EVALUATION OF RIPARIAN COMMUNITY CAPITALS AND THEIR RELATIONSHIP
TO ADAPTIVE LAKE MANAGEMENT OUTCOMES
By
Jennifer Lynn Jermalowicz-Jones

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Community, Agriculture, Recreation, and Resource Studies-Doctor of Philosophy
2017

ABSTRACT
EVALUATION OF RIPARIAN COMMUNITY CAPITALS AND THEIR RELATIONSHIP
TO ADAPTIVE LAKE MANAGEMENT OUTCOMES
By
Jennifer Lynn Jermalowicz-Jones
While inland lakes in Michigan provide multiple community benefits, the aquatic ecosystems in
many of these lakes are now significantly degraded, resulting in damages to ecological integrity,
decreased utility by citizens and a decline in the municipal tax base due to loss in property values.
Lake communities vary significantly in their capacity to identify problems and implement
programs to improve lake water quality over time.

This study utilizes the Community Capitals Framework (CCF) to investigate what community
capitals (assets) lead to better capacity and outcomes in terms of improved management of lake
resources through implementation of Best Management Practices (BMPs). It also evaluates the
Trophic State Index (TSI) of the lakes relative to the various community capitals. The exploration
of this approach may be used in the future to specify which assets are most needed for improving
water quality. This research increases our ability to understand capital resources and ultimately
make recommendations to individual communities for optimal management capacity.

Statistically significant findings include a positive correlation between cultural capital and BMPs
in riparian communities with active Lake Management Plans (LMPs) demonstrating that as
cultural capital increased, communities were more likely to implement BMPs. Additionally, there
were significant differences between BMPs and financial capital for the riparian communities.
Communities with higher financial capital that had LMP’s were more likely to also implement

BMPs. Lastly, there were significant differences between human capital and BMPs for the LMP
communities, indicating that higher human capital was associated with a greater ability to
implement BMPs.

Copyright by
JENNIFER LYNN JERMALOWICZ-JONES
2017

For Blue, who taught me the true meaning of persistence.

v

ACKNOWLEDGEMENTS

Special thanks and appreciation are in order for my spouse, Grant L. Jones, my immediate family,
Denise L. Sigmon, Glenn Jermalowicz Jr., and Graham B. Sigmon. Also to be recognized are my
in-laws, Susan and Lewis Jones, and brother and nephew in law, Kurt Jones and Michael Jones.
Also, some big thanks for the support from my coworkers, Michael Solomon, Nick Gressick, and
Kevin Sylvester. Special thanks to my closest friends and of course my wonderful graduate
committee members: Dr. Jon Bartholic, Professor, Institute for Water Research, and Department
of Community, Agriculture, Recreation, and Resource Studies, Dr. Dawn Reinhold, Associate
Professor, Biosystems Engineering, Dr. Peter Kakela, Professor, Department of Community,
Agriculture, Recreation, and Resource Studies and Dr. Stephen Gasteyer, Associate Professor,
Department of Sociology, and Dr. Lois Wolfson, Specialist, Institute for Water Research,
Department of Fisheries and Wildlife. Sincere gratitude and thanks are also for Dr. Niles Kevern,
Professor Emeritus, and Department of Fisheries who offered valuable advice on this dissertation
study.

Dr. Jon Bartholic, my major advisor was very patient with me as I pursued my own

consulting company while in the middle of my doctorate program. I am also grateful for support
from two non-profit organizations that I serve with including the Michigan Lake and Stream
Associations and the Michigan Chapter of the North American Lake Management Society. Lastly,
thanks to all of the riparian volunteers who completed the survey instrument and offered valuable
feedback.

vi

PREFACE

This dissertation is meant to assist riparian communities with the assessment of community
resources as a tool for better management outcomes. The proxy indicators developed as a part of
this research are based on various community capital resources that exist in riparian communities.
This dissertation may also be used to bring awareness to riparian communities relative to
deficiencies in resources.

Existing research on the direct influences of capital resources on

environmental sustainability is quite limited. The purpose of this research was to measure the
varying levels of capital and investigate the strength of relationships between the capitals and
sustainable management outcomes (such as best management practices or BMPs). My research is
not meant to replace larger-system model concepts since recognition of hydrologic and upstream
influences on water quality are important for mitigation. This research aims to determine the types
and relative abundance of community capitals that are available in various riparian communities
as tools for enhancing implementation of Best Management Practices (BMPs) as components of
adaptive lake management.

vii

TABLE OF CONTENTS

LIST OF TABLES

x

LIST OF FIGURES

xiv

KEY TO ABBREVIATIONS

xvii

CHAPTER I:
I.1
I.2
I.3
I.4
I.5

CHAPTER II:
II.1

II.2

FRAMING THE INLAND LAKES WATER QUALITY PROBLEM
Introduction to Lake Ecosystems, Their Valuation, and Human Influence
Lakes as Common Pool Resources and Sense of Place
Elements of a Sustainable Lake Community
Elements for Sustainable Lake Management: Local Governance
Lake Management and Governance at the Local Scale: Components
for a New SES Framework
DEVELOPMENT OF HYPOTHESES AND THEORY ON THE ROLE OF
COMMUNITY CAPITALS IN INLAND LAKE MANAGEMENT
Community Capital Resources and Their Roles in Riparian Communities
II.1.1
Social Capital
II.1.2
Cultural Capital
II.1.3
Natural Capital
II.1.4
Built/Technical Capital
II.1.5
Financial Capital
II.1.6
Human Capital
II.1.7
Political Capital
Research Questions & Hypotheses
II.2.1
Hypotheses

1
1
6
8
11
18

23
23
24
26
27
28
29
29
30

31
32

CHAPTER III: RESEARCH METHODOLOGY
III.1 Selection of Proxy Indicators for Assessing Community Capitals
III.2 Development of the Survey Instrument
III.3 Data Coding
III.3.1 Proxy Indicators for Riparian Communities (Associations):
III.3.2 Social Capital:
III.3.3 Natural Capital:
III.3.4 Cultural Capital:
III.3.5 Human Capital:
III.3.6 Political Capital:
III.4 Data Analysis Methods

34
34
36
38

CHAPTER IV: RESULTS
IV.1 Social Capital with LMPs and Social Capital with non-LMPs
IV.2 Financial Capital with LMPs and Financial Capital with non-LMPs

46
47
48

viii

40
40
41
41
42
43

45

IV.3
IV.4
IV.5
IV.6
IV.7

Political Capital with LMPs and Political Capital with non-LMPs
Natural Capital with LMPs and Natural Capital with non-LMPs
Cultural Capital with LMPs and Cultural Capital with non-LMPs
Human Capital with LMPs and Human Capital with non-LMPs
Built/Technical Capital with LMPs and Built/Technical Capital with
non-LMPs
IV.8 Statistical analyses on BMPs in LMP and non-LMP
Riparian Communities
IV.9 Statistical analyses on correlations of BMPs and community capitals
in LMP Riparian communities.
IV.9.1 Social Capital:
IV.9.2 Financial Capital:
IV.9.3 Political Capital:
IV.9.4 Natural Capital:
IV.9.5 Cultural Capital:
IV.9.6 Human Capital:
IV.9.7 Built/Technical Capital:
IV.10 Statistical analyses on correlations of BMPs and community capitals
in non-LMP riparian communities.
IV.10.1 Social Capital:
IV.10.2 Financial Capital:
IV.10.3 Political Capital:
IV.10.4 Natural Capital:
IV.10.5 Cultural Capital:
IV.10.6 Human Capital:
IV.10.7 Built/Technical Capital:
IV.11 Statistical analyses on means of TSI in LMP and non-LMP riparian
community populations.
IV.12 Statistical analyses on correlations of TSI and community capitals
in LMP riparian community populations.
V.13 Statistical analyses on correlations of TSI and community capitals in
non-LMP riparian community populations.
CHAPTER V: DISCUSSION AND IMPLICATIONS FOR FUTURE RESEARCH
V.1
The Roles of Cultural, Human, and Social Capital in LMP
Riparian Communities
V.2
The Roles of Financial Capital in LMP Riparian Communities
V.3
Future Research Needs

49
50
51
52
53
54
55
55
56
57
58
59
60
61

62
62
63
64
65
66
67
68

70
71
78
86
87
91
92

APPENDICES
Appendix A Statistical Tables
Appendix B Survey Instrument

96
97
104

LITERATURE CITED

122

ix

LIST OF TABLES

Table 1. Commonly implemented BMPs for riparian communities .............................................40
Table 2. Summary statistical table for differences in community capitals for Non-LMP
and LMP riparian community populations .....................................................................53
Table 3. Summary statistical table for correlations between community capitals
and BMPs for non-LMP and LMP riparian community populations .............................69
Table 4. Summary statistical table for correlations between community capitals
and TSI for Non-LMP and LMP riparian community populations ................................85
Table B-1. Descriptive statistics for social capital in both LMP and non-LMP riparian
community populations. ..........................................................................................105
Table B-2. Statistical test on the social capital means in both LMP and
non-LMP riparian community populations ..............................................................105
Table B-3. Descriptive statistics for financial capital in both LMP and non-LMP
riparian community populations ...............................................................................105
Table B-4. Statistical test on the financial capital means in both LMP and non-LMP riparian
community populations ............................................................................................106
Table B-5. Descriptive statistics for political capital in both LMP and non-LMP riparian
community populations ............................................................................................106
Table B-6. Statistical test on the political capital means in both LMP and non-LMP
riparian community populations ...............................................................................106
Table B-7. Descriptive statistics for natural capital in both LMP and non-LMP riparian
community populations. ...........................................................................................107
Table B-8. Statistical test on the natural capital means in both LMP and non-LMP riparian
community populations ............................................................................................107
Table B-9. Descriptive statistics for cultural capital in both LMP and non-LMP riparian
community populations ............................................................................................107
Table B-10. Statistical test on the cultural capital means in both LMP and non-LMP
riparian community populations ............................................................................108
Table B-11. Descriptive statistics for human capital in both LMP and non-LMP
riparian community populations .............................................................................108

x

Table B-12. Statistical test on the human capital means in both LMP and non-LMP
riparian community populations .............................................................................108
Table B-13. Descriptive statistics for built/technical capital in both LMP and
non-LMP riparian community populations ............................................................109
Table B-14. Statistical test on the built/technical capital means in both LMP and
non-LMP riparian community populations .............................................................109
Table B-15. Descriptive statistics for BMPs in both LMP and non-LMP
riparian community populations .............................................................................109
Table B-16. Statistical test on the BMP means in both LMP and non-LMP riparian
community populations ...........................................................................................110
Table B-17. Statistical test on the correlation between BMPs and social capital in
LMP riparian community populations ....................................................................110
Table B-18. Statistical test on the correlation between BMPs and financial capital
in LMP riparian community populations ................................................................110
Table B-19. Statistical test on the correlation between BMPs and political capital
in LMP riparian community populations ................................................................111
Table B-20. Statistical test on the correlation between BMPs and natural capital in
LMP riparian community populations ....................................................................111
Table B-21. Statistical test on the correlation between BMPs and cultural capital
in LMP riparian community populations ................................................................111
Table B-22. Statistical test on the correlation between BMPs and human capital
in LMP riparian community populations ................................................................112
Table B-23. Statistical test on the correlation between BMPs and built/technical
capital in LMP riparian community populations ....................................................112
Table B-24. Statistical test on the correlation between BMPs and social capital in
non-LMP riparian community populations ............................................................112

xi

Table B-25. Statistical test on the correlation between BMPs and financial capital
in non-LMP riparian community populations.........................................................113
Table B-26. Statistical test on the correlation between BMPs and political capital
in non-LMP riparian community populations.........................................................113
Table B-27. Statistical test on the correlation between BMPs and natural capital
in non-LMP riparian community populations.........................................................113
Table B-28. Statistical test on the correlation between BMPs and cultural capital
in non-LMP riparian community populations.........................................................114
Table B-29. Statistical test on the correlation between BMPs and human capital
in non-LMP riparian community populations.........................................................114
Table B-30. Statistical test on the correlation of BMPs and built/technical capital
in non-LMP riparian community populations.........................................................114
Table B-31. Descriptive statistics on TSI chlorophyll-a in LMP and non-LMP riparian
community populations ...........................................................................................115
Table B-32. Statistical test on the means of TSI chlorophyll-a in LMP and
non-LMP riparian community populations .............................................................115
Table B-33. Descriptive statistics on TSI phosphorus in LMP and non-LMP riparian
community populations ...........................................................................................115
Table B-34. Statistical test on TSI phosphorus in LMP and non-LMP riparian
community populations ...........................................................................................116
Table B-35. Descriptive statistics on TSI Secchi in LMP and non-LMP riparian
community populations ...........................................................................................116
Table B-36. Statistical test on the means of TSI phosphorus in LMP and non-LMP
riparian community populations .............................................................................116
Table B-37. Statistical test on the correlation between TSI and social capital in
LMP riparian community populations ....................................................................117
Table B-38. Statistical test on the correlation between TSI and financial capital in
LMP riparian community populations ....................................................................117
Table B-39. Statistical test on the correlation between TSI and political capital in
LMP riparian community populations ....................................................................117
Table B-40. Statistical test on the correlation between TSI and natural capital in
LMP riparian community populations ....................................................................118

xii

Table B-41. Statistical test on the correlation between TSI and cultural capital in
LMP riparian community populations ....................................................................118
Table B-42. Statistical test on the correlation between TSI and human capital in
LMP riparian community populations ....................................................................118
Table B-43. Statistical test on the correlation between TSI and built/technical
capital in LMP riparian community populations ....................................................119
Table B-44. Statistical test on the correlation between TSI and social capital in
non-LMP riparian community populations .............................................................119
Table B-45. Statistical test on the correlation between TSI and financial capital in
non-LMP riparian community populations .............................................................119
Table B-46. Statistical test on correlation between TSI and political capital in non-LMP
riparian community populations .............................................................................120
Table B-47. Statistical test on the correlation between TSI and natural capital in
non-LMP riparian community populations .............................................................120
Table B-48. Statistical test on the correlation between TSI and cultural capital in
non-LMP riparian community populations .............................................................120
Table B-49. Statistical test on the correlation between TSI and human capital in
non-LMP riparian community populations .............................................................121
Table B-50. Statistical test on the correlation between TSI and built/technical capital
in non-LMP riparian community populations.........................................................121

xiii

LIST OF FIGURES

Figure 1.

A diagram showing the interconnectedness and interdependence of lake
resources, municipalities, and riparian communities ...................................................15

Figure 2. The overlapping of critical community capital forms and the positive
shared outcomes (Flora, 2004).....................................................................................24
Figure 3. Randomly selected lake association sites included in this resource study.
Note: The Upper Peninsula of Michigan is not represented here because
there were no survey responses received from that region of the state. ......................37
Figure 4. Relationship between social capital and BMPs in riparian communities
with an LMP ................................................................................................................55
Figure 5. Relationship between financial capital and BMPs in LMP riparian
communities .................................................................................................................56
Figure 6. Relationship between political capital and BMPs in LMP riparian
communities .................................................................................................................57
Figure 7. Relationship between natural capital and BMPs in LMP riparian
communities .................................................................................................................58
Figure 8. Relationship between cultural capital and BMPs in LMP riparian
communities .................................................................................................................59
Figure 9. Relationship between human capital and BMPs in LMP riparian
communities .................................................................................................................60
Figure 10. Relationship between built/technical capital and BMPs in LMP riparian
communities .................................................................................................................61
Figure 11. Relationship between social capital and BMPs in non-LMP riparian
communities .................................................................................................................62
Figure 12. Relationship between financial capital and BMPs in non-LMP riparian
communities .................................................................................................................63
Figure 13. Relationship between political capital and BMPs in non-LMP riparian
communities .................................................................................................................64
Figure 14. Relationship between natural capital and BMPs in non-LMP riparian
communities .................................................................................................................65

xiv

Figure 15. Relationship between cultural capital and BMPs in non-LMP riparian
communities .................................................................................................................66
Figure 16. Relationship between human capital and BMPs in non-LMP riparian
communities .................................................................................................................67
Figure 17. Relationship between built/technical capital and BMPs in non-LMP
riparian communities ...................................................................................................68
Figure 18. Relationship between social capital and TSI in LMP riparian
communities .................................................................................................................71
Figure 19. Relationship between financial capital and TSI in LMP riparian
communities .................................................................................................................72
Figure 20. Relationship between political capital and TSI in LMP riparian
communities .................................................................................................................73
Figure 21. Relationship between natural capital and TSI in LMP riparian
communities .................................................................................................................74
Figure 22. Relationship between cultural capital and TSI in LMP riparian
communities .................................................................................................................75
Figure 23. Relationship between human capital and TSI in LMP riparian
communities .................................................................................................................76
Figure 24. Relationship between built/technical capital and TSI in LMP riparian
communities .................................................................................................................77
Figure 25. Relationship between social capital and TSI in non-LMP riparian
communities .................................................................................................................78
Figure 26. Relationship between financial capital and TSI in non-LMP riparian
communities .................................................................................................................79
Figure 27. Relationship between political capital and TSI in non-LMP riparian
communities .................................................................................................................80
Figure 28. Relationship between natural capital and TSI in non-LMP riparian
communities .................................................................................................................81
Figure 29. Relationship between cultural capital and TSI in non-LMP riparian
communities .................................................................................................................82

xv

Figure 30. Relationship between human capital and TSI in non-LMP riparian
communities .................................................................................................................83
Figure 31. Relationship between built/technical capital and TSI in non-LMP
riparian communities ...................................................................................................84

xvi

KEY TO ABBREVIATIONS

BMP

Best Management Practices

CCF

Community Capitals Framework

EPA

Environmental Protection Agency

GDP

Gross Domestic Product

LMP

Lake Management Plan

NPS

Non-Point Source

SES

Socio-Ecological System

TSI

Trophic State Index

U.S.

United States of America

xvii

CHAPTER I:

I.1

FRAMING THE INLAND LAKES WATER QUALITY PROBLEM

Introduction to Lake Ecosystems, Their Valuation, and Human Influence

Lakes are valuable resources that provide many ecosystem services to riparian (lake) communities
and others who utilize them. Services may include recreational activities such as boating, fishing,
and swimming, aesthetic opportunities, water supply, and dilution of pollutants (Postel and
Carpenter, 1999). Furthermore, lakes uses differ, depending on their position in the landscape and
how they provide services to the communities (Riera et al., 2001). Naiman and Turner (2000)
determined that water has become the key limiting factor for both environmental and human
populations, inferring a fair and sustainable balance should be dispersed among the two groups.
Lakes offer a multitude of ecosystem services as described above and thus are vulnerable to
environmental changes that result from human behaviors which may ultimately compromise these
services. Costanza et al., (1997) estimated that the value of global ecosystem services is around 33
trillion U.S. dollars which is higher than the value of the global annual Gross Domestic Product
(GDP).

In addition to the intrinsic values that lakes provide for the organisms within them, they also
provide value to communities that are located proximate to them. Stynes (2002) estimated that
Michigan’s 11,000 inland lakes (Fuller and Minnerick, 2008) support a recreational industry that
is valued at approximately 15 billion dollars per year. Inland lakes also provide economic and
aesthetic values to riparian waterfront property owners through increased residential lot values and
scenic views. A survey of approximately 485 riparian land owners that represented five lakes in

1

Kalamazoo County, Michigan, USA, was conducted by Lemberg et al. (2002) and revealed that
the most cited benefit of lakefront ownership was the view. Unfortunately, this benefit is coveted
by so many citizens that it results in over-development of lake shorelines. Morton and Brown
(2011) are quick to emphasize the paradox between humans and degraded waters since humans
inherent both the ability to pollute waters and solve water degradation issues. They further explain
that existing policies for addressing water quality also ignore human social components as they
were originally developed for agricultural practices to reduce soil erosion.

A growing amount of literature attempts to explain the relationships between water quality of lakes
and the associated property values. Scientists and economists have conducted these studies to
assist policy makers with environmental decision-making and to help local economies through
protection of municipal tax bases with lake improvements. The inclusion of local stakeholders
involved in the conservation and management processes is desirable. Doss and Taft (1996) studied
wetlands and lakes in Ramsey County, Minnesota, USA, and determined that the size of the living
area and lake view were the most determinant variables on property values. Additionally, most
riparian owners desire an open-water system or a scrub-shrub wetland over forested wetlands or
those that contain predominantly emergent aquatic vegetation. Halstead et al. (2003) assessed the
impacts of the invasive aquatic macrophyte, Myriophyllum heterophyllum on lakefront properties
in New Hampshire, USA, and found that property values declined significantly for uninfested
lakes. Additionally, Krysel et al. (2003) conducted a hedonic analysis of multiple water quality
variables and how impaired states of these variables reduced lakefront property sales in the
Mississippi Headwaters (USA) region. A major finding in their study was that water clarity
reductions in area lakes would result in property value losses estimated at millions of dollars.

2

Other regions such as Chesapeake Bay, USA, have also been jeopardized by water quality
degradation due to land use activities such as overdevelopment and agricultural runoff. A hedonic
analysis by Leggett and Bockstael (2000) showed that homeowners were willing to make the
necessary financial contributions to reduce concentrations of fecal coliform in area waters to
protect human health and the water resource.

Lakes are complex ecosystems with metabolisms that drive the cycling of nutrients and sustain
multiple forms of life (Smith and Prairie, 2004; Staehr and Sand-Jensen, 2007, among many
others).

These ecosystems are vulnerable to pollution and disruptions from the surrounding

watersheds which usually include non-point source (NPS) pollutants such as nitrogen and
phosphorus (Carpenter et al., 1998), and the introduction of invasive species (National Research
Council, 1992; Bohn and Kerschner, 2002; Joyce et al., 2006; Crowl et al., 2008). This idea of
ecological connectedness is not new, and in 1969, Odum defined an ecosystem as a unit that is
comprised of all organisms in a location which interact with the physical environment and results
in energy fluxes and trophic structure within the system. The metabolism of the local area (urban
metabolism) as initially defined by Wolman (1960), may also be subject to energy flows from
outside the boundary of the natural resource (Huang et al., 2010) and thus consideration of these
extraneous flows can be critical.

The diffuse nature of NPS pollution makes mitigation of negative impacts challenging and
sometimes impossible. Per the Millennium Ecosystem Assessment (2005), the major causes of
lake degradation are overdevelopment, land use conversions, water withdrawal, anthropogenic
eutrophication and pollution, invasive species introductions, and overexploitation and resource

3

overuse. It is estimated that approximately 85% of the streams in the United States (U.S.) possess
phosphorus concentrations that exceed U.S. Environmental Protection Agency (EPA) criteria for
clean waters (Heinz Center, 2008). Thus, there is an inherent vulnerability of lake systems to the
actions of humankind. Additional research is needed on the thresholds of aquatic ecosystems and
the alternative stable states that can follow, since these states often possess less desirable water
quality characteristics than the original state (Scheffer et al., 2001). Walker and Salt (2006) offer
the term “resilience”, which they define as: “The capacity of a system to absorb disturbance and
still retain its basic function and structure.” Although it can be argued that an alternative stable
state, often precipitated by pollution events, allows an aquatic ecosystem to maintain a “basic
structure and function”, this is usually not the ideal goal since degradation leads to a decline in
water quality and biodiversity as evidenced by computed indices of biotic integrity (Karr, 1991;
Blockstein, 1992; Hughes and Noss, 1992). Vitousek et al. (1997) discusses the substantial
impacts imparted by the human population on resource systems through multiple influential
vectors that ultimately decrease biodiversity. Scheffer et al., (2001) concludes that construction
and maintenance of ecosystem resilience through facilitation of the desired state is the
recommended strategy for sound ecosystem management in an environment in constant flux.

Incentives for lake conservation and management are often large and impact the system on both
economic and ecological scales. O’Riordan (1999) recommends that the economic valuation
involved in the management of a natural resource be a participatory process with stakeholders so
that all parties can weigh the costs and gains of the management outcome(s). This was especially
important in the Oregon (USA) Plan for Salmon and Watersheds, which was designed to restore
the populations of coastal salmon through the encouragement of residential property owners to

4

grow riparian vegetation buffers that reduced streamside water temperatures and provided salmon
spawning habitat. A hedonic pricing analysis of streamside property values before and after buffer
implementation showed a reduction in property values after the buffers were installed (Mooney
and Eisgruber, 2001). Conflicts between the use of water resources and the proper management
are common in riparian habitats since financial resources are at stake. It is therefore important that
incentives to protect both the lake resource and property values be projected to the residents to
increase the probability of citizen support. It must be realized that even with the existence of
“private” lakes, all lakes can be considered “common pool” resources since they are shared with
other people either within or external to the riparian community.

5

I.2

Lakes as Common Pool Resources and Sense of Place

It is arguable that public-access lakes could be classified as “Common Pool Resources” (CPR’s)
since populations outside of the riparian community have access to the same resource, and the
consumptive actions of the individuals reduce resource availability to others (Hardin, 1998).
Hardin (1998) further warned that resource depletion beyond the carrying capacity of a commons
system is inevitable if the system goes unmanaged. Alternatively, lakes held in completely private
ownership by shoreline riparians are also vulnerable to resource depletion if consumptive rules
and regulations are not in place. Privatization alone cannot guarantee protection of a natural
resource. Libecap and Wiggins (1985) discussed the inherent challenges that privatized resources
face due to the inability of the organization to agree on use regulations of the resource. Feeny et
al., (1990) emphasized the need for the acknowledgement of a local property rights regime relative
to the resource since this is an often-overlooked component of a Socio-Ecological System (SES).
He describes the classification of property ownership in four groups including: 1.) Open access,
where use of the resource is available to all and more likely subjected to overuse and degradation,
2.) Private property, where there is exclusive ownership, sometimes collectively, of the resource
and where rational exploitation does not guarantee a sustainable outcome, 3.) Communal property
where there is voluntary oversight and management of the resource, and 4.) State property where
state ownership utilizes regulations for management of the resource but sustainability is not
guaranteed and is dependent upon regulation enforcement and awareness of such regulations by
external users. If open-pool resources (commons) and privatized systems are not exempt from
resource exploitation, then it will be challenging to manage natural resources such as lakes to
guarantee ecosystem services for future generations.

6

Another necessary component that cannot be overlooked is the sense of place that the riparian
community has in relation to the water body. Jorgensen and Stedman (2006) surveyed 290
lakefront owners in Northern Wisconsin (USA) about their perceptions of sense of place for their
waterfront properties and determined that owners with greater property development believed that
the lake was the central focus of their perception of their properties. A study of multiple natural
resources such as forests, streams, lakes, wetlands, prairies, and open fields was conducted by
Wang et al. (2012) in southeast Michigan, USA, who found that lakes and streams had the highest
level of importance to property owners. This finding emphasizes the importance of these water
resource systems to riparian communities. How can riparians around the lake enjoy their sense of
place and act in an environmentally responsible way? The solution will likely involve not only
adequate knowledge of the ecosystem and its vulnerability, but incentives from the riparian
community to preserve the resource for the entire lake community. A framework for governance
and management would allow for consistent management of many inland lakes that could utilize
similar resources with beneficial outcomes. At the heart of such a framework lies the concept of
sustainability since it must be present to allow for long-term management of a commonly shared
lake resource.

7

I.3

Elements of a Sustainable Lake Community

The concept of sustainability is likely an evolving concept that should be tailored to the specific
locale under study to allow for maximum flexibility under changing conditions (Thompson, 2007).
Thus, it is likely that a “universal model” for sustainability may not be achieved.

Meadows et al. (2004) cautioned that the ever-expanded global population will eventually be
forced to reduce resource over-consumption through human behavior, organization, or
technologies, or be compromised by inevitable resource limitations which could result in an
unhealthy environment. Orr (2003) also warned us that: “A world of ever increasing economic,
financial, and technological complexity cannot be sustained because sooner or later it will
overwhelm our capacity to manage”. He further elaborated that the major barriers to sustainability
lie in social, political, and psychological forms, as opposed to purely technological forms.
Lubchenco (1998) offered a concrete and functional definition of sustainable systems which would
possess attributes of ecological soundness, economic feasibility, and social justice. In regards to
these attributes, ecological soundness can be derived from careful management, economic
feasibility can be assured through prioritization of improvement goals and corresponding cost
analyses, and social justice can be obtained through inclusion of all stakeholders that have an
association with the lake resource. Thus, it is synonymous to label a sustainable system as a
derivation of an adaptive management process.

The scale of the community regarding the resource being exploited must also be considered for
successful sustainable conservation measures (Dresner, 2002).

For the conservation and

management of lake systems, the riparian community combined with the local municipality seems
8

appropriate in size for sustainable governance of lake resources. Clearly, this proposed community
structure would involve only those that have a genuine stake in the lake resource and would lead
to greater participation and ultimately conservation of the lakes. Flora (2004) discovered that
communities need a shared vision of their future existence to drive change. It must not be ignored
that lakes which span more than one municipal jurisdiction may be susceptible to more
externalities and the riparian and municipal community may be larger in size. Everard (1999)
cautioned that sustainable management of still water resources (i.e., lakes) must account for
externalities that may complicate conservation efforts due to distant sources. An example of such
an externality would include materials brought to the lakes from the watershed. Many watersheds
in Michigan transcend multiple jurisdictional boundaries which make exclusive local governance
of a lake resource more complicated.

How can we meet the demands of society in relation to development and ecosystem services
without degradation of the resource in question? In the context of inland lakes and shoreline
development, users and especially owners of the resources are often shocked when the ecosystem
collapses in the form of hyper-eutrophication (as in the case of lake-wide Harmful Algal Blooms,
or HAB’s) or dramatically altered water levels (most commonly associated with excessive water
withdrawals). Ostrom (2009) warned that the users may not be informed of the carrying capacity
of the resource which ultimately results in resource commodity failures and ultimate destruction
or depletion of the resource. This statement emphasizes the strong need for an understanding of
the resiliency of the lake resource by all that benefit from it and its limits and thresholds to pollution
sources, as well as accountability by the public to protect the lakes and gain services without
exceedance of these thresholds.

Ostrom also elaborated that there must be a thorough

9

understanding of conditions specific to a certain region so that governance rules created by the
users and governmental units remains sustainable. Information on thresholds for specific lakes is
lacking, along with a sense of riparian lifestyle needs. Soranno et al. (2010) describe the model for
landscape limnology which incorporates components of the lake ecosystem, land, and human land
and water uses to effectively explain the landscape mosaic and allow for different variables to be
used based on unique management goals. This model could be applied to any water body where
land uses impact the water quality and are dependent on continuous changes. Barles (2010)
elucidated that the interactions between society and the biosphere are completely inter-dependent
and form the basis of sustainable development.

It becomes clear that knowledge of thresholds for each lake needs to be accurately understood,
and the lake must be utilized in a manner favorable to support the local community and avoid
depletion of the lake resources. Thus, we must have a sound idea of the capacity of the resource
to support the local population, and of the population to manage the resource with sustainable
methodologies. The capacity to manage can be increased through a thorough understanding of
who is responsible for governing and managing the resource and what degree of management
capacity they possess.

10

I.4

Elements for Sustainable Lake Management: Local Governance

Many inland lakes within Michigan, USA, are impaired and often rely on funds from Special
Assessment Districts (SADs) established by local municipalities for improvements since external
funding sources are scarce. Scheffer et al. (2001) suggest that prevention of catastrophic shifts in
natural resource systems requires that an acceptable level of resilience be maintained. Such
resilience may be evaluated with the community capitals framework (CCF) which could then be
used to prevent ecological and economic losses of an ecosystem such as inland lakes. Once a
catastrophic shift occurs the costs to mitigate the damages and restore an ecosystem to its original
state may be excessive and impossible to attain. Holling (1973) was the first ecologist to define
resilience as the time it takes post-disturbance for an ecosystem to return to a normal state of
equilibrium.

The opportunities for local empowerment and governance in Michigan are high since there are
1,240 townships throughout the state. There is much concern about the ability of municipalities
and riparian residents to effectively manage aquatic ecosystems due to limited knowledge of these
complex ecosystems. Furthermore, management of these systems in the context of sustainability
is uncertain if best management options of the natural, social, and economical components of the
system are not well understood. Everard (1999) makes it clear that the high diversity among
decision-makers requires a thorough understanding of the regional hydrological cycle so that
upstream behaviors and impacts relative to specific water bodies are known. In other words, if the
local lake system is considered as an isolated entity while outside or upstream factors influence its
water quality, then a solution to mitigate impacts becomes nearly impossible. Inevitably, this will
include stakeholders upstream of some water resources as well as additional water resources that
11

may enter the local lake system. Morton and Wright (2011) explore the importance of multicitizen stakeholder participation since everyone can bring unique perspectives, knowledge, and
values to the forefront so that acceptance of solutions can be more widely understood.

A socio-ecological system (SES) framework is clearly needed to facilitate and govern the human
roles in conservation and management of complex lake ecosystems. An SES may be defined as a
highly integrative term that describes the nested variables such as resource sub-systems, resource
units, users, and governance systems within the larger system (Ostrom, 2009). Ostrom also offers
a framework to assist in the analysis of SES sustainability, which is undoubtedly crucial for
optimum function of the system. Lake ecosystems must be viewed through the lens of an SES to
begin to understand the human behaviors and activities that result in degradation since prevention
is eventually needed for ecosystem sustainability.

Municipal governance was recommended by Burström and Korhonen (2001) as a means to reduce
impacts of industry and society on natural resources (a.k.a, industrial ecology). They further cited
that over the past three decades, municipalities have accumulated increased responsibilities for
environmental actions. The responsibilities include the contributions of technical services, social
services, infrastructure operations and management, purchasing of materials, employment of
citizens, local planning, authoritative roles, and transference of information to residents.
Undoubtedly, continued assurance of these services results in increased stability and resilience of
the municipal community. The capacities of these municipal responsibilities can be determined
through the recognition and measurement of community capitals present within the municipality.

12

Ostrom (2009) mentioned that although scientific knowledge (technical capital) is beneficial for
solving complex resource management problems, there has been a disconnect between the
ecological and social sciences. She mentions that SESs are necessary for addressing environmental
issues but they must be sustainable. This would require some metrics to evaluate the sustainability
of an SES. Ostrom offers many metrics that could be used as a framework for SES evaluations.
However, in a riparian-lake SES, a possible web of metrics could include the community capitals
framework proposed by Flora (2004).

Flora et al. (2004) recognized seven types of capital including natural, cultural, human, social,
political, financial, and built capitals that are acknowledged by the most sustainable communities.
Capitals are defined as existing resources that are used to develop new and stronger resources.
Natural, cultural, and human capitals comprise the foundation of community composition. Mäler
(2000) reminds us that natural capital often encompasses complex, non-linear systems that are
difficult to predict. The behavior of these systems must not be overlooked since there exists an
urgent need to measure the resilience of these systems and a thorough understanding of them is
pivotal for successful conservation. Additionally, consideration of both the costs of conservation
and the costs of a potentially damaged resource state are necessary to make sound and often
sacrificial management decisions.

Magis (2010) discusses the components of community

resilience and emphasizes that in times of uncertainty a community strong in resources that are
shared among members is more likely to thrive than one without ample resources. Pretty (2003)
concluded that communities with higher social capital can participate more readily in collective
activities and have more trust that results in increased cooperation. Precise evaluation metrics are
needed to determine the magnitude of capitals present within a municipal community for water

13

resource conservation and management. Increased cooperative efforts would likely result in
achievement of common objectives and goals and lead to a successful conservation outcome.

Michigan riparian communities offer a unique opportunity for the study of these forms of capital
and the inherent importance of them for the management of inland lakes because riparian
communities often lie within one or a few municipalities and can be clearly delineated since the
responsibilities of lake management activities rely almost entirely on the bordering riparian
community and the local municipality (Jermalowicz-Jones, personal observation; Figure 1).
Morton (2008) stated that a combination of regulation (policy) and scientific knowledge is not
enough to solve a major environmental problem such as NPS pollution. Morton recommends that
civic engagement is a necessary and effective tool for achieving complex goals. Civic groups need
to have access to management tools such as those utilized by farmers in Iowa, USA and Ohio,
USA since they rely on data from metrics such as the phosphorus index and soil condition index
for data-driven management decisions to protect their yields.

Although riparian communities would need different tools than those utilized by farmers, they
could benefit from a collection of various tools that assist them with the co-management of their
unique lake resource. This is one advantage that riparian lake associations have in that they are a
civic group of collective thoughts and actions that determine management outcomes. In reference
to the CCF, policy would be in the political capital category, whereas scientific knowledge would
lie in the technical capital category and civic engagement may fall into human and social capital
categories. For purposes of this research, I will focus solely on riparian communities as a beginning

14

approach to a broader SES model that may then encompass municipalities as an additional
resource.

Municipality

Lakes

Riparians

Figure 1. A diagram showing the interconnectedness and interdependence of lake resources,
municipalities, and riparian communities.

A local framework for the conservation and management of inland lakes is lacking in Michigan,
USA, and it is critical that one needs to be developed. In 1997, the Michigan Department of
Environmental Quality (MDEQ) and the United States Geological Survey (USGS) formed the
Lake Water-Quality Assessment Monitoring Program (LWQA) to assess the conditions of over
700 inland lakes by 2015 (Minnerick, 2004). Even though these efforts are critical to determine
the baseline conditions of many recreational lakes in the state and recommend continued lake water
quality monitoring, they do not establish a long-term process for the conservation and management
of these systems. Environmental parameters and their responses to mitigation efforts (i.e., such as
BMPs) are sometimes difficult to measure, especially in short time intervals, due to inherent “lag

15

times” that may be present once an improvement effort is implemented (Meals et al., 2010).
Dobson et al. (2007) acknowledge that lake restoration methods require significant amounts of
time to show intended improvements and thus recommended that restorative measures mimic the
natural system to expedite any revelations. This finding further emphasizes the need for a
framework that also supports long-term monitoring in addition to initial baseline water quality
assessments to measure restorative efficacies.

Federal and state involvement in lake management is minimal, limited only to the issuance of lake
improvement permits or regulatory oversight as required by the Clean Water Act of 1972 and the
Natural Resources Environmental Protection Act (P.A. 451 of 1994), respectively and also to
periodic fish stocking and surveys. The capacity is lacking for these regulatory levels to efficiently
and effectively manage most of the lakes in the state. It can therefore be argued that lake
conservation and management should be executed at the local level, with enhanced cooperation of
the municipalities involved and the riparian communities. Kennedy (2003) reminds us of the
relatively greater number of successes for local conservation through balancing of conservation
objectives with recreational values compared to conservation management at the global scale.
However, Birch and McCaskie (1999) cautioned that management of shallow urban lakes
(London, U.K.) by local municipalities can be disadvantaged through the lack of knowledgeable
experts to manage the lakes and to funding limitations. Because of this revelation, retention of
environmental experts to effectively educate and guide the community is strongly encouraged.
The expert could produce models that link together elements of local SES’s that would ultimately
provide policy makers with a comprehensive view of their proposed actions, as recommended by
Naiman (1996). The Sustainable Development Records (SDR) method proposed by Nilsson and

16

Bergström (1995) offers key indicators that measure the sustainability of an operational SES
relative to the resource base and ecosystem services provided. A primary benefit of this method
is that it allows stakeholders from different components of the system (i.e. policy makers,
scientists, municipal leaders, etc.) to look at the system sustainability from an economic viewpoint,
even given different foci relative to their fields of expertise or roles in society.

While involvement of the municipality is critical, we cannot undermine the importance of other
stakeholder involvement. Riparians are lakefront property owners who have a direct stake in the
conservation of lake resources. Many environmental management programs have failed because
of a scarcity in stakeholder participation. Herath (2004) described the loss of wetlands in Victoria,
Australia and attributed this to poor management that occurred without stakeholder input which
was critical to the development of alternative objectives.

Ultimately, the fate of the lake resource depends on the coherency and decisions of the community
and therefore requires robust community participation.

To determine the components that

comprise a well-structured community in regards to sustainable lake conservation and develop a
functional framework, the concept of sustainability must be thoroughly investigated as it applies
to riparian communities. Investigation of riparian communities allows for an initially simple
model that incorporates a clearly defined community. In other words, there must be a thorough
understanding of local level governance to understand what additional resources may be
contributed from the municipalities.

17

I.5

Lake Management and Governance at the Local Scale: Components for a New SES
Framework

Conservation scientists and policy-makers have uncertainties regarding the efficacy of local
governance to manage inland lakes and other waterways. This phenomenon is not limited to lakes
in the United States, as Europe also struggles to regulate lake eutrophication through the
classification of lakes as Sites of Special Scientific Interest (SSSI’s). The SSSI’s are physical as
well as legislative units where surrounding land is not included in regulations but may have
devastating impacts to water quality through contributions of agricultural nutrient loads (Wilson,
1999).

Certainly, such legal segregation of the natural resources (land and water) is not

harmonious with the connectedness of these resources and makes potential management of nonpoint sources to the lakes nearly impossible. The water framework created by the European Union
has clearly intended to remove nitrogen in addition to phosphorus as a regulatory mandate (Chave,
2001). The European Union Water Framework Directive (WFD) was adopted by the European
Commission in 2000 for priority lakes that have the greatest benefit to cost ratio. The primary
goal is to achieve good ecological health in these lakes by 2015. A study of recreation data by
Vesterinen et al. (2010) determined that if a one-meter water clarity improvement was made for
these lakes, then there would be a 6% increase in local swimming and 15% increase in local fishing
activities. It is critical that lake improvements not only be effective but also do not contribute to
overuse of the resource, although use by the local community would likely help support the local
taxation base for the municipality. A thorough analysis of the European Union (EU) Life
Programme for the management of urban lakes in the London Borough of Wandsworth yields a
highly informative approach of the transference of ecological knowledge of specific lake systems
to lake managers. The proposed management model allows for a tailored management process

18

that addresses the use of each lake system (relative to the lake users) in a context that corresponds
to compatible management or restoration methodologies (Birch and McCaskie, 1999). Holling
and Meffe (1996) warned that a “command-and-control” management approach leads to a
reduction in heterogeneity of an ecosystem, which inherently reduces its resilience due to loss of
essential functions provided by biodiversity. Thus, this approach would not be ideal for the
management of inland lakes.

Minnesota, USA, utilizes mandatory Watershed Management Organizations (WMO’s) to manage
surface waters within a watershed district. The WMO’s lack the power of property taxation but
can be funded by municipalities since they are governed by members appointed by municipalities.
There have been difficulties, however, with the management of local waterways at a watershed
scale due to observed differences in jurisdictional government and social structures, among other
attributes. Furthermore, reliance on state and federal programs to manage inland waterways at the
local level is unrealistic given the high quantities of these systems and their continuous need for
effective management. White et al., (2009) elaborated on the importance of Critical Source Areas
(CSA’s) for the detection of specific environmental problem areas that could be addressed more
readily and efficiently.

Even if local participation in lake conservation and management from riparian communities and
municipalities is successfully executed, there exists a strong need for a framework with sustained
funding mechanisms and environmental expertise. Furthermore, exclusion of the roles of property
owners associated with the resource and heavy reliance on governmental representatives has been
significantly linked to watershed planning failure. Differences in values as well as priorities

19

between these two key groups have resulted in irresolvable conflict and thus the planning process
should consider the entire watershed which includes all property owners and not predominantly
ecological advocates (Woolley and McGinnis, 1999).

Dietz et al. (2003) cautioned that

governance at the local scale may also have to consider outside forces (such as externalities such
as non-point source pollution) and thus it may be necessary for layered institutions to be involved
to resolve possible resource use conflicts. Conflict over management regimes of CPR’s likely
arises from differences in knowledge, interpretation, pre-conceptions, and priorities about the
resource in addition to material interests (Adams et al., 2003).

A sustainable management process should be adaptive in natural resource issues (Williams, 2011)
where uncertainty and controllability are high (Allen et al., 2011). The National Research Council
(2004) identified adaptive decision-making as the formulation and sharing of objectives,
identification of management alternatives, consideration of resource management consequences
and possible uncertainties, along with monitoring procedures. There is indeed high uncertainty in
the management of inland lake systems because they are not strictly ecological, but rather socioecological systems. However, the management potential of inland lake problems is significant
since behaviors of riparians or those that utilize the lake resource can be readily altered, often when
adequate knowledge of the resource is given. Allen et al., (2011) also offered a structural recipe
for the components of adaptive management which included a clear vision of the goals, flexibility
with the determination of alternative management objectives and causative hypotheses, and
methods for data collection which were amenable to evaluation and repetitive measurement. In
addition, Allen and Gunderson (2011) described the nine most likely reasons for the failures of
adaptive management programs. Such failures included a lack of consensus among stakeholders

20

on the definition or ingredients of an adaptive management plan, lack of coordination between
scientists and policy makers, lack of leadership, lack of metrics for determining adaptive
management success, lack of funding for necessary continued monitoring, research goals
dominated by research or biased interests, scientific overstatement of research capacities, and lack
of shared decision making. Martínez de Anguita et al. (2008) emphasize the need for integration
of stakeholder ethics into decision-making frameworks that aim to achieve sustainable and longterm environmental conservation objectives.

From the literature review above, it is apparent that a multitude of variables needs to be considered
for the development of a sustainable inland lake conservation framework for Michigan. First, there
needs to be a sound analysis of the capacity of a local municipality and the riparian community to
effectively manage the resource. Second, there needs to be a clear understanding of the lake
resource governance, how decision-making processes should be executed, and who is ultimately
responsible for sustainable management of the lake ecosystem. It cannot be emphasized enough
how critical the sociological component is to continued sustainability of any water quality
improvement or conservation program (Gooch et al., 2012; Morton and Brown, 2011; Mascia et
al., 2003). For example, the sociological interaction of farmers in an agricultural watershed may
allow for more successful performance-based goals for non-point source pollution reduction
(Morton, 2008). Ultimately, the CCF may assist in the prediction of degree of success of costly
experimental policies to minimize wasted costs and efforts (Walters, 1997). Third, adequate
access to knowledge of the complex lake ecosystem (either contributed from experts or from local
institutions), including critical thresholds, must be available and effectively relayed to all the
stakeholders. Fourth, the compilation of any critical information for conservation of the resource

21

(i.e., water quality monitoring, data collection, CSAs, modeling, etc.) should be participatory and
involve citizens from both the riparian community and municipality, and if applicable include
institutions from outside of the system that impact the lake resource. Lastly, the proposed
framework should also engage an adaptive management strategy due to the rapidly changing
conditions of the SES.

Since it is important that a proposed framework for sustainable inland lake conservation and
management must be developed, I would offer the hypothesis that sustainable lake conservation
and management is dependent upon sustainable and resilient communities that are, in turn,
dependent on well-managed resources or municipal community capitals for continuous
sustenance. A careful analysis of each of the components necessary for a sustainable and resilient
community and lake resource is needed to generate predictive models that could determine the
potential long-term benefits from the proposed framework. Huang and Xia (2001) mentioned that
the myriad biological, physico-chemical, hydrological, socio-economic, and environmental
management elements that comprise complex aquatic SES’s cannot be considered separately but
must rather be integrated in a system model. The model must be tested with inland lake riparian
communities in Michigan to determine if the framework would have standing. The following
chapter introduces critical components for the evaluation of management capacities of individual
riparian communities and the theoretical rationale for the selection of these components.

22

CHAPTER II:

II.1

DEVELOPMENT OF HYPOTHESES AND THEORY ON THE ROLE
OF COMMUNITY CAPITALS IN INLAND LAKE MANAGEMENT

Community Capital Resources and Their Roles in Riparian Communities

There are seven major categories of community capitals that are used as resources for community
function and sustainability. Flora (2004) defines capitals as “resources used to create new
resources”. In reference to riparian communities, evaluation of available forms of capital will
allow for the development of a sustainable capital resources tool to assist them with the comanagement of their lake resource. Flora (2004) further demonstrates that there exists significant
overlap of the capitals and that this overlap becomes pivotal for best management outcomes
(Figure 2). In fact, Miller and Buys (2008) indicate that to date only a small amount of studies has
investigated the relationship between social capital and sustainability. Furthermore, sustainable
development is dependent upon future generations having access to at least the existing levels of
capitals (Serageldin, 1996). Scholars have agreed on most of the definitions of these capitals,
although some individual characteristics can be modified across disciplines. There is significant
debate about the indicators for these capitals and how those indicators predict community functions
and outcomes. Thus, careful analysis of all indicators that could be applied to community inland
lake management are being pursued to assure the most precise measurements for future predictions
and models. Additionally, collection of baseline values for each capital will allow for the
determination of future community sustainability. A description of each form of community capital
is discussed below.

23

Figure 2. The overlapping of critical community capital forms and the positive shared outcomes
of that relationship (Flora, 2004).

II.1.1 Social Capital

Social capital was first introduced to the literature by L.J. Hanifen in 1916 as he learned that
community involvement was critical for successful education in rural schools. Social Capital was
defined by Putnam (2000) as: “The social connectedness of a community or the glue that enables
people, organizations, communities, and nations to work together collaboratively for mutual
benefit.” Social Capital can be more broadly defined as the interactions among individuals and
groups within a community (Flora, 2004). It includes attributes of trust, reciprocity, cooperation
and membership, common visions and goals, acceptance of alternative views, leadership, and
diverse representation and contributes to economic and social development. In fact, problemsolving capacities are generally higher in communities with high social capital (Cohen and Prusak,
2001), which is important for natural resource management efforts. This was evidenced in the

24

case of the San Francisco Bay Plan which occurred post-Clean Water Act since social capital was
the only form of capital initially available to drive conservation in the area. Putnam (2000) defined
social capital as connection that allows community members to work together for mutual benefits.
It is critical to realize that not all social capital results in positive outcomes and so special attention
should be directed to the situation and context involved (Miller and Buys, 2008; Hogg et al., 2012).
It can however, be a useful tool for policy makers since measurement of its capacity can determine
the diversity in social capital, if any that may be useful for avoiding land use and natural resource
management conflicts (Dale and Sparkes, 2007.)

Flora (2004) mentions that not all bonding social capital is constructive since divisions among
community members and the formation of cliques may prevent unity among an entire community.
Bridging social capital can help overcome this but by itself is also not effective (Hernandez, 2003).
Bonding and bridging social capital are different in that the latter refers to social connections
between heterogeneous groups whereas the former refers to the connections of homogenous
groups. In fact, if when bonding social capital is high and bridging social capital is low, the
community is less likely to accept “outsiders” that may be critical to solving a given problem.
Alternatively, when bridging social capital is high and bonding social capital is low, the internal
community is weak regarding external forces and ownership struggles may result. Furthermore,
Woolcock (2000) introduced the term linking social capital as the relationship between community
groups and power figures. Miller and Buys (2008) emphasize that not all social capital is
“positive” and certain forms may not assist with the development of a sustainable community.
They discuss favorable environmental behaviors and unfavorable forms of behavior. Unfavorable
behavior may originate from socially proactive people that are more concerned with speaking out

25

on an issue or appeasing neighbors than favoring what is best from a sustainability perspective. In
reference to my research, this would be manifested by communities that refuse to implement BMPs
not necessarily due to cost but due to a judgement that particular BMPs may not be aesthetically
pleasing or may not be in their best interest (as opposed to nature).

II.1.2 Cultural Capital

Cultural capital refers to world views and it is usually cultivated over generations and includes
language, behavior, recognition, celebration, world view, and values. Flora (2004) explains that
cultural capital may influence natural capital since human behaviors can have marked impacts on
natural systems. It is possible that different individuals within a given community can have unique
world views due to their life history and experiences but still share a common goal or objective.

Cultural capital also encompasses the idea of legacy which is what a current generation passes on
to the next or a previous generation passed on to an existing generation. This has a potentially
important bearing on riparian communities since some of them have had several generations living
on a specific lake. These people may have significant knowledge of the lake’s history and therefore
be more tuned in to current issues involving the lake’s health. Conversely, some riparian
communities have fast turnover of lake residents and this could potentially lead to confusion
regarding the lake’s issues since historical knowledge and baseline data on the lake may be limited
or scarce. Legacy is associated with a sense of place. So if a lakefront home is passed down to
younger generations, there could be a strong probability that those younger generations would
inherit that same sense of place. Flora (2008) also points out that sometimes cultural capital can

26

be reduced by the introduction of new governing policies or political capital. This is perhaps a
good reason why there needs to be a thorough understanding of cultural capital within a community
so that future policy change can take that into consideration and avoid the potential loss of valued
cultural capital.

II.1.3 Natural Capital

Natural capital refers to natural geographic resources such as land, water, climate, scenery, living
biota, and biodiversity. In other words, natural capital refers to natural geographic features.
Additionally, natural capital may be considered a foundational form of capital since it is a resource
which often determines human behavior and action. The maintenance of natural capitals in a
community allows for sustainable resource use while populations increase if the stock is not
substantially reduced. Sseguya et al. (2009) explain that reduced resources (such as natural) capital
are often due to deficiencies in other community resources. This is one reason why protection of
natural capital is complex and must investigate many different types of resources. Furthermore,
understanding the costs that natural capitals provide also allows for better conservation efforts with
a growing population (Costanza and Daly, 1992). Folke et al. (1994) remind us that natural capital
depends on other forms of capital such as human and financial capital preservation and protection.
This is crucial for the natural resource to continue with the contributions of ecosystem services.
As described above, lakes are a form of natural capital as are other facets of natural landscapes. In
reference to lake systems, this form of natural capital essentially creates financial capital in the
form of waterfront properties and the associated tax revenue base. Of particular importance is the
realization that natural capital is often non-renewable and thus these resources are highly

27

vulnerable to depletion and degradation. Natural capital is thus susceptible to exploitation from
the “commons” and a thorough understanding of its stock is critical for its preservation in a lake
community. This is important to realize since natural capital must be present for economic
development to progress in a community (Berkes and Folke, 1992). Jansson and Jansson (1994)
discuss the importance of biodiversity for preservation of natural capital stock. In reference to
lake systems, this would include protection of the fish, benthos, and other wildlife.
Implementation of BMPs for the protection of lake biodiversity would thus conserve natural
capital. This concept is discussed in further detail relative to the methodology executed by this
study.

II.1.4 Built/Technical Capital

Built capital refers to infrastructure, buildings, hardware, housing, and utilities or producer goods.
Technical capital may refer to learning resources and their availability. In riparian communities,
this may include the lakefront properties, local libraries, GIS, and informational centers, schools,
and other built structures. These forms of capital may also be limiting if other forms of capital
such as social capital are low. In other words, the needs of local communities through open
communication may best dictate the types of built capital for a given community as this is often
need-based. One advantage that a riparian community may have with respect to abundant built
capital is the ability to have roads altered or graded that may be contributing to runoff into the
lakes. Many rural lakes have dirt roads that may sometimes wash into the lake and contribute
sediment and nutrient loads which leads to water quality degradation. Abundant built capital may
assist such communities with technologies that could improve the local environment.

28

II.1.5 Financial Capital

Financial capital includes debt and investment capitals, savings, tax revenues, donations, contracts,
reallocation, loans, poverty rates, grants, and tax abatements. Financial capital may be closely
linked to political capital when governmental entities are involved in the granting of funds. A
natural resource such as land or a lake may also be considered as a component of financial capital
if those resources yield new resources. In riparian communities, this would manifest itself in
lakefront properties which have considerably higher value when on waterfront versus the same
property not on a waterbody. This may also serve as an impetus for moving forward with a Lake
Management Plan (LMP) since the plan itself requires significant financial capital to improve the
lake resource and have it yield more financial benefits to the riparian community. Riparian
communities with high financial capital would thus be able to implement more improvement
methods since the costs could be paid. It is important to note that even with high financial capital,
other forms of capital may be needed to drive change or implement solutions since human capital
also contributes to financial capital through labor activities. Another significant source of financial
capital may arise from the local municipalities since they may be able to contribute financial
resources to riparian communities through the acquisition of grants or direct donations.

II.1.6 Human Capital

Human capital refers to the population, creativity, educational attainment, skills, health, and
abilities of community individuals. Economists consider investments in the attributes as additions
to human capital that cannot be separated from the individual the same way that physical or

29

financial assets could be (Becker 2002). In general, smaller communities are less diverse and
usually have lower human capital. However, that may not always be the case since a smaller
community that is well educated may also have a high level of human capital. The type of training
and education may be a significant contributor to the activities that would be implemented in a
riparian community. For example, a riparian community with a high number of citizens educated
in medicine would likely have different knowledge of lake issues relative to riparians trained in
lake ecology.

Innovation is another attribute that lies within human capital and may also be an impetus for
moving forward with new technologies for lake improvement such as Best Management Practices
(BMPs). Human capital also refers to the “power in numbers” rule since more people can assume
more roles and not be overtaxed for numerous duties. It also incorporates the concept of work
ethic since laborers in the workforce are an example of strong human capital. In relation to riparian
communities, a lake association with a higher number of participants would be expected to be able
to execute more activities than a considerably smaller group. Flora (2004) explains that the trio of
natural, cultural, and social capital form the community base and at least those forms of capital
must be recognized before outside experts or resources can assist with water resource management
issues.

II.1.7 Political Capital

Political capital refers to the mobilization of community resources through political proponents
such as policy makers. The major components of political capital are connections, power, voice,

30

ability of local governments to acquire resources for a community, and organization (Flora, 2004).
For example, many small communities will directly contact a legislator to influence regulations
rather than work together as a small group to create plans for themselves to request distribution of
governmental resources.

II.2

Research Questions & Hypotheses

Research Question:

How do we measure the capacity of lake associations to respond to water

quality impairment?

Sub-question 1: Which community capitals influence lake association riparian community
capacity to implement water quality best management practices (BMPs)?

Sub-question 2: Are there any significant correlations between community capitals and
water quality?

Research Design:

Use the community capitals framework (Flora, et al. 2012) to measure

differences in capital assets (e.g. human, social, cultural, political, natural, built/technical, and
financial capitals) among riparian communities and determine the relationships between these
community capitals and BMPs and Trophic State Index (TSI) in sample populations with lake
management plans (LMP) and without lake management plans (non-LMP).

31

II.2.1 Hypotheses

Hypothesis #1:
H0: There are no significant differences in mean capital for LMP and non-LMP riparian
communities.
HA: There are significant differences in mean capital for LMP and non-LMP riparian
communities.
Note: These hypotheses are tested for all seven forms of capital.
Hypothesis #2:
H0: There are no significant differences in mean BMPs for LMP and non-LMP riparian
communities.
HA: There are significant differences in mean BMPs for LMP and non-LMP riparian communities.

Hypothesis #3:
H0: There are no significant correlations between BMPs and each form of capital in LMP riparian
communities.
HA: There are significant correlations between BMPs and each form of capital in LMP riparian
communities.
.Hypothesis

#4:

H0: There are no significant correlations between BMPs and each form of capital for non-LMP
and riparian communities.
HA: There are significant correlations between BMPs and each form of capital for non-LMP
riparian communities.

32

Hypothesis #5:
H0: There are no significant differences in mean TSI for non-LMP and LMP riparian
communities.
HA: There are significant differences in mean TSI for non-LMP and LMP riparian communities.

Hypothesis #6:
H0: There are no significant correlations between TSI and community capitals for LMP riparian
communities.
HA: There are significant correlations between TSI and community capitals for LMP riparian
communities.

Hypothesis #7:
H0: There are no significant correlations between TSI and community capitals for non-LMP
riparian communities.
HA: There are significant correlations between TSI and community capitals for non-LMP riparian
communities.

33

CHAPTER III: RESEARCH METHODOLOGY

III.1

Selection of Proxy Indicators for Assessing Community Capitals

Specific indicators for evaluating communities differ across the landscape, evaluate various
attributes of a community and explain the existing functions of a community. One program that
utilizes indicators representative of the environment, population and resources, economy, youth
and education, and health and community is the Sustainable Seattle Model (Belesme and Mullin,
1997). These indicators are related to community health and sustainability but are most applicable
to larger communities such as local municipalities.

To assess the degree of community capitals present within defined riparian communities, it was
necessary to generate a survey instrument that measured community capitals in individual riparian
communities. Development of this survey instrument consisted of methodical generation of proxy
indicators that were specific to riparian communities and represented the community capitals
present in the community capitals framework (CCF) developed by Flora (2004). Proxy indicators
are defined as indirect measures that approximate an attribute in the absence of a direct measure.

It was critical to include all forms of capital, as Fey et al. (2006) emphasize that overlaps of various
forms of capital are common and may be critical in explaining how they combine to create an outcome
at the community level. For example, Alston (2004) showed that a decline in human capital was
associated with a loss of economic and social capital. Due to this observed level of overlap, the
designation of indicators to a specific form of capital is challenging. Thus, indicators that dominantly
represent each form of capital were selected for this research. The textbook “Rural Communities (third

34

edition, Westview Press)” by Flora and Flora (2008) was instrumental in the selection of proxy
indicators specific to lake communities. Although the textbook did not have a section solely on lake
indicators, the basic principles for each form of capital could be applied to many types of natural
resource communities. Flora (2004) emphasizes that although scientific data and technical

information may inform communities of infrastructure deficiencies that may highlight immediate
needs for improvement, the information may not lead to a long-term, sustainable future
improvement. It was important that the selection of riparian communities be used for subjects
because having place-based communities allowed for a better understating of how these
communities interact with the lake ecosystem using various forms of capital. Armitage (2005)
emphasized the need for specific and place-based metrics for optimum natural resource
management capacity. Additionally, Flora (2004) describes the individual forms of capital that
may be evaluated for water resource sustainability. For this study, the community capitals to be
evaluated included social capital, financial capital, political capital, natural capital, cultural capital,
human capital, and built/technical capital.

The proxy indicators were grouped by sections

indicated by the capital form being measured for the sake of careful analysis.

35

III.2

Development of the Survey Instrument

In addition to the development of the proxy indicators for each form of capital to be evaluated, the
survey questions were created to be closed-ended to assure that each respondent would be able to
answer in a consistent manner. This was done to minimize any effects from myself as the
interviewer and thus avoid interviewer bias. The respondents were individuals that were actively
leading a lake association (such as an acting president or vice-chair). This was important since it
reduced respondent bias which could have been a factor if the respondents could not provide
accurate answers. Additionally, each question on the survey instrument had an opt-out answer that
was stated as: “I do not know”. Sixty lakes were randomly selected from a master list of lake
associations. All of the respondents were mailed a complete survey with instructions and a letter
explaining that the surveys were voluntary and would be used to develop a tool for management
by inland lake riparian communities. A total of 33 survey instruments were returned and included
in the data analysis. This represents a response rate of approximately 55% which is favorable for
a mailed survey.

Lake respondents were well scattered throughout the state of Michigan

(Figure 3).

36

Figure 3. Randomly selected lake association sites included in this resource study. Note: The
Upper Peninsula of Michigan is not represented here because there were no survey
responses received from that region of the state.

37

III.3

Data Coding

After all surveys were received, the survey instruments were divided into two key groups to
investigate the ability to implement BMPs and included lake associations with lake management
plans (LMP) and those without lake management plans (non-LMP). Additional data on the number
of BMPs implemented for the lakes returned was collected independently by gathering the data
from lake association websites or reviewing lake management plans online or by directly
contacting lake association board leaders for the information.

A list of BMPs commonly

implemented for riparian communities was created (Table 1) based on my experience with lake
improvement projects and the various BMPs that are most often executed for such improvements.
A total number of individual BMPs was calculated for each riparian respondent. Each form of
capital had questions that ranged from 0 points to up to a maximum of 5 points. Community capital
scores were calculated by adding up the total number of points for each capital measured and
dividing that number by the total number of points possible for each type of community capital.
Such quantitative data allowed for statistical analysis of the data. Each individual question in the
capital categories was tallied for total points and added into an Excel spreadsheet. A total capital
score was then generated for each respondent and each form of capital. If the answers were in the
“I do not know” category, they were assigned a value of zero (0). Answers that included “I do not
know” were assigned a value of zero since the survey instrument assessed overall abundance of
each form of community capital and randomly selected subject lakes were associated with lake
leaders that would have great knowledge of these local community resources. Respondents with
high levels of capital had higher overall scores. This process was conducted for each type of capital
and included social capital, natural capital, political capital, human capital, cultural capital,
financial capital, and built/technical capital. The latter form of capital was combined since the
38

survey instrument included questions pertaining to infrastructure as well as educational and
informational (material) resources.

The use of a different dependent variable in place of BMPs was decided upon to see if actionoriented attributes such as BMPs would yield different outcomes from more static variables such
as water quality. A Trophic State Index (TSI) provided a quantitative measure for scoring lakes
based on three major parameters: 1) total phosphorus, 2) Secchi transparency, and 3) chlorophylla. Each parameter can have a separate index or a combination of the three parameters can be
calculated to formulate a new index. The TSI was created by Carlson (1977) for use in categorizing
lakes based on trophic status. The TSI scores range from 0-100 with the lowest scores referencing
lakes with excellent water clarity, low nutrients, and low chlorophyll-a and those with the highest
scores revealing lakes with higher nutrients concentrations, lower water clarity, and higher
chlorophyll-a. In general, lakes with a score <30 are the most oligotrophic (nutrient-poor) and
lakes with a score of >80 are the most eutrophic (nutrient-rich). A TSI index for each subject lake
respondent was derived for all three parameters. Water quality data used to develop the TSI were
collected from a combination of sources including existing lake management plans, the State of
Michigan MiSWIM database, the MiCORPS Cooperative Lakes Monitoring Program database, or
from the EPA Storet water quality database. The following equations (Carlson, 1977) were used
to calculate the TSI Index for each water body:
TSI-Phosphorus (in µg L-1) = 14.42*Ln [TP] + 4.15
TSI-Chlorophyll-a (in µg L-1) = 30.6 + 9.81 Ln [Chl-a]
TSI-Secchi (in meters) = 60-14.41*Ln [Secchi]
Mean TSI = [TSI-Phosphorus + TSI-Chlorophyll-a + TSI-Secchi]/3

39

Table 1. Commonly implemented BMPs for riparian communities.
Best Management Practices
Water Quality Monitoring
Control of Submersed Invasive Exotic Plants
Control of Submersed Nuisance Native Plants
Control of Algae/Phytoplankton
Control of Emergent Invasive Plants
Riparian Education/Outreach/Seminars
Muck Reduction (aeration or dredge)
Watershed Plan/Monitoring
Drain Sampling/Stormwater Control
Tributary Sampling/Inlets to Lake
Natural Shoreline Promotion/Preservation
Reduction of Lake Nutrients
Reduction of other aquatic invasives (i.e. zebra
mussels)
Fishery Study/Evaluation
Fish Stocking
Waterfront District Ordinance Development
Boat Washing Station

Implemented? (Yes or No)

III.3.1 Proxy Indicators for Riparian Communities (Associations):

The questions below were derived to include representative proxy indicators for each form of
community capital. Possible responses to the questions are available in Appendix A.

III.3.2 Social Capital:

1) How many years has your lake association been in existence?
2) How many times per year does your lake association hold meetings?
3) How many social media groups does the association belong to?
40

4) Estimate the degree of trust among lake Association residents:
5) How many times in the past year have you reached out to other lakes (Associations of other
groups) for guidance or information on lake issues?
6) What percentage of your Association has turned over membership in the past 2 years?

III.3.3 Natural Capital:

1) How many lakes lie within five miles of your lake?
2) How many years has your lake been managed by your Association or a lake professional?
3) Is there a nature center within five miles of the lake?
4) What is the lake trophic status (i.e. nutrient-rich or poor or moderate)?
5) How many parks are within five miles of lake?
6) Percentage of green space/forest/wetlands/ within five miles of lake?
7) How many miles of walking (non-paved) trails are within five miles of lake?
8) What is the population density of your riparian community?
9) Does the lake have public access?

III.3.4 Cultural Capital:

1) How many unique items have been created as marketing products for your lake or
association?
2) What is the average annual time investment (in hours) of the Association on lake projects?
3) How many lake-specific events are held in your lake Association community annually?

41

4) How many traditions exist that are specific to your lake Association?
5) Approximately what percent of the population around your lake is seasonal?
6) Approximately what percent of the population around your lake is generational or has
relatives that once lived on the lake?

III.3.5 Human Capital:

1) What is the average age of a riparian in the Association?
2) What percentage of your Association has the following educational attainment:
3) How many riparians in the Association have skills that pertain to natural resources
management or protection?
4) How many different civic associations do lake activities around your lake (i.e. Elks,
Masons, Rotary, Schools, etc.)?
5) How many businesses donate time to lake activities each year?
6) How many businesses donate time to lake protection each year?
7) How many businesses donate time for lake monitoring each year?
8) How many small businesses/start-ups exist within your lake community?
9) What percentage of the lake population is in your Association?

42

III.3.6 Political Capital:

1) How many grants have been obtained by the Association in the past five years? (Grants
can be municipal, private, non-governmental organization, state, county, national, or
township)
2) On average, how many times have riparians worked annually with government or political
groups?
3) How many riparians on the lake occupy a political office in the area or state?
4) Is there evidence of the ability of the Association to influence regulations (i.e. ordinances)
due to the structure/composition of your lake community?
5) Is there evidence of the ability to implement regulations (such as ordinances) to benefit
water quality due to the structure/composition of your lake community?

III.3.7 Built/Technical Capital:

1) How many homes are there around your lake?
2) How many of the homes on your lake are on a sewer system?
3) How many paved trails exist within five miles of your lake?
4) What is the percentage of homes with internet access on your lake?
5) Is there access to a local community center or library within five miles of your lake?
6) Is there a lake expert that manages your lake?
7) Is there access to scientific/technical information on your lake?
8) Is there access to GIS/maps on your lake (regarding your lake)?

43

9) How many industries or businesses are within five miles of your lake?

III.3.8 Financial Capital:

1) What is the average annual per capita income per riparian in the Association?
2) What is the annual amount of money received for annual lake Assoc. dues?
3) What is the annual amount of grant money received for lake improvement/watershed
grants?
4) How much money is spent annually on lake management?
5) How much money is donated annually to the lake for management or preservation?
6) What is the approximate percentage of riparians on the lake at poverty level?
7) What is the average cost of housing on your lake?
8) What is the percentage of home ownership in your Association?
9) Would the Association have the ability to obtain a loan for lake improvements?

44

III.4

Data Analysis Methods

Data entered into Excel were analyzed first for correlations between each form of capital (the “x”
independent variables) and implementation of BMPs (the “y” dependent variable). An “r squared”
value and linear regression equation was obtained for each form of capital among all respondents
separately in the lake management plan (LMP) and non-lake management plan (non-LMP) groups.

Additionally, another dependent variable (Trophic Status Index or TSI) was selected and graphed
for correlations between the individual forms of community capitals also for both LMP and nonLMP populations. Tests of statistical significance on the correlations were then conducted using
the Pearson correlation coefficient, two-tailed test with SPSS Version 22 (IBM® Corp.). The
Pearson correlation coefficient measures “r” which measures linear dependence between x and y
variables.

A comparison of the means (independent samples t-test; two-tailed) was conducted for all forms
of capital for the LMP and non-LMP riparian community groups.

45

CHAPTER IV: RESULTS

The results presented below correspond to each hypothesis derived for this study. This section
contains only the quantitative results which consists of graphs and tables. Analysis of qualitative
answers was not executed for this study. If statistically significant results were found, then the
table indicates these findings.

An independent samples t-test of the means was conducted on both data sets (non-LMP and
LMP) relative to each form of community capital to determine significance differences of each
form of community capital for both riparian populations. Table 2 shows the summary for
differences in community capitals for Non-LMP and LMP riparian community populations.

46

IV.1

Social Capital with LMPs and Social Capital with non-LMPs

There were no significant differences in social capital for riparian community populations with
and without LMPs (p=0.4; Tables B-1 and B-2; Appendix B). This finding indicates that there is
not a significant difference in social capital for the two groups and therefore having a management
plan does not necessarily mean higher social capital will be present within a riparian community.
Thus, the null hypothesis for social capital was accepted.

47

IV.2

Financial Capital with LMPs and Financial Capital with non-LMPs

There were significant differences in financial capital for riparian community populations with and
without LMPs (p=0.04; Tables B-3 and B-4; Appendix B). This finding indicates that there is a
significant difference in financial capital for the two groups and riparian communities that had
active LMP’s also had more financial capital than communities without LMPs. Thus, the null
hypothesis for financial capital was rejected.

48

IV.3

Political Capital with LMPs and Political Capital with non-LMPs

There were no significant differences in political capital for riparian community populations with
and without LMPs (p=0.6; Tables B-5 and B-6; Appendix B). This finding indicates that there is
not a significant difference in political capital for the two groups and therefore riparian
communities that had active LMP’s did not have more political capital than communities without
LMPs. Thus, the null hypothesis for political capital was accepted.

49

IV.4

Natural Capital with LMPs and Natural Capital with non-LMPs

There were no significant differences in natural capital for riparian community populations with
and without LMPs (p=0.9; Tables B-7 and B-8; Appendix B). This finding indicates that there is
not a significant difference in natural capital for the two groups and therefore riparian communities
that had active LMP’s did not have more natural capital than communities without LMPs. Thus,
the null hypothesis for natural capital was accepted.

50

IV.5

Cultural Capital with LMPs and Cultural Capital with non-LMPs

There were no significant differences in cultural capital for riparian community populations with
and without LMPs (p=0.7; Tables B-9 and B-10; Appendix B). This finding indicates that there
is not a significant difference in cultural capital for the two groups and therefore riparian
communities that had active LMP’s did not have more cultural capital than communities without
LMPs. Thus, the null hypothesis was accepted.

51

IV.6

Human Capital with LMPs and Human Capital with non-LMPs

There were significant differences in human capital for riparian community populations with and
without LMPs (p=0.03; Tables B-11 and B-12; Appendix B). This finding indicates that there is
a significant difference in human capital for the two groups and therefore riparian communities
that had active LMP’s had more human capital than communities without LMPs. Thus, the null
hypothesis was rejected for human capital.

52

IV.7

Built/Technical Capital with LMPs and Built/Technical Capital with non-LMPs

There were no significant differences in built/technical capital for the two riparian community
populations (p=0.2; Tables B-13 and B-14; Appendix B). This finding indicates that there is not
a significant difference in built/technical capital for the two groups and therefore riparian
communities that had active LMP’s did not have more built/technical capital than communities
without LMPs. Thus, the null hypothesis was accepted for built/technical capital.

Table 2: Summary statistical table for differences in community capitals for Non-LMP and
LMP riparian community populations

Community
Capital

Non-LMP
Mean ± SD

LMP
Mean ± SD

Diff. Non-LMP & LMP
P-value

Social

0.65 ± 0.17

0.70 ± 0.20

0.4

Financial

0.42 ± 0.13

0.54 ± 0.18

0.04*

Political

0.29 ± 0.17

0.33 ± 0.18

0.6

Natural

0.59 ± 0.15

0.58 ± 0.15

0.9

Cultural

0.46 ± 0.15

0.48 ± 0.17

0.7

Human

0.31 ± 0.15

0.43 ± 0.13

0.03*

Built/Technical

0.31 ± 0.15

0.43 ± 0.13

0.2

SD= Standard deviation
* Denotes statistically significant result
LMP = lake management plan population
Non-LMP = non-lake management plan population

53

IV.8

Statistical analyses on BMPs in LMP and non-LMP Riparian Communities

There were no significant differences (p=0.1; two-tailed t-test; Tables B-15 and B-16; Appendix
B) in BMPs for the LMP and Non-LMP riparian community populations. Thus, the null
hypothesis for BMPs was accepted. This means that having a lake management plan does not
necessarily guarantee that more BMPs will be implemented in riparian communities. Table 3
demonstrates the correlations between community capitals and BMPs for Non-LMP and LMP
riparian community populations.

54

IV.9

Statistical analyses on correlations of BMPs and community capitals in LMP
Riparian communities.

IV.9.1 Social Capital:

There was not a significant correlation between social capital and BMPs for riparian communities
with an LMP (p=0.1; Figure 4 and Table B-17; Appendix B). Thus, the null hypothesis was
accepted for the relationship between social capital and BMPs.

Relationship between Social Capital and BMPs
14
12

y = 5.1969x + 2.9483
R² = 0.1204

BMP Score

10
8
6
4
2
0
0.00

0.20

0.40
0.60
Social Capital

0.80

1.00

Figure 4. Relationship between social capital and BMPs in riparian communities with an LMP.

55

IV.9.2 Financial Capital:

There was not a significant correlation between financial capital and BMPs for riparian
communities with an LMP (p=0.3; Figure 5 and Table B-18; Appendix B). The relationship
between BMPs and financial capital was a positive one. Thus, the null hypothesis was accepted
for the relationship between financial capital and BMPs.

Relationship between Financial Capital and BMPs
14
12
y = 3.5539x + 4.7075
R² = 0.0452

BMP Score

10

8
6
4
2
0
0.00

0.20

0.40
0.60
Financial Capital

0.80

1.00

Figure 5. Relationship between financial capital and BMPs in riparian communities with an
LMP.

56

IV.9.3 Political Capital:

There was not a significant correlation between political capital and BMPs for riparian
communities with an LMP (p=0.2; Figure 6 and Table B-19; Appendix B). The relationship
between BMPs and political capital was a positive one. Thus, the null hypothesis was accepted
for the relationship between political capital and BMPs.

Relationship between Political Capital and BMPs
14
12
y = 4.9859x + 4.9648
R² = 0.0956

BMP Score

10
8
6
4
2
0
0.00

0.10

0.20

0.30
0.40
Political Capital

0.50

0.60

0.70

Figure 6. Relationship between political capital and BMPs in riparian communities with a lake
management plan.

57

IV.9.4 Natural Capital:

There was not a significant correlation between natural capital and BMPs for riparian communities
with an LMP (p=0.7; Figure 7 and Table B-20; Appendix B). The relationship between BMPs and
natural capital was a negative one. Thus, the null hypothesis was accepted for the relationship
between natural capital and BMPs.

Relationship between Natural Capital and BMPs
14
12
y = -1.9561x + 7.7467
R² = 0.0103

BMP Score

10
8
6
4
2
0
0.00

0.20

0.40
0.60
Natural Capital

0.80

1.00

Figure 7. Relationship between natural capital and BMPs in riparian communities with an LMP.

58

IV.9.5 Cultural Capital:

There was a significant correlation between cultural capital and BMPs for riparian communities
with an LMP (p=0.04; Figure 8 and Table B-21; Appendix B). The relationship between BMPs
and cultural capital was a positive one. Thus, the null hypothesis was rejected for the relationship
between cultural capital and BMPs.

Relationship between Cultural Capital and BMPs
14
12

BMP Score

10
8

y = 7.3665x + 3.0419
R² = 0.1818

6
4
2
0
0.00

0.20

0.40
0.60
Cultural Capital

0.80

1.00

Figure 8. Relationship between cultural capital and BMPs in riparian communities with an LMP.

59

IV.9.6 Human Capital:

There was not a significant correlation between human capital and BMPs for riparian communities
with an LMP (p=0.1; Figure 9 and Table B-22; Appendix B). The relationship between BMPs and
human capital was a positive one. Thus, the null hypothesis was accepted for the relationship
between human capital and BMPs.

Relationship between Human Capital and BMPs
14
12

BMP Score

10
8
y = 8.2029x + 3.0865
R² = 0.1291

6
4
2
0
0.00

0.10

0.20

0.30
0.40
0.50
Human Capital

0.60

0.70

0.80

Figure 9. Relationship between human capital and BMPs in riparian communities with an LMP.

60

IV.9.7 Built/Technical Capital:

There was not a significant correlation between built/technical capital and BMPs for riparian
communities with an LMP (p=0.9; Figure 10 and Table B-23; Appendix B). The relationship
between BMPs and built/technical capital was a negative one. Thus, the null hypothesis was
accepted for the relationship between built/technical capital and BMPs.

Relationship between Built/Technical Capital and BMPs
14
12
y = -0.3556x + 6.848
R² = 0.0004

BMP Score

10
8
6
4
2
0
0.00

0.20

0.40
0.60
0.80
Built/Technical Capital

1.00

1.20

Figure 10. Relationship between built/technical capital and BMPs in riparian communities with
an LMP.

61

IV.10 Statistical analyses on correlations of BMPs and community capitals in non-LMP
riparian communities.

IV.10.1 Social Capital:

There was not a significant correlation between social capital and BMPs for riparian communities
without an LMP (p=0.9; Figure 11 and Table B-24; Appendix B). The relationship between BMPs
and social capital was a positive one. Thus, the null hypothesis was accepted for the relationship
between social capital and BMPs.

Relationship between Social Capital and BMPs
12
10

BMP Score

8

y = 0.3241x + 4.4909
R² = 0.0003

6
4
2
0
0.00

0.20

0.40
0.60
Social Capital

0.80

1.00

Figure 11. Relationship between social capital and BMPs in riparian communities without an
LMP.

62

IV.10.2 Financial Capital:

There was not a significant correlation between financial capital and BMPs for riparian
communities without an LMP (p=0.2; Figure 12 and Table B-25; Appendix B). The relationship
between BMPs and financial capital was a positive one. Thus, the null hypothesis was accepted
for the relationship between financial capital and BMPs.

Relationship between Financial Capital and BMPs
12
10
y = 11.208x + 0.0302
R² = 0.2252

BMP Score

8
6
4
2
0
0.00

0.10

0.20

0.30
0.40
Financial Capital

0.50

0.60

0.70

Figure 12. Relationship between financial capital and BMPs in riparian communities without a
lake management plan.

63

IV.10.3 Political Capital:

There was not a significant correlation between political capital and BMPs for riparian
communities without an LMP (p=0.8; Figure 13 and Table B-26; Appendix B). The relationship
between BMPs and political capital was a positive one. Thus, the null hypothesis was accepted
for the relationship between political capital and BMPs.

Relationship between Political Capital and BMPs
12
10

BMP Score

8

y = 1.4795x + 4.2685
R² = 0.0068

6

4
2
0
0.00

0.10

0.20

0.30
0.40
Political Capital

0.50

0.60

Figure 13. Relationship between political capital and BMPs in riparian communities without a
lake management plan.

64

IV.10.4 Natural Capital:

There was not a significant correlation between natural capital and BMPs for riparian communities
without an LMP (p=0.6; Figure 14 and Table B-27; Appendix B). The relationship between BMPs
and natural capital was a negative one. Thus, the null hypothesis was accepted for the relationship
between natural capital and BMPs.

Relationship between Natural Capital and BMPs
12
10

BMP Score

8
y = -3.8182x + 6.9545
R² = 0.0372

6

4
2
0
0.00

0.20

0.40
0.60
Natural Capital

0.80

1.00

Figure 14. Relationship between natural capital and BMPs in riparian communities without a lake
management plan.

65

IV.10.5 Cultural Capital:

There was not a significant correlation between cultural capital and BMPs for riparian
communities without an LMP (p=0.7; Figure 15 and Table B-28; Appendix B). The relationship
between BMPs and cultural capital was a positive one. Thus, the null hypothesis was accepted for
the relationship between cultural capital and BMPs.

Relationship between Cultural Capital and BMPs
12
10

y = 2.7349x + 3.4444
R² = 0.0171

BMP Score

8
6
4
2
0
0.00

0.10

0.20

0.30
0.40
Cultural Capital

0.50

0.60

0.70

Figure 15. Relationship between cultural capital and BMPs in riparian communities without a lake
management plan.

66

IV.10.6 Human Capital:

There was not a significant correlation between human capital and BMPs for riparian communities
without an LMP (p=0.3; Figure 16 and Table B-29; Appendix B). The relationship between BMPs
and human capital was a positive one. Thus, the null hypothesis was accepted for the relationship
between human capital and BMPs.

Relationship between Human Capital and BMPs
12
10

y = 7.5517x + 2.3961
R² = 0.133

BMP Score

8
6
4
2
0
0.00

0.10

0.20

0.30
0.40
Human Capital

0.50

0.60

0.70

Figure 16. Relationship between human capital and BMPs in riparian communities without a lake
management plan.

67

IV.10.7 Built/Technical Capital:

There was not a significant correlation between built/technical capital and BMPs for riparian
communities without an LMP (p=0.2; Figure 17 and Table B-30; Appendix B). The relationship
between BMPs and built/technical capital was a negative one. Thus, the null hypothesis was
accepted for the relationship between built/technical capital and BMPs.

Relationship between Built/Technical Capital and
BMPs
12

BMP Score

10
8
y = -4.41x + 7.1159
R² = 0.1453

6
4
2
0
0.00

0.20

0.40
0.60
0.80
Built/Technical Capital

1.00

1.20

Figure 17. Relationship between built/technical capital and BMPs in riparian communities
without a lake management plan.

68

Table 3. Summary statistical table for correlations between community capitals
and BMPs for non-LMP and LMP riparian community populations

Community
Capital

Non-LMP
Correlation
P-value and R2

LMP
Correlation
P-value and R2

Social

0.9; R2 = 0.000

0.1; R2 = 0.120

Financial

0.2; R2 = 0.225

0.3; R2 = 0.045

Political

0.8; R2 = 0.007

0.2; R2 = 0.096

Natural

0.6; R2 = 0.037

0.7; R2 = 0.010

Cultural

0.7; R2 = 0.017

0.04*; R2 = 0.182

Human

0.3; R2 = 0.133

0.1; R2 = 0.129

Built/Technical

0.2; R2 = 0.133

0.9; R2 = 0.000

* Denotes statistically significant result
LMP = lake management plan population
Non-LMP = non-lake management plan population

69

IV.11 Statistical analyses on means of TSI in LMP and non-LMP riparian community
populations.

Independent samples t-tests at the 95% confidence interval (α=0.05) were applied to each TSI
parameter for both non-LMP and LMP populations and no significant differences were found
between the two groups for TSI-phosphorus (p=0.7), TSI-chlorophyll-a (p=0.2), and TSI-Secchi
transparency (p=0.8; Tables B-31-B-36; Appendix B).

70

IV.12 Statistical analyses on correlations of TSI and community capitals in LMP riparian
community populations.

To determine if any correlations existed between TSI and the community capitals, a correlation
regression was performed for each community capital type for the LMP population. A correlation
test of significance was then conducted using the Pearson Correlation Coefficient which is a
measure of “r” or the linear dependence (correlation) between the dependent (TSI) and
independent variables (community capitals). The test was conducted at the 95% confidence level
(α=0.05). No statistically significant findings were found for these correlations (Tables B-37-B43 and Figures 25-31).

Relationship between Social Capital and TSI
65
60

TSI

55
y = -7.5888x + 48.438
R² = 0.0763

50
45
40
35
30
0.00

0.20

0.40
0.60
Social Capital

0.80

1.00

Figure 18. Relationship between social capital and TSI in riparian communities with an LMP.

71

Relationship between Financial Capital and TSI
65
60

TSI

55
y = -11.666x + 49.474
R² = 0.1287

50
45
40
35
30
0.00

0.20

0.40
0.60
Financial Capital

0.80

1.00

Figure 19. Relationship between financial capital and TSI in riparian communities with a lake
management plan.

72

Relationship between Political Capital and TSI
65
60
55

TSI

50
y = -1.1499x + 43.483
R² = 0.0014

45
40
35
30
0.00

0.10

0.20

0.30
0.40
Political Capital

0.50

0.60

0.70

Figure 20. Relationship between political capital and TSI in riparian communities with a lake
management plan.

73

Relationship between Natural Capital and TSI
65
60
55
50

TSI

y = -10.314x + 49.185
R² = 0.0784

45
40
35
30
0.00

0.20

0.40
0.60
Natural Capital

0.80

1.00

Figure 21. Relationship between natural capital and TSI in riparian communities with a lake
management plan.

74

Relationship between Cultural Capital and TSI
65

60

TSI

55
y = -2.2736x + 44.2
R² = 0.0051

50
45
40
35
30
0.00

0.20

0.40
0.60
Cultural Capital

0.80

1.00

Figure 22. Relationship between cultural capital and TSI in riparian communities with a lake
management plan.

75

Relationship between Human Capital and TSI
65
60
55
y = -2.0624x + 43.991
R² = 0.0022

TSI

50
45
40
35
30
0.00

0.10

0.20

0.30
0.40
0.50
Human Capital

0.60

0.70

0.80

Figure 23. Relationship between human capital and TSI in riparian communities with a lake
management plan.

76

Relationship between Built/Technical Capital and TSI
65
60
55
y = -9.7612x + 49.708
R² = 0.084

TSI

50
45
40
35
30
0.00

0.20

0.40
0.60
0.80
Built/Technical Capital

1.00

1.20

Figure 24. Relationship between built/technical capital and TSI in riparian communities with a
lake management plan.

77

V.13

Statistical analyses on correlations of TSI and community capitals in non-LMP
riparian community populations.

To determine if any correlations existed between TSI and the community capitals, a correlation
regression was performed for each community capital type for the non-LMP population. A
correlation test of significance was then conducted using the Pearson Correlation Coefficient
which is a measure of “r” or the linear dependence (correlation) between the dependent (TSI) and
independent variables (community capitals). The test was conducted at the 95% confidence level
(α=0.05). No statistically significant findings were found for these correlations (Tables B-44-B50 and Figures 18-24).

Relationship between Social Capital and TSI
60
55
50

y = -0.8837x + 41.695
R² = 0.0005

TSI

45
40
35
30
25
20
0.00

0.20

0.40
0.60
Social Capital

0.80

1.00

Figure 25. Relationship between social capital and TSI in riparian communities without a lake
management plan.
78

Relationship between Financial Capital and TSI
60
55
y = 25.274x + 30.814
R² = 0.234

50

TSI

45
40
35
30
25
20
0.00

0.10

0.20

0.30
0.40
Financial Capital

0.50

0.60

0.70

Figure 26. Relationship between financial capital and TSI in riparian communities without a lake
management plan.

79

Relationship between Political Capital and TSI
60
55

50

y = 15.338x + 36.992
R² = 0.1268

TSI

45
40
35
30
25
20
0.00

0.10

0.20

0.30
0.40
Political Capital

0.50

0.60

Figure 27. Relationship between political capital and TSI in riparian communities without a lake
management plan.

80

Relationship between Natural Capital and TSI
60
55
50
y = -5.1108x + 44.14
R² = 0.0143

TSI

45
40
35
30
25
20
0.00

0.20

0.40
0.60
Natural Capital

0.80

1.00

Figure 28. Relationship between natural capital and TSI in riparian communities without a lake
management plan.

81

Relationship between Cultural Capital and TSI
60
55
50

y = 3.1198x + 39.677
R² = 0.0048

TSI

45
40
35
30
25
20
0.00

0.10

0.20

0.30
0.40
Cultural Capital

0.50

0.60

0.70

Figure 29. Relationship between cultural capital and TSI in riparian communities without a lake
management plan.

82

Relationship between Human Capital and TSI
60
55
50

TSI

45
y = 26.775x + 33.241
R² = 0.3394

40
35
30
25
20
0.00

0.10

0.20

0.30
0.40
Human Capital

0.50

0.60

0.70

Figure 30. Relationship between human capital and TSI in riparian communities without a lake
management plan.

83

Relationship between Built/Technical Capital and TSI
60
55
50

y = -7.566x + 45.314
R² = 0.0914

TSI

45
40
35
30
25
20
0.00

0.20

0.40
0.60
0.80
Built/Technical Capital

1.00

1.20

Figure 31. Relationship between built/technical capital and TSI in riparian communities without
a lake management plan.

84

Table 4. Summary statistical table for correlations between community capitals and TSI for NonLMP and LMP riparian community populations.
Community
Capital

Non-LMP
Correlation
P-value

LMP
Correlation
P-value

1.0

0.2

0.2

0.1

0.3

0.9

0.8

0.2

0.9

0.7

0.1

0.8

0.4

0.2

Social
Financial
Political
Natural
Cultural
Human
Built/Technical

There were no statistically significant results for this test.
LMP = lake management plan population
Non-LMP = non-lake management plan population

85

CHAPTER V:

DISCUSSION AND IMPLICATIONS FOR FUTURE RESEARCH

In summary, the statistically significant findings of this study include:
1) The correlation between cultural capital and BMPs for the LMP population is statistically
significant. This means that in riparian communities with an active LMP, cultural capital
is important for increasing implementation of BMPs.
2) There is a significant difference between financial capital for the LMP population and nonLMP population. There is a higher mean financial capital score in the LMP population
which means that riparian communities with access to more financial capital are more
likely to have LMPs.
3) There is a significant difference between human capital and BMPs for the LMP population
and non-LMP population. There is a higher mean human capital score in the LMP
population which means that riparian communities with access to more human capital, are
more likely to implement BMPs especially if there is an active LMP being followed.

86

V.1

The Roles of Cultural, Human, and Social Capital in LMP Riparian Communities

Human capital includes the educational attainment of individuals and encompasses the broad
subject of knowledge (such as skills, ability to analyze problems, etc.) that people within a
community possess. In riparian communities, human capital would be advantageous for the
implementation of BMPs since some general understanding of various BMPs and their
applications would benefit from such educational background. This form of capital is more than
just “power in numbers” relative to the size of a riparian community and also considers the
knowledge base. It is also recognized that there is a considerable connection between human
capital and social capital since having a sound knowledge base allows for enhanced social
communication (Apaliyah et al., 2012). Thus, a community that has high bridging and bonding
social capital has an advantage in that both forms of this capital can improve human capital.
Perhaps some riparian communities with lower human capital can facilitate increasing social
capital as a means to improve human capital. This may be especially beneficial to communities
that have limited human capital and limited access to educational and informational resources. It
may also allow for enhanced civic engagement that can motivate riparian communities on moving
forward with management solutions such as BMPs while utilizing a broader lake management
plan. Carpenter and Lathrop (1999) mention that restoration of a lake ecosystem from degradation
of water quality depends not only upon implementation of suitable restoration strategies but also
the inclusion of communities with lake managers in the decision-making process relative to BMP
implementation. This would require adequate human and cultural capital to drive this process
forward for the betterment of lake systems. The mere presence of natural capital can also increase
human capital (Stijns, 2006) since riparians utilize the lake for ecosystem services discussed earlier
in this study. This study demonstrated how human capital is important for lake communities to
87

implement BMPs in the presence of a lake management plan. If human capital continues to
increase, it may then be attributed to an increase in the availability of natural capital through actiondriven decisions such as implementation of BMPs. Human capital is as important for the growth
and sustenance of riparian communities much as it is for entrepreneurial operations (Kim et al.,
2006). There may be some parallels between these two different populations since riparian
communities may be innovative which is a key attribute present in entrepreneurs. This innovation
would also likely lead to development and/or implementation of BMPs and other lake
improvement programs.

Becker (1994) discusses the links between per capita income (financial capital) and human capital
since specialized training and education often results in a more skilled labor force which in turn is
associated with increased overall economic growth. Based on this research, financial capital itself
is not necessarily a guarantee of ability to implement BMPs; however, having a coherent lake
management plan or program may be a product of adequate financial capital since such plans are
often costly and also usually have on-going implementation costs.

Douglas (2009) offered a sustainability framework that is centered around four attributes which
are representative of human, cultural, and social capital. The elements of cultural capital include
individual internal beliefs. Human capital elements include the role that knowledge plays in the
network process and how the learning process is influenced by individual experience. He
emphasizes that in order to have a sustainable program, these key principles need to be thoroughly
understood. The path to a sustainable program then would incorporate a thorough understanding

88

of cultural, human, and social capital of a given community. These attributes dictate how adaptable
a program can be in the context of sustainability.

Since there is a significant correlative relationship between cultural capital and the ability to
implement BMPs in populations with lake management plans, this indicates that the world views,
experiences, traditions, and knowledge of the riparian communities likely play a significant role
in the development of a lake management plan. Bourdieu and Passeron (1977) suggested the link
between cultural capital and behavior. This is important for lake communities since the amount
and types of cultural capital could influence the behavior of riparians in response to the lake
resource.

Specific objectives that are a part of a lake management plan require some level of background
knowledge of the lake baseline conditions and existing issues in order to bring about change. The
presence of an expert on lake issues may also contribute significant human and cultural capital but
it is important that the riparian community must also understand lake issues and improvement
solutions available to them. Community engagement or the direct participation of riparian
communities in an improvement program has been shown to be important for successful project
completion (Green et al., 2013). Lake management plans do not always include community
engagement but for optimal resource allocation they should. Cochrane (2006) explains the positive
influences that cultural capital has on management objectives and process efficacy. This would
support the findings in this study since BMPs can be considered a management objective. The
relationship between cultural capital and BMPs although significant, was not strongly positive

89

(R2=0.18) and this may have been due to a relatively small sample size. Further research is needed
to increase the sample populations for a deeper understanding of these relationships.

90

V.2

The Roles of Financial Capital in LMP Riparian Communities

Putnam (2000) concluded that social capital is critical in the formation of prosperous communities
since high social capital is associated with higher public trust and communication. These actions
are more likely to result in higher availability of financial resources within a community. In this
study, communities with higher financial capital were most likely to have active LMPs. This may
allow for specific communities to implement BMPs that are generally inherent in LMPs. The LMP
itself usually has a significant cost associated with it and the long-term implementation of BMPs
would incur further costs. It seems logical that communities without access to adequate financial
resources may encounter hurdles relative to the implementation of solutions. Financial capital
may then be considered a vehicle by which other forms of capital are able to augment and create
additional resources. It is clear in this study though, that financial capital itself is not enough to
drive environmental change in lake communities. The elements of human and cultural capital are
also critical.

91

V.3

Future Research Needs

This research evaluated the types of resources available to riparian communities that live around
an individual lake. To evaluate resources from a systems approach, the same research would have
to be conducted for local municipalities such as townships. For this research, only riparian
communities were included to represent a relatively confined community for the ease of evaluation
at a smaller scale. A complete model could then be created which would include all available
resources for a given area.

This study had a few limitations. First, it sampled only riparian communities and not local units
of government. It was also limited to lake associations since they are coherent groups of riparians
that are not true governing bodies. A similar study evaluating the resources of a community and
local unit of government is needed to determine the similarities, differences, or even synergistic
relationships that may exist between the two populations. Furthermore, this study utilized proxy
indicators that were specifically tailored to riparian communities. Such indicators may need to be
modified over time and new indicators may need to be developed for future studies of municipal
community capital resources. This study also did not investigate interactions between the various
forms of capital although that may be conducted soon while this research is ongoing. Such
research would be beneficial and could be conducted on a similar data set that is applied to a stock
and flow type of model (Meadows, 2008). Each form of capital could be varied in the analysis
and this would allow for the determination of the function of each capital form relative to changes
in the others. A study of the interactions of community capitals by Pigg et al., (2013) discusses
the ability of some forms of capital that are needed to augment other forms of capital. Inland lakes

92

would be categorized as natural capital and this study has shown that human and cultural capital
are important in the protection of natural capital.

Of particular interest is the finding of Trophic State Index (TSI) not being correlated with all of
the capital forms. This indicates that the state of inland lakes is not necessarily a product of
community capital but rather that implementation of BMPs or other behaviors may depend on
community capitals. In the long-term, these resources and the ability to implement solutions could
have impacts on the lake health. Further studies of heavily degraded lake systems and pristine
water bodies along with measurement of community capitals could offer more insight to the
importance of community capitals in water quality and ecosystem protection.

Sebatier (1998) discussed an advocacy coalition framework for driving policy change and that
such a framework is dependent upon fair representation of values, beliefs, and perspectives. This
capital framework may be implemented by riparian communities, local units of government, and
outside experts if the cultural and human capital attributes are thoroughly understood by all
stakeholders. The survey instrument used in this study offers some indicators that may be used to
measure the human and cultural capital capacities within riparian communities but more indicators
can be evaluated in the future. In an effort to inform riparian communities of the degree of capital
they possess relative to each form of capital, Phillips (2003) recommends visual graphs and charts
that can be easily understood by a community and that can allow them to readily visualize what
resources are in abundance and what ones are scarce.

93

Of interest were the findings that political capital did not play a role in riparian communities
relative to BMP implementation which suggests that these communities may be likely to
implement BMPs without having to utilize outside governing resources. Additionally, it was not
surprising that there was a negative correlation between natural capital and BMPs. This may be
because some communities that have more pristine lakes may not need to implement BMPs.
Although there is some confidence in the significant findings above, further studies are needed to
evaluate more riparian communities with a greater sample size. Additionally, since TSI did not
appear to be correlated with the various forms of capital but BMPs were with some capitals, this
may indicate that community capitals are more likely to have a significant influence on actionoriented parameters such as BMPs.

Increased education (human capital) and return on investment (financial capital) were both
considered important for the implementation of BMPs for beef cattle production (Kim et al., 2005).
Thus, these forms of capital may be applied to many different natural systems.

Getz et al., (1999) found that sustainable natural resource conservation must include scientists and
citizen-scientists for the ultimate long-term success of a management program. This is another
example of how increases in human capital can lead to increases in natural capital. Other forms of
capital may be needed to increase natural capital such as social and human capital (Pretty and
Ward, 2001). In fact, when natural capital is depleted, the accumulation of other favorable forms
of community capital also decline which would be a substantial hurdle for sustainable management
(Stratford and Davidson, 2002). For riparian communities, it appears that human capital is more
critical to implementing improvements than social capital. This may be because the individual

94

interactions between riparian members are not as critical in driving change as their knowledge base
and world views. Similarly, the presence of indigenous knowledge also becomes important for
natural resource protection (Gadgil et al., 1993). Such knowledge would incorporate both cultural
and human capital which were shown to have importance in riparian communities. It may also be
beneficial in future studies of community capitals in riparian communities to lay out the context,
process, and impacts that each unique form of capital may have on the management capacity of
lake systems (Gasteyer and Araj, 2009). This research serves as a beginning for the evaluation of
lake community resources which ultimately are needed for a sustainable management of lake
ecosystems.

95

APPENDICES

96

APPENDIX A

Survey Instrument

97

Social Capital:

1)

How many years has your lake association been in existence?
a) < 1 year b) 1-5 years
c) 5-10 years
d) > 10 years e) I do not know

2)

How many times per year does your lake association hold meetings?
a) 0
b) 1-3 times c) 3-5 times
d) > 5 times e) I do not know

3)

How many social media groups does the association belong to?
a) 0
b) 1
c) 2
d) > 2
e) I do not know

4)

Estimate the degree of trust among lake Association residents:
a) No trust b) low trust
c) moderate trust d) high trust e) I do not know

5)

How many times in the past year have you reached out to other lakes (Associations of other
groups) for guidance or information on lake issues?
a) 0
b) 1
c) 2
d) >2
e) I do not know

6)

What percentage of your Association has turned over membership in the past 2 years?
a) 0%
b) 1-20%
c) 20-40%
d) > 40%
e) I do not know

Natural Capital:

7)

How many lakes lie within five miles of your lake?
a) 0
b) 1-3
c) 3-6
d) 6-9+

e) I do not know

8)

How many years has your lake been managed by your Association or a lake professional?
a) 0
b) 1-3
c) 3-6
d) 6-9+
e) I do not know

9)

Is there a nature center within five miles of the lake?
a) Yes
b) No
c) I do not know

98

10) What is the lake trophic status (i.e. nutrient-rich or poor or moderate)?
a) Eutrophic (highly nutrient enriched and low in clarity)
b) moderate in nutrients and clarity (mesotrophic)
c) oligotrophic (low in nutrients and clarity)
d) I do not know
11) How many parks are within five miles of lake?
a) 0
b) 1-3
c) 3-6
d) >6

e) I do not know

12) Percentage of green space/forest/wetlands/ within five miles of lake?
a) 0%
b) 1-10%
c) 10-20%
d) 20-30%
e) > 31% f) I
know

do

not

13) How many miles of walking (non-paved) trails are within five miles of lake?
a) 0
b) 1-5
c) 5-10
d) 10-15
e) > 15
f) I
know

do

not

14) What is the population density of your riparian community?
a) < 10 people b) 10-50 people c) 50-90 people d) >90 people e) I do not know
15) Does the lake have public access?
a) Yes
b) No
c) I do not know

Cultural Capital:

16) How many unique items have been created as marketing products for your lake or association?
a) 0

b) 1-3

c) 3-6

d) >6

e) I do not know

17) What is the average annual time investment (in hours) of the Association on lake projects?
a) 0 hours b) 1-50 hours c) 50-100 hours d) > 100 hours e) I do not know
18) How many lake-specific events are held in your lake Association community annually?
a) 0
b) 1-2
c) 3-4
d) > 4
e) I do not know
19) How many traditions exist that are specific to your lake Association?
a) 0
b) 1
c) 2
d) 3
e) >3

f) I do not know

20) Approximately what percent of the population around your lake is seasonal?
a) 0
b) 1-10%
c) 10-20%
d) 20-30%
e) >30% f) I
know

99

do

not

21) Approximately what percent of the population around your lake is generational or has
relatives that once lived on the lake?
a) 0%
b) 1-10%
c) 10-20%
d) 20-30%
e) > 30% f) I do not
know

Human Capital:

22) What is the average age of a riparian in the Association?
a) Under 20 b) 20-40 c) 40-60 d) > 60 e) I do not know

23) What percentage of your Association has the following educational attainment:
a) Non high school b) high school c undergrad degree (BS or Associates)
d) Graduate degree e) I do not know

24) How many riparians in the Association have skills that pertain to natural resources
management or protection?
a) 0
b) 1-5
c) 5-10 d) > 10 e) I do not know

25) How many different civic associations do lake activities around your lake (i.e. Elks, Masons,
Rotary, Schools, etc.)?
a) 0
b) 1
c) 2
d) 3
e) > 3 f) I do not know

26) How many businesses donate time to lake activities each year?
a) 0
b) 1
c) 2
d) 3
e) > 3
f) I do not know

27) How many businesses donate time to lake protection each year?
a) 0
b) 1
c) 2
d) 3
e) > 3
f) I do not know

28) How many businesses donate time for lake monitoring each year?
a) 0
b) 1
c) 2
d) 3
e) > 3
f) I do not know

29) How many small businesses/start-ups exist within your lake community?
a) 0
b) 1-5
c) 5-10 d) >10 e) I do not know

100

30) What percentage of the lake population is in your Association?
a) 0% b) 1-10% c) 10-30% d) 30-50% e) > 50%

f) I do not know

Political Capital:

31) How many grants have been obtained by the Association in the past five years? (Grants can
be municipal, private, non-governmental organization, state, county, national, or township)
a) 0
b) 1
c) 2
d) 3
e) > 3
f) I do not know

32) On average, how many times have riparians worked annually with government or political
groups?
a) 0
b) 1-3
c) 3-6
d) > 6
e) I do not know

33) How many riparians on the lake occupy a political office in the area or state?
a) 0
b) 1
c) 2
d) > 2
e) I do not know

34) Is there evidence of the ability of the Association to influence regulations (i.e. ordinances)
due to the structure/composition of your lake community?
a) Yes b) No
c) I do not know

35) Is there evidence of the ability to implement regulations (such as ordinances) to benefit water
quality due to the structure/composition of your lake community?
a) Yes b) No
c) I do not know

Built/Technical Capital:

36) How many homes are there around your lake?
a) 20-80 b) 80-140 c) 140-200
d) >200

e) I do not know

37) How many of the homes on your lake are on a sewer system?
a) 0
b) 1-20% c) 20-40%
d) 40-60% e) > 60 %

f) I do not know

38) How many paved trails exist within five miles of your lake?
a) 0
b) 1
c) 2
d) 3
e) >3
f) I do not know

101

39) What is the percentage of homes with internet access on your lake?
a) 0
b) 1-20% c) 20-40% d) 40-60%
e) > 60%

f) I do not know

40) Is there access to a local community center or library within five miles of your lake?
a) Yes
b) No
c) I do not know

41) Is there a lake expert that manages your lake?
a) Yes
b) No
c) I do not know

42) Is there access to scientific/technical information on your lake?
a) Yes
b) No
c) I do not know

43) Is there access to GIS/maps on your lake (regarding your lake)?
a) Yes
b) No
c) I do not know

44) How many industries or businesses are within five miles of your lake?
a) 0
b) 1-5
c) 5-10
d) > 10
e) I do not know

Financial Capital:

45) What is the average annual per capita income per riparian in the Association?
a) < $20,000 b) $20,000- $40,000
c) $40,000-$60,000 d) > $60,000
e) I do not know

46) What is the annual amount of money received for annual lake Assoc. dues?
a) $0-500
b) $500-$1,000 c) $1,000-$1,500 d) > $1,500
e) I do not know

47) What is the annual amount of grant money received for lake improvement/watershed grants?
a) $0
b) $1-500
c) $500-$1,000
d) > $1,000
e) I do not know

48) How much money is spent annually on lake management?
a) $0
b) $1,000-$30,000 c) $30,000-$60,000 d) > $60,000

102

e) I do not know

49) How much money is donated annually to the lake for management or preservation?
a) $0
b) $1-$500
c) $500-$2,000
d) > $2,000 e) I do not know
50) What is the approximate percentage of riparians on the lake at poverty level?
a) 0% b) 1-10%
c) 10-20% d) 20-30% e) > 30%
f) I do not know
51) What is the average cost of housing on your lake?
a) < $100,000 b) $100,000-$200,000
c) $200,000-$300,000
e) I do not know

d) > $300,00

52) What is the percentage of home ownership in your Association?
a) 0% b) 1-30%
c) 30-60% d) > 60%
e) I do not know

53) Would the Association have the ability to obtain a loan for lake improvements?
a) Yes
b) No
c) I do not know

103

APPENDIX B

Statistical Tables

104

Table B-1. Descriptive statistics for social capital in both LMP and nonLMP riparian community populations.

NONLMP
LMP

N

Mean

Std. Deviation Std. Error Mean

1.00

10

.6450

.17070

.05398

2.00

23

.7043

.19593

.04085

Table B-2. Statistical test on the social capital means in both LMP and non-LMP riparian
community populations.

Table B-3. Descriptive statistics for financial capital in both LMP and non-LMP riparian
community populations.

NONLMP
LMP

N

Mean

Std. Deviation Std. Error Mean

1.00

10

.4180

.12744

.04030

2.00

23

.5361

.17601

.03670

105

Table B-4. Statistical test on the financial capital means in both LMP and non-LMP riparian
community populations.

Table B-5. Descriptive statistics for political capital in both LMP and nonLMP riparian community populations

N

Mean

Std. Deviation

Std. Error Mean

NONLMP

1.00

10

.2920

.16884

.05339

LMP

2.00

23

.3291

.18231

.03801

Table B-6. Statistical test on the political capital means in both LMP and non-LMP riparian
community populations

106

Table B-7. Descriptive statistics for natural capital in both LMP and non-LMP riparian
community populations.

N

Mean

Std. Deviation

Std. Error Mean

NONLMP

1.00

10

.5900

.15122

.04782

LMP

2.00

23

.5822

.15166

.03162

Table B-8. Statistical test on the natural capital means in both LMP and non-LMP riparian
community populations.

Table B-9. Descriptive statistics for cultural capital in both LMP and non-LMP riparian
community populations.

N

Mean

Std. Deviation

Std. Error Mean

NONLMP

1.00

10

.4590

.14587

.04613

LMP

2.00

23

.4843

.17061

.03557

107

Table B-10. Statistical test on the cultural capital means in both LMP and non-LMP riparian
community populations.

Table B-11. Descriptive statistics for human capital in both LMP and non-LMP riparian
community populations.
N

Mean

Std. Deviation

Std. Error Mean

NONLMP

1.00

10

.3050

.14653

.04634

LMP

2.00

23

.4296

.12872

.02684

Table B-12. Statistical test on the human capital means in both LMP and non-LMP riparian
community populations.

108

Table B-13. Descriptive statistics for built/technical capital in both LMP and non-LMP riparian
community populations.
N

Mean

Std. Deviation

Std. Error Mean

NONLMP

1.00

10

.5490

.26274

.08309

LMP

2.00

23

.6748

.16208

.03380

Table B-14. Statistical test on the built/technical capital means in both LMP and non-LMP
riparian community populations.

Table B-15. Descriptive statistics for BMPs in both LMP and non-LMP riparian community
populations.

N

Mean

Std. Deviation

Std. Error Mean

NONLMP

1.00

10

4.7000

3.02030

.95510

LMP

2.00

23

6.6087

2.93473

.61193

109

Table B-16. Statistical test on the BMP means in both LMP and non-LMP riparian community
populations.

Table B-17. Statistical test on the correlation between BMPs and social capital in LMP riparian
community populations.
Correlations
SOCCAP
SOCCAP

Pearson Correlation

BMP
1

.347

Sig. (2-tailed)
N
BMP

.105
23

23

Pearson Correlation

.347

1

Sig. (2-tailed)

.105

N

23

23

Table B-18. Statistical test on the correlation between BMPs and financial capital in LMP
riparian community populations.
Correlations
FINCAP
FINCAP

Pearson Correlation

BMP
1

.213

Sig. (2-tailed)
N
BMP

.328
23

23

Pearson Correlation

.213

1

Sig. (2-tailed)

.328

N

23

23

110

Table B-19. Statistical test on the correlation between BMPs and political capital in LMP
riparian community populations.
Correlations
POLCAP
POLCAP

Pearson Correlation

BMP
1

.310

Sig. (2-tailed)

.150

N
BMP

23

23

Pearson Correlation

.310

1

Sig. (2-tailed)

.150

N

23

23

Table B-20. Statistical test on the correlation between BMPs and natural capital in LMP riparian
community populations
Correlations
NATCAP
NATCAP

Pearson Correlation

BMP
1

-.095

Sig. (2-tailed)
N
BMP

Pearson Correlation
Sig. (2-tailed)

.666
23

23

-.095

1

.666

N

23
23

Table B-21. Statistical test on the correlation between BMPs and cultural capital in LMP
riparian community populations.
Correlations
CULTCAP
CULTCAP

Pearson Correlation

BMP
.427*

1

Sig. (2-tailed)
N
BMP

.042
23

23

Pearson Correlation

.427*

1

Sig. (2-tailed)

.042

N

23

23

*. Correlation is significant at the 0.05 level (2-tailed).

111

Table B-22. Statistical test on the correlation between BMPs and human capital in LMP riparian
community populations.
Correlations
HUMCAP
HUMCAP

Pearson Correlation

BMP
1

.359

Sig. (2-tailed)
N
BMP

.092
23

23

Pearson Correlation

.359

1

Sig. (2-tailed)

.092

N

23

23

Table B-23. Statistical test on the correlation between BMPs and built/technical capital in LMP
riparian community populations.
Correlations
BLTCAP
BLTCAP

Pearson Correlation

BMP
1

-.021

Sig. (2-tailed)
N
BMP

Pearson Correlation
Sig. (2-tailed)
N

.925
23

23

-.021

1

.925
23

23

Table B-24. Statistical test on the correlation between BMPs and social capital in non-LMP
riparian community populations.
Correlations
SOCCAP
SOCCAP

Pearson Correlation

BMP
1

.018

Sig. (2-tailed)
N
BMP

.960
10

10

Pearson Correlation

.018

1

Sig. (2-tailed)

.960

N

10

10

112

Table B-25. Statistical test on the correlation between BMPs and financial capital in non-LMP
riparian community populations.
Correlations
FINCAP
FINCAP

Pearson Correlation

BMP
1

.475

Sig. (2-tailed)
N
BMP

.166
10

10

Pearson Correlation

.475

1

Sig. (2-tailed)

.166

N

10

10

Table B-26. Statistical test on the correlation between BMPs and political capital in non-LMP
riparian community populations.
Correlations
POLCAP
POLCAP

Pearson Correlation

BMP
1

.086

Sig. (2-tailed)
N
BMP

.813
10

10

Pearson Correlation

.086

1

Sig. (2-tailed)

.813

N

10

10

Table B-27. Statistical test on the correlation between BMPs and natural capital in non-LMP
riparian community populations.
Correlations
NATCAP
NATCAP

Pearson Correlation

BMP
1

-.197

Sig. (2-tailed)
N
BMP

Pearson Correlation
Sig. (2-tailed)
N

.585
10

10

-.197

1

.585
10

10

113

Table B-28. Statistical test on the correlation between BMPs and cultural capital in non-LMP
riparian community populations.
Correlations
CULTCAP
CULTCAP

Pearson Correlation

BMP

1

.133

Sig. (2-tailed)
N
BMP

.714
10

10

Pearson Correlation

.133

1

Sig. (2-tailed)

.714

N

10

10

Table B-29. Statistical test on the correlation between BMPs and human capital in non-LMP
riparian community populations.
Correlations
HUMCAP
HUMCAP

Pearson Correlation

BMP
1

.368

Sig. (2-tailed)
N
BMP

.296
10

10

Pearson Correlation

.368

1

Sig. (2-tailed)

.296

N

10

10

Table B-30. Statistical test on the correlation of BMPs and built/technical capital in non-LMP
riparian community populations.
Correlations
BLTCAP
BLTCAP

Pearson Correlation

BMP
1

-.384

Sig. (2-tailed)
N
BMP

Pearson Correlation
Sig. (2-tailed)
N

.273
10

10

-.384

1

.273
10

10

114

Table B-31. Descriptive statistics on TSI chlorophyll-a in LMP and non-LMP riparian
community populations.
Group Statistics
N

Mean

Std. Deviation

Std. Error Mean

NONLMP

1.00

9

37.2222

8.21246

2.73749

LMP

2.00

22

41.3636

8.56652

1.82639

Table B-32. Statistical test on the means of TSI chlorophyll-a in LMP and non-LMP riparian
community populations.

Table B-33. Descriptive statistics on TSI phosphorus in LMP and non-LMP riparian community
populations.
Group Statistics
N

Mean

Std. Deviation

Std. Error Mean

NONLMP

1.00

9

43.3333

7.71362

2.57121

LMP

2.00

21

44.5238

7.24306

1.58057

115

Table B-34. Statistical test on TSI phosphorus in LMP and non-LMP riparian community
populations.

Table B-35. Descriptive statistics on TSI Secchi in LMP and non-LMP riparian community
populations.
Group Statistics
N

Mean

Std. Deviation

Std. Error Mean

NONLMP

1.00

9

42.8889

6.77208

2.25736

LMP

2.00

20

43.7000

4.60092

1.02880

Table B-36. Statistical test on the means of TSI phosphorus in LMP and non-LMP riparian
community populations.

116

Table B-37. Statistical test on the correlation between TSI and social capital in LMP riparian
community populations.
Correlations
SOCCAP
SOCCAP

Pearson Correlation

TSI
1

-.276

Sig. (2-tailed)
N
TSI

Pearson Correlation
Sig. (2-tailed)
N

.214
22

22

-.276

1

.214
22

22

Table B-38. Statistical test on the correlation between TSI and financial capital in LMP riparian
community populations.
Correlations
FINCAP
FINCAP

Pearson Correlation

TSI
1

-.360

Sig. (2-tailed)
N
TSI

Pearson Correlation
Sig. (2-tailed)
N

.100
22

22

-.360

1

.100
22

22

Table B-39. Statistical test on the correlation between TSI and political capital in LMP riparian
community populations.
Correlations
POLCAP
POLCAP

Pearson Correlation

TSI
1

-.037

Sig. (2-tailed)
N
TSI

Pearson Correlation
Sig. (2-tailed)
N

.870
22

22

-.037

1

.870
22

22

117

Table B-40. Statistical test on the correlation between TSI and natural capital in LMP riparian
community populations.
Correlations
NATCAP
NATCAP

Pearson Correlation

TSI
1

-.276

Sig. (2-tailed)
N
TSI

Pearson Correlation
Sig. (2-tailed)
N

.213
22

22

-.276

1

.213
22

22

Table B-41. Statistical test on the correlation between TSI and cultural capital in LMP riparian
community populations.
Correlations
CULTCAP
CULTCAP

Pearson Correlation

TSI

1

-.074

Sig. (2-tailed)
N
TSI

Pearson Correlation
Sig. (2-tailed)
N

.744
22

22

-.074

1

.744
22

22

Table B-42. Statistical test on the correlation between TSI and human capital in LMP riparian
community populations.
Correlations
HUMCAP
HUMCAP

Pearson Correlation

TSI
1

-.050

Sig. (2-tailed)
N
TSI

Pearson Correlation
Sig. (2-tailed)
N

.825
22

22

-.050

1

.825
22

22

118

Table B-43. Statistical test on the correlation between TSI and built/technical capital in LMP
riparian community populations.
Correlations
BLTCAP
BLTCAP

Pearson Correlation

TSI
1

-.285

Sig. (2-tailed)
N
TSI

Pearson Correlation
Sig. (2-tailed)
N

.198
22

22

-.285

1

.198
22

22

Table B-44. Statistical test on the correlation between TSI and social capital in non-LMP
riparian community populations.
Correlations
SOCCAP
SOCCAP

Pearson Correlation

TSI
1

-.022

Sig. (2-tailed)

.955

N
TSI

Pearson Correlation
Sig. (2-tailed)

9

9

-.022

1

.955

N

9

9

Table B-45. Statistical test on the correlation between TSI and financial capital in non-LMP
riparian community populations.
Correlations
FINCAP
FINCAP

Pearson Correlation

TSI
1

.482

Sig. (2-tailed)
N
TSI

.189
9

9

Pearson Correlation

.482

1

Sig. (2-tailed)

.189

N

9

9

119

Table B-46. Statistical test on correlation between TSI and political capital in non-LMP riparian
community populations.
Correlations
POLCAP
POLCAP

Pearson Correlation

TSI
1

.358

Sig. (2-tailed)

.344

N
TSI

9

9

Pearson Correlation

.358

1

Sig. (2-tailed)

.344

N

9

9

Table B-47. Statistical test on the correlation between TSI and natural capital in non-LMP
riparian community populations.
Correlations
NATCAP
NATCAP

Pearson Correlation

TSI
1

-.123

Sig. (2-tailed)
N
Pearson Correlation
TSI

Sig. (2-tailed)

N

.752
9

9

-.123

1

.752

9

9

Table B-48. Statistical test on the correlation between TSI and cultural capital in non-LMP
riparian community populations.
Correlations
CULTCAP
CULTCAP

Pearson Correlation

TSI

1

.071

Sig. (2-tailed)
N
TSI

.855
9

9

Pearson Correlation

.071

1

Sig. (2-tailed)

.855

N

9

9

120

Table B-49. Statistical test on the correlation between TSI and human capital in non-LMP
riparian community populations.
Correlations
HUMCAP
HUMCAP

Pearson Correlation

TSI
1

.590

Sig. (2-tailed)

.095

N
TSI

9

9

Pearson Correlation

.590

1

Sig. (2-tailed)

.095

N

9

9

Table B-50. Statistical test on the correlation between TSI and built/technical capital in nonLMP riparian community populations.
Correlations
BLTCAP
BLTCAP

Pearson Correlation

TSI
1

-.307

Sig. (2-tailed)
N
TSI

Pearson Correlation
Sig. (2-tailed)
N

.422
9

9

-.307

1

.422
9

9

121

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