18 - Acceptance/Enforcement of regulation variables, explanation, derivation,
and type ........................................................................................................... 97

Table 19 - Awareness toward mangrove potential threat variables, explanation,
derivation, and type ........................................................................................ 98

Table 20 - Summary of geometric transformation to UTM system for both dates
based on 6 control poits and linear polynomial .............................................. 106

Table 21 - Equations for radiometric corrections of bands 4,5, 6, and 7 .......................... 109

Table 22 - Spectral signatures of coastal classes: 1) developed land (d1),
2) evergreen (eg), 3) unconsolidated wet soil (usl), 4) unconsolidated

dry soil (usZ), 5) deciduous (de), 6) dune vegetation (dv) ............................. 112
Table 23 - Training stage confusion matrix and Khat output statistics for February
1986 image ........................................................................................................ 114
Table 24 - Training stage confusion matrix and Khat output statistics for April
1990 image ........................................................................................................ 115
Table 25 - “F rom-To” matrix for coastal changes from 1986 to 1990 .............................. 116
Table 26 - Conditional and overall Kappa coefﬁcient of agreement (k) for land cover
changes ............................................................................................................... 118
Table 27 - Signiﬁcant change for coastal land cover proportions ...................................... 119
Table 28 - Differences between February 1986 and April 1990 per class .......................... 119
Table 29 - Area 1, Chuburna “ﬁ'om-to” matrix 1986 - 1990 ............................................. 120
Table 30 - Area 2, Chelem “from-to” matrix 1986 - 1990 ................................................ 121
Table 31 - Area 3, Progreso “from-to” matrix 1986 - 1990 ............................................... 121
Table 32 - Area 4, Prochub “from-to” matrix 1986 - 1990 ................................................ 122

Table 33 - General information for variables: age, sex, marital status, years of
resident, and schooling ....................................................................................... 128

xi

Table 34 - Contingency tables for mangrove direct use variables ﬁrewood, conwood,
and artwood ........................................................................................................ 130

Table 35 - Observed minus expected frequencies for mangrove direct use
variables ........................................................................................................ 131

Table 36 - Contingency tables for indirect use variables ﬁnﬁsh, crust, mollu,
birds, mamma, reptil, insects, and recrea ...................................................... 133

Table 37 - Observed minus expected frequencies for mangrove indirect use
variables ........................................................................................................ 135

Table 38 - Contingency tables for indirect use variables promat, soilpro, hurpro,
nesit, nurs, and clewa ... ................................................................................ 138

Table 39 - Observed minus expected frequencies for mangrove environmental
beneﬁts variables .......................................................................................... 140

Table 40 - Contingency tables for long term resource use variables: moral,
natura, ecoll, ec012, ecol3, scienc, negat, short, option, equity,
exist, extem, willi, and parti ............................................................................... 143

Table 41 - x2 values and p values for long term resource use variables: moral,
natura, ecoll , ec012, ecol3, scien, negat, short, option, equity, exist,
exter, willi, and parti .......................................................................................... 145

Table 42 - Observed minus expected frequencies for long term resource use.

Variables: short and extem .......................................................................... 145

Table 43 - Contingency tables for awareness to environmental threats variables
urgro, porin, roads, garba, trewa, oilspi, woode, ﬁshe and hunte ................ 147

Table 44 - Results )8, d.f., and p values for awareness regarding threats to
mangroves. Variables ugro, porin, roads, garba, trewa, oilspi, woode,
ﬁshe and hunte ................................................................................................... 148

Table 45 - Observed minus expected frequencies for awareness regarding threats to
mangroves. Variables: urgro and oilspi ...................................................... 148

Table 46 - Contingency tables for regulatory variables lgral, Icons, 1cont, lwood,
lﬁsh, lhunt, and ltran .................................................................................... 150

Table 47- Result of x 2values and p values for regulatory variables lgral, Icons,
lcont, lwood, lﬁsh, lhunt, and ltran .................................................................. 151

xii

Table 48 - Variables selected for relating natural and human variables ........................ 153

Table 49 - Summary of the social variables. Proportion of the “yes/agree”
answers, conﬁdent interval (+/-), x2 for homogeneity by town,
degree of freedom and probability level ....................................................... 159

Table 50 - Summary of the social variables. Proportion of the “yes/agree”
answers and 2 values for p = 0.5 vs. p 1: 0.5, or = 0.05
(accept the null hypothesis if abs (2) < 1.96) ............................................... 160

Table 51 - Departures from homogeneity for mangrove direct use: ﬁrewood,
home construction, and ﬁshing tool construction ........................................ 165

Table 52 - Departures from homogeneity for mangroves indirect products:
ﬁnﬁsh, crustacean and mollusk ..................................................................... 167

Table 53 - Departures from homogeneity for mangrove indirect products:
birds, mammals and reptiles ........................................................................ 169

Table 54 - Departures from homogeneity for mangrove environmental beneﬁts ......... 172

Table 55 - Departures from homogeneity for awareness regarding threats to
mangroves ................................................................................................... 175

xiii

LIST OF FIGURES

Figure 1 - General land use decision-making model (Rayner et al. 1994) ....................... 5
Figure 2 - Study area in the Yucatan Peninsula ............................................................. 13
Figure 3 - Conceptual model of land use decision-making (Rayner et a1. 1994) ............ 22
Figure 4 - Major contribution of mangrove detritus (from Odum, 1970 in

Mitsch and Gosselink 1993) ............................................................................ 35
Figure 5 - Mangrove species zonation in the Yucatan Peninsula

(after Morales 1992) ........................................................................................ 37
Figure 6 - Geographic characteristics of the studied area: 1) coastal

barrier, 2) coastal lagoon, and 3) mangrove wetland ...................................... 71
Figure 7 - Zonal classiﬁcation and categories of Progreso coastal area ............................ 75
Figure 8 - Mangrove wetland use format question .......................................................... 85
Figure 9 - Mangrove indirect product format question ......... 86
Figure 10 - Mangrove wetland conversion format question ............................................ 86
Figure 11 - Mangrove environmental services format question ...................................... 87
Figure 12 - Attitudes towards mangrove resource use format question ........................... 92
Figure 13 - Land use regulation format question ............................................................. 93
Figure 14 - Awareness of current menaces to mangrove wetland format question ......... 94
Figure 15 - Chuburna sample frame .............................................................................. 101
Figure 16 - Chelem sample frame .................................................................................. 101

xiv

Figure 17 - Prochub sample frame “a” ........................................................................... 101

Figure 18 - Prochub sample frame “b” ......................................................................... 102
Figure 19 - Progreso sample frame ................................................................................ 102
Figure 20 - Control point geographic positions for image corregistry .............................. 104
Figure 21 - UTM grid coordinate system for MSS images in the study area ................. 107
Figure 22 - Correlograms from independent variable (MSS, Feb 1986) .......................... 108

Figure 23 - Change binary masks for change vector analysis: a) distance of 13
for 81%, b) distance of 20 for 90%, 0) distance of 31 for 95%, and
d) distance of 56 for 99% ................................................................................. 1 10

Figure 24 - Change binary masks for vegetation index: a) from -.35 to .45 for 0 at 75%,
b) from -.45 to .74 for O at 80%, c) from -.55 to .84 for O at 85%, and

d) from -.75 to 1.34 for 0 at 90% ..................................................................... 111
Figure 25 - Total radiometric changes: Change vector at 90% conﬁdence interval (OR)

vegetation index at 85% conﬁdence interval ................................................... 1 11
Figure 26 - Classiﬁcation of changed areas for February 1986 (date 1) ........................... 114
Figure 27 - Classiﬁcation of changed areas for April 1990 (date 2) ................................. 115
Figure 28 - Selected areas for change detection: Chuburna, 2) Chelem,3) Progreso

and 4) Prochub .................................................................................................. 120
Figure 29 - Total land cover changes and road proximity map .......................................... 123
Figure 30 - Relationship between road distances and total land-cover change ................. 123
Figure 31 - Total new developed land and road proximity map ......................................... 124
Figure 32 - Total dune vegetation losses and road proximity map ................................... 124
Figure 33 - Total green vegetation losses and road proximity map ................................. 125
Figure 34 - Total unconsolidated soil losses and road proximity map ........................... 125

Figure 35 - Town proximity distances to selected areas in Chuburna, Chelem
and Progreso ................................................................................................ 126

XV

Figure 36 - Changes at different lags for new developed land (ndl), dune vegetation
(dv), evergreen losses (egl), and uncosnolidated soil losses (us) for
Chuburna, Chelem and Progreso (labels: x = lags, l lag = 180 m,
y = pixels) ................................................................................................... 127

Figure 37 - General information: a) age, b) sex, c) marital status,
(1) number of resident, and e) schooling ...................................................... 129

Figure 38 - Interaction plots of direct use for variables: a) ﬁrwood (ﬁrewood),
b) conwood (wood for constructions), and c) artwood (ﬁshing tool
constructions) ............................................................................................... 1 32

Figure 39 - Interaction plots of mangroves indirect use variables: a) ﬁnﬁsh,
b) crust, c) mollu, d) birds, e) mamma, t) repti, g) insect
and h) recrea ................................................................................................. 136

Figure 40 - Interaction plots of mangrove environmental services variables:
a) promat, b)soilpro, c)hurpro, d) nesit, e) nurs, Dclewa ............................. 139

Figure 41 - Similarities among residents in studied places: a) direct use,
b) ndirect use and c) environmental services provided by the mangrove
wetland ....................................................................................................... 141

Figure 42 - Interaction plots of long term resource use: a) short (short-term
exploitation) and b) extem (extemalities) .................................................. 146

Figure 43 - Interaction plots for threats to mangroves variables:
a) ugro (urban growth) and b) oilspi (oil spills) ......................................... 149

Figure 44 - Similarities between residents for: a) long term resource use, variables:
short and extem and b) awareness regarding threats to mangroves,

variables: urgro and oilspi ........................................................................... 152
Figure 45 - Vegetation losses and direct mangrove consumption ................................. 154
Figure 46 - Vegetation losses and mangrove ecological beneﬁts .................................. 155
Figure 47 - Vegetation losses and attitudes toward long-term resource use .................. 156

Figure 48 - Evergreen losses, dune vegetation losses and awareness toward threats
to mangrove wetland: 1) urban expansion (urgro), 2) port infraestructure
(porin), 3) roads (roads), 4) solid garbage dumps (garba) ........................... 157

Figure 49 - Evergreen losses, dune vegetation losses and awareness toward threats
to mangrove wetland: 1) untreated sewage (trewat), 2) oil spills (oilspi),
3) wood extraction (woode) and 4) ﬁshing intensity (ﬁshe) ........................ 157

Figure 50 - New developed land (ndl), evergreen losses (egl), dune vegetation losses
and acceptance of: l) enforced urban regulation (Icons), 2) enforced
wood extraction regulation (lwood), and 3) enforced hunting regulation
(lhunt) ............................................................................................................ 158

Figure 51 - Tree cluster analysis for mangroves indirect products: ﬁnﬁsh, mollusk
and crustacean ............................................................................................. 168

Figure 52 - Tree cluster analysis for mangroves indirect products: birds, mammals

and reptiles .................................................................................................. 170
Figure 53 - Tree cluster analysis for mangrove environmental beneﬁts ........................ 172
Figure 54 - Tree cluster analysis for awareness regarding threats to mangroves .......... 175

Chapter 1

INTRODUCTION

This chapter focuses on the importance of coastal ecosystems, resource depletion and
environmental degradation in the coast. It supports the need to devise a conect policy for long-term
development and environmental protection. It introduces data requirements for policy planning and
implementation and provides a description of the objectives and hypotheses that guided this work. It
also shows the opportunities provided by the coastal areas of the State of Yucatan to assess changes
in the wetlands and the perceptions of people who live there. Finally, it provides a summary of the
major procedures used in this study for data collection and analysis.

 

 

1.1 Background

1.1.1 Natural Resources and Coastal Zone Development.

The marine environment, including estuarine coastal areas, is an essential
part of the life-support system. Estuarine coastal areas are rich in natural
resources, diverse, and dynamic in biological and physical interactions. Estuarine
coastal areas are among the most productive ecosystems, the most beautiful
landscapes, and highly valued areas for agriculture, ﬁshing, recreation,
aquaculture, mineral extraction, and transportation. Resource demands on these
areas go back to earliest records of humanity, however the contemporary intense
and increasing urban, industrial, and agricultural activities have resulted in

excessive resource consumption, mountainous sites of waste, resource use

inefﬁciency, and destabilization of the coastal zone, with economic, social,‘and
environmental implications for present and future generations (Mayor 1991, Chiras
1992, OECD 1993a, Qing-nan 1987). Serious concerns regarding the scarcity of
essential fragile resources resulting from this intense development of the coastal
zone was evidenced during UNESCO's Coastal Marine (COMAR) meeting for
“Coastal System Studies and Sustainable Development” (UNESCO 1992).

Habitat losses, ﬁsh and other wildlife population declines, water
contamination and land degradation caused by large and dense settlements,
ocean-related industries, recreation and tourism, ﬁshing, and inadequate coastal
management have been clearly documented in the literature by Pimentel et al.
(1992), Hawker and Connell (1992), Saenger and Holmes (1992), Dennison et al.
(1993), Chua Thia Eng et al. (1989), Cronin (1992), Butler et al. (1993), Pearce
(1984), Boto (1992), Ongkosongo (1992), McClees (1993), Amett (1991), Yaﬁez-
Arancibia et al. (1992), Swaminathan (1992), Stigliani et al. (1993), McCain et al.
(1992), OECD (1993b), Suman (1994), and others.

Current efforts in management of coastal ecosystems and their resources
have not always stopped the impoverishment of the area. Consequently, there is
an increasing need to provide adequate information for precautionary and
anticipatory management programs. This need is critical at all levels of
management to avoid unacceptable environmental damage, maintain necessary
natural resources for future generations, and meet the needs of the present

population.

1.1.2 Coastal Ecosystems

Mangrove wetlands, seagrasses, lagoons, and other estuarine ecosystems
make the coastal zone rich in natural resources, beautiful landscapes, and are
ecologically important. In the equatorial region mangrove wetlands play a very
important role in controlling coastal hydrodynamics and sediment movements.
Mangroves also provide habitat for a wide range of dependent biota from plankton
to human communities (Lugo and Snedaker 1974, Odum and Heald 1975,
Parsons 1992, Boto 1992).

Unfortunately, mangrove wetlands are severely impacted by human
activities in coastal areas (Parson 1992). Increased settlement, aquacultural
developments, and nutrient loading are the main causes of their destruction
(Benites et al. 1992, Boto 1992, Butler et al. 1993). In spite of the ecological,
aesthetic, and economical values, and their role as an excellent indicator of an
ecosystem health, the coastal zones around the world have experienced
destruction and governments have difﬁculties in protecting the mangrove wetland
(Ongkosongo 1992, Semesi 1992, Lin and Fan 1992).

The primary reasons given for mangrove wetland destruction include: 1)
people's attitudes, 2) low market value of mangrove products, 3) incomplete
knowledge about the services provided by the mangroves, 4) property rights, 5)
negative externalities, and 6) political and administrative obstacles for their
management (Parsons 1992, Hamilton et al. 1989, OECD 1993a, Suman 1994).
As a result hundreds of hectares of mangrove wetlands are destroyed each year in

the world and in the Caribbean and Latin America there are considerable losses in

Mexico, Ecuador, Guatemala, El Salvador, Honduras, and Nicaragua (Boto 1992,
Butler et al. 1993, Suman 1994).
Caribbean and Latin American coastal areas contain approximately

2,400,000 ha of mangroves, 500,000 ha of which are in Mexico (Suman 1994).

1.1.3 Sustainable Development in Coastal Areas

Sustainable development (SD) is an increasingly important concept for
managers, policymakers, politicians, scientists, and the public interested in the
development and conservation of the coastal zone. The concept, deﬁned by the
World Conservation Strategy in 1980, illustrates their concerns for improving
peoples' livelihoods while at the same time maintaining the quality of the
environment. The Brundtland Report (1987), deﬁnes sustainable development as:
"development that meets the needs of the present without compromising the ability
of future generations to meet their own needs" (VVCED 1987, p43).

Several objectives and strategies for sustainable development were
recommended by the Conservation World Strategy in 1980, by the Brundtland
Report in 1987, and by the Rio de Janeiro Earth Summit Conference in 1992.
Common objectives included: 1) using natural resources at rates that do not
destroy or severely deplete the natural resource base, 2) preserving biological
diversity, and 3) producing wastes according to the assimilative capacity of the
environment. Suggested strategies include: 1) comprehensive approaches, 2)
community participation, and 3) agency coordination. Axinn (1988) suggests that a

comprehensive approach that includes biological, physical, cultural, social,

economic, administrative, political, and diplomatic forces; and their relationships, is
needed even for simple technological innovation. These forces represent a
complex system many times incompletely understood (Axinn 1988). A conceptual
model (Figure 1) developed by Rayner et al. (1994) to indicate the effects of land
use and land cover on global environmental changes shows this complexity. The
model links socioeconomic, political and cultural human behavior with land use
and land cover and their impacts on global environmental processes. The model
spells out the basic drivers, human aspirations and population and the inﬂuence of

the decision system on soil, water and vegetation.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

STUDIES or DECISION SYSTEM BIOGBOCHEMICAL
SOCIAL DRIVERS ANALYSIS ASSESSMENTS
G
L
0 GLOBAL POLITICAL “0:2"
2 ECONOMY F cm 5 ®
1. POPULATION
R I I
E‘.
(I; MICRO DECISION 25610:;
0 SYSTEM 4 m
N CHEMISTRY
A
L I
L
0 HUMAN
c ASPIRATIONS MICRO DECISION LAND ASSESSMENT
A SYSTEM «——> —@
L #

 

 

 

 

 

 

 

 

 

8

Figure l - General land use decision-making model (Rayner et al. 1994)

Difﬁculties for development and conservation of the ecosystem increase as
people's interests, capabilities, and perceptions are included in decision making.
Past experience indicates that if all these forces remain misunderstood and
unbalanced there is little hope for long-term development and conservation. The
dimension in complexity between nature and human society for the purposes of

sustainable development is clear in Baba Dioum's (Sénegal) statement:

"in the end we will conserve only what we love; we will
love only what we understand; and we will understand
only what we are taught" (in Lasserre 1992, p. 38).

1.1.4 Coastal Zone Management Experiences in OECD Countries

In the examination of their experiences, 16 countries in the Organization for
Economic Co-operation and Development (OECD) summarize the causes of
coastal zone problems in three areas: policy deﬁciencies, intervention deﬁciencies,
and market failures (OECD 1993a). The problems are the result of: 1) poor
policies, poor implementation, and unresolved conﬂict between or among policies,
2) inadequate data, predictions, and monitoring, 3) lack of agency coordination
and demarcation of responsibilities, 4) under-valuation Of resources, 5)
externalities, 6) lack Of long-run cost, and 7) past ignorance and mistakes (OECD
1993a).

Common conclusions from the above indicate that lack of information and
insufﬁcient scientiﬁc understanding adversely affect the selection of policy

instruments for coastal zone management. Studies by the World Bank also show

that Instrument selection is inﬂuenced by cultural perspectives, environmental
perceptions, personal biases (based on life-stage, education, ability, and
personality), and political motivations (World Bank 1992). The range of
instruments used by 16 OECD studied countries included: zoning, permits, taxes,
bonds, limiting discharges, as well as other developmental controls.‘ Instruments,
currently used, are heavily oriented towards regulation (i.e., zoning). Improving the
availability and usability of information for decision makers, and selecting adequate
monitoring programs are fundamental for informed and anticipatory governmental
actions (i.e., selection of the right mix of regulatory and economic instruments and

extending the types of management instruments).

1.1.5 Coastal Zone Management in Latin America

Problems in Latin America are similar to those experienced by OECD
countries. Olsen and Gordon (1992) identiﬁed that the major characteristic limiting
the initiatives to improve management in coastal areas of Central America is the
absence of effective implementation. Among common impediments with OECD
countries (i.e., fragmented authority, overlapping jurisdictions, etc), the short
tenure of administrators and technical personnel and the absence of a
constituency are major fundamental problems (Olsen and Gordon 1992).

Coastal zone management programs implemented in Latin America use
different strategies, instruments and have different levels of development:

1) "The shoreland restrictions" approach implemented in Costa Rica's

program for coastal zone management (1977), law 6043, was a response to

public complaints regarding coastal degradation caused by unplanned residential
and tourism developments (Sorensen et al. 1990). In response to the problems
inherent in a national program, Mack et al. (1992) indicated that for Law 6043 to
be successfully implemented land tenure and land use studies are of priority.
Furthermore, that there is a need to identify priority areas and to implement and
utilize plans, education and training at a local level.

2) Cuba in 1981, enacted law No. 33 to provide basic principles for
environmental protection and the rational use of natural resources in response
to natural and anthropogenic impacts. Included in this law is a system of
protected areas. The law designated the Ministry of Agriculture (MINAGRI) as
the responsible agency in the protection of ﬂora and fauna. More recently
(1991), a multidisciplinary group from several Cuban organizations has written a
law-decree that focuses on two management belts running parallel to the coast:
1) the coastal zone and 2) the buffer zone (Menéndez et al. 1994).

3) Ecuador has explored strategies to implement coastal management
programs in the context of third world nations, this means that close attention
was given to cultural and government traditions in the design of effective CZM
strategies. The program was conducted by the University Of Rhode Island as
part of the "Coastal Resource Management Project". (Merschrod 1989). More
recently and focusing on the mangrove wetland management, Bodero (1994)
recommended integrated programs to include: protection, management for

multiple users, restoration, ecotourism, education and research.

4) Mexico enacted in 1988 "Ley General del Equilibrio EcolOgico y la
ProtecciOn al Ambiente (LGEEPA)" (SEDESOL 1988b). This general law provided
the basis for the management of the natural resources for sustainable
development and environmental protection. The Secretariat of the Environment,
Natural Resources, and Fisheries (Secretaria del Medio Ambiente, Recursos
Naturales y Pesca (SEMARNAP)) is responsible for the formulation and
enforcement of the environmental policy. The law provided several instruments
such as: ecological order (zoning), promoting development with ecological
criteria, environmental impact assessment (EIA), Ofﬁcial technical norms (NOM),
natural protected areas, research, education and information (Chap. V,
LGEEPA). Loa (1996), in relationship to the management and conservation of
coastal wetlands, identiﬁed several problems in two areas Of current legislation:
1) incomplete body of speciﬁc laws and regulations and 2) non-enforcement of
speciﬁc regulations. He said that several factors contribute to a weak
enforcement of the regulations such as: non-systematic use of the regulations,
partial use of regulations, use of out of date regulations, unclear demarcation of

responsibilities, and poOrly informed users and the public.

1.1.6 Coastal Zone Management in the State of Yucatan, México

In 1995, the Consejo Estatal de Consultoria Ecologia (CECE) identiﬁed the
need to formulate an integrated management plan in the coast primarily due to: 1)
the growing socio—economic role of the coast, 2) the increasing coastal human

population (immigration of the rural poor) from agricultural areas, 3) the biodiversity

10

of the area, 4) failure to consider the ecological beneﬁts of the ecosystems , 5)
short run beneﬁts and 6) limits imposed by the ground water system (Flores et al.
1995). In addition to the management of commercial ﬁsheries, the coast of the
State Of Yucatan has four protected areas: Rio Lagartos (48,000 ha), Celestun
(60,000 ha), Las Bocas de Dzilam (62,000 ha), y El Palmar (50,000 ha). Total
protected area is approximately 220,000 ha. Outside of these protected areas
important ecological and economic areas exist that must be studied to be

considered in an integrated program. Changes in the LGEEPA have given to
Mexican states an opportunity to broaden their own local environmental
legislation (e.g., The Yucatan Law for Environmental Protection). Current local
legislation in the state of Yucatan is under revision and today, a growing
awareness exists that local legislation must be changed to provide appropriate

and speciﬁc indications to meets the needs Of the local conditions‘.

1.1.7 Research Needs

The United Nations World Commission on Environment and Development,
the U. S. National Science Foundation, and the Social Science Research Council,
have emphasized the need for interdisciplinary research efforts to achieve a long-
term quality of life and halt environmental impoverishment in the world (Stonich
1992). Research needs include: 1) gaining a better understanding of the
biophysical coastal processes, 2) relating human activities to coastal natural

resource changes, 3) gaining knowledge regarding local people's interest,

 

1Minutes of the discussions for modifying the “Ley de Proteccibn al Ambiente del Estado de

11

attitudes, and behavior in the use and management of the coastal zone, and 4)
how to integrate social, biophysical, and economic factors needed for sustainable
coastal zone management programs. These research needs are essential given
that the United Nations estimates that 2 out of 3 people will live within a strip 60
kilometers wide along the coast by the year 2000 (Swaminathan 1992). This
means that 75% of the world's total population will be living on 8% of the earth's
surface increasing the pressure on coastal natural resources including fragile
ecosystems.

The United Nations conference on Environment and Development in Rio de
Janeiro encourages Coastal States to give priority to improving their capacity to
collect, analyze, assess and use information for management purposes (United
Nations 1992). Developing countries like Mexico face several problems of limited
information regarding the state of their natural resources, human activities that
affect their natural resources, and human perceptions regarding the use of the
natural resources when there is public pressure for increasing their use. In the
Yucatan Peninsula mangrove wetlands are being destroyed and threatened by: 1)
conversion to urban areas, aquacultural systems, and port infrastructure, 2)
unenforced and unregulated use of mangrove wetland products, 3) increasing
pollution from industrial and residential sewage and solid wastes, and 4)
modiﬁcation of hydrological regimes because Of the construction of roads and
ditches (Zizumbo 1986, Benites et al. 1992, Valdes 1995). These factors have

reduced the mangrove wetlands, polluted the water and affected other dependent

 

Yucatan”. COPLADE, Merida, Yucatan, February, 1997.

12

resources in the area. This situation has environmental, and socio-economic
implications for the local communities.

In the Yucatan Peninsula mangrove wetlands located in the coastal areas
of Campeche, Yucatan, and Quintana Roo, support important commercial shrimp
ﬁsheries (e.g., Penaeus duorarum) and subsistence ﬁsheries: (e.g., Fundus
grandissimus, Cichlasoma urutalmus, Centropomus undecimalis). In addition
mangrove wetland support a high animal diversity (e.g., Phoenicopterus mber,
Fragata magniﬁcens, Porphyrula martinica); protect the coast from erosions; are
a source of energy; and are sink for nutrients and other substances released
from human activities.

The wetlands in the Yucatan Peninsula are among the most important
pristine ecosystems in this hemisphere? To face the conservation-development
dilemma in the Yucatan Peninsula, information from social, biophysical, and
economic dimensions must be available for decision-making. The ecosystem and
local human communities need to be properly assessed and incorporated into
planning for the long-term beneﬁt of both the ecosystem and the inhabitants of

coastal areas of the State of Yucatan. Proposed study area is shown in Figure 2.

 

2 Described by Dr. Guy Baldasarre, New York State University (NYSU) during his conference on
"Ecology and Wetlands Management" Cinvestav, Merida, Yucatan, March 1994 .

 

 

 

 

 

 

 

58
56
54
52
51]
UTM: Zxx 000 E, 2310: 0111] N, 16, N
Figure 2 - Study Area in the Yucatan Peninsula
1.2 Purpose of the Study

1.2.1 Problem Statement

Unintegrated environmental and developmental decision-making at the
policy planning and management levels inﬂuence the actions of all groups in
society with implications for the efﬁciency and sustainability of development
(UNCED 1992). Critical to designing and implementing enforceable and effective
coastal policy that is economically efﬁcient, socially equitable, responsible, and
environmentally sound, is the need to: (1) assess natural resources, (2) relate
human activities to coastal natural resource changes, and (3) gain knowledge

regarding local people.

14

1.2.2 Research Goals

For the purpose of this study the following goals are proposed:

(A) To assess the extent of physical changes of the coastal ecosystems
including the mangrove wetland.

(B) TO determine the knowledge base Of individuals that live in the area
as it relates to mangrove wetland use and environmental signiﬁcance
in coastal ecosystem performance.

(C) To identify critical factors that inﬂuence successful implementation of

coastal management plans.

1.2.3 Hypotheses

A better understanding of the natural system must begin with the analysis of
the extent and rate of major changes in coastal areas. The land cover is
constantly changing due to natural process, infrastructure (roads, urban and
industrial areas, piers) and human activities (agriculture, salt extraction). These
changes have a profound impact on resources and the environment as well as on
human populations living in the coast. In the absence of data for coastal areas of
the State of Yucatan, indicating where, when, what and the extent of coastal
changes, current data can only suggest that coastal deterioration of the
environment is occurring. For these reasons, the following hypothesis (H1) has

been proposed:

H102

H113

15

There has not been signiﬁcant changes in the coastal areas of
Progreso, Yucatan, Mexico from 1986 to 1990.
There has been signiﬁcant changes in the coastal areas of Progreso,

Yucatan, México from 1986 to 1990.

In looking at the social system, important drivers for the use of natural

resources and environmental protection are the human aspirations (needs and

wants) and the public policy. An appreciation and analysis of the knowledge base

of residents in coastal areas will assist to devise alternative policies and instrument

implementation in a coastal management program. The following hypothesis (H2)

has been proposed to guide part of this work:

H202

H213

Permanent residents from Chicxulub, Chelem, and Progreso and
non-permanent residents along the coast are not signiﬁcantly
different from each other in their individual knowledge base related
to: 1)the use and environmental signiﬁcance of the mangrove
wetland, and 2) their attitudes for long term resource use and in

accepting potential regulatory interventions for their management.

Permanent residents from Chicxulub, Chelem, and Progreso and
non-permanent residents along the coast are signiﬁcantly different
from each other in their individual knowledge base related to: 1) the

use and environmental signiﬁcance of the mangrove wetland, and 2)

16

their attitudes for long term resource use and in accepting potential

regulatory interventions for their management.

1.2.4 Objectives

The research Objectives are:

(1) To quantify changes in mangrove wetland and dunes from 1986 to
1990 in coastal areas of the municipality of Progreso, Yucatan.

(2) To identify critical converted or impacted mangrove wetland and
dune areas.

(3) To identify residential, industrial, commercial and infrastructure
developments linked to the physical destruction of the mangrove
wetland.

(4) To determine people's knowledge about mangrove wetland uses,
and mangrove environmental signiﬁcance.

(5) To determine people's attitudes to accept regulatory management
instruments to protect the mangrove wetlands.

(6) To use a geographic information system to analyze and integrate

data from the natural, and social systems.

1.2.5 Methodology Summary
Changes in coastal areas were detected when a change detection analysis
was performed using two Landsat Multispectral Scanner (MSS) satellite images

taken 4 years apart. Remote sensing (RS) and geographic information systems

17

(GIS) techniques were used in the analysis of the images. Changes were detected
by subtracting and dividing corrected radiometric values from two images and
thresholds used to record signiﬁcant changes. Mangrove wetland cover was
calculated from both images. The resulting thematic cartography of the area and
changes were employed to quantify losses in the mangrove wetland. These data
and results from the change detection analysis were used to test hypothesis H1.
Observed changes in mangrove wetland and dune cover was linked to human
actions from residential, commercial, industrial and infrastructure developments.
Individual knowledge about mangrove uses and environmental signiﬁcance
was collected using survey data. Attitudes toward long term resource use and the
acceptance of management instruments were measured with a self report Of the
type suggested by Henerson et. al., (1987). Indicators for the variables of interest
were integrated in a questionnaire and applied to four sampled groups (i.e.,
Chuburna, Chelem, Progreso, and Prochub). Data were statistically analyzed to
test hypothesis H2. The sample frame was constructed using a GIS to establish
the spatial boundaries of the areas. The number and location of the samples was
drawn from a stratiﬁed aligned design according to the size of the area, economic

resources and the time available for the research.

1.2.6 Assumptions
This study was based on ﬁve assumptions: 1) that sustainable development
of the coastal zone through integrated coastal zone management (ICZM) is

desirable; 2) that public and private citizens in coastal areas of Yucatan, Mexico do

18

play key roles in modifying the coastal environment are interested and willing to
engage in sustainable management practices, 3) that the national and state
governments are interested in promoting and enforcing the policies that promote
sustainable management practices; 4) that the study area has many of the
problems associated with coastal zone management in Yucatan; and 5) that the
results of this study will expand the knowledge base needed to achieve an
integrated coastal zone management program in the Yucatan peninsula.

Data collected from interviews, reports, proposals and meetings indicated
that ICZM is desirable for the coastal areas of the State of Yucatan. Residents,
decision makers, and politicians have all manifested their willingness to participate
and promote an integrated approach to solve coastal problems associated with
resource use and environmental protection. The studied areas (Chuburna,
Chelem, Progreso and Prochub) have also shown that many of the problems
associated with the loss and degradation of the mangrove wetland in the Yucatan
coast can be attributed in part to: 1) increasing urban developments, 2) road and
port infrastructure and 3) solid garbage dumps. These increasing developments
are also inﬂuenced by the knowledge and attitudes of the residents living there.
An appreciation of the differences between these studied towns may assist in

deﬁning a better policy for the purpose of coastal management.

Chapter 2

LITERATURE REVIEW

 

The central focus Of this study is tO gain knowledge about the state Of coastal natural
resources, human activities that affect coastal ecosystems, and public knowledge as factors for
implementing management instruments in coastal mangrove wet/ands. This chapter provides
support for? 1) the need tO integrate biological, physical, economic, and cultural infonrration for policy
design, correction, and/or implementation, 2) the need tO gain knowledge about the state Of natural
resources, human activities, and individuals knowledge and attitudes as key elements for decision
making in coastal management, 3) the selection Of appropriate tools to collect and analyze data
from the natural, and social systems, and 4) the selection Of the proposed study area.

2.1 Coastal Zone Management

2.1.1 World Experiences

Resource and environmental management is one of the most important and
challenging coastal problems. Contemporary development Of the world’s coastal
areas has resulted in: 1) excessive resource consumption, 2) resource use
inefﬁciency, 3) encroachment and poverty, 4) increasing waste streams, 5) water
contamination, 6) soil erosion, 7) loss of biodiversity, 8) reductions in ﬁsh and
wildlife populations, and 8) degradation and loss Of habitats (Pimentel et al. 1992,
Hawker and Connell 1992, Saenger and Holmes 1992, Dennison et al. 1993,
Chua Thia Eng et al. 1989, Cronin 1992, Butler et al. 1993, Pearce 1984, Boto

1992, Ongkosongo 1992, McClees 1993, Amett 1991, Yaﬁez-Arancibia etal.

19

20

1992, Swaminathan 1992, Stigliani et al. 1993, McCain et al. 1992, OECD 1993a,
Suman 1994, Moffat and Linden 1995).

These problems have been attributed by Chua and White to: 1) population
growth, 2) poverty, 3) short term economic gains, 4) lack of awareness about
management for resource sustainability, 5) lack of understanding of the full
economic contributions of coastal resources and 6) absence of governmental
support and enforcement (Clark 1992). Similarly, a report from OECD countries
identiﬁed deﬁciencies and failures in policy, intervention and the market as the
causes of coastal zone management problems. In particular the following
problems were highlighted: 1) absence of policies, 2) contradictory policies, 3)
improper management strategies, intervention mechanism and instrument
deﬁciencies, 4) lack Of adequate information and monitoring, 5) lack Of
coordination, 6) unclear demarcation of responsibilities, 7) pricing that does not
reﬂect full resource value, 8) externalities, and 9) failure to incorporate long run
costs (OECD 1993a).

TO correct or at least to mitigate environmental damage and inefﬁcient
resource allocation in the coast, there is a need to establish a management
policy based on scientiﬁc knowledge from both the natural and social systems.
Political commitment, community participation and power for implementation are
also needed (OECD 1993a). The United Nations Conference on Environment
and Development at Rio de Janeiro encouraged coastal states to seriously
improve their capacity to collect, analyze, assess and use information for
management purposes (United Nations 1992). This was supported by a number

of participating countries and the World Bank, who felt that lack of information

21

and insufﬁcient scientiﬁc understanding has adversely affected coastal
management. In addition to economic and ecological factors, other important
management considerations are the cultural perspectives; environmental
perceptions; personal biases based on life-stage, education, ability, and

personality; and political motivations of all the participants (World Bank 1992).

2.1.2 Comprehensive Approach

A conceptual model developed by Rayner et al. (1994) for the study of
land use/cover change that relates social drivers, political and economic decision
systems, and the environment is used as a framework (Figure 3). The model
was selected to describe the human and bio-physical dimensions and their
relationships for the purpose of coastal zone management.

Relationships in the model are described by Rayner et al. (1994) at three
levels: local, regional and global. The model stresses at the local level the
importance of human needs, wants, perceptions, attitudes and values in
motivating behavior, as well as population as basic drivers for resource use.
Feedback loops inﬂuence human characteristics according to our understanding
of the political and economic decision system (shown in the middle of the
diagram) and also by the modiﬁed climatic conditions (right hand side of the

diagram) (Rayner et al. 1994).

22

 

 

 

 

 

 

 

 

 

 

 

 

 

 

,_____
I GLOBAL
GLOBAL POLITICAL
l
l _, ,
: POPULATION mums Emma“
I ,
, I I
MACRO DECISION REGIONAL CLMIE/cmasmy
uz__KLL

STRUCTURE AIP REGIONAL
CHDIISTRY CLIMATE

 

 

 

 

 

 

 

 

 

 

l PUBLIC POLICY |

 

 

 

 

 

 

HUMAN ASPIRATIONS

I

MICRO DECISION

 

 

CULTURE

NEEDS

\Nl'.‘
{ms H

 

 

 

CONSUMPTION PRODUCTION
DEC"; RAP H'r'
ANLI

CLIMATE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

8

Figure 3 - Conceptual model of land use decision-making (Rayner et al. 1994)

23

The natural system as modeled by the land assessment block, which
shows the natural processes and changes due to the inﬂuence of the decision
system. For the purposes of coastal management the model provides a clear
and complete description Of the major components of the system and their
relationships between man and nature. This model shows the high level of
complexity in the system requiring information from several disciplines in order to
design a correct and proactive coastal policy to prevent and reduce the negative
environmental effects caused by humans. There is also the need for holistic
approaches and adequate monitoring programs to better understand the system.

Among others, holistic approaches that have been presented for the
purpose of management are: 1) ecosystem management, 2) biodiversity
management, 3) ecosystem health, 4) adaptive management, and 5) integrated
management. These management schemes share several characteristics: 1)
hierarchical context, 2) ecological understanding of the system, 3) data
collection, 4) monitoring, 5) adaptive management, 6) interagency cooperation,
7) organizational change, 8) user participation and 9) values (Grumbine 1994).

As suggested by the model major dimensions to be assessed for
integrated natural resource management are: a) the natural system and b) the
social system.

a) The natural system, represented by land assessment module, indicates
the need to collect physical, biological and ecological data about the biophysical
processes in order to understand: 1) physical alteration, 2) rates of replacement,
3) maintenance of ﬂoral and faunal population levels and sustainable yields, and

4) changes in one resource that have an impact on other resources. This

24

information is critical to relate the impacts of consumption/production decisions
on resource changes (OECD 1993a).

This need has been supported by Balkas et al. (1993), in his discussion of
environmental considerations in CZM in the Izmir Bay in Turkey where a baseline
monitoring program and an environmental impact assessment study were
conducted. Additional studies were recommended for assessing the assimilative
capacity of the bay and preparing environmental impact assessment for the new
water treatment system. Also in Norway, the LENKA project was established "as
a standardized, efﬁcient, integrating mechanism to provide coastal zone planners
with an information base on aquatic capacity to aid in making choices as to the
best use of the coastal zone" (Stewart et al. 1993, p. 271).

b) The social system, represented by the human aspirations block, shows
that the key variables to be considered for natural resource use decisions are: 1)
needs, 2) wants, 3) willingness and unwillingness to pay, 4) perceptions, 5)
beliefs, 6) attitudes and 7) values. The analysis of cultural variables helps us to
understand the transformation of basic needs into goals and values of activities
in the decision system that transform the natural environment (Rayner et al.
1994)

In the United States (1972) a federal-state program was created for the
management and protection of coastal resources called the Coastal Zone
Management Act (CZMA). According to Imperial et al. (1992), ﬁve programs: 1)
the Delaware River Basin Commission, 2) the federal river basin commissions, 3)
the section 208 area-wide waste treatment planning, 4) the federal coastal zone

management program, and 5) the Chesapeake Bay Program have, during the

25

past 25 years represented the best effort in management of the United State Of
America coastal zone. From these studies several lessons were learned: 1)
coastal environmental planning initiatives must have an interested audience, 2)
plans and policies must address important public‘issues, and 3) successful
programs included all the relevant actors affected by the plan (Imperial et al.
1992)

Clark (1992) suggests that for an Integrated Coastal Zone Management
(ICZM) program to succeed interested groups must be involved if new coastal
policies and rules are to be supported. It is argued that local communities
support sustainable and ecologically sound uses, but this tradition is being lost in

modern and overpopulated areas. Clark (1992 p. 60) indicates:

When people decide tO "participate", they as
resource users will make the real difference in
protected area management. The solution thus lies in
helping people to decide tO participate in a
constructive manner. Once resource users decide tO
dO so and receive the associated beneﬁts, the
process will perpetuate itsel

2.1.3 Policy Implementation and Integration

A common practice when resource use patterns are out of balance or
socially undesirable is to use non-market institutions (Emel and Peet 1989).
Current public policies for coastal management are based on strategies and
methods that help to organize natural resource use and environmental
conservation. Coastal states in developed and developing countries have
adopted a comprehensive and integrated approach for policy development and

implementation called integrated coastal zone management (ICZM) or integrated

26

coastal area management (ICAM) (Imperial et al. 1992, OECD 19933, Boelaert-
Suominen and Cullinan 1994, Sorensen et al. 1992, Flores et al. 1995). ICZM is
one Of the several systems that attempt to central development that negatively
impacts resources and the environment (Clark 1992). ICZM, in response to
ineffectiveness of sectoral approaches, considers the functioning Of the coastal
system as a whole with implications and challenges to current legal perspectives
and mechanism implemented for the management of the coast.

In 1994 during the workshop on integrated coastal management at the
Bedford Institute Of Oceanography, Biliana Cicin-Sain, University of Delaware
and Editor of the Ocean & Coastal Management Journal, described the meaning
of Integrated Management. She addressed the several dimensions of
integration in the context of the ocean and the coast: 1) among sectors, 2)
between coastal land and coastal waters, 3) among levels of government, 4)
between nations, and 5) among disciplines. She said that integration in this
context is a continuous process that moves from less-integrated to more-
integrated situations as they pass from fragmented approaches, communication,
coordination, harmonization, and integration (100 Seminar on Integrated Coastal
Management 1995)

She also stressed the need to identify factors and incentives to facilitate
integration and described how local and national perspectives and knowledge
are different. She said that the local community concentrates on economic well-
being, infrastructure needs and quality of life and the locals know best the local
needs and have the best information and understanding of the area. The

national perspective, on the other hand, is concerned with the need to provide

27

orientation to lower levels and uniform approaches for the entire nation (lOC
Seminar on Integrated Coastal Management 1995). This suggest that
appropriate approaches must consider the local social, economic and cultural
conditions.

To implement coastal zone management policies, two categories of
instruments are of the greatest importance today: regulatory and economic.
Regulatory instruments attempt to regulate process and products. by limiting
discharges or restricting activities in time and space. Land use zoning is a
regulatory instrument used to designate dominant uses at the regional level and
speciﬁc land uses at the local level. At the local level, zoning speciﬁes
residential, industrial, commercial, recreational, reserves and parks areas.
Zoning, well implemented, may contribute to reduced urban expansion, aesthetic
controls, separation of incompatible activities, and protection of important natural
areas (OECD 1993a). Disadvantages are lack of ﬂexibility and in the economic
and environmental inefﬁciencies that can result in suboptimal allocation of land
uses.

Economic instruments, on the other hand, attempt to stimulate desired
actions with the use of ﬁnancial stimuli including: charges, subsidies, market
creation, and ﬁnancial enforcement incentives. Economic instruments can be
cost effective and easy to modify, but they are Often incapable of fully
incorporating environmental values (OECD 1993a). Effective management of
the coast requires knowledge about public preferences regarding management

alternatives and must be incorporated by decision makers. Knowledge of public

28

beliefs and preferences are required for new management models to select an
appropriate mix of regulatory and economic policies.

Additional instruments to promote ICZM objectives discussed by Boelaert-
Suominen and Cullinan (1994) are: exclusion zones, public ownership,
expropriation and acquisition , environmental impact assessment, environmental

compensation and recognition of traditional and/or indigenous rights.

2.2 Coastal Zone Management in Latin America

2.2.1 Regional Efforts

Countries in Latin America have reached different levels in Coastal Zone
Management (CZM). For example, the “shoreland restrictions” approach,
implemented in Costa Rica's program (Law on the Marine and Terrestrial Zone
6043, 1977), divides the coastal zone in two areas: the public zone (50 meters
inland from the high tide mark) and the restricted zone (additional 150 meters)
(Sorensen et al. 1990, Mack et. al. 1992). This law attemptsto assure public
access and regulate development in the marine terrestrial zone. Law 6043
excludes from the restricted zone important areas such as estuaries and
mangrove wetlands. In spite of the dynamic character of this Law, 200 meters of
the coast may not be sufﬁcient to control deforestation, pollution, and
environmentally unsound uses (Mack et al. 1992). Honduras has nine protected
areas on its coasts, only one of the areas currently has a management plan
(Oyuela 1994). Two increasing industries in Honduras are mariculture and

tourism. Mariculture has consumed more than 5,000 hectares of coastal areas to

29

install shrimp ponds. The development of ponds, wood to fuel salt ovens, and
domestic consumption have resulted in the destruction of mangroves and loss Of
access to traditional ﬁshing grounds. Even with the institutional framework and
agency efforts to control human activities along the coast weaknesses in reviewing
applications, granting permission, training personnel in resource management,
monitoring, and fostering public education still exist (Foer 1992).

In Panama, public lands on the coast extend from the high tide to 200m
inland (COdigO Agrario de 1962, articulo No. 116). Executive Decree #39 (1966,
articles 8 and 34) prohibits the destruction of trees that regulate the ﬂow of water,
prevent erosion, and protect unique species located within 30m of river banks,
lagoons, and lakes respectively. Forest Law No.1 enacted in 1994 requires a
license for wood harvesting and building aquacultural ponds in mangrove
wetlands. Law No. 21 (1980) regulates pollution from boats with ﬁnes up to
$200,000 for spilling toxic substances. Unfortunately, these laws are unknown to
the public and, many times by the ofﬁcials charged with their enforcement. Osoric
(1994) concludes that it is unclear when mangroves are included in the current
laws, sometimes it is necessary to deduce it. Because Of these and other
difﬁculties, a strategy for coastal management was recommended in 1992. Part of
the management needs were: 1) to deﬁne critical habitats; determine adequate
measures to preserve ecological process, ecosystems and biodiversity; 2) to
design strategies to establish a coastal tourism industry which causes the least
possible disturbance to the environment while maximizing the social beneﬁt; 3)
develop a set of legal standards for coastal buildings; and 4) public education

(Vasconez 1991 ).

30

Ecuador uses a strategy based on 'Working Groups" developed by the
University of Rhode Island as part of their "Coastal Resource Management
Project". This approach combines ﬂexibility and informality in procedures, and
facilitates maximum participation and at the same time avoids institutional rigidity.
Cultural and governance traditions in Ecuador are different from ﬁrst world
traditions and are considered crucial to the design of effective coastal zone

management strategies (Merschrod 1989).

2.2.2 Mexico's Environmental and Natural Resource Policy and Law

In the Mexican constitution, article 27 indicates that the property Of the
land and water belong, originally, to the nation, which has the right to transfer
them to individuals to become private property. The Constitution indicates that
public and private property has to be regulated because of public interest. This
is in the interest of an equitable distribution of the wealth from natural resources,
resource conservation, balanced development, and improvement in the life styles
of the rural and urban population (ConstituciOn Politica de los Estados Unidos
Mexicanos 1995).

Laws and regulations related to article 27 of the Mexican constitution with
relevance to the management of the coast are dispersed in the following laws: 1)
Ley de Aguas Nacionales (National Water Law), 2) Ley de Pesca (Fishing Law),
3) Ley de Caza (Hunting Law), 4) Ley Forestal (Forest Law), 5) Ley Minera
(Mining Law), 6) Ley Federal del Mar (Federal Marine Law), 7) Ley de
ConservaciOn del Suelo y Agua (Soil conservation and Water Law), 8) Ley

General de Asentamientos Humanos, (Human Settlement General Law), 9) Ley

31 .

General de Bienes Nacionales (National Property General Law) , 10) Ley de
PlaneaciOn (Planing Law), 11) Ley sobre Metrologia y Normalizacicn (Metrology
and Normalization Law), y 12) Ley General de Equilibrio ECOIOQICO y la
ProtecciOn al Ambiente (General Law for Ecological Equilibrium and
Environmental Protection).

In 1988, the law “Ley General del Equilibrio EcolOgico y la Proteccidn del

Ambiente (LGEEPA)” was enacted (SEDESOL 1988b). This Law provided the

basis:
"I. TO deﬁne the principles for a general ecological policy and
regulate the instruments for its application.
II To pursue an ecological order.

Ill. TO preserve, rehabilitate and improve the environment.

IV. TO protect the natural areas and the aquatic and land ﬂora
and fauna.

V. TO guide a rational use Of the natural elements where
economic beneﬁts and ecological equilibrium be compatible.

VI. TO prevent and control water, air, and soil contamination.

VII. TO coordinate the participation Of the federal government,
states, and municipalities.

Vl/l. To coordinate the participation among the federal public
administration entities and Ofﬁces and society" (Translated
from LGEEPA, 1988).

In addition to other factors, a more intense use of the natural resources is
anticipated in Mexico with the elimination Of trade barriers included in
international agreements such as the North American Free Trade Agreement
(NAFTA). NAFTA will place considerable attention on the need for
environmental protection. LGEEPA should play an important role in the long
term management of the natural resources via environmental protection. In
1992, the Secretaria de Desarrollo Social (SEDESOL) was created, replacing

the former Secretaria de Desarrollo Urbano y Ecologia (SEDUE). SEDESOL,

32

the new created agency, was charged with the formulation and enforcement of
the Mexican environmental policy. In 1995, a new secretariat was created,
Secretaria del Medio Ambiente, Recursos Naturales y Pesca (SEMARNAP). It
replaced Secretaria de Pesca (SEPESCA) and assumed the functions of
SEDESOL.

Following LGEEPA Mexican states have enacted their own local
environmental legislation (i.e., The Yucatan Law for Environmental Protection).
Current local legislation in the state of Yucatan is based on generalities of
LGEEPA, however a growing awareness exist that local legislation must be
changed to provide speciﬁc and focused legislation that meets the needs of the
local conditions‘.

Even with this body of laws and agencies, current problems have been
identiﬁed in the following two areas: 1) incomplete body of speciﬁc laws and
regulations and 2) non-enforcement of speciﬁc regulations (Lea 1996). Factors
that contribute to a weak enforcement of regulation are: 1) discretionary use
(ofﬁcials make the decisions that they consider convenient), 2) ofﬁcial does not
‘ know the regulation, 3) out Of date regulation use, 3) unclear demarcation of
responsibilities, 4) lack of public education, and 5) public does not know

regulation (Lea 1996).

 

1Minutes of the discussions for modifying the “Ley de Proteccicn al Ambiente del Estado de
Yucatan". COPLADE, Merida, Yucatan, 1997.

33

2.2.3 Project of Ecological Order in Mexico

A project for an ecological order called proyecto de ordenamiento
ecologico (POE) was included in LGEEPA. POE provides the basis for land use
planning in the country including coastal areas. Ecological order was deﬁned by
this law as: ”The planning process directed tO evaluate and program the land use
and the management Of the natural resources through out the nation and also
over the zones through which the nation exerts its sovereignty and jurisdiction tO
preserve and rehabilitate ecological equilibrium and protect the environment"
(Translated from LGEEPA 1988, Art 3th-XX, p37).

Projects have been conducted under guidelines provided in the "Manual
de Ordenamiento EcolOgico del Territorio" (SEDUE 1992). This guide introduces
the criteria for integrating ecological and socio-economic studies to create a
model for an ecological order. At national level the Secretaria de Desarrollo
Social (SEDESOL) and the Instituto de Geografia de la UNAM published the
results of a National Project for a General Ecological Order under this criterion.
At the regional level four out of ten projects were concluded in 1991 emphasizing
the coastal zone located in Coatzacoalcos, Veracruz; Salina Cruz, Oaxaca;
Jalisco; and Rio Panuco, Veracruz. Currently more than six projects in the
coastal zone are underway in the states of Baja California, Tamaulipas, Sonora,
Oaxaca, and Guerrero (SEDESOL 1993a). Reports regarding the success or
failure of these projects are scarce and thus knowledge that may facilitate project
acceptance for future efforts does not exist. In general, there is a lack of
information about economic and cultural factors that either support or detract

from traditional physical and biological interventions. In the State of Yucatan a

34

growing awareness exist that a POE can be a useful planning tool and that the
state and municipalities must provide the funds to initiate the implementation of

an ecological project (ICZM) in the coast.

2.3 Ecosystems and Human Communities in the Coast

2.3.1 Mangrove Wetlands Fragility

Of primary interest to coastal management is the conservation of critical
habitats. This point is especially important in developing countries because
people in poverty depend on many products from the natural coastal ecosystems
for food, fuel, and construction materials. A critical habitat in the coast is the
mangrove wetland. Mangroves are salt tolerant plants that range from small
shrubs to tall trees. They developed in intertidal coastlines in low-wave energy
environments. The substrate is typically muddy, saline, acidic, and anaerobic.
Mangroves play an important role in controlling coastal hydrodynamics and
sediment movements that result in shoreline stabilization. The extraction of
nutrients by the mangroves and the ability Of the soil to hold toxins and metals
helps in the maintenance Of the water quality. Mangroves also provide habitat
for a wide range of dependent biota from plankton to human communities (Lugo

and Snedaker 1974, Odum and Heald 1975, Parsons 1992, and Boto 1992).

See Figure 4.

35

 

    

fl; 0
Phytoplankton and BACTERIA
Benthic Algae WKPROTOZOA

hilarpactrcoi Copepod

r 2 a

Qgﬁm—J/g Mysi d Shrimp

Shea

innow
a

2ND CONSUMERS

 

 

 

Figure 4 - Major contribution of mangrove detritus (From Odum 1970 in Mitsch and
Gosselink 1993)

Unfortunately, mangrove wetlands are most severely impacted by human
, activities in coastal areas (Parsons 1992). The potential use of mangrove
ecosystems for urban development, agriculture, aquaculture, port infrastructure,
waste treatment area, and energy production are a serious menace to their
conservation. Mangrove clearing by increased settlement and aquacultural
developments, and pollution from sewage are the main causes of destruction of
the mangroves (Benites et al. 1992, Boto 1992, Butler et al. 1993). Even though
it is well known that they have important ecological, aesthetic, and economical
values and a role as an excellent indicator of the health of the coastal

ecosystems; the coastal zones in Pakistan, China, Indonesia, East Africa,

36

Australia, Ecuador and other places have still experienced signiﬁcant destruction
and difﬁculties in protecting the mangrove forest (Ongkosongo 1992, Semesi
1992, Lin and Fan 1992).

Mangrove wetlands in Mexico cover approximately 4900 km2 (Loa 1994).
The State of Yucatan has a surface area of 630 km2 of mangroves (13% of the
nation) and is the forth largest area after Nayarit with 1,342 kmz, Campeche with
804 kmz, and Sinaloa with 705 km2 (Lea 1994). There are 4 mangrove species
in the Yucatan Peninsula: 1) Rhizophora mangle also called red mangrove or
“xtapche’” by the local Maya people, 2) Avicennia germinans or black mangrove
also called “taabche’” by the locals, 3) Laguncularia racemosa or white mangrove
called by the locals as “sak-olhom”, and 4) Conocarpus erectus or buttonwood or
“canche’” (Morales 1992, Sanchez 1994). Relative distribution of mangrove

species in the Yucatan is shown in Figure 5.

37

 

 

 

 

 

 

 

 

 

Red Mangrove Black Mangrove * i ’ ,
(Rhizophora mangle) (Avicennia germinans) "
Xtapche' Taabche’ ,«o
. « g f
'1 ' 4 ' . - I
_' a; .‘ﬁh
. I
,- , ....
__‘___ 1.... __,_ _ ..__.,..._ y" ' White Buttonwood
- Mangrove (Conocarpus
(Languncularia greets)
racemosa) anc e
Sak-Olhom

 

 

 

 

 

 

 

 

Figure 5 - Mangrove species zonation in the Yucatan Peninsula (After Morales 1992)

Mangrove wetlands in the coastal areas of the Yucatan have been
destroyed and impoverished by the road network, increasing ﬁlling for residential
and industrial developments, urban sewage, mangrove harvesting for
construction and energy production, solid waste, and maintenance of port

infrastructure, channels, vessels and oil pipes (Zizumbo 1989, Valdés 1995).

2.3.2 Changes Affecting Mangroves

There is evidence that the mangrove forest changes its boundaries as
part of its natural evolution. Mangroves are considered stable as long as they
occupy the same area and unstable if their boundaries are advancing or
retreating (Bird and Barson 1982). Changes in the mangrove structure and

species composition impact species productivity. Human activities that change

38

the conditions of the mangrove ecosystem appear to directly alter the natural
mangrove evolution to levels that may result in undesirable vegetation changes
including mortality. Critical parameters that have been identiﬁed and reported in
the literature (Bird and Barson 1982) that affect the mangrove system are:

1) Salinity. Damage has been reported in places were mangroves were
cut for boat landings, building harbors, or allowing runoff to escape from
manmade canals. Variation in salinity may be affected by interrupting the
frequency Of inundation by the seawater, and changes in the pattern of
freshwater inﬂow. Fresh water inﬂow can occur by stream ﬂows or groundwater
ﬂows.

2) Sea level. There is evidence that the rise of the sea level promotes the
regression of the mangroves to landward with recolonization of the immediate
hinterland.

3) Sediment supply. Mangrove dieback is reported from places where
accumulation of sand eroded from other places and carried by drainage
channels has buried pneumatophores.

4) Nutrient Pollution. Sewage efﬂuent, aquaculture ponds, and
agriculture runoff are the major sources of nutrient input. Boto (1992) mentions
that 300 Kg N and 30 Kg P per hectare annually should be capable Of being
processed by the mangrove forest. However he assumes that very high
concentration of ammonium would be toxic to dependent biota and substantial
amounts of organic nutrients will lead to extreme eutrophication, which is

detrimental to the associated fauna.

39

5) Oil spills. It is known that mangroves die if oil reaches the roots.
Experiments with young mangroves showed that high concentrations above
1250 to 12,500 ppm did have effects on black and white mangroves (Thorhaug
1992)

6) Clearing and Reclamation. Much of the reduction of mangrove areas
is reclamation of land for housing, aquaculture, solar salt production, agriculture
and industry. In addition to the direct effect of landﬁlling for reclamation

purposes, other adjacent areas may be sensitive to this change.

2.3.3 Coastal Land Use and Control

As population grows, the amount of land has become scarce and in the
absence of proper land use planning many natural populations and their
environments have been negatively impacted. Pressure on land has caused
many coastal countries to strengthen land use planning and regulation with the
purpose of protecting the welfare of present and future generations. However,
implementing land use controls requires that people understand and accept
required trade-offs as beneﬁting all. Opposition to land use planning are often
associated with perceptions regarding property rights and the real estate
markets. To the individual motivated by proﬁt, free markets may lead to the
highest and best use of land. This behavior is not necessarily safe for the
environment or desirable to the public. Others feel that in the future land will be

treated more as a public resource than a commodity Mokma (1994).

40

House et al. (1976), have proposed several instruments for shaping land
use: 1) ownership, 2) regulation, 3) taxation, 4) subsidies, 5) incentives, 6)
investments, 7) information, 8) education, and 9) persuasion. The problem is
how to select the most appropriate set of instruments for a particular case. To
deal with this problem, the cause and effect relationship between land use and
the environment must be understood. This necessitate that we be able to predict
the likely environmental consequences related to each different land use

alternative.

2.4. Data Collection and Analysis

2.4.1 Natural System
2.4.1.1 Environmental Impact Assessment

Identiﬁcation and evaluation of the environmental impacts of development
in the coastal zone are essential if sustainable mitigative measures and
recommendations regarding project design and Operation, land use planning and
other management interventions are to be implemented. Environmental Impacts
(i.e., changes in the attributes of the environment) are largely determined by the
way resources are used (Schultink 1993). Environmental Impact Assessment
(EIA) is a process that provides indicators of the likely consequences of current
and proposed actions. Spatial and temporal changes of environmental
parameters that occur as consequences of a particular activity are called an

impact (Wathern 1988). In general EIA is used as a tool for: impact

41

identiﬁcation, prediction, interpretation, communication, and in designing
monitoring schemes (Wathern 1988).

Schultink (1992) provides examples of major actions potentially affecting
the resource base (Table 1). These actions, in the coastal zone, may result as a
consequence of development projects from urban, commercial, industrial, and
agricultural developments, such as: 1) building homes, 2) motor road and airport
construction, 3) construction of water ways and marinas, 4) marine product
manufacturing, 5) disposal facilities, 6) salt extraction, 7) aquaculture ponds, 8)

ﬁshing, 9) hunting, and 10) wood extraction.

Table 1 - Examples of major actions on the Environment (Schultink 1992).

 

1 Modiﬁcation of Ecosystem functions, properties and boundaries

 

Land transformation and change

 

Resource extraction and depletion

 

Resource utilization and processing

 

Resource alteration and conservation

 

Resource renewal and redevelopment

 

 

 

 

#0301wa

Introduction and storage of hazardous materials

 

EAls are required by Multinational Development Banks for sanctioning
and liberating funds for development projects and mandatory in many countries,
including México according to LGEEPA (1988).

Table 2, list the output components of an EIA called Environmental Impact

Statement (EIS) (Devuyst 1993, pag. 152).

42

Table 2 - Components of an Environmental Impact Statement (Devuyst 1993)

 

. Description of the development: the purpose(s) of the development should be described as
should the physical characteristics, scale. and design. Quantities of materials needed
during construction and Operation should be included and, where appropriate, a
description of the production processes.

 

. Site description: the on-site land requirements of the development should be described and
the duration of each land use.

 

. Residuals: the type and quantities of residual and/or waste matter and energy created should
be estimated, the expected rate of production given, and the proposed disposal routes
to the environment described.

 

. Environment description: the likely geographical extent of the affected environment should be
descnbed.

 

. Baseline Conditions: a description of the affected environment as it is currently, and as it
could be expected to develop if the project were not to proceed, should be presented.

 

. Deﬁnitions of impacts: potential impacts of the development on the environment should be
investigated and described. Impacts should be broadly deﬁned to cover all potential
effects on the environment and should be determined as the predicted deviation from
the baseline state.

 

. Identiﬁcation of impacts: methods should be used which are capable of identifying all
signiﬁcant impacts.

 

. Prediction of impact magnitude: the likely impacts Of the development on that environment
should be described in exact terms wherever possible.

 

. Assessment of impact signiﬁcance: the expected signiﬁcance that the projected impacts will
have for society should be estimated. The sources of quality standards plus its
rationale, assumptions and value judgments used in assessing signiﬁcance should be
fully described.

 

10. Alternatives: alternatives to the proposed project should be considered. The alternatives

should be outlined, the environmental implications of each presented, and the reasons
for their rejection brieﬂy discussed.

 

11. Mitigation measures: all signiﬁcant adverse impacts should be considered for mitigation.
Evidence should be presented to show that proposed mitigation measures will be
effective gee et al., 1990)

 

12. Monitoring: the predictions included in the EIS and the effect of the proposed mitigation
measures could be evaluated when a permit for the project has been issued. A plan
which describes the best way to monitor the project could already be proposed in the
EIS.

 

 

13. Annex: interesting additional information, external sources etc., should be presented at the
end of the document in an annex.

 

 

43

Environmental impact identiﬁcation can be conducted with the use of
checklists, matrix methods, networks, overlays, and simulation (Wathern 1988,
Devuyst 1993). Checklists are simple lists to collect impacts associated with a
project activities. These simple lists do not provide: 1) interactions between
effects, 2) the most signiﬁcant impacts, and 3) the likelihood of occurrence.
Matrices introduced by Leopold (1971) are the most popular method. Matrices
relate development actions with environmental components and can be used to
measure and interpret impacts. Such matrices focus on direct impacts, not the
cumulative impacts. Networks are graphs relating actions with secondary and
higher-order impacts, which can result from an initial impact. Overlays are
suitable for the consideration of spatial distribution and intensity Of individual
impacts. In this case, layers can be overlaid with the use of geographic
information systems (GIS) commands to determine amongst others: 1) total
impacts, 2) select locations with speciﬁc criteria, 3) compute areas, 4) change
weighting values, and 5) add new impact type layers. Computer simulation is
another tool that allows analysts to use a GIS model to test several scenarios
and compute likely outcomes from the real system (Wathern 1988).

The above tools utilize physical models, experimental methods, and
mathematical models to measure and predict the extent and nature Of impacts.
An additional method without predictive capabilities is the valuational method.
It is used to compare alternative locations on the basis of the value of the
environmental services that will be damaged by the proposed project (Wathern

1988).

44

2.4.1.2 Data Types and Methods

Policy design and implementation requires a good understanding of the
condition of the natural resources and environmental impacts of human
activities on natural systems. This task involves: 1) the detection, distribution,
and quantiﬁcation of physical changes, 2) evaluation of the quality Of the natural
system, 3) quantifying present and future rate of resource use, and 4) quantifying
current and future waste generated in the region .and other regions discharged in
the coastal zone. Land use and land cover patterns are required to detect
changes during the impact analysis. Land cover is deﬁned as the type of feature
present on the surface of the earth, and land use as the types of human activities
associated with a speciﬁc piece of land (Lillesand and Kiefer 1987).

Most of the data is collected from large areas and multiple ecosystems
where the use of traditional survey methods does not provide enough synoptivity.
Remote sensing techniques (RS), geographical information system (GIS), and
global positioning system (GPS) Offer improved capabilities to be used in
detecting landscape changes. A GIS is one of the recommended tools for
integration and analysis of the pre-classiﬁed spatial data and to produce zoning
maps (Thackway and Cresswell 1992, Welch and Remillard 1992). GIS oriented
analysis provides ﬂexibility and alternative solutions and it is convenient for a
wide range of applications, including conservation assessment, planning and
management. GIS also offers greater versatility because the data base can be
cheaply analyzed numerous times to prOduce alternative maps (T hackway and

Cresswell 1992).

45

A data base that includes land use and land cover types can be built with
the use of remote sensing techniques. Remote sensing involves photographs or
digital images taken by satellites or airborne sensors Of the Earth's surface. The
analysis of the images provides information about the objects, areas of interest
and various phenomena. Digital image interpretation and GIS provide the tools
for the analysis of remotely sensed imagery (RSI). Land cover information can
be directly interpreted from remote sensors, however, information about human
activities cannot always be taken from them.

Digital image processing through computers is a common way to analyze
remotely sensed imagery. The various types of computer assisted Operation for
this task involve: 1) image rectiﬁcation and restoration, 2) image enhancement,
3) image classiﬁcation, and 4) data merging (Lillesand and Kiefer 1987). Image
rectiﬁcation and restoration (preprocessing) includes geometric and radiometric
procedures to correct distorted or degraded images caused by atmospheric
conditions, platform and sensor changes, and GIS requirements. Image
enhancement procedures display images usually for visual interpretation. Image
classiﬁcation involves quantitative techniques for automatic feature identiﬁcation.
Procedures that involve the interpreter's knowledge to train the classiﬁer are
called supervised, others are called unsupervised. Statistical analysis of multiple
bands is used in classiﬁcation processes. Data merging procedures combine

remotely sensed data with other data sources (Lillesand and Kiefer 1987).

46

2.4.1.3 Coastal Classiﬁcation and Detection

From a comprehensive approach, the management and conservation of
the. mangroves under ICZM requires an adequate description of the coastal zone
including: 1) mangrove forest, 2) open waters, 3) barrier island, 4) urban areas,
5) agricultural developments, and 6) industrial developments. Necessary
information for these categories includes: 1) geographic location and extent, 2)
composition, 3) production, 4) condition, 5) trend, and if possible 6) rates of
change. The Coastal Change Analysis Program (C-CAP) that is part of the
Estuarine Habitat Program (EHP) of NOAA Coastal Ocean Program (COP), has
developed a C-CAP system classiﬁcation for three major coastal components:
wetlands, submerged vegetation, and uplands (Dobson et al. 1995).

This classiﬁcation system is used in: 1) inventories and 2) monitoring
changes in coastal areas. Three major components are called superclasses and
were divided into classes and subclasses reﬂecting ecological relationships and
classes that can be discriminated from data provided by satellite image, aerial
photography, and ﬁeld data (Table 3).

To collect data for classiﬁcation and monitoring of the coastal zone, the
use of level II United States Geological Survey (USGS) is suggested for land
use/land cover classiﬁcation. For level II, small scale photography, Landsat TM
(30 m), and SPOT (20 m) imagery were recommended (Lillesand and Kiefer
1986). For other landscape features, Jensen and Tell (1982) successfully used
Landsat MSS band 5 to study residential land-use developments. MSS band 5
(600-700 nm) is also used to separate vegetated and nonvegetated surfaces

(Jensen and Toll, 1982). To study changes in the urban areas, Jensen and Toll

47

(1982), in addition to band 5 raw data, used MSS band 5 for texture analysis to

improve change detection accuracy in residential land-use development.

48

Table 3 - C-CAP Coastal Land-Cover Classiﬁcation System (Dobson et al. 1995)

 

C_-CAP Coastal Land-Cover Classiﬁcation System
Superclasses 8. Classes

 

1.0 Upland

 

1.1 Developed Land

 

1.2 Cultivated Land

 

1.3 Grassland

 

1.4 Woody Land

 

1.5 Bare Land

 

2.0 Wetland

 

2.1 Marine/Estuarine Rocky Shore

 

2.2 Marine/Estuarine Unconsolidated Shore

 

2.3 Marine/Estuarine Emergent Wetland

 

2.4 Estuarine WOOdLWetland

 

2.5 Riverine Unconsolidated Shore

 

2.6 Lacustrine Unconsolidated Shore

 

2.7 Palustrine Unconsolidated Shore

 

2.8 Palustrine Emergent Wetland

 

2.9 Palustrine Woody Wetland

 

3.0 Water and Submerggd \lgggtation

 

3.1 Water

 

3.2 Marine/ Estuarine Reef

 

3.3 Marine/ Estuarine Aquatic Bed

 

3.4 Riverine Aquatic Bed

 

3.5 Lacustrine Aquatic Bed

 

 

3.6 Palustrine Aquatic Bed

 

 

Source: Dobson et al. 1995: NOAA Coastal Change Analysis Program (C-CAP)

Rep. NMFS123

. NOAA Tech.

49

For mapping coastal vegetation, Loubersac and Slimani (1985) suggest
that dense Rhizophora forest can be delimited using vegetation indices. Visible
(MSS band 5) and infrared (MSS band 7) are preferred spectral bands to
calculate vegetation indices (Green et al. 1994). These electromagnetic bands
are selected because chlorophyll is a compound that highly absorbs energy
from blue (400-500 nm) and red (600-700 nm) bands in the electromagnetic
spectrum with low percent reﬂectance. Another characteristic is that internal leaf
structure responds to near-infrared (NIR) band (700-1350 nm) by increasing NIR
percentage of reﬂectance as leaf layers increase. Thus, NIR percentage of
reﬂectance is typically bigger than visible bands for vegetated areas.

A simple vegetation index that uses this property is calculated by
subtracting the visible channel from the infrared channel. This vegetation index
gives high values for vegetation because of the relatively high near-infrared
reﬂectance and low visible reﬂectance. In contrast, water and clouds give
negative values because visible reﬂectance is greater than the near-infrared
reﬂectance, and rock and bare soil areas result in vegetation indices close to
zero since they have similar values in both bands (Lillesand and Kiefer 1987).
Vegetation indices can be computed using MSS Landsat images using band 5
(600-700 nm) and band 7 (800-1100 nm) data. Proposed vegetation indices
suggested by Lillesand and Kiefer (1987) are:

(a) Difference index as given by:

VI = Band(7) - Band(5) ( 1)

50

where values of VI > 0 are taken as vegetated areas, VI = 0 rock and bare soil,
and VI < 0 clouds, snow, and water.

(b) Normalized Difference Vegetation Index (NDVI) as given by :

Band - Band5

 

NDVI = ( 2)
Band +Band

7 5

The normalized index is sometimes preferred since it compensates for changes

in the illumination, surface slope, and other factors.

2.4.1.4 Change Detection Procedures

To discriminate land cover changes between two dates, a change
detection analysis is usually conducted with the use of maps, photographs, and
satellite images. Change detection is performed by a combination of techniques
that include: image differencing, image ratioing, and change vector analysis. For
these techniques, geometric and radiometric corrections are needed. Geometric
corrections are accomplished to register the images, this means that pixels (i.e.,
elements (i,j) of the image array) in both images with the same row (i) and
column (j), must refer to the same place on the ground. Radiometric distortions
caused by degradation of the sensor, platform changes, and changes in light
conditions should be corrected by algorithms that take into account, sensor
calibration, changes in the orbit, sun seasonal variations, and haze (Dobson et

al. 1995, Eastman et al. 1991).

51

In addition to raw images for change detection, images can be
transformed for better analysis using vegetation indices, principal components,
regression, texture images, or classiﬁed images. Suggested manipulations are:
(a) Image Differencing as given by:

D=(Band5(tl)- Band5(t2))/+127 ( 3)
and values of D > 127 are considered as clearing, D = 127 no forest change,
and D < 127 regrowth.

(b) Ratioing as given by:

R=Band7(tl)/Band7(t2) (4)

and R is normalized to ﬁt 8 bits and :1: N standard deviations from the mean are
used to separate change from no-change pixels
(c) Standardized Principal Components (Fung and LeDrew, 1988). The index as

given by:

D=SPC3(t])-SPC3(12) (5)

and SP0, is the third principal component and it is a measure of the greenness.
To separate change from no-change pixels, :1: N standard deviation from the

mean can be used.

(d) Ratio Image Differencing as given by:

Band7(tl) - Band5(tl)

6
band7(t1)+band5(tl) +l)*127 ( )

 

Rl=(

52

_ Band7(t2) - Band5(t2)

R — +1 *127 7
2 (band7(t2)+band5(t ) ( )

 

2)

R = R1 - R2 1 ( 3)

Equation (6 and 7) are highly correlated with the amount of green biomass

and equation (8) is useful in the estimation of green biomass change between
different dates.

(9) Vegetative Index Difference (Nelson, 1983) as given by:

=Band.(t.) Band.(t.)+
Band5(t,) ' Band5(t,)

 

C (9)

C is a constant to produce a non-negative real image. Nelson (1983) in a single
and multi-band analysis found that this index produces the highest accuracy
against differencing and ratioing procedures similar to those described in cases
(a) and (b).

(f) Linear Regression as given by:

PredictedBand,(t,)=a+b*Band,(t,) (10)

A linear relationship is assumed to exist between the two images. The
linear relationship that result from regressing one image against the other is used
to create a predicted image in t2. Predicted and true images are subtracted
according the procedure in case (a).

(g) Post-Classiﬁcation comparison. This is a method that involves independent

classiﬁcations for dates t1 and t2, and veriﬁcation of corresponding pixels (i.e., no-

53

change = pixels with same number category, change = pixels with different
number category). In this method, the accuracy of the change detection is the
product of multiplying the accuracy (measured on a scale from 0 to 1) of each
classiﬁcation.

(h) Change Vector Analysis. Multispectral images involve more than one band.
Bands from time t1 image are used to ﬁx one point in a multidimensional space
and bands from time t2 image are used to ﬁx a second point. The magnitude of
the Euclidean distance between these two points is calculated and represents a
measure of difference or change. In addition, direction of change can be
computed when two bands are involved. Direction can provide additional
information.

(i) Texture Analysis. Interpreters use texture to distinguish features with similar
reﬂectance. Spatial Gray-Level Dependence (SGLD) matrices are common
sources of features. Popular features are: energy, entropy, correlation, inertia,
and local homogeneity. Images created with the SGLD approach can be

subtracted for change detection (Ballard and Brown 1982).

2.4.1.5 Field Sampling and Thresholds

A common need in these procedures involves the use of a threshold to
decide change from a no change situation. As noted in the literature, there is not
a unique or effective way to set a threshold. One approach assumes that
residuals have a normal distribution, therefore a threshold can be chosen based

on statistical conﬁdence intervals. For example, a 95% conﬁdence interval

54

suggests :t 1.96 s.d. from the mean to ﬁx the area of no-change, in this case
2.5% of the points in both tails of the distribution is taken as the true change.
This approach does not always provide the best results. Fung and LeDrew
(1988) found, using six indices that as the number of standard deviations
increases the accuracy of the indices increases until they reach a maximum and
afterwards they decrease.

Unfortunately, to ﬁnd this maximum value for indices, ﬁeld data must be
collected and multiple conﬁdence intervals tested to ﬁnd the best threshold.
Fung and Ledrew (1988) suggest that the Kappa indexof agreement is the best
way to select the threshold between change and no change areas using ground
truth data. The Kappa index measures the difference between observed
agreement and the agreement that might occur by chance. A simpliﬁed Kappa

index is deﬁned as follows:

 

17.30. (11)
where p0=2:_,p” and p,=Z:-1p,p.

p0 = the proportion of units which agree
pc = is the proportion of units for expected chance agreement and
C = the number of classes used.

It is assumed that error matrices are products of appropriate sampling
design and an adequate number of samples (Congalton 1983). The proposed
sampling scheme by Rosenﬁeld et al. (1982) mentioned that for each category

the number of samples is taken according the cumulative binomial probability

55

with the assumption that a preliminary percent of the accuracy can be estimated

(p0). The maximum error tolerated (E) and a conﬁdent level (alpha) are also

involved in the computation of sample size (n). Table 4 shows values for (n) at

95% conﬁdence level (alpha = 0.05) and E = 10%.

Table 4 - Sample size from Rosenfield et aL (1982)

 

P0 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85

 

 

 

 

 

 

 

 

 

 

 

 

2.4.1.6 Mapping and Precision
In the case of quantitative data where radiation values are the basic data,
maps should be submitted with statistical results that justify threshold selection
and estimation of the propagation error due to image manipulation with GIS
operations. Error matrices and their Kappa coefﬁcients that result from several
thresholds must be evaluated. The highest Kappa value is the criteria for
threshold selection. Kappa is a measure of the change detection precision. GIS
data transformation creates changes in the thematic classiﬁcation precision. The
intersection of two layers (e.g., and operations) has precision equal to the
product of the proportion of the points correctly classiﬁed (PCC) from each layer.
In the case of qualitative data where values of radiation were transformed
into categories, the standard form used to report the differences between two
images is a crossclassiﬁcation table. Crossclassiﬁcation is an n by n array where

columns and rows are labeled with the categories. Diagonal values in the array

56

are the number of pixels with no change, and off-diagonal values are the number
of pixels with change. Inspection of the array provides quantitative data about
the changes which have occurred in the study area. Statistics used include Chi

Square, Cramer's V correlation coefﬁcient, and the Kappa Index of Agreement.

2.4.2 Social Systems

Basic precepts for wildlife management were introduced by Aldo Leopold
in 1933, but it is in the last 25 years that the human dimension has emerged in
contemporary wildlife management. Today, efforts are conducted to integrate
the human dimensions with biological dimensions as a way to present sound
solutions in management programs (Decker et al. 1992).

Decision makers need to know people’s responses both short and long-
term regarding the effects of current or projected actions. Proactive decision
makers need to anticipate and calculate the reactions that their proposals will
produce on people. In general, data from the socio-cultural dimension will
enhance policy selection and implementation of strategies for resource
management (Payton 1990, Decker et al. 1992). Relevant data for the purpose
of natural resource management includes: 1) traditional uses, 2) preferences by
type of project, 3) short vs. long term economic returns, 4) preferences by project
location, 5) environmental knowledge, 6) organization, 7) aspirations, 8) potential
response to management programs, 9) competing groups, 10) attitudes, and 12)
values. Assessment of the individual knowledge base and attitudes of people

(i.e., ﬁshermen, students, industrial workers, tourist, etc.) will provide information

57

about the realistic success in the design, implementation and future results of

management plans.

2.4.2.1 Knowledge Base

In coastal areas where the mangrove wetland is the primary ecosystem,
public knowledge critical to the management of the area include: knowledge
concerning the direct and indirect mangrove products and use; threats to
mangroves; mangrove ecological beneﬁts; economic and cultural values; and
attitudes.
1) Resource use and ecological functions. Data about mangrove wetland
products are in general divided in: 1) mangrove wetland direct products and 2)
mangrove indirect products. Principal direct and indirect products and their uses
are shown in Tables 5 and 6. More information about direct and indirect products

can be found in (Suman 1994, Dugan 1992, Hamilton and Snedaker 1984).

Table 5 - Mangrove Direct Products

 

 

 

 

 

 

 

 

Category Use

1. Wood for energy production Cookingor heating, smokingﬁsh, and bumigq bricks

2. Wood for construction Beams and poles for houses, boat-building materials,
fence post, furniture, and toys

3. Fishing Poles for ﬁsh traps, ﬁshing floats, tannins for net
preservation, and ﬁsh attractiggg shelters.

4. Agriculture Fodder, and green manure

5. Food and Drugs Tea, condiments, and medicines

 

 

58

Table 6 - Mangroves Indirect Products

 

 

 

 

 

 

 

 

 

 

 

Category Use
1. Finﬁsh Food, fertilizer, and bait
2. Mammals Food, fur, observation, and sport Hunting
3. Crustaceans Food, fertilizer, and bait
4. Reptiles Food, skin, observation, and siort hunting
5. Mollusks Food, fertilizer, bait, and shells
6. Insects Food, honey, wax
7. Birds Food, feathers, observation, and sport hunting
8. Recreation Swimming, walking, sailing

 

In addition to residents knowledge about direct and indirect mangrove
products use, it is important to understand how much residents know about the
ways mangrove wetlands are transformed (by direct and/or indirect actions) with
effects that reduce or eliminate its natural production. Table 7, shows some of

the mangrove wetland conversion frequently reported in the literature.

Table 7 - Mangrove Wetland Conversions

 

 

 

 

 

 

 

 

 

 

Category Category
1. Residential use 7. Marine infrastructure
2. Roads 8. Disposal of Solid waste
3. Pipes 9. Salt pond developments
4. Parking lots 10. Oil industry
5. Aquacultural ponds 11. Sewage
6. Agriculture 12. Recreational and tourism facilities

 

Less tangible for residents are the environmental beneﬁts provided by the

mangrove wetland. Dugan (1992) and Hamilton et al. (1989) provided a good

 

59

description" of the mangroves environmental beneﬁts. A summary is provided in

Table 8.

Table 8 - Mangrove Environmental Services

 

Category

 

. Production of organic matter and feeding nursery

 

. Protection of shore erosion

 

. Protection from hurricanes and storms

 

. Habitat for nesting of birds

 

. Spawning area and protective cover

 

. Removal of nutrients, heavy metals, and other substances from the water

 

. Transportation by water

 

. Retention basin for ﬂood waters

 

 

 

(DQNQOIAODN-t

. Export of Nutrients

 

2) Long-Term Resource Use. Information about people’s feelings, beliefs and
values related to resource conservation and protection of critical processes in
coastal ecosystems is needed. Kellert (1980) studied attitudes toward animals
creating a taxonomic division of cultural values: naturalistic, ecologistic,
humanistic, moralistic, scientistic, aesthetic, utilitarian, doministic, negativistic,
and neutralistic. In addition to cultural values, economic values other than use
such as option, bequest, and existence have been recommended to be
introduced in natural resource management (Randall 1987, Hamilton et aI .
1989, Barbier 1994).

Also important are beliefs regarding the consequences of human activities
on vegetation, wildlife, and ﬁsh that may adversely affect the mangrove wetland.
For example: 1) conversion (urban expansion, port Infrastructure, aquaculture ‘

development, salt ponds, road expansion) 2) residuals (domestic and Industrial

60

solid wastes, untreated domestic and industrial sewage, oil spills), and 3) rates of
resource consumption (trees, ﬁsh, birds, mammals).

Another important factor is the acceptance of regulation for controlling
resource use and environmental protection. Controls may involve the protection
of nursery areas and creation and protection of corridors where almost all human
activities would be banned. For activities such as ﬁshing, hunting, and wood
harvesting, the following control may be required: obtaining hunting permits,
limiting or rotating the number of ﬁshing grounds, the establishment of seasons,
etc. For urban and industrial activities, the controls required include: controls for
home construction, roads and port infrastructure (marinas), as well as regulation

for solid garbage and sewage is used.

2.4.2.2 Data Collection Methods

To collect data from the human dimension and test proposed hypotheses,
it is necessary, as Singleton et al. (1993) suggest, to deﬁne the concepts
outlined in the hypotheses, operationalize these concepts into variables, select
proper indicators for further analysis, and select the best approach to generate
information. Singleton et al. (1993), examined three of the various approaches
to operationalization: 1) verbal reports, 2) observation, and 3) archival records.
Verbal reports or self-reports provide simple and generally accurate measures of
background variables, as well as subjective experience (Singleton et al. 1993).
To measure complex concepts, two or more responses are combined to create

an index or scale. Indices simplify the analysis and increase precision.

61

Henerson et al. (1987) have suggested the following instruments to measure
attitudes: 1) Self reports, which include interviews, surveys, polls, questionnaires
and attitude rating scales, logs, journals, and diaries; 2) reports of others, which
include interviews, questionnaires, logs, journals, reports, and observation
procedures; 3) sociometric procedures, which include peer rating, and social
choice techniques; and 4) records, which include counselor ﬁles, and attendance
records.

Self-reports are procedures for asking people to provide information (oral
or written) about age, gender, education and their own attitudes. Attitude
assessment with the use of self report procedures are the most direct approach.
It has been recommended that when the people understand the questions asked
of them and have sufﬁcient self-awareness to provide the necessary information,
that they will answer honestly and tend not deliberately, to falsify their responses
(Henerson et al. 1987). Reports of others, refer to a procedure in which a person
(the observer) is informed about the behavior and attitudes of others. This is
useful when people are unlikely to provide accurate information. Two main
concerns in this approach are provided by Henerson et al. (1987): 1) the bias
introduced by the observer (lack of objectivity) and 2) the completeness of the
information.

Sociometric procedures are in general used for asking people to provide
information about the social struCture or pattern of a group (i.e., Who are the
group leaders?) Finally, archival records refer to the analysis of existing
recorded data such as statistical records, public and private documents, and

mass communication.

62

2.4.2.3 Population and Sampling

As Singleton et al. (1993) suggest, deﬁning the target population requires
keeping in mind the scope of the desired generalization and the practical needs
to draw the sample. Generalization can be made regarding individuals, groups,
or organizations with geographic (target area) and time referents and
appropriated samples that involve economic and technical feasibility. To
operationalize research on a given population a sampling frame must be deﬁned.
A nonspatial sampling frame is the set of all cases deﬁned by either listing all
cases or providing a membership rule. A spatial sampling frame might be a map
where sample units are identiﬁed using a coordinate system (McGrew and
Monroe 1993 )

To capture population heterogeneity requires a careful procedure to make
sure that population variation is represented adequately in the sample. The
quality of the sample is related to the sampling design. Samples are drawn
using probability and non-probability procedures. Probability procedures are
preferred over non-probability sampling design to avoid bias in the selection of
cases and because it allows the use of probability to measure the accuracy of
the sample. Under probability sampling, random selection is required at some
point in the process. Simple random sampling, stratiﬁed random sampling, and
cluster sampling are methods that involve the use of random selection.

The size of the sample is affected by several factors: heterogeneity,
desired precision, type of sampling design, available resources, and the number
of breakdowns (Singleton et al. 1993). Heterogeneity indicates that more cases

are required for the sample. Statistical measures, such as the standard

63

deviation (6), provide an indication about the data heterogeneity. In computing
the sample size the standard deviation or a close approximation is required.
Samples with geographical location are of special interest for
management. Spatial sampling can be conducted in several ways including:
point sampling, line sampling and area sampling. Point sampling may involve
the following sampling strategies: systematic, random, proportional and
disproportional stratiﬁed, and clusters. Selected types of hybrid point sampling
provided by McGrew and Monroe (1993) include: stratiﬁed systematic unaligned,
disproportional stratiﬁed systematic aligned, cluster systematic, and

disproportional stratiﬁed cluster.

2.4.2.4 Data Analysis

Techniques used in the analysis are usually selected according the type
of the data collected: nominal, ordinal, interval, or ratio level (Craft 1990). As
mentioned by Singleton et al. (1993), the analysis of these univariate measures
helps to ﬁnd the most appropriate variables, collapse categories, and determine
the shape of frequency distributions. When hypotheses involve more than single
variables, bivariate analysis provides tools to assess relationships or
associations between two variables. Contingency tables, cross-tabulation, and
cross-classiﬁcation are tables for the analysis of variables with few categories,
usually nominal and ordinal scale measures. The test of statistical signiﬁcance
that allows the conclusion that evidence of statistical association exists, is the

chi-square (x2) (Bhattacharyya and Johnson 1977, Craft 1990 ). The strength of

64

statistical association between the two sets of measures is computed with the
use of Cramer's contingency coefﬁcient, Parson's coefﬁcient of mean square
contingency, or Parson's phi coefﬁcient in 2x2 table. Large values of these
indexes indicatea strong association (i.e., < .10 weak, .11 to .25 weak to
moderate, .26 to .40 moderate, .41 to .50 moderate to strong, over .50 to strong
(Craft 1990)).

For interval and ratio scale measurement, regression analysis is used to
determine the best mathematical equation that describes relationship. Linear
' regression ﬁts a straight-line to the data and provides an index :2 that represents
the portion of the dependent variable variability explained by the linear relation
with the independent variable. Small values of r2 indicate that a straight-line
does not ﬁt well for the data. Some nonlinear models can be tested with the use
of linear regression using their linearized transformations.

To explore more complex relationships such as causation with more than
one causal variable, models are tested with multivariate analysis. Multivariate
analysis with nominal and ordinal data and contingency tables uses a technique
called elaboration. Interval and ratio level measurements use a multiple-
regression analysis, similar to the simple linear regression model. In this case,
Pearson's R2 is the index that reports the proportion of the variation in the y

(dependent) variable explained by x,, x2, x,, (independent) variables in the

analysis.

65

2.4.3 The Study Area
2.4.3.1 Boundaries

The coastal zone is that region on the earth where land and fresh water
reach salty ocean waters. The coastal zone or simply the 'coast' is also called
the land-sea interface. According to OECD (19933), the interface is
characterized by two axes: the long-shore axis and the onloff-shore axis. The
former runs parallel to the shore axis and the later is perpendicular to the shore
line. It is especially difﬁcult to deﬁne the boundaries for the onloff axis. This axis
can include, for the inland deﬁnition, entire watersheds to a small strip of
shoreline, and for the seaward limit can extend as far as the 200 nautical miles
limit.

The boundary problem arises since the coastal zone is diverse in the
number of the ecological systems present in the area such as: marshes,
mudﬂats, shallow open waters, mud and sand bottom, beaches, and dunes.
Each of these systems consist of interrelated populations of plants and animals,
and ﬂows of water, nutrients and other materials. These ecosystems may exist
on various scales and complexity and varyfrom region to region. These
ecological systems, although different, are linked together to form an
interdependent system where the activities or changes in one system inﬂuence
the functioning of the others. These ecosystems are also inﬂuenced when the
ocean and nutrient materials ﬂushed from the land move to this area.

For these reasons, the boundaries for a study area should be deﬁned
based upon an ecosystem approach according to the objectives of management.

This approach, when applied to the onloff-shore axis should cover part of the

66

hinterland and include aquatic ecosystem and portions of tributaries (OECD
1993a).

It is suggested by Clark (1992) that the bounded area must be divided in
management sub-units called tiers, each unit is characterized by resources,
issues and jurisdictions. A four tier approach could be: 1) Marine and coastal

areas, 2) transitional area, 3) shorelands, and 4) uplands.

2.4.3.2 Opportunities in the Yucatan Peninsula

Coastal areas in the State of Yucatan provide opportunities to test and
study the proposed hypotheses. Outside the protected areas, mangrove
wetlands in the coastal areas of Chuburna, Chelem, and Progreso also
contribute to the support of: 1) commercial shrimp ﬁsheries (e.g., Penaeus
duorarum, Penaeus aztecus, and Penaeus brasiliensis), 2) subsistence fisheries:
(e.g., Fundus grandissimus, Cichlasoma umtalmus, Centropomus undecimalis,
Lutjanus gn'seus, Lutianus analis, Pogonias cromis, Bardie/la ronchus, Cynoscion
arenan'us, Cynoscion nebulosus, Scombemmorus macu/atus, Lutjanus Synagn's,
Ocyurus Chrysurus, Pepn'lus paw, Caranx hippos, Xancus angulatus, Melongena
corona and others), 3) animal diversity including birds (e.g., Fragata magniﬁcens,
Porphyrula martinica, Jacana spinosa, Fulica amen'cana, Himantopus mexicanus,
Gallinago gallinago, Calidn's minutilla, Pandiion haliaetus, Buteogallus anthracinus,
Eudocimus albus, Ajaia ajaja, Casmerodiius albus, Buton'des stn‘atus, bubulcus
ibis, Phoenicoptems ruber, and many other ), 4) protect the coast from erosion,

and human settlements from hurricane and storms, 5) are a source of energy

67

(wood and other organic materials); and 6) are sink for nutrients and other
substances released from human activities in the area.

Mangrove ecosystems in this area are threatened by intentional landﬁlls,
improper road constructions, gas pipes, industrial developments, maintenance of
port and urban infrastructure, industrial and residential solid wastes, and
improper sewage treatment (Zizumbo 1989, Valdes 1995). Threats to
mangroves due to previously described human activities originate from urban
developments. A selected area in the state of Yucatan provides the opportunity
to study a coastal community in a fragile ecosystem with pressures for
environmental protection, economic development and population growth. The
area includes three towns at different stages of economic development
(Chuburna, Chelem and Progreso) mixed with a popular vacation area.
Chuburna is an small rural town where residents heavily dependent on the
coastal natural resources (small scale ﬁshing, hunting, wood for energy
production). Chelem is a rural town bigger than Chuburna. Residents also
depend on coastal resources (small scale and mechanized/industrial ﬁshing
activities), however, the industrial plant located in Progreso and Mérida (ﬁsh
processing and maquila) and services required by non-permanent residents
along the year (demand increases during the months of July and August) provide
alternatives for jobs. Progreso, the larger and more urban area, offer job
opportunities in the commercial ﬁshing industry, transportation, construction, and
services. Progreso has many restaurants and hotels, state ofﬁces including the
Mexican navy, the lighthouse and a pier to receive large vessels. This area, also

provides the opportunity to study a large non-permanent group of residents that

68

use the area during several holiday periods throughout the year. Full occupation
occur during the months of July and August when schools have a long vacation
period. To protect the environment and resources in this area, the mangrove
ecosystem and local human communities need to be properly assessed and
incorporated into planning for the long-term beneﬁt of both, the mangrove
wetlands and inhabitants of coastal areas of the State of Yucatan. The selected

study area is shown in Figure 1, Chapter 1.

Chapter 3

RESEARCH DESIGN

 

This chapter provides a complete description of the procedures used in this work to: 1)
determine and compare changes in the mangrove wetland over time, 2) link environmental changes to
cultural features over speciﬁc distances, 3) assess the individual's knowledge base related to
mangrove use and conservation, and 4) assess the acceptance of current and proposed general
controls for regulation. It shows hypothesis operationalization, survey design, sampling design, criteria
for accepting or rejecting the hypotheses, and proposed statistical analysis.

 

3.1 General Approach

As suggested by Rayner et al. 1994, an assessment of the natural and social
systems is the ﬁrst step. Natural coastal areas that have been damaged or
converted by human activities were identiﬁed using synoptic data provided by remote
sensors. A Change Detection Analysis (CDA) was conducted using photographs
and M88 satellite images taken from the same geOgraphical area. Remote sensing
techniques were used in quantifying changes in the mangrove wetland and adjacent
land with the production of land cover maps. Data from this analysis were used to
identify major changes, link cultural features to changed areas, and assist the

sampling design for assessing the social system.

69

70

In assessing the social system, a pilot study was conducted to prepare an
intensive survey in three semi-homogeneous strata to collect from local residents
data about: 1) use, 2) conversion, 3) ecological knowledge and 4) attitudes toward
resource use and regulation. A preliminary questionnaire was designed and tested
in the study area and a pre-tested questionnaire was applied using a spatially
stratiﬁed sampling design. The sampling process was based on photographs and
GIS tools to select sampled areas. A total of 139 interviews were conducted and

the data collected was captured and statistically analyzed with a software package.

3.2 Analysis of the Natural System

The natural system was studied under the hypothesis H1 that stated:
"There have not been signiﬁcant changes in the coastal areas of Progreso,
Yucatan, Mexico from 1986 to 1990." Hypothesis was tested under an EIA
approach utilizing a change detection analysis (CDA), photographs, maps and

M88 satellite images.

3.2.1 Boundaries and Location

The boundaries for the study area were deﬁned based upon an ecosystem
approach (Clark 1992). The onloff-shore axis (perpendicular to the shore)
included the transitional areas and shorelands tiers. The transitional area was
deﬁned as the edge of the sea, which is intertidal and included coastal lowlands
subject to ﬂooding during seastorms as well as intertidal mangroves, tideﬂats and

beaches. Also, the shoreland tier was deﬁned as: lands adjacent to the

71

transitional area which generate signiﬁcant impacts on coastal resources such as
the urban, recreational and industrial areas that usually disrupt the edge zone and
generate pollution. The longshore axis (parallel to the shore) included the coastal
lagoon in the area.

The extension of these axes provided the limits for sampling, data
collection, and analysis. The selected area has coordinates 89° 35' - 89° 50' West
and 21° 11' - 21° 18' North. The main geographic characteristics of the area
include: 1) coastal barrier, 2) coastal lagoon, and 3) the mangrove wetland ( see

Figure 6)

 

 

 

 

 

Figure 6 - Geographic characteristics of the studied area: 1) coastal Barrier, 2) coastal Lagoon, and 3)
mangrove Wetland.

Three coastal towns are located on the area: 1) Chuburna, 2) Chelem, and

3) Progreso, as well as a large stripe of vacation homes 4) Prochub. Towns are

72

located in the municipality of Progreso. This area is the ﬁfth most populated area
in the state of Yucatan with 18, 945 males and 18,861 females for a total of 37,806

permanent residents (INEGI1 1990).

3.2.2 Land Cover Classiﬁcation

Land cover of coastal areas was classiﬁed using the system proposed in
the Coastal Change Analysis Program (C-CAP) developed by NOAA Coastal
Ocean Program (Dobson et al. 1995). C-CAP was selected because the several
characteristics considered during its development: 1) detecting coastal upland and
wetland land cover, 2) monitoring changes in the coastal region, 3) calibration
among regions and scenes, 4) regional ecological modeling, 5) using remotely
sensed data, and 6) using geographic information systems. The C-CAP
classiﬁcation system involved three major coastal components for classiﬁcation: 1)
wetlands, 2) submerged vegetation, and 3) uplands. Six classes were selected to
classify coastal areas based on the objectives of this study and the main
geographical coastal characteristics. Photographs, previous studies, and ﬁeld trips
helped to select proposed subclasses as follows:
1) Upland/Developed Land. This category included areas of intensive
anthropogenic use such as: “cities; towns; villages, strip developments along
highways; transportation, power, and communications facilities; and areas such as
those occupied by mills, shopping centers, industrial and commercial complexes,
and institutions that may, in some instances, be isolated from urban areas”

(Anderson et al. 1976 in Dobson et al. 1995 ). This category was selected

73

because it represents the greatest threat to the mangrove wetland and dunes in
the coastal area of Progreso.

2) UplandNVoody Land/Deciduous/Scrub-Shrub. Includes all upland areas having
a predominance of shrub (plants < 6m in height) that lose their leaves or needles
at the beginning of the dry season. This category of tropical upland vegetation
was selected because their destruction increases run-off and sedimentation that
affect the mangrove wetland.

3) Upland/Bare Land. Categories include “ Dry Salt Flats; Beaches, Sandy Areas
other than beaches; Bare exposed Rock; Strip Mines, Quarries; Gravel Pits;
Transitional Areas; and Mixed Barren Land” (Dobson et al., 1995). This category
was selected because once mangroves are cleared and ﬁlled, then the area
develops similar characteristics to upland/bare land.

4) Estuarine Woody Wetland/EvergreenlScrub-Shrub. This category includes
areas with at least 67 % shrub that remain green throughout the year. Areas are
located in semi-enclosed tidal wetlands in which marine water is diluted by
freshwater runoff from the land. This low-energy area favors plants and animals,
such as mangroves (Rhizophora mangle, Avicenia Genninans) and oysters. This
is the most important category because mangroves have ecological, economical
and cultural values for society as described in chapter 2. Their loss has
implications for water quality conservation, ﬁsh production and protection, wood
production, and as a habitat for many birds and mammals.

5) Marine/Estuarine Unconsolidated Shore/mud-organic. Include areas with 75%
unconsolidated substrate (stones, boulders or bedrock), 25% pioneering

vegetation, and temporarily ﬂooded. Subclasses provided by Cowardin et al.

74

(1979) are: cobble-gravel, sand, and mud. The reason that this category is so
important is because it is no longer accepted that mangroves are “land builders”.
Mangroves usually follow the land building as a consequence of currents and tidal
energies (Mitchs and Gosselink 1993). Lugo (1980) indicated that mangroves
contribute to land building only after the soil has been established (Mitchs and
Gosselink 1993). This class also has implications in detecting mangrove clearing
and mortality, since hurricanes, droughts and human-inﬂuenced changes
(modiﬁcation of hydrology, dredging, and sedimentation) cause mangrove
destruction.
6) Marine/Estuarine Water. Refers to permanent ﬂooded areas that are sufﬁciently
deep so that they do not support emergent vegetation. This is the seaward
mangrove boundary where marine and estuarine waters are frequently the routes
by which mangroves are reached for wood extraction. Mangroves are also cleared
for boat landing, and afterwards the area appears as open water.

More details about the original deﬁnition of these classes can be found in
Dobson et al. (1995).

A coastal proﬁle of the Yucatan coastal area is also used to indicate the
relevance of selected classes: 1) marine system, 2) coastal barrier, 3) mangrove

wetland and lagoon, and 4) uplands (see Figure 7).

75

 

Marine Barrier Wetland Upland
System

.. ﬁlﬁﬂe -

 

 

 

 

 

. Water 0 Sand . Water . Vegetation
. Developed . Soil (DecidUOUS)
. Vegetation . Vegetation
(Dune vegetation) (Evergreen)

 

 

 

Figure 7 - Zonal classiﬁcation and categories of Progreso coastal areas.

3.2.3 Change Detection

A multiple-date change detection method using a binary mask (Dobson et
al. 1995) was used for the analysis of two MSS satellite images taken from the
same geographical area with four years lag and seasonally close. The procedure
involved the following stages:
1) Geometric corrections were performed on the images so that corresponding
pixels refer to the same place on the ground (coregistry). The following steps were
conducted: .
. Two Landsat multispectral images (MSS) taken in February, 1986 and April,

1990 were used. Images corresponded to dry season.

. Resampling was used to create a multi-raster project to construct a set of

coregistered raster images. Six points were used for coregistry.

76

Images were georeferenced using six control points. Their geographical
position were recorded by the use of a GPS. A 1st order polynomial function

was selected.

2) Radiometric distortion caused by degradation of the sensor and light conditions

was corrected according the following steps:

Moran’s l index of autocorrelation was computed for independent variable (X =
February 1986) for bands 4,5,6 and 7 at several distances or lags.

Images were radiometrically corrected to eliminate differences in calibration of
the sensors, atmospheric conditions, and solar elevation with regression. A
new set of corrected images were produced using linear equations from

regression.

3) Change was determined by computing the magnitude of a change vector in a

multidimensional space created by a multispectral image and added (logical OR)

to the magnitude of a vegetation index difference. The procedure involved the

following steps:

Change vector analysis. Bands 5 and 7 were used to compute the magnitude
of the change vector.

Distance = sqrt( (band5 (t2) - band5 a,»2 + (band7(t2)-band7(t1))2)
Thresholding. Cumulative frequency distribution of the distance was used to
select several thresholds to create binary masks that indicated potential sites
of change.

Vegetation difference index. A vegetation index was created with bands 7 and

5:

77

DlV= (band7 (t2) / band5 (t2) - (band7(t1)lband5(ti) + C

Tresholding. Cumulative frequency distribution of the difference was used to
select several thresholds to create binary masks that indicated potential sites
ofchange.

Change Binary mask. One threshold value was selected from each method to
create two binary masks. Binary mask were added (logical OR) to produce a
total binary mask for changes in the area. Changes were limited to uplands,
and wetlands. Deep water was masked out as well as the areas covered by

clouds.

4) Classiﬁcation of changed areas was conducted following a standard supervised

classiﬁcation.

The C-CAP Coastal Land Classiﬁcation System was used to convert spectral
clusters into information classes already described.

Training sites. At least ﬁve training sites were selected per class to extract
statistical information for classiﬁers. Photographs and ﬁeld data were used to
select the areas. Resulting spectral signatures were analyzed with the
inspection of the histogram. Extreme values in both sides of the histogram
were eliminated to adjust minimum and maximum values. Training sites inside
the area of change were eliminated.

Minimum distance classiﬁer. A minimum distance classiﬁer was used for
classiﬁcation. No limit was imposed on the distance and standard deviation
values were used to compare distances.

No ﬁeld inspections were conducted to assess the quality of the classiﬁcation.

78

5) Change analysis was conducted overlaying a binary mask on classiﬁed images.
. The binary mask was overlaid on classiﬁed images for date1 and date2.

. A “from-to” matrix was computed to determine changes in the area.

3.2.4 Linking Environmental Changes with Development

The following developments were selected to explore their relationship with
changes on environmental attributes based on distance: 1) urban areas, which
have been developed for permanent residences, commercial and industrial
purposes; and 2) the coastal road network.

Relationship between development and ambient environmental quality were
established over one component: land. From many possible attributes, total land
cover changes, vegetation losses, new developed land and unconsolidated soil
losses were used. Proximity maps to selected cultural developments were overlaid
on these land cover changes.

A town proximity map (i.e., the Euclidean distance between each cell and
the nearest town feature) was created starting at the main squares of Chuburna,
Chelem, Yucalpetén, and Progreso. lsolines to the target features were created
using an interval of 3 pixels (1 pixel = 60 m). A number of 30 intervals of 180 m (3
pixels of 60 m) were selected. A road proximity map was also constructed with 30
intervals of 180 m for major roads in the area. Proximity maps were overlaid on
total coastal changes, vegetation loss, new developed land and unconsolidated
soil. The number of changes per distance interval was computed and plotted to

determine their relationships.

79

3.2.5 Criteria for Rejection of the Null Hypothesis H1

Signiﬁcant changes, as it was stated for hypothesis (H1), means that the
1990 mangrove wetland individual category proportions are not signiﬁcantly
different from 1980 proportions. This step is to test two binomial proportions, the
null hypothesis Ho: p1 = p; vs. the alternative H1: p, at p2. To compute the
proportions p1 and p2, for all individual classes, the number of marginal pixels per
class were divided by the total number of changed pixel n the “from-to” matrix.

The statistic Z= (p1-p2)/ ( \/(p2*(1- p2)/n) was used. The overall hypothesis
will be rejected if evergreen (eg) or dune vegetation (dv) or both land cover
categories are rejected (eg(date1) <> eg(date2), dv(date1 <> dv(date2)) for the two

study dates

3.3 Analysis of the Social System

3.3.1 Resource Use and Ecosystem Environmental Services

Second hypothesis states: “Permanent residents from Chubuma, Chelem,
and Progreso and non-permanent residents along the coast are not signiﬁcantly
different from each other in their individual knowledge base related to: 1) the use
and environmental signiﬁcance of the mangrove wetland, and 2) their attitudes for
long term resource use and in accepting potential regulatory interventions for their
management“. Elements of survey and causal-comparative research were used.
A structured approach was selected for testing the proposed hypothesis. A
questionnaire was developed for data collection with open and closed-ended

questions with two response formats: 1) multiple choice and 2) agreement scale.

80

Hypothesis H2 part 1, was subdivided into three parts to operationalized the

concepts (Appendix A). Parts were restated as is shown below:

H2ao: No differences exist across residents from Chuburna, Chelem, Progreso
and non-permanent residents in their knowledge about the mangroves
direct use

H2bo: No differences exist across residents from Chuburna, Chelem, Progreso
and non-permanent residents in their knowledge about the mangroves
indirect use

H2co: No difference exists across residents from Chuburna, Chelem, Progreso
and non-permanent residents in their knowledge about the environmental

services provided by the mangrove wetland.

3.3.1.1 Mangrove Use and Environmental Signiﬁcance Concepts

The individual knowledge base was deﬁned as the knowledge that
residents and temporary residents living in the study area have about the
mangrove wetland uses and ecological functions. To determine the state of the
public’s knowledge about mangrove wetlands, data were collected in four
categories: 1) mangrove wetland direct products, 2) mangrove indirect products, 3)
mangrove wetland conversion, and 4) mangrove ecological functions. Proposed
direct and indirect products, and ecological beneﬁts of the mangrove wetland were
taken from Hamilton and Snedaker's (1984) as reported in Hamilton et al. (1989)

and by Dugan (1992).

81

a) Mangrove Direct Products
The following mangrove direct product use categories were selected and
included in the questionnaire (see Table 9) because of the importance of

mangrove wood to rural economies and critical to the poor who depend upon it.

Table 9 - Mangrove direct products included in the survey and the name of the variables

 

 

 

 

 

 

 

 

 

 

Category Variable Use

1. Wood for energy ﬁrewood Cooking or heating, smoking ﬁsh, and burning

production bricks

2. Wood for construction conwood Beams and poles for houses, boat-building
materials, fence post, furniture, and toys

3. Fishing Arts artwood Poles for ﬁsh traps, ﬁshing ﬂoats, tannins for
net preservation, and ﬁsh attracti:ng shelters.

4. Agriculture agricul Fodder, and green manure

5. Food and Drugs foodru Tea, condiments, and medicines

 

b) Mangrove indirect Products

The following categories in Table 10 were selected and included in the
questionnaire because mangrove indirect products (ﬁsheries and hunting) are in
many rural communities the only alternative to include protein as part of the daily
food. Mangroves also support in the area 14 bird species with value to sport
hunters that during the months of January and February have its higher activity
(Batllori 1995). For the Gulf of Mexico, 90% of the capture (US$700

millions/year) is attributed to mangrove dependent species (Dugan 1992).

82

Table - 10 Mangrove indirect products included in the survey and the name of the variables

 

 

 

 

 

 

 

 

 

 

Categg Variable Use
1. Finﬁsh finfish Food, fertilizer, and bait
2. Mammals mamma Food, fur, observation, and sport hunting
3. Crustaceans crust Food, fertilizer, and bait
4. Reptiles repti Food, skin, observation, and sport hunting
5. Mollusks mollu Food, fertilizer, bait, and shells
6. Insects insect Food, honey, wax
7. Birds birds Food, feathers, observation; and sport hunting
8. Recreation recrea Swimming, walking, sailing

 

 

 

0) Environmental Signiﬁcance

In addition to the beneﬁts from direct and indirect products, mangroves

have functions and attributes with environmental signiﬁcance unknown to the

users and coastal residents.

appreciation of the mangroves with negative consequences for their sustainable

use and conservation. The environmental signiﬁcance to residents was tested

This situation contributes to low valuation and

with the functions (ecological services) and attributes shown in Table 11.

Table 11 - Mangroves ecological services included in the survey and the name of the variables

 

 

 

 

 

 

Category Variable
1. Production of organic matter and feeding promat
nursery
2. Protection of shore erosion soilpro
3. Protection from hurricanes and storms hurpro
4. Habitat for nesting of birds nesit
5. Spawning area and protective cover nurs

 

 

6. Removal of nutrients, heavy metals, and other clewa
substances from the water.

 

 

 

 

83

d) Mangrove Wetland Conversion

Mangrove wetlands are transformed (ﬁlling and drying or removing trees)
to make it suitable for other uses. Resident knowledge about changes (change)
in the area and rate of change (rchan) were collected to assist the change
detection analysis. Possible categories for conversion were included in the

questionnaire to be used as a guide (see Table 12).

Table 12 - Mangrove wetland conversions included in the survey and the name of the variables

 

 

 

 

 

 

 

 

 

 

Category Cateﬂry
1. Residential use 7. Marine infrastructure
2. Roads 8. Disposal of Solid waste
3. Pipes 9. Salt pond developments
4. Parking lots 10. Oil industry
5. Aquacultural ponds 1 1. Sewage
6. Agriculture 12. Recreational and tourism facilities

 

3.3.1.2 Instrument Design for Use and Environmental signiﬁcance

To collect data for testing hypothesis H2, three sections were included in
the questionnaire according the categories and needs described above for: 1)
mangrove wetland use (direct and indirect products), 2) mangrove wetland
conversion, and 3) mangrove ecological functions. The questionnaire included 6
sections: 1) mangrove wetland use, 2) mangrove wetland conversion, 3) attitudes
toward sustainable resource use, 4) land use, 5) perceptions towards menaces, 5)

Environmental beneﬁts, 7) general information, and 8) ﬁnal comments.

84

Mangrove Wetland Use

In section 1 of the questionnaire a picture was to show respondents the
studied area (photo No. 1). The image included the Chelem lagoon, the
surrounding mangrove wetland, and the uplands where Chuburna, Chelem, and
Progreso are located. A list of names were collected in Q1 in order to identify
how residents referred to the study area and the associated vegetation. Q1:
Would you like to tell us the name you use to designate the area that is
shown inside the RED circle in PHOTO #1?. Using a second picture (photo
#2) trees that live partially submerged in water where showed to the
respondents. Names were registered in question 2. Q2: Would you like to tell
us the name you use to designate those trees shown inside the RED circle
in PHOTO #2? To know if residents use the mangrove wetland and their
products, Q3: Do you make use of any part of these trees such as the wood,
roots, ﬂowers, leaves or any other part of the tree? Respondents were told
that based on previous discussions with members of the community, some
possible uses of these trees were identiﬁed and that we would like to know more
about them. Q4: Please indicate from the following list your current uses
and add other uses people make of these trees. Add any others uses not

represented on the lists. Question format is shown in Figure 8.

85

 

Own Use Others Use
1. Wood for energy production ( ) Q4.1.a ( ) Q4.1b
Additional information was collected for speciﬁc use such as:
Cooking or heating .................. ( ) ( )
Smoking ﬁsh ........................... ( ) ( )
burning bricks ....................... ( ) ( )
Other ....................................... ( ) ( )

2. Wood for construction ( ) ( )Q

3. Fishing Arts construction ( )Q4.3a ( )Q4.3.b
4. Agriculture ( ) ( )Q
5. Food and Drugs ( ) ( ) Q

 

 

 

Figure 8 - Mangrove wetland use format question

Surveyors also asked, 05: Do you know of any other trees harvested
in this area (photo #1) that people make use of? and 06: Can you mention
the names of those trees?

To determine what indirect products are used, Q7: What other beneﬁts
have you received from this area ( Photo #1) in practicing hunting, ﬁshing,
swimming, bird watching or any other activity? An example of the question

format is shown in Figure 9.

86

 

Own Use Others Use
1. Finﬁsh ( ) Q7.1a ( )Q7.1.b
Additional information was collected for speciﬁc use such as:

Food ............................. () ()

Fertilizer ........................ () ()

Bait ............................... () ()

Other .......................... . () ()
2. Mammals ( )Q7.2a ( )Q7.2.b
3. Crustaceans ( )Q7.3a ( )Q7.3.b
4. Reptiles ( )Q7.4a ( )Q7.4.b
5. Mollusks ( )Q7.5a ( )Q7.5.b
6. Insects ( )Q7.6a ( )Q7.6.b
7. Birds ( )Q7.7a ( )Q7.7.b
8. Recreation ( )Q7.8a ( )Q7.8.b

 

 

 

Figure 9 - Mangrove indirect product format question

Mangrove Wetland Conversion

Three questions were used to collect data for comparison in section 2 of
the questionnaire. QB: Do you think that important changes in the land use
or cover have occurred in the area shown in Photo #17, 09: How fast do
you think these changes are occurring? (0 to 5), (5 to 15), or (more than 15
years) and Q10: Can you tell us the current use(s) of the changed area(s)?

Figure 10 shows question format.

 

 

Residential use ........................................... ( ) Salt pond developments .............................. ( )

Road constructions ..................................... ( ) Storage areas .............................................. ( )

Pipe Introductions ....................................... ( ) Oil Industry .................................................... ( )
Parking lots ................................................. ( ) Fish industry .................................................. ( )
Aquacultural ponds ..................................... ( ) Sewage ......................................................... ( )

Agriculture ................................................... ( ) Recreational and tourism facilities ................ ( )

Marine infrastructure ..................................... ( ) Other ............................................................ ( )

Solid waste disposal .................................... ( ) .

 

Figure 10 - Mangrove wetland conversion format question

 

87

Environmental Services.

Section 6 of the questionnaire was developed to measure to what extent
residents knew about the environmental beneﬁts provided by the mangrove
wetland. Six out of thirteen services mentioned in Hamilton et al. (1989) were
used. Q14: Below we have a list of environmental services provided by
mangrove wetland. For each one, please check those that you know.

Figure 11 shows the question format.

 

 

1. Mangroves are a source of food for many small animals that .......................... ( ) ( ) Q14.1
live in the swamp. These small animals are also food
for other creatures.

2. Mangrove protect the soil. Without these plants tides would ........................... ( ) ( ) 014.2
erode the soil.

3. Mangrove trunks and branches protect other plants and animals .................... ( ) ( ) 0143
from hurricanes and storms.

4. Mangrove branches are the place where birds construct their nests ................ ( ) ( ) 014.4
to reproduce, protect, and grow.

5. Calm waters close to mangrove roots are the place where ﬁsh ....................... ( ) ( ) 014.5
and crustaceans reproduce, protect, and grow.

6. Mangrove roots and soil help to keep water clean. They trap ......................... ( ) ( ) Q14.6

substances mixed in the water.

 

Figure 11 - Mangrove environmental services format question

3.3.1.3 Criteria for Rejection of the Null Hypothesis H2a, H2b, and H20

To test difference across towns (Chuburna, Chelem, Progreso and
Prochub) and across coastal stripes (beach, inner and wetland), proportions
were calculated for all variables needed to test the null hypothesis of

homogeneity (H20: p1=p2=p3=p4). The test statistics were given by X2-

 

88

The following tables, Table 13, 14 and 15 show variable name,
explanation, derivation and type that were used in the contingency tables to test

H2a, H2b and H2c.

Table 13 - Mangrove direct products variables, explanation, derivation, and type.

 

 

 

 

 

 

 

 

Variable Explanation Derivation Type
Name
ﬁrewood Respondent currently uses or ﬁrewood=1 if Q4.1A OR NQM|NAL
has known others use wood Q4.1B IS “YES” ELSE
for energy production ﬁrewood=2
conwood Respondent currently uses or conswood=1 lF Q4.2A OR NOM|NAL
has known others use wood Q4.2B IS “YES” ELSE
for home constructions conswood=2
artwood Respondent currently uses or artwood=1 IF Q4.3A OR NQM|NAL
has known others use wood Q4.3B IS “YES" ELSE
for ﬁshing_gear constructions artwood=2

 

 

Table I4 - Mangrove indirect products variables, explanation, derivation, and type.

 

 

 

 

 

 

 

 

 

 

 

Variable Explanation Derivation Type

Name

finfish Respondent currently captures ﬁnﬁsh=1 if Q7.1A OR Q71 B NOMINAL
ﬁnﬁsh or has known others IS “YES” ELSE ﬁnﬁsh=2
capture in the area

crust Respondent currently captures crusta=1 IF Q7.3A OR 0738 NOM|NAL
crustaceans or has known IS “YES” ELSE crusta=2
others capture in the area

mollu Respondent currently captures mollus=1 IF Q7.5A OR Q7.5B NOM|NAL
mollusks or has known others IS “YES” ELSE mollus=2
capture in the area

birds Respondent currently hunts birds=1 IF Q7.7A OR Q7.7B NOM|NAL
birds or has known others IS “YES” ELSE birds=2
hunt in the area

mamma Respondent currently hunts mamma=1 IF Q7.2A OR NOM|NAL
mammals or has known Q7.2B IS “YES" ELSE birds=2
others use the area for hunting

recrea Respondent currently uses or recrea=1 IF Q7.8A OR Q7.8B NOM|NAL
has known others use the IS “YES" ELSE recrea=2
area for recreational purposes

 

 

 

89

Table 15 - Mangrove environmental services variables, explanation, derivation, and type

 

Variable
Name

Explanation

Derivation

Type

 

promat

Respondent has known
about mangrove food
production for small ﬁsh.

answer to Q14.1

I NOMINAL

 

soilpro

Respondent has known
about the protection
mangrove provides to soil.

answer to 014.2

NOMINAL

 

hurpro

Respondent has known
about the protection
mangrove provides during
storms.

answer to Q14.3

NOMINAL

 

nesit

Respondent has known
about the mangrove as a
place for bird nesting and
reproduction.

answer to 014.4

NOMINAL

 

nurs

Respondent has known
about mangrove roots as a
ﬁsh nursery and a place for
ﬁsh reproduction.

answer to Q14.5

NOMINAL

 

 

clewa

 

Respondent has known
about mangrove role for water

 

quality.

answer to Q14.6

 

NOMINAL

 

3.3.2 Regulatory Interventions

Hypothesis H2 section 2, was subdivided into three parts and were

restated as is shown below:

H2do: No differences exist across residents from Chuburna, Chelem, Progreso

and non-permanent residents for their attitudes towards long term

resource use.

H2eo: No difference exist across residents from Chuburna, Chelem, Progreso

and non-permanent residents in their awareness regarding threats to

mangrove wetland.

H2fo: No difference exist across residents from Chuburna, Chelem, Progreso,

and non-permanent residents to accept mangrove wetland regulation.

 

90

To test the acceptance of regulatory interventions, data were collected in
three categories: 1) attitudes towards long terms resource use, 2) awareness
towards potential threats that will increase conversion and pollution, and 3)

acceptance towards regulation.

3.3.2.1 Attitudes Toward Long Term Resource Use

Information about people’s feelings, beliefs and values related to resource
conservation and protection of critical processes in coastal ecosystems, was
collected with an agreement scale type questionnaire. The survey included
questions to measure attitudes towards animals proposed by Kellert (1980):
moralistic, naturalistic, ecologistic, utilitarian, and negativistic. In addition
to these cultural values, we also included questions to test other values such as:

existence, option, as well as for intergenerational equity.

3.3.2.2. Awareness Regarding Threats to Mangroves

From current human activities in the area, mangrove wetland can be
affected by conversion, residuals from residential, commercial and industrial
activities and rates of resource consumption. Selected categories and

corresponding types are listed in Table 16.

91

Table 16 - Mangrove potential threats included in the survey

 

Category type

 

1. Land use Urban expansion

Port Infrastructure
Aquaculture Development
Roads expansion

 

Domestic and Industrial solid wastes
Untreated domestic and industrial sewage
Industrial and Vessels/pipes oil spills

2. Waste

 

 

Trees
Fish
Hunting Birds and Mammals

3. Resources consumption

 

 

 

3.3.2.3 Acceptance of Regulation

As mentioned in Chapter 2, many coastal areas are environmentally
sensitive and some of them, such as the mangrove wetlands, are considered
critical to rural communities and to biodiversity conservation. To explore to
what extent regulation can be enforced and accepted by residents in the area,
hypothetical regulation was proposed to protect nursery areas and corridors
where commercial or recreational human activities would be banned. For current
extractive activities such as ﬁshing, hunting, and tree cutting the need for the
following regulation was proposed: permits, limiting or rotating the number of
ﬁshing grounds, or the establishment of seasons. In urban activities, the
following proposals were used: controlling new home construction, roads and
port infrastructure (marinas) as well as regulation for solid garbage disposal

and sewage.

92

3.3.2.4 Instrument Design for Long Term Resource Use
Attitudes Toward Resource Use

An initial list of 60 sentences were developed for section 3 of the
questionnaire. The list was given to three judges (i.e., mangrove and reef
experts) to see which questions clearly contribute or do not contribute to long
term coastal resource use. After several discussions with the judges, 33
questions were removed. During the training period difﬁculties were observed in
keeping respondent interest and respondents also had difﬁculties in
understanding some of the questions. Therefore an additional 13 sentences
were removed from the questionnaire. Q11: We want you to rate the
following sentences using the next options: Agree (3), neutral (2), Disagree

(1), and (0) for avoiding the question. The following sentences in Figure 12

 

 

were used:

1. We have a moral obligation to protect the ﬁsh and the wildlife .................... ( ) ( ) ( )
2. It cause me satisfaction to see the wetland and its wildlife .......................... ( ) ( ) ( )
3. Sewage discharged into the wetland affects the ﬁsh ................................... ( ) ( ) ( )
4. Regardless of how much we exploit resources nature will always ............... ( ) ( ) ( )

continue to provide enough for our needs

5. More wetland vegetation means more ﬁsh ................................................... ( ) ( ) ( )

6. We need to conserve the wetland to be studied ........................................... ( ) ( ) ( )

7. Wetlands only produce dangerous animals to people .................................. ( ) ( ) ( )

8. Wetlands may not exist in the future. Therefore, I must exploit ................... ( ) ( ) ( )
them as much as possible now.

9. Wetlands conservation is needed because may be in the future ................. ( ) ( ) ( )
we will ﬁnd valuable things in it

10. I do not have an obligation to conserve the wetlands for others ................... ( ) ( ) ( )

11. The reason that wetlands will exist in the future is sufﬁcient ......................... ( ) ( ) ( )
important for me to participate in conservation.

12. Anything people do on their property will not create problems .................... ( ) ( ) ( )
for others

13. lwould sacriﬁce something to uphold the purpose of wetland conservation ( ) ( ) ( )

14. Wetland meetings are a waste of time ......................................................... ( ) () ( )

 

Figure 12 — Attitudes towards mangrove resource use format question

 

93

Land Use Regulation

Questions in section 4 of the questionnaire were developed to determine if
residents were willing to accept more regulations for using the wetland and
conservation. Q12: Thinking about the area shown in PHOTO #1, would you
like to tell us your answer for the next questions (Yes, Undecided, No)?

Figure 13 shows the format used in the questionnaire.

 

 

1. Are you willing to accept /enforce the law to control some .............................. ( ) ( ) ( )
activities to protect land, vegetation, wildlife, and water in this area?

2. This law may demand more control on homes, roads, and boat ..................... ( ) ( ) ( )
landing constructions. Is this acceptable? '

3. This law may prohibit the use of wetlands for dumping. ................................... ( ) ( ) ( )
Is this acceptable?

4. This law may prohibit the cutting of living trees. ............................................... ( ) ( ) ( )
Is this acceptable ?

5. This law may prohibit hunting during some seasons and ................................ .( ) ( ) ( )
require a hunting license. Is this acceptable?

6. This law may close ﬁshing grounds, regulate ﬁshing arts, ............................... ( ) ( ) ( )
and require a permit. Is this acceptable?

7. This law may control the number of boats and trips. Is this acceptable ? ......... ( ) ( ) ( )

8. Do you support the creation of protected areas where, construction, .............. ( ) ( ) ( )

hunting, and ﬁshing would be completely banned?

 

Figure 13 - Land use regulation format question

Awareness of Current Menaces

Questions in section 5 included: 1) increasing developments that may get
off part of the wetland, 2) waste management in the urban, commercial and
industrial area, and 3) extraction of natural populations. Q13: From the
following list please tell us which ones you consider a current threat to
mangrove wetlands (plants and animals). Figure 14 shows the format used

for this section in the questionnaire.

 

94

 

 

 

 

1 . Is it the actual size of the urban area of Chuburna, Chelem, and Progreso? ......... ( ) ( )
2. Is it the number of port facilities? ............................................................................ ( ) ( )
3. Is it the type of road construction ? .......................................................................... ( ) ( )
4 Is it solid garbage? ................................................................................................... ( ) ( )
5. lsitsewage? ........................................................................................................... ( ) ( )
6 - Is it oil pipes and vessels leaks ................................................................................ ( ) ( )
7 - Is it the wood rate extraction ? ................................................................................. ( ) ( )
8 - Is it the commercial and subsistence ﬁsheries ........................................................ ( ) ( )
9 . Is it Hunting .............................................................................................................. ( ) ( )

Figure 14 - Awareness of current menaces to mangrove wetland format question

General Information

General information was collected from the respondents in section 7.
Q15: the birth date (mmlddIYY). Q16: sex (M=malelF=female), Q17: marital
status (married/not married), Q18: job type, Q19: the number of years living

in the area or using the area, and Q20: the number of years in school.

Final Comments

At the end of the questionnaire, section 8, respondents were thanked for
Participating and offered the opportunity to make suggestions or comments
about the interview. 021: We appreciate the time that you have set aside for
this interview and I am happy to make a note of any of your suggestions

related to this interview.

95

3.3.2.5 Criteria for Rejecting of the Null Hypothesis H2d, H2e, and H2f
To test differences across residents, proportions of each of the variables
per strata were calculated to test the null hypothesis of homogeneity (H2d, H2e,
H2f: p1=p2=p3=p4). The test statistics is given by X2- The following tables, Table
1 7, 18, and 19 list the names, explanations, derivations and types of variables
that were used for testing H2d for long term resource use, H2e to accept/enforce

regulation, and H2f for awareness toward current mangrove wetland threats.

96

Table 17 - Long term resource use variables, explanation, derivation, and type.

 

Explanation Derivation Type

I Variable
Name

 

"

rmoral

Respondent believes we have
a moral obligation to protect
the wetland

response to 011.1

NOMINAL

 

natura

Respondent experiences
satisfaction when he is in
contact with the wetland

response to Q11.2

NOMINAL

 

-—'

\ ecol1

 

Respondent accepts that
sewage affect life in the
wetlands

response to Q11.3

NOMINAL

 

(ecolz

Respondent recognizes limits
in nature

response to 011.4

NOMINAL

 

ecol3

Respondent relates
vggeiation with animals.

response to 011.5

NOMINAL

 

scien

Respondent accepts the
need of conservation for
mangroves be studied

response to 011.6

NOMINAL

 

negat

Respondent believes that only
dangerous creatures live in
the wetland

response to 011.7

NOMINAL

 

shon

Respondent preference is for
short time

response to 011.8

NOMINAL

 

option

Respondent accepts
conservation for mangroves
because of their possible
future uses

response to 011.9

NOMINAL

 

equity

Respondent accepts his
obligation to protect the
mangroves for others

response to Q11.10

NOMINAL

 

exist

Respondent participates in
conservation with the only
reason mangroves will exist in
the future

response to 011.11

NOMINAL

 

Respondent accepts that
some actions can create

response to 011.12

NOMINAL

 

negative externalities
Respondent is willing to
sacriﬁce something for
wetland conservation

response to Q1 1.13

NOMINAL

 

 

 

Respondent say that
participation in wetland
conservation is not waste of
time

 

response to Q11.14

 

NOMINAL

 

 

97

Table 18 - Acceptance/enforcement of regulation variables, explanation, derivation, and type.

 

 

 

 

 

 

 

 

 

 

 

regulation to maintain core
areas where conversion and
extractive activities would be
banned.

 

 

I Variable Explanation Derivation Type
Name
I gral Respondent accepts response to 012.1 NOMINAL
regulation as a way to protect
natural resources
I cons Respondent accepts - response to Q12.2 NOMINAL
regulation as a way to control
urban development, roads,
and boat Iandingareas
i cont Respondent accepts response to Q12.3 NOMINAL
regulation as a way to
regulate discharges in the
wetland
“ﬂood Respondent accepts response to Q12.4 NOMINAL
regulation for wood extraction
lﬁsh Respondent accepts response to Q12.5 NOMINAL
regulation for fishinL
Ihunt Respondent accepts response to 012.6 NOMINAL
regulation for hunting
Ireserv Respondent accepts response to 012.8 NOMINAL

 

 

98

Table 19 - Awareness toward mangroves threats variables, explanation, derivation, and type

 

Variable Explanation Derivation Type
Name

 

urgro Urban growth is currently a response to 013.1 NOMINAL
threat for the mangrove
wetland

 

porin Port infrastructure is currently response to Q13.2 NOMINAL
a threat for the mangrove
wetland

 

roads The road network is currently response to 013.3 NOMINAL
a threat for the mangrove
wetland

 

garba Garbage is currently a threat response to Q13.4 NOMINAL
for the mangrove wetland

 

trewa Untreated sewage is currently response to 013.5 NOMINAL
a threat for the mangrove
wetland

 

oi|spi Oil spills are currently a threat response to 013.6 NOMINAL
for the mangrove wetland

 

woode Wood extraction is currently a response to Q13.7 NOMINAL
threat for the mangrove
wetland

 

fishe Fishing effort is currently a response to 013.8 NOMINAL
threat for the mangrove
wetland

 

 

hunte Hunting is currently a threat response to 013.9 NOMINAL
for the mangrove wetland

 

 

 

 

3.4 Sampling Design

3.4.1 Population

The target population was deﬁned as all permanent and temporary
residents in the coastal area of Yucatan from Chuburna to Progreso. Target
population included all adult males and females (>18 years) living in the area
from February 1996 to August of 1996. The target area was deﬁned as a set of
5 locations: 1) residential homes in Chuburna, 2) residential homes in Chelem, 3)

residential homes in Progreso, 4) vacation homes from Chuburna to Chelem

 

99

(Prochub a) and 5) vacation homes from Chelem to Yucalpeten, Progreso
(Prochub b).

To delineate the spatial boundaries for the sampling frame, several
pictures were generated using photographs and city maps. Vacation homes
were all included in one non contiguous semi-homogenous stratum (A), and
towns were divided in three semi-homogenous stratums (A, B and C) that run
parallel to the coast. Coastal area segmentation was decided according to the
spatial distribution of permanent residents and an appreciation of their socio-
economic conditions. Residents in stripe A are integrated in neighborhoods with
a better economic condition than those living in B and C. Stripe B characterizes
all resident that live in the inner part of the towns where stores, schools, and in
general services are located. Stripe C characterizes residents that live in difﬁcult
and poor conditions close to or inside the wetland. Figures 15, 16, 17, 18, and
19 shown these stripes and subdivisions created for the purpose of organizing
interviewers and ﬁnding sampled elements.

The sampled population was drawn using a disproportional (each town
with a different sampling distance) stratiﬁed (three stripes) systematic aligned
(same street) sampling to determine the location of the respondents. A
nonprobability sampling design was selected due to the absence of lists of
residents and maps with large scales to identify single homes. Used design
allowed a better access to residents, however the underlying problem is that
generalization must be carefully made to other groups or areas. As
recommended by Fraenkel (1993), in general a minimum of 30 samples must be

collected per group. In this study and because limited funds, 28 samples were

100

collected in Chuburna, 30 in Chelem, 37 in Progreso, and 45 in Prochub for a

total of 139 interviews.

3.4.2 Data Collection

Seven interviewers (6 males and 1 female) received training for data
collection at Centre de lnvestigaciones y Estudios Avanzados del IPN
(CINVESTAV-IPN) en Mérida, Yucatan. They received information about the
main survey objectives and procedures. During the sessions for training
interviewers, they worked with two respondents with characteristics similar to
people living in the studied area. During the training sessions and pilot study
several inadequacies were detected and corrected in the questionnaire.

Samples were taken from each of the deﬁned strata during February and
March for permanent residents and during August 1996 for temporary residents.
The interviewers were instructed to interview ﬁve people in their area at regular
intervals. All interviews were conducted face-to-face to a person who identiﬁed
themselves as one of the heads of the household, when this person was not at
home, the interview was conducted with another adult. Residents who accepted
the interview were informed that they were free to reject a question that they felt
were inappropriate. They were also told that their answers would not be

identiﬁed in any way.

101

 

 

Figure 15 - Chuburna sample frame

 

Figure 16 - Chelem sample frame

 

Figure 17 - Prochub sample frame “a”

102

 

Figure 18 - Prochub sample frame “b”

 

Figure 19 - Progreso sample frame

Chapter 4

RESULTS

 

This chapter provides the results from the change detection and proximity analysis conducted
in the coastal areas of the Yucatan, Mexico to identify environmental changes and their spatial
relationships with cultural features: 1) reads and 2) urban centers. It also presents the results ﬁom
interviews conducted in four towns assessing residents individual's knowledge related to the use and
ecological beneﬁts of mangrove wetlands, threats to mangroves and attitude towards resource use and
acceptance of regulation.

 

4.1 State of the Natural System.
4.1.1 Data Preparation

A coregistered multi-raster project data was created with 2 M88 images, 4
bands per image (2 visible and 2 infrared) acquired in February 1986 and April 1990.
The image from February 1986 was selected as a base map and a set of 6 control
points were used for April 1990 image coregistry. An overall RMS error of 0.706
pixels (i.e., area of 1 pixel = 4424 m2) was obtained. Residuals for a ﬁrst order
polynomial and nearest neighbor selection for selected points are: 1) 1.009 pixels for
the old marine terminal in Progreso, 2) 0.8193 pixels Chicxulub saltpond, 3) 0.2084
pixels Chuburna main square plaza, 4) 0.4988 pixels road intersection, 5) 0.6588

pixels Chelem salt pond, and 6) 0.7618 pixels Chicxulub main square.

103

104

Control Point No. 1

   

Progreso Old Marine Terminal Progreso Old Marine Terminal
X = 282.5. Y = 227 Februarv 1986 X = 285, Y = 227 April 1990
N 2 358 065 W 222 741

Control Point No.2

   

Chicxulub Salt Pond Chicxulub Salt Pond
X = 427, Y = 228 February 1986 X = 428, Y = 228 April I990

Control Point No.3

   

Chuburna Main Square Plaza

Chuburna Main Square Plaza
X = 30, Y = 88 April 1990

X = 29, Y = 87 February 1986
N 2 352 655 W 207 734

Figure 20 - Control point geographical positions for image coregistry.

105

Control Point No. 4

 

Road Intersection Road Intersection

X = 337 , Y = 45.5 February 1986 X = 336.5, Y = 44.5 April 1990
N 2347 331 W 224116

Control Point No.5

 

Chelem Salt Pond

Chelem Salt Pond
X = 34, Y = 45.5 February 1986

X = 33.5. Y = 46.5 April 1990

Control Point No.6

   

Chicxulub Main Square Plaza Chicxulub Main Square Plan
X = 394.4, Y = 225.5 February 1986 X = 395-5. Y = 224-5 Aprll 1990
N 2 356 916 W 229 522

Figure 20 - (Cont’d).

the UTM coordinate system and their (x,y) coordinates can be found in Figure 20.

was georeferenced using six ground control points. The correspondence ﬁle was
created with a total of six points, three points selected from those used for
coregistry (i.e., point 3, 4 and 6) and three additional points at which geographical
positions were easily recorded. The geographical position of visited points was
recorded in the ﬁeld with a global positioning system (GPS) calibrated for the area.
Residuals for linear geometric transformation used in this procedure are shown in
Table 20 and an overall RMS = 0.839. A UTM coordinate system was overlaid on
MSS images for the study area with coordinates from 206 000 E to 230 000 E and

from 2 348 000 N to 2 360 000 N (Figure 21).

Table 20 - Summary of geometric transformation to UTM system for both dates based on 6 control

106

points and linear polynomial

Six subimages indicating the geographic positions of these control points in

After coregistry, the set of images acquired in February 1986 and April 1990

 

 

 

 

 

 

 

 

 

 

 

 

 

Num x y Easting Northing Residual
(new x) (new y)
1 258.01 304.5 222741 2362815 0.309
2 336.47 45.5 224512 2347331 1.031
3 158.79 138.0 215448 2354397 1.159
4 394.06 224.0 229522 2356916 0.470
5 29.91 87.0 207734 2352655 0.716
6 339.37 32.5 224526 2346679 0.993

 

 

107

 

06 08 10 12 14 16 18 20 22 24 26 28 30

 

 

 

 

 

 

 

 

1" 8"?" «1,... .-

 

.' is“: [if

 

 

 

 

 

 

 

 

 

Meters
w ‘. Moan UTM 220000 E, 23n000 N —
4,757.64

 

 

 

Figure 21 - UTM grid coordinate system for MSS images in the study area

Moran's | index of autocorrelation was calculated for all bands of the
independent variable X (February 1986) bands 4, 5, 6 and 7 at several distances
or lags: 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, and 110. Results from these
calculations, for bands 4,5 and 7, indicated that suitable lags to have non
autocorrelated samples occurred at distances of: 1) 80 for band 4, 2 random, z<2
and an index value of 0.1213, 2) 80 for band 5, 2 random, z<2.5, and an index
value of 0.1708 and 3) 100 for band 7, 2 random, z<2.7 and an index value of
0.3297. Correlograms calculated from data of the independent variable are shown

in Figure 22.

108

 

IBand4_l I 2 I”-..

1l
Moran's I

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 22 - Correlograms from independent variable (MSS, Feb. 1986)

13:“: my 4%)" Band4(1986)I. [—1on ‘
2 12: 0%‘17166 1' 1 100°
161 067111 f 2 60 . _ 0.8I \ ‘g‘
‘20 p 054177 J 23 ..g 0.6+ i r--. 710° g
30l 05766 16 l £0.41 I . .‘ .__ 210 f
2 2 ﬁ 240‘ 0.4043I 4' y8ﬂ 02 I . i i I 3
2 504017351 _ 2 0 f + g g 21-2. III,
601 0.4084 6 .- o
1111111 ,. 2 22- 1 8 8 8 8 8° 8
- :82 833(1); - 147'? DistancelLag
. 7 lBand5 I 1 v 2 A ‘ ; Nbran'sl
{Lagv Moran'sl Tz(randorn) Band5(1986): , I _ m
' 09644: *1: 7 -901 1 1.. “706;  .
_ 10": 9242i -339. 1 _ 0.8 , g =
20l 06218I 27.51 I" -, 06 i .2 100 E I
30, 0.6409 20.13 i gm '1}: l
7 40703807 778.6 » 2302, III. 10 :1‘
-- 501 0.2871 -- 4.8 o. - 2 - 2 - 2 s1 2
33133617 3;; "‘988888 8 ~
I 23%‘ I ‘
‘77’81’61768‘ 62491 - , 2:212:
Band7 l l I 3 ann‘sl
Lag Moran'sl lz(random‘ Band 7 (1985)I _'
7““— 1 0.976 911 l 1 7 “ 7 ‘ "
10 0.7866 73 1 ”I1, ‘0: ‘5‘ I
20 0.6749 30 ‘ - ° " o l
_ I .u OBI - ls,‘ 11$ l
30 0.619, 18.5 , g - 1 1 . - l s l
40 0.5877T 12.8 t a 0.4 2 l'a' I ' I e ?
50 04108 706 I 02‘ ‘mﬂm 10 :1
T 607 0.412 6245 , '02,.“ ... , -22 ,_, L, 1‘1 -°
, i: 33:22 .2 ewes
90 0.5676 5.16 Distance/Lag
100 0.3297[ 2.75 _

109

With reference to previous correlograms, a distance lag of 87 pixels was
used in an unaligned random sample design to select samples for a linear
regression. Regressed 19 pair values per band produced the following set of

equations for radiometrically corrected images (Table 21 ).

Table 21 - Equations for radiometric corrections of bands 4,5, 6, and 7

 

 

 

 

 

Regression r t n df
line

y4=-1.20+1.3x4 0.86 6.86 19 17

y5=-1.59+1.3x5 0.90 8.72 19 17

y5=1.60+1.23x6 0.90 8.52 19 17

y7=0.82+1.46x7 0.94 11.65 19 17

 

 

 

 

 

 

 

4.1.2 Change Detection

Resulting values from the change vector analysis had a Gaussian
distribution. Thresholds were selected with the use of the cumulative frequency
distribution at 80%, 90%, 95%, and 99% above the lower end point.
Corresponding radiometric distances of 13, 20, 31, and 56 of these previous
percentages resulted with potential thresholds to decide change between the two
dates. These radiometric values were selected to create 4 binary masks to
indicate potential sites of change (Figure 23). The threshold value of 20 for a 90%
cumulative frequency distribution was selected for potential land cover changes

detected by vector analysis (10% of the largest change values were selected).

110

From the vegetation index by Nelson (1983) and its cumulative frequency
distribution four ranges -0.35 to 0.45 , -0.45 to 0.74, -0.55 to 0.84 and -0.75 to 1.34
at 75%, 80%, 85%, and 90% respectively were selected to create 4 binary masks
to indicate potential changes (Figure 24). The interval from -O.55 to 0.84 at 85%
was selected for potential land cover changes detected with vegetation index.
Looking at the work by F ung and LeDrew (1988), threshold values selected for this
study may underestimate change since the number of s.d. was greater than 1.
These two indices were combined with the OR operation to produce a binary mask
for 1986—1990 total potential change (Figure 25). Clouds and oceanic waters were

masked out in the right-lower corner and upper parts of the image respectively .

 

 

 

 

 

 

 

 

 

 

E5

Figure 23 - Change binary masks for change vector analysis: a) distance of 13 for 81%, b) distance
of 20 for 90%, c) distance of 31 for 95%, and d) distance of 56 for 99%

 

 

 

1‘11

 

 

 

 

 

 

 

 

 

(C) (d)

 

 

 

 

 

 

Figure 24 - Change binary masks for vegetation index: a) from -.35 to .45 for 0 at 75%, b) from -.45 to
.74 for 0 at 80%, c) from -.55 to .84 for 0 at 85%, and d) from -.75 to 1.34 for 0 at 90%

 

 

 

 

 

Figure 25 - Total radiometric changes: Change vector at 90% confidence interval (OR) vegetation
index at 85% conﬁdence interval

112

For a supervised classiﬁcation, spectral signatures of training sites for
developed land (dl), evergreen (eg), unconsolidated wet soil (us1), unconsolidated
dry soil (us2), deciduous (de), and dune vegetation (dv) are shown in Table 22.
Samples of each category were selected using photographs, false color images,

and site inspections with the use of a GPS.

Table 22 - Spectral signatures of coastal classes: 1) developed land (dl), 2)evergreen (eg), 3)
unconsolidated wet soil (usl), 4) unconsolidated dry soil (usZ), 5) deciduous (de) and 6) dune vegetation
(dV)

 

 

Band 4 | Band 5 1 Band 6 Band 7

 

 

 

 

 

 

N Name min max mean min max moan mm max mean min max mean
1 d1 44 58 51.4 52 73 63.4 62 82 71.6 58 84 68.9
2 eg 21 33 25.6 18 32 25.3 37 48 42.2 42 53 46.7
3 us1 26 46 36 32 58 45.1 22 52 37.4 6 22 12.7
4 U32 59 84 70.6 67 1 12 87.9 80 102 91.1 65 95 80.1
5 de 20 27 22.1 14 22 17.4 54 64 58.4 64 71 67.1
6 dv 27 37 31.7 27 41 33.1 55 7O 62 62 73 67.4

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Land cover classes that resulted from a supervised classiﬁcation algorithm
(minimal distance/ normalized s.d.ﬁnﬁnite) were reclassiﬁed to collapse and solve
the confusion between classes. Classes were collapsed using the following
criteria: The image was segmented in two parts: 1) dunes, and 2) wetland and
upland. Dunes were reclassiﬁed as follows: a) dl = dl or us1, this means that
beaches were included as developed land, b) dv = dv or 69 or do, this means that

all types of vegetation on the dunes were classiﬁed as dune vegetation. Wetlands

and uplands were reclassiﬁed as follows: a) us1 - us1 or dl, incorrect

 

113

classiﬁcation of unconsolidated soil as developed land was corrected, 69 = 69 or
dv, vegetation that was classiﬁed as dune vegetation in the wetland was included
as ever green. Finally, wet unconsolidated soil (us1) was collapsed with dry
unconsolidated soil (usZ). Results of the classiﬁcation process and training stage
confusion matrices for C-CAP coastal classes inside the potential areas of change
are shown in Figures 26 and 27 and error matrices and the Kappa coefﬁcient of

agreement in Tables 23 and 24.

114

 

No Change 1 986 (a)
Developed Land
Evergreen
Unconsolidated Soil
Deciduous

Dune Vegetation

  
  

ﬁllllﬂ

 

 

 

Figure 26 - Classification of changed areas for February 1986 (date 1)

Table 23 - Training stage confusion matrix and Khat output statistics for February 1986 image

 

Training stage confusion matrix

 

ACTUAL CLASS
1 2 3 4 5
MAP
CLASS
1 349 0 0 0 30
2 0 254 5 9 0
3 0 4 1083 0 0
4 0 0 0 58 0
5 28 0 O O 185
PCC = 96.209476

Khat = 0.940838 +/- 0.012829
Individual Class Accuracy
Class User's (PCC and Kappa) Producer's (PCC and Kappa)
1 92.1, 0.903 +/- 0.044, 92.6 0.908 +/- 0.044
2 94.8, 0.940 +I- 0.043, 98.4 0.982 +l- 0.045
3 99.6, 0.992 +l- 0.044, 99.5 0.990 +/- 0.044
4 100.0, 1.000 +/- 0.047, 86.6 0.862 +/- 0.041
5 86.9, 0.853 +/- 0.044, 86.0 0.844 +I- 0.044

 

 

 

 

6...... 1990 (b)
Developed Land
Evergreen
Unconsolidated Soil
Deciduous

Dune Vegetation

   

 

   
 

 

., '3 , to: ' 5,840.00

0. o .1 ‘1' ‘3‘.
a n . \ a‘, ' -

x. - ‘ . I 1 '
.1 0.2“ Abel .. ..

Figure 27 - Classiﬁcation of changed areas for April 1990 (date 2)

 

 

Table 24 - Training stage confusion matrix and Khat output statistics for April 1990 image

 

Trainﬂg stage confusion matrix

 

ACTUAL CLASS
1 2 3 4 5
MAP CLASS

1 377 0 5 0 31
2 0 248 6 32 0
3 0 8 1077 0 0
4 0 2 0 35 0
5 0 0 0 0 1 84

PCC = 95.810474
Khat = 0.934416 +I- 0.013463
Individual Class Accuracy
Class User's (PCC and Kappa) Producer's (PCC and Kappa)
1 91.3, 0.893 +l-0.041, 100.0 1.000 +/- 0.046
2 86.7, 0.848 +I-0.041, 96.1 0.955 +/- 0.046
3 99.3, 0.984 +/-0.044, 99.0 0.978 +/-0.044
4 94.6, 0.944 +I-0.059, 52.2 0.513 +/- 0.032
5 100.0, 1.000 +/- 0.048, 85.6 0.841 +/- 0.040

 

 

 

116

4.1.3 Coastal Land-Cover Changes Hypothesis H1

Quantitative measurements of aerial changes by land-cover class for the

studied area are shown in a “from-to” matrix (Table 25). Columns represent values

from the 1986 total classes “changed_to”, numbers along the diagonal indicate the

area that did not change, and row values indicated 1990 total classes

“came_from”. Marginal values in the matrix represent the total area in hectares for

 

 

 

 

 

 

 

 

classes.
Table 25 - “From-To” matrix for coastal changes from 1986 to 1990
1986 Class
Class dl eg us do dv Total Difference
dl 500.35 5.75 181.83 0 269.86 957.79 429.12
1990 eg 0 1012.21 48.22 192.44 1.77 1254.64 -137.15
Class us 19.91 345.52 1747.48 12.39 3.53 2128.83 140.68
de 0 27.43 2.21 98.21 0 127.85 -175.19
dv 8.41 0.88 8.41 0 190.67 208.37 -257.46
Total 528.67 1391.79 1988.15 303.04 465.83 4677.48

 

 

 

 

 

 

 

 

 

 

Developed land (dl).

Total developed land in 1986 accounted for 528.67 ha.

Large sections of developed land (as expected) 94.64%, remained as part of the

total developed land in 1990. Small parts of developed land went to 5.36 % of

unconsolidated and dune vegetation. Conversion to developed land mainly came

from 5.75 ha of evergreen, 181.83 ha unconsolidated, and 269.86 ha dunes, to

have in March 1990, a total of 957.79 ha of developed land. An increment of

429.12 ha (82.5 °/o) resulted for this land cover type.

 

117

Evergreen (erg). Total evergreen areas in 1986 accounted for 1391.79 ha. Part of
this total, 72.7% remained as part of the evergreen in 1990. The rest, 27.3 %
mainly changed to the following classes: developed land by 5.75 ha,
unconsolidated by 345.52 ha, and deciduous by 27.43 ha. Contributions came
from unconsolidated wet by 4822 ha, and deciduous with 192.44 to end 1990
with a total of 1254.64 of the evergreen category. A reduction of (9.8 %)
resulted.

Unconsolidated (us). Total unconsolidated soil in 1986 accounted for 1,988.15 ha
and 87.89 % of this total remained as part of the total unconsolidated soil in 1990.
The rest, 12.10 % mainly changed to developed land by 181.83 ha, evergreen by
48.22. Contributions to 1990 unconsolidated came from 345.52 evergreen to have
a total of 2128 ha in 1990. An increment of 140.68 ha (7.07 %) resulted. .
Deciduous (de). Total deciduous in 1986 accounted for 303.04 ha. Only 32.4 % of
this total remained as part of the total deciduous in 1990. The rest, 67.6 % mainly
changed to evergreen in 192.44 ha, and with minor changes went to
unconsolidated soil by 12.39. Contributions came from 27.43 ha of evergreen, and
2.21 ha of unconsolidated soil. 1990 ﬁnished with a total of 127.85 of deciduous
vegetation. A reduction of 175.19 ha (57.81 %) was observed.

Qine vegetatign (d_v)_. Total dune vegetation in 1986 accounted for 465.85 ha.
Only 40.9 % of the total remained as part of the total dune vegetation in 1990. A
greater proportion of the total change (59.1 %), went to 269.86 ha of developed
land, and a minor proportion changed to 1,77 evergreen, and 3.53 unconsilidated.

Contributions to 1990 dune vegetation came from 8.4 ha developed land, and

118

8.41 ha unconsolidated soil. Resulting in a total of 208.37 ha of dune vegetation.
A reduction of 257.04 ha (55.17 %) resulted.

Conditional and overall Kappa coefﬁcient of agreement (k) computed
between these two images for changed area is shown in Table 26. A coefﬁcient
value k=0 indicates obtained agreement equals chance agreement. Positive
values occur from greater than chance agreement and negative values from less

than chance agreement.

Table 26 - Conditional and overall Kappa coefﬁcient of agreement (k) for land cover changes

 

 

 

 

 

 

 

Class KIA when CAM90 KIA when CAM86
as reference as reference
dl 0.4573 0.9330
eg 0.7251 0.6280
us 0.6890 0.7780
de 0.7521 0.3050
dv 0.9035 0.3818
Overall Kappa 0.6527 i .011180 '

 

 

 

 

 

Proportions between 1986 and 1990 are shown in Table 27, they were used
for the Z test statistic Z= (p1-p2)l V(p2*(Pz -1)/n). Selected rejection region R:
abs(Z) 2. 1.96 correspond to or=.05 (see table 27). Results for signiﬁcant
difference between proportions indicated that individual cover classes were
rejected for Ho: p1 = p2 (see Table 27). The overall hypothesis H1 was rejected
as evergreen and dune vegetation had a statistically signiﬁcant different

proportions between 1986 and 1990.

119

Table 27 - Signiﬁcant change for coastal land cover proportions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Class 1986 1990 1986 1990 abs(z) Ho: p1=p2
# # proportion proportion abs(z) 21.96

pixels pixels a=.05

dl 1195 2165 0.11 0.20 30.3 rejected

eg 3146 2836 0.30 0.27 6.6 rejected

us 4494 4812 0.43 0.46 6.3 rejected

de 685 289 0.06 0.03 15.6 rejected

dv 1053 471 0.10 0.04 18.9 rejected

Total 10573 10573 1 1

 

 

Four selected areas in the boundaries of Chuburna, Chelem, Progreso and
Prochub were deﬁned using four mask polygons 1, 2, 3, 4a and 4b indicated in
Figure 28. Inside these polygons, “from-to” changes were selected using the
results of the procedure described above. “From-to” matrices are shown in Tables

29, 30, 31 and 32. A summary of the differences between dates are in Table 28.

Table 28 - Differences between February 1986 and April 1990 per class

 

 

 

 

 

 

Area Developed Evergreen Unconsolidated Dune
Land soil Vegetation
Chuburna 27.43 0.88 -7.96 -17.70
Chelem 56.63 -4.87 -6.19 -45.12
Progreso 150.42 -148.65 50.88 -23.89
Prochub 44.24 -0.44 -7.97 -35.83

 

 

 

 

 

 

120

 

 

 

Figure 28 - Selected areas for change detection: Chuburna, 2) Chelem, 3) Progreso and 4) Prochub

Chuburna (Area 1). Total new developed land become 27 ha from 1986 to
1990. Conversion to developed land affected coastal dunes by 17 ha and
unconsolidated soil by 10 ha. Mangroves, included in the evergreen (eg). in this

area did not indicate major changes between these two dates (see Table 29).

Table 29 - Area 1, Chuburna “from-to” matrix 1986 - 1990

 

Chelem (Area 2).

Conversion to developed land came from dune vegetation (dv) with 45 ha and

unconsolidated soil (us) by 11 ha. Evergreen vegetation (eg) had a total loss of 4

121

ha to contribute increment unconsolidated soil (us) (Table 30).

Table 30 - Area 2, Chelem “from-to” matrix 1986 - 1990

Total developed land (dl) grew 56 ha from 1986 to 1990.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1990

1986 dl 89 us de dv Total Difference
dl 29.20 0.00 11.06 0.00 45.57 85.83 56.63
:9 0.00 14.16 3.541 0.88 0.00 18.58 -4.87
us 0.00 8.8 126.97 0.00 0.00 135.82 6.19
de 0.00 0. 0.00 0.44 0.00 0.88 044
dv 0.00 0.00 0.44] 0.00 10.18 10.62 -45.12
Total 29.20 23.46 142.01] 1.3 55.74 251.73

 

 

Progreso (Area 3). Total developed land (dl) growth 150 ha from 1986 to 1990.
Conversion to developed land came from dune vegetation (dv) with 27 ha and

unconsolidated soil (us) with 117 ha. Evergreen vegetation (eg) had a total loss of

148 ha to increment unconsolidated soil (us) (Table 31).

Table 3] - Area 3, Progreso “from-to” matrix 1986 - 1990

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1990

1986 dl 89 us de dv Total Difference
dl 88.48 5.31 117.6 0.00 27.43 238.90 150.42

42L 0.00 439.30 35.83 21.24 t 0.00 496.37 448.65
us 0.00 198. 285.35 10.62 0.00 494.60 50.8
de 0.00 1.77 1.3 0.4 0.00 3. -28.76
dv 0.00 0.00 3. 0.00 11.50 15.04 -23.89.

Total 88.48 645.02 443.71 32.30 38.93 1248.45 J

 

122

Prochub (Areas 4a and 4b). Total developed land (dl) growth 44 ha from 1986 to
1990. Conversion to developed land came from dune vegetation (dv) with 45 ha
and unconsolidated soil (us) with 6 ha. Evergreen vegetation (eg) did not showed

a total cover change (Table 32).

Table 32 - Area 4, Prochub “from-to” matrix 1986 - 1990

 

 

 

 

 

 

 

 

 

 

 

 

1990

1986 dl 89 us do dv Total Difference
dl 107.50 0.00 6.1 0.00 45.12 158.82 44.24
eg 0.00 0.00 0.00 0.00 0.00 0.00 -0.44
us 0.00 0.00 0.00 0.00 0.00 0.00 4.96l
de 0.00 0.0 0.00 0.00 0.00 0.00 0.00
dv 7.08 0.44 1.77 0.00 60.17 69.46 -35.83
Total 114.5 0. 7. 0.0 105.29 228.28

 

 

 

 

 

 

 

4.1.4 Environmental Changes and Development

The following results were obtained when proximity maps, based on the
distances to the road system and selected towns, were overlaid on coastal land
cover changes. Distances to roads, selected at intervals of approximately 180 m
(3 pixels of 60 m) and the total land cover change area are shown in Figure 29.
The total number of pixels per interval are shown in Figure 30 and shows the
shape of the relationship between distance to road and change. Distance to roads
were overlaid on new developed land, dune vegetation losses, evergreen

vegetation losses and uncosolidated soil losses.

123

 

 

 

 

 

Figure 29 - Total land cover changes and road proximity map

600

#01
CO
CO

No. of pixels
N 00
O O
O O

..x
O
00

Figure 30 - Relationship between road distances and total land-cover change

I

" ‘-
Jill-llama".-
10 20 30

Distance (1=180 m)

No. of Pixels

No. of Pixels

124

500 r l
: "
400 1

300 i 1

l w
200., -

100 ~ 'l

O I 7 ' - I. r - 7 7 ‘ V ‘ i "i 777‘ if I' _ I

O 5 10 15 20 25 30 1
Distance (1=180m) ‘

Figure 31 - Total new developed land and road proximity map

500 -~,

l I
400—4
300 f

l
200~i E , ~
1 1
100 1— 1 1

 

0.——-iIm-unuu1 -——~-- —-- _,
0 5 10 15 20 25 30

Dista nce (1 =1 80m)

Figure 32 - Total dune vegetation losses and road proximity map

125

500 .
400 .

|

300 1
l

200 .~

l

No. of Pixels

100 ,I -

I. I
0 + - ~ *1 ‘IIII-IIIIIIIIIII ~

0 5 10 15 20 25 30
Distance (1=180m)

Figure 33 - Total green vegetation losses and road proximity map

500
400 ;
300

200

N0. 01 Pixels

100 -1

I '-
0 . 1 --~—-I---I---
O 5 1 O 1 5 2 0 2 5 3 0

Distance (1=180m)

Figure 34 - Total unconsolidated soil losses and road proximity map

126

Similarly to the road proximity map, a town proximity map was created for
Chuburna, Chelem and Progreso with Intervals of 180 m (Figure 35) according the
polygons for these areas described in Figure 28. Land cover changes per class at
selected lags (1 lag = 3 pixels or 180 m) for new developed land (ndl), dune
vegetation losses (dvl), evergreen losses (egl) and unconsolidated soil losses(usl)
are shown in Figure 36. Plots in the columns of Figure 36 indicate changes per

class and the rows indicate the towns. In these plots, x axis indicates lag

numbers and “y" axis indicates the number of pixels with change.

 

 

 

Figure 35 - Town proximity distances to selected areas in Chuburna, Chelem and Progreso

Chuburna

98.5588383
9

ndl ‘

1015 Z) 25 I!)
dvl

0 5 10 15 20 25 30
egl -- ,3 ---

5.
a-

15i

1.11... '

1L W

'59 5 10 15 Z) Z a)
usl‘ . - ,, 7--.

Cycle!!! . ,- PEOEICSB , 3
‘401 401T
”J $4 . :i 357 7.."
1&1 . 1w ‘b’.
11 5+1 1 15‘ ‘ 1‘ ‘1;
1mm ‘, 2... .1
11 151i ,1 i1 159'? 31
110+ “ 1 101 1.’ 19.
l 51 5

1 l l;

i! 0* a— #° “# +— + "ﬂ 1 0+0—-“-——+——- r—— 'a— 4 ——l 1
I'051015m2531l.051015202530

'1 so. 9 30 .7
‘ w l 1
1251" 1251.
' z)- i .1 201 1
l 1 1 1 111
115W 1 1115-1 1.1
‘1 10" . l 10‘ 1' 11".
5. 5, f‘
O? ‘ ' ‘ ' 7' 01M. ‘ 7' ""“’ ’
0 5 10 15 Z) 25 I) 1 0 5 10 15 20 25
1, A _|__ 2 .,_- , -
l 1 is 7 7 7 1 ~61 7-—— ‘ ~ 7 ~ — ’— v
. - 1
‘1 501' 1150i 1"",
1 l 1 l .11
2 40 | 404 1"
1' l l 1
1 sol 301 3.1" 1”
l 1 1‘ . ‘ '11“,
l l 1 1 ‘ 11 .
1120 11:1 ,0 b1, 1
. 104 1 9
I l. t
1" CW 11 owe 5 57—19
0 5 1o 15 20 25 30 ,1 0 5 1o 15 20 25
, -1 - _ ,
~ 1 ‘45?
‘ 40' 35i 7d
11301 i1 ! * W
,, l 25% ’ 1
.1 1
“120,r 1* “ l.
15 ll 15' ‘ V
1 1 l l 1 .
’10- ye i ;
1i 0 I; illlllll’llllllll'I". ‘ 1 l
1105101520253033‘59510152025

127

30

30

Figure 36 - Changes at different lags for new developed land (ndl), dune vegetation losses (dvl),
evergreen losses (egl), and unconsolidated soil losses (usl) for Chuburna, Chelem and Progreso
(labels: x=lags, l lag = 180 m, y=Pixels)

128

4.2 State of the Social System

Results from the survey conducted with permanent residents of Chubumé,
Chelem, Progreso and non-permanent residents (Prochub) in the coastal areas of

the Yucatan Peninsula are presented.

4.2.1 General Information

The study area in the Yucatan Peninsula included: Chuburna, Chelem,
Progreso, and Prochub. These towns are located in the north west part of the
State of Yucatan. Results from age (age), sex (sex), marital status (marit),
years of residence in the area (resti) and educational level (school) of residents

from Chuburna, Chelem, Progreso and Prochub, as well as from the total

sample are summarized in Table 33 and Figure 37.

Table 33 - General information for variables: age, sex, marital status, years of resident, and schooling

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Place Average Males Females Married Single Average years of Average
age °/. % % % residence educational
level
n=104 n=137 n=137 n=137 n=137 n=l33 n=126
Chuburna 41.85 18 10 25 3 30.53 7.03
13.14% 7.30% 18.25% 2.19%
Chelem 48.30 19 10 23 7 29.26 5.03
13.87% 7.30% 16.79% 5.11%
Progreso 37.17 16 21 25 10 28.97 8.37
11.68% 15.33% 18.25% 7.30%
Prochub 37.20 21 22 36 8 17.66 14.05
15.33% 16.06% 26.28% 5.84%
Totals 40.89 74 63 109 28 26.05 8.96
54.01% 45.99% 79.56% 20.44%
F 2.49
p .064

 

 

129

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ﬁlmmmf
,5 7 , . . . f _
l l
l .
5- l .
1 "‘j ‘
S» 1 ‘r
u 2
W45 .
9 u
5 U 10 1 ...“.1 1
‘ l
25 .
zsuow ' .
‘5 A 1 MEI! H l
enmi- Chelem Pwm Prue-a ‘ “'9‘ ‘
PLACE
(a)
WW'WIW
n .-
l) ‘t
. . l
1nl r'
10 l l
—.——..1
a 1. “PT! 1
1 z 1 z
n m RICE
8 than 0.-
3» . ,
z ..._
ro~ l
1 l
10 . i 1
L D 1 lawn.
a ' 4“ ' 15115:
i l l
m m ° h"
l"WI-u M
(C)
autumn-Perm
N
‘1 v ‘
16~ 1 1
E
1 2 .
12-
8 5| 1 °
3
‘ 1;
‘i
xsuou
0‘ 1mm
Oatm- Ct‘n Prey-a m u H
m

 

 

 

Figure 37 - General information: a) age, b) sex, c) marital status, d) years of resident, and e)
schooling

130

4.2.2 Use and Environmental Signiﬁcance Hypothesis H2a, H2b and H2e
Hypothesis H2 was developed to test the differences between residents
knowledge related to: a) natural resource use of mangrove direct products, b)
mangroves indirect products, 0) mangroves ecological role, d) attitudes toward
long term resource use, e) awareness of potential threats that will increase

conversion and pollution, and f) acceptance of regulation.

4.2.2.1 Direct Use (H2a)

To test resident knowledge about direct use of the mangroves, three
contingency tables were constructed for the following variables: 1) wood for energy
production (ﬁrwood), 2) wood for construction (conwood), and 3) ﬁshing tool

construction (artwood) (Table 34).

Table 34 - Contingency tables for mangrove direct use variables ﬁrewood, conwood, and artwood.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Place ﬁrewood conwood artwood

yes no total yes no total yes no total
Chuburna 22 6 28 13 15 28 7 21 28
Chelem 23 7 30 17 13 30 10 20 30
Progreso 1 5 22 37 4 33 37 3 34 37
Prochub 9 35 44 4 4O 44 3 41 44
total 69 70 139 38 101 139 23 116 139
Expec. % 49.64 27.34 16.55

 

The main purposes of the analysis was to test whether the populations

 

were homogeneous with respect to the total cell probabilities. This allowed the null
hypothesis for homogeneity Ho: p1 = p2 = p3 = p4 to be tested. The following

values for X2 resulted: ﬁrewood = 34.35, p = 0.0; conwood = 30.59, p = 0.0 and

131

artwood = 12.49, p =0.006. Tabulated upper 5% value of X2 was 7.815 for d.f. =
3. As a result the null hypotheses were rejected at a = 0.05 for ﬁrewood,
conwood, and artwood.

To explain the reason for rejecting the null hypothesis, observed minus
expected frequencies (O-E) and interaction plots of frequencies were calculated
and are shown in Table 35 and Figure 38. Table 35 shows the major departures
of homogeneity. The signs of the differences that make the largest contribution to
the X2 indicated the nature of the departures from homogeneity (i.e., Prochub for
variable ﬁrewood with 7.55 and 7.44). Figure 38, a graphical representation of the
contingency tables, shows the absolute number of responses (1= yes and 2 = no)
per town per variable (i.e., Chuburna, in contrast to Prochub has a large number of

residents (22) using wood for energy production.

Table 35 - Observed minus expected frequencies for mangrove direct use variables.

 

 

 

Place ﬁrewood conwood artwood
)8: 34.35 x2= 30.59 x2= 12.49
p=0.0 p=0.0 p=0.006
(O—E) (O—E)2/E (O-E) (O-E)2/E (O-E) (O-E)2/E
yes no yes no yes no yes no yes no 165 no

 

Chubumé 8.10 -8.10 4.72 4.65 5.35 -5.35 3.73 1.40 2.37 -2.37 1.21 0.24

 

Chelem 8.11 -8.11 4.41 4.35 8.80 -8.80 9.44 3.55 5.04 -5.04 5.11 1.01

 

Progreso -3.37 3.37 0.62 0.61 -6.12 6.12 3.70 1.39 -3.12 3.12 1.59 0.32

 

 

Prochub -12.84 12.84 7.55 7.44 -8.03 8.03 5.36 2.02 -4.28 4.28 2.52 0.50

 

 

 

 

 

 

 

 

 

 

 

 

 

 

132

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Interaction Plot PLACE x F IRWOOD
4o.
35 >
30 >
25 >
I!)
,o
g 20 * 4
S
"‘ 15 —O- PLACE
Chuburna
10 0 PLACE
Chelem
5 0 PLACE
Progreso
0 --e- PLACE
G 1 1 G 22 Pm
FIRWOOO
(8)
Interaction Plot. PLACE x CONWOOD
45 ,
40 x.
/
35 , X
x o
/
30 ,I
/
3 25 /
g /
o I
12° 1’
“ ,5 4» PLACE
Chuburna
10 '9‘ PLACE
Chelem
Progreso
0 «t- PLACE
G_1 1 632 ”WM”
CONWOOD
Interaction Plot. PLACExARTWOOD
45 -
40 > 1‘
I
/
35 1’ o
I
so ,’
a, 25
8
g 20
3
S 15
‘L 4» PLACE
1o Chubuna
~11 PLACE
5 Chelem
4» PLACE
° Progreso
.5 «a» PLACE
G 1 1 G_2:2 m
ARTWOOO

 

 

 

Figure 38 - Interaction plots of direct use for variables: a) ﬁrwood (ﬁrewood), b) conwood (home
construction), and c) artwood (ﬁshing tool construction).

133

4.2.2.2 Indirect Use (H2b)

Variables used for testing resident knowledge about mangrove indirect
use included: 1) ﬁnﬁsh used as food, fertilizer and bait (ﬁnﬁsh), 2) crustaceans
used as food, fertilizer and bait (crust), 3) mollusc used as food, fertilizer, bait,
and handcraft production (mollu), 4) birds Used as food, sport hunting,
watching and handcraft production (birds), 5) mammals used as food, fur, and
sport hunting (mamma), 6) reptiles used as food and skin, (repti), 7) insects

used as food, wax, honey (insect), and 8) used for recreational area (recrea).

Eight contingency tables were constructed for these variables (Table 36).

Table 36 - Contingency tables for indirect use variables ﬁnﬁsh, crust, mollu, birds, mamma, repti,
insect and recrea.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Place ﬁnﬁsh crust mollu

yes no total yes no total yes no total
Chuburna 20 8 28 16 12 28 22 6 28
Chelem 25 5 30 25 5 30 28 2 30
Prggreso 21 16 37 21 16 37 16 21 37
Prochub 18 26 44 1 43 44 12 32 44
Total 84 55 139 63 76 139 78 61 139
Expec. % 60.43 45.32 56.12
Place birds mamma repti

yes no total yes no total yes no total
Chuburna 15 13 28 5 23 28 4 24 28
Chelem 13 17 30 15 15 30 3 27 30
Prggreso 1 1 26 37 1 36 37 3 34 37
Prochub 3 41 44 13 31 44 0 44 44
Total 42 97 139 34 105 139 10 129 139
Expec. °/o 30.22 24.46 7.19
Place insect recrea

yes no total yes no total
Chuburna 3 25 28 9 19 28
Chelem 1 29 30 17 13 30
Progreso 0 36 36 10 27 37
Prochub 9 35 44 18 26 44
Total 13 125 138 54 85 139
EQec. % 9.42 38.85

 

134

The purposes of testing H2b was to determine whether the populations are
homogeneous with respect to the cell probabilities. The null hypothesis for
homogeneity was tested as follows: Ho: p1 =5 p; = p3 = p4. Results were the
following values for x2 : ﬁnﬁsh = 15.21, p = 0.00164; crust = 53.92, p = 0.0;
mollu = 39.96, p = 0; birds = 21.11, p = 0.00001, mamma = 21.34, p = 0.0 , repti
= 12.49, p = 0.006, insect = 21 .11, p = 0.0, and recrea = 6.8, p = 0.08.

The null hypotheses were rejected at a = 0.05 for all X2 values of variables
except recrea. The tabulated upper 5% point of X2 is 7.815 for d.f. = 3. To explain
the nature for rejecting the null hypothesis, observed minus expected frequencies
and interaction plots of frequencies were calculated and are shown in Table 37
and Figure 38. An examination of Table 37 indicates a major departure from
homogeneity. The differences in the contribution to the x2 shows the departures

from homogeneity primarily at Prochub in the case of ﬁnﬁsh with 2.77 and 4.24.

135

Table 37 - Observed minus expected frequencies for mangroves indirect use variables

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Place ﬁnﬁsh x2= 15.2, p = 0.001 crust x2= 53.92, p = 0.0 mollu 1?: 39.96, p = 0.00
(O-E) (O-E)2/E (O-E) (O-E)2/E (O-E) (O-E)2/E
yes no yes no yes no yes no yes no yes no
Chuburné 3.06 -3.08 0.56 0.66 3.31 .331 0.66 0.72 6.29 -6 29 2.52 3.22
Chelem 6.67 5 67 2.60 3 98 11.40 -1140 9.56 7.93 11.17 -1117 7.41 9.47
Progreso -136 1.36 0.06 0.13 4 23 4.23 1.07 0.68 4.76 4.75 1.09 1,40
PTOChUb 6.59 6.59 2.77 4 24 18.94 16.94 17.99 14.91 -12.69 12.69 6.52 834
Place birds x2= 21 .11, p = 0.0 mamma x2= 21.3, p = 0.0 repti x2= 12.49, p = 0.006
(O-E) (O-E)2/E (O—E) (O-E)2/E (O-E) (O—E)2IE
yes no yes no yes no yes no yes no yes no
Chubumé 6.54 -6.54 5,05 2.19 -165 1.85 0.50 0.16 1 99 .199 1.96 0.15
Chelem 3.94 3.94 1.71 0.74 7.66 -7.66 6.00 2.59 0.64 0.64 0.33 0.03
Progreso -018 0.16 000 0.00 -805 6.05 7.16 2.32 0.34 034 0.04 000
PTOChUb -10 29 10 29 7 97 3.45 2.24 -2 24 0.47 0.15 -3.17 3.17 3.17 0.25
Place insectTf: 21 .11, p = 0.0 recrea {2: 6.8 , p = 0.08
(O-E) (O-E)2/E (O-E) (O-E)2/E
yes no yes no yes no yes no
Chubumé 0.36 036 0.05 0.01 -166 1.88 0.32 0.21
Chelem -163 1.63 1.16 0.12 5.35 .535 2.45 1.56
Progreso -339 3.39 3.39 0.35 4.37 4.37 1.33 0.65
PTOChUb 4.66 .466 5.69 0.59 0.91 091 0.05 0.03

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

136

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

mmmemmsu MMWIM
E a
a
25 41 ,x
/
I
//
20 1 3° /
V] r 0 II,
g6 20 °* V . //
' d
., ,
5 N
“,0 mo: 10 7” + ms
1 clan , cum-
‘1 91.405 ’7’ ° 1* PUCE
5 a 01m 11 , cum
- Puce 1» met
I _ Proms) Pm
ogi 7 A J ~ PUCE ,,OL_72 7.7 + m
G 11 6_22 PM” 0.11 G_22 W
ansu CRUST
(2) lb)
WMPMCEIMOUU WMPUCExBRDS
”(‘7 6
A
1) 0 (7
as 4’
25 /
an ,’
20 1’ o
9 325 / "
.2 .6 ,
1915 820 //
ﬂ 3 / 0
E10 £15 KR
PlACE 9 ',’ + m
crum- 10 ° ,1 cum
5 mos / p m
in Claim 5 / CM!“
0 PLACE ' .2 mos
Prove-n 0 Prague
‘5 ,2“? A Puss 5 + mos
c_1 1 G_22 m G_1 1 6]] “m
MOLLU BIRDS
(C) (d)
Impumcsxm map-11191111211105.1190
405—7 ~————v—-7 so 7,
‘ 1
35 7° ‘ A i
0 1
3° 1 1
1 ° 1
as 31; .
1 D J
0 I)
'§‘° § ° 1
0 Q N
315 ﬁ-
“ .0 PLACE " ,0 . 4 m
01mm 1 enm-
5 rue: a ‘ 0 me
Chem 0" l M
0 o rues , «1 moi
P109330 Mn
5 PlACE 401 7 _ 7 .9 PLACE
G_11 G_22 9"“ c_11 s_z2 "m
mm 115711
(e) (0

Figure 39 - Interaction plots of mangroves indirect use variables: a) ﬁnﬁsh, b) crust, c) mollu, d)
birds, e) mamma, f) repti, g) insect and h) recrea.

 

 

137

 

 

 

 

 

 

 

 

 

 

“erection Plot. PLACE x NSECT hencnon Pet PLACE x RECREA
40 11
5 o
A
I) 25
25
m .
’3’ 20 f" t . ° .
§ 5 1 a j
1 .
g g '5 1: 1
LL 10 '0' PM 0 :
Chum '
5 0 me ’
Chelem ‘0 ‘ 1°: *
0 0 me
Progeso
_5 g . + PLACE 5 _ ,
G_1.1 G_2:2 W G_1 1 (332
lNSECT RECREA
(g) (11)

Figure 39. (can’t)

 

138

4.2.2.3 Environmental Services (H20)

The variables used for testing resident knowledge about environmental

services were: 1) production of organic matter (promat), 2) soil protection

(soilpro), 3) hurricane protection (hurpro), 4) nesting site (nesit), 5) nursery site

(nurs), and 6) water quality (clewa). Contingency tables are shown in Table 38.

Table 38 - Contingency tables for indirect use variables promat, soilpro, hurpro, nesit, nurs, and

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

clewa.

Place promat soilpro hurpro

yes no total yes no total yes no total
Chuburna 20 8 28 19 9 28 21 6 27
Chelem 13 17 30 20 10 30 18 12 30
Progreso 29 8 37 29 7 36 26 1 1 37
Prochub 32 12 44 28 16 44 32 1 1 43
total 84 55 139 96 42 138 97 40 137
Expec. % 67.63 69.57 70.8
Place nesit nurs clewa

yes no total yes no total yes no total
Chuburna 26 2 28 23 5 28 17 11 28
Chelem 28 2 30 28 2 30 10 20 30
Progreso 32 2 34 31 6 37 18 19 37
Prochub 40 4 44 31 12 43 1 1 32 43
total 126 10 136 113 25 138 56 82 138
Expec. % 92.65 81.88 40.58

 

 

 

 

 

 

 

 

 

 

The null hypothesis of homogeneity was tested (Ho: p1 = p2 = p3 = p4)

and the results were (12) : promat = 10.74, p=.013; soilpro = 2.94, p= .4; hurpro

= 2.60, p=.45; nesit = 0.32, p=.95; nurs = 5.52, p=.13, and clewa = 10.37,

p=0.015. The null hypotheses was rejected at 01:05 for promat and clewa

(tabulated upper 5% for 12 is 7.815, d.f. = 3).

(Figure 40) and contributions to X2 (Table 39) were calculated.

Interaction plots of frequencies

 

 

139

 

 

WMWXPRMT WMWISULPRO
:15 . e S e
. A '
:0 301 c
1 ° ‘ i
1 5,
251 .

Frequendes
. .8
9
a
Frequencies
a 4

'o'
0

$2153.29
ésiéisis

7011 W 7 7 7 E22
mom some

 

 

 

 

(a) (b)

 

mmmxmnm mmPhtPLACErNESﬂ

3133.23.53

a
6'1

Frequencies
8
u
quuenaos

6

ésé’éisEé
121111;;

a
a

r
b

HURPRO ' NESU

 

(C) (d)

 

 

WNWINURS WMWIQEWA

81

. 3
a

Frequencies
Frequencies
o
1
o

8

O
6

321156112
asséiéss

u-
o
a

o
.

NURS CLEWA

 

 

 

 

(e) (0

Figure 40 - Interaction plots of mangroves environmental services variables: a)promat, b)soilpro,
c)hurpro, d)nesit, e)nurs, and f) clewa

140

Table 39 shows major departures from homogeneity, the signs of the

differences that make the largest contribution to the X2 indicate the reason for the

departures. For example, Chelem for variable promat contributed with 2.62 and

5.47.

Table 39 - Observed minus expected frequencies for mangrove environmental beneﬁts variables

 

Place

promat?= 10.7 p = 0.013

soilpro 12: 2.94, p = .4

hurpro 18: 2.5, p = 0.45

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

(O-E) (O-E)2/E (O-E) (O-E)2/E (O-E) (O-E)2/E
yes no yes no yes no es no yes no yes no
Chuburna 1.06 -1.06 0.06 0.13 -0.48 0.48 0.01 0.03 1.88 -1.88 0.19 0.45
Chelem -7.29 7.29 2.62 5.47 -0.87 0.87 0.04 0.08 -3.24 3.24 0.49 1.20
Progreso 3.98 -3.98 0.63 1.32 3.96 -3.96 0.63 1.43 -0.20 0.20 0.00 0.00
Prochub 2.24 -224 0.17 0.35 -2.61 2.61 0.22 0.51 1.55 -1.55 0.08 0.19
Place nesit 18*: 0.32, p = 0.95 nurs x2 = 5.52, p = 0.13 clewa x2= 10.37, p = 0.015
(O-E) (O-E)2/E (O-E) (O-E)2/E (O-E) (O-E)2/E
yes no yes no yes no yes no yes no yes no
Chuburna 0.06 -0.06 0.00 0.00 0.07 -0.07 0.00 0.00 5.64 -5.64 2.80 1.91
Chelem 0.21 -0.21 0.00 0.02 3.43 -3.43 0.48 2.17 -2.17 2.17 0.39 0.27
Progreso 0.50 -0.50 0.01 0.10 0.70 -0.70 0.02 0.07 2.99 -2.99 0.59 0.41
Prochub -0.76 0.76 0.01 0.18 -4.21 4.21 0.50 2.28 -6.45 6.45 2.38 1.63

 

 

 

 

 

 

 

 

 

 

4.2.2.4 Similarities using H23, H2b and H2c

To summarize similarities among the communities a cluster analysis was

used to compare mangrove direct use, mangrove wetland indirect use and

mangrove wetland environmental services. Results of the analysis, based on

Euclidean distances, were graphed in a tree cluster (Figure 41). Distances were

calculated based on variables that were rejected for homogeneity (i.e., direct

use: ﬁrwood, conwood and artwood; indirect use: promat and clewa; and

environmental beneﬁts: recrea).

 

141

 

 

 

 

 

 

 

 

 

 

 

Tree Diagram ior4Cases
Single Linkage
Euclidean distance:
55,-—*—-7- ~i—~#—
1 l 1 I
so- 1 l
' |
1
l
40 - 1 1
2 35}
8 l
I.
5 30‘
g 251
5
20; 7 i
151 l
10‘ j 1 ‘ l
5 :_ . . . . . - , 1
PROCHUB PROGRESO CHELEM CHUBURNA
(a)
Tree Diagram 101 4 Cases
Single Linkage
Euclidean distances
75175 27‘2 _-,.._-__ —-—-————‘
1 1 * “‘“ ‘ ‘ _ ”'
1 j |
70 l 1 1
1 l
' l
65 . .
8
c
E!
3 60
8 i ,
g 1 l
1: .
:1 55-
l .
1 1 z z -
w . . 7 l
I 1
45 1 7 . , ~
PROCHUB CHELEM PROGRESO CHUBURNA
0))
Tree Diagram for 4 Cases
Single Linkage
Euclidean distances
‘0 . if f . if z, 7 z,
1 1 l
- 1
1 I ‘ .
35» l . l
1 l
| l
, A 7 2 -
g 30; 1 ‘ l
5 1
.2 ' , l 1
° 1
11' 25. 1 1 1
x 1
C.
'3' 1
20 1
I l
. 1 j
15‘, . __.. . . __.__ . __. .‘ zAiz.
CHELEM PROCHUB PROGRESO CHUBURNA

 

 

 

(C)

Figure 41 - Similarities among residents in studied places for: a) Direct use , b) Indirect Use and c)
Environmental services provided by the mangrove wetlands

142

4.2.3 Regulatory Interventions Hypotheses H2d, H2e and H2f
4.2.3.1 Attitudes toward long term Resource Use (H2d)

In this section and in the two following sections, results from questions
measuring the following are presented: attitudes toward long term resource use,
awareness of threats that will increase conversion and pollution, and acceptance
of- regulation.

Results from questions to measure attitudes toward long term resource use
are shown in contingency tables for the following attitudinal variables: 1) moralistic
(moral), 2) naturalistic (natura), 3) ecologistic (ecol1, ecol2, ecol3), 4) scientistic
(scienc), 5) negativistic ( negat), 6) short term beneﬁts (short), 7) .option value
(option), 8) equity (equity), 9) existence value (exist), 10) externalities (exter), 11)
willingness to contribute (willi), and 12) willingness to participate (parti) (Table 40).
Respondent options included: 1) yes/agree, 2) uncertain and 3) no/disagree.

The main purpose of this analysis was to test whether the populations were
homogeneous with respect to the total cell probabilities computed by town. The
null hypothesis for homogeneity (Ho: p1 = p2 = p3 = p4) was tested and accepted
for 12 variables. The chi-square (X2) statistic, the degrees of freedom (d.f.), and

probabilities of occurrence (p) for variables are shown in Table 41.

143

Table 40 - Contingency tables for long term resource use variables: moral, natura, ecoll, ecol2, ecol3,
scienc, negat, short, option, equity, exist, extern, willi, and parti.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

moral natura
Place yes unce- no total yes unce- no total
taln taln
Chuburna 27 1 0 28 28 O O 28
Chelem 29 O 1 30 30 0 0 30
Progreso 37 O 0 37 36 0 1 37
Prochub 44 O 0 44 39 2 3 44
total 137 1 1 139 133 2 4 139
Expect. % 98.56 95.68
ecol1 ecol2
Place yes uncer- no total yes unce- no total
tain taln
Chuburna 24 1 3 28 17 3 8 28
Chelem 29 O 1 30 22 1 7 30
Progreso 36 0 1 37 26 3 8 37
Prochub 43 0 1 44 31 6 7 44
total 132 1 6 139 96 13 30 139
Expect. % 94.96 69.06
ecol3 scienc
Place yes uncer- no total yes unce- no total
tain taln
Chuburna 24 2 2 28 27 1 0 28
Chelem 28 1 1 30 29 1 0 30
Progreso 33 2 2 37 33 2 2 37
Prochub 42 2 0 44 42 1 1 44
total 127 7 5 139 131 5 3 139
Expect. % 91.37 94.24
negat short
Mace yes uncer- no total yes unce- no total
taln taln
Chuburna 2 1 25 28 7 1 20 28
Chelem 1 O 29 30 7 0 23 30
ﬂggreso 4 3 30 37 15 1 21 37
Prochub 1 0 43 44 1 3 40 44
total 8 4 127 139 30 5 104 139
Expect. % 91.37 74.82
(mtion eguity
Place yes uncer- no total yes unce- no total
taln taln
Chuburna 27 0 1 28 25 1 2 28
Chelem 30 O 0 30 29 0 1 30
Pro reso 34 1 2 37 33 2 2 37
Prochub 42 2 0 44 43 1 O 44
total 133 3 3 139 130 4 5 139
Expect. % 95.68 93.53

 

 

 

 

 

 

 

 

144

Table 40 - (con’t).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

exist extern

Place yes uncer- no total yes unce- no total
taln taln

Chuburna 22 4 2 28 1 1 3 14 28
Chelem 29 1 0 30 6 2 22 30
Progreso 32 3 2 37 13 4 20 37
Prochub 39 2 3 44 4 4 36 44
total 122 10 7 139 34 13 92 139
Expect.% 87.77 66.19

willi parti
Place yes uncer- no total yes unce- no total

taln tain

Chuburna 26 2 O 28 6 4 18 28
Chelem 29 1 0 30 2 2 26 30
qureso 32 3 2 37 6 4 27 37
Prochub 35 6 3 44 7 4 33 44
total 122 12 5 139 21 14 104 139
Expect. % 87.77 74.82

 

 

 

 

 

 

 

 

 

145

Table 41 - xz values and p values for long term resource use variables: moral, natura, ecoll, ecol2,
ecol3, scienc, negat, short, option, equity, exist, extern, willi, and parti.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable X2 d.f. p result
moral 7.63 6 .26 accept
natura 8.71 6 .19 accept
eco|1 7.65 6 .26 accept
ecol2 3.85 6 .69 accept
ecol3 3.56 6 .73 accept
scienc 3.75 6 .70 accept
_legat 9.39 6 .15 accept
short 19. 58 6 .003 rejected
option 6.24 6 .39 accept
equity 4.9 6 .55 accept
exist 5.64 6 .46 accept
extern 12.89 6 .04 rejected
willi 6.72 6 .34 accept
parti 4. 1 6 .66 accept

 

Short and extern were rejected for or < .05. To further explain the reason
for rejecting the null hypothesis, observed minus expected frequencies and

interaction plots of frequencies are shown in Table 42 and Figure 42.

Table 42 - Observed minus expected frequencies for long term resource use. Variables: short and

 

extern
Place short x2= 19.58 p = 0.003 extern x2= 12.89, p = 0.04
(O-E) (O-E)2/E (O-E) (O-E)2/E
yes uncer- no yes unoer- no yes uncer- no yes uncer- no
tain tain tain taln

 

 

Chuburna -0.95 -0.01 0.96 0.04 0.00 0.15 -4.53 0.38 4.15 1.11 0.06 2.52

 

Chelem 0.55 -1.08 0.53 0.01 1.08 0.04 2.14 -0.81 -1.34 0.23 0.23 0.24

 

Progreso -6.68 -0.33 7.01 1.61 0.08 6.16 4.49 0.54 3.95 0.82 0.08 1.72

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Prochub 7.08 1.42 -8.50 1.52 1.27 7.60 6.88 -0.12 -6.76 1.62 0.00 4.25

 

 

146

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Interaction Plot: PLACE x SHORT
45
40 1
35 >
30 1
n 25»
_o
g 20 1
E 15
“' [ —0— PLACE
10 Chuburna
-~o-~ PLACE
5 * Chelem
0‘ PLACE
0 Progreso
.5 A t , ”A- PLACE
no unoertal yes Prochub
SHORT
(3)
Interaction Plot: PLACE x EXTER
4° ‘ V ﬁ
35 .
30 . '
25 >
a
.3 20 1
C
8
g 15
“‘ 10 , —0— PLACE
Chuburna
5 ’ 4:» PLACE
Chelem
0 1 , "0“ PLACE
Progreso
-5 L + g - -a~ PLACE
no unoertai yes Prochub
EXTER

 

 

 

Figure 42 - Interaction plots of long term resource use. a) short (short term exploitation) and b)
extern (externalities).

147

4.2.3.2 Awareness Toward Mangrove Threats (H2e)

Variables used for testing resident perceptions to environmental threats
were: 1) urban growth (urgro), 2) port infrastructure (porin), 3) road system
(roads), 4) garbage dumps (garba), 5) domestic and industrial wastes (trewa),
6) oil spills (oilspi), 7) amount of wood extracted (woode), 8) ﬁshing volumes
(fishe), and 9) hunting pieces (hunte). Nine contingency tables were

constructed to evaluate the responses (see Table 43).

Table 43 - Contingency tables for perceptions to environmental threats variables urgro, porin, roads,
garba, trewa, oilspi, woode, ﬁshe and hunte.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

urgro porin roads
Place yes no total yes no total yes no total
Chuburna 9 19 28 11 17 28 8 20 28
Chelem 12 18 30 13 17 30 6 24 30
Prggreso 21 16 37 10 27 37 16 21 37
Prochub 27 17 44 21 23 44 16 28 44
total 69 70 139 55 84 139 46 93 139
Expect. % 49.64 39.57 33.09

arba trewa oilspi

Place yes no total yes no total yes no total
Chubuma 24 4 28 20 8 28 13 15 28
Chelem 25 5 30 21 9 30 20 10 30
queso 35 2 37 32 5 37 34 3 37
Prochub 33 1 1 44 31 13 44 34 10 44
total 1 17 22 139 104 35 139 101 38 139
Expect. % 84.17 74.82 72.66

woode fishe hunte
Place yes no total yes no total yes no total
Chuburna 9 19 28 10 18 28 11 17 28
Chelem 12 18 30 13 17 30 13 17 30
Prggreso 17 20 37 18 19 37 24 13 37
Prochub 24 20 44 27 17 44 23 21 44
total 62 77 139 68 71 139 71 68 139
Expect. % 44.6 48.9 51.08

 

 

 

 

 

 

 

 

 

 

148

The purpose of the analysis was to test whether the populations were
homogeneous with respect to the total cell probabilities. The null hypothesis for
homogeneity (H0: p1 = p2 = p3 = p4) was tested and accepted. The chi-square
(X2) statistic, the degree of freedom (d.f.), and probabilities of occurrence (p) are

shown in Table 44.

Table 44 - Results of XI, d.f. and p values for awareness regarding threats to mangrove. Variables
urgro, porin, roads, garba, oilspi, woode, fishe, and hunte

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable x2 d .f. p result _
_u_rgro 7.713037 3 .05235 accept
porin 3.837443 3 .27958 accept
roads 4.515430 3 .21094 accept
_garba 5.861271 3 .11858 accgt
trewa 3.659044 3 .30073 accept
oilspi 17.60199 3 .00053 reject
woode 3.803901 3 .28345 accept
fishe 5.056409 3 . 16774 accept
hunte 5.1 17863 3 . 16339 accept

 

 

To explain the reason for rejecting the null hypothesis, observed minus

expected frequencies and interaction plots of frequencies were used (see Table

45 and Figure 43).

Table 45 - Observed minus expected frequencies for awareness regarding threats to mangroves.
Variables: urgro and oilspi

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Place urgro x’= 7.71, d.f. = 3 p = 0.052 oilspi x’= 17.60, d.f. = 3, p = 0.00053
(O-E) (O-E)2IE (O-E) (O-E)’IE
yes no yes no yes no yes no
Chuburna -4.90 4.90 1.73 1.70 -735 7.35 2.65 7.05
Chelem -2.89 2.89 0.56 0.55 -1.80 1.80 0.15 0.39
Progreso 2.63 -2.63 0.38 0.37 7.12 -7.12 1.88 5.00
Prochub 5.16 -5.16 1.22 1.20 2.03 -2.03 0.13 0.34

 

 

149

 

WMWIURGRO

requenclee
‘55.. §
6500
Frequencies
L3_
? D
|
1
1
8

e
‘7’

isiéisis
12815:;

O
O

o
9

0741-17 7 7 70725
0110110 use

(a) (b)

 

 

 

 

 

Figure 43. Interaction plots for threats to mangroves: a) urgro (urban growth) and
b) oilspi (oil spills)

4.2.3.3 Acceptance of Regulation (H2f)

Variables used for testing the acceptance/enforcement of regulation for
the mangrove wetland products and services were: 1) acceptance of regulation
(Igral), 2) construction controls (Icons), 3) dumping controls (Icont), 4) wood
cutting controls (lwood), 5) ﬁshing regulation (Ifish), 6) hunting regulation
(lhunt), and 7) wetland transportation regulation (ltran). Seven contingency
tables were constructed to evaluate residents responses. They are shown in

Table 46.

Table 46 - Contingency tables for regulatory variables lgral, Icons, lcont, lwood, lfish, lhunt, and ltran

150

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Lgml Icons
Mace no uncer- yes total no uncer- yes total
taln tain
Chuburna 3 1 24 28 6 1 20 27
Chelem 0 0 29 29 3 0 24 27
Prog reso 1 0 36 37 4 1 28 33
Prochub 1 0 43 44 0 1 42 43
total 5 1 132 138 13 3 114 130
Expect. °/o 96.6 87.7
lcont lwood
P|ace no uncer- yes total no uncer- yes total
tain taln
Chuburna 0 0 28 28 3 1 24 28
Chelem 0 0 30 30 4 0 25 29
Progreso 0 0 36 36 0 1 30 31
Prochub 1 0 43 44 1 0 42 43
total 1 0 137 138 8 2 121 131
Expect. % 99.3 92.4
lﬁsh lhunt
Place no uncer- yes total no uncer- yes total
taln taln
Chuburna 2 1 24 27 3 0 25 28
Chelem 3 1 25 29 6 1 22 29
Progeso 6 0 29 35 5 2 29 36
Prochub 1 0 42 43 1 0 42 43
total 12 2 120 134 15 3 118 136
Expect. % 89.5 86.7
ltran
Place no uncer- yes total
taln
Chuburna 2 2 1 5 19
Chelem 5 1 22 28
Progreso 3 3 24 30
Prochub 1 2 39 42
total 1 1 8 1 00 1 19
Expect. % 84.0

 

151

The purpose of this analysis was to test whether the populations were
homogeneous by town with respect to the cell probabilities. The null hypothesis of
homogeneity Ho: p1 = p2 = p3 = p4 was tested and accepted. Values of x2 ,
degree of freedom (d.f.), and probabilities (p) were calculated and are shown in
Table 47. All variables were accepted according the criteria that p 2 0.05

indicating that no differences exist between towns.

Table 47 - Result of 12 values and p values for regulatory variables

 

 

 

 

 

 

 

 

Variable x2 d.f. p result
_lgEl 9.549910 6 .14497 accept
Icons 10. 53542 6 .10388 accept
lcont 2.151958 3 .54148 accept
lwood 9.580509 . 6 .14351 accept
lﬁsh 8.186590 6 .22479 accept
lhunt 10.44450 6 .10718 accept
ltran 6.586734 6 .36079 accept

 

 

 

 

 

 

 

4.2.3.4 Similarities using H2d and H26

To summarize similarities among communities, a cluster analysis was
used to compare long term resource use and awareness regarding threats to
mangroves. Tree clusters were graphed based on Euclidean distances (Figure
44). Distances were calculated based on variables that were rejected for

homogeneity (i.e., long term resource use: short and extern; and awareness

regarding threats to mangroves: urgro and oilspi).

152

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Tree Diagram for 4 Cases
Single Linkage
Euclidean distances
20
19 >
§ 18 >
£9
8 17
8
a
E 16 1
15 .
14
PROCHUB CHELEM PROGRESO CHUBURNA
(a)
Tree Diagram for 4 Cases
Single Linkage
Euclidean distmces
18
16 >
14 1
§
3 12
_in
CS
8. 10 »
2
.E
_J
a .
6 b
4
PROCHUB PROGRESO CHELEM CHUBURNA
(b)

Figure 44 - Similarities between residents for: a) long term resource use, variables: short and extern
and b) awareness regarding threats to mangroves, variables: urgro and oilspi

153

4.2.4 Relating Physical Changes to resident knowledge and attittudes

To integrate the physical and human dimensions, variables from
hypotheses H1 and H2 shown in Table 48 were selected for the analysis.
Variable selection was based on the test of homogeneity between towns
(Chuburna, Chelem, Progreso and Prochub) according the. A summary of the
social variables including percentages, conﬁdent intervals, x2 values, degree of

freedoms and probabilities are shown in Table 49 at the end of this section.

Table 48 - Variables selected for relating natural and human variables

 

Physical Dimension Human Dimension

 

3) Evergreen losses (egl) a) Use of mangrove direct products : 1)
ﬁrewood (ﬁrewood), 2) home repairs and
construction (conwood), and 3) ﬁshing
tool constructions (artwood).

 

b) Dune vegetation losses (dvl) b) Mangrove ecological beneﬁts : 1)
production of organic matter and feeding
nursery (promat), 2) protection of shore
erosion (soilpro), 3) removal of nutrients,
heavy metals and other substances from
the water (clewa), and 4) spawning area
and protective cover (nurs).

 

c) Attitudes towards long-term resource
use : 1) limits on nature (ecol2), 2)
negativist (negat), 3) short time
preferences (short), and negative
externalities (extern).

 

d) Threats to mangroves : 1) urban
expansion (urgro), 2) port infrastructure
(porin), 3) roads (roads), 4) solid garbage
dumps (garba).

 

 

e) Acceptance of regulation : 1) untreated
sewage (trewa), 2) oil spills (oilspi), 3)
wood extraction (woode), and 4) ﬁshing
intensity (ﬁshe).

 

 

 

154

a) The sample percentages of residents with knowledge of the use and/or as
consumers of the direct mangrove products were related with changes on the

mangrove wetland. The following three social variables were selected based on

the higher X2 values : 1) ﬁrewood (ﬁrewood), 2) home repairs and constructions

(conwood), and 3) ﬁshing tools construction (artwood). They were related with

evergreen vegetation losses (egl) and dune vegetation losses (dvl) per habitant

 

 

(Figure 45).
0.8 0.025
0.7
4 0.02 , i, 7,. pg 7

a 0'6 1+Firewood
C
° 1
a "' +Home re irs and
0 .5 : pa
2 0 0015 g construction
8 a ‘+Fishing tool

0.4 g .
:7 E construction
3 0.01 4g 1 —e— Evergreen losses (y')
'0 0.3 z
27 1+Dune vegetation
'7 072 losses (y')
m . 0.005 ,. 7 7

0.1

0 v . . 0

 

 

 

Chubuma Chelem Progreso

 

 

Figure 45 - Vegetation losses and direct mangrove consumption

b) The percentage of residents from Chuburna, Chelem and Progreso based on

their knowledge of ecological beneﬁts of the mangroves were related with

changes on vegetation. The following three social variables were selected

 

155

based on their highest value for )8 statistic: 1)production of organic matter and
feeding nursery (promat), 2)protection of shore erosion (soilpro), 3)removal of
nutrients heavy metals and other substances from the water (clewa), and 4)
spawning area and protective cover (nurs). They were related with evergreen
vegetation losses (egl) and dune vegetation losses (dvl) per habitant (Figure

46).

 

 

  

 

 

 

 

1 0.025
0.9
3 0-3 ’ 0-02 ‘7—x7— Pgduction of orgarﬁcmatter
3 0 7
8. 7 E +Protection of share erosion
3 0.5 4 0.015 g
"g E +Removal of nutrients. metals and
5‘ 0'5 E other substances
3 04 001 § ‘—o—Spawning area and protective
'2 2 comer
E- 0.3 ‘+Evergreen losses (y')
"’ 0.2 0.005 . _
‘+Dune vegetation losses (y')
0.1 . . , ,
0 4/ 1 1 0
Chuburna 7 Chelem Progreso

 

 

 

Figure 46 - Vegetation losses and mangroves ecological beneﬁts

c) Result from the change detection and residents attitudes towards long-term
resource use allow to relate the percentage of residents from Chuburna, Chelem
and Progreso about their attitudes towards the long-term resource use and with
changes on evergreen vegetation (egl) and dune vegetation (dvl) losses (Figure

47). The following three variables were selected based on their highest values

156

for X2 statistic: 1) limits on nature (ecol2), negativist (negat), 3) short time

preferences (short), and 4) externalities (exter).

 

 

 

 

0.7
3 — 0.02
., 0.6
C
0
nos E '77 L' '1 717 7
g . 1 _ 0.015,: —x—1misonnaure
- ﬂ +Negativist
3 04 1:“
f 7 E +Short term preferences
3 03 . 0.01 g +Extemalities
% ‘ .1: 1~§~ Evergreen losses (y')
E 0.2 » 1:4-Bumﬁgelfflmfste‘? 90.1
in 0.005

0.1 »

0 - 1 7 0
Chuburna Chelem Progreso

 

 

 

Figure 47 - Vegetation losses and attitudes toward long-term resource use

d) The percentage of residents from Chuburna, Chelem and Progreso that
accepted that: 1) urban expansion (urgro), 2) port infrastructure (porin), 3)
roads (roads), 4) solid garbage (garba), are threats to the mangrove wetland
and linked to vegetation losses are shown in Figure 48. Also 1) untreated
sewage (trewat), 2) oil spills (oilspi), 3) wood extraction (woode) and 4) ﬁshing
intensity (ﬁshe) were included and related to vegetation losses and are shown in
Figure 49. The graphs illustrate the number of hectare/habitant of greeen
vegetation losses (egl) and also dune vegetation losses (dvl) per town during the

studied period.

157

 

 

 

 

 

1 , 0.025
0.9
0 8 « 0.02
8 a
2 0.6 ~ - 0.015 g
. .a . . .
g 0. 5 g ——x— Urban growth .
‘5 E + Port infrastructure 1
-\' 0.4 . 0-01 g 1+Road network 1
O
7E7 0 3 + Solid garbage dumps 1
'3 7 ; 7+ Evergreen losses (y')
0.2 - < 0.005 +Dune vegetation losses (y')
0.1
o - a r l _ 0
Chuburna Chelem Progreso Prochub

 

 

 

Figure 48 - Evergreen losses, dune vegetation losses and awareness toward threats to mangrove
wetland : 1) urban expansion (urgro), 2) port infrastructure (porin), 3) roads (roads), 4) solid

 

 

 

 

 

garbage dumps (garba).
1 0.025
0.9 »
0 8 « 0.02
.3;
S 0.7
U
E 0.6 » - 0.015 5
g 05 > 1°: .-x—Untreated sewage
"6 ' f +00 spills ‘
z! 0.4 « 0.01 g +Wood extraction
.3 -= +Fishing
E 0.3
5 -.I.... Evergreen losses (y')
0.2 4 0.005 1—A—Dune vegetation losses (y')
0.1
O - . . .. 0
Chuburna Chelem Progreso Prochub

 

 

 

Figure 49 - Evergreen losses, dune vegetatrion losses and awareness toward threats to mangrove
wetland : 1) untreated sewage (trewat), 2) oil spills (oilspi), 3) wood extraction (woode) and 4) ﬁshing
intensity (ﬁshe).

158

e) Acceptance of regulation indicated by the percentage of residents from
Chuburna, Chelem and Progreso for: 1) enforced urban regulation (Icons), 2)
enforced wood extraction regulation (lwood), and 3) enforced hunting regulation
(lhunt) was related with the number of hectar/habitant of new developed land
(ndl), green vegetation losses (egl) and dune vegetation losses (dvl) during the

studied period.

 

 

 

 

 

1 0.03
0.9 1
3 0 8 0.025
2 0 7 » - ’ ' '
8. . . 0.02 .. —x—— Enforced regulation for
g 0 6 1:5 constmction
7: ‘ g + Enforced regulation for wood
;. 0 5 0.015 % extraction
3 0 4 5 + Enforced regulation for hunting 1
' 0 1
l» - 0 01 2
a. 0.3 ' + New developed land (y') 1
" 1
in 0.2
‘ 0.005 4» Evergreen losses (y')
0.1
0 0 —A— Dune vegetation losses (y')
Chuburna Chelem Progreso Prochub

 

 

 

Figure 50 - New developed land (ndl), evergreen loses (egl), dune vegetation losses and acceptance of
I) enforced urban regulation (Icons), 2) enforced wood extraction, and 3) enforced hunting
regulation (lhunt).

Table 49— Summary of the social variables. Proportion of the ‘yes/agree’ answares, conﬁdent

159

interval (+/-), x2 for homogeneity by town, degree of freedom and probability level.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Vaiable Chuburna Conf. int. Chelem Conf. int. Progreso Conf. int. Procchub Conf. int. X2 d.f. l
Firewood 0.78 0.15 0.76 0.15 0.4 0.15 0.2 0.11 34.35 3 q
conwood 0.46 0.18 0.56 0.17 0.1 0.1 0.09 0.08 30.59 3 o]
artwood 0.25 0.16 0.33 0.16 0.08 0.08 0.06 0.07 12.49 3 0.006l
ﬁnﬁsh 0.71 0.16 0.83 0.13 0.56 0.15 0.4 0.14 15.21 3 0.001

crust 0.57 0.18 0.83 0.13 0.56 0.15 0.02 0.04 53.92 3 0

mollu 0.78 0.15 0.93 0.08 0.43 0.15 0.27 0.13 39.96 3 0

birds 0.53 0.18 0.43 0.17 0.29 0.14 0.06 0.07 21.11 3 0

mamma 0.17 0.14 0.5 0.17 0.02 0.05 0.29 0.13 21 3 0

repti 0.14 0.12 0.1 0.1 0.08 0.08 0 0 12 3 0.006]
insect 0.1 0.11 0.03 0.06 0 0 0.2 0.11 21 3 0]
recrea 0.32 0.17 0.56 0.17 0.27 0.14 0.4 0.14 6.8 3 0.08]
Ipromal 0.71 0.16 0.43 0.17 0.78 0.13 0.72 0.13 10.74 3 0.013]
[soilpro 0.67 0.17 0.66 0.16 0.8 0.12 0.63 0.14 2.94 3 0.4]
[hurpro 0.77 0.15 0.6 0.17 0.7 0.14 0.74 0.12 2.6 3 0.45]
nesit 0.92 0.09 0 93 0.08 0.94 0.07 0.9 0.08 0.32 3 0.95]
nurs 0.82 0.14 0.93 0.08 0.83 0.11 0.72 0.13 5.52 3 0.13]
clewa 0.6 0.18 0.33 0.16 0.48 0.16 0.25 0.12 10.37 3 0.015]
moral 0.96 0.06 0.96 0.06 1 0 1 0 7.63 6 0.26]
natura 1 0 1 0 0.97 0.05 0.88 0.09 8.71 6 0.19]
ccoll 0.85 0.12 0.96 0.064 0.97 0.05 0.97 0.044 7.65 8 0.26]
ecol2 0.6 0.18 0.73 0.15 0.7 0.14 0.7 0.13 3.85 6 0.69]
ecol3 0.85 0.12 0.93 0.08 0.89 0.1 0.95 0.08 3.56 6 0.73]
scienc 0.96 0.068 0.96 0.06 0.89 0.1 0.95 0.06 3.75 6 0.7]
negal 0.07 0.095 0.03 0.06 0.1 0.1 0.02 0.04 9.39 6 0.15]
short 0.25 0.16 0.23 0.15 0.4 0.15 0.02 0.04 19.58 6 0.003]
option 0.96 0.06 1 0 0.91 0.08 0.95 0.06 6.24 6 0.39]
equity 0.89 0.11 0.96 0.06 0.89 0.1 0 97 0.04 4.9 6 0.55]
exist 0.78 0.15 0.96 0.06 0.86 0.11 0.88 0.09 5.64 6 0.46]
extern 0.39 0.18 0.2 0.14 0.35 0.15 0.09 0.08 12.89 6 0.04]
willi 0.92 0.09 0.96 0.06 0.86 0.11 0.79 0.11 8.72 6 0.34]
parti 0.21 0.15 0.06 0.08 0.16 0.11 0.15 0.1 4.1 6 0.66]
urgro 0.32 0.17 0.4 0.17 0.57 0.15 0.61 0.14 7.71 3 0.05]
porin 0.39 0.18 0.43 0.17 0.27 0.14 0.47 0.14 3.83 3 0.27]
roads 0.28 0.16 0.2 0.14 0.43 0.15 0.36 0.14 4.51 3 0.21]
garba 0.85 0.12 0.83 0.13 0.94 0.07 0.75 0.12 5.86 3 0,11]
trewa 0.71 0.16 0.7 0.16 0.86 0.11 0.7 0.13 3.65 3 0.3]
oilspi 0.46 0.18 0.66 0.16 0.91 0.08 0.77 0.12 17.6 3 0]
woode 0.32 0.17 0.4 0.17 0.45 0.16 0.54 0.14 3.8 3 0.28]
ﬁshe 0.35 0.17 0.43 0.17 0.48 0.16 0.61 0.14 5.05 3 0.16]
lhuntc 0.39 0.18 0.43 0.17 0.64 0.15 0.52 0.14 5.11 3 0.18I
llgral 0.85 0.12 1 0 0.97 0.05 0.97 0.04 9.54 6 0.14

neons 0.74 0.16 0.88 0.11 0.84 0.11 0.97 0.04 10.53 6 0.1

WW 1 0 1 0 1 0 0.97 0.04 2.15 3 0.54

ﬂood 0.85 0.12 0.86 0.12 0.96 0.05 0.97 0.04 9.58 6 0.14

llﬁsh 0.88 0.11 0.86 0.12 0.82 0.12 0.97 0.04 8.18 6 0.22

Fhunt 0.89 0.11 0.75 0.15 0.8 0.12 0.97 0.04 10.44 6 0.1

[En 0.78 0.15 0.78 0.14 0.8 0.12 0.92 0.07 6.58 6 0.36]

 

 

 

 

 

 

 

 

 

 

 

 

 

160

Table 50 - Summary of the social variables. Proportion of the ‘yes/agree’ answers and 2 values for p
= .5 vs p at .5, a = 0.05 (accept the null hypothesis if abs(z) Z < 1.96).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Vaiable Chuburné Chelem Progreso Procchub Chuburna Chelem Progreso Prochub
zforp¢.5 zforp¢.5 zforp¢.5 zforpac.5

Firewood 0.78 0.76 0.40 0.20 2.96 2.85 -1.22 -3.98
conwood 0.46 0.56 0.10 0.09 -0.42 0.66 -4.87 -5.44
artwood 0.25 0.33 0.08 0.06 -2.65 -1.86 -5.11 -5.84
ﬁnﬁsh 0.71 0.83 0.56 0.40 2.22 3.61 0.73 -1.33
crust 0.57 0.83 0.56 0.02 0.74 3.61 0.73 -6.37
mollu 0.78 0.93 0.43 0.27 2.96 4.71 -0.85 -3.05
birds 0.53 0.43 0.29 0.06 0.32 -0.77 -2.55 -5.84
mamma 0.17 0.50 0.02 0.29 -3.49 0.00 -5.84 -2.79
repti 0.14 0.10 0.08 0.00 -3.81 -4.38 -5.11 -6.63
insect 0.10 0.03 0.00 0.20 -4.23 -5.15 -6.08 -3.98
recrea 0.32 0.56 0.27 0.40 -1.90 0.66 -2.80 -1.33
promat 0.71 0.43 0.78 0.72 2.22 -0.77 3.41 2.92
soilpro 0.67 0.66 0.80 0.63 1.80 1.75 3.65 1.72
hurpro 0.77 0.60 0.70 0.74 2.86 1.10 2.43 3.18
nesit 0.92 0.93 0.94 0.90 4.44 4.71 5.35 5.31
nurs 0.82 0.93 0.83 0.72 3.39 4.71 4.01 2.92
clewa 0.60 0.33 0.48 0.25 1.06 -1.86 -0.24 -3.32
moral 0.96 0.96 1.00 1.00 4.87 5.04 6.08 6.63
natura 1.00 1.00 0.97 0.88 5.29 5.48 5.72 5.04
ecol1 0.85 0.96 0.97 0.97 3.70 5.04 5.72 6.24
eco|2 0.60 0.73 0.70 0.70 1.06 2.52 2.43 2.65
60013 0.85 0.93 0.89 0.95 3.70 4.71 4.74 5.97
scienc 0.96 0.96 0.89 0.95 4.87 5.04 4.74 5.97
negat 0.07 0.03 0.10 0.02 -4.55 -5.15 -4.87 -6.37
short 0.25 0.23 0.40 0.02 -2.65 ~2.96 -1.22 -6.37
option 0.96 1.00 0.91 0.95 4.87 5.48 4.99 5.97
equity 0.89 0.96 0.89 0.97 4.13 5.04 4.74 6.24
exist 0.78 0.96 0.86 0.88 2.96 5.04 4.38 5.04
extern 0.39 0.20 0.35 0.09 -1.16 -3.29 -1.82 -5.44
willi 0.92 0.96 0.86 0.79 4.44 5.04 4.38 3.85
parti 0.21 0.06 0.16 0.15 -3.07 -4.82 -4.14 -4.64
urgro 0.32 0.40 0.57 0.61 -1.90 -1.10 0.85 1.46
porin 0.39 0.43 0.27 0.47 -1.16 -0.77 -2.80 -0.40
roads 0.28 0.20 0.43 0.36 -2.33 -3.29 -0.85 -1.86
garba 0.85 0.83 0.94 0.75 3.70 3.61 5.35 3.32
trewa 0.71 0.70 0.86 0.70 2.22 2.19 4.38 2.65
oilspi 0.46 0.66 0.91 0.77 -0.42 1.75 4.99 3.58
woode 0.32 0.40 0.45 0.54 -1.90 -1.10 -0.61 0.53
ﬁshe 0.35 0.43 0.48 0.61 -1.59 -0.77 -0.24 1.46
hunte 0.39 0.43 0.64 0.52 -1.16 -0.77 1.70 0.27
lgral 0.85 1.00 0.97 0.97 3.70 5.48 5.72 6.24
Icons 0.74 0.88 0.84 0.97 2.54 4.16 4.14 6.24
lcont 1.00 1.00 1.00 0.97 5.29 5.48 6.08 6.24
lwood 0.85 0.86 0.96 0.97 3.70 3.94 5.60 6.24
lﬁsh 0.88 0.86 0.82 0.97 4.02 3.94 3.89 6.24
lhunt 0.89 0.75 0.80 0.97 4.13 2.74 3.65 6.24
ltran 0.78 0.78 0.80 0.92 2.96 3.07 3.65 5.57

 

 

 

 

 

mc

uni
stat
PrOg
ever
199(
vege

the (c

Chapter 5

CONCLUSIONS &
RECOMMENDATIONS

Findings from the change detection analysis and survey research provided the foundations
for the following: 1) green vegetation losses, dune vegetation losses and urban and unconsolidated
soil growth are changing the mangrove wetland, 2) differences were found between Chuburna,
Chelem, Progreso and Prochub residents in their knowledge base for mangrove direct and indirect
products use, and also in their knowledge of mangrove environmental services, attitudes toward
resource use and awareness toward threats to mangroves. The implications of these ﬁndings to
coastal management are discussed which leads us to several recommendations. '

 

 

5.1 Assessment of the Natural Systems
5.1.1 Total Land Cover Changes and Road Proximity Map

Results of the change detection analysis indicated that land cover
modiﬁcations have occurred for dune vegetation, evergreen vegetation and
unconsolidated soil in the coastal areas of the State of Yucatan. Hypothesis H1,
stating that there has not been signiﬁcant changes in the coastal areas of
Progreso, Yucatan, México from 1986 to 1990, was rejected. Dune vegetation,
evergreen vegetation and unconsolidated soil has been converted from 1986 to
1990 to: 1) urban areas and 2) garbage dump facilities. Additional losses of green
vegetation that changed to unconsolidated soil has been attributed to the effects of

the road system.

161

162

A reduction of approximately 250 ha of dune vegetation was observed
during the period 1986 - 1990 in the whole area. Dune vegetation was converted
to urban area and providied nearly 61% of the total urban land growth. Total
dune vegetated area was calculated to be 733 ha in 1990.

Unconsolidated soil also supplied land for urban development in the
period, their contribution accounted for a total of 162 ha. This amount
represents approximately 38% of the total urban land growth. Unconsolidated
soil, even though converted to urban, grew during the studied period. This
increment of unconsolidated soil was mainly a consequence of the loss of green
vegetation (345.52 ha). Changes of unconsolidated soil were mainly located in
the area of Progreso where clearing and ﬁlling for conversion to urban were
taking place.

Green vegetation losses were linked to areas for solid garbage disposal.
A large area was detected that changed form green vegetation to unconsolidated
soil close to an unpaved road located between Progreso and Lactun. The area
was cleared and impoverished by the dumping of solid garbage.

Dead mangrove trunks were found in the “Estero de Yucaltepen” on the
road 261 between Progreso and Mérida (State capital) where loss of green
vegetation was identiﬁed. The area of dead trees is contiguous to previously
destroyed areas and located in semi-enclosed areas artiﬁcially created by the
road network (see Figure 31 for main roads). Mangroves in this area are dying

at the edge of the forest.

163

To illustrate possible trends in the development of the changed areas
including the mangrove wetland, relationships were established between

variables representing total changing areas and their distance to roads.

Road proximity maps and total changes indicated that:

1. Land cover changes decrease as distance to roads increases. It was
observed that 52% of the total changes detected from 1986 to 1990 occurred
at a distance of 600 m from the roads and 92% of the total changes occurred
in the ﬁrst 1800 m (Figure 30).

2. Similar patterns were also observed when new developed land, green
vegetation losses and dune vegetation losses were overlaid on the road
proximity map (Figure 31, Figure 32 and Figure 33)

3. A different pattern was observed for unconsolidate soil losses when it was
overlaid on the road proximity map. Changes increase as distance from
towns increase until they reach a maximum and start decreasing again

(Figure 34).

5.1.2 Towns Land Cover Changes and Town Proximity Maps

Further analysis of the physical changes limited to the polygons in Figure
28 indicated that Progreso, the larger town, grew by approximately 150 ha during
the period from 1986 to 1990. In contrast, Chuburna (the smaller town) grew by
approximately 27 ha and Chelem by 56 ha during the period. These ﬁgures
when divided by the population in Progreso 30,037, Chelem 2249 and Chuburna

1244 (INEGI 1990) shown that Chelem is the area that grew more per habitant.

164

Chuburna, Chelem and Prochub have affected the dune vegetation while
Progreso has affected the unconsolidated soil including the dead mangrove
areas. The reason Progreso is incorporating unconsolidated soil to urban is
because almost all the dunes have already been converted to urban. Green
vegetation losses in the area of Progreso accounted for approximately 148 ha in
contrast to Chelem with 5 ha and Chuburna where regrowth was observed.
Green vegetation in the area of Progreso changed primarily to unconsolidated
soil due to solid garbage dumps and probably because of the effect of the road

system.

Town proximity map and land cover changes per town indicated that:

1. Land cover changes had a similar pattern for all categories, it was observed
that changes increase as distance from towns increases until they reach a
maximum and start decreasing again (Figure 36).

2. Changes in Chuburna, Chelem and Prochub occurred primarily between
developed land (T) and dune vegetation (Jr) with a maximum peak of change
close to a distance of 500m from downtown (Figure 36).

3. Changes in Progreso occurred primarily between developed land (T) and
unconsolidated soil (l) with a maximun peak of change close to a distance of
1200m from downtown and less with the dune vegetation (l) with larger

changes close to a distance of 2300m from downtown (Figure 36).

165

5.2 Assessment of the Social Systems
5.2.1 Knowledge about Mangrove Direct Use

Hypothesis for the analysis of mangrove direct use H2a, stating that no
differences exist across residents from Chuburna, Chelem, Progreso, and non-
pemranent residents in their knowledge about mangroves direct use, was
rejected. The test of homogeneity among Chuburna, Chelem, Progreso and
Prochub was rejected, as 3 out of 3 variables did not meet the criteria to accept
(i.e., X2 goodness of ﬁt). Results showed that different proportions of the
residents use or/and know that other residents in the area use mangrove direct
products for: 1) energy production, 2) home repairs and construction, and 3)
ﬁshing tools construction.

An examination of Table '51 for the departure of homogeneity and a tree
diagram for cluster analysis Figure 41(a) revealed that residents from Chuburna
and Chelem were the most similar. Observed values in the number of
responses for all mangrove direct use variables were above the expected values.

Similarities were also found with residents from Prochub and Progreso where

all observed values were below the expected values.

Table 51 - Departures from homogeneity for mangrove direct use: firewood, home construction, and
fishing tool construction

 

 

 

 

 

Variable Expected Chelem Chuburna Progreso Prochub
Value (residuals) Qesiduals) (residuals) (residuals)
Firewood 49.64% Above Above Below Below
(8.1L (8.1) (3.3) (12.8)
Construction 27.34% Above Above Below Below
(5.3) (8.7) (6.1) (8.0)
Fishing tools Construction 16.55% Above Above Below Below
(2.36) (5.03) (3.12) (4.28)

 

 

 

 

 

 

 

166

From these data the following was concluded:

1.

Larger proportions of residents from Chuburna and Chelem use or/and
know that others use mangrove wetland wood for energy production than
do residents from Progreso and Prochub.

Larger proportions of residents from Chuburna and Chelem use or/and
know that others use mangrove wetland wood for home construction than
do residents from Progreso and Prochub.

Larger proportions of residents from Chuburna and Chelem use or/and
know that others use mangrove wetland wood for ﬁshing tool construction
than do residents from Progreso and Prochub.

Larger proportion of residents from Chuburné and Chelem know that direct
products are taken from the mangroves, than do residents from Progreso
and Prochub.

Expected proportions in the number of answers computed from the total

population for mangrove direct products (energy production 49.64%, home

construction 27.34%, and ﬁshing tool construction 16.55%) indicate: 1) 1 out of 2

residents from the whole sample use and/or know that wood from the mangrove

wetland is used in the area for energy production, 2) 1 out of 4 use and Ior know

that mangroves are used for home repairs and construction and 3) 1 out of 6 use

and/or know that mangroves are used for ﬁshing tool construction.

167

5.2.2 Knowledge about Mangrove Indirect Use

Hypothesis for the analysis of the mangrove indirect product use under
H2b, stating that no differences exist across residents from Chuburna, Chelem,
Progreso, and non-permanent residents in their knowledge about mangroves
indirect use, was rejected. Hypothesis H2b, which test for homogeneity
between Chuburna, Chelem, Progreso and Prochub was rejected, as 7 out of 8
variables did not meet the criteria to accept. Analysis showed that different
proportions of the residents use or/and know that other residents in the area use
the following mangrove indirect products: 1) ﬁnﬁsh, 2) crustacean, 3) mollusk, 4)
bird, 5) mammal, 6) reptiles, 7) insects, and 8) recreational purposes.

Deviation from homogeneity (Table 52) and cluster analysis (Figure 51)
for ﬁshing activities related to ﬁnﬁsh, mollusks and crustaceans indicated that
residents from Chuburna and Chelem are the most similar in their knowledge

about ﬁsh and shellﬁsh mangroves indirect use products.

Table 52 - Departures from homogeneity for mangroves indirect products: finfish, crustacean and

 

 

 

 

 

mollusk.
Variable Expected Value Chuburna Chelem Progreso Prochub
Qesiduals) (residuals) (residuals) (residuals)
Finﬁsh 60.53% Above Above Below Below
(3.0) (6.8) (1.3) (8.5)
Crustacean 56.12% Above Above Above Below
(3.3) (11.4) (4.2) (18.9)
Mollusk 45.32% Above Above Below Below
(6.29 (11.1) (4.7) (12.7)

 

 

 

 

 

 

 

168

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Tree Diagram for 4 Cases
Single Linkage
Euclidean distances
60
55
50
§
.9
o
'0’» 40
.3
.E
..l
35
30
25
PROCHUB PROGRESO CHELEM CHUBURNA

 

 

 

Figure 51 - Tree cluster analysis for mangroves indirect products: finﬁsh, mollusk and
crustacean.

From this data the following was concluded:

. Larger proportions of residents from Chuburna and Chelem harvest or/and
know that others harvest finﬁsh than do residents from Progreso and
Prochub.

. Larger proportions of residents from Chuburna, Chelem, and Progreso
harvest or/and know that others harvest crustaceans than do residents
Prochub.

. Larger proportions of residents from Chuburna and Chelem harvest or/and

know that others harvest mollusks than do residents from Progreso and

Prochub.

169

4. Lower proportion of residents from Prochub harvest or/and know that others
harvest ﬁnﬁsh, mollusks and crustaceans than do residents from
Chuburna, Chelem and Progreso.

5. Chuburné and Chelem were the most similar towns followed by Progreso
and Prochub when variables for ﬁnﬁsh, crustacean and mollusk were taken

simultaneously in a cluster analysis (Figure 51).

Deviation from homogeneity (Table 53) and cluster analysis (Figure 52)
showed that hunting activities related to birds, mammals and reptiles indicated
that residents from Progreso and Chuburna were the most similar when use

and/or knowledge about hunting in the mangrove wetland was considered.

Table 53 - Departures from homogeneity for mangrove indirect products: birds, mammals and

 

 

 

 

 

reptiles.
Variable Expected Value Chuburna Chelem Progreso Prochub
(residuals) (residuals) (residuals) (residuals)
Birds 30.22% Above Above Below Below
(6.5) (3.9) (0.2) (1 0.3)
Mammal 24.46% Below Above Below Above
(1.8) (7.6) (8.0) (2.2)
Reptiles 7. 19% Above Above Above Below
(1.9) (.84) (.34) (3.17)

 

 

 

 

 

 

 

170

 

Tree Diagram for 4 Cases
Single Linkage
Euclidean distances

Linkage Distance

 

PROCHUB CHELEM PROGRESO CHUBURNA

 

 

 

Figure 52 - Tree cluster analysis for mangroves indirect products: birds, mammals and reptiles.

The following conclusions were drawn:

. Larger proportions of residents from Chuburna and Chelem hunt birds
or/and know that others hunt birds than do residents from Progreso and
Prochub.

. Larger proportions of residents from Chelem and Prochub hunt mammals
or/and know that others hunt mammals than do residents from Progreso and
Chuburna.

. Larger proportions of residents from Chuburna, Chelem and Progreso hunt
reptiles or/and know that others hunt reptiles than do residents from
Prochub.

. Residents from Progreso and Chuburna grouped as well as residents from
Prochub and Chelem when birds, mammals and reptiles variables are

considered.

171

5. Larger proportions of residents from Chelem and Prochub use the area for
recreational purposes and/or know that others use the area for recreational

purposes than do residents from Chuburna and Progreso.

5.2.3 Knowledge about Environmental Services

Resident knowledge about the mangrove environmental services were
studied under H2c for homogeneity. H2c stating that, no differences exist across
residents from Chubumé, Chelem, Progreso, and non-permanent residents in
their knowledge about mangroves environmental services, was accepted. This
analysis found that there was not a signiﬁcant variability amongst the residents
responses in 4 out of 6 mangrove environmental service variables (i.e., soil
protection, hurricane protection, nesting site and nursery places). Signiﬁcant
differences, however, were found for production of organic matter and water
quality.

Expected values and deviations from homogeneity for each town are
shown in Table 54. Cluster analysis in Figure 53 showed that residents from
Progreso and Chuburna are the most similar, followed by residents from
Prochub and Chelem when the production of organic matter and the remotion of

nutrients by the mangroves were introduced in the analysis (environmental

benefits).

172

Table 54 - Departures from homogeneity for mangrove environmental benefits.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable Expected Chuburna Chelem Progreso Prochub
Value (residuals) (residuals) (residuals) (residuals)
Production of 67.63% above(1.06) below(7.28) above(3.9) above(2.24)
_qganic matter
Soil protection 69.57 % below(.47) below(.86) above(3.95) below(2.6)
Hurricane 70.8 above(1.88) below(3.24) below(.19) above(1.55)
grotection
Nesting site 92.65% above(.05) above(.2) above(.5) below(.76)
Nursery area 81.88% above(.07) above(3.4) above(.7) below(4.2)
Water qualim 40.58 % above(5.6) below(2.17) above(2.9) below(6.4)
Tree Diagram for 4 Cases
Single Linkage
Euclidean distances
40
35
9:3 30
13
O
a
g 25
20
15 CHELEM PROCl-IUB PROGRESO cnuauw

 

 

 

Figure 53 - Tree cluster analysis for mangrove environmental benefits.

Even though statistical differences were not found in 4 out of 6 variables,
non-permanent residents (Prochub) had the largest negative distance from the

expected value for variables, soil protection, nesting site, nursery area and water

173

quality. Additional information provided by residents during the interviews, it
appears that permanent residents are more knowledgeable about the
environmental beneﬁts provided by the mangrove wetland. Examples were
given by permanent residents about hurricane and storm protection, and
mangrove wetland use as a feeding and nursery areas for ﬁsh, crustacean and

mollusks and for bird nesting sites.

5.2.4 Attitudes toward Resource Use

Attitudes toward resource use were studied under H2d. Hypothesis H2d
stating that, no differences exist across residents from Chuburna, Chelem,
Progreso and non-permanent residents in their attitudes towards long term
resource, was accepted. Results from the 14 variables analyzed for attitudes
(H2d), provided little evidence about the differences between residents, as 12
out of 14 variables were not rejected for homogeneity. The following expected
values for attitude variables were found: 1) moral obligation 98.56% (which
means that a high percentage of residents were in agreement), 2) cause
satisfaction 95.68%, 3) environmental changes may affect ﬁsh 94.96%, 4)
excessive harvesting affect natural production 69%, 5) vegetation the basis of
ﬁsh production 91.37%, 6) conservation for research purposes 94.24, 7)
wetland as producer of dangerous creatures 91.5%, 8) no short term
beneﬁts 74.82%. 9) option for the future 95.68%, 10) wetlands for future
generations 93.53, 11) existence value 87.77, 12) externalities 66.19, 13)
willingness to contribute (i.e., money, work) for mangroves conservation

87.77, and 14) willingness to participate 74.82.

174 ‘

The following conclusions were drawn (percentages and their conﬁdent intervals

are shown in Table 49):

1.

Close to thirty percent (30%) of the residents do not agree that intense
harvesting would affect the natural production and may reduce ﬂora and
fauna populations to undesirable levels for harvesting.

Close to thirty percent (30%) of the residents do not believe that their
actions impact other residents.

Close to twenty ﬁve percent (25%) of the residents believe that is better to
receive at the present as many benefits as possible from the wetland
whatever the consequences in the future.

An overall value of 86.7% of responses had a positive attitude. This value
was interpreted as residents who have a positive attitude for long term

resource use.

5.2.5 Awareness regarding threats to mangroves

Awareness regarding threats to mangroves was studied under H29 for

homogeneity. Hypothesis H2e, stating that no differences exist across residents

from Chubumé, Chelem, Progreso and non-permanent residents in their

attitudes regarding threats to mangroves, was accepted. This analysis found

that 8 out of 9 nine variables (urban growth, port infrastructure, road network,

solid garbage disposal, untreated waste water, wood extraction, ﬁshing, and

hunting) met the criteria for acceptance. Oil spills was signiﬁcantly different

between groups.

175

Expected values and deviations from homogeneity for each area are
shown in Table 55 for urban growth (p = 0.5235) and oil spills. Cluster analysis
in Figure 54 showed that resident from Progreso and Prochub are similar and
more aware than are residents from Chuburna and Chelem regarding the

impacts that urban growth and oil spills may have on the mangrove wetland.

Table 55 - Departures from homogeneity for awareness regarding threats to mangroves.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variable Expected Chuburna Chelem Progreso Prochub
Value (residuals) (residuals) (residuals) (residuals)
urban growth 49.64 % below below Above Above
(4.9) (2.89) (2.63) (5.16)
oil spills 72.66 % below below Above Above
(7.35) (1.8) (7.12) (2.03)
Tree Diagram tor4 Cases
Single L'rikage
Euclidean distances
18
16 .
14
g ..
a
g. 10
S a,
6 r i
4 PROCHUB PROGRESO CHELEM CHUBURNA

 

 

 

Figure 54 - Tree cluster analysis for awareness regarding threats to mangroves.

 

176

The following conclusions were drawn:

1._ Larger proportion of residents from Progreso and Prochub are aware of
urban growth and oil spills than are residents from Chuburna and Chelem.

2. Residents felt that there is a current threat to mangroves and associated
fauna. Resident responses indicated that: a) threats above the 50%
response rate are: solid garbage (84.17%), untreated water (74.82%), and
oil spills (72.66%); b) threats with 50% response rate: urban growth
(49.64%), wood extraction (44.6%), ﬁshing (48.9%), and hunting
(51.08%); and c) threats below the 50% response rate: port infrastructure
(39.57%) and road system (33.09%). Table 50 at the end of Chapter 4

summarize the variables with z values for testing p = 0.5 vs. p at 0.5.

5.2.6 Acceptance of Regulation

Testing of hypothesis H2f, stating that no difference exist across residents
from Chuburna, Chelem, Progreso, and non-permanent residents to accept
mangrove wetland regulation, resulted in the acceptance of the null hypothesis
Ho . Results indicated that all proposed needs of regulation had a high rate of
acceptance and no signiﬁcant differences were found across the groups. The
expected values (percentages of responses) for all variables were as follows: 1)
95.65% agreed with the need to enforce regulation, 2) 87.69% agreed with
strength regulation for construction, 3) 99.28% agreed with the prohibition of

dumping in wetland, 4) 92.37% agreed with the prohibition of cutting living trees,

177

5) 89.55% agreed with the need for licenses and seasons for hunting, 6) 86.76%
agreed with the need for licenses, art regulation and seasons for ﬁshing, and 7’)
84.03% agreed with motor boat trafﬁc regulation.

A test for signiﬁcant difference indicated that all responses were above
50% rate of response. The researcher concludes that residents from the study
areas are in general willing to accept regulation in order to control several
activities to protect vegetation, wildlife, ﬁsh, and water in the area. Signiﬁcant

differences were not found between groups.

5.3 Implications and Recommendations

Observed physical changes in the study area provide indications of how
urban development and infrastructure projects have changed fragile ecosystems
such as dune vegetation and mangrove ecosystems. Study results can
contribute to the design of environmental policies in the area that account for the
heterogeneity in residence knowledge base concerning: (i) the potential use a
non-use values of mangrove wetlands, (ii) the intertemporal preferences in
resource use, and (iii) the awareness of environmental impacts of urban
expansion, solid waste disposal in wetlands, and coastal road network. Effective
implementation of Mexican environmental policy in the study area most consider
the above mentioned ﬁndings in order to design and implement effective coastal
policies and regulations. The identiﬁed resident knowledge base could provide a
relevant input for the design of environmental education programs dealing with
coastal resource development and conservation. Both, regulatory and

environmental education may help in the mitigation of reversible negative effects

178

created by coastal growth, and provide a solid base for sustainable development
in areas close to coastal wetland ecosystems of the Yucatan.

a) Mangrove direct use. The mangrove wetland is a source of important
resources for coastal communities in Chuburna, Chelem and Progreso. It
provides wood for energy production, home repairs and construction, and ﬁshing
tool construction. Results from this study indicated that a proportion of 78% _t
1.5 of the residents from Chuburna and 76% i 1.5 from Chelem with populations
around 1250 and 2250 habitants use or/and know about the uses of the
mangrove wetland ﬁrewood (see Table 48 for % ﬁgures related to home repairs
and ﬁshing tool construction). In Chuburna and Chelem the evergreen losses
detected were close to 0.0 and 4.0 ha respectively. Progreso has a population
close to 30,000, and 40% :l: 1.5 of the residents have knowledge about the direct
use of the mangroves for ﬁrewood. This area has sustained evergreen losses
close to 150 ha from 1986 to 1990.

The above ﬁndings have strong implications for Progreso residents who
daily depend upon the mangrove wood (especially for those residents living on
the borders of the mangroves where the town ﬁgure of 40% changed to 67%
when data is analyzed by stripes). A large and increasing. population and the
decreasing resource size will increase the pressure on the wood mangrove
wetland and on the people living in Progreso in the stripe facing the mangrove
wetland.

b) Mangrove indirect use. Mangrove wetland also supports ﬁsh and other

wildlife populations with value for coastal communities. As indicated by the

179

survey, 71% i 1.6 residents from Chuburna , 83% i 1.3 from Chelem and 56% i
1.5 from Progreso capture or/and know others who capture ﬁnﬁsh. Percentage
ﬁgures for mollusks and crustaceans percentage are in Table 48. Awareness
that many of the captured species rely on the abundance and health of the
mangrove wetland vegetation, means that mangrove losses in the area of
Progreso will lead to fewer ﬁsh, mollusk, crustaceans and birds for ﬁshing and
hunting. This in turn will increase the pressure on people already living at the
poverty level who depend on daily capture for food.

d) Mangrove ecological beneﬁts. In the survey, the proportion of residents
who indicated that they knew the ecological functions of the mangroves were as
follows: for production of organic matter: Chuburna (71% :t 1.6), Chelem (43% i
1.7, Progreso (78% i 1.3), and Prochub (72% i 1.3. Similar results were also
found for mangroves soil erosion protection, hurricane protection and removal of
nutrients in the water (these results can be found in Table 48). These ﬁndings
have implications for mangrove conservation as well as for the dependent biota
because this lack of resident knowledge may lead to practices which contribute
to mangrove alteration. An Improvement in resident knowledge concerning the
ecological functions and a recognition of the beneﬁts gained in sustaining
healthy the mangrove wetlands in the area will help the communities in
Chuburna, Chelem and Progreso to appreciate and give more value to
mangroves.

e) Attitudes toward resource use. Vegetation and other dependent

resources from the mangrove wetlands are also negatively affected by residents

180

attitudes. Even though there was a the high rate of positive responses
(statistically signiﬁcant p a: 0.5), awareness must be raised in these communities
concerning for the long term use of mangrove wetlands and their natural
resources in the following areas:

1) That a balance must exist between natural production and harvesting. At
present 60% i 1.8 of residents in Chuburna, 73% i 1.5 in Chelem, 70% :t 1.4 in
Progreso, and 70% i 1.3 in Prochub agree that intense harvesting would affect
the natural production. Attention must be paid in Chuburna where p at 0.5 was
not rejected. This has implications for regulations that attempt to limit captures
or extraction, because closing seasons or limiting ﬁshing may not be well
received.

2) That we need to think more regarding the future of the mangroves since 25%
i 1.2 of the residents from Chuburna, 23% i 1.5 from Chelem, 40% i 1.5 from
Progreso and 2% from Prochub want to receive as many beneﬁts as possible in
the present from the mangroves regardless of future consequences. Chuburna,
Chelem, and Prochub were statistically rejeCted to be p = 0.5 (with values in the
lower tail), however Progreso was not rejected indicating more preference in
receiving the beneﬁts today even though residents were aware (70% :r 1.4) that
the natural production will be affected.

3) That we must understand that some of our actions have a negative effect on
others. This is in contrast with the belief of 40% :t 1.8 of the residents from
Chuburna, 20% i 1.4 from Chelem, 35% i 1.5 from Progreso and 9% i 8.0 from

Prochub who think that their actions do not have negative consequences for

181

others. In this case % ﬁgures from Chuburna and Progreso were not rejected for
p ¢ 0.5. This have implications in connection with the regulation of residents
action towards the mangrove wetland.

e) Awareness regarding threats to mangroves. Lack of awareness
regarding threats to mangroves also have implications for mangrove wetland
conservation and resource use.

1) Urban areas as a threat to mangroves were perceived by 32% i 1.7 from
Chuburna and 40% i 1.7 from Chelem in contrast to 57% i 1.5 of residents from
Progreso, 61% i 1.4 from Prochub. These ﬁgures do not support a clear
positive or negative position concerning urban developments as threats to
mangroves (-1.88, -1.09, 0.82, 1.5, p a: 0.5 accepted) Table 49. However, this
data could help to understand residents reaction towards a policy for the control
of urban expansion. Urban constructions in Chuburna, Chelem and Progreso
have eliminated close to 130 ha of dune vegetation. Furthermore in Progreso
decisions relating to the new developments have also led to the ﬁlling of the
mangrove wetland.

2) The road system is perceived as an even lower threat to mangroves since
28% i 1.6 from Chuburna, 20% i 1.4 of the residents from Chelem, 43% i 1.5
Progreso, and 36% i 1.4 from Prochub perceived that the road system has an
impact on the mangrove wetland (-2.26, -3.28, -0.82, -1.8) Table 49. This,
probably, is a reason that new road developments have encountered little
resistance. Residents did not even request that roads were constructed in a way

that minimize their ecological damage to the mangrove wetland.

182

3) Solid garbage and untreated water dumped and discharged in the wetland
were seen as high threats to mangroves in all of the towns (3.7, 3.6, 5.4, 3.3).
This suggests that there would be support from residents against untreated
discharges and disposal of solid~ garbage in the mangrove wetland. This also
suggests that resident may wish to move current garbage facilities to other safety
areas.

4) Regarding ﬁshing, hunting and wood extraction residents do not appear to
favor a position that these actions are now a threat to mangroves and dependent
resources since all towns were not signiﬁcant different to p at 0.5. These in
addition to previous discussed factors may not facilitate the implementation of
regulation and control of these activities.

Results from this study indicate that mangrove wetland wood and related
finﬁsh and shellﬁsh in the Chelem Lagoon are important resources for residents
living in Progreso, Chuburna and Chelem all year around and appear to be
especially important during the hurricane season. In the light of the following: 1)
that mangrove wetland vegetation support coastal resources, 2) that mangroves
are threatened by a complex mix of cumulative effects of conversion, solid
garbage dumps and the road network, 3) that residents seems to have in general
a positive attitude towards long term resource use, 4) that residents are aware of
some of the threats to mangroves, and 5) that residents are willing to accept and
participate in enforcing regulation, the following recommendations were made:

1) decisions affecting the mangrove wetland must take note of the results of this

study in considering projects that may negatively impact the mangrove wetland.

183

This also is true for the well being of the people in the area (i.e., Chelem and
Chuburna).

2) The ongoing ﬁsheries in the lagoon must be studied to keep them in balance
with their natural production and with the environment. The low capture from
marine ﬁsheries, increasing coastal population, and the alternative use of the
lagoon for ﬁshing during storms and the hurricane season, will increase the
pressure on ﬁnﬁsh and shellﬁsh resources (i.e., the ﬁsheries of the Melongena
corona, Callinectes sapidus and Libinia dubia, as well as, the multispeciﬁc
ﬁnﬁshery in the coastal lagoon.

3) The amount of wood extracted and their economic value must be known (i.e.,
for energy production and construction).

4) A complete and precise calendar of coastal activities to identify critical dates
and activities must be identiﬁed (i.e., who, when, what, and where is to be
harvested).

5) Food and health conditions of the human population must be known in the
stripe facing the mangrove wetland (i.e., contribution of wetland products to
protein intake and the state of health of these population)

6) Urban growth in Progreso must be controlled as the primary threat to the
mangrove wetland, as well as construction of vacation homes that are
eliminating the dune vegetation.

7) A regular water-quality monitoring program must be considered and initiated in
several critical points (i.e., Progreso, where dump facilities are located and also
in the lagoon Chelem, where oil spills produced by boat repair and maintenance

may have an impact on mangroves and related fauna).

184

8) Critical information about the ecological role of the mangrove wetland must be
provided to residents to promote the long term use of the coastal natural
resources.

9) Environmental problems that were frequently perceived by residents must be
considered as topics for future research and implementation (i.e., the solid
garbage disposal, the treatment of sewage, and oil disposal)

10) An integrated coastal zone management program must be implemented as
an alternative to minimize the negative effects on the environment created. by the
needs, lack of knowledge and attitudes of all participants in the development of
the coast. The integrated program will beneﬁt all and specially those groups

who’s daily survival is based on the mangrove wetland.

APPENDICES

APPENDIX A

Coastal Zone Management
Research Project

Mangrove Wetlands:

Uses, Environmental Knowledge,
and Attitudes of their People

Centro de lnvestigacidn y de Estudios Avanzados del IPN
Department of Marine Resources
Unidad Mérida

1996

185

186

Introduction and Consent

Good morning, my name is José . I am from CINVESTAV, a research and educational institution in
Mérida, Yucatan, Mexico. We are talking to people living in the coastal areas of Progreso Yucatan,
Mexico. The main subjects in this interview are related to: 1) current uses of the mangrove wetland, 2)
information about the mangroves ecological functions, and 3) attitudes towards long term use of natural
resources in the area. lnforrnation learned from this study will help to us understand coastal zone
development.

The characteristics of this survey are as follows:

The questionnaire has several short sections

The survey will take about 1/2 an hour.

All your answers will remain conﬁdential.

You do not have to answer any question you feel is inappropriate.
There is no right or wrong answare

You may also withdraw from the interview

All your answers are very valuable to the project

Your cordial participation is important and greatly appreciated.

To continue with the interview, I WANT TO MAKE SURE YOU UNDERSTAND THE PROCEDURE
AND THAT YOU AGREE TO PROCEED WITH THE INTERVIEW.

Please check the appropiate box: YES C NO ii
If you have any questions or would like to receive a copy of our ﬁndings, please contact:

Jorge I Euan-Avila of the Department of Marine Resources, AP 73 Cordemex, 97310, Mérida, Yucatan,
Mexico at (99) 81-29-03 or (99) 81-29-05 Ext 273, CINVESTAV Unidad Mérida.

Thank you very much.

Q.l

Q2

Q3

0.4

187

Section I. Mangroves Wetland Use

A picture (photo No. 1) of the area where the respondent live is used to indicate the site of

interest of this study .
Would you like to tell us the name you use to designate the area that is shown inside the RED

circle in PHOTO #1?

R

 

In picture (Photo #2) we have trees that live partially submerged in water.
Would you like to tell us the name you use to designate those trees shown inside the RED circle
in PHOTO #2?

R:

 

Do you make use of any part of these trees such as the wood, roots, ﬂowers, leaves or any other
part of the tree?

Yes _.1 No

In talking with various members of the community, we learned of some possible uses of these

trees.
Please indicate from the following list your current uses and add other uses people make of these

trees. Add any others not present on the list.

 

 

 

He (" )
Others (V)
I, Firewood Comercial purposes ................................. .
Cooking or heating .................................. Other ......................................................... _S 1
Smoking ﬁsh .............................................
Burning bricks ..........................................
Comercial purposes .................................. 4. Agriculture
Other ..........................................................
Fodder .....................................................
2. Wood for construction Green Manure .......................................... -;
Beams and Poles for houses .................. i ' Comercial purposes ................................ F. i.
Boat-building Materials ........................... ' Other ........................................................ ;_J . 1
Fence posts ................................................ l L. '
Furniture ...................................................
TOYS .......................................................... 5. Food and Drugs
Comercial purposes .................................. Tea ...........................................................
Other ......................................................... Condiments .............................................
Medicines ...............................................

3. Fishing Arts Comercial purposes ................................ . .
Poles for ﬁsh traps ...................................... Other ......................................................... .. . 1
Fishing ﬂoats ............................................... -

Tannins for net preservation .......................
1 Fish attracting shelters ...............................

 

 

 

 

 

 

 

 

188

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Other

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Do you know of any other trees harvested in this area Photo #1 that people make use of them?
Yes No 1‘
Q6 Can you mention them?
R:
What other beneﬁts you receive from this area ( Photo #1) in practicing hunting, ﬁshing,
swimming, bird watching or any other activity? Use the following list.
He (" )
Others (/)
I. Finﬁsh 2. Mammals
Food 3 [3 Food E El
Bait S [3 Fur l] U
Fertilizer C D Watching ............................ E] D
Comercial purposes ................................ E] [J Sport Hunting .................... D C]
Other D D Comercial purposes .............................. D D
Other D D
3. Crustacean
Food D D 4. Reptiles
Bait U D Food [:1 D
Fertilizer Cl C] Skins [:1 Cl
Shells l] l] Comercial purposes ............................... Cl 1:]
Comercial purposes ................................ B D Other D [:1
Other D D
5. Mollusc 6- Insects
Food D D Honey U B
Hair [1 l] Wax l3 3
Fertilizer [1 D Comercial purposes ................................ Ll Cl
thllq B C] Other ..................................... D I:
Comercial purposes ................................ Cl C]
Other [I D
8. Services
Swiming B [:1
7. Birds Walking 2 ﬂ
Food 1: El See the Sunrise L [l
Feather D [1 Playing L [3
Watching [1 D Other 1:] [I
Hunting E] D
Comercial purposes .................................. D U
C L
at

 

 

 

189

Section 2. Mangroves Wetland Con version

 

 

 

0.8 Do think that in the area shown in Photo #1 there are important changes in the land use or
cover?
Yes No
Q9 How do you think this changes are ocurring?
Fast (in less than 5 years) moderate ( 5 to 15) Low (more than 15)
Q. 10 Can you tell us the current use(s) of the area(s), starting year, and place. Use the following list.
Check Here (J) Year place
Residential use ............................................ El ------------------------
Road Constructions .................................... D ------------------------
Pipe Introductions ...................................... E] ........................
Parking Lots ............................................... I] ........................
Aquacultural ponds .................................... E] ------------------------
Agriculture ................................................. E] ------------------------
Marine Infraestructure ................................ D ------------------------
Solid waste Disposal ................................. D ------------------------
Salt pond developments ............................. D ------------------------
Storage Areas ............................................. l] ------------------------
Oil Industry ............................................... El ------------------------
Fish Industry ............................................. D ------------------------
Sewage ....................................................... D ........................
Recreational and Tourism facilities ........... D ------------------------

 

 

 

 

 

 

 

 

 

0.11

190

Section 3. Attitudes Toward Sustainable Resource Use

We want you to rate the following sentences using the following options: Agree (3), Neutral (2),

Disagree (1), and No answare (O).

 

 

 

 

 

 

 

 

99953935”?

l0.
ll.
12.
13.
14.

 

15.
16.
'17.
'18.
19.
20.

21.
22.

'23.
24.
.25-

26.
27.

 

 

 

We have a moral obligation to protect ﬁsh and wildlife

It cause me satisfaction to see the wetland and its wildlife
Nature is interesting

Wetlands are ugly places

Wetland animals do not become sick

Sewage discharged into the wetland aﬁ'ects the ﬁsh

Regardless of how much we exploit resources nature will always
continue to provide enough for our needs

More vegetation mens more ﬁsh

Fish in the sea depend on the wetlands

We need to conserve the wetlands for study

It is the same to catch young or old animals

Wetlands are not important for ﬁsh and birds

We know everything about the wetlands

We have large wetlands and should not be worried now for

their future

Different plants and animals add value to wetlands

Wetlands only produce dangerous animals to people

Crocodiles and aligators should exclusive be kept in zoos
Wetlands are only valuable because their products

Wetlands may not exist in the future. Therefore, 1 must exploit them
as much as possible now.

Wetlands conservation is needed because may be in the future
we will ﬁnd valuable things in the wetland

I do not have an obligation to conserve the wetlands for others
The only reason that wetlands will exist in the feature is
important to me to participate in conservation.

Anything people do on their property will not create problems for
others

Wetlands areas are not sufficiently important for us to make too many
provision efforts to save them.

I would sacriﬁce something to uphold the purpose of wetland
conservation

Because elections are important, I always vote

Wetland meetings are a waste of time

 

 

 

 

Agree Neutral Dengue
@ ® ® N
0
3 2 1 answer
l 3 2 1 0
3 2 l 0
3 2 l 0
3 2 l 0
3 2 l 0
3 2 l 0
3 2 l O
3 2 l 0
3 2 l 0
3 2 l O
3 2 l O
3 2 l 0
3 2 l O
3 2 l O
3 2 l 0
3 2 l 0
3 2 l 0
. 3 2 l 0
I 3 2 l 0
3 2 l O
3 2 l 0
3 2 l 0
3 2 l O
3 2 l O
3 2 l 0
3 2 1 O
3 2 l O

 

 

 

 

 

191

Section 4. Land Use

Q.12 Thinking in the area shown in PHOTO # l , would you like to tell us your opinion about the
next questions?

 

 

 

 

 

 

 

 

 

 

Agree Neutral became
© e a N,
3 2 1 answer
1. Are you willing to accept the use of the law to 3 2 1 0
control some activities to protect land, vegetation,
wildlife, and water in this area?
2. This law may demand more control on home, 3 2 l 0
roads, and boat landing constructions. Is this
. acceptable?
. 3. This law may prohibit the use of wetlands for 3 2 l O

dumping. Is this acceptable?
4. This law may prohibit the cutting of living trees. 3 2 1 0
Is this acceptable ?
5. This law may prohibit hunting during some 3 2 1 0
seasons. Is this acceptable?
6. This law; may close ﬁshing grounds and regulate 3 2 l 0
ﬁshing arts. Is this acceptable?

7. This law may control the number of boats and 3 2 1 0
trips. Is this acceptable ?

8. Do you support hunting permits? 3 2 1 O

9. Do you support ﬁshing permits? 3 2 1 O

10. Do you support wetland conversion? 3 2 l O

l 1. Are you willing to participate in discussing this 3 2 1 0

regulations?

 

 

 

 

 

Section 5. Perceptions towards menaces

Q. 13 From the following list please tell us which ones you consider current threats to mangrove
wetlands and your perception of the environmental impact magnitude (1 minor, 2 moderate, 3
mayor)

192

(3 mayor, 2 moderate, 1 minor, 0 I can not rate)

 

Growing Land Uses

y/n

 

Urban expansion

 

 

Port Infrastructure

 

Salt pond expansion

 

Aquaculture Development

 

Roads expansion

 

 

 

Cattle 3 2 l 0
Agriculture 3 2 l 0
Wastes

 

Home/lndustrial/markets/ or commercial wastes

 

Septic Tank Leaks

 

Garbage from Vessels

 

Oil pipes Leaks

 

 

 

Animals waste

 

Use Rate

 

Wood extraction

 

Commercial Fishing

 

Subsistance Fishing

 

Hunting

 

Population Growth

 

Population Size

 

Location

 

Proposed Developments

 

Srimp aquaculture

 

Open sea ﬁshing

 

Port infraestructure

 

Artificial reefs

 

 

Tourism

 

 

 

 

 

193

Seccién 6. Servicios al Ambiente.

Q. 14 Below we have a list of environmental services provided by mangrove wetland. For each one,

please check those that you know and your opinion about
environment.

how important are they for the

 

 

Know? + important -
(Y/N) H M L N

 

l. Mangroves are a source of food for many small animals that live in the swamp.
These small animals are also food for other useful animals.

 

2. Mangrove protect the soil. Without this plants tides would erode the soil.

 

3. Mangrove trunks and branches protect other plants and animals from hurricanes
and storms.

 

4. Mangrove branches are the place where birds construct their nests to reproduce,
protect, and grow.

 

 

in the water.

 

5. Calm waters close to mangrove roots are the place where fish and crustaceans 3 2 l O
reproduce. protect, and grow.
6. Mangroves roots and soil help to keep water clean. They trap substances mixed 3 2 l O

 

 

 

 

l———_.

Section 7. General Information

Q.15 Birth Date (mm/dd/yy)

 

Q. l6 Sex (M=male/F=female) Q. 17 Married (Y/N)

Q.18 Job

 

 

Q. 19 Number of years living in the area (years)

 

Q.20 Number of years in School (years)

 

Section 8. Final Comments

Q.21 We appreciate the time that you have set aside for this interview and I am happy to make a note

of any of your suggestions.

 

 

 

APPENDIX B

DICTIONARY OF VARIABLES

Natural system

Land cover changes

dl Developed land

eg Evergreen vegetation
us Unconsolidated soil
dv Dune vegetation

de Deciduous

ndl New developed land

egl Evergreen vegetation losses
usl Unconsolidated soil losses
dvl Dune vegetation losses

Social system

Mangrove direct uses

ﬁrewood Cooking or heating,

conwood Beams and poles for houses, boat-building materials, fence post, furniture, and
toys

artwood Poles for ﬁsh traps, ﬁshing ﬂoats, tannins for net preservation, and ﬁsh
attracting shelters.

194

195

agricul * Fodder, and green manure

foodru *

ﬁnﬁsh

rnaunnna

crust
repti
mollu
insect
birds

recrea

promat
soilpro
hurpro
nesti
nurs

clewa

moral
natura
ecol1
ecol2

ecol3

Tea, condiments, and medicines

Mangrove indirect products
Food, fertilizer, and bait

Food, fur, observation, and sport Hunting
Food, fertilizer, and bait

Food, skin, observation, and sport hunting
Food, fertilizer, bait, and shells

Food, honey, wax

Food, feathers, observation, and sport hunting
Swimming, walking, sailing

Mangroves ecological beneﬁts
Production of organic matter and feeding nursery
Protection of shore erosion

Protection from hurricanes and storms
Habitat for nesting of birds

Spawning area, and protective cover

Removal of nutrients, heavy metals, and other substances from the water.

Attitudes towards resource use

Respondent believes we have a moral obligation to protect the wetland
Respondent experiences satisfaction when he is in contact with the wetland
Respondent accepts that sewage affect life in the wetlands

Respondent recognizes limits in nature

Respondent relates vegetation with animals.

scienc
negat
short

opﬁon

equity

exist

extern
willi

parti

lgral

Icons

lcont
lwood
lfish

lhunt

lreserv *

urgro
porin
roads

garba

196

Respondent accepts the need of conservation for mangroves be studied
Respondent believes that only dangerous creatures live in the wetland
Respondent preference is for short time

Respondent accepts conservation for mangroves because of their possible
future uses

Respondent accepts his obligation to protect the mangroves for others

Respondent participates in conservation with the only reason mangroves will
exist in the future

Respondent accepts that some actions can create negative effects on others
Respondent is willing to sacriﬁce something for wetland conservation

Respondent say that participation in wetland conservation is not waste of time

Acceptance of regulation
Respondent accepts regulation as a way to protect natural resources

Respondent accepts regulation as a way to control urban development, roads,
and boat landing areas

Respondent accepts regulation as a way to regulate discharges in the wetland
Respondent accepts regulation for wood extraction

Respondent accepts regulation for ﬁshing

Respondent accepts regulation for hunting

Respondent accepts regulation to maintain core areas where conversion and
extractive activities would be banned.

Threats to mangroves

Urban growth is currently a threat for the mangrove wetland
Port infrastructure is currently a threat for the mangrove wetland
The road network is currently a threat for the mangrove wetland

Garbage is currently a threat for the mangrove wetland

197

trewa Untreated sewage is currently a threat for the mangrove wetland
oilspi Oil spills are currently a threat for the mangrove wetland
woode Wood extraction is currently a threat for the mangrove wetland
fishe Fishing effort is currently a threat for the mangrove wetland

hunte Hunting is currently a threat for the mangrove wetland

* Not used in this study

 

BIBLIOGRAPHY

BIBLIOGRAPHY

ACIUC, 1986, Australia's Marine and Estuarine Areas - A Policy for Protection
Australian Committee for the International Union for Conservation of Nature
and Natural Resources. Occasional Paper Number 1.

Adams, W. M., 1990, Green Development. Routledge.

Anderson, J. R., E. E. Hardy, J. T. Roach, and R. E. Witmer, 1976, A land use
and land cover classiﬁcation system for use with remote sensor data. U.S.
Geol. Surv. Prof. Pap., 964p.

Amett, R., 1991, Estuarine Pollution: a case study of the Humber. Geography, 76
(330), 67-69.

Axinn, George H., 1991, Sustainable Development Reconsidered. Development, 1,
120-122.

Balkas, T., U. Yetis, and C. Chung, 1993, The Integration of Environmental
Considerations into Coastal Zone Management: Izmir Bay, Turkey. In
Coastal Zone Management: Selected Case Studies. Organization for
Economics Co-operation and Development. Head of Publication Services,
OECD. Paris, France.

Ballard, D. H. and C. M. Brown, 1982, Computer Vision. Prentice Hall.

Barbier, E. B., 1994, Valuing Environmental Functions: Tropical Wetlands. Land
Economics, 70(2), 155-173.

198

199

Batllori, E., 1995, Hidrologia de la regién costera noroccidental del Estado de
Yucatan. Tesis de Doctorado, Facultad de Geografia, Universidad de la
Habana.

Bhattacharyya, G. K. and Richard A. Johnson, 1977, Statistical Concepts and
Methods. John Wiley & Sons.

Benites Jorge A., David Zarate L., Jose L. Rojas G., Alejandro Yaﬁez-Arancibia,
1992, Expansion Urbana y Deterioro Ambiental en la Regién de la
Laguna de Términos, Campeche. Jaina, 3 (2), 4-4.

Bird, E. C. F. and M. M. Barson, 1982, Stability of Mangroves Systems. In
Mangroves Ecosystems in Australia: Structure, function and management.
B. F. Clough (ed). Proceedings of the Australia National Mangroves
Workshop 1979, Australian Institute of Marine Science.

Birkeland, Charles, 1992, Differences among Coastal Systems: The controlling
Inﬂuences of Nutrient Inputs and the Practical Implications for
Management. In Coastal System Studies and Sustainable Development.
Proceedings of the COMAR Intemegional Scientific Conference, UNESCO
Technical Papers in Marine Science No. 64.

Bodero, Alejandro, 1994, Los Manglares en Ecuador. In El Ecosistema de
Manglar en America Latina y la Cuenca del Caribe: Su manejo y
conservacion. Daniel Suman (Ed), Rosenstiel School of Marine and
Atmospheric Science y The Tinker Foundation.

Boelaert—Suominen, S. and Cormac Cullinan, 1994, Legal and Institutional
Aspects of Integrated Coastal Area Management in National Legislation.
FAO, Rome.

Boto, Kevin G., 1992, Nutrient and Mangroves. In Pollution in Tropical Aquatic
Systems. Des W. Connell and Darryl W. Hawker Eds., CRC Press.

Butler, James M, James Burnett-Herkes, John A. Barnes, and Jack Ward,
1993. The Bermuda ﬁsheries: a tragedy of the commons averted?.
Environment, 35 (1), 6-23.

 

200

Chiras, Daniel, 1992, Lessons from Nature: Ieaming to live sustainably on the
earth. Island Press, Washington, DC, Covelo, California.

Chua Thia Eng, James N. Paw, and Flordeliz Guarin, 1989, The environmental
Impact of Aquaculture and the Effects of Pollution on Coastal
Aquaculture Development in Southeast Asia. Marine Pollution Bulletin,
20 (7), 335-743.

Clark, John R. 1992. Integrated Management of Coastal Zones. FAO, Fisheries
Technical Paper 327.

Congalton, Rusell G., 1983, A Quantitative Method to Test for Consistency and
Correctness in Photointerpretation. Photogrammetric Engineering and
Remote Sensing, 49 ( 1), 69—74.

Constitueién Politica de Ios Estados Unidos Mexicanos, 1995, Edicién 108,
Editorial Prorrua, S.A.

Contreras-Espinosa, Francisco, 1993, Ecosistemas Costeros Mexicanos.
Comision Nacional para el Conocimiento y Uso de la Biodiversidad.
Universidad Autbnoma Metropolitana, Mexico.

Cowardin, L. M., V. Carter, F. C. Golet, and E. T. LaRoe, 1979, Classiﬁcation of
wetlands and deep water habitats of the United States. US. Fish and
Wildlife Service. FWSIOBS-79/31.

Cronin, J., 1992, Save the Hudson's ﬁshermen. (pollution of Hudson River and
threat to ﬁshing industry; case of General Electric's environmental
contamination of Hudson River) (Column). The New York Times May 9,
141, 15(N), 23(L), col. 2.

Daly, Herman E. and John Cobb, Jr., 1989, For the Common Good. Beacon
Press.

Decker, D. J., T. L. Brown, N. A. Connelly, J. W. Ean, G. A. Pomerantz, K. G.
Purdy, and W. F. Siemer, 1992, Toward a Comprehensive Paradigm of
Contemporary Wildlife Management: Integrating the Human and
Biological Dimensions of Management. American Fish and Wildlife

201

Policy: The Human Dimension. W. R. Mangun (ed.), So. lll, University
Press, Carbondale.

Dennison, W. C., R. J. Orth, K. A. Moore, J. C. Stevenson, V. Carter, S. Kollar,
P. W. Bergstrom, and R. A. Batiuk, 1993, Assessing Water Quality with
Submersed Aquatic Vegetation. BioScience, 43 (2).

Devuyst, Dimitri, 1993, Environmental Impact Assessment. In Environmental
Management, 3, Instruments for Implementation, B. Nath, L. Hens, and D.
Devuyst Eds, VUB press.

Dobson, J. E., E. A. Bright, R. L. Ferguson, D. W. Field, L. L. Wood, K. D.
Haddad, H. lredale III, J. R. Jensen, V. V. Klemas, R. J. Orth, and J. P.
Thomas, 1995, NOAA Coastal Change Analysis Program (C-CAP): Guide
for Regional Implementation. NOAA Technical Report NMFS 123.

Dugan, Patrick J. (ed), 1992, Conservacién de Humedales: Un analisis de
Temas de Actualidad yAcciones Necesarias. UICN, Gland, Suiza, 100 p.

Emel J. and Richard Peet, 1989, Resource management and natural hazards. In
New models in geography, R.Peet and N. Thrift (Eds), Unwin Hyman,
Boston.

Eichbaum, William M. and Brock B. Bernstein, 1990, Current Issues in
Environmental Management: A case Study of Southern California's
Marine Monitoring System. Coastal Management, 18, 433-445.

Eastman, R. and McKendry J., 1991, Change and Time Series Analysis.
Exploration in Geographic Information Systems Technology, a Workbook
Series, Vol. 1. The United Nations Institute for Training and Research
(UNITAR)

Flores, J. S., S. Manzanilla, D. Aldana, D. Fuentes, G. de la Cruz, A. Mendoza,
E. Batllori, J. Correa, E. Dunhe, L. Morales, M. Villasuso, y J. Andrews,
1995, Marco de Referencia para el Manejo Integral de la Zona Costera.
Consejo Estatal de Consultoria Ecolégica, documento técnico No. 1,
Mérida, Yucatan, México.

202

Foer, Gordon, 1992, Proﬁle of the Coastal Resources of Honduras. In Central
America's Coasta: Proﬁles and an Agenda for Action. Gordon Fear and
Stephen Olsen (Eds), USAID, ROCAP, and CRC.

Fung, Tung and Ellsworth LeDrew, 1988, The Determination of Optimal
Threshold Levels for Change Detection Using Various Accuracy Indices.
Photogrammetric Engineering and Remote Sensing, 54 (10), 1449-1454.

Green, E. P., P. J. Mumby, A.J. Edwards, and C. D. Clark, 1996, A review of
Remote Sensing for the Assessment and Management of Tropical
Coastal Resources. Coastal Management, 24, 1-40.

Grumbine, R. E, 1994, What is Ecosystem Management?. Conservation Biology,
8 (1), 27-38.

Hamilton, Lawrence, John A. Dixon, and Glenys Owen Miller, 1989, Mangrove
Forest: An Undervalued Resource of the Land and of the Sea. In Ocean
Yearbook 8, Elisabeth Mann Borgese, Norton Ginsburg, and J. Morgan
(Eds), The University of Chicago Press.

Hamilton, Lawrence and Samuel C. Snedaker (Eds), 1984, Handbook for
Mangrove Area Management. United Nations Environment Programme
and East-West Center, Environment and Policy Institute.

Hawker D. W. and Des W. Connell, 1992, Standards and Criteria for Pollution
Control in Coral Reef Areas. In Pollution in Tropical Aquatic Systems,
Des W. Connell and Darryl W. Hawker (Eds), CRC Press.

Henerson, M. E., L. L. Morris, and C. T. Fitz-Gibbon, 1987, How to Measure
Attitudes. Program Evaluation Kit, SAGE Publications.

House, V., J. Quinn, N. Wengert, and G. Wunderlich, 1975, Policy Instruments
for Shaping Land Use Choices, No. 3. In Land Resources Today,
University of Illinois at Urbana-Champaign.

203

Imperial, Mark T., Donald Robadue, Jr., and Timothy M. Hennessey, 1992, An
Evolutionary Perspective on the Development and Assessment for the
National Estuary Program. Coastal Zone Management, 20, 311-341.

INEGI, 1990, YUCATAN: Resultados Deﬁnitivos. Datos por AGEB Urbana. XI
Censo de Poblacién y Vivienda. Instituto Nacional de Estadistica,
Geografia e Informética.

IOC Seminar on Integrated Coastal Management, 1995, Intergovermental
Oceanographic Commission, UNESCO, Workshop Report No. 90.

Jensen, John and David L. Toll, 1982, Detecting Residential Land-Use
Development at the Urban Fringe. Photogrammetric Engineering and
Remote Sensing, 48(4), 629-643.

. Johnsen, Per K., Gerrit J. Knaap, and Larry J. Smith, 1992, Public Perceptions
and Attitudes Toward Environmental Rehabilitation of the Lower Green
Bay Watershed. Coastal Zone Management , 20, 9-23.

Lasserre, P., 1992, Marine Biodiversity, Sustainable Development and Global
Change. In Coastal System Studies and Sustainable Development.
Proceedings of the COMAR lnterregional Scientiﬁc Conference, UNESCO
Technical Papers in Marine Science No. 64.

LGEEPA, 1988, Ley General de Equilibrio Ecolégico y la Proteccion al Ambiente.
Diario Oﬁcial de la Federacién, 28 de enero de 1988, Mexico.

Lillesand, T. M. and Ralph W. Kiefer, 1987, Remote Sensing and Image
Interpretation. Second Edition. John Wiley & Sons.

Lin, P. and Fan, H, 1992, Development of the Natural Reservations of
Mangroves along China Coast. In Coastal System Studies and
Sustainable Development. Proceedings of the COMAR lnterregional
Scientiﬁc Conference, UNESCO Technical Papers in Marine Science No.
64.

204

Loa, Eleazar, 1994, Los Manglares de Mexico: Sinépsis General para su
Manejo. En El Ecosistema de Manglar an America Latina y la Cuenca del
Caribe: Su manejo y conservacion. Daniel Suman (Ed), Rosenstiel
School of Marine and Atmospheric Science y The Tinker Foundation.

Loa, Eleazar, 1996, Legislacion, Politica y Gestién sobre humedales. En Manual
para el Manejo y conservacién de los humedales de Mexico. F. J. Abarca
y M. Cervantes (Eds), Publicacion especial bajo la colaboracién del
Instituto Nacional de Ecologia de la SEMARNAP, US. Fish and Wildlife
Service, Arizona Game and Fish Department y Wetlands International the
Americas Program, Mexico.

Loubersac, L. and M. Slimani, 1985, Cartography and Modelization of Tropical
Littoral Environment from High Resolution Satellite Data. An Application
of Spot Simulated Data to the Management of a Tropical Salt Marsh.
Proceedings of the International Conference of the Remote Sensing
Society, Center of Earth Resources Management. 9-12th September,
University of London.

Lugo, A. E. and Snedaker, S. C., 1974, The ecology of mangroves. Annual
Review of Ecology and Systematics, 5,39—64.

Mack, Stephen, Bruce Epler, Paul Atelsek, and David Dudenhoefer, 1992,
Proﬁle of the Coastal Resources of Costa Rica. In Central America's
Coasta: Proﬁles and an Agenda for Action. .Gordon Fear and Stephen
Olsen (Eds), USAID, ROCAP, and CRC.

Mayor, Federico, 1991, Introduction. In Coastal Systems Studies and
Sustainable Development. Report of the COMAR lnterregional Scientiﬁc
Conference, 21-25 May. UNESCO, Paris.

McCain, B. B., Sin-Lam Chan, Margaret M. Krahn, Donald W. Brown, Mark S.
Myers, John T. Landahl, Susan Pierce, Robert C. Clark, Jr., and Usha
Varanasi, 1992, Chemical Contamination and Associated Fish Diseases
in San Diego Bay. Environ. Sci. Technol, 26 (4). American Chemical
Society.

McClees, Joseph D., 1993, Can Small Commercial Fishermen Survive on the
US Atlantic Coast?. Research and Exploration, 9 (1).

205

McGrew J. Chapman and Charles B. Monroe, 1993, An Introduction to Statistical
Problem Solving in Geography. Wm. C. Brown Publishers, Dubuque,
Iowa.

Mehan III, G. T., 1996, Ecosystem Management in the Great Lakes Basin.
Fisheries, 21 (4).

Menéndez, Leda, Pedro Alcolado, Santiago Oharriz, and Ciro Milian, 1994,
Mangroves of Cuba: Legislation and Management. En El ecosistema de
manglar en America Latina y la cuenca del Caribe: Su manejo y
conservacion, Daniel O. Suman (Ed), Rosenstiel School of Marine and
Atmospheric Science, University of Miami, Miami, Florida.

Merschrod, Kris, 1989, In Search of a Strategy for Coastal Zone Management in
the Third World: Notes from Ecuador. Coastal Management, 17, 63-74.

Mitsch, William J. and J. G. Gosselink, 1993, Wetlands. Second Edition, Van
Nostrand Reinhold.

Moffat, David and Olof Lindén, 1995, Perception and Reality: Assessing
Priorities for Sustainable Development in the Niger River Delta. Ambio, 24
(7-8), 527-538.

Mokma, D, 1994, Land Use Planning. Futures, Vol. 12, No. 2, Michigan State
University, Agricultural Experiment Station.

Morales, J. J., 1992, Los Humedales, un Mundo Olvidado. Una publicacibn de
Amigos de Sian Ka’an, A.C. México.

Nelson, Ross F., 1983, Detecting Forest Canopy Change Due to Insect Activity
Using Landsat MSS. Photogrammetric Engineering and Remote Sensing,

54 (10), 1449-1454.

206

Odum, W. E. and E. J. Heald, 1975, Mangrove forest and aquatic productivity.
In Coupling of Land and Water Systems. A. D. Hasler (Ed),
Spring-Verlang, Ecological Studies 10, 129-136.

OECD, 1993a, Coastal Zone Management: Integrated Policies. Head of
Publication Services, Organization for Economics Co—operation and
Development (OECD). Paris, France.

OECD, 1993b, Coastal Zone Management: Selected Case Studies. Head of
Publication Services, Organization for Economics Co-operation and
Development (OECD). Paris, France.

Ongkosongo, Otto S. R, 1992, Human Activities, Environmental Problems and
Management of the North Coast of West Java, Indonesia, with Emphasis
on the Jakarta Bay. In Coastal System Studies and Sustainable
Development. Proceedings of the COMAR Intenegional Scientiﬁc
Conference, UNESCO Technical Papers in Marine Science No. 64.

Osorio, Orlando, 1994, Situacién de los Manglares de Panama. En El
Ecosistema de Manglar en America Latina y la Cuenca del Caribe:, Su
manejo y Conservacién. Daniel Suman (Ed), Rosenstiel School of Marine
and Atmospheric Science y The Tinker Foundation.

Oyuela, Omar, 1994, Los Manglares del Golfo de Fonseca - Honduras. En El
Ecosistema de Manglar en America Latina y la Cuenca del Caribe: Su
manejo y Conservacién. Daniel Suman (Ed), Rosenstiel School of Marine
and Atmospheric Science y The Tinker Foundation.

Paré, Luisa y Julia Fraga, 1994, La Costa de Yucatan: Desanollo y
Vulnerabilidad Ambiental. Instituto de lnvestigaciones Sociales.
Universidad Nacional Autbnoma de Mexico.

Parsons, T. R., 1992, Biological Coastal Communities: Productivity and
Impacts. In Coastal System Studies and Sustainable Development.
Proceedings of the COMAR lnterregional Scientiﬁc Conference, UNESCO
Technical Papers in Marine Science No. 64.

207

Payton, R. B., 1984, A typology of Natural Resource Issues with Implications for
Resource Management and Education. Michigan Academician XVII, 1,
49-58

Payton, R. B., 1990, Institutional and Public Constraints on Dynamic
Management of Fish and Wildlife Resources. In Management of Dynamic
Ecosystems, J. M. Sweeney (Ed), North Cent. Sect, The Wildl. Soc.,
West Lafayete, lnd., 155-169.

Pearce, J., 1984, Assessing the Health of the Oceans and International
Perspective. In Contaminant effects on ﬁsheries. Victor W. Cairns, Peter
V. Hodson, Jerome O. Nriagu (Eds), Advances in environmental science
and technology 16.

Penning-Rowsel, E. C., 1993, Introduction. In Coastal Zone Management:
Selected Case Studies. Organization for Economic Co-operation and
Development (OECD), Head of Publications Services, Paris, France.

Pezzey, John, 1992, Sustainable Development Concepts: An economic
Analysis. World Bank Environmental Paper Number 2. The World Bank.
Washington, DC.

Pimentel, D., H. Acquay, M. Biltonen, P. Rice, J. Nelson, V. Lipner, S. Giordano,
A. Horowitz, and M. D'Amore. 1992, Environmental and Economic Costs

of Pesticide Use. BioScience, 42 (10).

Postma, Henk, 1992, Conference on Coastal System Studies and Sustainable
Development. In Coastal System Studies and Sustainable Development.
Proceedings of the COMAR lntenegional Scientiﬁc Conference, UNESCO
Technical Papers in Marine Science No. 64.

Qing-nan, Meng, 1987, Land-Base Marine Pollution: International Law
Development, Graham & Trotman Pub., London.

Randall, A., 1987, Resource Economics, An Economic Approach to Natural
Resource and Environmental Policy. Second Edition, John Wiley & Son.

 

208

Rayner, S. (rapporteur), F. Bretherton, S. Buol, M. Fosberg, W. Grossman, R.
Houghton, R. Lal, J. Lee, S. Lonergan, J. Olson, R. Rockwell, C. Sage
and E. lmhoff, 1994, A Wiring Diagram for the Study of Land-UseICover
Change: Report of Working Group A. In Land Use and Land Cover: A
Global Perspective, Meyer W. and L. Turner II (Eds), Cambridge
University Press.

Rosenﬁeld, G. H., K. Fitzpatrick-Lins, and H. S. Ling, 1982, Sampling for
Thematic Map Accuracy Testing. Photogrammetric Engineering and
Remote Sensing, 49 (1), 69-74.

Ryan, John C., 1992, Conserving Biological Diversity. In State of the World.

Saenger, P. and N. Holmes, 1992, Physiological, Temperature Tolerance, and
Behavioral Differences between Tropical and Temperate Organism. In
Pollution in Tropical Aquatic Systems, Des W. Connell and Darryl W.
Hawker (Eds), CRC Press.

Sanchez, R. D., 1994, Comparacion Estructural de la Comunidad de Manglar en
dos Sistemas Lagunares Costeros del Estado de Yucatan, Mexico. Tesis
de Licenciatura, Facultad de Medicina Veterinaria y Zootécnica de la
Universidad Auténoma de Yucatan.

Schultink, Gerhardus, 1992, Evaluation of Sustainable Development
Alternatives: Relevant Concepts, Resource Assessment Approaches and
Comparative Spatial Indicators. Intem. J. of Environmental Studies, 41,
203-224.

SEDESOL, 1993a, Informe de la Situacién General en Materia de Equilibrio
Ecolégico y Proteccién aI Ambiente 1991-1992. Instituto Nacional de
Ecologia, Secretaria de Desarrollo Social, Mexico.

SEDESOL, 1993b, Ley General del Equilibrio Ecologico y la Proteccién al
Ambiente. Secretaria de Desarrollo Social. Mexico.

SEDU E, 1992, Manual de Ordenamiento Ecolégico del Territorio. Secretaria de
Desarrollo Urbano y Ecologia, Mexico.

209

Semesi, A. K., 1992, The Coastal Systems of East Africa: Mangrove Ecosystem,
Case Study. In Coastal System Studies and Sustainable Development.
Proceedings of the COMAR lnterregional Scientiﬁc Conference, UNESCO
Technical Papers in Marine Science No. 64.

Singleton, R. A. Jr., B. C. Straits, and M. M. Straits, 1993, Approaches to Social
Research. Second Ed., Oxford University Press.

Smith-Sreen Poonam (Ed), 1992, Sustainable International Development,
Department of Resource Development, Michigan State University.

Sorensen, J., T. McCreary, and A. Brandani, 1992, COSTAS: Aneglos
Institucionales para Manejar Ambientes y Recursos Costeros. Primera
Edicion Castellana, USAID, CRC.

Stewart, J. E., E. C. Penning-Rowsell, and S. Thornton, 1993, The LENKA
Project and Coastal Zone Management in Norway. In Coastal Zone
Management: Selected Case Studies. Organization for Economics Co-
operation and Development. Head of Publication Services, OECD. Paris,
France.

Stigliani, W. M., Peter R. Jaffe, and Stefan Anderberg, 1993, Heavy Metal
Pollution in the Rhine Basin. Enviom. Sci. Technol, 27 (5). American
Chemical Society.

Stonich, Susan C., 1992, Society and Land Degradation in Central America:
Issues in Theory, Method, and Practice. In Anthropological Research:
process and applications. John J. Poggie, Jr., Billie R. DeWaIt, William
W. Dressler (Eds), SUNY series in advances in applied anthropology.

Suman, Daniel, 1994, Status of Mangroves in Latin America and the Caribbean
Basin. In El Ecosistema de Manglar en America Latina y la Cuenca del
Caribe: Su manejo y conservacion. Daniel Suman (Ed), Rosenstiel
School of Marine and Atmospheric Science y The Tinker Foundation.

Swaminathan, M. S., 1992, Human Inﬂuence and Evolution of Demography in
the Coastal Zone. In Coastal System Studies and Sustainable

210

Development. Proceedings of the COMAR lntenegional Scientiﬁc
Conference, UNESCO Technical Papers in Marine Science No. 64.

Thackway, R. and I. D. Cresswell, 1992, Environmental Regionalisations of
Australia. A User-Oriented Approach. Environmental Resources
Information Network. Astralian National Parks and Wildlife Services.
Canberra, Australia.

Thorhaug, Anitra, 1991, Oil Spills in the Tropics and Subtropics. In Pollution in
Tropical Aquatic Systems. Des W. Connell and Darryl W. Hawker (Eds),
CRC Press.

Trejo-Torres, Jorge C., Rafael Duran e Ingrid Olmsted, 1993, Manglares de la
Peninsula de Yucatan. En Biodiversidad Marina y Costera de Mexico. S.
I. Salazar-Vallejo y N. E. Gonzalez (Eds), Corn. Nal. Biodiversidad y
CIQRO, Mexico, 660-672.

UNESCO, 1992, Coastal System Studies and Sustainable Development.
Proceedings of the COMAR lnterregional Scientiﬁc Conference,
UNESCO Technical Papers in Marine Science No. 64.

UNITAR, 1993, Applications in Coastal Zone Research and Management.
Explorations in Geographic lnfonnation Systems Technology. Vol. 3.
Kevin St. Martin ed. United Nations Institute for Training and Research.

United Nations, 1992, Protection of the Oceans, all kinds of Seas, including
enclosed and semi-enclosed seas and coastal areas and the protection,
rational use and development of their living resources. In Report of the
United Nations of the Environment and Development. Chapter 17, Rio de
Janeiro, 3-14 June.

Valdes-Lozano, D. S., 1995, Procesos que Regulan el Nitrdgeno inorganico y
Variaciones Estacionales de la Hidrologia, en la Laguna de Chelem,
Yucatan. Tesis de Doctorado, Colegio de Ciencias y Humanidades,
Universidad Nacional Auténoma de Mexico.

211

Vasconez, Jose, 1991, Proﬁle of the Coastal Resources of Panama. In Central
America '3 Coast: Proﬁles and Agenda for Action. USAID, ROCAP, and
CRC.

Wathern, P., 1988, An introductory Guide to EIA. In Environmental Impact
Assessment. Theory and Practice. Peter Wathern (ed), Unwing Hyman,
London.

Welch, R. and M. Remillard, 1992, Integration of GPS. Remote Sensing, and
GIS Techniques for Coastal Resource Management. Photogrammetric
Engineering and Remote Sensing, 58 (11), 1571-1578.

World Bank, 1992, Natural resource and environmental information for
decisionmaking, Hassan M. Hassan and Charles Hutchinson (Eds),
World Bank Publication, Washington, DC.

WCED, 1987, Our Common Future. The World Commission on Environment
and Development Oxford University Press.

Yaﬁez-Arancibia, Alejandro y David J. Zérate-Lomeli, 1992, Implicaciones
Ecolégicas y de lmpacto Ambiental: Descarga de la Ria de San Francisco
al Litoral de Campeche. Jaina, 3 (2).

Zizumbo, Daniel, 1989, El Deterioro del Sistema Ecolégico, Ciénaga de
Progreso. Secretaria de Ecologia, Gobierno del Estado de Yucatan.
Mexico.

 

212

General References

Allaby, Michael, 1994, The Concise Oxford Dictionary of Ecology. Oxford
University Press

Barlowe Raleigh, 1978, Land Resource Economics. The Economics of Real
State. 3rd edition, Prentice-Hall.

Bettinetti, A., P. Pypaert and J. Sweerts, 1996, Application of an Integrated
Management Approach to the Restoration Project of the Lagoon of
Nenice. Joumal of Environmental Management, 46, 207-227.

Dent, Borden D., 1993, Cartography, Thematic Map Design. Third Edition.
WCB Publishers.

Garmonsway, G. N., 1971, The Penguin English Dictionary. Penguin Books.

MSU, 1996, The Graduate School Guide to the Preparation of Master’s Theses
and Doctoral Dissertations. Michigan State University.

 

9—u

 

11191119111911191111911119111911119111911119111191191119111191111

 

__..—‘1—_‘