n 1....
3

.3,

'usoiv

5:!-

31...,
‘ 4s
.33

 

M " _, LIBRARY
13 , Michigan State
2: CO7 _ University

This is to certify that the
dissertation entitled

ACOUSTICAL AND NEMATODE COMMUNITY ASSESSMENT
FOR ECOSYSTEM CHARACTERIZATION

presented by
MARISOL ANDREA QUINTANILLA TORNEL

has been accepted towards fulfillment
of the requirements for the

PhD . degree In Entomology

Z/// My

Mayo, Professor’s Signature

 

May 14, 2009

Date

MSU is an Affinnativa Action/Equal Opportunity Employer

 

 

 

PLACE IN RETURN BOX to remove this checkout from your record.
To AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE DATE DUE DATE DUE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

5/08 K:IProj/Aoc&Pres/ClRC/DateDue.lndd

TITLE: ACOUSTICAL AND NEMATODE COMMUNITY ASSESSMENT FOR
ECOSYSTEM CHARACTERIZATION

By

Marisol Andrea Quintanilla Tornel

A DISSERTATION

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

DOCTOR OF PHILOSOPHY
Entomology

2009

ABSTRACT

ACOUSTICAL AND NEMATODE COMMUNITY ASSESSMENT FOR
ECOSYSTEM CHARACTERIZATION

By
Marisol Andrea Quintanilla Tornel

Ecosystems with different levels and types of human management were
compared and characterized using nematode community structure
methodologies and acoustical recordings. Conventional tillage, no-till, bio-based
(organic), early and mid-successional fields with a history of tillage and mature
deciduous forest sites were compared using these two methodologies. These
methodologies were used to characterize the biological and physical aspects of
these ecosystems with different levels of human management. For the
nematode community structure analysis, nematodes naturally found in the soils
from the conventional tillage, no-till, bio-based, successional fields and
deciduous forest systems from Kellogg Biological Station Long Term Ecological
Research (KBS/LTER) were identified to the lowest possible taxon and the
results analyzed for taxon biodiversity, evenness, ecosystem stability and
nutrient enrichment. Multivariate canonical correspondence analyses were
performed in order to find associations between nematode taxa, ecosystems and
soil characteristics. The results were compared and images of the nematodes
identified can be found at http://www.nemasoil.com. As expected, the greatest
biodiversity, evenness and ecosystem stability were often found in the deciduous

forest and field succession ecosystems, and the lowest levels of the same

parameters were most often found in the no-till and conventionally-tilled
ecosystems. The acoustical methodologies were used to measure both biological
and physical characteristics of ecosystems. The physical characteristics, such
as soil aggregate stability, of soils with different levels of human management
were measured with acoustical methods and the results were compared to
conventional methods. Soil aggregates from conventional tillage, no-till, and
native systems were immersed in water and the sounds of the rapid hydration
were recorded with hydrophones for 30 seconds. The results were as expected
and agreed with conclusions arrived at using conventional methods. The
greatest stability and lowest sound intensity was found in the native ecosystems
and the greatest sound intensity and lowest aggregate stability was found in the
conventionally-tilled ecosystems, with the no-till ecosystems being intermediate.
Acoustical recordings were used additionally to characterize the biological insect
sounds from the same ecosystems used previously at KBS/LTER. The native
ecosystems had significantly more insect sounds, especially at night, compared
to any of the human-managed ecosystems. In the native ecosystems, the
sounds were in a wave cubic regression form, with the sounds increasing and
decreasing at regular time intervals. Additionally, acoustics were used to
determine the biological activity of compost, using water as an activating agent.
In conclusion, our methods showed that both nematode community structure
methods and acoustical methods can be effectively used to characterize

ecosystems.

ACKNOWLEDGMENTS

Support for this research was provided by a Michigan Agricultural
Experimental Station assistantship, a MOTT Fellowship, and the NSF Long-Term
Ecological Research Program at the Kellogg Biological Station.

I would also like to specially thank Dr. George Bird for his incredible help,
support and patience in guiding my scientific endeavors. I would like to thank Dr.
Gage for the help in analysis and formation of my dissertation. Also I would like
to thank DiFonzo for her careful revisions of my manuscripts, to Dr. Robertson
and Dr. Smucker for their guidance, revisions and participation in my committee.
Also to Dr. Delfosse for guidance in presenting this research in the 2008
Entomological Society of America meetings. I would like to thank Dr. Ayers and
Dr. Miller for being good and supportive teachers. John Davenport, Fred Warner,
Belinda Roman, Cassandra Bates, Leslie Schumacher, Chris Chen, Claudio
Quintanilla, Goran and Valera Jotanovic for taking an active part in contributing to
my research. And to my family (Marisol Tornel, Valera Pascual, Valeria Tornel,
Daniel Tornel, Jobita Hausdorf, y Carolina Quintanilla) for supporting me in my
PhD research, studies and life. Finally I would like to acknowledge the guidance,

help and love of the Creator in my life and academic endeavors

iv

TABLE OF CONTENTS

LIST OF TABLES ........................................................................... vii
LIST OF FIGURES .......................................................................... x
INTRODUCTION ............................................................................ 1

CHAPTER 1: IMPACT OF TILLAGE ON ACOUSTICAL SIGNATURES
DURING RAPID HYDRATION OF AIR-DRIED SOIL AGGREGATES ...... 6

Abstract ............................................................................... 6
Introduction ......................................................................... 7
Materials and Methods ........................................................... 11
Results ................................................................................ 16
Discussion ........................................................................... 20
Conclusions ......................................................................... 30
Tables ................................................................................. 32
Figures ............................................................................... 35
References .......................................................................... 42
CHAPTER 2: NEMATODE COMMUNITY STRUCTURE OF SOIL FROM
ALTERNATIVE MANAGEMENT AND NATURAL ECOSYSTEMS ........... 46
Abstract .............................................................................. 46
Introduction ......................................................................... 48
Materials and Methods ........................................................... 51
Results ............................................................................... 55
Discussion ........................................................................... 68
Tables ................................................................................. 90
Figures ............................................................................... 113
References .......................................................................... 121

CHAPTER 3: TEMPORAL DYNAMICS OF ACOUSTICAL SIGNATURES
ASSOCIATED WITH ALTERNATIVE MANAGEMENT AND NATURAL

ECOSYSTEMS .............................................................................. 131
Abstract .............................................................................. 131
Introduction ......................................................................... 1 32
Materials and Methods ........................................................... 136
Results ............................................................................... 1 38
Discussion ........................................................................... 142
Tables ................................................................................. 146
Figures ............................................................................... 147
References .......................................................................... 164

CHAPTER 4: IMPACT OF MICROWAVES AND WATER ON ACOUSTICAL

SIGNATURES OF A COMPOST ...................................................... 166
Abstract .............................................................................. 166
Introduction ......................................................................... 167
Materials and Methods ........................................................... 169
Results ............................................................................... 171
Discussion ........................................................................... 171
Tables ................................................................................. 174
Figures ............................................................................... 175
References .......................................................................... 179

CONCLUSION .............................................................................. 1 83

APPENDICES .............................................................................. 188

vi

LIST OF TABLES

Table 1.1 Soil collection locations, managements, carbon (%) and bulk density
of the six soil air-dried aggregates sets used in this research ....................... 32

Table 1.2 Soil collection locations, site establishment dates, management
descriptions, and taxonomy of the of the six soils aggregate sets used in this
research ........................................................................................... 33

Table 1.3 Matlab code for computation of the average Power Spectral Density
(acoustical intensity) for 1 kHz frequency bins (plus total) from a .wav file
segment ........................................................................................... 34

Table 2.1 Description and abbreviations for the ecosystems used in this
dissertation. The ecosystems are in the Kellogg Biological Station Long Term
Ecological Research (KBS/LTER) project, Hickory Comers, Michigan. Each
replication plot consists of one hectare. The agricultural and the old field
succession established in 1989 (early succession) have six replications each (six
one-hectare plots). The deciduous forest and the mid-succession (KBS/LTER
treatment SF) have three one hectare replications each .............................. 90

Table 2.2 Nematode community structure indicator formulae used for
descriptive, comparative and temporal analysis of ecosystem biodiversity,
evenness, abundance, maturity, and enrichment of six ecosystems at
KBS/LTER, Hickory Corners,

Michigan .......................................................................................... 91

Table 2.3 Nematode taxa recovered from the deciduous forest ecosystem at
KBS/LTER, Hickory Corners Michigan. Table contains nematode Taxa,
Oligocheates and Tardigrades densities and frequencies in 29 samples from
three deciduous forest sites at KBS/LTER ............................................... 93

Table 2.4 Mid-succession (abandoned in ~1967) ecosystem characterization
consisting of: Nematode Taxa, Oligocheates and Tardigrades densities and
frequencies in 4 samples from succession sites at KBS/LTER, Hickory Corners,
Michigan .......................................................................................... 96

Table 2.5 Early succession (last tilled in spring of 1989 and occasional burning)
ecosystem characterization consisting of: Nematode Taxa, Oligocheates and
Tardigrades densities and frequencies in 12 samples from six field old field
succession sites at KBS/LTER, Hickory Corners, Michigan............................. 98

vii

Table 2.6 No Till system characterization consisting of: Nematode Taxa,
Oligocheates and Tardigrades densities and frequencies in 18 samples from six
no-till cropping sites at KBS/LTER, Hickory Corners, Michigan ................... 101

Table 2.7 Bio-based system characterization consisting of Nematode Taxa,
Oligocheates and Tardigrades densities and frequencies in 18 samples from six
bio-based agricultural cropping sites at KBS/LTER, Michigan ..................... 103

Table 2.8 Conventional Tillage system characterization consisting of: Nematode
Taxa, and Oligocheates densities and frequencies in 18 samples from six
conventionally tilled sites at KBS/LTER, Hickory Corners, Michigan ............ 106

Table 2.10 Index of similarity (Sorensen 1948). Based on the presence or
absence of taxa in six ecosystems at KBS/LTER, Hickory Comers, Michigan..108

Table 2.11 Nematode community structure, biodiversity, evenness and
ecosystem maturity (Table 2) analysis table for September 25, 2007 sample
date at KBS/LTER with means, standard errors (SE), analysis of variance (AOV)
and non-parametric mood median (MM) test ........................................... 109

Table 2.12 Nematode community structure and biodiversity (Table 2) analysis
Table for December 12, 2007 sampling date at KBS/LTER with means, standard
errors (SE), and analysis of variance (AOV) ............................................ 110

Table 2.13 Nematode Community Structure and Biodiversity (Table 2) Analysis
Table for September 25, 2008 sampling date at KBS/LTER: with Means,
Standard Errors (SE), and Analysis of Variance (AOV) .............................. 111

Table 2.14 Mean daily temperatures and precipitation for Kellogg Biological
Station, Hickory Corners Michigan for the two weeks prior to three soil sampling
dates ............................................................................................... 112

Table 3.1 Sound recording sampling times, dates and sunset, dusk, dawn and
sunrise times at Kellogg Biological Station/Long Term Ecological Research,
Michigan in 2007 ............................................................................... 146

Table 4.1 Matlab code to compute average Power Spectral Density (PSD,

watts/kHz) for 1 kHz frequency bins (plus total) from a .wav file segment. The last
calculation (Tab (i,12)) is the sum for all 11 kHz levels ............................... 174

viii

LIST OF FIGURES

Figure I.1. Organization and objectives model of the Dissertation of Marisol
Quintanilla ........................................................................................ 4

Figure 1.1 a-d: Spectrograms of rapid water hydration of soil aggregates from
three different soils: a) Hoytville, Ohio continuous till, b) Wooster, Ohio native
forest, c) KBS, Hickory Corners, Michigan native soil and d) background sound
with no soil aggregates ............................................................................ 35

Figure 1.2 Relationship among Power Spectral Density (acoustical intensity,
sum of kHz frequency levels 3-11) of soil aggregates from three
soil/management systems: a native succession at the Kellogg Biological Station
(KBS), Hickory Comers; Michigan, a conventionally-tilled soil from Hoytville,
Ohio; and a native forest soil from Wooster, Ohio. Samples were immersed in
water (H20), 50 g/L Na-Hexametaphosphate (NaHmP), or 5mM CaSO4. Error
bars indicate standard error of the mean of four replicate aggregates ............ 36

Figure 1.3 Relationship between Power Spectral Density Mean (acoustical
intensity, sum of kHz 2-11 frequency levels) of rapidly hydrated (immersed in
water) soil aggregates and six soil/management systems: native succession at
KBS (Hickory Corners, Michigan); conventionally tilled and no tilled soil from
Hoytville (Ohio); and native forest, no-till and conventionally tilled soil from
Wooster (Ohio). Error bars indicate standard error intervals from the mean of 30
replicates. Letters represent significant difference using 95 % confidence
intervals for the mean ......................................................................... 37

Figure 1.4 Relationship between Power Spectral Density (acoustic intensity),
frequency (kHz level 2 — kHz level 11) and six soil/management systems
(Hoytville Ohio tilled and no-till, KBS, Hickory Corners Michigan native soil and
Wooster Ohio native, no—till and tilled soil) .............................................. .38

Figure 1.5 Relationship between visual assessment of soil aggregate slaking in
water (Relative Stability: 1.0= no slaking and 6.0= complete disintegration when

immersed for 30 seconds). Graphs bars represent the means (soil/management
systems= 6, replications=30) and 95% confidence intervals ........................ 39

Figure 1.6 Relationship between air bubbler tube size (1= small 0.3mm, 5=
large 1cm) and Power Spectral Densities (sound intensity) from kHz 1-3 levels
sound frequency produced by air bubbles in water. KHz 4-11 had low values and
could not be seen in the graph .............................................................. 40

ix

Figure 1.7 Relationship between air bubbler tube size (1= small 0.3mm, 5=
large 10m) and Power Spectral Density (sound intensity). Air bubbles of differing
sizes were released in water and the sounds recorded ............................... 41

Figure 2.1 Canonical Correspondence Analysis Biplot. The sites are found in
Kellogg Biological Station, Hickory Corners, Michigan. The sites were
conventionally tilled, no-till and no—input (bio-based) agricultural ecosystems,
deciduous forest, early and mid-succession fields. The variables represent
percent soil elemental carbon (C), percent soil elemental nitrogen (N), soil bulk
density (BD) and pH ......................................................................... 113

Figure 2.2 Nematodes per 100 cm3 (100 cc) of soil, and standard error intervals
for trophic groups (Ba=bacterievores, Ca= Carnivores, Fu=fungivores,
He=Herbivores, Om=Omnivores, UnlD=Unidentified nematodes), in five
ecosystems at KBS/LTER, Michigan. Soil sampled September 25, 2007.... 116

Figure 2.3 Mean Number of Nematodes per 100 cm3 (100 cc) of soil, separated
by trophic group (Ba=bacterievores, Ca= Carnivores, Fu=fungivores,
He=Herbivores, Non-Nem=Non-Nematodes (Oligocheates and Tardigrades),
Om=Omnivores, UnlD=Unidentified nematodes) on December 12, 2007. Error
bars represent standard error from the mean .......................................... 117

Figure 2.4 Mean Number of Nematodes per 100 cm3 of soil, and standard
errors for trophic groups (Ba=bacterievores, Ca= Carnivores, Fu=fungivores,
He=Herbivores, Om=Omnivores, Non-Nem=Non-Nematodes (Oligocheates and
Tardigrades), and UnlD=Unidentified nematodes), in five ecosystems at
KBS/LTER, Michigan. Soil sampled September 25, 2008 ........................ 118

Figure 2.5 Mean Number of Nematodes per 100 cm3 of soil, and standard errors
for trophic groups (bacterievores, carnivores, fungivores, herbivores, omnivores,
non-nematodes (Oligocheates and Tardigrades), and unidentified nematodes), in
five ecosystems (deciduous forest, succession abandoned last tilled circa 1955
(mid-succession) and last tilled in 1989 (early succession), and no-till, bio-based
(organic) and conventionally tilled agricultural cropping systems) at KBS/LTER,
Michigan. Three sampling dates combined September 25, 2008, December 12,
2007 and September 25, 2008 ............................................................ 119

Figure 2.6 Nematode families associated with five soil systems at KBS/LTER,
Michigan in 2007 and 2008 ................................................................. 120

Figure 3.1 Aerial image of the Kellogg Biological Station/Long Term Ecological
Research site at Hickory Corners, Michigan
(httpzliwww.lter.kbs.msu.edu/maps/images/allLTERsites.jpg). Treatments (T)
used: T1= conventional tilled crop rotation, T4= no-input (organic) crop rotation,
T7= early succession last tilled in 1989 and occasionally burned, SF= mid-

succession last tilled circa 1955, DF= mature deciduous forest, CF = mature
coniferous forest. .............................................................................. 147

Figure 3.2 Photographic image of recording equipment used for acoustical
recordings ....................................................................................... 148

Figure 3.3 Acoustical recording equipment in the deciduous forest, Kellogg
Biological Station, Hickory Corners, Michigan ......................................... 148

Figures 3.4a-c Acoustic characterization sonograms of deciduous forest, early
succession, and conventionally tilled corn/soybean/wheat crop rotation at 21 :20,
August 29, 2007. The sounds were recorded at Kellogg Biological Station,
Michigan. The x axis represents 0-30 seconds in time and the y axis represents
frequencies of 0-11 kHz. Color intensity indicates sound intensity. Cool blues
and greens indicate lowest intensity, yellow medium and the highest sound
intensity is orange, and red .................................................................. 149

Figure 3.5 Total power spectral density (sum power spectral density (PSD) of the
frequency levels 2-11 kHz) for recordings of July and August 2007 during
sunrise, noon, sundown, and midnight at the Kellogg Biological Station/Long
Term Ecological Research Station, Hickory Corners, Michigan. Error represents
95% confidence interval from the mean ................................................. 150

Figure 3.6 Total sound power spectral density (PSD) of frequencies 1-11 kHz
for August 22-26, 2005 sound recordings at the Kellogg Biological Station, Long
Term Ecological Research station at Hickory Corners, Michigan. The
ecosystems recorded were: deciduous forest, coniferous forest, no-input
(organic) crop rotation, and conventionally tilled crop rotation. Errors represent
95 % confidence intervals from the mean. .............................................. 151

Figure 3.7 Deciduous forest total sound power spectral density (PSD watt/kHz
plot of the frequency levels 2-11 kHz) for recordings of July and August 2007
from 20:00-22:20 at the Kellogg Biological Station/Long Term Ecological
Research Station, Hickory Corners, Michigan. Each dot represents the sound
PSD of one sample. The sounds PSD increase exponentially as the evening
progresses in late August and early September ..................................... 152

Figure 3.8 Deciduous forest total sound power spectral density (sum power
spectral density (PSD watt/kHz) plot of the frequency levels 2-11 kHz) for
recordings of July and August 2007 from 6:00-00:00 at the Kellogg Biological
Station/Long Term Ecological Research Station, Hickory Corners, Michigan.
Each dot represents the sound PSD of one sample. The sounds PSD increase
exponentially as the evening progresses in late August and early September.
The sounds are close to 0 in the morning and noon in both July and August...153

xi

Figure 3.9 Deciduous forest total sound power spectral density (sum power
spectral density (PSD watt/kHz) plot of the frequency levels 2-11 kHz) for
recordings of July and August 2006 from 6:00-00:00 at the Kellogg Biological
Station/Long Term Ecological Research Station, Hickory Corners, Michigan.
Each dot represents the sound PSD of one sample. The sounds PSD increase
exponentially as the evening progresses in late August and early September.
The sounds are close to 0 in the morning and noon in both July and August... 154

Figure 3.10 Cubic regression fit of the relationship of time and sound power
spectral density (PSD) in the mid-succession with history of tillage. The
recordings were done in the evenings of July 23 and August 22, 2007. For mid-
succession with history of tillage in 2007, the sound produced in July 23, 2007
were significantly different from those of August 22, 2007 (p = 0.001).‘ At both
dates, time effectively (p< 0.001) predicted the insect sound intensity. The later
in the evening the louder the sounds (regression analysis, analysis of variance p
< 0.001, R2 = 53.7%) .......................................................................... 155

Figure 3.11 Cubic regression fit of the relationship between time and sound
Power Spectral Density (PSD) in a mid-succession field last tilled in circa 1955.
The sounds were recorded August 22, 2007. .......................................... 156

Figure 3.12 Three dimensional plot of the sum sound Power Spectral Density
(PSD) of frequency levels 2-11 kHz. In 2007, the sounds of four ecosystems
were recorded: deciduous forest, mid-succession field last tilled in 1955, early
succession last tilled in 1989 (occasional burning) and a conventionally tilled
corn/soybeanlwheat rotation field at Kellogg Biological Station, Michigan ....... 157

Figure 3.13 Distribution of sound power spectral density (PSD) in the frequency
levels 1-6 kHz recorded in deciduous forests at Kellogg Biological Station,
Hickory Corners, Michigan. The sounds were recorded in July and August of
2007. Sound power spectral density in the frequency levels 7-11 kHz was not
different from 0, so they are not included in the graph. Errors represent 95 %
confidence intervals from the mean ........................................................ 158

Figure 3.14 2007 sound power spectral density (PSD) of kHz frequencies 1-5
recorded in mid-successional field last tilled circa 1955 in Kellogg Biological
Station, Hickory Corners, Michigan. The recordings were done between ~20:00
and 22:20 in July and August 2007. Sound power spectral density in the
frequency levels 6-11 kHz was not different from 0, so they are not included in
the graph. Errors represent 95 % confidence intervals from the mean ............ 159

Figure 3.15 2007 sound power spectral density (PSD) of frequency levels 1-11

kHz recorded in early successions field occasionally burned and last tilled in
1989 at the Kellogg Biological Station, Hickory Corners, Michigan. The

xii

recordings were done between ~20:00 and 22:00 in August 2007. Errors
represent 95 % confidence intervals from the mean .................................. 160

Figure 3.16 Sonogram of Deciduous forest sounds in August 22, 2007, 10:15
pm. Color intensity means greater sound intensity (blue/green = low or no
sound, red/maroon = very intense sound, or high PSD) ........................... 161

Figure 3.17 Sonogram of deciduous forest sounds in August 30, 2007 at 7:05
am. The insect sounds are the blurry line in kHz 4-5. The bird sounds, are seen
as complex communication patterns and repetition of sounds. More sonograms
can be seen in www.nemasoil.com ................................................................... 162

Figure 3.18 Sonograms of the sounds from an early succession with occasional
burning last tilled in 1989. The recordings were done August 29, 2007 at 20:55.
...................................................................................................... 163

Figure 4.1 Sonograms of sounds of non-microwaved and microwaved compost
with and without water as an activating agent. ........................................ 175

Figure 4.2 Power Spectral Densities (PSD, sum of kHz frequency levels 1-11) of
microwaved and non-microwaved compost, without and with water (p< 0.0005),
with 95% confidence interval bars for the means. ..................................... 176

Figure 4.3 Comparison of Power Spectral Densities (PSD, kHz levels 1-5) of
microwaved (MC) and non-microwaved (C) compost with (+w) or without water (-
w) added as an activating agent. The four treatments where: microwaved
compost with no water added (MC-w), microwaved compost with water added
(MC+w), compost with no water added (C-w), and compost with water added
(C+w). Each treatment had five repetitions; the bars represent the mean for the
five repetitions. The error bars represent 95% confidence intervals for the means.
Each kHz frequency level represented is shown separate, i.e. kHz level 1 is
named khz-L1 on the graph .................................................................. 177

Figure 4.4 Comparison of mean relative Power Spectral Density (PSD,
watts/kHz) for four treatments in the kHz frequency levels 2-11. The four
treatments were: microwaved compost with no water added (MC-w),

microwaved compost with water added (MC+w), compost with no water added
(C-w), and compost with water added (C+w). Each treatment had five repetitions.
Each kHz level represented is shown separate, i.e. kHz level 2 is named kHz-2
on the graph. The error bars represent 95% confidence intervals (CI) for the
means .............................................................................................. 178

xiii

INTRODUCTION

The objective of this dissertation was to obtain a greater understanding of
the effect of agronomic disturbance on physical and biological aspects of
ecosystems (Fig. L1). The work also compared the characteristics of native
forest and old field successions, with agricultural no-till, no-input and
conventionally tilled crop systems. The research was performed at the Michigan
State University, W. K. Kellogg Biological Station, Long Term Ecological
Research (KBS/LTER) in Hickory Comers, Michigan. Soil aggregates for
acoustic research were collected at KBS/LTER and also obtained from Wooster,
Ohio and Hoytville, Ohio. The research was divided into four mayor parts: 1)
Impact of tillage on acoustical signatures during rapid hydration of air-dried soil
aggregates, 2) Nematode community structure of soil from alternative
management and natural ecosystems, 3) Temporal dynamics of acoustical
signatures associated with alternative management and natural ecosystems, and
4) Impact of microwaves and water on acoustical signatures of a compost.

Soil physical characteristics were measured through acoustical methods
(Chapter 1). The sound produced by soil aggregates during the slaking process
(breaking and bubbling when immersed in water) were recorded, analyzed and
compared to traditional methods of water-aggregate stability measurements.
The complete data set for these analyses can be found at www.nemasoil.com.
Measuring of water-aggregate stability through acoustics had not been reported

in the literature at the time of this dissertation. Water aggregate stability is an

important soil characteristic that is related to soil quality. Soils with high
1

proportion of water-stable aggregates are less vulnerable to erosion, leaching,
and soil organic matter loss. Soils with high organic carbon tend to have greater
resistance to slaking (Zaher et al. 2005). Soils resistant to slaking also perform
many ecosystem services more efficiently than soils with low water-aggregate
stability. Management practices, such as tillage, affect water-aggregate stability
(Park and Smucker 2005).

The biological characteristics of the KBS/LTER ecosystems were also
studied. The nematode community structures of soils from alternative
managements and natural ecosystems at KBS/LTER were investigated. Soil
ecosystem biodiversity, evenness, maturity, and structure are can be calculated
using nematode communies (Bongers 1990, Bongers & Ferris 1999). A
relationship between nematode community structure and soil quality has been
reported in the literature (Neher 2001 and Bird & Birney 1998). Images and
descriptions of the nematodes, Oligocheates and Tardigrades recovered from the
various ecosystems at KBS/LTER can be found at www.nemasoil.com.
Comparison of systems yields, soil bulk density, pH, soil carbon and nitrogen (%)
can be found in appendices 26-31.

Sounds can also be used to determine the biological health of ecosystems
(Gage et al. 2001). Krause and Gage (2003) stated that “A landscape’s
acoustics signature is a unique component of the evaluation of its function”. The
objective of the research reported in Chapter 3 is to measure effects of

ecosystem disturbance (agricultural and intensity of management) type using

acoustical procedures through comparison with non-managed systems.

2

 

Acoustical methods do not require destructive sampling and can be done
relatively easily and inexpensively, compared to other procedures. One of the
reasons for this research is to enhance our understanding of ecosystem
disturbance. The sounds of forest, old field successions, and no-till and no-input
crops agricultural fields are reported and analyzed. The acoustical temporal
dynamics of the various ecosystems were studied.

It was hypothesized that the reason no significant sound intensity from
biological activity at the soil surface in soils from any of the KBS/LTER
ecosystems, was because of a lack of a specific activation event, or low
population density of invertebrates on the soil near the recording hydrophone.
Soil organisms tend to be in a dormant state and need to be activated in order to
function to their full potential (Lavelle et al., 1995). In order to test this, water was
added to soil/compost with and without living invertebrates, the resulting sounds
were recorded. Increase of soil insect acoustics following an activation event
have been reported by Mankin et al. 2006. The effects of water as a pulsing
agent over the activity and acoustics of soil/compost invertebrates were reported

in chapter 4.

6th 3:53:50 Barns. Lo cozmtowmfi 0:10.289: 32838 new cozmnEmQO .2 2:9“.

 

 

m

«3.59. S mean: 55me um 568;...
«Enoch .83....utmm mcfiaob 3.3.3:?
ucm 5.3803 ucm 32380:...

Rm: .328 w>zmc Be: .520.

     
 

3an8
Lo 332.com of :0
Emma mafia m 3 .395

a. 3:. .88 9.5 gum: om?» mam
Co 605 .v .3926 . .c 8 . L _

.muzmsoom 9.39m 9.302% >u_:=EEou
o>on< .m .335 wuoumEoz .N Lmuamsu

  

 

.8325...

 

 

.mcozmum .mucmEtwnxm
ozo .25 «Ewe. Ea:
m=om .28:qu .8338.”

595:: 85¢.me
mummmewmm :3 :0 main?
9 8:223. .25 3:33.“.

mumemmmLBmB co
mum...“ Co tutu A .335

 

 

 

.25 .983:ch ucm E593 “35-0: .=:
.05 mEBm>m0um _~u_Eo:ocmm not Em: 53> Amp—.3333 c2332;

, 2w: new :28 mean: EmanU 8 new $833.33 Lo 333m
333.03 Em .3323 a...“ co EEocome :o 260: 89.3536
Co tutu 2: Co msucfiflmnc: 858m < "8:033. £83m 19:30

 

 

 

REFERENCES

Bird, George W. and M.F. Berney. 1998. Relationship Between Nematode
Community Structure and Soil Quality. KBS/LTER Meetings.
http:/llter.kbs. msu .ed u/Meetings/1 998_All_lnv_Meeting/bird.htm.

Bongers, T. 1990. The maturity index: an ecological measure of environmental
disturbance based on nematode species composition. Oecologia 83:14-19.

Bongers, T. and H. Ferris. 1999. Nematode community structure as a bio-
indicator in environmental monitoring. Trends in Ecology and Evolution 14:224-
228.

Gage, S. H., B. M. Napoletano, and M. C. Cooper. 2001. Assessment of
ecosystem biodiversity by acoustic diversity indices. The Journal of the
Acoustical Society of America 109: 2430.

Krause, B. and S. Gage. 2003. Testing Biophony as an Indicator of Habitat
Fitness and Dynamics. SEKI Natural Soundscape Vital Signs Pilot Program
Report, February 3.

Lavelle, P.C. Lattaud, D. Trigo, and I. Barois. 1995. Mutualism and biodiversity
in soils. In book by: Collins, H.P., G. P. Robertson and M. J. Klug (eds.). 1995.
The Significance and Regulation of Soil Biodiversity. Kluwer Academic
Publishers, Dordrecht, the Netherlands. 23-33

Neher, DA. 2001. Role of nematodes in soil health and their use as indicators.
Journal of Nematology 33: 161-168.

Mankin, R. W. 2006. Increase in acoustic detectability of Plodia interpunctella
larvae after low-energy microwave radar exposure. Florida Entomologist 89:
416-418. ӣ145 kB]

Park, E.J. and A. J. M. Smucker. 2005. Erosive strengths of concentric regions
within soil macroaggregates. Soil Science Society of America Journal 69: 1912-
1921.

Zaher, H., J. Caron, and B. Ouaki. 2005. Modeling aggregate internal pressure
evolution following immersion to quantify mechanisms of structural stability. Soil
Science Society of America Journal 69: 1-12.

CHAPTER 1
IMPACT OF TILLAGE ON ACOUSTICAL SIGNATURES DURING RAPID

HYDRATION OF AIR-DRIED SOIL AGGREGATES
ABSTRACT
The objective of this research was to determine if sound can be used to
discriminate among soils and soil managements. Air-dried soil aggregates were
immersed in water, CaSO4, or Sodium Hexametaphosphate solutions. Sounds from
each aggregate hydration were digitally recorded using a hydrophone. Sounds were
analyzed using Matlab software to produce sonograms and bar charts of frequency
distributions. Soil aggregates were obtained from a native ecosystem at Kellogg
Biological Station (Michigan); forest, tilled and non-tilled agricultural sites at the Wooster,
Ohio Agricultural Experiment Station and tilled and non-tilled agricultural sites in
Hoytville (Ohio). Sounds recorded from tilled soil aggregates had significantly greater
sound Power Spectral Density (PSD) and variability than sounds from soils from non-
managed ecosystems. We concluded that tilled soils contain a mixture of stable and
less stable aggregates. Soil aggregates from tilled soils are generally less stable and
contain less carbon than no-till and non-managed soils. Air released during wetting and
slaking appears to cause greater sound in tilled soil than either the native soils or no-
tilled soils. Either slow absorption of water or slow release of air resulted in lower PSD
in aggregates from no-till and native, compared with the tilled soils. This is probably
caused partly by organic matter that reduces the rate of entry of water into soil
aggregates and reduces pressure buildup inside aggregates. This results in less
breaking and bubbling, therefore less noise. Use of sound to record and quantify soil

characteristics can be a useful way to evaluate resistance to slaking.

 

Abbreviations: KBS/LTER, Kellogg Biological Station/Long Term Ecological Research,
Michigan; NaHMP, Na-Hexametaphosphate; PSD, Power Spectral Density.

Soils with the ability to resist degradation and respond to management in
an optimal manner usually contain stable aggregates that are not degraded by
the actions of water and extemal mechanical stresses (Dexter 1988). The
process of aggregation takes place when particles of mineral matter are joined
together by various organic and inorganic substances. The numerous biological,
chemical, and physical components contributing to the formation of stable soil
aggregates are impacted by ecosystem disturbances, including the agricultural
practice of tillage (Amezketa 1999, Six et al. 1998, Pikul et a/ 2009). Recently,
acoustical transmission analyses have been used to identify specific soil

properties (Grift et al. 2005; Moore and Attenborough 1992).

Soil Acoustics

The physical characteristics of soil have been measured by active
acoustics (sending sounds into a soil and receiving the signal) and passive
acoustics (recording sound). The National Center for Physical Acoustics has a
website section dedicated to active acoustical assessment of soil
(httpzllwww.olemiss.edu/depglncoa/PorMat/SA.htm). Soil characteristics such as air-filled
porosity and relative air permeability have been effectively measured through
acoustic propagation (Moore and Attenborough 1992). Passive acoustics have
been tested to measure soil compaction (Grift et al. 2005). Additional studies on
the acoustics of soil physical characteristics, including acoustics of soil aggregate

hydration, are limited.

Soil Aggregation, Stability and Slaking

Soil aggregation processes include both the formation and stabilization of
aggregates. Flocculation of clay, wetting, drying, freezing, thawing, and the
dynamics of both microbes and roots play important roles in aggregation. In
addition, inorganic stabilizing agents like Ca2+, Al3", Fe3", oxides, and hydroxides
of aluminum and iron, and carbonates of magnesium and calcium are involved in
the process. Persistent, intermediate, and transient organic stabilizing agents
may also have a role in the process. Soil organic matter can also be protected
from decomposition inside an aggregate. When the aggregate breaks, the
protected organic matter is subjected to microbial decomposition, releasing 002
(Jastrow and Miller 1998).

Soil aggregation is crucial for preventing erosion, increasing water
infiltration and maintaining soil surface integrity (F ranzluebbers et al. 2000).
Stability of soil aggregates, in part, determines nutrient and pollutant leaching,
mineralization of nutrients, and the extent and rate of conversion of soil carbon
into C02. Soil aggregate stability is one of the physical characteristics of soil that
can serve as an indicator of soil quality (Arshad and Coen 1992, Hortensius and
Welling 1996). Additionally, soil properties such as erosive and crusting potential
can be estimated when soil aggregate stability is measured (Amezketa 1999).
Models of soil aggregation have been developed by Oades and Waters (1991),
Elliot (1986), Tisdall and Oades (1982), and Edwards and Bremmer (1967); and

reviewed by Amezketa (1999).

Rapid hydration or flooding is a disruptive wetting method leading to
aggregate slaking. Air slaking is caused by increasing internal pressures when
dry soil aggregates are submerged in water causing the rapid escape of air
bubbles and some breakage of the soil aggregate (Amezketa 1999; Dickson et
al. 1991; Jastrow and Miller, 1991). Zaher etal. (2005) indicated that organic
carbon reduced the rate of water entry inside the aggregate, and decreased the
internal pressure of the aggregate, thereby, reducing slaking and rapid bubbling.
Causarano (1993) reported that polar tensile strength does not always match soil
aggregate stability. When evaluated by rapid water hydration, soil organic matter
increases the strength of moist soil aggregates and may decrease the strength of
dry soil aggregates. Factors that affect the strength of soil aggregates include
the types and quantities of cations, organic matter, dispersible clays, drying and
wetting cycles, the intensity of wetting and drying, and management practices
(Park and Smucker 2005b).

Soil aggregate stability modifications by alternative tillage operations have
been quantified using wet/dry sieving, rainfall simulators and other conventional
soil analysis methodologies (Amezketa 1999). Six et al. (2000), immersed soil
aggregates in water (slaked treatment) as a pretreatment to soil stability

assessment by wet sieving.

Ecosystems Disturbance: Tillage
Tillage is an agricultural practice that can increases aggregate slaking,
increase 002 loss; reduce organic matter, and increase soil erosion and nutrient

leaching (Six et al. 1998; Angers 1998). Reduced tillage practices can increase

9

soil aggregate stability, organic matter (Pikul et al. 2009), water infiltration and
earthworm population densities. It can reduce soil erosion and preserve soil
aggregate stability (Katsvairo et al. 2002). Tillage also impacts soil organic
matter, nitrogen and carbon dynamics, and microbial community structures
(Carter and Rennie 1982; Campbell et al. 1989). Robertson et al. (1993) found
that cultivation reduced soil carbon by 50%, compared to a non-disturbed soil.
Grandy and Robertson (2006) concluded that plowing once immediately and
substantially alters aggregation and Light Fraction and particulate Carbon
dynamics and that these conditions persist. The presence of vegetation
increases soil aggregate stability, especially where there has been little
disturbance. Additionally, plant cover can protect soil from erosion and nutrient
leaching, serve as a source of nitrogen, provide organic matter, reduce disease
potential, and enhance soil moisture holding capacity (Bowman et al. 1998).
Ecosystem disturbance and management practices affect soil aggregate
stability and pore arrangements. Soil aggregate stability and porosity also affect
bio-geo-chemical processes (Park and Smucker 2005a). Soil aggregate polar
tensile strength and rapid hydration are commonly used as measures of stability.
Tensile strengths are quantified from the crushing forces on generally dry soil if
the diameter of the soil aggregate is known. The methods of measuring tensile
strengths are described by Dexter and Kroesbergen (1985). Erosive strengths
methods were also used by Park and Smucker (2005b). Other macro soil
aggregate stability measuring methods include wet sieving, dry sieving, and

rainfall simulation. A review of these methods can be found in Amezketa (1999).

10

Objectives and Hypothesis

The objectives of our research were: 1) to evaluate the suitability of
acoustical recordings to quantify water-soil-aggregate-stability, associated with
various soil tillage management systems and 2) compare our results to
conventional methods of measuring soil aggregate stability and slaking. The
method for achieving this objective was completed by recording the acoustical
signatures of air-dried soil aggregates during rapid re-hydration (immersion). Our
hypothesis is that sound caused by the rapid rehydration and sometimes
breaking (air slaking, Hillel 2004) of air-dry aggregates can be used to identify the
variation in soil aggregate stability in a gradient of soils from non-managed
(forest) to highly managed (conventional agriculture). We hypothesize that each
soil type and management combination will produce a unique sound or signature
which will be statistically different from other soil types and management
systems. Also, it was expected that the escape of air at different rates from soil
pores would produce different sounds, and that the results of our recordings
would be comparable with conventional methods of measuring resistance to

slaking under the stress of rapid hydration.

MATERIALS AND METHODS
General Methods

Soil aggregates were obtained from a native ecosystem at Michigan State
University, Kellogg Biological Station/Long Term Ecological Research site

(KBS/LTER, Hickory Corners, Michigan); native forest, tilled and non-tilled

11

agricultural sites at the Ohio Agricultural Experiment Station (Wooster, Ohio); and
tilled and non-tilled agricultural sites in Hoytville, Ohio (Tables 1.1-2). Air-dried
soil aggregates were rapidly rehydrated by immersing them in water, 5mM
CaSO4 (stabilizing solution), or 509/L Na-Hexametaphosphate (dispersive
solution). The acoustical signatures of each soil aggregate were recorded
continuously for 30 seconds during the rapid hydration and slaking process.

The sounds emanating from soil aggregates were recorded by placing an
Aquarian hydrophone (A03, www.aquarianaudio.com, Anacortes Wyoming) in a
container of water and adding aggregates to the water. The hydrophone was
connected to an M-audio Mobile Preamp connected to a laptop computer
(www.m-audio.com, lrwindale, California). Recordings were captured using Audio
Audition (2004) as the soil aggregates were added to the water. The sounds
were recorded at 16 bits, monaural at 22.050 kHz in .wav file format for 30
seconds. The sound files were named according to soil treatment
(site/management) and replication number. The 30 second sound segments
were processed using Matlab (MathWorks, www.mathworks.com). The Welch
(1967) method was used to quantify the acoustical power (Power Spectral
Density) in watts for each recording. The program produced several graphical
representations of the sound profile from each 30 second sound sample,
including a: 1) sonogram, 2) ocillogram, 3) trace of the Power Spectral Density
(PSD) in watts/Hz, 4) bar chart of the PSD at one kHz intervals (watts/kHz) and
5) table of the PSD values in watts/kHz for each one kHz frequency interval from

0-1 1 kHz (Table 1. 3). The MatLab function “pwelch” implements Welch's method

12

of determining the power density in watts (\Nelch 1967) and is a way to estimate
PSD using periodograms (finite segments of the original signal that are converted
into a spectral representation using the Fourier transform). This computation
was automated so that multiple sound samples could be processed. The PSD
values for each 1.0 kHz increment were used to compare sounds produced by
each of the soil aggregates tested.

For statistical and interpretation purposes, sounds where
compartmentalized into 1.0 kHz frequency intervals from 0-11 kHz. Low
frequency sounds (kHz level 1 and sometimes level 2) were eliminated from the
analysis, as these low frequencies contain background noise. For comparison
of the total PSD among the treatments, graphs were made from the sum of all
kHz levels, excluding kHz level 1, or levels 1 and 2. These represent the mean
of the total PSD (sum of kHz levels 2-11 or 3-11).

Two groups of experiments were conducted. Group-1 compared the
acoustical signatures of soil aggregates from continuously tilled and native sites
in water, a stabilizing solution and a dispersive solution. Group-2 compared the
acoustical properties of soil aggregates from conventional and no-till

management systems and native sites using only water.

Preliminary experiments (Group 1)
Three soils were used. These included: 1) Wooster silt loam (fine loamy,
mixed, mesic, Typic Fragiudalf) from a native forest site in Wooster, Ohio; 2)

conventionally tilled soil from Hoytville Ohio; silty clay loam (silty clay loam; fine,

13

illitic, mesic, Mollic Epiaqualfs) and 3) native, never-tilled soil from Hickory
Corners, Michigan (KBS/LTER), sandy loam-silty clay loam (Kalamazoo Fine
loamy, mixed, mesic, Typic Hapludalfs and Oshtemo coarse-loamy, mixed, mesic
Typic Hapludalf). The soils were collected and processed for air-dried soil
aggregates as described by Park and Smucker (2005b). The aggregates used in
this research were part of the collection used by Park and Smucker (2005a & b).
During the air-drying process, the large soil aggregates were broken manually
along their natural planes of weakness using gentle manual forces, resulting in
aggregates of s 20 mm. The air-dried aggregates were manually dry sieved for
less than one minute to obtain a distribution of <10, 1.0-2.0, 2.0-4.0, 4.0-6.3, 6.3-
9.5, and > 9.5 mm in diameter aggregates and stored in separate plastic
containers at room temperature. Aggregates used in this research were 4.0-6.3
and 6.3-9.5 mm in diameter (Park and Smucker 2005b). Details about the soils
and management systems are presented in Tables 1.1-2.

Individual soil aggregates, 4.0 to 9.5 mm in diameter, from each of the
three soils were submerged in separate glass beakers containing 250 mL of
purified water (total dissolved solids, 4 ppm), 5mM CaSO4, or 50 g/L NaHMP.
Each individual soil aggregate was placed on the surface of the water. It then fell
a distance of about 5 cm. The resulting acoustical signatures were continuously
recorded for 30 seconds with the hydrophone located directly above the
immersed aggregate. The process was repeated four times for each

soil/management and solution treatment, resulting in 36 individual recordings.

To determine the level of background sound, a recording was done in the

14

absence of soil aggregates. Analysis of the sound was done with Matlab using
the P-welch method (Table 1.3) and bar graphs of the means were produced with
Minitab using standard error bars. Minitab statistical software was used for

statistical analyses of the data.

Impact of Tillage on Aggregate Acoustics (Group 2)

Air-dried soil aggregates from conventionally tilled, non tilled and native
soils were analyzed in the second group of experiments (Table 1.1-2).
Aggregates from each treatment were placed on a 1.0 mm mesh screen held by
a wire and submerged to a depth of 10 cm in seven liters of water in a 25 cm-
wide and 20 cm-deep glass desiccator. The hydrophone was placed to the side
of the sample in a horizontal position. Sounds generated by each immersed soil
aggregate were recorded for 30 seconds. Measurement of the acoustics of each
of three soil types and three management systems were replicated 30 times.
The recordings were done in a sound-proof chamber at the Audiology Laboratory
at Michigan State University.

Sounds were analyzed using the same Matlab code that was used for the
first group of experiments (Table 1.3). Minitab 15 software was used for
statistical analysis including ANOVAs, T-tests, and 95% confidence intervals.
Sounds were also graphically portrayed with sonograms. The analysis of
acoustic observations was compared with conventional methods of aggregate

stability and resistance to slaking measurements. Also, the same group of

15

aggregates from the erosive strength measurements reported by Park and

Smucker (2005a, 2005b) were used.

Supplementary Experiments

Two additional tests were done to address questions related to Group 1
and Group 2 experiments. The same soil aggregates that were used for the
Group 2 acoustical experiments were evaluated visually for slaking using a scale
of 1-6 (1= soil stayed intact, no slaking for the 30 seconds of the recording and
6= soil aggregate disintegrated immediately after immersion). The data were
compared to our acoustical results. In the other supplementary test, Tygon
(Akron, Ohio) tubing rubber tubes (bubblers) of different diameters (small= 0.3
mm & large: 1 cm) were inserted into water. The sounds produced by the air
bubbles were recorded with a hydrophone. This was done to quantify the sounds
of different size bubbles and compare it with the soil aggregate slaking acoustic
recordings.

All of the sonograms and sound recordings for each replication of each
treatment and additional statistics, graphs, and video recordings (movies) can be

found at www.nemasoil.com.

RESULTS
Preliminary Experiments (Group 1)
Hydration of aggregates from each of the three soils produced different

acoustical signatures. Tilled Hoytville soils aggregates generated extensive

16

acoustical intensity (PSD) during the first 10 seconds of the recording sessions
and lower intensity and less frequency during the last 20 seconds of the
recording sessions (Figure 1.1a). Soils from the native sites at Wooster and
Kellogg Biological Station exhibited less sound intensity (Figures 1.1b-c). The
sounds produced by the tilled Hoytville soil aggregates were detected at all 11
kHz levels measured. The two native soils were consistently quieter that the tilled
soils. The sonograms were distinct for each soil management system. The
sonogram of the background sound had only a faint signal in kHz level 1 (Figure
1.1d).

Soil aggregates from tilled soil from Hoytville (conventional tillage)
displayed significantly (p = 0.017) greater PSD values than Wooster and KBS
native soils in water, 5mM CaSO4, and 50 g/L NaHMP solutions (Figure 1.2; one-
way ANOVA). Soil aggregates from the Kellogg Biological Station and Wooster
native areas generated lower PSD values than aggregates from the
conventionally tilled Hoytville treatment. Soil management system (treatment)
had a greater impact than wetting solution (H20, CaSO4, or Na-HMP) on the
acoustical signatures of rapidly hydrated air-dry aggregates. Soil wetting solution
did not significantly affect the results (Figure 1.2).

Sounds from hydrating aggregates from the native soils from Wooster and
KBS exhibited low PSD values and low variability. The PSD and variability of
acoustical power associated with Hoytville-tilled soil aggregates was greater than
that associated with the Wooster and KBS native soil aggregates (Figure 1.2).

Immersion of aggregates in H20, 50 g/L Na-HMP or 5 mM CaSO4 solutions did

17

not significantly alter the acoustical power generated by soil types or

management.

Impact of Tillage on Aggregate Acoustics (Group 2)

Sounds from soil aggregate hydration from native soils had lower PSD
than the sounds from no-till and conventional till soil/management systems
(Figure 1.3). Soil aggregates from KBS native soil remained stable when
immersed in water for the duration of the measurement. Soil aggregates from
conventionally tilled sites in both Hoytville and Wooster soils began slaking
immediately following immersion. PSD values for the sum of kHz levels 2-11
from hydrated Hoytville aggregates from continuous tillage site were 57% greater
than aggregates from Hoytville no-till (p< 0.001, T-test). The mean acoustical
power for continuously tilled Hoytville aggregates was 0.381 (standard error
SE=0.036 for the sum of kHz levels 2-11), while Hoytville aggregates managed
without tillage produced lower mean acoustical intensities of 0.2180 (SE =
0.0035, Figure 1.3).

Acoustical power values (sum of kHz 2-11, Figure 1.3) of aggregates from
conventionally tilled Wooster silt loam soils were 200% greater (mean= 1.2, SE =
0.33; p= 0.092, two sample T-test) than acoustical power from the aggregates
from the Wooster no-till site (mean= 0.592, SE = 0.12) and 550% greater than
aggregates from the Wooster native soil (mean= 0.2236, SE=0.0052; p= 0.006,
two sample T-test). The lowest acoustical power and lowest variability occurred

for the Wooster silt loam soils from the native forest (Figure 1.3).

18

Tilled soil from Wooster, Ohio had the greatest PSD and highest
variability of all soil types and treatments tested. Native soils had the lowest
PSD and variability (Figure 1.3, p= 0.05, 95% confidence intervals for the mean
(95% CI)). No-Till had intermediate values between the tilled ecosystems and the
native ecosystems. Hoytville-Till had a lower sound intensity (PSD) value than
the Wooster-Till (p= 0.05).

A fully nested ANOVA indicates that there is significant evidence (p =
0.04) that soil management (till and no-till) had an effect on the PSD of hydrated
soil aggregates. There was no significant evidence for site (Wooster vs.
Hoytville) effect (p = 0.204) on the PSD of soil aggregate hydration.

The acoustic frequencies that dominated in the sounds of the soil
aggregate hydration from Hoytville and Wooster till, and Wooster no-till soils,
were 1-4 kHz frequencies (Figure 1.4, kHz level 1 is not shown in the graph). The
higher frequencies (5-11 kHz) did not vary significantly between or within
treatment. In the native soils, sound was more equally distributed between the

different kHz levels (Figure 1.4).

Supplementary Experiments

When soil aggregates were immersed in water, they had different Visual
Slaking lndices (breaking). The tilled soils had the highest visual slaking values
of all the soil/management systems (p = 0.05 (95% Cl), Figure 1.5). The native
soils had the lowest slaking values and the no-till treatments were intermediate,

as expected. This agreed with the acoustical data, giving us an indication that

19

the sounds recorded were in part created during the slaking and subsequent
bubbling process.

When air bubbles of different sizes were recorded using the same
methods as used for the soil aggregate hydration, most of the sounds produced
were in the kHz 1-3 levels (Figure 1.6). Larger bubbles produced louder sounds

than the smaller bubbles (Linear regression R2= 95.8%, p=0.004, Figure 1.7)

DISCUSSION
Comparison of Conventional and Acoustical Methods for Soil Water-
Aggregate Stability Analysis

Mahboubi and Lal (1998) measured the soil aggregate stability on air dried
aggregates for no-till and tilled treatments at Wooster and South Charleston Ohio
using the wet sieving technique (Yoder, 1936). Although the results were
expressed in different ways, mean weight diameter and percent total aggregation
are selected for comparison with acoustical methodology. Total percent
aggregation is the percent of the total soil dry weight of a sample made up of
water stable aggregates greater than 0.1 mm. The mean weight diameter of the
no-till treatments was higher than both of the tillage systems measured (no-till
1.19 a, chisel plow 0.71 b, and moldboard plow 0.53 c). The total percent
aggregation was higher in the no-till treatment compared to the two till treatments
tested (no-till 41.4a, chisel plow 29.6 b, and moldboard plow 25.8 b). This
compares well with our results, in which tilled soils had greater PSD (sound

intensity) than no-till soils and native soils (Figures 1.2 & 1.3). Our evidence

20

suggest that the louder the sounds, the lower the water-stability of soil
aggregates and resistance to slaking. The sounds are probably caused by the
effects of slaking, including air bubbling (Zaher et al. 2005).

Six et al. (2000) did aggregate separations, soil stability assessments and
carbon and nitrogen measurements of soils from Wooster Ohio, KBS/LTER
Michigan, and two others that we did not test (Sidney, NE and Lexington, KY).
They also immersed air-dried soil in water (slaked treatment); the “slaked
aggregate-size distribution differed between management treatments at all sites”.
Native soil had the greatest proportion of macro-aggregates, whereas,
conventional tillage had the lowest and no-till was intermediate. In Wooster, they
did not observe a significant difference between the native and the no-till soil.
They indicated that the cause was the similar values of total carbon and
particulate organic matter (Six et al. 1999). This is also consistent with our
aggregate slaking and sound recordings. Conventionally tilled soil had the
highest PSD values, whereas, the no-till were intermediate and native soils had
the lowest PSD. It Is consistent with the literature and our own observations to
hypothesize that the sound came from the slaking process. We visually
categorized levels of slaking in aggregates when they were immersed. The
results indicated that the conventional tillage had significantly (p= 0.05) more
slaking when immersed in water than both the native and no-till soils. The native
soil had the lowest slaking with the no-till having an intermediate value (Figure

1.5). There was no significant difference in the level of slaking between Wooster

21

no-till and Wooster native regimes. This is consistent with the results obtained
by Six et al. (2000).

The high variability among soil aggregate stabilities from tilled soils,
reported by Park and Smucker (2005a and 2005b), were also reflected by the
large variability of acoustical signatures of tilled soils from different soil types. In
contrast, the more stable soil aggregates from native forest soils generated lower
variability and lower acoustical power values. These acoustical indices support
current knowledge that soil tillage diminishes aggregate stability as reported by
Amezketa (1999), Six et al. (1998), and Angers (1998). Furthermore, the greater
above- and below-ground accumulations of plant and microbial biomass,
associated with native succession and forest ecosystems decreased PSD, and
increased aggregate stability (Amezketa 1999).

The analysis of acoustical power from different soils/management regimes
separated the stabilities of aggregates from the same soil types and
management systems reported by Park and Smucker (2005b). Their method that
identifies erosive strengths of concentric layers from aggregates compared well
with wet sieving and crushing strength measurements of aggregates. Acoustical
signatures, erosive strengths, and wet sieving have all demonstrated
comparative differences among aggregate resistances to mechanical and water
forces and established that soil aggregate strengths were highest among soils
from native ecosystems, moderate in the no-till ecosystems and weakest in

conventional tillage (Yoo and Wander, 2008).

22

Role of Soil Organic Carbon on the Slaking Process

Carbon increases water-stability of soil aggregates. In tests done by
Bipfubusa et al. (2008), carbon increased the mean weight diameter of water-
stable aggregates by 45% (p=0.05) compared to the soil without carbon
amendments. In addition, the carbon amendments increased the aggregates
resistance to slaking. The effect is lasting, as these results were obtained two
years after adding the organic carbon.

The higher organic carbon contents in the native and no-till soil most likely
affected our results. The soil aggregates with lower carbon content slaked most
when immersed in water, and therefore produced the most noise.

Most of the acoustics from the rapid hydration of soil aggregates from
tilled soils were produced during the first 10 seconds of recording (Figure 1.1a).
This is consistent with the findings of Zaher et al. 2005, that “aggregates rupture
rapidly in contact with water, within the first 8 seconds of immersion”.
Aggregates, especially from tilled sites, ruptured within the first 10 seconds of
immersion, matching with the acoustics produced. It appears that soil aggregate
rapid hydration acoustics are recording the effects of soil aggregate disruption,
especially air bubble release. Zaher et al. (2005) measured the rate of air
escape from immersed soil aggregates during 8 seconds in time and compared
different soil types and organic matter contents. They showed that soils with high
organic matter lose air more slowly than soils with low organic matter content.
They came to the conclusion that the two factors controlling aggregate

disintegration, namely pressure buildup and swelling are significantly reduced by

23

the addition of organic matter... and indicate that organic matter plays a role in
soil stability by improving cohesion and contributing to a decrease in water entry
so, the organic matter leads to a decrease in swelling and intra-aggregate
pressure. The two mechanisms of action of the organic matter in reducing the
rate of water entry are occlusion and the increase in the rugosity and
hydrophobicity of the pore space by the carbonaceous fractions. A good image
of the conceptual model of the factors controlling aggregate stability when rapidly
hydrated can be found at httpzllsoil.sciiournals.org/cgi/content/full/69/1/1/FIG6
_Z_aher et al. 2005. This hypothesis stated by Zaher et al. (2005) fits our results.
In general the higher the carbon content of the soil, the lower the PSD (sound
intensity) recorded (Table 1.1 and Figure 1.3).

Sullivan (1990) indicated that “hydrophobic properties of soil organic
matter can increase the amount of air encapsulation within soil materials during
water uptake; and secondly, that this increased air encapsulation can reduce
water uptake rates sufficiently to prevent slaking.” It is known that native and
no-till soils tend to have more soil organic matter than conventionally tilled soils.
The organic matter increases air encapsulation and reduces rate of water uptake,
therefore reducing slaking when soil is rapidly wetted (Sullivan 1990). We
believe that this is in part responsible for the lower PSD’s in the native and no-till
soils compared to the conventionally tilled soils.

Six and Paustian (1999), reported that carbon content in Wooster (Ohio),
KBS (Michigan) and Sydney (Nebraska) were greatest in the native forest, lowest

in the conventional tillage and intermediate in the no-till. “Greater soil carbon

24

contents increased water stability of soil aggregates” in the wet-sieving
aggregate stability measured by Park et al. (2005) in Wooster and Hoytville soils
from native, no-till and conventionally tilled sites. They also noted that soil
carbon contents in no-till soils were 1.6 and 2.2 fold greater than in conventional

tillage in Hoytville and Wooster.

Soil Aggregate Hydration Acoustics

Acoustical methods appear to be appropriate for estimating degree of
slaking during rapid hydration. They are also useful in distinguishing between soil
types and soil management systems.

Comparing the acoustical signatures of soil aggregates from the same
location, it is clear that soil tillage increases variability among aggregate samples.
The greater variability in acoustical signatures from tilled soils can be explained
by the different histories of each soil aggregate. Not all aggregates are affected
by the same disturbance. Some might be protected in some way, such as being
in the middle of a much larger soil clod. Other aggregates might be significantly
reduced in size and stability by tillage tools.

In both experiments, the most stable soil aggregates were from the KBS
Long Term Ecological Research, native succession and the Wooster native
forest. These soils generated extremely low PSD’s and the tilled soils generated
higher PSD’s. Also native soils had low variability compared to the more
disturbed agro-ecosystems (Figures 1.2 & 1.3). Generally, the lower

frequencies of 2, 3 and 4 kHz were the predominant frequencies produced from

25

the hydration of the aggregates with more agricultural disturbance in their
histories (Figure 1.4). In comparison, sounds from the native soils were more
equally distributed among the different frequencies (Figure 1.4), while sound
distribution (in kHz 2-11) and variability among aggregates from no-till Wooster
soils were between the tilled and native successional soils (Figures 1.3 & 1.4).
Comparing the low variability from native soils with the extremely high variable
data for aggregates sampled from the conventionally tilled soil demonstrates how
our efforts to homogenize soil aggregate fractions by conventional tillage actually
causes greater heterogeneity among the tilled aggregates.

Differences between Hoytville and Wooster: Differences among the
acoustical signatures of tilled Hoytville soils compared to tilled Wooster soils
(Figures 1.3 & 1.4) appears to indicate that different soil types are more variable
in relation to the effects of tillage on the resistance to slaking of soil aggregates.
Acoustical analysis of both the tilled and non-tilled Wooster silt soils showed that
there was a three-fold greater amount of PSD than that computed for the
Hoytville silty clay soils. Like the acoustics results, Park and Smucker (2005b)
erosive strengths test showed a greater stability in the Hoytville soils, compared
to the Wooster soils. The difference in the erosive strength of the concentric
layers within dry soil aggregates of the two soils were nearly 200-fold (Park and
Smucker 2005b). Some of the differences between the lower wet acoustical
signatures and the higher dry erosive strengths values for aggregate layers of
Wooster and Hoytville soils can be explained by the lower intra-aggregate

porosities and higher bulk densities of Hoytville aggregates (Park and Smucker

26

2005a). Causarano (1993) reported that carbon contents increased the strength
of moist soil aggregates. Consequently, the rapid hydration of high carbon
aggregates would increase the stability and decrease the acoustic signature.

The Hoytville soils may be naturally more stable than Wooster soils, most likely
because of the greater clay and soil organic matter contents. The results in our
research using rapid soil aggregate hydration and acoustical recording lead us to
conclude soils with higher carbon content are more stable when less disturbed by
tillage.

Jarecki and Lal (2005) compared the water-aggregate stability of Hoytville,
and South Charleston Ohio till no-till soil. They concluded that “the plow till
treatments had a small impact on soil aggregation in clayey soil (Hoytville). The
decline in water-stable aggregates with reference to no-till was no more than one
sixth. In the silt loam soil, however, the water-stable aggregates in plow till
treatments were merely one third of that in the no-till treatments”, but both soil
types had more water-stable aggregates in the no-till treatments compared to the
tilled treatments. This is the same conclusion that we reached through our
acoustical analysis. Our results indicate that agriculture disturbance did not have
as strong an impact on Hoytville clayey soil, compared to the silt loam from
Wooster Ohio. This conclusion was also reached by Jarecki et al. 2005. It, in
part, explains our results for the second set of experiments. The Hoytville till soil
generated significantly more sound than the Hoytville no-till, but the difference
was not as great as associated with the till and no-till aggregates from Wooster

(Figure 1.3). The difference is, in part, likely caused by the soil type and the

27

organic matter content. Jarecki et at. (2005) said that the historic loss of soil
organic carbon was 25 to 35% in Hoytville (clayey soil) and 19 to 25% in S.
Charleston (silt loam). Hoytville had more water-stable aggregates in the no-till,
compared to the tilled treatments and more than the silt loam from S. Charleston.

Differences between Group 1 and 2 Experiments: We began the first
group of experiments with the objective to test if there was any difference in the
resistance to slaking between the tilled and the native soils and if our acoustical
methods could be used to document these differences. We also wanted to
determine if dispersive (Na-HMP) or stabilizing (CaSO4) solutions would have an
effect on the sounds, during the slaking process, for these soils. When the
results became clear, we increased the number of soils and the replications in
the second group of experiments. Since the wetting solutions did not have a
significant effect on the sounds, we did not include them in the second group of
experiments. The high variability and the low number of replications (four)
probably obscured the effects of the wetting agents.

The PSD observed in the first set of experiments were much higher than
those recorded in the second set of experiments (Figures 1.2 and 1.3). This was
most likely because in the first set of experiments the hydrophone was placed
directly above the slaking aggregate, so the air bubbles were hitting the
hydrophone directly. In the second set of experiments, the hydrophone was
placed besides the aggregate. The air bubbles did not hit the hydrophone

directly.

28

Supplementary Experiments: Use of a Visual Slaking Index (1-6)
produced similar results as the acoustical analysis. The observations indicated
that the tilled soils had the highest slaking when immersed in water and the
native soils had the lowest slaking, with the no-till having intermediate values
(Figure 1.5); in the visual slaking supplementary experiment, however, we did
not see the differences between Hoytville and Wooster as documented in the
group 2 acoustical experiments. Wooster-Till had significantly more PSD than
Hoytville Till (Figure 1.3). This difference was not seen in our visual assessment
(Figure 1.5), suggesting that our visual assessment was an adequate procedure.
Maybe soil aggregates appeared to totally disintegrate, but in reality, smaller
aggregates were formed or the higher carbon content of Hoytville had an effect of
reducing the noise, but not the visual breaking. Since our Wooster and Hoytville
acoustical results were more consistent with the literate than our visual
assessment, we believe than our visual assessment was not accurate.

The sounds produced by the soil aggregate rapid hydration were likely
produced by air bubbles and the aggregate breaking during the slaking process.
In our recordings of air bubbles in water, the lower three kHz levels were
dominant. This is similar to what we observed in the soil aggregate hydration
experiments (Figure 1.6 & 1.4). The larger the air bubble size, the higher the
sound (R2= 95.8%, p=0.004, Figure 1.7)

29

CONCLUSION

Our results concurred with the conclusions reached by the majority of the
scientific literature, indicating that tillage increases vulnerability to slaking. Our
conclusion that native soils are more water stable than no-till soils and that no-till
is more resistant to slaking than soil with a conventional tillage management, is
also in agreement with the literature.

We concluded that: 1) the results of the acoustical research indicate that
soil aggregates from no-till sites have higher resistance to slaking and generate
less sound (PSD) than those from conventional tillage systems. 2) Native soils
(non-disturbed soil) had the least slaking and less PSD when hydrated rapidly.
3) Acoustical methods appear to have potential for estimating water soil
aggregate stability and degree of slaking. 4) Furthermore, the impacts of tillage
on aggregate slaking can be potentially quantified using acoustical technology.
5) Our acoustic methods are faster and simpler than most of the conventional
methods of determining soil aggregate stability, but more research needs to be
done in order to determine the quantifiable correlation between the acoustical
methodologies and the conventional methods. 6) Tillage reduces water-soil
aggregate stability of soil; this in part is caused by the decrease in organic
carbon in tilled soils (Six et al. 2000 and Zaher et al. 2005). Acoustical methods
provide comparable results to the conventional methods of water-aggregate
stability. Acoustical methods are rapid providing quantifiable results. Currently,
more work needs to be done to determine what exactly are the sounds recording,

most likely it is the bubbling and breaking during the slaking process. More

30

quantification of the relationship between the sound produced and porosity,
aggregate stability, carbon contents et al. is needed. The effects of
hydr0phobicity caused by soil carbon on the soil aggregate rapid hydration

sounds produced also need to be determined and quantified.

31

TABLES

Table 1.1. Soil collection locations, management, carbon (%) and bulk
density of the six soil air-dried aggregates sets used in this research
(adapted from Park and Smucker, 2005a, 2005b; Six et al. 2000).

 

Soil aggregate characteristics

 

 

Bulk Carbon content

Location and management density
system (9 cm ) (%)
Hoytville, 0H (Silty clay loam)

1. Conventional Till I“ 1.76 2.09

2. No-Till * 1.64 3.31
Wooster, OH (Silt loam)

3. Conventionally Till * 1.78 0.77

4. No-Till * 1.56 1.96

5. Native Forest 1 1.40 2.84
Kellogg Biological Station
(KBS), Ml (Loam)

6. Native Succession * 1.62 1.70

 

T Used in the first and second group of experiments
* Used in the second group of experiments only

32

Table 1.2. Soil collection locations, site establishment dates, management
descriptions, and taxonomy of the of the six soils aggregate sets used in this
research (adapted from Park and Smucker 2005a, 2005b; Mahboubi and Lal
1998, Dick et al. 1997, Robertson et al. 1993 and Six et al. 2000 &1999).

 

Location

Management description

Soil taxonomy

 

Hoytville, Ohio
Established 1963
41°03’ N, 84°04’ W

Conv. TillT Continuous corn; fall

moldboard plow, 20-25cm deep
and one or two 10cm deep
secondary tillage treatments.

No-TiIF Continuous corn; No-Till
planting (cutting through crop
residues), no additional tillage.

Silty clay loam; fine,
illitic, mesic, Mollic
Epiaqualfs

 

Wooster, Ohio
Established 1962
40°48’ N, 82° 00’ W

Conv. Tiltf Continuous corn;
spring moldboard plow, 20-25cm
deep and one or two 10cm deep
secondary tillage treatments

No-Till* Continuous corn: No
tillage was used.

Native Forest’r Nearby Forest Soil

Fine loamy, mixed,
mesic, Typic
F ragiudalf

 

KBS/LTER,

Hickory Corners
Michigan
Established1986
42°24’ N, 85° 24’ W

Native Soil T without recent
history of tillage

Kalamazoo fine
loamy, mixed,
mesic, Typic
Hapludalfs and
Oshtemo coarse-
Ioamy, mixed mesic
Typic Hapludalf

 

7 Used in the first and second group of experiments
* Used in the second group of experiments only

33

Table 1.3. Matlab code for computation of the average Power Spectral Density
(acoustical intensity) for 1 kHz frequency bins (plus total) from a .wav file
segment. The last calculation (Tab(i,12)) is the sum for all 11 kHz levels.

 

 

96**iit*************i****iii*************************iiii************i************

% Calculate the average Power Spectral Density (PSD) of the frequency bands using Welch
method.

96************************************************t*****tifi***********************

[y,Fs] = wavread(FileSet); % Set wavefile to variable for plot
[signal,Fs] = wavread(FileSet); % Access the wav file segment
Pxx = pwelch(signal,[],[],512.22050); % Calculate pwelch

Hpsd = dspdata.psd(Pxx,'Fs’,22050,'spectrumType','onesided');

96************i*i*************i*****************t*ii**************************i*****

% Create the matrix of average power based on parameters set for PSD for each 1 kHz
frequency band (plus total) in a .wav file segment. Multiply % values by a constant (1,000,000) to
scale values.

96*******i**i**************************i*************************i**i*****i*********

Tab(i,1) = avgpower(Hpsd,[OOOO 1000])*1000000; % average PSD 0000-1000 Hz
Tab(i,2) = avgpower(Hpsd,[1000 2000])*1000000; % average PSD 1000-2000 Hz
Tab(i,3) = avgpower(Hpsd,[2000 3000])*1000000; % average PSD 2000-3000 Hz
Tab(i,4) = avgpower(Hpsd,[3000 4000])*1000000; % average PSD 3000-4000 Hz
Tab(i,5) = avgpower(Hpsd,[4000 5000])‘1000000; % average PSD 4000-5000 Hz
Tab(i,6) = avgpower(Hpsd,[5000 6000])*1000000; % average PSD 5000-6000 Hz
Tab(i,7) = avgpower(Hpsd,[6000 7000])*1000000; % average PSD 6000-7000 Hz
Tab(i,8) = avgpower(Hpsd,[7000 8000])“1000000; % average PSD 7000-8000 Hz
Tab(i,9) = avgpower(Hpsd,[8000 9000])*1000000; % average PSD 8000-9000 Hz

Tab(i,10) = avgpower(Hpsd,[9000 10000])*1000000; % average PSD 9000-10000 Hz
Tab(i,11) = avgpower(Hpsd,[10000 11000])*1000000; % average PSD 10000-11000 Hz
Tab(i,12) = avgpower(Hpsd)* 1000000; % average PSD 0-11000 Hz

 

34

 

FIGURES

a. Hoytville-Till b. Wooster-Native

kHz 1-11

C. KBS-Native CI. Background

kHz 1-11

  

Time 0-30 seconds

Time 0-30 seconds

Figure 1.1 a-d: Spectrograms of rapid water hydration of soil aggregates from
three different soils: a) Hoytville, Ohio continuous till, b) Wooster, Ohio native
forest, c) KBS, Hickory Corners, Michigan native soil and d) background sound
with no soil aggregates. The color intensity and brightness indicates sound
intensity. Black indicates no sound. Lighter color means higher sound intensity.

35

 

 

505

30-

20-

 

10-

Power Spectral Density (watts/ kHz)

      

    

, ..1
.: h‘

 

 

 

 

 

 

CaSO4 H20 NaHmP CaSO4 HZO NaHmP CaSO4 l-l20 NaHmP
Hoytville-Till KBS-Native Wooster-Native

 

 

 

 

 

Figure 1.2 Relationship among Power Spectral Density (acoustical intensity,
sum of kHz frequency levels 3-11) of soil aggregates from three
soil/management systems: a native succession at the Kellogg‘Biological Station
(KBS), Hickory Corners; Michigan, a conventionally-tilled soil from Hoytville,
Ohio; and a native forest soil from Wooster, Ohio. Samples were immersed in
water (H20), 50 g/L Na-Hexametaphosphate (NaHmP), or 5mM CaSO4. Error
bars indicate standard error of the mean of four replicate aggregates.

36

 

 

Power Spectral Density (watts/ kHz)

 

 

 

Mill—Till
KBS Hoytville Wooster

 

 

 

Figure 1.3 Relationship between Power Spectral Density Mean (acoustical
intensity, sum of kHz 2-11 frequency levels) of rapidly hydrated (immersed in
water) soil aggregates and six soil/management systems: native succession at
KBS (Hickory Corners, Michigan); conventionally tilled and no tilled soil from
Hoytville (Ohio); and native forest, no-till and conventionally tilled soil from
Wooster (Ohio). Error bars indicate standard error intervals from the mean of 30
replicates. Letters represent significant difference using 95 % confidence
intervals for the mean.

37

 

 

 

 

Frequency
E .kl-lthl
x .kl-lthO
\
g DkHzIs
g .kHzLB
E [JkHzIJ
'a .kI-lzL6
Elm-125
_ II<HzL4
g II<HzL3
g @kl—lsz
U)
6
t
n.

 

 

 

 

 

 

Figure 1.4 Relationship between Power Spectral Density (acoustic intensity),
frequency (kHz level 2— kHz level 11) and six soil/management systems
(Hoytville Ohio tilled and no-till, KBS, Hickory Corners Michigan native soil and
Wooster Ohio native, no—till and tilled soil).

38

 

 

 

Figure 1.5 Relationship between visual assessment of soil aggregate slaking in
water (Relative Stability: 1.0= no slaking and 6.0= complete disintegration when
immersed for 30 seconds). Graphs bars represent the means (soiVmanagement
systems= 6, replications=30) and 95% confidence intervals.

39

 

 

  
   

 

 

 

 

       

  

 

 

400. F
A eq Y
g kH23.
\n I kHzZ.
U E kHzl
300- '
g i'.
V >..
> 'o’
’0
.1: ;.,
In 3.
r: .-
.
:0
.;
,
200- ..
- 30.0
“ :44
h m 33 ,
5 °.;..
:4-
..
.
In
‘- .;.;.;.;.;.;.;. 5;.
1
. .
0.0.0.0
,.
vvv‘vv v 0.1
b.0'07v.0. '0’; 0.4
3;.

 

 

 

Figure 1.6 Relationship between air bubbler tube size (1= small 0.3mm, 5= large
1cm) and Power Spectral Densities (sound intensity) from kHz 1-3 levels sound
frequency produced by air bubbles in water. KHz 4-11 had low values and could
not be seen in the graph.

40

 

 

 

 

 

 

   

 

 

 

 

 

 

A

N

I

X

E 400 -

S 300 -

Jr

:1"!

8

v 200 -

E

2 5 35.6368
3 100 _ R-Sq 95.3%
a R-Sq(adj) 94.4%
E o p value: 0.004

e 0 _ I r I j I7

g 1 2 3 4 5

a. Air Babbler Tubes Sizes

 

 

 

Figure 1.7 Relationship between air bubbler tube size (1= small 0.3mm, 5=
large 1cm) and Power Spectral Density (sound intensity). Air bubbles of differing
sizes were released in water and the sounds recorded. Equation is Sum kHzL1-
11(PSD) = - 58.45 + 93.15 bubble size. R2= 95.8%, p=0.004.

41

REFERENCES

Adobe Systems Incorporated. 2004. Adobe Audition 1.4 User Guide for
WIndows 299 pp.

Amezketa, E. 1999. Soil Aggregate Stability: A Review. Journal of Sustainable
Agriculture 14: 83-151.

Angers, D. 1998. Water-stable aggregation of Quebec silty clay soils: some
factors controlling its dynamics. Soil and Tillage Research 47:91-96.

Arshad, MA and GM. Coen. 1992. Characterization of soil quality: Physical and
chemical criteria. American Journal of Alternative Agriculture 7:25-32.

Bipfubusa M., D.A. Angers, A. N’Dayegamiye and H. Antoun. 2008. Soil
aggregation and biochemical properties following the application of fresh and

composted organic amendments. Soil Science Society of America Journal 72:
160-166

Bowman, G., C. Shirley, and C. Cramer. 1998. Managing cover crops profitably.
2"“ Edition. Sustainable Agriculture Network, Beltsville, Maryland. 214 pp.

Campbell, CA, V.O. Bierderbeck, M. Schnitzer, F. Selles, and RP. Zentner.
1989. Effect of six years of zero tillage and N fertilizer management on changes

in soil quality of an orthic brown Chernozen in southeastern Saskatchewan. Soil
Tillage Research 14:39-52.

Carter, MR. and DA. Rennie. 1982. Changes in soil quality under zero tillage
farming systems: Distribution of microbial biomass and mineralizable C and N
potentials. Canadian Journal of Soil Science 62:587-597

Causarano, H. 1993. Factors affecting the tensile strength of soil aggregates.
Soil Tillage Research 28:15-25.

Dexter, AR. 1988. Advances in characterization of soil structure. Soil and
Tillage Research 11:199-238.

Dexter, AR, and B. Kroesbergen. 1985. Methodology for determination of
tensile

strength of soil aggregates. Journal of Agriculture Engineering Research 31 :139—
147.

Dick, W.A., W.M. Edwards and EL. McCoy. 1997. Continuous application of no-

tillage to Ohio soils: changes in crop yields and organic matter-related soil
properties. In: soil organic matter in temperate agroecosystems: long-term

42

experiments in North America. Eldor Alvin Paul, Keith A. Paustian, E. T. Elliot, C.
V. Cole (eds). CRC Press, pp 414.

Dickson, E.L., V. Rasiah and P. H. Groenevelt. 1991. Comparison of four pre-
wetting techniques in wet aggregate stability determination. Canadian Journal of
Soil Science 71 :67-72.

Edwards, AP. and J.M. Bremner. 1967. Microaggregates in soils. Journal of Soil
Science 18:64-73.

Elliott, ET. 1986. Aggregate structure and carbon, nitrogen and phosphorous in
native and cultivated soils. Soil Science Society of America Journal 50:627-633.

Franzluebbers A.J., Wright S.F., Stuedemann J.A. 2000. Soil aggregation and
glomalin under pastures in the Southern Piedmont USA. Soil Science Society of
America Journal 64:1018-1026.

Gage, S. H., Napoletano, B. M. and MC. Cooper. 2001. Assessment of
ecosystem

biodiversity by acoustic diversity indices. The Journal of the Acoustical Society of
America 109(5): 2430.

Grandy, A. S. and G. P. Robertson. 2006. Aggregation and organic matter
protection following tillage of a previously uncultivated soil. Soil Science Society
of America Journal 70: 1398-1406 '

Grift, T.E., M.Z. Tekeste, and R.L. Raper. 2005. Acoustic compaction layer
detection. American Society of Agriculture Engineering 48 (5): 1723- 1730.

Hillel, D. 2004. Introduction to Environmental Soil Physics. Academic Press,
San Diego, CA. 494 pp.

Hortensius, D. and R. Welling. 1996. International standardization of soil quality
measurements. Communications in Soil Science and Plant Analysis 27:387-402.

Jarecki, M.K., L. Rattan. 2005. Soil organic carbon sequestration rates in two
long-term no-till experiments in Ohio. Soil Science 170 (4): 280-291.

Jastrow, JD. and RM. Miller. 1991. Methods for assessing the effects of biota on
soil structure. Agriculture, Ecosystems and Environment 34:279-303.

Jastrow, JD. and RM. Miller. 1998. Soil Aggregate stabilization and carbon
sequestration: Feedbacks through organomineral associations, In R. Lal, et al.,
(eds). Soil Processes and the Carbon Cycle. pp 207-223. CRC Press LLC, Boca
Raton, Florida.

43

Katsvairo, T., W. J. Cox, and H. Van Es. 2002. Tillage and rotation effects on
soil physical characteristics. Agron. J. 94:299-304.

MatLab. 2007. MatLab ver. 2007b. The MathWorks, Inc. (www.mathworks.com).
Natick, Mass.

Mahboubi, AA. and R. Lal. 1998. Long-term tillage effects on changes in
structural properties of two soils in central Ohio. Soil & Tillage Research 45: 107-
1 18.

Minitab 15. 2008. Minitab Inc. www.minitab.com

Moore H. M. and K. Attenborough. 1992. Acoustic determination of air-filled
porosity and relative air permeability of soils. European Journal of Soil Science
43 (2): 211—228.

Oades, J.M. & A.G. Waters. 1991. Aggregate hierarchy in soils. Australian
Journal of Soil Research 29:815-828.

Park, E.J., K. Dria, D. Gamblin, T.R. Filley, and A.J.M. Smucker. 2005. Spatial
Distribution of Lignin Biopolymers and Aggregate Stability of Forest and Tilled
Soil Types. Third USDA Symposium on Greenhouse Gases and Carbon
Sequestration in Agriculture and Forestry, Baltimore, Maryland.

Park, E.J., and A.J.M. Smucker. 20053. Saturated hydraulic conductivity through
macroaggregates modified by tillage. Soil Science Society of America Journal
69:38-45.

Park, E.J. and A. J. M. Smucker. 2005b. Erosive strengths of concentric regions
within soil macroaggregates. Soil Science Society of America Journal 69: 1912-
1921

Pikul, J.L., G. Chilom, J. Rice, A. Eynard, TE. Schumacher, K. Nichols, J.M.F.
Johnson, S. Wright, T. Caesar and M. Michael. 2009. Organic matter and water
stability of field aggregates affected by tillage in South Dakota. Soil Science
Society of America Journal 2009 73: 197-206

Robertson, G. P., J.R. Crum, and B.G. Ellis. 1993. The spatial variability of soil
resources following long-term disturbance. Oecologia 96:451-456

Six, J., E. Elliott, K. Paustian, and J. Doran. 1998. Aggregation and soil organic
matter accumulation in cultivated and native grassland soils. Soil Science Society
of America Journal 62:1367-1377.

Six, E.T. Elliot and K. Paustian. 1999. Aggregate and soil organic matter
dynamics under conventional and no-tillage systems. Soil Science Society of
America Journal 63: 1 350-1 358.

Six. J., K. Paustian, E.T. Elliott, and C. Combrink. 2000. Soil Structure and
Organic Matter: l. Distribution of Aggregate-Size Classes and Aggregate-
Associated Carbon. Soil Science Society of America Journal 64: 681-689.

Sullivan, LA. 1990. Soil organic matter, air encapsulation and water-stable
aggregation. European Journal of Soil Science 41: 529-534.

Tisdall, J.M. & J.M. Oades. 1982. Organic matter and water-stable aggregates in
soils. Journal of Soil Science 33:141-163.

Welch, PD. 1967. The use of fast Fourier transformation for the estimation of
power spectra: A method based on time averaging over short, modified
periodograms. IEEE Transactions on Audio Electro-acoustics. 15:70-73.

Yoo G. and MM. Wander. 2008. Tillage effects on aggregate turnover and
sequestration of particulate and humified soil organic carbon. Soil Science
Society of America Journal 72:670-676

Yoder, RE. 1936. A direct method of aggregate analysis and a study of a
physical nature of erosion losses. Journal of the American Society of Agronomy
28: 337-351.

Zaher, H., J. Caron, and B. Ouaki. 2005. Modeling aggregate internal pressure

evolution following immersion to quantify mechanisms of structural stability. Soil
Science Society of America Journal 69: 1-12.

45

CHAPTER 2

NEMATODE COMMUNITY STRUCTURE OF SOIL FROM ALTERNATIVE
MANAGEMENT AND NATURAL ECOSYSTEMS

ABSTRACT

Soil is a largely unexplored natural resource and nematodes can be used
to provide a view into its processes and conditions. Nematodes and nematode
community structure analysis have been shown to be useful indicators in
ecological studies of the impact of management on soil quality. In this research
we compared deciduous forest, successional old field successions, no-till, bio-
based (organic), and conventionally tillage ecosystems using nematode
community structure methodology. The study was conducted at the Long-Term
Ecological Research (LTER) site at the Kellogg Biological Station (KBS), which is
uniquely designed to evaluate alternative ecosystems. The objectives of this
research were to: 1) compare soil nematode community structures among three
agricultural managements systems, a natural forest and two successional old
fields (grasses and forbs); 2) determine if the community indices which measure
ecosystem biodiversity, stability and maturity can be used to distinguish among
these ecosystems in relation to the level of soil disturbance. We found that
nematode communities in the forest and old field successions were the most
biodiverse, mature and stable. The nematode communities were different from
those associated with the agriculture soils. The conventional till or the no-till
agricultural managements tended to have lowest biodiversity, maturity and

evenness. Ecosystem specific taxa were observed. Differences were greatest

46

when the soil was dry, probably because increased soil moisture makes
resources more available such that the opportunistic nematodes (r-strategist or
lower c-p values) become more abundant. In conclusion, management affected

nematode communities.

Abbreviations: KBS/LTER = Kellogg Biological Station/Long Term Ecological
Research, 95 % CI = 95% Confidence Interval of the mean.

47

Nematodes (Nematoda) are believed to be the Earth’s most abundant
metazoan and are commonly found in soils throughout the world’s
ecosystems(Bongers 1990). The majority of biological diversity in terrestrial
ecosystems occurs in soil. Soil is said to be one of the earth’s final frontiers
(Andre et al. 2001). Nematode communities in soil are very diverse and
composed of many species of herbivores, bacterivores, fungivores, omnivores,
and carnivores. Some nematodes fulfill a role in cycling nutrients through
mineralization, making nutrients available for use by autotrophs in primary
production. Because nematode taxa have different life spans, survival strategies
and reproductive capacities, they are used to measure ecosystem change and
disturbance (Gomes et al. 2003, Freckman 1982, Samoiloff 1987, and Bongers
1990).

Since 1985, substantial effort has been devoted to documenting and
understanding nematode community structure changes associated with different
plant communities, management practices and ecosystem disturbance levels
(Neher et al. 2005, Gomes et al. 2003, Niblack 1989, Hyvonen & Persson 1990,
Coleman et al. 1991, Wasilewska 1991, Freckman & Ettema 1993, Neher &
Campbell 1994 and lngham 1985). Typically, a soil sample of 100 g has
approximately 3,000 nematodes of numerous species with different food web
functions (Bongers and Bongers 1998). The combination of taxons provides an
opportunity to use nematode community structure as a bio-indicator of soil
disturbance and quality (Ferris & Matute 2003, Ferris et al. 1996 and Bongers &

Bongers 1998) and thus to make in-situ assessments of ecosystem disturbance.

48

Nematodes also have been used to assess vulnerability of soil ecosystems in
faunal and environmental studies (Bonger and Ferris 1999, Kozlowska 1989, Van
Kessel et al. 1989). Nematodes are convenient indicators because their trophic
functions can be deduced by observing their easily observed digestive system
morphology (Bongers and Bongers 1998) because most soil nematodes are
transparent and dissection is not required for taxonomic identification.

Nematodes inhabit almost all terrestrial and aquatic environments (marine
and freshwater) and similar nematode species have similar r-or-K selection
strategies. Species from related taxa, therefore, have similar responses to
environmental perturbation. Recently, however, it has been shown that some
nematode species manifest characteristics of both strategies (Lee 2002). For
example, when Caenorhabditis elegans first colonizes a soil it has high
reproduction and a short adult survival period (r-strategy). When populations
increase and competition is high it produces dauer larva (a developmentally
arrested developmental stage produced under stress conditions) are produced
which survive for long periods (K-strategy).

When there is enrichment of soil (e.g. added organic matter), there is a
rapid increase in the rate of bacterial-feeding nematode colonization. This is
followed by more persistent species as resources are used (Bonger and Ferris
1999). Plant parasitic nematodes are different. They tend to respond in an
inverse way to environmental enrichment.

Study Site: We conducted this study at the KBS/LTER site, which was

established in 1988 to study the ecology of diverse agricultural and native

49

ecosystems (Robertson 2008b). KBS/LTER is located at 42°24’ N, 85° 24’ W,
Hickory Corners, Michigan. Soils at KBS are Typic Hapludalfs in the Kalamazoo
(fine loamy, mixed, mesic) and Oshtemo (coarse-loamy, mixed, mesic) series
(Robertson et al. 1993 and F reckman & Ettema 1993). The KBS/LTER focus is
designed to compare several agricultural systems with successional field and
forest ecosystems.

This dissertation seeks to accomplish a very small part in the challenge of
sustainable development (Francis, Poincelot & Bird, 2006). An increased
understanding of ecosystem services and biodiversity by the public is an
important prerequisite for appropriate social and behavioral change. When we
understand what can be done to improve an ecosystem biodiversity and its
services, appropriate innovations can be put into practice (Bailey, 1916).

The objective of this research is to determine the comparative nature and
dynamics of the nematode community structure associated with six ecosystems
(three agricultural, two old-field succession, and a deciduous forest) at the
Michigan State University Kellogg Biological Station/Long Term Ecological
Research (KBS/LTER) site in Hickory Corners, Ml.

We tested the following hypothesis: 1) Nematode taxa, biodiversity,
evenness and maturity vary in their responses to ecosystem management.
Forest and old field succession ecosystems will have the greatest biodiversity,
evenness and maturity indices. Conventionally-tilled and no-till (higher herbicide

use) systems will have the lowest values for these parameters. 2) The sensitivity

50

to ecosystem disturbances does not vary among nematode taxa and nematode

community structure.

MATERIALS AND METHODS

KBS/LTER: Six KBS/LTER ecosystems: deciduous forest, two old-field
successions, conventional tillage, no-till, and bio-based systems were selected
for nematode community structure analysis (Table 2.1). The deciduous forest
has no history of tillage or cutting. One of the successional sites was last tilled
circa 1950 (mid-succession) and the other in 1989 (early succession, occasional
burning). The 1989 site is comprised of grasses and forbs and is burned
occasionally to keep it in an early succession stage; whereas the ~1967 site has
more shrubs and small trees and is considered to be in a mid-succession stage.
The agricultural systems consist of corn, soybean and winter wheat rotations.
The conventionally till and no-till ecosystem use conventional pest management
practices. The bio-based system is tilled and has cover crop. No fertilizers or
pest control inputs are used. At the 2007 sampling dates the fields were planted
to wheat, and in 2008 to corn.

Sampling: Soil samples were collected with a one-inch-in-diameter soil
corer. Each core consisted of approximately 100 cm3 of soil from the top 10-15
centimeters of soil. In 2006 and 2007 the deciduous forest and mid-succession
were sampled by taking five soil cores taken in a 1.0-meter diameter circle
around each KBS/LTER sampling flag (makers dividing the 1 ha plot into 5

sections) in each of the three KBS/LTER plots, resulting in a total of 15

51

composite soil samples each ecosystem (Table 2.1). In 2008, the deciduous
forest plots was divided in half, and each section had one composite sample
(made of five samples), providing six replications instead of three. The mid-

succession treatment was not sampled in 2008.

For the conventional-till, no-till, bio-based, and early succession plots
(Table 2.1), a composite soil sample was taken from each 1.0 hectare plot.
Each composite sample consisted of five subsamples taken one meter from the
KBS/LTER sampling station flags (makers dividing the 1.0 ha plot into five
subsections) from each replicate on September 25, 2007, December 12, 2007
and September 25, 2008. There were four ecosystems and six replications
totaling 24 composite samples. Samples were placed in plastic bags and stored
in at 5 C°.

Nematode Extraction, Identification and Population Assessment:

The soil samples were processed using a modified Jenkins (1964) centrifugation
and flotation technique (Appendix 1), using 100 cm3 subsample. Subsequently a

1.0 ml aliquot was placed in a cover slip-bottom dish and all the nematodes
present were identified to the lowest taxon possible. Additionally, Tardigrades
and Oligocheates abundance was recorded.

DeLey’s and Blaxter’s (2002) system of classification was used for most
nematode classification. A summary of the references for specific identification is
presented following the reference section. Photographic images were taken of

most nematodes taxa reported at found in this study. Images can be found at

52

www.nemasoil.com. Microscopes Nikon Eclipse TS100 and E600W, and Spot
Insight Firewire camera and program were used for the pictures.

For the nematode-specific maturity, structure and enrichment indices,
Bongers (1990) c-p values were used. Bongers used an older system of
classification, and for the c-p value calculations, this systems were used.
Bongers c-p values are listed by family, so the differences did not affect the
genus Or species level, but the family in which nematodes were placed.
Individual nematodes were separated into trophic groups according to Yeates et
al. (1993). The absolute value for each trophic group (mean of the sum of all the
nematodes within a trophic group, for example, bacterievores) per treatment and
sampling date was analyzed using Minitab 15 software (2008).

Nematode Community Structure Analysis: Data from the three sampling
dates were combined to characterize the ecosystems (forest, field succession,
no-till, bio-based, and conventional till). The guild (trophic group & Bongers
(1990) c-p value), absolute frequency, relative frequency, total absolute density,
mean absolute density and the relative density were calculated for each taxon
recovered from each system using the following formulas:

Absolute frequency (%) = 100*(number of samples containing taxa/ number of
samples collected)

Relative frequency (%) = 100*(frequency of taxon/sum frequency of all taxa)

Sum Absolute Density = Total amount of nematodes in each individual taxa per
system

Mean Absolute Density = Sum Absolute Density of nematodes] number of
samples collected

53

Relative Density (%) = 100*(number of individuals per taxon per system/total
sum of all individuals in the treatment)

Sorensen’s (1948) Similarity Index was used to compare the six
ecosystems. In our case, we used presence (1) or absence (0) of taxa for
comparison. The calculation was performed with the taxa recovered from the
three sampling dates combined.

The equation is: Index of Similarity = c , where
1/2 (a + b)

c = number of species that the two samples or areas have in common
a and b= the number of species in samples or area a and b.

A Canonical Correspondence Analysis Bi-plot was performed to determine
the relationships among specific taxa, ecosystem sites and their characteristics
for use as best bioindicators. The Canonical Correspondence Analysis Bi-plots
were created using XLSTAT (1995-2009) software. The mean number of
individual nematodes per taxa per 100 cm3 of soil per ecosystem for the three
sampling dates combined was used. Soil elemental carbon (%), elemental
nitrogen (%), bulk density, and pH data were obtained from the KBS/LTER
website data catalog and used as a quantitative variable in the analysis. A
permutation test was performed (1000 permutations) and a symmetric chart of
the sites and taxa created. Correlations were used for the calculations. In the
chart, points represent the number of nematodes by taxon (genus, species or
family); abundances decrease in distance from each site or variable point in a
unimodal way (ter Braak and Smilauer 2002, Neher et al. 2005)

Ecosystem biodiversity, maturity, stability and abundance were

described and compared using methods and equations for biodiversity,

54

evenness, stability and enrichment as presented in Table 2.2. They were
calculated for. the ecosystems and sample dates (Tables 2.1 & 2.2).

Taxa means, standard errors and 95% confidence interval differences for
nematodes per 100 cm3 of soil in each ecosystem and each sampling date were
calculated using Minitab 15 and Microsoft Excel software. The trophic group
(Yeates et al. 1993) and the c-p value (c = colonizer, p = persister; Bongers
1990) were also determined and presented as Guild. The trophic groups are
represented by letter designations: b = bacterievore, f= fungivore, o = omnivore,
h = herbivore, and c = carnivore. The c-p value ranges from 1-5. A value of one
represents taxa with rapid colonization (r-strategist) that do not necessarily need
high ecosystem maturity, and respond quickly to new food sources. K strategy
taxa needing more ecosystem stability (considered persisters) are given values
closer to five (Appendix 2-4; Bongers 1999 and 1990).

“Images in this dissertation are presented in color.”

RESULTS

Nematode Community Structure Analysis: Each of the six ecosystems had a
unique nematode community structure. A total of 67 taxa (nematode,
Oligocheates and tardigrades) were recovered from the KBS/LTER deciduous
forest site (Table 2.3). Eighteen taxa were recovered from the mid-succession
(Table 2.4) and 51 taxa from the early succession (Table 2.5). A total of 38, 43
and 40 taxa were recovered from the no-till, bio-based and conventional tillage

systems, respectively (Tables 2.6-8).

55

The Sorensen (1948) Index of Similarity for the three sampling dates
indicated that the agricultural systems had 70% or more taxa in common (Table
2.10). The systems with the most taxa in common (79%) were the no-till and the
bio-based. The forest and agricultural systems were less similar. They only had
circa 50% of taxa in common (51-56 %). The deciduous forest ecosystem had
64% taxa in common with the early succession. The early field succession was
most similar to the bio-based, having 68% taxa in common (Table 2.10).

The Canonical Correspondence Analysis indicates that the nematode
communities associated with the agricultural systems are similar to each other
(Figure 2.1). The deciduous forest and the field succession were different from
each other and from the agricultural systems. The deciduous forest ecosystems
had several taxa that were not found in any of the other sites. The deciduous
forest had the highest % elemental organic carbon and nitrogen. The highest
bulk density and pH was associated with the agricultural systems (Figure 2.1 and
Appendices 28-31).

Ecosystem biodiversity, maturity, stability and abundance: the
highest Absolute Population Densities (mean of total nematode per 100 cm3 of
soil) in September, December 2007 and September 2008 were found in the field
successions (340, 519 and 1677 nematodes per 1006m3 respectively). In all
three sample dates significantly more nematodes were recovered from the field
succession (AOV p = 0.022, p = 0.001 and p = 0.001 respectively, Tables 2.11-
13) than the forest and often the agricultural sites. The lowest population

densities were recovered from the deciduous forest in the September 2007 (3? =

56

124.89 nematodes per 100cm3 of soil) and 2008 (f = 324.2) samples. In
December 2007, the bio-based site had the lowest value (Tables 2.11-12).
Taxonomic distribution mainly by genus in the different sites are combined by
date in Tables 2.3-8, separated by date and site in Appendix 2-4 and Appendix
8-25. Family distributions can be seen in Figure 2.6 for all sampling dates
combined and Appendix 5-7 by sampling date.

The Deciduous forest ecosystem had the highest Total Number of Taxa
(sum number of taxa found in each ecosystem and replications) at all sample
dates. In 2007, the no-till treatment had the lowest Total Nematode Taxa. In
September 2007, bio-based and mid-succession field were equally low in Total
Nematode Taxa. The conventionally tilled treatment had the lowest total
nematode value in September 2008 (Tables 2.11-13).

For all sample dates the Mean Number of Nematode Taxa (mean
species richness) was significantly higher (p< 0.05) in the field succession site.
The lowest value in the September 2007 sample was associated with the
conventional tillage, while the lowest value in December was from the no-till
system (Tables 2.1 1-13).

The highest Shannon-Weiner Diversity Index values in September 2007
were associated with the mid-succession (E = 1.95) and deciduous forest (3? =
1.76) ecosystems, indicating greater nematode diversity. The lowest value was
from the conventional tillage system (3 = 1.42). The difference between the
forest and succession ecosystems and the conventionally tilled system was

significant (p = 0.048, AOV; T-test (p = 0.016) between forest & conv. till; Table

57

2.11). No-till and bio-based indices were not significantly different from the other
systems sampled. In December 2007 and September 2008, there were no
significant differences in the Shannon-Weiner Diversity Index values among any
of the ecosystems (l’ ables 2.12-13).

The results for Simpson Dominance (higher numbers indicate greater
diversity) in September, 2007, were similar to the Shannon-Weiner Diversity
results. The deciduous forest and mid-succession field values were significantly
(p = 0.013, AOV) lower, indicating greater diversity than the conventional tilled
ecosystem (Tables 2.2 & 2.11). No difference was found among the no-till and
the bio-based systems and the remaining ecosystems. In December 2007 and
September 2008, no difference was found for Simpson Dominance among the
ecosystems (Table 2.12-13).

In the September 2007, Evenness was higher in the deciduous forest
than all other ecosystems sampled (P = 0.006 AOV, Table 2.11). The
conventionally tilled systems (bio-based and conventionally tilled with
conventional fertilizer and pest management) were significantly lower than
deciduous forest and the mid-succession. There was no significant difference in
Evenness in the December 2007 and September 2008 samplings (Table 2.12-
13).

The deciduous forest had a significantly (p< 0.001, AOV, Table 2.11)
higher value in the Maturity Index (ZMI or MI 15) total with plant parasites,
than the other sites in the September 2007 sampling. A higher Maturity Index

value indicates greater ecosystem maturity or stability. When non-nematodes

58

(tardigrades and Oligocheates) were added in the analysis in September 2008,
the deciduous forest was significantly (p = 0.023, T-test, Table 2.13) higher in
ZMI than the conventional till.

The Maturity Index 1-5 (Ml, without plant parasites, Table 2.2), showed
no difference among any of the sample dates (Tables 2.11-13). In September
2007 the Maturity Index 2-5 (only c-p values 2-5 and non-herbivores included,
Table 2.2), there was no difference found with an AOV test (p = 0.519), but a T-
test (p = 0.059) revealed differences with 91% confidence between the mid-
succession and the no-till system, indicating greater ecosystem stability in the
mid-succession ecosystem than in the no-till system, which would be expected
(Table 2.11).

In the December 2007 and September 2008 samples, there were no
differences among the ecosystems for the Maturity Index 2-5 (Tables 2.12 &
2.13). In the December 2007 sample, when non-nematodes such as
Tardigrades and Oligocheates (and plant parasites) were included in the analysis
of the Ml25, there was statistical difference (p = 0.002, AOV, Table 2.12) among
the ecosystems. The deciduous forest had the highest Maturity Index 2-5 value
(‘55 = 2.92), indicating greatest ecosystem stability, second highest was the bio-
based system and the lowest value, was found in the no-tilI ecosystem (Y = 2.27).
The deciduous forest was statistically different from the no-till and conventionally
tilled ecosystem, but not different from the bio-based or the early succession. A
similar result occurred in September 2008, when plant parasites and non-

nematodes (Oligocheates and tardigrades) were added to the analysis (le, Ml 1-5

59

+ PPI and non-nematodes), a T-test revealed a significant (p = 0.023) difference
between the deciduous forest and the till ecosystem (Table 2.13).

In September 2007, the highest value for the Plant Parasitic Index (PPI)
was found in the field succession (E = 3.91, p = 0.000, AOV). This was caused
by the dominance of Xiphinema sp. in the mid-succession (grasses and forbs).
Xiphinema has the highest c-p value assigned to nematodes. The deciduous
forest had second highest value (PPI = 2.83) statistically (p< 0.001, AOV) higher
than the other three remaining ecosystems (no-till= 2.21, bio-based = 2.27 &
conv.-till= 2.23, Table 2.11). No-till had the lowest PPI value, indicating the
lowest ecosystem stability when measured with plant parasitic nematodes.

In December 2007, the highest PPI value was found in the deciduous
forest ecosystem (PPI = 2.74); whereas, the lowest value was associated with
no-till (2.39). There was significant (p = 0.032, T-test) difference between these
two ecosystems (Table 2.10). The second highest value was found in the early
succession field (2.57). The forest or field succession had the highest PPI value
for both September and December 2007 and the no-till ecosystem the lowest. In
September 2008 there was no significant difference between the treatments
(Tables 2.12-13).

In September 2007 the highest values for the Structural Index (higher
values indicate greater environmental stability and ranges from 0-100, Ferris et
al. 2001) were found in the mid-succession field (63.23), which was significantly
higher (28.88, p = 0.028 T-test), and more than twice value of the no-till

ecosystem. The second highest value was found in the Deciduous Forest

60

(49.27). In December 2007 and September 2008, there was no significant
difference between the five ecosystems for the Structure Index (Tables 2.12 and
13).

In September 2007, the deciduous forest had the highest Enrichment
Index value, indicating an ecosystem with more nutrients. The deciduous forest
had a greater proportion of bacterial and fungal feeding nematodes in the lower
c-p values (r-strategist). The bio-based had the lowest enrichment, but there
was no statistical difference between the treatments (p = 0.833, AOV). In the
December 2007, however, there was significant (p = 0.004 AOV) difference
between the ecosystems. The deciduous forest had the highest value (86.78) for
the enrichment index and the no-till ecosystem had the lowest value (69.73). The
bio-based had the second highest value (83.12) which was significantly different
from the no-till ecosystem. For all the ecosystems, the Enrichment Index values
were higher in December, compared to September. In September 2008 there
was no significant difference between for the Enrichment Index between the
ecosystems (Table 2.11-13).

In the September 2007, the mid-succession field (0.90) and the
deciduous forest (0.86) ecosystems had the highest value for the Nematode
Channel Ratio. As the Nematode Channel Ratio approaches 1.0, it is more
bacterial feeder dominated, and as it approaches to 0.0 it is more fungal feeder
dominated. The no-till ecosystem had the lowest Nematode Channel Ratio value
(0.606). With an AOV, no significant difference was found between the

ecosystems (p = 0.093, Table 2.11), but T-tests, revealed that there was

61

significant difference between the deciduous forest and the no-till (p = 0.045, T-
test) and the field succession and the no-till (p = 0.015). In September 2008, the
deciduous forest ecosystem the highest (p = 0.011 AOV, Table 2.13) Nematode
Channel Ratio value and the bio-based had the lowest value.

Trophic Group Abundance: In the September 2007 and 2008, there
were no statistically significant differences in the number of bacterievores
among the five ecosystems (p = 0.05, Figure 2.2, 4). In December 2007,
however, there were more bacterievore nematodes in the early succession than
all the other ecosystems (p = 0.035 AOV, Figure 2.5). In September and
December 2007 and September 2008, there were no significant differences (p =
0.05, 95% Cl) among the ecosystems for the population density of carnivorous
nematodes (September 2007 p = 0.105; December p = 0.279 AOV; Figures 2.2-5
& Appendices 12-14).

In the September 2007 sample there were no differences among the six
ecosystems for the population density of fungivores (p = 0.216, AOV). In the
December sample, however, there were significantly more fungivore nematodes
in the early succession compared to all other treatments (p < 0.001, AOV). In
September 2008, the early succession had the highest number of fungal feeding
nematodes and the forest had the lowest number (p = 0.05, 95% CI). The bio-
based had the second highest value and the no-till and bio-based had a similar
number of fungivores (Figures 2.2-5 & Appendices 15-17).

In September 2007, no statistically significant differences were found in

the number of herbivores among the systems (p = 0.104 AOV). In December

62

2007 and September 2008, however, more herbivorous nematodes were
recovered from the early succession, with the least number of herbivorous
nematodes associated with the forest (p = 0.05, 95% CI). The agricultural
systems had intermediate population densities of herbivores (Figures 2. 2-5 &
Appendices 18-20).

In September 2007, a higher number of omnivores were found in the mid-
succession, followed by the bio-based. The lowest populations densities of
omnivores was associated with the no-till site (p = 0.071, AOV). In December
2007, the early succession had the highest number of omnivores (3.667)
followed by the till (2.33). The early succession field was significantly higher than
the no-till, bio-based and forest (p = 0.017, AOV). No significant difference was
found in December 2008 (Figures 2. 2-5 & Appendix 21-23).

In December 2007, there were significantly more Tardigrades and
Oligocheates in the deciduous forest (29.64; p = 0.000 AOV) than in the other
systems. The early succession field had the second highest value (9.17) and the
bio-based had a mean of 0.83. No Tardigrades or Oligocheates were found in
the no-till and conventional till systems. In September 2008, the deciduous forest
had significantly (p = 0.05 95% Cl) more tardigrades and Oligocheates than the
bio-based and the conventional till systems. (Figures 2.1-5 & Appendices 24-25)

Taxon Characterization (alphabetically): 1) There were no significant (p
= 0.05) differences among the ecosystems for genus Acrobeles in September
2007. The highest values, however, were associated with the deciduous forest

and in the mid-succession. The no-till and bio-based had no Acrobeles, and the

63

till had a low population density. In the December 2007 samples, there was a
significant difference (p = 0.05, 95% CI). The highest value (35 = 22.1) was
recovered from the deciduous forest and the lowest values were found in the bio-
based (2 = 0), 1989 old field succession (E = 1.7) and no-till (Y = 3.30). No
significant differences were observed among the September 2008 samples
(Appendix 2-4 & 8-25).

2) The genus Aphelenchoides was found in all the ecosystems tested,
and in similar numbers for both the 2007 September and December sampling. In
September 2008, there were significantly (p = 0.05, 95% Cl) more
Aphelenchoides nematodes in the no-till than the forest. 3) There was significant
difference between the treatments for the mean abundance of the genus
Aphelenchus in both the September and December 2007 sampling. For both
dates, the deciduous forest had one of the lowest values, and the no-till was
significantly (p = 0.05) higher than the deciduous forest. The till and the bio-
based had both higher values than the deciduous forest, but did not reach
significant difference because of their variability. The successions were a mixed
story. In September 2007, there were no Aphelenchus found in the 1967 old field
succession and in December the highest number of the genus Aphelenchus was
found in the 1989 old field succession. In September 2008 the early succession,
bio-based and till had higher numbers (p = 0.05, 95% CI) of Aphelenchus than
the deciduous forest.

4) Members of the family Criconematinae were only found in the

deciduous forest and the field successions in both September and December

64

samples from 2007. They were not detected in any of the agricultural systems
(Appendix 5-6. In September 2008, there were significantly (p = 0.05, 95% Cl,
Appendix 4, 7) more nematodes from the family Criconematidae in the forest,
compared to all the agricultural systems. A low population density was recovered
from the early succession.

5-7) The genera Bastiania and Belondira were only found in the
deciduous forest in December 2007, and most samples did not contain these
genera (Bastiana E = 1.40 and SE= 1.40, Belondira 0.70 mean individuals per
sample in 100 cm3 of soil and SE= 0.70). The genus Bunonema was only found
in the deciduous forest and the early succession in December 2007 and
September 2008 (Appendix 2-4).

8-9) the genus Cephalobus was recovered from all the ecosystems and
in all three sampling dates except for the mid-succession in September 2007. In
September 2008, Diphtherophora was only found in the agriculture and early
succession ecosystems. The early succession was significantly (p = 0.05) higher
than the forest. In December 2007, the number of Diptherophora in the
agriculture and early succession was higher than the forest, and in September
2007 Diptherophora was only found in the no-till and bio-based ecosystems.

10) The genus Discolaimus was only recovered in the deciduous forest
on December 2007. Disco/aimus has a cp value of 5, which favors very stable
environments. It is expected to be found in deciduous forest but the variability
was high because it was found only in very few samples (2 = 0.70, and SE= 0.70;

Appendix 3).

65

11) There were significant differences for the number of members of the
genus Eucephalobus found per 100 cm3 of soil in the different treatments tested
in the December 2007 sample at KBS/LTER, Michigan. The early succession
had significantly (p = 0.05) more Eucephalobus than both the deciduous forest
and bio-based ecosystems.

12) The highest number of Helicotylenchus was found in the bio-based
(Dec. 2007 and Sept. 2008) and the till in September 2007. The lowest number
was in the forest soil. Helicotylenchus was only found in the agriculture and early
succession ecosystems in September 2008.

13) The genus Hemicycliophora was only found in the deciduous forest
in all the soil sampling dates.

14-15) Panagrolaimus and Plectus seem to be represented in most of
I the ecosystems tested in 2007 September, 2007 December and September
2008. There was no statistical difference between the treatments 2-4)

16) The Genus Pratylenchus was found in all the ecosystems tested at
KBS/LTER except in the deciduous forest (p = 0.05, Appendix 2-4). This was
true for the 2007 September and December sampling. In 2008, the lowest value
for Pratylenchus was found in the deciduous forest ecosystem, it was abundantly
represented in the agricultural and early succession ecosystems.

17) The family Rhabditidae (genera Rhabditis, Rhabditella, and
Mesorhabditis) was abundant in all ecosystems tested. It was generally more
represented in the deciduous forest and the field successions than in the

agricultural treatments. In the December 2007 sample, the deciduous forest had

66

significantly more Rhabditidae nematodes than the no-till, and the till treatments
(p = 0.05, 95% Cl). This difference was not observed in the September 2007
sample.

18-19) In the September 2007 sample, there were significantly more (p =
0.05, 95% CI) Tylenchorhynchus nematodes in the mid-succession than in all
other treatments measured. Also in September 2007 there were significantly (p =
0.05, Appendix 2) more Tylenchus nematodes in the no-till and bio-based
ecosystems than in the deciduous forest ecosystem. The genus Tylenchus was
found in all the treatments tested in all three dates.

20-21) Xenocriconemella macrodora was only found in the Deciduous
Forest ecosystem in all three dates. This nematode was only found in a few of
the samples tested in the deciduous forest, making for high variability. The
genus Xiphinema was mostly found in the field successions and deciduous
forest ecosystems. In the 2007 September sample there were significantly more
(35 = 114 individuals per 100 cm3 of soil, p = 0.05) in the mid-succession than all
other treatments measured (no-till E = 0, bio-based E = 0, till E = 1.33 and
deciduous forest 2 = 1.78). In the December 2007 sample, the genus Xiphinema
was only found in the early succession and in the deciduous forest. In
September 2008, Xiphinema was only found in the early succession. For the
three sampling dates, there was no difference in the number of unidentified
nematodes between the treatments tested. The nematodes that were not

identified were in bad conditions (dead & decomposing) or were in a life stage

67

were identification was made difficult. The number of unidentified nematodes
was minimal all soil sampling dates (Appendix 2-4 8 Figure 2.6).

22) In December 2007 and September 2008, Oligocheates (Annelida)
were significantly more abundant in the deciduous forest compared than the
agricultural treatments (p = 0.05, Appendix 3-4).

23) In December 2007, Tardigrades (Arthropoda) were only found in the
deciduous forest and the early succession. The deciduous forest had
significantly (p = 0.05) more Tardigrades than all the agricultural treatments. In
September 2007, Tardigrades where not counted. In September 2008,
Tardigrades were very abundant in a sample in the no-till ecosystem, they were

also found in the forest and the early succession (Appendix 34).

DISCUSSION
Nematode Community Structure Analysis
Multivariate Analysis: The agricultural treatments are closely associated
with each other (Fig.1). Also, the combined early and mid-successions sites
have a low association to the no-till and conventional till sites. The forest
ecosystem was not closely associated with any other system. Agriculture
management (corn/soybean/wheat) had an effect on nematode community
structure, as indicated by the separation from the field successions and forest.
This could have been caused by the different vegetation and plant biodiversity
found in the crop rotations compared to the forest and field successions.
Freckman and Ettema (1993) performed a canonical discriminant analysis

of their KBS/LTER nematode data collected in 1991. The analysis separated the

68

agricultural treatments into high chemical input (no-till and till) and bio-based
(organic). The early succession (at that time on had 2 years since the last tillage)
and the succession with no history of tillage (KBS/LTER treatment 8, mowed
yearly) formed clusters apart from the agricultural treatments, but closer in
distance to the bio-based (organic) than to the high chemical input no-till and
conventionally tilled systems. Their analysis is comparable to our Canonical
Correspondence Analysis (Figure 2.1).

Freckman and Ettema (1993) found that cumulatively, the genera
Panagrolaimus, Pratylenchus and Aphelenchoides explained 86% of the
dispersion of ecosystems in the canonical discriminant analysis. Pratylenchus
had a lower abundance in the early succession and the never-tilled (KBS/LTER
Treatment 8) than the agricultural plots. We found that Pratylenchus was also
significantly more abundant in the agricultural plots, compared to the forest, and
the field succession. Our data did not show significant differences in the
treatments for Panagrolaimus numbers at any of the sampling dates. The
September and December 2007, samplings did not show a significant difference
in the number of Aphelenchoides among the treatments. In September 2008,
however, no Aphelenchoides were found in the forest and the highest
abundances were found in the no-till and field succession.

Ecosystem biodiversity, maturity, stability and abundance:

In all sampling dates, the deciduous forest ecosystem had the lowest
absolute population density (Tables 2.2 & 2.11-13). Freckman and Ettema

(1993) also reported low absolute population densities in the woody site and

69

higher population densities in the early succession site, that was last tilled in the
spring of 1989 (KBS/LTER treatment T7). Neher et al. (2005) also reported
lower abundance of nematodes in forest compared to an agricultural system.

In all sampling dates the highest absolute values (mean total number of
nematodes found per 100 cm3 of soil), were associated with the mid-succession
and early succession (Table 2.11-13). Freckman and Ettema (1993) found that
the high input agricultural systems (no-till, conv. till) and the bio-based (organic)
had the highest absolute values. However the ecosystems have changed since
1993. Higher observed plant diversity in the early and mid-succesional fields,
could, in part, explain the reason they had the highest nematode absolute values
for all sampling dates (T ables 2.11-13). Also, the field successions are the
highest in soil carbon sequestration (Fig 1). More organic matter sequestration
indicates more plant growth and food substrate for bacteria, and fungi, and
therefore more food for nematodes in the different levels of the food chain.
Higher plant diversity would also provide niches for more specialized nematodes,
especially the plant parasites. The absolute vales (divided by families, Figure
2.6) of nematodes were distributed differently in each ecosystem.

In the December 2007 sampling, the deciduous forest ecosystem had the
highest number of Total Nematode Taxa (sum of taxa in all replications, Table
2.12). The absolute density was relatively low (518.6) compared to the field
succession (1833.7). In the forest, however, the number of taxa was 46, while
the field succession had only 35 taxa. The no-till ecosystem had lowest number

of nematode taxa (19) while the conventional till and the bio-based systems had

70

both a value of 27. In September 2007 sampling, the deciduous forest had the
highest number of Total Nematode Taxa. In September 2008, the early
succession and the forest had the highest number of taxa.

The deciduous forest appears to be the most stable and undisturbed
ecosystem, followed by the field successions ecosystem. As expected, the
biodiversity associated with the deciduous forest system was greater than that of
the other systems. The agricultural systems had the lowest number of taxa.
Agriculture, especially large monocultures, reduces plant biodiversity. This
generally causes a “reduction in animal and microbial diversity which result in a
loss in ecosystem function (Swift and Anderson, 1994). This does not only apply
to tillage disturbance because some of the lowest numbers of taxa values were
found in the no-till ecosystem. Even though the deciduous forest had the lowest
Absolute Densities it had the highest total number of nematode Taxa. Stable
undisturbed ecosystems tend to have greater biodiversity, and the organisms are
distributed more evenly, while disturbed environments have a few organisms
dominating the ecosystem (Wilsey & Potvin 2000). In our study, nematodes
where distributed more evenly in the deciduous forest than in all other
ecosystems in September 2007 and 2008 (Tables 2.11 & 2.13). Neher et al.
(2005) also found that the forest had lower absolute densities of nematodes and
higher family diversity than agricultural sites and that disturbance tended to
reduce family richness. Freckman and Ettema (1993) reported a higher total
number of taxa in the succession never tilled (mowed yearly, KBS/LTER

treatment 8).

71

The field successions systems had the highest Mean Number of
Nematode Taxa (mean taxonomic diversity). This was true for all three sampling
dates (Tables 2.11-13). F reckman and Ettema (1993) also reported that
taxonomic diversity was greatest in the successional treatments at KBS/LTE'R.
The deciduous forest had lower Mean Number of Nematode Taxa (mean species
richness) than the field successions, even though the Deciduous Forest had the
highest Total Nematode Taxa among the ecosystems. This can be explained,
even though the Total Nematode Taxa was high in the deciduous forest, the
Absolute Population Densities was low, and the Evenness was high. The taxa
were dispersed in the different samples, and samples were not generally
dominated by a few taxa. Commonly, in one sample we found taxa that were not
found in other samples. Even though the Total Nematode Taxa per Treatment
was high, the Mean Number of Nematode Taxa per sample was low (Tables
2.11-13). The value for the Mean of Total Nematodes in 100 cm3 of soil
(Absolute densities) in the field succession was higher compared to the other
ecosystems evaluated. This can also in part explain why the Mean N° of
Nematode taxa were higher in the field succession ecosystems compared to the
forest which had a higher Total Nematode Taxa value. The field succession
ecosystems often had double the nematodes recovered than the deciduous
forest ecosystem. Even though it had less Total Nematode Taxa (includes taxa
from all the samples) than the forest, it had so many nematodes per sample that
each sample had more taxa than the deciduous forest. Several taxa were

recovered only in the forest (Figure 2.1). Some of these were encountered only

72

once. This resulted in a high absolute number of taxa, but a low mean number of
taxa. On December 2007, the no till ecosystem had the lowest Mean Number of
Nematode Taxa per sample, and the lowest Absolute Value of Nematode per
sample compared to all other ecosystems. It also had the lowest Total
Nematode Taxa per Ecosystem. So the no-till had few nematodes, both in
diversity and quantity.

In general, agricultural systems with the greatest disturbance (chemically
or mechanically) were those with the lowest in the mean number of nematode
taxa. On September 25, 2007, the conventional till had the lowest value. In
December 2007, the no-till (additional herbicide use) was the lowest and in
September 2008 the conventionally tilled system had a lower value than the field
succession (p< 0.05; Tables 2.11-13). These data support the hypothesis that
disturbance reduces biodiversity (\Nilsey and Potvin 2000).

The Shannon-Weiner diversity values for September 2007 indicate
greater diversity in the field succession and the deciduous ecosystems,
compared to the conventional till agriculture ecosystems. F reckman and Ettema’s
(1993) previous study of the nematode community structure at KBS/LTER
reported a statistical difference (for Shannon-Weiner Diversity H) between the
succession never-tilled systems (mowed yearly, T8 at KBS/LTER) and all the
agricultural systems sampled at KBS/LTER. In December 2007 no significant
difference was found among these systems. The cause is suspected to be
increased soil moisture (Table 2.14). In December 2007, the soil was wet and

there was very limited plant growth. There was also decomposing litter in most

73

Of the systems, especially in the deciduous forest. This drastically increased the
Enrichment Index. There were more bacterial feeders dominating the
ecosystems decomposing the litter in December 2007, which is believed to have
masked the differences found in September 2007 (Tables 2.11-13).

Wetting can act as an activating agent, increasing the activity and
population density of opportunistic organisms. This is part of what is called the
“Sleeping Beauty Paradox” (Lavelle 1996 and Brown et al. 2000). These referred
to the increase of activity that micro-organisms exhibit after passing through
earthworm gut, or the increased activity created by the earthworms’ mucus,
through the creation of an optimal moisture and temperature regime. We found
that water increased the activity of soil organisms (Chapter 4). Also, since the
conditions are ideal in moist environments for bacterial replication, it is logical
that bacterial feeders would increase. This was observed in our results. In
December 2007 and September 2008 (moist soil), the bacterial feeder proportion
of the population was higher compared to the September 2008 sample, when the
soil was dry (Tables 2.11-14). Similar biodiversity results were obtained with
Simpson’s Dominance as with Shannon-Weiner Diversity estimations.

In September 2007, Evenness was highest in the deciduous forest,
second highest is the field succession, and lowest in the conventional till system.
This was expected. In December 2007, no difference was found. It is possible
that the reason there was no significant difference for Evenness for the
December 2007 is that the samples were bacterial-feeder dominated. In the

December 2007 forest sample, the nematode channel ratio (higher number

74

indicates more bacterial feeders) was significantly higher than all other
ecosystems sampled. It also had a high Enrichment Index value. This indicates
that there was likely a rapid increase of bacterial feeders that dominated the
ecosystem when the soil was wet and covered with decomposing leaves (from
the deciduous trees) that provided nutrients in December 2007. This probably
drove the evenness down, since the bacterial feeders dominated in this period of
the season.

Freckman and Ettema (1993) reported higher Maturity Index values in
the succession never-tilled system (mowed yearly, KBS/LTER T8) and the
succession-last tilled in 1989 (early succession) compared to the agricultural
systems. There was no significant difference among the agricultural systems. In
our research, no significant differences were detected in the 2007 and 2008
sampling dates for the Maturity Index values among the ecosystems. However,
the ecosystems have changed since the Freckman and Ettema sampling in
1991. Even though Neher et al. (2005) said that Maturity lndices have the ability
to respond to specific disturbances, this has not been consistent by region,
ecosystem and season. Neher et al. (2005) reported that the Maturity Index was
inconsistent in discerning differences between disturbed and undisturbed
ecosystems. They also reported that nematode family composition was more
effective in separating between disturbed and undisturbed ecosystems and
indicating ecosystem function. A greater understanding of the Maturity Index

effectiveness is needed before it can be applied universally.

75

The results with the Maturity Index 2-5 from September 2007 indicated
that the field succession was a more stable ecosystem than the no-till. It
Probably, the crops used and the agronomical inputs had an effect on the
Maturity Index 2-5 of the no-till ecosystem. This in part shows that nematodes
are sensitive to more than the physical disturbance, which has been reported by
Neher (2001).

Because we found no difference in the Maturity Index when all the c-p
‘ values (1-5, Bongers 1990) were included and found differences with the Maturity
Index with only the c-p values 2-5, we, in part, confirm the previous conclusions
that the lower cp-value of 1 masks the effect of the ecosystem. This relates to
the rapid capacity of reaction and reproduction to the nutrient enrichment or rapid
death when nutrients are limited (Bongers and Bongers 1998).

For the Maturity Index 2-5 in the December 2007 sampling, there was no
difference found, but when other organisms such as Tardigrades and
Oligocheates and plant parasitic were included in the analysis, there was
difference among them (p = 0.002 AOV). The forest had the highest Maturity
Index 2-5 value, indicating greater ecosystem stability. The addition of non-
nematodes was based on the observation that Tardigrades and Oligocheates
(order Enchytraeida) were only found in only the non-agricultural and least
disturbed ecosystems, including the deciduous forest and the field succession (in
this research). This interpretation needs to be taken with caution, because no
research was done by us to determine the cp-value given (5) and they are not

closely related to nematodes. But, it did, however, provide a distinction between

76

the ecosystems. For some reason, the Oligocheates and Tardigrades in the
December sampling were only found in the majority in the Deciduous Forest and
some were found in the succesional field. This could, in part, be based the
conditions found in these ecosystem that favored their survival. Many
Tardigrades feed on mosses and lichen, and are associated with leaf litter,
mosses, lichens and fungi, certain species of Tardigrades are reported to predate
on nematodes (Lehmann et al. 2007). Then soil Enchytraeida (Oligocheates)
were said to be detritivores by Sampedro & Dominguez (2008) and to be
secondary decomposers by Scheu & Falca (2000) feeding on fragmented litter,
microbial residues and microorganisms. They are likely to be found in
decomposing organic matter; like the soil covered with deciduous leaves found in
our forest. This seems to be particularly true at KBS/LTER. The authors have
observed Oligocheates in samples from agricultural plots, such as Sugar Beet
fields, from sites other than KBS/LTER. Enchytraeida are commonly extracted
from soil using the modified Jenkins centrifugation and flotation technique.

The highest Plant Parasitic Index (Table 2.2) value was found in the
September 2007 early successional system this was the result of an abundance
of Xiphinema spp. (Table 2.11). Xiphinema spp. have a c-p value of 5, the
highest c-p value possible Bongers (1990) scale. The abundance of grasses and
forbs in the field succession might have been a cause for this abundance.
Xiphinema spp. seem to be sensitive to both chemical and physical disturbance,
as there low abundance and almost total absence of Xiphinema from any of the

row crops agricultural systems in the three sampling dates (Appendix 2-4).

77

Freckman and Etttema (1993) also found Xiphinema spp. in the least disturbed
ecosystems. They were detected in the Poplar, the early succession, with history
of tillage and in the mid-succession, never tilled, but mowed (KBS/LTER
Treatment 8), but there was no Xiphinema detected in the row crops with corn,
wheat and soybeans rotations in all the different managements (till, no-till, low-
input and bio-based). Neher (1999) did not report Xiphinema in either the
conventional or organically managed agricultural soils. Yeates et al. (1999) did
not report Xiphinema in the annual (maize) or the perennial crops (asparagus).
Xiphinema amen'canum is common in orchards and vineyards throughout
Michigan.

In September 2007, the highest value for the Plant Parasitic Index
(indicating greater ecosystem stability), was found in the field succession and the
lowest value in the no-till ecosystem. In December 2007, the results were
similar. In December, however, the forest had the highest Plant Parasitic Index
value, the field succession had the second highest and no-till had .the lowest
value. In the September 2008 sample, no statistical significance difference was
found (Tables 2.11-13). Freckman and Ettema (1993) had conflicting results for
the Plant Parasitic Index. The no-tilI had the highest value and the field
successions had the lowest.

The Plant Parasitic Index seems to be a good indicator of environmental
stability. In our case it gave us clearer conclusions than the Maturity Index 1-5,
but F reckman and Ettema (1993) results, however, would lead to the conclusion

that the Maturity Index is a better indicator than the Plant Parasitic Index.

78

In September 2007, the results were as expected. The field succession
or the deciduous forest had the highest values for stability and diversity and the
no-till or the till had the lowest values. The highest value of the Structural
Index was associated with the field succession, which was more than twice the
value of the no-till ecosystem (Table 2.11). This is an indication that the greatest
ecosystem stability was associated with the field succession compared to the no-
till ecosystem. There was no statistically significant difference between the
treatments in December for the Structural Index Measurement. In part, this could
have been caused by the change of ecosystem in December. There was very
little active growth of plants, the soil had high moisture content, and there was
plant matter in the process of decomposition. This may have changed the
nematode community structure of the deciduous forest and the field succession
to a lower Structural Index in December compared to September 2007 (Table
2.11-12). The high residue on the soil surface of the deciduous forest and the
FIELD SUCCESSION field and the high moisture probably produced the
abundance of bacterial feeders in the lower c-p scale values. This would result in
a basal condition compared to the September 2007 sample (Ferris et al. 2001).

For the Enrichment Index, there was a significant difference among the
treatments for the 2007 December sampling and not for the 2007 September
sampling. Additionally, all the Enrichment Index values were greater in
December compared to September 2007. This probably happened because the
plant biomass was in the process of decomposition in December and because of

the higher moisture content compared to September 2007 (Table 2.14). The

79

nutrients released were not being taken up by plants in active growth, because
most plants were dormant or dead. This increase in free resources probably
produced the increase in nematodes that indicate enrichment. The ecosystem
(forest) with the highest plant residue on the surface had the highest Enrichment
Index. This was true for both dates, but in December there was statistically
significant difference (Table 2.11-12). The cover crop probably enriched the bio-
based ecosystem, and caused the Enrichment Index to be significantly higher
than that of the no-till in December 2007 (Table 2.12).

Results for the Nematode Channel Ratio for September 2007 reveal that
the field succession and the deciduous forest was more bacterial feeder
dominated than the agricultural systems. There was significant difference
between the two native ecosystems compared to the no-till. The no-till was more
fungal feeder dominated. In December 2007 and September 2008, the
deciduous forest ecosystem was more bacterial dominated than all other
ecosystems tested (Tables 2.11-13).

Yeates & Bongers (1999), said that “it is clear that bacterial feeders were
the dominant group” in forest ecosystems. We also found that bacterial feeders
were dominant in our deciduous forest ecosystem at KBS/LTER. Neher et al.
(2005) also found that forest soils were more bacterial feeder dominated
compared to fungal feeders.

Summary of Diversity and Maturity Indicators: It is interesting that the
highest values (September 2007) of ecosystem stability and diversity were found

in successional fields or deciduous forest. This was expected (Table 2.11-13).

80

The lowest values were found in the no-till or till. When using nematode-specific
measurements, such as Maturity Index, Plant Parasitic Index or Structural Index,
the no-till was always the most unstable, and the field succession the most
stable, with the deciduous forest usually having the second highest values (Table
2.11). These results were expected, and confirm the effectiveness of these
methods to measure ecosystem stability. They also appear to indicate that
nematodes are as sensitive to chemical disturbance as to physical disturbance,
since the no-till was always last among nematode specific measurements.
Measurement such as the Maturity Index have been used effectively to
distinguish changes caused in soil by heavy metals, manure, tillage, fertilizers
and fumigation (Neher et al. 2005; Table 2.2).

In September 2007, the field succession and sometimes the deciduous
forest were consistently higher in biodiversity and Evenness, and the
conventional-till or no-till ecosystems were lowest in biodiversity and evenness.
This was true in December 2007, but only if the measurements were statistical
significant. When statistical difference was reached (p = 0.05), the deciduous
forest or the field succession had the highest values for biodiversity and
nematode-specific ecosystem stability measurements. The no-till ecosystem
was commonly the lowest value for ecosystem biodiversity and stability (Table
2.12). This leads to the conclusion that the most unstable least diverse
ecosystems measured by Nematode Community Structure are the conventional-
tillage and the no-till systems. The most stable and diverse are the deciduous

forest and field succession ecosystems.

81

Neher et al. (2005) found that Maturity Index 1-5, Plant Parasitic Index,
Maturity Index 2-5, and Structural Index (Table 2.2) did not reliably separate
disturbed from undisturbed ecosystems and that the results varied by seasons.
Separation by family, and genera, however, separated the ecosystems logically
when analyzed with Canonical Correspondence Analysis. Ecosystems with
different disturbance levels (i.e. undisturbed and disturbed forest) were more
closely associated to each other than to different ecosystems (i.e. forest and
agriculture). We also found that the different types of row crops systems (no-till,
till and bio-based) were more closely associated to each other than to different
ecosystem types as the forest and field succession (Figure 2.1).

Neher et al. 2005 found that tillage reduced diversity. This was true for
September 2007 KBS/LTER. In December 2007 and September 2008, we had
mixed results. In some cases the no-till or the till had the lowest values. This
could be because of the higher herbicide input in the no-till system.

Trophic Group Abundance: Freckman and Ettema (1993) reported
that bacterial feeders, herbivores, and fungivores dominated the ecosystems.
We also found that fungivores, herbivores, and bacterievores were most
abundant groups (Figure 2.5).

According to Wasilewska (1998), the proportion of bacterial feeders
increases when there are organic inputs (i.e. plant litter, manure). This is
especially true of c-p1 colonizers. This increase was correlated to an increase in
microbial activity. Wasilewska also found that in accordance to the theory of

succession, extreme r-strategist (c-p 1) decreased in number with the increasing

82

stability or development of the ecosystem, and the reverse was also true, that the
higher c-p values increased in more stable ecosystems compared to the more
unstable ecosystems. Acid rain reduced the decomposition rate, and therefore
decreased the bacterievores of lower c-p values. Papatheodorous et al. (2004)
did not find the correlation between bacteria and bacterial feeders as reported by
Wasilewska.

Taxon Characterization: In the last row of Appendices 2-4 the statistical
difference was determined with 95% confidence intervals for the means
(Appendix 2-4 & 8-25). We chose this method because of the often large
variability between samples in the same treatment (AOV pool the standard
deviation for all the treatments). Extensive nematode population density spatial
variability is common (Robertson & Freckman 1995). For example, in the
conventional tillage system, contained circa 40 Aphelechus sp. per 100 cm3;
whereas, none might be recovered from other sample replicates on the same
date.

In September 2007, there were no Aphelenchus in the mid-succession
and in December the highest number of the genus Aphelenchus was found in the
early succession. One of the causes could be that the successions are
temporally different in their succession. One was an agriculture field with field
succession beginning ~1967. The other was last tilled in 1989. The plant
species composition also differed. The mid-succession has a higher amount of
woody plants. Additionally, in September 2007, the soil was dry and in

December it was wet. This is believed to have made a difference on which taxa

83

were detected. Nematodes are preferentially aquatic and in dry conditions many
nematodes go into resting stage, die or migrate deeper were moisture is present.
Following a precipitation event many taxa rapidly increase in numbers and take
advantage of the available food (bacteria, fungi, other nematodes, plants, algae
et al.). The r—strategist are able to more rapidly respond to this ecosystem
change, whereas, in dry conditions, the K-strategists would be more successful,
especially in more stable environments such as the native forest or field
successions. This may at least partially explain differences among results of the
three sampling dates. The soil was only dry in September 2007. In September
2008 and December 2007, it was wet.

Some K-strategist nematodes with high c-p values of 5 were only present
in the forest or field succession as expected. In many cases they were present
only rarely in some samples in the forest or field succession, thereby increasing
the variability and not resulting in statistical significant differences. Disco/aimus,
Longidorus, and Belondira are examples of nematodes with high c-p values that
were rare, but detected in the forest or succession ecosystems. These
nematodes are good indicators of stable ecosystems. Because of their rarity,
however, they do not seem like reliable indicators. The genus Xiphinema has a
cp value of 5. Our data seems to confirm this, because it was more abundantly
found in the more stable environments.

Bastiania (b3), Bunonema (c—p value of 1), Xenocriconemella macrodora
(h3) and Hemicycliophora are some nematodes with relatively low c-p value that

were found only in the native forest or field successions. Neher et al. 2005

84

reported that the family Bunonematidae was only found in the undisturbed soils,
confirming our observations that that Bunonema was only found in the native
undisturbed soils. We believe that some c-p values might need to be revised.
This recommendation is based on our ecological observations, prior to the
recommendation of any changes experimentation and observation would be
needed. The family Criconematidae (h3) was found mostly in forest, but Neher et
al. 2005 reported higher numbers of Criconematidae in the agricultural systems
compared to the forest. This difference is probably species related. Species of
the Criconematidae are common in tree fruit production in Michigan.

In September and December 2007 the Genus Pratylenchus was found in
all KBS/LTER ecosystems studied except the deciduous forest. In 2008 this taxa
was very abundant in the agriculture plots but rare in the forest (Appendix 24).
Pratylenchus is a genus that is not commonly found in the deciduous forest at
KBS/LTER, Michigan. Boag 2008 listed Pratylenchus as a common genus in
deciduous forest in Scotland. Pratylenchus macrostylus were collected from the
roots of Fraser fir and red spruce in North Carolina (Hartman and Eisenback,
1991). Additionally, Pratylenchus brachyurus was also reproduced on pine trees
by Ruehle (1969) and in North Carolina Pine forest (Neher et al. 2005), so the
genus Pratylenchus can be found in forest. Yeates et al. 1999 reported lower
population densities of Pratylenchus in the perennial crops compared to the
annual crops. The perennial or native fields also did not contain as many
Pratylenchus as the annual agriculture cropping systems at KBS/LTER in 1991,

as reported by Freckman and Ettema (1993). Pratylenchus penetrans is one of

85

the most common plant parasitic nematodes species in Michigan. The observed
differences are probably species specific.

Neher et al. 2005 found that Mylonchulus was associated with agriculture
ecosystems. We also found that Mylonchulus was associated with agriculture
ecosystems (Figure 2.1 & Appendix 13). Neher et al. 2005 reported that the
family Tylenchidae (genus Tylenchus), Plectidae (Plectus) and Cephalobidae
were found in both forest and agricultural systems. We found that these families
were common in all ecosystem sampled (Figure 2.6, Figure 2.1, & Appendix 2-4).

Effects of Soil Moisture on Nematode Community Structure:
Temperature and moisture can affect nematode abundance and the nematode
community structure of ecosystems (Bakonyi & Nagy 2001). The mean daily
temperature for two weeks before sampling in September 25, 2008 was 18.79 0°
and the mean daily precipitation was 1.83 cm. The soil was moist on the
sampling date. For the December 12, 2007 sampling date, the mean daily
temperature for two weeks prior was -2.8 0° and the mean daily precipitation was
0.58 cm. The soil was very moist. On September 25, 2007, the soil was dry; the
mean daily precipitation for the two previous weeks was 0.1 cm, and it had not
rained for 9 days before the sampling. The mean daily temperature for was 18.35
C° (Table 2.14).

It is possible that the clear and significant results of stability, maturity and
biodiversity in September 2007 were caused by the low moisture contents in the
soils (Table 2.14). It is hypothesized that water acts as an agent that makes

nutrients more available for the microbes and nematodes, therefore permitting

86

the r-strategist (low c-p values) to reproduce in great quantities. This is probably
what happened in December 2007 and September 2008, soil moisture was
abundant. Nematodes of lower c-p values, and of just a few taxa, dominated the
ecosystems, reducing the evenness and increasing the variability of most of the
measurements. In addition, since mostly the lower c-p values were abundant,
this reduced the Maturity Index, and Structural Index values, which measure the
maturity of ecosystems through nematodes c-p values. This can be supported
by the literature, but the results in the literature are mixed.

In a study of the effect of temperature and moisture on nematode
community structure of semiarid grassland in Hungary by Bakonyi & Nagy
(2001), the soil that was warm and dry had greater generic richness than the
warm and wet on most dates. But in the same study, the Maturity Index was
highest in the warm and wet soils compared to the cold and warm dry soils, cold
and wet soil and the control. We cannot therefore makeany conclusions of why
the results were so clear in the September 2007 sample. It is likely, however,
than under the water stressed conditions in September 2007, the more
undisturbed native plots had more K-strategist that could persist under such
conditions.

According to Papatheodorous et al. (2004), the family Cephalobidae is
characteristically dominant in dry soils. We also noticed a similar result. Taxa of
the family Cephalobidae were more proportionately abundant in the September
2007 sampling, when the soils were dry, in comparison with December 2007 and

September 2008 samplings, were the soils were moist. Greater irrigation,

87

therefore moisture, increased the abundance of nematodes in a study of Taylor
et al. (2004). In our contribution, we report greater nematode abundances in
December 2007 and September 2008 when the soil was moist, compared to
September 2007 when the soil was dry.

Family Abundances and New Taxa Compared to Freckman and
Ettema’s (1993): In samples taken in 1991, the family Rhabditidae,
Cephalobidae, Aphelenchidae, Tylenchidae and Pratylenchidae were abundant
in all systems studied at KBS/LTER (Freckman & Ettema 1993). The 2007 and
2008 samples yielded similar results. The most abundant families in most
ecosystems were Rhabditidae, Tylenchidae, Cephalobidae, Pratylenchidae (in
agriculture ecosystems), Aphelenchidae (not abundant in the forest), and
Plectidae (Figure 2.6).

Thirty-six new taxa were recovered from the 2007 and 2008 soil samples
that were not reported by Freckman and Ettema (1993). These were:
Amphidelus, Amphidorylaimus, Bunonema, Crossonema, Cruznema,
Diplogaster, Diplogasteroididae, Ditylenchus, Dory/aimoides, Enchodelus,
Gracilacus, Hemicycliophora, Labronema, Leptonchus, Longidorus,
Macropostonia, Meloidogyne, Medinius, Mesomabditis, Mononchus,
Nothotylenchus, Odontolaimus, Ogma decalineatum, Oligocheates (non-
nematode), Oscheius, Pungentus pungens, Panagrobelus, Pn'onchulus,
Rhabditella, Rotylenchus, Scutellonema, Stegelletina, Stomadorhabditis,
Tardigrada (non-nematode), Tn'pyla, Tylencholaimus, Tylocephalus, and

Xenocriconemella macrodora.

88

We conclude that Nematode Community Structure is a useful tool to
determine ecosystem function, biodiversity and stability, and that the KBS/LTER
forest and old field succession ecosystems tend to have higher biodiversity,
maturity and stability than agricultural systems. Among the agricultural systems,
the bio-based (organic) was more closely related to the native ecosystems than
the no-till and conventional till ecosystems. Nematode community structure
methods successfully separated the ecosystems. There were, however, some
problems with the methodology because of frequent variability and unclear
results associated with specific measurements. These methods need to be
investigated further in order to be applied effectively in a universal scale. The
multivariate analyses logically separated the ecosystems studied, and were
useful in identifying taxa that were ecosystem specific. One further problem in
using these methods universally is that they require a great deal of taxonomic
knowledge and training. I found the learning curve to be relatively steep. The
nematode taxa recovered from KBS/LTER are photographed and described at

www.nemasoil.com.

89

.mmbamx Ewmsmu‘wfis a; so: 8598 was Q28 5:3 new ._._a .582: .828 =8 .8 $8 anew 5239; :85
9: Co 29.25 8:25:00 38 5:5 nocflfiflmo mocwamtfi 30.0 n 3 #50535 3:56 cEEoo some :_£_>> mambo. E9220 3 “3032.2 2.85. L.

 

n 80.0 m Noe 0 mad 9 $6 £39: 52820 .8 m_w>o_ Emucflw
82 .uwfiaamm x8263 7823 __= 683828
cmogoflcaoozmoca .mcozcgcoo

E. ”E296
was: .mco_Eo>coo

 

a Ford m «me 9 mad n 8.0 new: omm=¢ dob
mom? .uocflaflmm .98 + ucm cmogoflEoobmozz thBQm umwmném
0:590 “3:1on 88va

 

 

n mote m «we on mad 9 56 935 32:55 Co 203. Emucfim
mm? .uocflzflww __o=:oo new; a2 now: maniac; thSmSAw =:.-oz
.cmon>0m\c.oo>mo§> ==-oz
n rid no one 9 Se m omé E958: 9:53
$2 9:8

$5588 5? .328 95 E 523803 250

8303-53
8899 EcsEEoo .mco_mmooo:w

 

 

 

 

 

 
 

n :_..o on 91 a mo; 0 afim $6: .9330: m
$630393 .
53885 8:95 can um .co_mmooo:w-u_E
Eo Em» omév wee doom—Em .20: 5:23.685
m med o N; m 3: 0 an 9550 Lo 03:: $92 .
+ + .F ._. .6 BBmE 0: 32.20% 528m 026: 9322 uo .320... 2.63280
02:. :8 MES :mcoo ooFEom In :8 cozntowoo aESmxwoow mco=£>95na mm:
>6 92 9 £5 :8 in 90 9 :32 E085 "new: «E2338.

 

cmmoez __ow :85. .x. c880 __om

 

 

:08 20:3: 9 998; 95 8...: m>mc Cm Eocene: mwhdmmxv co_mwooo=m-u_E or:
cam $92 2.966% of. .303. 2985-98 x3 comm 226239 5.». 02...... 323803 258 mm? c_ vocflfiflmw cofimmoozw 2o: 20 05 new
65:30:06 on... .938; 95 Co Emacoo Boa cozmoima comm .8952: 90500 >6on .Hoo_oa Emiamxv coamomom .8628
cozflm .mo_mo_o_m_ 30:3. 9: 5 9w mEEm>mooo or; .cozmtomwi £5 5 new: mEouw>mooo ms E8 2253053 98 cozazowww Shaka &
~

0.0

«A

 

90

 

meow ..0 .0 :00
000. 0.00000 .0 0.00000
000.. 0.00000

.0.0...00030 .0 0.9000 0. 0000.000: .0 .0003: .0.0..0.n.E00

0. . 00x0. .0 0000.00.00 .0 .00030. 0.9000 0. . 00x0. .0 500300..
"A... 000 . 00x0. .0 03.0> 0-0 H €> $000.50 00:00.00 .00...

o: 0...... :2. 0.02000 00. 0. 0003.00. .0: 0.03 00:00.00 .00... 0000
0.0. 0. ...>....00.0.. .0.00E:0..>:0 0...... 00000.00. .5. .0 03.0> 00..

e. .. 8.0 u .s.

:2. 00:00.0: 00.:
.85.; 080:. 0.5.0.2

 

 

000. 0.00000 0 0.00000
000.. 0.0000 .0 000.00.“.
000. 0.00000

 

0. E00030 .0 0. 0.00 0. 0000.00.00
.0 .00E3: 0.9.0.0000 0. . 00x0. .0 0000.000: .0 .00830.
0.0E00 0. . 00x0. .0 300300.. "A... 000 . 00x0. .0 03.0> 0-0 H €>
0.02000 00. 0. 0003.00. 0.03 00:00.00 .00.: 0000 0.0. 0.
.._5...00.0.. .0.00E:0..>00 0...... 00000.00. :2 .0 03.0> 00 ..

3.10.0 n _.>.w

saw. 080:. 0:30;.

 

 

 

 

 

 

 

 

 
 

 

 

 

 

 

 

 

 

 

 

0000. 0:000 500:: m. 0.0000000 00:00 00 00.0 :00 A .00 I Am. 000000>m
.000::0>0 0.0.0:.00 0:.00 . ...-o .00.. 00:0. 003.0> 00 0 w . . I I m.
0030.0 03 .00.0 00.
0000 00000 .0 000.00. 5.0.020 0.00.0 00.00.00. .0003: .00.0. 00. 00.0.0.0 $.05... 0: :00:E.0
_0>...3.0. 0.0E 0000:0000 00000.0 00. 00.0.0 0. 0003 0. 0.0..
0030.0 :03 .00.0 00.
0000 00000 .0 000.00. 5.0.020 0.00.0 05.00.00. .0003: .00.0. 00. 00.0.0.0 Amdwvé 0-. 00090.0
.02..3.:. 0.0E 00:00.0.00 00090.0 00. 00.0.0 0. 0003 0. 0.0..
380.05 .0002: 2 .o 0. 000:. .0020
.800 ..0 .0 00800 00. 000.0 .0. .00E3: 00. .0..0Em 000 .090. 0.000 00. 0. Ndw n n. .0 00005000 00000.0
.080 ..0 .0 :00 00 :05 00000. .0 00.0E00 0.030.200. .00. 000000 00. 00000000
:80 90:0 :90. 5. .1. 0.0.02:
080 ..0 .0 :00 00. 0. 0.030.200. .0 00.0000... n a 5.0.020 00.000.00. 00.00.00. .0 .0 .00.0>>.:o::00m
I 0. 0000.00. :0 5.0.020 0.0.0090. 0.3000E 0. :03 0 0... A . v :_ . w - u I
0.0003 0.0000 .00 0x0.
0000. 0:000 00:0.0005 00000 0. 000000.... 00.0000 00:00 0.9000 .00. 0000.0E02 .0 oz 000.).
00....000m 000 000.00.: .00 0x0. 0000.000: .0 .0003: 000.2
moom ..0 .0 :00 5:00.000 .0 .0.0. .030000 0 0. 0030. 0x0. .0 .0003: .08.. 0x0. .0 .0003: .0.0...
meow ..0 .0 :00 :00 .0 0.00 00. .00 0000.0E0: .0 .0003: .0.0... 000.0000 0.3.000... .0 .00
000.. 00:02 0000.000: .0.0... .0 000.2
000020.00. 003 0:0 0000.30.00 00:03 0 .0. 00:000. xm :0..0m0m 0.002
000.00....

.0 0.02000 .0.0000. 000 02.0.00E00 02.9.0000 .0. 0003 00.3E.0. 5.00.00. 0.3.02.0 5.:3EE00 000.0.002 .N.N 0.00 .r

91

 

meow ._0 .0 :00

o 0. 00:0. 2.0. 00. 0.00.000 0.0000. .0003. ..

F 0. 00:0. 000. 00. 0.00.000 0.0000. 000.000 ..
00000000000 .0 .000000 00. 000.90 0. >02, 0 0...
0000.000:

00.000. .0003. .0 000.000 .0 00.00090 .0 00000030<

3m+mm\mm

0.02. 2.00
.000000 000.0002

 

meow ._0 .0 =00
.Foom ._0 .0 0.00“.

.00 .0 200.30 000 N00 000 N3“. .0 000000300 200.30

0 .00 0 .P .0 .00 0. 0000.000 00.0.5 ..00. >0 N3“. 000 .00

.0 000000300 20...3.2 .002, 000. .0000 0 0.00.00. .00. 00:30
.0003. 000 .0..0.000 .0 000000300 H0 000 .0..000 0000000
0.00.00. .00. 0.0000. .0003. 0:0 .0..0.000 .0 000000300 "0
oo.

-o 00.. 00000. 03.0> 000.3000. 0.00.0 00000 .0 0.00.0

.3 + 0.0. . 8.10

00. 00:. 000.6000

 

meow ._0 .0 =00
room ._0 .0 0.00”.

000.0000

00.5 000. .0000 0 0.00.00. .00. 0.0000. .0003. 0:0 000.000
.0 000000300 "0 000 003.0> 0.0 00.0. 003 000. 00.3.0300
0 0.00.00. .00. 00:30 .0 000000300 n0 09-0 00.. 00000.
03.0> 5500.0 .0.00000..>:0 0.00.0 00000 .0 0.00.0

.3 + 0.0. . 8. u _0

00. x00... 0.3.02.0

 

000. 0.00000 .0 0.00000
80. 0.0080

0.0000

0. . 00x0. .0 000300.. v.3. 000 . 00x0. .0 03.0> 0-0 n €>
.0.02000 00. 0. 0003.00. 0.03 .00. 00.00.00 00.0 200
...5...00.0.. .0.000:0..>00 0...... 00000.00. .00 .0 03.0> 00 ..

e. . .5 n .00

0%..
00:. 00.00.00 00.0

 

000. 0.00000 .0 0.00000
80. 0.0080

0.02000 00. 0. 0003.00. 0.0 0-0 003.0> 0-0 200
.0.0000
0. . 00x0. .0 000300.. "0.. 000 . 00x0. .0 03.0> 0-0 H €>

.0.02000 00. 0. 0003.00. .00 0.03 00200.00 00.0 0000 0.0. 0.

...5...00.0.. .0.00000..>00 0...; 00000.00. :2 .0 03.0> 00 ...

s. . 3.0 n 00.2

0-0 00... 0.5.05.

 

002.000 0.0 0.00.

92

93

 

3... 3.0 v N2. 9‘...” F No sousnoacaco
2.: 3.: 2 N2. 9% F N: 3:8.on
3... we... 2 8.: 8.: N N... 2852.2.
2.: 8.: 8 8.: 8.0 N N: 83255....
8.: 8.. 9. N2. 9.». F N: Nazca...
8.: 8.. cc N3 mt» v N: .338...
and 8.. cc mN; 2.9 .V N: 2.3.395
one 3...: mm. 3.? 8.8 o N: 3:22:85
v? 2.2 8.. 86 8. 3. N. N: 8.383
9.... 8:. new 3.0 Nag 2 N: 23.22%".
98 .VNé 8m :6 3.8 8 N: .338...
8.: 8: N no... 8: N 3 £33.30
2... 3,... 2 de ova. F E 3.2.225:
8.: 8.. on N3 me...” P E «auaafiouassm
an... 8.. o... .5... 3.2 m B «23233:.
8.: m3 2. mod 8.: N E 3.2.80
3.: mm... 9. NNe 2.9 c 3 «£2.85
k... EN 8 5.: 3.2 m 5 82228833.:
5. and N2 N...” 3.3” S E 35.2295:
3.... 2.8 So on... «N? 3 E 5.32.:
8.2 8.8 NFVN :6 3...: ON 3 83.32.:
Aha-vinegotsoam
:38. 3.: 33>
.x. 3.28 3.28 x. .\. 5.; :6 a :35
3:200 3202? 33.023 30:309.“. N 5:362“. 3.95m 02:2... «29:3 mm
3333. :8: Eam @5201 83.03:. No .3532 6:30 .380". .o :_ 58...

 

.8635: .0 Emma? v. osmzxo n m ucm 55:28 .0 $5993 . 9:2on

uF .Eaaczcoo v... 9: :. mmam> .2 $56 88? 23.59 m:_m> a6 ocm 9.20. 2.39.. n 2.50 $3. 0.3.. .mm5\
398 26:28: 8.5 Ea: 3383 mm :_ 88:33.... Em $3.23 $35.28. ucm $520095 .96... 39m mmx *m $5

wank .:mm_:o=2 90:50 >3on .mmhdmmx am 899608 390: 30:28: 9: E0... 3.989 mxm. woofiEme: «58:8
z .03
.3.

 

00.0
3.0
00.0
00.0
No...
00.?
006
00.0
9.6
3.0
3.0
CNN?

Fm;
owd
00.0
00.0
00...

3.0
2.0
0v.o

00.9
9.6
0.20
00.0

00.?
00...
EN
no.0
«0.0
No.0
«0.0 _.
004
v0.0
00.0
05.0
3.00

5.0
v0.0
Nu...
VNN
00.0

v0.0
$0.0
Nu...

00.0
N06
00.0
wad

92

mm?
0..
on
00
o:

o _.
ON
on

o:
0 _.
ON
00

00.0
~06
00.0
3. _.
3. w
NNN
vud
and
N06
N06
00.0
00.0

N04
«0.0
50.0
mm...
vmd

N06
00.0
50.0

had
N06
«0.0
0N2.

8.0
00.0
8.0
oodm
8.0m
v P .VN
00.3”
00.0
00.0
0Y0
8.0
5.00

0N:
010

00.9
052
0V€0

010
8.0
v0.9.

0.0.9
00.0
02.0
05.9.

(DIONPN

[x

OVC‘OFID

CON

VFPOO

00
00
00
00
00

2
2
0 .2
u;
N;
N;
a;
2. .2333:
s
0
0
0

Q
5 0203000".

no
vo

v0
.0. 08020.50

3
00
00
00

2080002002
000002320
000002301
000020000000

200%—.3250:
8002000.
23800000000}

080000000002
00000255
000002.00
.N-000002>._.
0002.020
6-0000023.

00000800..
8000205005
00000230502

000002000<
0020000200.?

00.50.0005
00000000!
00000022

000003
0022000000

000003

94

0005.00... 00. 0. 0.000.200. =0 .0 500 .0.050000000 .00 0x0. 00. 0. 0.000.200. .0 00505.09 "00 5000c 02.0.00.

00.02.00 00.0500 .0 00050500020500 .0 3.0000 20.0000. 500 "0.003 0.0.000... 0000.
5000000 .00 0x0. .000.>.00. 0000 0. 0000.00.00 .0 .00050 .0.05 "0.00%. 0.0.0000. 50%

A0000. :0 .0 50000000 5000x010 50000000700 3* 50000005. 02.0.0»...

60.02.00 00.0500 .0 000500.008. 00.0.2000 00.0500 .0 00500.60. "2. 0.0000000 0.0.0005

 

00.00..
050
00.0
00.N
N00
05.5
9.0
9....
9.0
05.0
3.0
00.0
00.0
00.0
mfo
00.0
00.0
00...
05.0
o...o
9.6
9.0
59.0

00.000
00.0
wNé
5.9
50.05
05.5N
00.0
00.0
00.0
00.0
0.20
0N6
0N6
mm...
00.0
..v.~
005
0.25
00.0
00.0
00.0
00.0
«0.0

00.5002.
or

00
00m

000
000
or
O?
0..
ON
0

0

0

0

0 w
05

00
SN
0..
0..
or
0..
05

8.00.. «0.000.. 00.000 50.003 0.0.0...
«0.0 00.0 F 0.0055020:
00.0 00.0 N 0.0055005:
000 5 .00 0F . 700000005...
0008050: 02.00005:
SN 00. .0 0 0080.000...
00.... 50.00 0. 020000025
0000.00.02-00:
«0.0 00.0 F 00 02.05.200.52
and 00.0 F 00 005.2022?
and 00.0 P 00 8.00200
~00 00.0 . no 0025.28.52
«0.0 00.0 _. 00 0000000 03000000
and 00.0 P we 000.00500m
00.0 00.0 w 00 0500200..
N06 00.0 F 00 003055.02
00.0 00.0 N 00 005.25.000m
«0.. 00.55 0 vo 0025.25.00
.0. 0202050
50.0 00.0. m 00 00.00.0000
00.0 00.50 0 0.. 0500.000.
and 00.0 F 00 050000020
~00 00.0 P 0; 0000020020:
«0.0 00.0 F 0.. 000.0280...
«0.0 00.0 v 00 50.30.5000 0500
000 8.0 N 2 05.08.20:

.00-00.0000 dd 0.00mi

95

 

 

05. F 00.0 00 v... N 00.00 F 00 00..05.200.00<
00.0 00.0 0 3.0 00.00 F vo 0500200..
0... 8... 0. 00.0 8.8 N 8 00.002.000.00
00.0 00.0 mm 00... 00.00 0 co 000.00500m.
.0. 00.02050
00.00 00.3 F 000 05.0 00.09 v 00 0500.00...
0 F. F 00.0 0F 3.0 00.00 F 00 000002320
00.5 00.00 .5 F 05.0 00.09 0 m0 0000050300000...
F50: 00.00 000 00.5 00.05 0 00 000005000000
.0. 8.3.0.0...
0...... 00.0 0 3.0 00.00 F 0.. .00 000000.500
00.0 00.00 00F 00.5 00.05 m 00 . 70000025...
05. F 00.0 .VN 00... 00.00 N N. 000.00002000<
c. 00.02000...
00.0 00.0 mm 00.5 00.05 m .0 00000000....
.0. 02020.00
05.. 8.0 .0. $0 8.3 . 0.. 0000.02.00
00.... 00.0F 05 00.5 00.05 m 00 . 700.02....
:10 00.00. 00F 05.0 00.00 F .V 00 02000.0<
05. F 00.0 00 00... 00.00 0 E 0.00000:
F5... 8.9 we 00.5 00.05 0 F0 005.20.00000
.0. 00.32.2000
:5. 300000 5.0000 .5. .x. 0.8... 5.3 020500

300000 0.0.000< 0.0.00.2 3000000.“. 3000000.... 020.000 0. 520000000
0.0.0.0”. 0002 .500 0.0.0.0”. 0.0.000< .0 .00502 0000 0.5 0. 0x0...

 

00.0.0000 .0 0.00.000 v. 00.0.0.0 n m 000

.0~.00.00 .0 0.00.000 . 050.08 n F 50000000 v... 00. 0. 000_0> .0. 0000.0 800.. 0.00000. 00_0> 0-0 000 000.0 0.000.. n 0000
0.0V. 0.00... 000.00.... 0.00.00 5.9.0.... .mm5...wmv. .0 02.0 00.000800 0.0.. 00.0500 0 0. 00.000000... 000 000.0000 0000.00.00.
000 020000095 .985 000.0502 00 05.0.0000 02.02.200.000 50.050000 .000 F... 0. 000000000. 0200000000.... .00 200...

96

95.5mm: m5 5 2322?: __m 8 E3 5055.58: .3 95 2: c. £9229: u_o .oneacvbe "R. 5.5ch mémEK
360:8 $383 6 .mnEsEmmuofiEmc 6 36ch 920mg Ezm "bacma o§omn< :82

E958: .ma mxfl 5:22.05 :03 E 83360.. ho 2:95 .38. nbficmc 250mc< Sam

Amxmu __m “_o 55:50: EstxS 3 55:35.8 Pix. Aozmaumt m>.=&mm

628:8 8383 “6 5963598 359:8 mmaEmm ho 59835.02 ufi 3532‘ 35035

2.66 v 2.63" 0:60» _. 09.9: r 2.6%: co. 3 Sauna? 239—.
and cod w 34.. oo.m~ v mo 5:32.933.—

Uozczcoo .V.N 034W.

97

 

2.: ~33 £2 mm.m mmwm n a 355.328:

98

 

5 $3233“.
de 2d o3 3d 8.8 m 3 m5.3.855.
3 0.3253
34 8.0m 8m mm.~ no.2. m 3 SEE?
mod 36 3 med mm.w H No 3.25.823
mod $4 8 med mg H 2 233.55.
mad mm.m ow mad 3.3 N Na «Eoccng
«No .36 om S: 8.3 m S 3.2.22
3.0 mm.m 8H 3: 8.2 m 3 $5380...
Rd 8.3 03 mmd 8.3 N 3 $25qu
m3 3% mom :3 8.8 m 3 “322333
34 Sam own B.~ 8.8 m .3 4-380...
34 2.3 .2}. «3 8.9: S S «32238
85 Ga om mg mg H 3 3.3835
mod 84 ON med mm.» H 3 «.3332:
36 oo.m om mm.~ 8.3. m 3 $332832
med Nms mm mm.~ 3.3 m E «Eococan
SA 3.3 0% Rd 8.8 o B SEEBEEE
mmdfi mmdNN ommN mm.~ 8.3 m 3 $332.:
3.3 oo.m- NHR mm.m 3.8 m B 32:32:.
:3 3.25.363
x +3....ch 3?ch x. x. SS... 5.3
+3.88 33.8.3 328.? 328369.". #3532“. 3.928 3353 .33.
035.3. :32 Sam gas-oz 328.? ”.o 53:52 230 ~« 229.33.." 2.50 88h

 

63.6.3 5 56.29% V. mngm u m new 55:28 8 33995 . mEmbxo n F .Ezsczcoo v... 9:

5 $29 .2 mucfim 88.. 209.03 m2m> 90 new 955. 2.30: n 2.56 58. 9an .5922: .9880 >8on .mm._.._\mmv_ am
8%.. 5.3303 Ema 20 gm: x_m So: $388 9 :_ 865:8: new $37,ch $3.928. new 35289.0 .mxmh 89952
no 9528 cozmutfloflmgo 899308 6553 _mco_mmooo new 89 ,6 95% c_ 3:: «mm: 5.380% 25m. .mN gawk

 

End
mud
and
mmd
mmd

mod
mod
mod
wmd
med
.VNA
mmé
<06
th
wmd
«ma
th
Nad

mod
hmd
an...
mod
mwé
0H6

om.N
mm.m
NNéN
8.3
code

Nada
mmd
hmé
nmd
mmd
oodN
mm.m~
mwdn
mmdw
had
mm.w~
Nmfi:
8.0m.”

nmé
coda
mmdm
mwd
NDHN
wofiofi

cm
0.»
o:
owN
3m

Omm
ON
ON
ow
OOH
Dem
owN
omw
owm
ow
ovm
mi;
onN

ON
CNN
Cum

2
0mm
mmNH

med
wed
mw.m
Syd
omd

mm.~
med
wed
mad
3.6
had
Nmé
wad
mw.m
med
mad
m~.m
Had

med
Naé
and.
mud
mm.m
Sum

mm.m
mm.»
node
oo.mN
node

504%
mmd
mmd
no.3
mmfi
oodm
mm.mm
soda
5900
mm.&
590..
NOAm
mm.mm

mmd
mm.mm
mm.mw

mm.»
node
8.8.”

Nmmt-lt-I

NHWNVLDHNHHLD

Q’I-l
H

OH

NH

.Vo 38.2333

.Vo «£2653

vo 23.83352.

we 3238023330

co 3335.3
.3 3.82ch

2 «£2.21...

2 3:25.33.

2 03.353936

2 «£33.33...

2 3.5.3.2

m... 355.32..

2 355.3033...

9. «833.8035.

N. a; 2.55.35

N; $355.3

N; 355......

N; 4-3553...

N; 3.35.395.
A... 3.2.3.0...

e. 3859.953...

v. 3..oE.n.2.u:u.>..

M. 82322220

35333

N. 322.323....

N. 322.323.}.

N. 3.35.2.3.

“.3538 .m.~ ~38.

 

99

0555.. m... c. 23229.. __m .0 Sam 5.055.599. 5a mxm. 9.. c. 3322?. .0 59835.00. u$ 3.5ch 525.3.
582.8 3.2.3 .o .BEscBmuofiEm: .0 .5550 55.084. 52.0, "3.6ch w.:.omn< 50...
E955: 5a mxm. .3229... 55 c. 50995: .o .595 .30... 5.235 m§omc< Sam.

.98. __w .o 55:52. E5553 .0 55:52....8 Fuck. 5538. 9.551

€909.00 wmaEmm .0 5983588. 9.59:8 3383 .0 59835.02. "ck. 5535.. 53.035

 

O0.00N
.VNO
MN...

Nm.N

m0.0
m0.0
mud
ONO
m0.0

unfimfin
had
NOAN

5.33

mm...
BOA
mm.m
OO.m
NON

OO.N@OHN O0.00N

Om
OON

Omm

ON
ON
OV
Om
ON

eeé
OWN

mud

med
wvd
wed
3A
wed

smash."
OO.mN
59.3

mm.mw

mmd
mmd
mm.m
OO.mN
mm.w

OOdON
m
m

HMHF‘IH

amuse... A2.59...
329.22.
35233.0
35553-52

55252::

3.35852 5.2.52.5
mo unu.E.n.ou.oa<
mo 35559.23
mo 3.55.5339.
mo «22.238
.Vo «3.8.3.609

53:39.8 .m.N 033

 

100

 

«H... 9...: 8m 8... 82 2 2 322.322.?

 

 

2... and. 3K HN... 3.3 3 2 35.5.2.2
5 3.288..
NH... mm... 8 Hm... on... H 3 3.2.2.2»:
NH... mm... 3 Hm... 23 H S 3.2.32...
om... HH... .K SN -.- e 3 3.2.2.2...
3... HH.H om Hm... 03 H mu «3...:
3 3.3.53
NH... mm... 8 Hm... 23 H .3 3.3.2.2..
«3 HH... oHH SH 3.3 m 3 32......
«Nd HH.H 8 8H HH.HH N m.. 35.3825...
3... 3... w Hm... 33 H S 33.8.8
NH... mm... 2 Hm... 23 H 3 3222.22
a... HH.H om Hm... mm... H S 3.2.22
3... SH cm «.3 HH.HH N 2 .333...
mm... 83 3 3H 3.3 m E «228......
mmm 3.3 wR 2... 3.3 m 3 38.2383
mm... 8.8 NS HN... 2...... 8H 2 38.2.38
3... 3R 8.. $3 8.8 m 2 Haste...
Hm... whm om 8H . HH.HH N H.. 3.32..
mm... 93 2 3H $3 m H.. 3:32.332
SN HH.HH 8m 2... 3.3. m 3 383.235..
on... 3.: 3m 2... 3.8 ~H H.. «3.8%....
3. 333.383
.x
x +3.33 +3.38 3.33 .3333... 8 .3333... 88... 5.3 3.923 3.928 as
3.3.2. 228.2 .522 228.... .53. 3.3.3. 23.8.2 8 .3232 2.5 ..=.oz c. 9.3
.88.an .0 8.3.28 v.

322.3 u m new 2.3.8.8 .0 8.8.28 . 3.20.23 n F .E::c....8 v.-. m... c. 33.9 .o. 388 Sam. 288m. 28> a-.. wcm 9.20

0.82. n 2.30 $8. 3.8.. 6.8.8.5. .2980 89.0.1 .mmfidmmv. .m 3..» 9.888 .56.. .8 So... 3328 3 c. am
new 338ch 385.23... ucm 3.38095 6me 38.232 no 9.8.38 8.853.888 Em.m>m =5. 0.7. 652.3.
. .o.~ w
3MP

101

3:958: 05 5 £95.22. ..m .0 E3 30558.8... .8 mxm. 9... c. £3222. .o .3E:c..oo.. a...» 3.5ch 2.33m
8.02.8 83.2me .0 5983583580: .0 3.200 2289... 65 "5.530 358$. :33.

295%... .3 mxm. .3223. :08 c. «.3295: .o .595 .80... "5.5on 958ch Sam

.96.. __m .0 >833... Eamaxm. .0 55:35.00 Hu& A0538». 9.33m

2.0.09.8 8.9:me .0 5952.95. 9.59:8 8358 .o .38:5.o3 "ox. 3:958. 23.035

 

8.8H $.93 8.82 8.2: 2.83 8.3H «weep 222
H; .an 8N H3 mm... H 3229.2.
oucunpcocncoz
8... ”SN .5. NH. 3mm 2 85.52::
85.52::
«Nd HH.H 8 H3 mm... H mo «RENEE
NH... mm... 2 Hm... on. H H5 35.85....
NH... mm... 3 Hm... mm... H co 33.2.2.3
NH... mm... 8 Hm... .3 H 5 82555.53
mmH mm... omH NH... 8.3 0H 3 «NEEEER.
.3 332ch
mo... NNH. e Hm... .3 H m... 35.55.55}.
RN 3.... 3.. 8m mm.mm o 2 8553825:
HH.HH 8.3 NHm HN... 3mm 3 m; 355.32“.
«NH HH... oHH 3N NN.NN e N. .2 355.35
8... NN.N 8 H3 .3 H N; $355...»
35 mm... 3 SN NN.NN m N; 355......
HEN mm.mHH SON m2. 863 NH N; H.355}
3. 3.2.3.5...
m2. NN.N H... H... .3 H a 3553.3
a? HH.m 3H Noe 8.8 m m. 2o....22.:...5
NH... 8... oH Hm... and H N. 35555
60:55—00 (502

 

@.N U—flflh

102

 

II 0‘ (\hf‘t-

 

 

8N NN... N..N m... ..... ..H N. 55.0.8035
.0. ..N. o... .0. .3. ..H N. 355.3%
..NH 3... ..NH ..N mm... . N. 3555252
5 0.0.0393“.
v... m... 8H m... a... n 5 3555:.
.0. .0.0...—:3
8... ..... o. .3. .... H 3 3.5.5:...
S... ..N o. .3 8... m e. 35......
..N... HH.H ..N .2. .... H m. 355.055....
..N... H... ..N .3. .... H N. 555....
..N... HH.H ..N .3... .... H u. 33.55....
H... NN.N 8 3.... .... H N. 35.5.2...
H... m... o. a... HH.HH N N. 5.3.qu
N... 3... o. 3... HH.H. N N. «.35»...
mH.H 3... .H. .0. a... n N. 332.53...
..N. .02 ..N. ..N. NN.N. .H N. 35.2.50
0.... 3.3 N.. .m... 0...... m N. 4-3.5...
..N... 2.. ..N we... .... H H. 05.05.33.290
S... NN.N o. a... HH.H. N H. 35.2255...
H... ..N o. 8... HH.H. N u. 5.32...
...N ..NH ..N N¢.N .08 . H. £32.53:
m... 3.3 .E H... HH.H. HH .. 8.5.32...
3. 3.2.0.3800
X .5300 .3500.— >zmcun .x .3530... .x .3530... 09.0... 5.3 .0.0...... 3
050.0: 0.3032 000.). 0.0.0.9. 800 03.0.0: 0.0.0.2 .0380... .0 .2 0.30 00.3.0.0 0. 0x2.

 

38. 0.00... 000.50.: £00.00 39...... .12....me .0 .0... 00.000... 5.3.0250 00000.0... x.m 0.0.. .0.0...... m. c
000 .00....00 30290.3 000 3.00.0095 .9...» 000.0802 ..0 9......000 00.55.080.020 E07,... 033.0

55.20.. .0 .030... v. 00.0.0.0 u m 000
.0~.:0_00 .0 5.00.0... . 0.00.0.0 n F €335.08 v... 0... c. .02.; .0. .00.... 809 0.09.09 03.0> 0-0 000 000.0 0200... 0 E30

.. g...
.m ..N ..N."

103

.98. ..0 .0 500000.. 0.3.0.... .0 300000.362"... 320000.. 02.0.0...
80.00.60 .0.0...... .0 .0083ka 9.0.0.000 0.0.00.0. .0 50.00550. "5. 300000... 0.0.0.05.

 

3&3
3.0
em...”

fled
Noé
mod
mod
mod
omd
:4
ma
mw.m

mad
med
me...
hmd
AWN
MHz"
mm.m
on:

o~.o
«ma
oma
o~.o

00...... 00.00H 00.0.: 00...0N .39..» .30»
..0 0H .30 .... H 3.0.5026
HH.H .N. H... H3. .H 4-02.0502:
non-0550:.coz .fl 3052.52.53
NN.N 03 3.0 .... H .0 «0.53.300
.... 00H .3.H ....H . .0 0.0.6.5020...
3.0 . 3.0 .... H ..0 5.3.55.
3.0 . 3.0 .... H 3 3.50055
3.0 . 3.0 .... H 3 35.0.0020...
...3 .. NeN .5N . ..0 0.0.5.500
00.. .0H N3.N .EN . co 35.500003...
n... 0N. .00 HEH N ..0 500.36.005.30
HH.HN o... 3.0 .... H ..0 3.00.5.3
.0. 3.02050
..0 .H 3.0 .... . m. 355.00..
3N 3 ...H ....H . 2 3555055....
00.3 N... .N.. NN.N. .H m. 355.00....
....0. 3H. 0... s... NH 2 35538.3.
2.3 0.N 0..N mm... . N. .N. 3553.0
H... 0HH N... ....N . N. 355....
3.2 0.. N...N .EN . N. 355.00....
HH... ...H HN.. 3... D N. .7355...
.5 3.9.3.0...
HH.H 0N 3.0 .... H 0. 3500.5.
m... 0.H 3.0 HH.HH N 3. 3.58.2230...»
8.. ..H ..3 00.0. m .. eJaeuVN ..
HH H 0N .0 0 H. .H N N. 9§§§\\\A.00 §

 

 

104

35859“ 9: E m_m:u_>_uc_ __m .0 Sam EoSSEfimb .3 923 9: E m_m:u_>_uc_ ho 59835.2: “o a€§W§§§

g

0053—00 mm-QENm ho LEEDmewUOuGEQC b0 bar—Q0 0u3_0mn< EQQII\\\%§QQ 0
acme—.5003 ha GXQ $32205 COMO E wmgumEOC uO “csoEm _$OP IX \WQOQ M~z0¢fi< cm
I N. ~3x00
a?

m:

53m.

105

 

 

o3 NN.HH oHH m3 3% n m. «3:835:99
Hm... 5H 8 3... a? H m. 363.3282
mm.~ 3.3 ma mum 8.x». H N. $2295.32
mm... oosm 08 2... 3:... NH H. 35:22.2
E «20293..
Hm... BH om 8H HH.HH N 3 3.2.8222
8H m3. 8H «hm 3% H 3 3555.2
.3 3.0253
mm... 3... 8 SH 8.3 m 3 32......
8... 3... m cm... 8.... H S asu:8_.3
Hed NN.N 8 8H HH.HH H B 8222.22
Hm... mg on 8H HH.HH H 2 2.8.3.5
8H 8.... 8H 3.... and H 3 $382..
H3 3.» mmH mH.~ NN.HH .V .3 “322.43....
E $.mH we... 3d. HH.HN e S 3.2.22
«mm 8.H~ mum H3 8.3 S E .333...
Ho... 8% 8... 3m HH.HH. HH 3 38.2.5.8
3.0 mm... 3 3.... mm... H E 255:5
3H 3.3 8H and ..NR m S 2:32.335.
..N... HH.HN qu m2”. 3% 5 Ha $.32...
Him mm.m~ gm 3... 3.3 m Hg 82:32...
8.... $8 Sm m5. 3% N. B 3533522.
3. 885.383
8 3.2.3 +3.33 3.33 X 35:3... x. .3539... $529.
0.50.3. 33.8.3 :35. 3289‘ 83. 025.3. 33.8.? 3.183 .0 oz 2.30 3. £5.63 c. 88?

 

P c.
m . w
E €929.50 22538..

 

.065. .0 «my. 905 «I afiefim

n m new 85:28 .0 $6283 . meets. u H €35.50 v... m... :. mm..._m> .o. 35% .83 23:09 2.5% bbw€§
n 2.8 max use. 896.... 92.8 39.0... .mmbamv. a 8% 8:: 2.22.528 xv, as... m , §\ ..
.ocm 83.22. 3.35895 new .98... 32952 no msfimcoo 8.5558920 E32» mm QRKE E

m 0..

ad wish

106

..00E.00.. 0... 5 0.005205 :0 .0 E00 .0.0...:0E.00.. .00 0x0. 0... 5 0.000.205 .0 50.00562. "00 5.002. 050.00..
00.00.60 00.00.00 .0 805050009900: .0 3.0000 0.0.0090. :50 "3.055 0.:.000< :00...

508.00.. .00 9.0. .005205 ..000 5 0000.080: .0 .5080 .0.0... "3.022. 0.0.00.3 Sam

.88. ..0 .0 500000.. 830.008. .0 500000550 WAR >0:0=00.. 02.0.0.0.

60.00.60 00.02.00 .0 5050598. 0550.000 00.0600 .0 30535.09 "X. A0530... 0.0.0005

 

00.03 00.30 00.003 00.03 00.33 00.00H 0.30.3 0.38
HN.0 HH.H 0N 00.0 00.0 H 00.028000
0...N 00.0H 00N 00... 00.00 0 H-00520200

00.00.0502 :02 a 00502 005302.02

H00 NN.N 00 00.0 00.0 H 00 30.5.2882
00.0 3.0 0 00.0 00.0 H 00 008.0.r00...05<
00.0 3.0 0 00.0 00.0 H 00 35.0.38
00.0 3.0 0 00.0 00.0 H .00 0.000053
0H.0 00.0 2 00.0 00.0 H ..0 03.00530
0H.0 00.0 0H 00.0 00.0 H 00 02035.3
HN.0 HH.H 0N 00.0 00.0 H 00 000.05.03.60
H00 00H 00 00.0 00.0 H 00 35.2.5000
3.0 00.N 0.. N0.H 00.3 0 00 005.230.3022
N00 .3... 00 00H HH.HH N 00 00.25.2000
.0. 00.9..ch
00.0 3.0 0 00.0 00.0 H 0.. 050500...
00.0 3.0 0 «0.0 00.0 H 0.. 0000500000002.
0H.0 00.0 0H 00H HH.HH N 0.. 02.000230...
00.0 00.3 000 00.0 HH.H0 HH 0.. 000000.30...
H00 00.0.. 0.0 H00 00.00 0H 0.. 000000.380"...
0N.N NN.NH 0NN EN 06R 0 N. .N.. 00500.3...
mH.N 00HH 00N 000 00.00 N N0 3500......
00.2 00.03 003 mud 8.03 0H 2 4.02.0022.
E. 00.2.3.0:

 

0000.500 .0.N 0.00..

107

 

 

.0 + 0.
..Immi ".0. 0. 00000.8 0.: .H
mud N50 006 006 end =.._. .0:0..=0>:0o
and mod 95 00.0 00000-0.m
..od and Ed _..._..02
and 006 00.0000000 2.0m
and 5.0000300...
.020... 0:30.009

00000 00.0000000 00.0000000

-0.m ...._.-0z >000 0.... .0000“. d 0.0.0.300...

 

0 000 0 00.0 .0 00.9000 0. 00.0000 .0 .0082. 00. "0 0:0 0

000.800 0. 0>00 000.0 .0 00.00.00 02.. 0.... .00. 00.0000 .0 .0050: u 0

0.0.3.

.. 600.00....— .0.0....00 bozo... .mw....=mmx .0 00.30.3000 .00 0. 0x0. .0 0000000

..0 000000.... 0... :0 0003 .83.. 00000.0»... 3.00:5? .0 x00:— dfiu 0.00...

 

108

l '1‘
‘rf:
a
2

 

 

p p .N 03.00.

.0000 u a. .0: 0.0 >03.

..0 00:0... c0>0 .8 .00 n 0. 5002 0:0 00:0 000 8300 u 0. 5502 000 .008. 0003.00 0000.0...0 003000 .00.- .0 80¢ .000000 000.0802 5 .
.6000 n 0 .00.-.. ___..r.>coo .0 00:0 0003.00. >0... ..0 ....3 0000 .0: 00000.0...0 .000...000 00.0..000E00 .00.-.. .0..0...000 000....0>m. c. 0
0000.0...0 0 053000 .050 n 0 0 00.. .00.- h 0 .:0 .5 0:0 .005. 0003.00 0000.0...0 0.. 003000 >O< c0 .0... >..0.0>.n. .050>> 00:00.5 5 0

..0.. 0.0 >O< ..0 00:0... ...0>0 0....02 000 00:0. 0.00.500: 0003.00 600.08. 0000.0...0 .00..0..0.0 003000 .0070 0 .x00... .0.:.0:..w 5 3

..00 0.0 >O< 00:0... c0>0 .__....02 000 .008”. 0:30.000. 0003.00 0000.0...0 003000 3000 u 0. .00.- .0 0 00:00.00 2.0.0 5.3 x00... >...:.0.2 c. 0
000000000 .05 5.3 0000.0...0 0 053000 .0000 .0 0:.0> 0 0 00.. 0E0.0>0000 _...-0.. 0:0 00.000080 0... 0003.00 .00.- ._. 0 .0.0 x00... >...:.0.2 5 0
.3000 .0: 0.0 >O< ..0 .0... 0.00.500... 0003.00 0000.0...0 0 00 .00.... 0.0... .0... 00.00.05 000.0 0.020.... .000 0.00000 0.03 000: 003 .09-...

 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

..00.0...0 >..c00_....0.0 0.0 030. 5 0.0..0. .00.0...0 >0 0032.0. 0000.2
.0000un. 0000 00.0 0000 300.0 .000 330.0 .. 000.0 .03000 002 0000.05.05 00200.02
0000un. 00.: 00.00 00.0. 00.0-m- .000 .000 0030 .00. E ._m.x000_.c0e..o_.cm
.3300"; 0.0. 0003 00.0. 30.0 000.0 .300 IE 00.: R03 ..0 x00..__0.aoe.0
000.0un. 00.0 030.0 .000 0000.0 0.00 H .000 IE 0.00 0.00.0 00006003000000.00
m000.0". 3000 00.0 000.0 0030.0 030.0 0300 «:0 .2000 00.00.00
.00: .0... x00... 0.5.0.2
N0.00:. .000 33.0 .000 000.0 000.0 03.0 a 02.0 000.0 00.5. 0-0 x005 :30...
.0001. E0 % 000.0 .000 03.0 00.0 5E 00.0 000.0 02.00.0900
.00... 0.. .0... 000.. 2.5.0.2
m.0000; 000.0 00...... 300.0 800.0 .000 803000 000.0 000000 0000 . . 0000020
0300",. :00 0.0.0 0030 0050.3 0000 003.3 .000 00030 0300 0000.0 00x00... 00% :00 5.0
0.00u0 0000 ..0 .0 0000 03.00 030.0 000000 0.00 00000 0000 00.0.0 2.0 0.0805500 00005.0
003003. 0.00 0003... 3.0 00000.. 00.0 0000... 000.0 . . 00.0 m000... .1. .0.0..0.0c.03...oc..0..0
.2200000u0 3000 02.. .000 00.000 .300 000000 0000 lam-40.0 0333.. 0x0.000.0E02-2 000.2
mm 0. 00 or a 0x0. 000.0...00 .0.0.0
0000",. 0.00 00.000 0000 000.000 03.00 000.000 0000 .. .. 0.30 .0030. 3.0000 0.28.2 .50
00. 5 0000.0E0c .0.0... .0 000.2
mm cam: mm :60.)— mm Pam—2 mm c692 mm cam—2

A25: ..0 >06. :2. «mm-.0“. NO\mN\m ”Gum—u mE—QENM

0:.0> n. .0..0..:0>..oo 00000.06 __...-02 00.00000:w-0..2 0:30.000

 

 

05.00.20... >0.0 5.3 00.00.05 0.0 00:.0> .0030. 000 05.00.20

 

.0 0.00.0 >0 00.00.05 0.0 0.00.5:

.0000... ..00. 22.2. 00.00... 000... 0.00.00.00-00... 0:0 90$ 00:0..0> .0 0.02000 .39 0.0..0 0.09.0.0 .0000... 0:3 mm......wmv. .0 0.00 0.00.00
Son .00 0060.000 .0. 0.00. 0.02000 .00 0.00 .5 3.5.0... Eo.0>0000 0:0 000cc0>0 .>..0.0>.00.0 .0.:.0:..0 >....:E...00 000.0502 .30 0.00 h

109

..00
0.0 <>Oz< 00 00:00. 00>0 .__.-_..02 000 .00.0. 0:0:0.000 0003.00 .0000 u 0. 0000.0...0 .00..0..0.0 003000 .00..-.. 0 .x000. 00.00.00 .000 00. 0. N
.00—.9.th .5050 .0: EU .09 <>OZ<

00 00>0 .000 003

00.0.0. . 00.0 00 00.0. 000 X000. 0.00.0005.
N.

onmmmd 3.0
.o

.000 00000.0. 00000.0. 000.0. 300.. E 0.0

.00 0x0.
.000 u 0 . . . . . w w . . .00 00 .-00...0000 0.2000...
0 000 m 0 00. a 0 003 m 0 000 0 :0. E 0 30. a 0 0.0 0 0000.20... .00. .o 08...

>O<

0:.0> 0 00.00000:0 >000

0..3 00.00.00.
0.0 00:.0> .0030. 000 05.00..00.0 0.00.0 >0 00.00.00. 0.0 0.00.5: .0000... ..>O<. 0000..0> .0 0.0>.000 000 ..00. 0800 00000.0 .00000.
0..3 00.5.09. .0 0.00 05.0000 .000 .0. 00.00000 .0. 0.00.- 0.0>_000 .00 0.00... 3.002050 000 0.3800 3.020.000 0090.002 0.0 0.005

 

110

.855:%

88.0 n 3 EmoEcQw m 3305 ___.-.m:o_Eo>coo 96 7.9.2 d 5923 ESP m .mmuBmEmcéoc new 3:228 :56 53, x35 areas. 5 N

 

 

  
 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
 

 

 

 

 

 

 

 

 

 

 
 

 

 

 

 

 

 

 

 

.305 go:
Eu >O< cm 65 wacoEummE cmmEmn mocwfitfi m on £98 9w£ $5 85065 numb 6225 852:8 5:0 Emucflm 90;? gm: mm; 39;.
:o. on n. E. o 938. o E. o 80. 0 £8 0 o: o 2 E o «8 o moz 25. 35.20 %an02
08.0 n n. 33 on: $3 8.5 83 8.8 83 8.8 R: iv 69: EmEfiEm
:05 u n. 83 $5, 83 3% 83 E 8% 83 8.8 85 2m 59: .9385
:3 u a 3.3 m3“ 2 to SN a; , $3 d :3 E En; 6?: 2:85 :55
mono u n. 2 to :N :3 "new 2.0 a 8.0 and 2.3 NN.N 8:85;
II Egg 5;, x35 €562
20.0 n a 83 SN 83 8a 23 on..." N26 2a 3.0 E
82. n n. 88 a3 83 SN 83 E1 S3 mam 33 mod :2 .3 829 o 8:223
EmE o: 53> xwuc. 3:365.
NBS u n. 83 ..th :3 min 53 Ba 23 now $3 . 22w. 852-8: a $58.8
:53 5;: 32. 3:32,.
88 u a 98.0 «3 Sod 8.0 9.0.0 :3 20.0 8... 08.0 . 326$
mono u n. 83 mum mad OS :3 mad 8+ 8e 8m 0: x85 gemzu :8 :5
«8.0 u n. «8.0 5.9 $90 2.0 25.0 H _. o 30.0 and «8.0 8.0 2 a e 8&558 commém
83 u n. 3.0 89 33 mod :3 a $3 8e 33 8.. iv 35% 5:63.852m
83 n n. 9 I 38.2 $3 $8.3 $3 852 33 . 83 £3 £9 muofiEmz oz :82
cu mm mm ‘ 5 922 829.5: :38.
53 n a 38 38.0 ..mom 98.8w o3 8.80 «.mwm E Em 8.an A :28 228% 6:5
cow 5 wmuofiEmc :30... .o :35.
mm cam—>— ww cam—>— mw cams— mw cam: mm :mws.
>O< «ween— .
3.9 n. __F 88565 =_._.-oz co_mmmoo:m 25m 262280 @93an mEEEmw

 

 

 

 

 

 

   

 

 

329sz $5 5? “.2862

9m 82? “332 can mEEQEmE x83 3 3.865 96 flmnEzc $29: 90$ 8cm_._m> S w_m>_m:< new .39 worm 0.5966 .28: 53>
£53me an Emu mEaEmm meow .mm .mnEmfiwm ..8 2an m_m>_mc< ANN mint £99605 cam 93025 >~_c:EEoo muSmEmz .meN meh

111

 

 

 

 

Bow .m~-: .mnEmEmw Eo 9.9 co 8.? Bow .3 52.53%

Boom .NF 5988093 598902 Eo wmd oo owd- Bow .NP .mnEmomo

meow dqmv .mnEmamw :5 mm: 00 m2: meow .mm 52.55%
:ozgfiofla 2322.53

mount .0 09.3. Emu 53.2 21.6 :35. 3aa 9:38am

 

 

 

 

.mmfichEEmw =3 3.5 8 .25 9.82, 0.2 05 .0.. $3.52.). €9.50
.5on .5sz 3050.05 30:3. .2 5335605 new 3.33382 28v cams. .3.~ 29w...

 

112

mN

§ 5.5:".
3 . no 0 no- r 3- N- ..N. m- on-

 

00.00:; . 0.0.5 o _

 

.83. .0
o 0

005-25.". 0

.832: _

 

 

o .1. 0% Em. 00:8 0:3 :8 ,sz 500...: 6.3220
Sac—-02 - :00 £00.00 .on 000.00 5:00.20 :00 €00.00
€000.00. 0030:? of. .020: 02800080....

=_._..>:oo 0:0 €00 000.0. 03002000 02.0.0380 6.23250
09. 80000-28 505-0: 0:0 :30: 00:: 2500300200
0.0.5 00:0 of. 009222 6.00.00 Boxof .cozflw

_mo_m0_o_m 80:3. 5 00:0. 0.0 00:0 9:. .835
m_m>_m:< 000000000980 $209.00 .3d 2:9"—

F
O
z
0

 

 

 

A: 3...... ”N“. 05 E 3.3. 3.55.50 . %s. <00

QT

md-

md

ma

(% 927:) as

113

ON

3.. 3.3V F."—

 

 

 

 

 

 

 

 

 

N OF F Do o md- F- OF- N- ..N. m- Om-
_ 0030:; . Sap . magma _ E M: 2 on 9 9. «F. N-
$- 3. R 2 8 a E
. T S. 9 FF 8 x. 0m. 8 .
T m F-
0? I no
NF WV 1: . F:
2 N. 00 $- NN 5 .H 8 0.H-20”. o
8 o. . . 8 8 w . .:
.00—6-23m mm 0 -- .o z . 3 no n
8" m3 N0 .. 2 F a t W.
mm I I I 5 .
_ . _ I F HI I _ h . ..ogmlc_: I z
_ u _ _ I .- - _ F ‘ _ u o 9
I I I I I I on 0/0
8 (
IMN N. \- v_. mm I. w ' mm on N0 an
Fans-oz - 8 %- . md
___H-mz . e
a w =_._-.>..00
I n I
0.. Jon mmr F
v Fm I
N I II A|||||||
8 m 2 8 mm B
o. 8 0F

 

 

.008 .me 9.. .._ .2.me 050:. ex. 8.? ”Nu. 0:0 F". 00x3 02.9083 . 022 <00

£4- 050..

114

 

 

 

 

 

 

3.0000000 00 086020.02 mv 800000.05 NN

30000.30; 00 0800000000.). Nv 05009000005 HN

00.82.... 00 000.02.03.00 00 8.8.003 S 2.03.50 8
0.000.008 .x «0 0300... N0 3000080.. ow 08003.0 0:
080000.05 mm 820009.000 H0 3.00>8.00 mm 080000090 0:
005800.03 N0 3080.30.00 8 08000.00. 00 000008000000 5
3.0000023 H0 38.20.00.000 00 0.0000..0>0.80I mm 0300.02.00 0:
30000.? ow 3.000.00000 00 3000030020... mm 3:00.>.0.0 0:
3000380000.}: 00 0000000 03000000 00 0000:0080 mm 30200000 3
38.0.0080...» 00 3.00000 00 38.03.0000 .Vm 000.000.00000 m.
0:..08.0.00000_>h R 0000000005 mm 3020000000 mm 08000000 NH
030.; on 800008.800 0800 3 008.03.00.00 Nm 00000.00 3
30.0.0.0: 00 38.208000 mm 3.0000000 :0 0.00.0000 0:
0.000—00.000830 .R 30000380002 Nm 30.008000 om 000.8.0_00.00< m
0000:0030 m.- 3_:0000_>_>. :0 38.03.00 0N 03.08.200.004. 0
0805:0800 N0 30000022 om 000.08.03.00 0N 3080.002 0
3080.300 K 0.0030005. 0.. 0028.03.00 0N 000.00000.000< 0
000800000 on 3000022 00 00.8.2000 0N 0:8.0_>.00.008< 0
2.0000000 00 080000.002). D. 30000.35 0 N 3.00.008< v
0000080020000 00 38.03.8032 0.. 38.0.0005 0N 38.03 0
30002.00 00 0800800000.). 0.. 0020.0.00000205 mN 000.0.000.0< N
38.20.0805 00 3.0.00.2 3‘ 0.000205 NN 00.000.0< H
.00 0.30... 0. 003 0.0083: 0300800200. 000 0x0...

 

 

0.00830

115

>0 008000.00. 0.0 0x0. 00 .- .08N ._0 .0 2800.00.00 ...0.0.0200.20 0.80.00 .0 .000 00. 0.500 30000 0.00 0.000 000 02.000.00.000 80.0.0 0......
00:0. .0 0000.0 00.0000 .0 00.000280. 00 02.08.08. 00.>o.0 000 0.0>.000 000002.35... ....0 000 500 3.0000 0:00 ..00 ..z. 0000...: 08080.0
..00 800.00 00. 000.00 08080.0 :00 800.00 8000.00. 00.00..0> 00 0 000.025. 0.0800 09.0.1 02.0.0 .020220 002.00 0. 00:0. 0.0

000.0 00 » 0028.00.00 00. .0. 000: 0.03 000020800 0000.00.08.00 .0 .00800nooo: 008.0000 00.5 .00. 000088.00 < .00_00..0> 02.00.0000
00 000000. 0.03 00.0000> 8050.0 .25.... 0...; 0000 0220 20088.6 0.0>.000. 0000000000000 .00.:0000 000,00 3030.0 .0.. 0«.N 2:0...

0.80 .00 .00..0..0.0 8.00.0. .60 000.00.... 005.000
.0 080.3080 02. 0. 000020800 000.800.03u083 .m0.0>.080u80 .00.0>.0.0Iu0... nm..0.0>.00_....u0n. 00.02800 n00
.00.0>0_.0.000umm. 0000.0 0.000.. .0. 0.0308. .000 0.00008 000 :00 .0 A00 005 .80 oo. .00 00020802 Nd 0.00.“.

 

 

 

 

 

m 0
m w m m. m. w.
..p n. my ..p ..p n."
S S S s S S
00.. 0.0. 00m 00. 0.0. 0.0.
0.... fl... 3.... E... ..N... ..2...
00.... 00...... .00... 00..... 00.... 000...
-8 u
1‘
-02 m
s
Axum
10
-08 m
-omm 0..
m.
.08 L
0002 05 80... .000 0.00080 000 0.0 0.00

 

 

116

 

.cmmE 9: E0: .25 Emucflm £398. 23 Stu Noom .NF 598800 :0 AmmuofiEmc umEEmuEDHQED
.wmzozcEOnEO Amway—2923 cam mmfimcoogov mmuoumEmzéozquzéoz .mmzoonznmI .mm.o>_m:2u:n_ .onzEmo
"mo .mm.o>m:mfiomnnmmv 955 oan: 3 0993mm .__om 3 A8 8: ”So cow .3 mmuoumEmz 96 59:32 cams. .m.N 9:9“.

 

 

   

w m M
3% m0 3% ,WO mm ma
mm ..w. Wm 1m. mm _.
flmfl mm? “mm .
1.1.1.1! I‘ll... I1IIE .... -o .
. U“. m
..‘.“_..,-8N m.
m -8, m
w -08 W
.25 m
w.
noon: “-
-OONH

 

 

 

 

 

 

117

.woom .mm .mnEmfimw quEmm __ow .AmEmEAw 8:: >=mcozcm>coo ucm 955-05 tom—“.563 .__:.0: 528803

23m .3909. 963283 59:22 £5...me 5 8.99380 9: :. .AmouoflmEmc umEEmUEDHDED cam $369923
new 38289.9 mmcoamezéozquzéoz .mw.o>_cEOnEO .mm.o>_n._wInoI .mm.o>_mc:v.n:n_ .wm.o>_Emo umo
.mm_o>m:90mnnmmv 339m 02%: .8 20:0 Emacflw ucm .__om 6 n=5 o9 Ba mouofiEoz ho 59:52 :85. .vN 9:9“.

 

 

 

 

 

 

 

w
n_.
m m
m m u w m. a m
a a a a a a a. a a a m...
m5. #0 m5. mic mm we no. mac mm mtg mm We ma who m
mmmmm mmmmm .mmwmm mammm mammm mmmsm mmmmm
mmmfiam www.mm mfg www.mm mam; www.mm www.mm m
—— — —— - _ _ n. — — — p p —_— - — .n — — — _—- p. - _ _ n— J
E El. E Sufi—Mafia. EE E10 m.
i E ISM
ION 1.
m.
[cm s
-oww
..
-8 m
-8 m
on m.
m.
low ”—

 

 

 

 

 

118

.woom .mw L0n_c._0a0m 0:0 Bow .NF

000E800 doom .3 000E0fi0w 00EnE00 00000 mEaEmm 003... 009222 .mwhdmmx um AmE0um>w 9.5090 .933250 00:: >__0co=c0>coo 0:0
AoEmuBV 00000-05 .__=-0c 9.0 52000003 2.00 .Eo_mm000:m-u_Ev $07. 00..: “00. 00:00:80 0200083 000.2 02022003 02.290000 0>c E
.3002me: 095522390: 0cm $009923 0cm 0000050029 w0090E02é02u0E0zé02 .m0.0>_cE0uEO 00.629050: .w08>_m:2u:n_
00.02500 "00 .m0_0>0:0~omnummv 8:05 0200: 000 90:0 Emccflm new .__00 00 nEo 8F .00 00090.52 00 00:52 :00: .md 05?.

 

 

VN
o
H.u H I: D m
n O N a n e m
U m 3 J U J
m N. N N N M
m o m m m m m
p
3 a a $ 9 9 9
G E a G G E E
I.“ I.“ I.“ JW J“ J“ JN
mm ~00 mw. wflo mm warn mm ware mm. ware mm. wan mm. ware
. SS _ SS _ SS . SS . SS . SS _ SS
UQNnnd UQNnnd UQNnnd UQNnnd UQNnnd UQNnnd UQNnnd
Aeowwm meowwm meowwm meowwm meowwm meowmm meowww
mwmfcm mwmmfluam Mmrmnqam MWMEUR M%M0am M%MB05 mwmflfam
PPhk—l—In —- —I— n n— - n n. ..n— F—Lblnh— —n h- n - n-h—n -
Jul. IO

 

 

 

:3
:3
:3
:3
:8
:8
:2
:8

E

E

—E:

a

I

'5'

E

a

I

E

+:‘
E:

c

E

E

i

+2

5

{1

fl

+1

+21

i

+:

E

E

4:

C

 

 

 

:00: 05 Eot Btu Emncfim 0:0 000 98
0050500 030n— mEEEmm 005:... n3 «0... .0335

nos :0 3300! Rd sapozewaN JO JaqwnN

 

 

 

H9

 

 

 

00000002: I
0025.29.80? I
0002802. I
000.22.53. I
0020.200 I
0000.32: I
000.00.55.83 I
08_=80__0._ I
03.5.0305000 I
000280.30... I
000.000... D
0002323050.. I
000_E_0_050:0._ I
30282.0 .H.
002.03: I
000280.382 I
0002085: I
000030.55: I
03.30.23 I
00028800.. I
03.2.0310: I
002.202.05.50: I
03.5.0.5 I
6.5.0.50 I
030820.30 I
000.950.0005 I
000.03.339.03 I
00200080an I
002.28.05.13 §
03.532005 I
03.005285 I
03.8.2000 I
000.000.0550 I
000.50.80.00 I
0000.05.00 I
000200000 I
06.2.0088< I
000.39.80.20... I
000280.002 I
0002.35 I
0.0.2.02 I

    

 

 

 

 

 

 

 

._F.>c8 0003-00 _.:oz 500083 0.0.... 0050 d
- o
- cow
885. an .
E _ _ _ _ -02
0x00 9.
- coo
r cow
.00080E0: 00 A008. 00 00:55 30:9 “00:9; : OOOH
05 0050.50 00.0 ._ =5 6000.00.00 *0 30:03.0
30. 0 00502.00 .0008 0:0:0.000 0c... €0.90
£000 Eot 00.0500. $2.0m .0.0E0x0v 0x0.
00 .00Esc 0E E00050. 200 05 0>000 00x00 02h OONH

 

 

 

woom .w Room 5 :09222 $3.539. ”—0 meum >800 02L 5:5 0306080 m0___E0¢ 0080802 .©.N .9”—

INS :0 33001 ad sapozauau

 

 

120

REFERENCES

Andre, H., X. Ducarme, J. Anderson, D. Crossley Jr., H. Koehler, M. Paoletti, D.
Walter and P. Lebrun. 2001. Skilled eyes are needed to go on studying the
richness of the soil. Nature 409: 761.

Barbercheck, M.E., D.A. Neher, O. Anas, S.M. El-Allaf and TR. Weicht. 2008.
Response of soil invertebrates to disturbance across three resource regions in
North Carolina. Environmental Monitoring Assessment DOI 10.1007/310661-
008-0315-5.

Bell N.L., L.T. Davis, S.U. Sarathchandra, B.|.P Barratt, C.M. Ferguson and R.J.
Townsend. 2005. Biodiversity of indigenous tussock grasslands sites in Otago,
Canterbury and the Central North Island of New Zealand: || Nematodes. Journal
of the Royal Society of New Zealand 35: 303-319.

Bending, G. D. and M. K. Turner. 1999. Interaction of biochemical quality and
particle size of crop residues and its effect on the microbial biomass and nitrogen
dynamics following incorporation into soil. Biology and Fertility of Soils 29:319-
327.

Bird, George W. and M.F. Berney. 1998. Relationship Between Nematode
Community Structure and Soil Quality. KBS/LTER Meetings.
httpzlllter. kbs. msu .ed u/Meetings/1 998_A|l_lnv_Meeting/bird.htm.

Boag. B. 2008. Nematodes associated with forest and woodland trees in
Scotland. Annals of Applied Biology 77: 41-50.

Bongers, T. 1990. The maturity index: an ecological measure of environmental
disturbance based on nematode species composition. Oecologia 83:14-19.

Bongers T. 1999. The Maturity Index, the evolution of nematode life history
traits, adaptive radiation and cp-scaling. Plant and Soil 212213-22.

Bongers, T. and M. Bongers. 1998. Functional diversity of nematodes. Applied
Soil Ecology 10:239-251.

121

Bongers, T. and H. Ferris. 1999. Nematode community structure as a bio-
indicator in environmental monitoring. Trends in Ecology and Evolution 14:224-
228.

Brodie, B. B., J. M. Good, and W. E. Adams. 1969. Population dynamics of
plant nematodes in cultivated soil: effect of sod-based rotations in Cecil sandy
loam. Journal of Nematology 1:309-312.

Brodie, B. B., J. M. Good, and C. A. Jaworski. 1970. Population dynamics of
plant nematodes in cultivated soil: effect of summer cover crops in old agriculture
land. Journal of Nematology 2:147-151.

Brown, G.G., l. Barois and P. Lavelle. 2000. Regulation of soil organic matter
dynamics and microbial activity in the drilosphere and the role of interactions with
other edaphic functional domains. European Journal of Soil Biology 36: 177-198.

Chang. 2004. Chang Bioscience @
httpzllwww.changbioscience.com/genetics/shannon.html

Coleman, D.C., Edwards, A.L., Belsky, A.J. and Mwonga, S. 1991. The
distribution and abundance of soil nematodes in East African savannas. Biology
and Fertility of Soils 12:67-72.

Ferris H., T. Bongers, and R.G.M. de Goede. 2001. A framework for soil food
web diagnostics: extension of the nematode faunal analysis concept. Applied
Soil Ecology 18: 13-29.

Ferris, H. and M. M. Matute. 2003. Structural and functional succession in the
nematode fauna of a soil food web. Applied Soil Ecology 23:93-110.

Ferris, H., R. C. Venette, and SS. Lau. 1996. Dynamics of nematode
communities in tomatoes grown in conventional and organic farming systems
and their impact on soil fertility. Applied Soil Ecology 32161-175.

Ferris, H., R. C. Venette, and K. M. Scow. 2004. Soil management to enhance

bacterivore and fungivore nematode populations and their mineralization
function. Applied Soil Ecology 25:19-35.

122

Freckman, D.W. 1982. Parameters of the nematode contribution to ecosystems.
In: F reckman, D.W. (Ed.) Nematodes in soil ecosystems. University of Texas
Press, Austin.

Freckman, D.W. and CH. Ettema. 1993. Assessing nematode communities in
agroecosystems of varying human intervention. Agricultural systems &
Environment 45:239-261.

Gomes, G.S., S.P. Huang, and J.E. Cares. 2003. Nematode community, trophic
structure, and population fluctuation in soybean fields. Fitopatologia Brasileira
28:258-266.

Hartman KM. and JD. Eisenback. 1991. Amended description of Pratylenchus
macrostylus Wu, 1971 with SEM observations. Journal of Nematology 23:104-
109.

Hyvonen, R., and T. Persson. 1990. Effects of acidification and liming on feeding
groups of nematodes in coniferous forest soils. Biology and Fertility of Soils
9:205—210. Find this article online

lngham, R. E., J. A. Trofymow, E. R. lngham, and D. C. Coleman. 1985.
Interaction of bacteria, fungi, and their nematode grazers: effect on nutrient
cycling and plant growth. Ecological Monographs 55:119-140.

Jenkins, W. R. 1964. A rapid centrifugal-flotation technique for separating
nematodes from soil. Plant Disease Reporter 48:692.

Kozlowska, J. 1989. The effect of sewage sediments on communities of soil
nematodes. Polish Ecological Studies 15:27-39.

Lavelle, P. 1996. Diversity of soil fauna and ecosystem function. Biology
International N° 33: 3-16.

Lee, D. 2002. The biology of nematodes. Edited by D. Lee, p. 69. New York,
NY: Taylor and Francis press pp. 1-634.

Lehmann, R.E., S.D. Shively and WP. Miller. 2007. Tardigrades of North

America: An historical collection from Kansas and Missouri. Transactions of the
Kansas Academy of Science 110: 169-178.

123

Minitab 15. 2008. Minitab Inc. www.minitab.com

Mulder, C., A.J. Schouten, K. Hund-Rinke and AM. Breure. 2005. The use of
nematodes in ecological soil classification and assessment concepts.
Ecotoxicology Environmental Safety 62: 278-289.

Neher, DA. 2001. Role of nematodes in soil health and their use as indicators.
Journal of Nematology 33: 161-168.

Neher, DA. and CL. Campbell. 1994. Nematode communities and microbial
biomass in soils with annual and perennial crops. Applied Soil Ecology 1: 17-28.

Neher, D.A., J. Wu, M.E. Barbercheck and O. Anas. 2005. Ecosystem type
affects interpretation of community measures. Applied Soil Ecology 30: 47-64.

Niblack, T. L. 1989. Application of nematode community structure research to
agricultural production and habitat disturbance. Journal of Nematology 21 :437-
443.

Papatheodorou E.M., M.D. Argyropoulou, and GP. Stamou. 2004. The effects
of large- and small-scale differences in soil temperature and moisture on
bacterial functional diversity and the community of bacterivorous nematodes.
Applied Soil Ecology 25: 37-49.

Robertson, GP. 2008. Long-term ecological research: Re-inventing network
science. Frontiers in Ecology and the Environment 6: 281.

Robertson, GP. 2008. About KBS LTER. Retrieved January 6, 2009, from
http://kbs/about/overview.php.

Robertson, G.P., E.A. Paul, and RR. Harwood. 2000. Greenhouse gases in
intensive agriculture: Contributions of individual gases to the radiative forcing of
the atmosphere. Science 289: 1922-1925.

Robertson, G. P., and D. W. Freckman. 1995. The spatial distribution of
nematode trophic groups across a cultivated ecosystem. Ecology 76: 1425-
1432.

Ruehle, J.L. 1969. Nematodes Parasitic on Forest Trees. Nematologica 15: 76-
80.

124

Quintanilla Tornel, M., G. Bird and S. Gage. 2009. Impact of Microwaves and
Water on Acoustical Signatures of a Compost. Accepted, Compost Science and
Utilization Journal.

Samoiloff, MR. 1987. Nematodes as indicator of toxic environmental
contaminants. In: Veech, J.A. & Dickson, D.W. (Eds) Vistas on Nematology,
p.433-439. Society of Nematologists, Inc. E.O. Painter Printing Co, DeLeon
Springs, Florida.

Sampedro L., & J. Dominguez. 2008. Stable isotope natural abundances (6‘30
and (TN) of the earthworm Eisenia fetida and other soil fauna living in two
different vermicomposting environments. Applied Soil Ecology 38: 91-99.

Sheu, S. & M. Falca. 2000. The soil food web of two beech forests (Fagus
sylvatica) of contrasting humus type: stable isotope analysis of a macro- and a
mesofauna-dominated community. Oecologia 123: 285-286.

Swift M. J. & J. M. Anderson. 1994. Biodiversity and ecosystem function in
agricultural ecosystems. ln: Biodiversity and ecosystem function. Schulze E., H.
A. Mooney Eds. Springer. 525 pp.

Taylor A.R., D. Schroter, A. Pflug and V. Wolters. 2004. Response of different
decomposer communities to the manipulation of moisture availability: potential
effects of changing precipitation patterns. Global Change Biology 10: 1313-

1 324.

ter Braak C.J.F., Smilauer, P. 2002. CANOCO Reference Manual and
CanoDraw for Windows User's Guide, Software for Canonical Community
Ordination (Version 4.5), Biometris, Wageningen and Ceske Budéjovice,
Netherlands, 500 pp.

Van Kessel, W. H. M., R. W. B. Zaalberg, and W. Seinen. 1989. Testing

_ environmental pollutants on soil organisms: a simple assay to investigate the
toxicity of environmental pollutants on soil organisms using CdCI2 and

nematodes. Ecotoxicology Environmental Safety 18:181-190.

Wasilewska, L. 1991. Long-term changes in communities of soil nematodes on

fen peat meadows due to the time since their drainage. Ekologia Polska 2:59-
104.

125

Wilsey B.J. and Potvin, C. 2000. Biodiversity and ecosystem functioning:
Importance of species evenness in an old field. Ecology 81: 887-892

XLSTAT©. 1995-2009. Addinsoft. www.x|stat.com

Yeates G. W, and T. Bongers. 1999. Nematode Diversity in Agroecosystems.
Agriculture, Ecosystems and Environment 74: 113-135.

Yeates, G.W., T. Bongers, R.G.M. De Goede, D. W. Freckman, and SS.
Georgieva. 1993. Feeding Habits in Soil Nematode Families and Genera - An
Outline for Soil Ecologist. Journal of Nematology 25: 315-331.

Yeates, G.W., D.A Wardle and RN. Watson. 1999. Responses of soil
nematode populations, community structure, diversity and temporal variability to
agricultural intensification over a seven year period. Soil Biology and
Biochemistry 31: 1721-1733.

126

Nematode Classification and Identification References“

Bert, W.and G. Borgonie. Order Rhabditida: Suborder Tylenchina. 2006. In:
Eyualem Abebe, W. Traunspurger, and I. Andrassy (eds) Freshwater
Nematodes: Ecology and Taxonomy. CABI Publishing, Cambridge, MA, pp. 648-
695.

Blaxter, M.L., P. De Ley, J.R. Garey, L.X. Liu, P. Scheldeman, A. Vierstraete, J.
R. Vanfleteren, L.Y. Mackey, M. Dorris, L.M. Frisse, J.T. Vida, and WK. Thomas.
1998. A molecular evolutionary framework for the phylum Nematoda. Nature,
392: 71-75.

Coomans A. and Eyualem-Abebe. 2006. Order Monhysterida. In: Eyualem
Abebe, W. Traunspurger, and I. Andrassy (eds) Freshwater Nematodes: Ecology
and Taxonomy. CABI Publishing, Cambridge, MA, pp. 574-603.

Decraemer W. and N. Smol. Orders Chromadorida, Desmodorida and
Desmocolecida. 2006. In: Eyualem Abebe, W. Traunspurger, and I. Andrassy
(eds) Freshwater Nematodes: Ecology and Taxonomy. CABI Publishing,
Cambridge, MA, pp. 497-573.

De Ley, P. and M. Blaxter. 2002. Systematic Position and Phylogeny. In: The
Biology of Nematodes. Lee, D. ed. Taylor and Francis, New York.

DeLey P., M. Mundo-Ocampo, and I. DeLey. 2001. Identification of Freeliving
Nematodes (Sercernentea). Workshop booklet from University of California,
Riverside, California. '

Fortuner R. and M. Luc. 1987. A reappraisal of Tylenchina (Nemata). 6. The
family Belonolaimidae Whitehead, 1960. Revue de Nématologie 1O : 183-202.

Freckman D.W. and CH. Ettema. 1993. Assessing Nematode Communities in
Agroecosystems of Varying Human Intervention. Agriculture, Ecosystems and
Environment 45: 239-261.

Goodey T. and J3. Goodey. 1963. Soil and Freshwater Nematodes. Butler and
Tanner Ltd, London, Great Britain pp 1-544.

Goodey J.B, and DJ. Hooper. 1965. A neotype of Aphelenchus avenae Bastian,
1865 and the rejection of Metaphelenchus Steiner, 1943. Nematologica 11:55-
65.

 

1 References not included in the manuscript, but used as resources for nematode identification
and classification.

127

Holovachov, O and P. DeLey. 2006. Order Plectida. In: Eyualem Abebe, W.
Traunspurger, and l. Andrassy (eds) Freshwater Nematodes: Ecology and
Taxonomy. CABI Publishing, Cambridge, MA, pp. 611-647.

Jairajpuri MS and W. Ahmad. 1992. Dorylaimida: free-living, predacious and
plant-parasitic nematodes. Oxford and IBH, New Delhi.

Mai W., P.G. Mullin, H.H. Lyon, and K. Loeffler. 1996. Plant-Parasitic
Nematodes: A Pictorial Key to Genera. Cornell University Press, Ithaca, NY.

Nguyen, Khuong B. 2006. Nematology website in the University of Florida.
httpzllkbn.ifas.ufl.edu/rhabdi/rhabkev.HTM

Nguyen, Khuong B. 2006. Nematology website in the University of Florida.
http:l/kbn.ifas.ufl.edu/cephalob/cephakev.htm

Pefia Santiago R. 2006. Dorylaimida Part I: Superfamilies Belondiroidea,
Nygolaimoidea and Tylencholaimoidea. In: Eyualem Abebe, W. Traunspurger,
and I. Andrassy (eds) Freshwater Nematodes: Ecology and Taxonomy. CABI
Publishing, Cambridge, MA, pp. 326-391.

Powers T. and P. Mullin. Plant and Insect Parasitic Nematodes. University of
Nebraska Nematology Website. httpzllnematodeunledu/

Raski, D. J. 1962. Paratylenchidae n. fam. with descriptions of five new species
of Gracilacus ng and an emendation of Cacopaurus Thome, 1943,
Paratylenchus Micoletzky, 1922 and Criconematidae Thorne, 1943. Proc.
Helminth. Soc. Wash., 29: 189-207.

Siddiqi, MR. 2000. Tylenchida. Parasites of Plants and Insects, 2nd Edition.
CABI Publishing. 848 pp.

Sher, SA. 1965. Revision of the Hoplolaiminae (Nematoda). V. Rotylenchus
Filipjev, 1936. Nematologica 11:173-198.

Sher, SA. 1963. Revision of the Hoplolaiminae (Nematoda). Ill. Scutellonema
Andrassy, 1958. Nematologica 92421-443.

SmoI, N.and A. Coomans. Order Enoplida. 2006. In: Eyualem Abebe, W.
Traunspurger, and I. Andrassy (eds) Freshwater Nematodes: Ecology and
Taxonomy. CABI Publishing, Cambridge, MA, pp. 225-292.

Stock, S.P, P. DeLey, l. DeLey, M. Mundo-Ocampo, J.G. Baldwin J.G. and SA.
Nadler. 2002. Panagrobelus stammen' Riihm, 1956 and Plectonchus hunti n.
sp.: implications of new morphological observations for characterisation of these
genera (Nematoda: Panagrolaimoidea). Nematology 4 (3): 403-419.

128

Thorne, G. 1974. Nematodes of the Northern Great Plains. Part II.
Dorylaimoidea in part (Nemata: Adenophorea). Technical Bulletin.

Vinciguerra, MT. 2006. Dorylaimida Part II: Superfamily Dorylaimoidea. In:
Eyualem Abebe, W. Traunspurger, and I. Andrassy (eds) Freshwater
Nematodes: Ecology and Taxonomy. CABI Publishing, Cambridge, MA, pp. 392-
467.

ZuIIini A. and V. Peneva. 2006. Order Mononchida. In: Eyualem Abebe, W.
Traunspurger, and I. Andrassy (eds) Freshwater Nematodes: Ecology and
Taxonomy. CABI Publishing, Cambridge, MA, p. 468-496.

Zullini A. 2006. Order Triplonchida. In: Eyualem Abebe, W. Traunspurger, and
I. Andrassy (eds) Freshwater Nematodes: Ecology and Taxonomy. CABI
Publishing, Cambridge, MA, pp. 293-325.

Websites:

Images and description of KBS/LTER nematodes:
http://www.nemasoi|.com/

Pictures and descriptions of soil nematodes:
http://nematode.un|.edul

Key to soil nematodes:
httpzllnematode.unl.edu/key/nemakev.htm
httpzllnematode.unl.edu/konzlist.htm.

Plant Parasites:

httpzllmgd.NACSE.ORG/hyperSQL/squiggles/
http://www.apsnet.oLq/education/LessonsPIantPath/LesionNema/

Key to the Order Diplogasterida:
http://kbn.ifas.ufl.ed u/gaster/dipmainhtm

Key to the Rhabditida-Rhabditina:
httpzllkbn.ifas.qu.edu/rhabdi/rhabmain.htm

Key to the Tylenchidae:
http://kbn.ifas.ufi.ed u/tvlench/tvlench.htm

Key to the orders:
http://p[pnemweb.ucdavis.ed u/nemaplex/Taxadata/orderkey.htm

129

Diverse keys and nematode info:
http://plpnemweb.ucdavisedu/nemaplex/Uppermnus/nematamnu.htm

Key to the families:
httpzllplpnemweb.ucdavisedu/nemaplex/‘l'axadata/Famkemtm

Key in Spanish:
httpzllwwwsenasa.qob.pe/intranet/capacitacion/cursos/curso arequma/clave ide
ntgeneropdf

 

130

CHAPTER 3

TEMPORAL DYNAMICS OF ACOUSTICAL SIGNATURES ASSOCIATED
WITH ALTERNATIVE MANAGEMENT AND NATURAL ECOSYSTEMS

ABSTRACT

Different habitats were monitored at the Kellogg Biological Station Long
Term Ecological Research (KBS/LTER), Hickory Corners, Michigan in 2005,
2006 and 2007 for their acoustical properties. Microphones where placed 30 cm
above ground and on the soil surface in a conventionally tilled corn-soybean
rotation, an organic tilled corn-soybean rotation, early succession and mid-
successional fields, a coniferous forest, and a deciduous forest. Acoustical
signatures were recorded on laptop computers at sunrise, noon, sunset, and
' midnight. Recordings were made every 5 minutes in 2007, and every minute in
2005 and 2006 for 30 seconds in duration at selected times of the day for about 2
hours. At midnight, there were significant differences between the above ground
sounds in the forest compared with the agricultural habitats. At midnight, the
deciduous forest habitat had a high intensity sounds made by tree crickets and
katydids; while the agricultural habitats were equally quieter throughout the day
and night. The sound Power Spectral Density (PDS) tended to increase as the
darkness increased in the deciduous forest and early and mid-successional
habitats. There were no differences among the habitats for the acoustical
signatures at the soil surface. We conclude that different ecosystems have
different acoustical characteristics.

131

INTRODUCTION

Sound can be used as one metric to measure ecosystem health. Gage et
al. (2001) developed an acoustic index to measure habitat quality. This index is
based on partitioning the acoustical frequency spectrum. Gage et al.
(www.real.msu.edu) developed analytical processes to measure acoustic
properties of ecosystems, and produced a web-based infrastructure that could
capture, process and relay ecosystem measurements through internet, in near
real-time (Butler et al. 2006).

Krause and Gage (2003) stated that an ecosystems’ acoustical signature
is a unique component of its function. Acoustical sampling methods do not
require destructive sampling and can be measured relatively easily and
inexpensively, compared to other procedures.

Bernie Krause, originator of Wild Sanctuary, Inc.
(http://www.wildsanotuarvcoml) is a soundscape pioneer and acoustical
technology researcher since 1968. Krause (2001) reported that sounds of
habitats are being lost. In part, he hypothesized that this is caused by habitat
destruction. Masking and disturbance created by human-induced mechanical
sound can impede many organisms from communicating and mating. This can
result in a decrease or even possible extinction of “key vocal creatures” and
accordingly the combination of shrinking habitat coupled with increased human
activity has produced conditions where non-human communications necessary
for survival of some organisms is in jeopardy (Krause 2001). Murray Shafer is
identified as the father of acoustic ecology who coined the term soundscape in

132

the late 70’s (Krause 2001). In Tuning of the World, Schafer (1977) observed
that the noise produced by humans contributes to the loss of soundscape in the
wild and that loud noise is emblematic of Western models of power (Schafer

1 977).

Krause (2001) noted that political, economic, and social aspects of our
culture has had a significant impact on ecosystem acoustics because in
undisturbed environments organisms distribute their sounds in space and time in
order to avoid competition, calling this the acoustic niche hypothesis. The niche
hypothesis predicts a positive correlation between species composition and
soundscape structure in terms of time, frequency and amplitude. When a habitat
reaches dynamic equilibrium, the spatial structure of the acoustic sonograms
illustrate complex features (both frequency and temporally based), indicative of
the relationship between vocal organisms (Krause and Gage 2003, Krause
2002). In other words, biodiversity tends to be positively correlated to acoustic
diversity over time, and the acoustics of an environment can provide indices of
ecosystem integrity and biodiversity. The acoustic niche hypothesis was
examined in an analysis of recordings made in the Sequoia National Park
(Krause and Gage 2003).

The definition of biophony is “the combined sounds that living organisms
produce in a given habitat” (Schmidt 2002). It is also believed that the biophony
of an ecosystem is a metric to aid in understanding its health. Gage and Krause
(2003) did not focus on recording and analyzing sounds of individual organisms

but analyzed the acoustical signature of entire systems. Embedded in this are

133

indications of the system’s health, location, season, weather, time, successional
stage and disturbance. The thesis tested was that each habitat’s biophony is
coupled to the health of a particular habitat.

According to Graber (Schmidt 2002), sounds...in Sequoia represents a
valuable component of a park’s natural resources inventory, much like producing
a vegetation map or a list of animal species...Should concordance among
various acoustic elements in a soundscape prove to be a widespread
phenomenon, it...holds promise for a window into a whole new aspect of
ecosystems that was heretofore undetected. Understanding and preserving the
acoustics of an ecosystem is important. For example, seventy-two percent of
visitors of national parks say that among the most important reasons for their visit
is to experience the peace and sounds of nature
(http://www.nature.nps.qov/naturalsounds/index.cfm). More multi-year and multi-
season recordings from other ecosystems are needed to gain an overall
understanding of biophony.

Objectives: The objectives of the research are to: 1) record the unique
acoustical signatures of different habitats, 2) measure the effect of disturbance
and successional levels on biodiversity through acoustical measures, and 3)
determine the temporal dimensions of acoustics.

The following three hypotheses were tested: 1) habitats have specific
acoustical signatures, 2) acoustical signatures of habitats have temporal
dimensions, and 3) agricultural systems impact ecosystem acoustics due to

disturbance.

134

Site Description: The research was conducted at the Michigan State University,
W. K. Kellogg Biological Station (KBS), Long Term Ecological Research (LTER)
site, in southwest Michigan (85° 24’ W, 42° 24’N, 288m elevation, Fig. 3.1). The
KBS/LTER was initiated in 1987 by Michigan State University, and the Michigan
Agricultural Experiment Station with support from the National Science
Foundation. It was founded with the objective of obtaining interdisciplinary
research in agricultural ecology (KBS/LTER website).

Soils at KBS/LTER are Typic Hapludalfs. They are either fine-loamy,
mixed Mesic of the Kalamazoo series, or coarse-loamy, mixed Mesic of the
Oshtemo series. Annual mean temperature is 9°C (min. in January -5°C, and
max. in July 22°C). The mean annual precipitation is 84 cm, evenly distributed
throughout the year, with half as snow.

The sites used for this experiment were: 1) corn/soybean/wheat rotation
with conventional management and tillage, 2) corn/soybean/wheat rotation
legume cover— bio-based (organic), 3) deciduous forest, 4) coniferous forest and
5) two old field successions, one is in an early succession stage because of
occasional burning and was last tilled in 1989 and the other is in a mid-
succession stage (grasses, forbs, shrubs and small trees) and was last tilled

circa1955 (Robertson et al. 1993, http://lter.kbs.msu.edu/siteDescription.htm, Fig. 3.1).

135

MATERIALS AND METHODS

Sounds were recorded in deciduous and coniferous forest, early
successional communities (yearly burning), mid-successional community and
agricultural habitats during July and August 2005, 2006 and 2007. Each
microphone (Aquarian A3 hydrophone) was placed 30 cm above the ground
(Figs. 3.2 & 3.3). The recording equipment included a laptop computer, two
hydrophones, an M-Audio sound processor, and a foam tube 30 cm in length to
support the two hydrophones (Fig. 3.2).

Total Recorder software, MobiIePre USB (httgzllwwwm-
wio.com/prodtm_/en us/MobilePreUSB.html) was used for automatic scheduling the
recordings, thus allowing simultaneous recording at all site. The battery powered
laptops limited recording time to a maximum of two consecutive hours. Cool Edit
2000 (now Adobe® Soundbooth® CS4) and Spectogram were used to make
sonograms and a program, developed in Matlab (2007) was used to analyze
sound samples.

In 2005, recording were made in four sites: 1) mature deciduous forest, 2)
coniferous forest, 3) bio-based (no-input, organic) agricultural system, and 4)
high input conventional-till system. From Monday, August 22 until Friday August
26, each site was monitored using two microphones. One was positioned at a 30
cm height and the other on the soil surface (O-horizon). This was repeated six
times per day: 6:00 (dawn), 9:30 (midmorning), 12:00 (noon), 16:30 (mid-

afternoon), 20:30 (dusk), and 00:00 (midnight). Recordings were made in all

136

sites simultaneously. There were seven acoustic samples of 30 seconds each
taken at all six times.

In 2006, similar methods and the same equipment were used as in 2005.
The microphones were placed in the same position as in 2005 (one 30 cm above
the ground and one on the soil surface). The recordings were made three times
during the summer: July 18-26, August 9-14, and August 23-27, 2006.

During observations in 2005, it was observed that the insect sounds
intensified as the night fell in late August. To investigate the increment of sound
as darkness increased, evening recordings where initiated about 30 to 45
minutes before and after sundown. The recordings were made every five
minutes for 30 second duration. Sounds were sampled in the evening starting
before sundown from ~8:30-10:00 pm and at dawn. Seven recordings were
made at noon every one minute, for 30 second duration based on the same
methods for the midnight recordings.

In addition, sounds were recorded in five habitats: 1) bio-based (no-input,
organic) corn/soybean/wheat rotation, 2) conventional corn/soybean/wheat
rotation, 3) mid-succession, 4) deciduous forest, and 5) coniferous forest.
Because sounds of audible interest were not detected in the organic
corn/soybean rotation and the coniferous forest, the sound recorders were
relocated to successional Pseudoacacia and grass fields that were adjacent to
the gravel road within the KBS/LTER main site, where there was more acoustical

activity. Even though recorders placed in the conventional corn/soybean rotation

137

did not produce significant audible sounds, the recorders were left recording to
enable comparison with other habitat types.

In July and August, 2007 sounds from the deciduous forest, mid and early
successions, and conventionally tilled corn/soybean/wheat rotation agricultural
ecosystems were recorded (Table 3.1). Recordings were made primarily at
times of lighting change such as sunrise and sunset (Table 3.1). The sounds
were recorded every 5 minutes for duration of 30 seconds. Replication was
obtained by recording the sounds of each replicate ecosystem at the same time
on a different day. Each KBS/LTER habitat is replicated three or six times. Each
replicate consists of a 1.0 hectare plot. The habitats sampled were: 1)
conventionally tilled (high input corn/soybean/wheat), 2) mid-succession field
last tilled circa 1955 (grasses, forbs, shrubs and small trees), 3) early
succession, yearly burned, with grasses and forbs, historically tilled last in 1989
and 4) mature deciduous forest.

“Images in this dissertation are presented in color.”

RESULTS
Ecosystem Acoustical Signatures

Sounds recorded in each habitat had unique acoustical signatures (Fig.
3.4a-c). In July and August 2007, sounds from the deciduous forest were
significantly (p = 0.05, 95% Cl, Fig. 3.5) louder than both the field successions
(early and mid—successions) and the conventionally tilled systems. In addition,

the sounds recorded in the field successions had significantly (p = 0.05) greater

138

Sound Power Spectral Density (PSD) than sounds emanating from the
conventional tillage (Fig. 3.5). In 2005, the deciduous forest had sounds with
greater PSD than sounds recorded the coniferous forest, and the agricultural
ecosystems (Fig. 3.6).

Temporal Dynamics of Ecosystem Acoustics

Time of day and date impacted the PSD of ecosystems at the KBS/LTER.
The change was more notable in the native ecosystems (forest and old field
successions) than in the agricultural system.

In the deciduous forest sounds were louder as the evening progressed
due to insect sound (Fig. 3.7). This was not observed in July. The PSD of
sounds recorded in the morning (~6:00-7:00 am) and noon in both July and
August 2007 were near 0 (Fig. 3.8). In 2006, the sounds in the deciduous forest
in August had significantly greater PSD in the evening and midnight (p = 0.05,
95% Cl) compared to sounds recorded in morning and noon (Fig. 3.9). In August
2007, the evening sounds were significantly greater in the evening and night than
in the morning and noon. In the July sampling date, time of day did not affect
sound PSD. On August 22-26, 2005 the deciduous forest at midnight had
significantly (p < 0.001, AOV) more sound than all other ecosystems recorded
(coniferous forest, conventional tillage and bio-based (organic)). In the
deciduous forest, the sounds were louder (p < 0.001, AOV) at midnight than at
6:00, 9:00, 00:00 (noon), and 20:30 (Fig. 3.6).

In the mid-succession ecosystem in 2007, the sounds produced on July

23, 2007, were significantly different from those of August 22, 2007 (p = 0.001).

139

On both dates, time effectively (p< 0.001) predicted the insect sound intensity.
The later in the evening, the louder the sounds (Regression Analysis, analysis of
variance p < 0.001, R2 = 53.7%; Fig. 3.10). Insect sounds in August 22, 2007
increased as a cubic regression form (R2 = 97.7%). As the evening progressed,
the sound increased, but it a wave fashion. After dusk, the sound increased in
an exponential fashion (Fig. 3.11).

In the evening, 2007, the sounds in the early and mid-successions were
generally not as loud as the sounds emanating from the deciduous forest (Figs.
3.12 & 3.5). Insect sounds were louder in the early and mid-successions in
August 27 2007 (Fig. 3.12) compared to earlier August and July recordings. On
August 29, 2007, however, the sounds in the early succession were as loud as in
the deciduous forest recordings.

The sounds recorded in the conventional tillage habitat had a low PSD on
all recording dates and times compared to the forest and the field successions (p
= 0.05, 95% Cl, Fig. 3.5). Since this experiment was conducted in a living field
laboratory consisting of different systems, sounds from the nearby field
successions or forests were recorded as background noise. These sounds were
heard at a distance, had significantly lower PSD values than that associated with
the deciduous forest and field succession in August in the evening. This was
observed in 2005, 2006 and 2007 (Fig. 3.5, 3.6 & 3.12).

Evening Sound PSD Distribution in the Different kHz Levels: The PSD value
(watts/kHz) for level 3 (2-3 kHz) was dominant in the deciduous forest in August

2007. It was significantly (p = 0.05, 95% Cl) higher than all other kHz levels

140

(Fig. 3.13). This was not the case in July. The PSD value for level 1 (largely
background sound) was similar in July and August 2007. There was significantly
more sound energy (PSD) in level 4 in August 2007 at night, compared to July at
the same time of day (Fig. 3.13). There were no significant differences between
July and August 2007 for any of the PSD frequency levels (1-11) associated with
the mid-succession. The 0-1 frequency kHz (level 1) had the greatest PSD (Fig.
3.14).

In the early succession, August 2007 evening recordings (~8:00-
10:00pm), the acoustic frequency (3-4 kHz) had the greatest PSD and was
significantly (p = 0.05, 95% CI) higher than lower frequencies (levels 2, 3) or
higher frequencies (5—11). The PSD values in Frequency levels 1 and 3 were
also higher than the frequency levels 5-11 (Fig. 3.15).

The sound pattern and PSD distribution can be visualized with a
sonogram. August nights in the deciduous forest in produced a sonogram with
reds and maroons Fig. 3.16). The highest sound intensity is between
frequencies of 2.5 kHz and 3.5 kHz, but the sound intensity of the insect calls can
be visualized in every kHz (Fig. 3.16). In the morning, the insect calls in the
deciduous forest were less intense, but bird calls were apparent in the spectrum.
Bird calls can be recognized by their diverse patterns in the sonogram (Fig. 3.17)
compared with insect calls which have lower diversity. The field succession in
August also tended to increase in sound PSD in the night. At night sonograms

tend to have a strong sound signal between the 3 and 4 kHz frequencies (Fig.

141

3.18). All the sounds and their respective sonograms for recordings in 2005,

2006 and 2007 can be heard and seen in www.nemasoil.com.

DISCUSSION

Time of day, date, and ecosystem type had an effect on the acoustical
signatures observed in our research. This agrees the Gages and Krause (2003)
hypothesis that acoustical signatures are different by location, season, time,
successional stage, disturbance and weather of ecosystems.

Ecosystem Specific Acoustical Signatures

At night in August, forest sounds had significantly more PSD in kHz 3-4
levels compared to July. Many of the sound making insects, such as katydids,
sing in the 3 or 4 kHz level (Walker and Moore, http://entnemdept.ufl.edu/walker/buzz/).
Audibly, our research indicated that there was an insect choir in August evenings
that was low or absent in July. Because kHz levels 3 and 4 were very low in the
mid-succession fields, we suppose that there was not a great presence of singing
insects on the evening recording dates as the deciduous forest.

At 20:00-22:00 in the early field succession in August, the kHz level 4 had
greater sound PSD than all other kHz levels. This is different from the forest on
the same times and dates. In the deciduous forest, kHz level 3 dominated and in
the early field succession kHz level 4 dominated the sound spectrum. This is
probably because different insects are singing in the forest than in the field

successions.

142

Temporal Dynamics of Acoustical Signatures: Recordings were made at
times of light intensity change, including sundown and sunrise, to determine if
insects increase their calls as darkness increases in the evening and night, while
birds increase their calls in the morning. This observation was supported by our
results. The insect sounds increased as darkness increased at night in the
deciduous forest and field successions in 2005, 2006 and 2007. Multiple
acoustical insect taxa stridulate at night and not in the day (lorgu and Musta
2008). Taxa can be differentiated by their acoustical signature and the temporal
dynamics of their calls (Walker 1962 and Oliveira et al. 2001)
Ecosystem Management Impact: On all dates and recording times, the
agricultural systems had a low sound PSD. During night in August, when the
deciduous forest and the field successions had multiple intense insect sounds,
the acoustics associated agricultural ecosystems were low or absent. KBS/LTER
agricultural systems do not seem to be a habitat propitious for acoustical insects.
The ability of birds to communicate using complex acoustics was evident
in their calls. They have very distinct calls that resemble language. Insect calls,
however, seem more mechanical. Many insect and birds can be identified to
genus or species by their acoustical signatures. There is also an apparent
difference between night and day. In KBS/LTER the forest and the field
succession increase in sound as darkness increases. At the same time the bird
calls are not commonly heard at night. It is probable that katydids, tree crickets,
crickets and other acoustic insect have a selective advantage by signing at night

when birds are less likely to prey on them, because of low visibility. Day calls

143

would disclose the well-camouflaged insects’ location and birds could easily find
them. This is a possible explanation for the increase of insect calls at night
compared to the day. One way to test this hypothesis would be to record insect
sounds in Guam where there are not many native birds left because of a non-
native snake invasion. It is hypothesized that insects in Guam have increased
their day calls because of no bird predation. This however was not tested by us.
In conclusion ecosystems have specific acoustical signatures that change
with time of year and time of day. Agriculture has a significant impact on the
biophony of ecosystems. Acoustical signatures can be used to distinguish

different ecosystems.

144

Acknowledgments:

"Support for this research was also provided by the NSF Long-Term Ecological
Research Program at the Kellogg Biological Station and by the Michigan
Agricultural Experiment Station." My appreciation for the help and support of Dr.
Bird, Dr. Gage, Dr. DiFonzo, Dr. Robertson, Dr. Smucker, Dr. Delfosse, Dr.

Ayers, Dr. Miller, Goran Jotanovic, and Claudio Quintanilla Hausdorf.

145

TABLES

 

Table 3.1. Sound recording sampling times, dates and sunset, dusk, dawn
and sunrise times at Kellogg Biological Station/Long Term Ecological

 

 

 

 

 

 

 

 

 

 

 

 

Research Michigan in July, August and September 2007.

Sampling Sampling Ecosystems and Time of Civil Civil Time of
Dates Times Replications recorded Sunset: Twilight Twilight Sunrise
2007

July 23 20:40-22:20 Deciduous forest rep. 1, 21:10 21:43 5:51 6:23
mid-succession rep. 1,
and Conv. Till rep. 1*

July 24 20240-22220 Deciduous forest rep. 2, 21:09 21:42 5:52 6:24
mid-succession rep. 2,
and Conv. Till rep. 2

July 25 20:40-22z20 Deciduous forest rep. 3, 21:08 21:41 5:53 6:25
mid-succession rep. 3,
and Conv. Till rep. 3

August 22 20:00-22:00 Deciduous forest rep. 1, 21:02 21:38 6:24 6:54
mid-succession rep. 1,
and Conv. Till rep. 1

August 27 20:00-22:00 Deciduous forest rep. 2, 20:25 20:54 6:30 6:59
early succession rep. 2,
and Conv. Till rep. 2

August 29 20:00-22100 Deciduous forest rep. 3, 20:21 21:51 6:32 7:02
early succession rep. 3,
and Conv. Till rep. 3 .

August 30 6:00-8:00 Deciduous forest rep. 1, 20:20 20:49 6:34 7:03
early succession rep. 1,
and Conv. Till rep. 1

August 31 12:00-1:00 Deciduous forest rep. 2, 20:18 20:47 6:35 7:04
early succession rep. 1,
and Conv. Till rep. 3

Sept. 2 12:00-1:00 Deciduous forest rep. 3, 20:15 21:18 6:37 7:06
early succession rep. 4,
and Conv. Till rep. 2

Sept. 3 6:00-8:00 Deciduous forest rep. 2, 20:13 20:42 6:38 7:07

 

 

early succession rep. 4,
and Conv. Till rep. 2

 

 

 

 

 

1' KBS/LTER treatment names: DF1, SF1, & T1
twww.sunrisesunset.com, retrieved March 27, 2009

146

 

FIGURES

 

Kellogg Biological Station
LTER Sites

x~w

l ’2 -- ‘ '51.: .
‘ 4 “q.-.“ 4*
. '_.-l‘~ 31, ' 1 '

 

 

km

 

Scaleup Fields [:1 Other LTER Studies 0 0 5 1
Unmanaged Sites E Experimental Plots Map prepared by KBS LTER GIS Lab. Dec. 2006

 

 

 

 

 

Fig. 3.1. Aerial image of the Kellogg Biological Station/Long Term Ecological Research site at
Hickory Corners Michigan (http: //www Iter kbs msu g ' TERsites jpg).
Treatments (T) used: T1= conventional tilled crop rotation T4= bio- based (no-input, organic) crop
rotation, T7= early succession last tilled in 1989 and occasionally burned, SF= mid- succession
last tilled circa 1955, DF= mature deciduous forest, CF = mature coniferous forest.

 

147

 

  

9" x
.

.‘ ‘3 5 . u
equipment used for acoustical recordings. The

 

Fig 3.2. Photographic image of recording
hydrophone is inserted in the purple foam.

Fig. 3.3. Acoustical recording equipment in the Deciduous Forest. Kellogg Biological Station,
Long Term Ecological Research, Hickory Corners, Michigan.

148

Figs. 3.4a-c. Acoustic
characterization sonograms of
deciduous forest, early succession,
and conventionally tilled
corn/soybean/wheat crop rotation at
21 :20, August 29, 2007. The
sounds were recorded at Kellogg
Biological Station, Michigan. The
x axis represents 0-30 seconds in
time and the y axis represents
frequencies of 0-11 kHz. Color
intensity indicates sound intensity.
Cool blues and greens indicate
lowest intensity, yellow medium
and the highest sound intensity is
orange, and red.

5!

F requencyfl-l 1 kHz

 

5 10 15 70 75

Time 0-30 seconds

Frequency 0-11 kHz

 

15 20 25
Tlmo (50600“)

Time 0-30 seconds

9,111 kHz

..- .3:

l‘u’

Frequen

 

149

 

 

18000 ‘
16000 -
14000 - EB
12000 4

 

Total PSD (watts/ kHz)

 

 

 

0.. a

 

Deciduous Forest Early Suchession ConventionalTill
KBS] L'l'El Sites

 

 

 

Fig. 3.5. Total power spectral density (sum power spectral density (PSD) of the
frequency levels 2-11 kHz) for recordings of July and August 2007 during sunrise, noon,
sundown, and midnight at the Kellogg Biological Station/Long Term Ecological
Research Station, Hickory Corners, Michigan. Error represents 95% confidence interval
from the mean.

150

 

 

 

 

 

60..
50-
:? 40‘
H
A
g 30‘
fi 20-
10-l
o- geéee 6‘9 $99 eeeeee eeeeee
IllIII‘I‘rI I II I I
9999 9999 9 9'9
Q.9 Q6 ”890999.95 9°699‘9g? “;,9°f§3>° 9°Qb°9°tmr§x 69 .9 9%“?
29‘ «9 Z
«0"? «0‘ .66? ‘
9- 9' e c

 

 

 

Fig. 3.6. Total sound power spectral density (PSD) of frequencies 1-11 kHz for August
22-26, 2005 sound recordings at the Kellogg Biological Station, Long Term Ecological
Research station at Hickory Comers, Michigan. The ecosystems recorded were:
deciduous forest, coniferous forest, bio-based (no-input, organic) crop rotation, and
conventionally tilled crop rotation. Errors represent 95 % confidence intervals from the
mean.

151

 

75000

PSD kI-lz 2-11 mo
25000

0

 

 

 

Fig. 3.7. Deciduous forest total sound power spectral density (sum power spectral
density (PSD watt/kHz) plot of the frequency levels 2-11 kHz) for recordings of July and
August 2007 from 20:00-22:20 at the Kellogg Biological Station/Long Term Ecological
Research Station, Hickory Comets, Michigan. Each dot represents the sound PSD of one
sample. The sounds PSD increase exponentially as the evening progresses in late August
and early September.

152

 

flaws/x

can b3. £8 E coo: use 9:59: 2: E o 8 owe—o 8w mwgom 2g .confiofiom 35o use 5395‘ 82 E 382on @555 2:

mm 3335598 88.55 Qmm £258 2? .2983 one he Qmm 950m 05 35838 How comm .cmeomE £280 @865 £288
socmomom EomwoBom 85H 985:0:me Egg—Em mmozovm 2: we oouooéouo Boa boom “Swag Ea bi. me @3988 com ANEx :-N
232 mocosaofi 2: ,Lo 83 323333 Qmmv 56:33 :28on $33 :53 36:26 :28on .5309 958 :38 $28 303283 .w.m .wE

 

. Boga 332MB
8.8 883m

 

 

2::
8;:

85° 8%

88m 3.” ":3. ..o 8..

ooomm

BS “83". 38289.32. a as: 2. Svu ”:5 am“. co 3... 88.5

 

 

 

153

.szwsxw

95 b3. Son 8 coo: can wEEoE 05 E o 8 086 88 $3538 2? .conEoEom base was “msmsxx 8.2 E 338on @555 2:

3 923388 3885 0mm £538 2:. .0383 28 .«o Qmm 950m 2: muaomoaoc 8c scam .zwmfioaz .mccfioo boo—oi flotsam
subsumed Rommsoom Each wagoufim 329365 mwo=oM 2: we oouooéono 89¢ ooom Hm=w=< was 33. mo meEoocp c8 ANEx :-N
.232 >253:on 2: mo 83 ANEVSEB Dmmv 5659 :26on 8309 833 3656 3.30on anion 958 :38 “mono.“ $5520an .06 .wE

 

oouoo . m2:
ooNH

         
                

  

      

88-3 oonoo 85¢
88.; I."
3.3 I‘MIIIIJ ~'
- 1‘ - I
88-3- #...ll.~.'..hhi."" o
.. .... .... ”"

... .

.. ... .

L .. ..I. ' . . I
.IIIIIIIuIIuiiiiiiiuiitttttthn 02
llllIIIWIIIIuIlr/IIIN/Il

88-x-
88-2..

     

   

    

 

@OON wagon—lush... can Quan— m> HHINNIx “—0 no... montam

 

 

 

154

m .. .mEF . .88. + N . .mEP .. mate I .mEP .. $93 + 9.. - I own. a.
88:8 8 8.8089 883. 9: ..m . me:- . 8848. I .N . we; . 888. + as: . 9488. I 888 I own. a. :88 m E 8882
0.8.8 22. one duos-mm .I- Nw. .250 v a occur? Co 29.29 .m_m>_mcm c2893.; 2258 9... .250. 9: 9.55 on. c. Loam. map 3.29:. 958
.88. as 8882.. :88 vs 22:88 as: .88 £8 a. .288 u a. B8 .8 882 .o 805 so: 8988 88:85 em; 38 .8 22.
c. coozuoa 250m 05 .Bom c. mag... .o 308.; 5:5 co_mmooo:m-u_E co”. Room .NN 6:921 ucm mm 22.. »o $553 on. 5 mean 99> 35989
one 892... .o ESQ... 5.2, co_mmmoo:m-u.E 2... :. awn: bacon .mbomqw 526.. 958 new 9:: .0 95:26.9 9.. Co E 5.89%. 05:0 .ofim .9“.

 

 

 

 

0E:-
8_n- 82m 85m
.0
e e Ix- III I I
o \ a
\ -88 m
\ l m
. -88 /
\ m
\ (
. -88 m
J m
. -88 m
\ Z
. w
. -oooo
888% III I
mooQMNR +
3mm. Iooon

 

 

 

 

 

 

 

 

155

Noom .NN 8:92. 8982 202, mucsom on... .32. mote :. no.5 “mm. Em: 5.8883
.25 m E awn: 3.950 .96on 5on ucsow use 8E: con-:38 95:28.2 9.. v6 E 88859 05:0 .38 .9“.

 

 

  
   

 

 

 

 

 

 

0E...-
853~ 8.8.3 888 85.18 8.88 8.88 8.38

i— . _ n _ _ _ lo I—
o e m
w
.83 m.
n
w

-ooo~
q.
a
-ooom )
m
n...
-83 fi
.88 W
m...
98.8 838-”. -ooom m
898 8-x W
#38 m .255 I

m**.mE_-.- wo+mmm- + ~**.oE_-r mo+m~fim -
.oEfi wo+mHN.m + mo+mm¢é - n 2-3-2&3

 

 

 

156

 

 

 

.85“. .o
.._....>:ou
8.8826 2.3
cofimmuuzméf
mEBmRoum

94

-.

 

 

 

88m Sun. NIV. Esm
£5395 om“.

oooooH

 

 

 

.5m322 .5sz .womwofimm awe—Ev— a 22m 8358 ”moaBEaufioWEoo

no...“ 32032380 m 98 Awning 32238 5.3. 32 E 8::

an. “SE82; 3.80 an no m3. 5 BE» awn. Eon 3868836? smoke.“ 30:2ch
60282 203 £53me8 Sow mo £258 0% .88 E .me :-m 205. 5:33
%o Swab 56ch 3.58% 538 958 Sam 05 mo 83 B=o_m:ofi% REF .N. .m .mE

 

 

 

 

157

 

 

35000 a

30000-

25000 -

 

20000 a

15000 a

wow- l

5000- E

Sound PSD (watts/ kit)

I
04 * t + 0" -o-

 

 

 

July August July August July August July August July August July August
kHz-1 kHz-2 kHz-3 kHz-4 kHz-5 kHz-6

 

 

 

Fig. 3.13. Distribution of sound power spectral density (PSD) in the frequency levels 1-6
kHz recorded in deciduous forests at Kellogg Biological Station, Hickory Corners,
Michigan. The sounds were recorded in July and August of 2007. Sound power spectral
density in the frequency levels 7-11 kHz was not different from 0, so they are not
included in the graph. Errors represent 95 % confidence intervals fi'om the mean.

158

 

 

 

 

 

 

 

25000-
20000— ‘-
\
*3 1soood a
5 l.
i 1°°°°l
2 _
5 5000.
0.. -_ I I I I -o- I -o- *-
July August Jle August July August July August fly August
kHz-1 kHz-2 kHz-3 kHz-4 kHz-5

 

 

 

Fig. 3.14. 2007 sound power spectral density (PSD) of kHz frequencies 1-5 recorded in
mid-successional field last tilled circa 1955 in Kellogg Biological Station, Hickory
Corners, Michigan. The recordings were done between ~20:OO and 22:20 in July and
August 2007. Sound power spectral density in the frequency levels 6-11 kHz was not
different from 0, so they are not included in the graph. Errors represent 95 % confidence
intervals from the mean.

159

 

 

25000 -

20000a

15000 a 4»

10000-1

PSD (watts! kHz)

 

5000- I "

ll

0-1 v o -o- -o o v 3

 

 

 

kl-E-i kH'z-Z HEB kI-llz-4 kHE-s kl-é-s kl-l‘z-7 kH'z-s kHE-s ld-lztlo ”E11

 

 

 

Fig. 3.15. 2007 sound power spectral density (PSD) of frequency levels 1-11 kHz
recorded in early successions field occasionally burned and last tilled in 1989 at the
Kellogg Biological Station, Hickory Comers, Michigan. The recordings were done
between ~20:00 and 22:00 in August 2007. Errors represent 95 % confidence intervals
from the mean.

160

Amucooomvoc=h
mm on m. o. m

oooF

OOON

coon

COO?

0000

0000

(2.4) Aouanbaij

ooom
..oooor

ooorr

 

5mm so... .o 6:38 3:25 bo> u 5995.99 .958 o: no 26. n 5993.8 3.9.2:. 958 .935
2.69: 3.29:. 5.00 .EQ mfior .38 .NN $392. c. $58 $90.. 303208 ho 2559.8 .0; .9”.

161

39.00va 0E...

(H) Aouenbaid

 

.Eloo.__.o|m.mE... loll: .33;

c. :03 on :8 mEmancom 20.2 .mucaom be 5.25%.. ago 2.33 cozmo_::EEoo xanoo mm zoom 20 .mucaom BB 9:.
mé N... c. 05. as... o5 2m 2258 88:. of. So noun am noon .8 “mama/x 5 $58 628 303.0% .0 Emacsom N; .9”.

162

AmUcOQowvoczh
2.

lru.

 

oooe

ooom

oooo

(2H) Aouanbaig

coon

oooo

ooom

oooow

coo:

.mmnom 3 Boom .om ~m=w=< 25v 203 $5982 BF dwa

E 3:: ES mam—:3 35638 HEB coimooosm 353 5 50¢ €8.58 05 mo msaumocom .w _ .m .mE

163

REFERENCES

Butler, R., M. Servilla, S. Gage, J. Basney, V. Welch, 8. Baker, T. Fleury, P.
Duda, D. Gehrig, M. Bletzinger, J. Tao, D. M. Freemon. 2006.
Cyberlnfrastrastructure for the Analysis of Ecological Acoustic Sensor Data: A
Use Case Study in Grid Deployment.
httpzqurid.ncsa.uiuc.edu/papers/CLADE2006 LTERgrid CCJ V3-FlNAL.pdf

Cool Edit 2000. Now Adobe Audition.
httpzllwww.adobe.com/special/products/audition/syntrillium.html

Gage, S. H., B. M. Napoletano, and M. C. Cooper. 2001. Assessment of
ecosystem biodiversity by acoustic diversity indices. The Journal of the
Acoustical Society of America 109: 2430

Gage, S. H. 2009 (retrieved). Remote Environmental Assessment Laboratory.
http://real.msu.edu/

Iorgu, l. T. and G. Musta. 2008. Bioacoustics studies on some cricket species
(Insecta: Orthoptera: Gryllidae) from Romania. Analele Stiintifice ale Universitatii
Al. I. Cuza” lasi, s. Biologie animala, Tom LIV

Kellogg Biological Station Long Term Ecological Station Website
http://Iter.kbs.msu.edu/

Krause, B. 1998. Into a Wild Sanctuary. Heyday Books.

Krause, B. 2001. Loss of Natural Soundscape: Global Implications of Its Effect
on Humans and Other Creatures. San Francisco World Affairs Council, January
31, 2001.

Krause, B. and S. Gage. 2003. Testing Biophony as an Indicator of Habitat
Fitness and Dynamics. SEKI Natural Soundscape Vital Signs Pilot Program
Report, February 3.

Krause, B. 2002. Wild Soundscapes: Discovering the Voice of the Natural
World. Berkeley, California: Wilderness Press.

MatLab. 2007. MatLab ver. 2007b. The MathWorks, Inc. (www.mathworks.com).
Natick, Mass.

Oliveira, P. A. P., P. C. Simoes & J. A. Quartau. 2001. Calling songs of certain

orthopteran species (Insecta, Orthoptera) in sourthern Portugal. Animal
Biodiversity and Conservation 24: 65-79

164

Robertson, G. P., J.R. Crum, and B.G. Ellis. 1993. The spatial variability of soil
resources following long-term disturbance. Oecologia 96:451-456.

Schafer, R. M. 1977. Tuning of the World, or Soundscape. Destiny Books.

Schmidt, B. 2002. Sound Signatures May Provide Clues to the Health of Park
Ecosystems. Natural Resource Preservation Program, annual review.

Total Recorder M-Audio MobilePre USB. 2005. Avid Technologies Inc.
httpzllwww.m-audio.com/products/en us/MobilePreUSB.html

Walker, T. J. 1962. The taxonomy and calling songs of United States tree
crickets (Orthoptera: Gryllidae: Oecanthinae). l. The Genus Neoxabea and the
niveus and van'comis groups of the genus Oecanthus. Annals of the
Entomological Society of America 55: 303-322

Walker J. W. and TE. Moore. 2009 (retrieved). Singing Insects of North
America. httpzllentnemdept.ufl.edu/walker/buzzl

165

CHAPTER 4

IMPACT OF MICROWAVES AND WATER ON ACOUSTICAL SIGNATURES
OF A COMPOST

ABSTRACT

Acoustical technologies have been used to evaluate characteristics of
ecosystems, including soil, demographics and organism behavior. In our
research, the acoustics of four states of yard and kitchen refuse compost were
analyzed. These included: compost with and without water, and microwaved
compost with and without water. The acoustics generated by the microwaved
and non-microwaved composts were recorded in a sound chamber. Water was
then added to the control and microwaved samples. Subsequently, the acoustics
were recorded again. Sonograms were produced for each of the four treatments
and the data analyzed in relation to sound Power Spectral Density (PSD). With
the addition of water, the non-microwaved compost became acoustically active.
The PSD was more than five times greater (p < 0.0001, ANOVA repeated
measures) than the microwaved compost with and without water and the non-
microwaved compost without water. Most of the PSD for all four treatments was
in the lower kHz frequency levels (1-4). KHz frequency level one contained the
majority of the sound. Compost plus water, had 46 times more PSD at kHz level
1 than the sum of kHz levels 2-11. Most of the remaining PSD was found in the
lower kHz frequencies. It is concluded that: 1) compost can be considered as a
place where organisms are often dormant, 2) compost can be activated with
water used as a pulsing agent and 3) acoustical technologies can be a useful,

non-destructive tool for assessment of the current state of composts systems.

166

INTRODUCTION

Acoustics have been used to measure soil characteristics such as
aggregate stability and degree of slaking (Quintanilla et al. submitted for
publication), Studies on the acoustics of biological activity associated with
compost and soil, however, are limited. Brandhorst-Hubbard et al. (2001) used
acoustical methods to map soil invertebrates. They compared their results with
traditional estimates (actual counts) and found positive correlations. June beetle
grub population densities overlapped significantly with their estimates of sound
pulse rate and had a R2 of 0.47 when compared with traditional methods. When
other invertebrates where added to the estimate, the R2 value increased (R2 =
0.59). They concluded that acoustic technology could be used to detect insect
pests in soil, but that further study and improvement of interpretation are needed.

Mankin et al. 2000 pointed out the need for economic and user-friendly,
non-destructive devices to detect and quantify insect populations in environments
hidden from view, like soils. Some organisms can be detected by their sounds or
vibrations used in communication, feeding, or moving. Insects can be detected
in soil (Mankin et al. 2007) or in trees (Mankin et al. 2008) by the use of
accelerometers and other acoustic sensors. The sounds of different soil
invertebrates could be distinguished. Their conclusion was that acoustic
detection systems have potential as a non—destructive organism mapping tools.
Sampling is easier than digging and flushing techniques, which are labor

intensive and destructive (Mankin et al. 2000).

167

Geophones have been used to detect subterranean termites and ants
(Mankin and Benshemesh, 2006), as well as sounds of wing-fanning vibrations of
male fruit flies used to increase the trapping of females (Mankin et al. 2004,
Mizrach et al. 2005). Acoustical systems have been used to detect insects in
plant stems (Mankin et al. 2004), and the activity and presence of white grubs
have been determined with acoustical methods (Zhang et al. 2003, 2000).
Mankin (2006) also found that a low-energy microwave radar exposure increased
the ability to detect Plodia interpunctella larvae with acoustical technology.
Lavelle et al. (1995) focused on the fact that many soil-borne organisms spend
much of their life in dormancy and Jost et al. (2004) used water as a pulsing
agent to activate compost in relation to organism motility.

In addition, Butler et al. (2001) and Gage (sonic.cevl.msu.edul) have
demonstrated that acoustical technologies have potential for analysis of
biodiversity, ecosystem integrity and biological activity. Acoustical methods
have the benefit of being portable, non-destructive, relatively economic
compared to other labor-intensive methods, and can be automated with the
signals processed via web-based systems

Objectives: The objective of this research was to determine the impact of
microwaves and water on the acoustics of compost.

Hypotheses: 1. Non-microwaved compost has a greater PSD than
. microwaved compost. 2. Water used as an activating agent results in an

increase in the intensity of the acoustical signature (PSD) of compost.

168

MATERIALS AND METHODS

The acoustics of four states of a compost were analyzed. Compost (200
cm3), made from leaves and kitchen refuse, and with its natural biota
(earthworms, Isopods, Collembola, Staphilinid beetles, nematodes and other
microscopic organisms) was placed in two 400 ml glass containers. One was
microwaved in a “Sharp Carousel R-326FS” model microwave for two minutes,
with an output power of 1100W, and the frequency 2450 MHz. A hydrophone
(Aquarian A03) was place on a 35° angle on the compost surface in both
containers and the sounds recorded for 30 seconds in a sound-insulated
chamber. The process was repeated five times. Subsequently, 50 ml of water
was added to both the original and microwaved compost. The resulting acoustics
were recorded as described above. The compost was moist (~40% moisture)
and at room temperature (~21 C°). The activities of the macro-invertebrates were
also visually observed.

The Aquarian hydrophone (AQ3) on the surface of the compost was
connected to a digital recorder (personal computer) and an M-audio Mobile
Preamp sound processor (www.m-audio.com). Cool Edit (now Adobe Audio
Audition) was used to record the sounds associated with the compost, at 16 bits,
monaural at 22.050 kHz in wav file format for 30 seconds. The 30 second sound
segments were processed using Matlab (MathWorks). The Welch (1967) method
was used to quantify the acoustical power (Power Spectral Density) in watts for
each recording. The Matlab processing code for computing the average Power

Spectral Density (PSD; Welch 1967) for each 1 kHz frequency band in the sound

169

file, plus the average (i12) Power Spectral Density of all frequencies is described
in Table 4.1. The program produced several graphical representations of the
sound profile from each 30 second sound sample, including a: 1) sonogram, 2)
ocillogram, 3) trace of the Power Spectral Density (PSD) in watts/Hz, 4) a bar
chart of the PSD at one kHz frequency intervals (watts/kHz) and 5) a table of the
PSD values in watts/kHz for each one kHz frequency interval from 0-11 kHz. The
MatLab function “pwelch” implements Welch's method of determining the power
density in watts (Welch 1967) and is a way to estimate PSD using periodograms
(finite segments of the original signal that are converted into a spectral
representation using the Fourier transform). This computation was automated so
that multiple sound samples could be processed. The PSD values for each 1.0
kHz increment were used to compare sounds produced by each of the soil
aggregates tested.

For statistical and interpretation purposes, sounds where
compartmentalized into 1.0 kHz frequency intervals from 0-11 kHz. The average
power spectral density values for each 1 kHz frequency band were analyzed to
determine the power in each 1 kHz frequency band produced by each
treatment/replicate. In addition to producing the average power for each 1 kHz
frequency band in each sound segment, a sonogram was produced for each
sound segment to enable visual evaluation of the frequency distribution of
sounds from different compost treatments. A repeated measures test ANOVA
was used. Minitab statistical software program and Excel were used for the data

graphics and statistical analysis.

170

RESULTS

The sound produced by the non-microwaved compost plus water was
greater than that associated with the non-microwaved compost without water or
the microwaved compost with or without water (Fig 4.1). The sound and
sonograms from each treatment and replication can be heard and seen at
www.nemasoil.com, on the compost acoustics link.

The PSD (sum of kHz frequencies 1-11) of non-microwaved compost with
water added as an activating agent was significantly greater (p< 0.001, repeated
measures ANOVA) than the PSD associated with non-microwaved compost
without water or microwaved compost with or without water (Fig. 4.2). The mean
PSD was more than five times greater in the non-microwaved compost plus
water, compared to the other three treatments (Fig. 4.2).

Most of the PSD for all four treatments was found in kHz frequency level 1
(Fig. 4.3). Non-microwaved compost plus water had 46 times more PSD at kHz
level 1 (mean = 24, Fig. 4.3) than the sum of kHz levels 2-11 (mean = 0.52; Fig.

4.4). Most of the remaining PSD was found in kl-Iz levels 2-4 (Fig. 4.4).

DISCUSSION

There was considerable difference between the non-microwaved (living)
compost plus water and all other treatments. The greater PSD found in the non-
microwaved compost activated with water was most probably caused by the
increase in movement and activity of the macro invertebrates found naturally in

the compost. This was observed visually and heard acoustically. lsopodans,

171

annelids, collembolans and staphilinid beetles were observed to drastically
increase their movement after the addition of water. It is interesting to note, that
although the organisms were alive before the addition of water, this did not
significantly increase the PSD compared to the microwaved compost were the
macro invertebrates were dead. This reminded us of the “sleeping beauty
paradox” described by Lavelle et al., 1995.

I think that the principles of the “Sleeping beauty paradox”, apply also to
macro-organisms, such as macro-invertebrates, and that they also were
animated with an activation event, such as the addition of water. It is understood
that in their description they were mainly pointing to microorganisms in a dormant
state waiting to “wake up” when the conditions were favorable (moisture,
nutrients, porosity, temperature...) It is likely that this principle also applies to
larger organisms, maybe not to such a drastic extent, but they also have periods
of rest and of greater activity. It is believed the adding of water in our test, acted
as an activation event, and that increased the PSD.

Mankin, 2006, found that a low-energy microwave radar exposure
increased the ability to detect Plodia interpunctella larvae acoustically. The idea
is similar to the pulsing agent; the organisms tend to be in an inactive stage
sometimes, and to detect them, something like water, heat, or the low microwave
exposure can cause them to be more active and be more effectively detected

using acoustical methods.

172

In our case, the microwave exposure was high enough to kill all the
macro-invertebrates observed to be in the compost. Water addition is
considered an activating agent and microwaving a sterilizing agent.

In the kHz levels 1-5, the treatment with the living organisms with water
added, had the greater PSD (Figs. 4.3 and 4.4). Through visual observation and
the acoustics recorded, it is concluded that most of the sound produced by the
organisms activated by water was due to movement. During the time of water
activation, pill bugs were observed running in the container and a staphilinid
beetle was following and biting one of them on the appendages. An increase In
movement was also observed in the earthworms and collembolans after the
addition of water.

The reason that most of the PSD was found in the lower kHz levels, and
kHz level one was 48 times greater in PSD than all the other kHz levels summed
together (compost with water), was likely because most of the sounds made by
these invertebrates were caused by movement. Movements can be considered
mechanical sounds, and mechanical sounds tend to be in the lower kHz levels
(Gage, 2007). The addition of water to the microwaved compost did not increase
the sound.

It is concluded that: 1) compost can be considered as a place where
organisms are often dormant, 2) compost can be activated with water used as a
pulsing agent and 3) acoustical technologies can be a useful and non-destructive

tool for assessment of the current state of composts or soil systems.

173

TABLES

Table 4.1. Matlab code to compute average Power Spectral Density (PSD,
watts/kHz) for 1 kHz frequency bins (plus total) from a .wav file segment. The last
calculation (Tab (i,12)) is the sum for all 11 kHz levels.

 

 

96********************************************************************************

% Calculate the average Power Spectral Density (PSD) of the frequency bands using Welch
method.

96********************************************************************************

[y,Fs] = wavread(FileSet); % Set wavefile to variable for plot
[signal,Fs] = wavread(FileSet); % Access the wav file segment
Pxx = pwelch(signal,[],[],512.22050); % Calculate pwelch

Hpsd = dspdata.psd(Pxx,'Fs',22050,'spectrumType',‘onesided');

96**********************************************************************************

% Create the matrix of average power based on parameters set for PSD for each 1 kHz
frequency band (plus total) in a .wav file segment. Multiply % values by a constant (1,000,000) to
scale values.

96**t**************fi****************************************************************

Tab(i,1) = avgpower(Hpsd,[OOOO 1000])“1000000; % average PSD 0000-1000 Hz
Tab(i,2) = avgpower(Hpsd,[1000 2000])*1000000; % average PSD 1000-2000 Hz
Tab(i,3) = avgpower(Hpsd,[2000 3000])*1000000; % average PSD 2000-3000 Hz
Tab(i,4) = avgpower(Hpsd,[3000 4000])*1000000; % average PSD 3000-4000 Hz
Tab(i,5) = avgpower(Hpsd,[4000 5000])*1000000; % average PSD 4000—5000 Hz
Tab(i,6) = avgpower(Hpsd,[5000 6000])“1000000; % average PSD 5000-6000 Hz
Tab(i,7) = avgpower(Hpsd,[6000 7000])*1000000; % average PSD 6000-7000 Hz
Tab(l,8) = avgpower(Hpsd,[7000 8000])“1000000; % average PSD 7000-8000 Hz
Tab(i,9) = avgpower(Hpsd,[8000 9000])*1000000; % average PSD 8000-9000 Hz

Tab(i,10) = avgpower(Hpsd,[9000 10000])*1000000; % average PSD 9000-10000 Hz
Tab(i,11) = avgpower(Hpsd,[10000 11000])*1000000; % average PSD 10000-11000 Hz
Tab(i,12) = avgpower(Hpsd)* 1000000; % avgge PSD 0-11000 Hz

 

174

 

 

kHz 1-2

kHz l-2

kHz 1—2

FIGURES

 

I I.
§ . 3 , Compost “Ii h'water '
.i'l‘ If I‘ . - j "

n

 

 

 

Compost without water

 

h: u.h“’l u‘u-UO—u-I-p-Ccu-hO—QIH-bt &- Wit-Ch uh .u‘h nfi—n-_—-“hn “o-afiu-om

 

 

 

Microwaved compost with water

11...“. —-" 4A-“-h + -—n --.5— -- —— .-—-—-—--

 

 

Microwaved compost without water

 

boohfio-u‘uh - g A _: h&h*“ohuh———n&n ‘00--*--0-I

 

 

 

Time (0-30 seconds)

Fig. 4.1. Sonograms of sounds of non-microwaved and microwaved compost

with and without water as an activating agent. The x axis is 30 seconds time and
the y axis represents kHz levels 1-2. The color intensity and brightness indicates
loudness. Sounds can be heard at www.nemasoil.com on the compost acoustics

page.

175

 

 

w
O
1

N
U1
1

 

I—‘ N
1.11 O
l l

t-‘-
O
1

PSD (kHz 1-11, watts/kHz)

 

 

 

 

 

9‘60 «We
«9°

 

 

 

Fig. 4.2. Power Spectral Densities (PSD, sum of kHz frequency levels 1-11) of
microwaved and non-microwaved compost, without and with water (p< 0.0005),
with 95% confidence interval bars for the means.

176

 

 

H
U1
.4

PSD (watts/ kHz)
N
o

 

 

 

 

 

liléliil --_.. ---- ---- --._-
I I l r Y

{935650er £1090?” {9105505 &icsgsos «9” say

(3

.\> ,0 ,0 >9 >9
8“ ‘3‘” ‘3‘” 8“ «9“

 

 

 

Fig. 4.3. Comparison of Power Spectral Densities (PSD, kHz levels 1-5) of
microwaved (MC) and non-microwaved (C) compost with (+w) or without water (—
w) added as an activating agent. The four treatments where: microwaved
compost with no water added (MC-w), microwaved compost with water added
(MC+w), compost with no water added (C-w), and compost with water added
(C+w). Each treatment had five repetitions; the bars represent the mean for the
five repetitions. The error bars represent 95% confidence intervals for the means.
Each kHz frequency level represented is shown separate, i.e. kHz level 1 is
named khz-L1 on the graph.

177

 

.x r.<'I—'T""I‘1. ‘- .
,.3’~ 1.2“:le 1....
" Liz?r:52—..“$‘

 

 

 

 

 

 

0.35
0.30j
1?
I 0.25-
.I
\
g 0.204
III
5 0.15“ x
n
(h
“- 0.10-
0.05- l
(1% ”gal r 9.312%: Li 1:134; 3:93.12 5-71.21...
EEEE .EEE :EEE EEEE EEE EEEE E E EEEE E EE EEEE-
U U U U
22°02: MUSEUUE’E’W Ezuzfiz’wg‘z’uugitpuzg’"
N m r L? “P 'T °° a O :

sssssstsgg

Fig. 4.4. Comparison of mean relative Power Spectral Density (PSD, watts/kHz)
for four treatments in the kHz frequency levels 2-11. The four treatments were:
microwaved compost with no water added (MC-w), microwaved compost with
water added (MC+w), compost with no water added (C-w), and compost with
water added (C+w). Each treatment had five repetitions. Each kHz level
represented is shown separate, i.e. kHz level 2 is named kHz-2 on the graph.
The error bars represent 95% confidence intervals (CI) for the means.

 

 

 

178

REFERENCES

Altieri, M. A. 1999. The ecological role of biodiversity in agroecosystems.
Agriculture, Ecosystems and Environment 74:19-31.

Adobe Systems Incorporated. 2004. Adobe Audition 1.4 User Guide for
Windows 299 pp.

Bertoldi, M., G. Vallini, and Pera A. 1983. The Biology of Composting: A
Review.
Waste Management and Research 12157-176.

Brandhorst-Hubbard J. L., K.L. Flanders, R. W. Mankin, E. A. Guertal, R.L.
Crocker. 2001. Mapping of soil insect infestations sampled by excavation and
acoustic methods. Journal of Economic Entomology 94:6 pp.1453—1458.

Butler, R., M. Servilla, S. Gage, J. Basney, V. Welch, B. Baker, T. Fleury, P.
Duda, D. Gehrig, M. Bletzinger, J. Tao, D. M. Freemon. 2006. Cyber
Infrastructure for the Analysis of Ecological Acoustic Sensor Data: A Use Case
Study in Grid Deployment.

http:l/grid.ncsa.uiuc.edu/papers/CLADE_2006 LTERgrid CCJ V3-FlNAl=gdj

Cornel composting, science and engineering.
http:l/www.css.cornell.edulcompost/invertebrates.html

Compost website
h_ttp://www.mansfieldct.org/schools/mms/compost/microbfi.htm

Ecological Acoustic Research Website. Purdue University.
h_ttp://ltm._agricultugpurdue.edu/ear/default files/Page424.htm

Gage S. 2007. Analysis of Acoustics. Stuart Gage, Listening to the Natural
World. Pulse of the Planet. http:l/39515.gridserver.com/sci-cjiaries/sd gage/

Gage S. Website: http:l/envirosonic.cevl.msu.edu/SoundGallerv.asp

Gunadi, B., C.A. Edwards and NO. Arancon. 2002. Changes in trophic
structure of soil arthropods after the application of vermicompost. European
Journal of Soil Biology 38 (2):161-165

Ferris H., R.C. Venette, H.R. van der Meulen and SS. Lau. 1998. Nitrogen
mineralization by bacterial-feeding nematodes: verification and measurement.
Plant and Soil 203:159—171.

”0' m r ”09' r..ouc0.\ n ‘13 0' row of n 0 . ‘. H.
Harrier, L. A. and CA. Watson. 2004. The potential role of arbuscular
179

mycorrhizal (AM) fungi in the bioprotection of plants against soil-borne pathogens
in organic and/or other sustainable farming systems. Pest Management Science
60:149-157.

Haugh, RT. 1993. The practical handbook of composting engineering. Lewis
Publishers. Boca Raton, FL.

Hoitink, H. A.J., A.G. Stone and D.Y. Han. 1996. Suppression of plant diseases
by compost. X Congreso Nacional Agronomico/ I II Congreso de
Fitopatologia.

Jost, M. S., J. Biernbaum J., Bird, G., Bates, C. Bates, R. Bounds, S. Hollosy, M.
Quintanilla, J. Smith, Walter. 2004. Impacts of substrates and pulsing agents on
reproduction of bacterial feeding-nematodes. J. Nematology 36:4.

Lavelle, P.C. Lattaud, D. Trigo, and I. Barois. 1995. Mutualism and biodiversity
in soils. In book by: Collins, H.P., G. P. Robertson and M. J. Klug (eds.). 1995.
The Significance and Regulation of Soil Biodiversity. Kluwer Academic
Publishers, Dordrecht, the Netherlands. 23—33

Mankin, R. W. 2006. Increase in acoustic detectability of Plodia interpunctella
larvae after low—energy microwave radar exposure. Florida Entomologist 89:
416-418. I£[45 kB]

Mankin, R. W., and J. Benshemesh. 2006. Geophone detection of subterranean
termite and ant activity. Journal of Economic Entomology 99: 244-250.

Mankin, R. W., J. Brandhorst-Hubbard, K. L. Flanders, M. Zhang, R. L. Crocker,
S. L. Lapointe, C. W. McCoy, J. R. Fisher, and D. K. Weaver. 2000.
Eavesdropping on insects hidden in soil and interior structures of plants. Journal
of Economic Entomology 93: 1173-1182.

Mankin, R. W., J. L. Hubbard, and K. L. Flanders. 2007. Acoustic indicators for
mapping infestation probabilities of soil invertebrates. Journal of Economic
Entomology 100: 790-800.

Mankin, R. W., M. T. Smith, J. M. Tropp, E. B. Atkinson, and D. Y. Jong. 2008.
Detection of Anoplophcra glabripennis (Coleoptera: Cerambycidae) larvae in
different host trees and tissues by automated analyses of sound-impulse
frequency and temporal patterns. Journal of Economic Entomology 101: 836-
849.

Mankin, R. W., Crocker, R. L., Flanders, K. L., and Shapiro, J. P. 1998. Acoustic

detection and identification of insects in soil. In: P. K. Kuhl and L. A. Crum,
[eds], Proceedings of the 16th International Congress of Acoustics and the 135th

180

Annual Meeting of the Acoustical Society of America, pp. 685-686. 1998.

(Digitized Sounds)
mpzllwwwars.usda.gov/spgUserFiIes/person/3559/publications/685 1.pdf

Mankin, R. W., C. W. McCoy, K. L. Flanders, J. Brandhorst, R. L. Crocker, and J.
P. Shapiro. 1998. Methods for acoustic detection of insect pests in soil, pp. 3-9,
In Proc. Soil Science Society Amer. Conference on Agroaccustics, Third
Symposium, Buoyoucos, MS. November 3-6, 1998 (Digitized sounds).

Mankin, R. W., J. B. Anderson, A. Mizrach, N. D. Epsky, D. Shuman, R. R.
Heath, M. Mazor, A. Hetzroni, J. Grinshpun, P. W. Taylor, and S. L. Garrett.
2004. Broadcasts of wing-fanning vibrations recorded from calling male Ceratitis
capitata (Wiedemann) (Diptera: Tephritidae) increase captures of females in
traps. Journal of Economic Entomology 97:1299—1309.

Mankin, R. W., D. K. Weaver, M. Grieshop, B. Larson, and W. L. Morrill. 2004.
Acoustic System for Insect Detection in Plant Stems: Comparisons of Cephus
cinctus in Wheat and Metamasius callizcna in Brcmeliads. Journal of Agriculture
and Urban Entomology 21: 239-248.

MatLab. 2000. MatLab, ver. 6.0. Natick, Mass: The Math Works, Inc.
Minitab. 2007. Minitab 15 Statistical Software, Inc.

Mizrach, A., A. Hetzroni, M. Mazor, R. W. Mankin, T. Ignat, J. Grinshpun, N. D.
Epsky, D. Shuman, and R. Heath. 2005. Acoustic trap for female Mediterranean
fruit flies. Transactions of the American Society of Agricultural Engineers 48:
2017-2022.

Nancarrow, L., and J.H. Taylor. 1998. The Worm Book. Ten Speed Press,
Berkeley Califomia.160pp.

Neher, D. 2001. Role of Nematodes in Soil Health and Their Use as Indicators.
Journal of Nematology 33 (4): 161-168.
http:l/wwwéuvm.edu/rLcaneher/Publications/JON3342001.wc

Odegaard, F., B.A. Tommeras. 2000. Compost heaps-refuges and stepping-
stones for alien arthropod species in northern Europe. Diversity and Distributions
6 (1):45-59.

Quintanilla Tornel, M., A.J.M. Smucker, S. Gage, G. Bird, and E.J. Park. 2008.
Impact of Tillage on the Acoustical Signatures during Rapid Hydration of Air-
Dried Soil Aggregates. Submitted for publication to the Soil Science Society of
America Jornal. In Review.

181

Rosenheim, J. A. 1998. Higher-Order Predators and the Regulation of Insect
Herbivore Populations. Annual Review of Entomology 43:421-447.

Tuomela, M., M. Vikman, A. Hatakka and M. Itavaara. 2000. Biodegradation of
Ligning in a compost environment: a review. Bioresourse Technology 72(2):
169-183.

Welch, PD. 1967. The use of fast Fourier transformation for the estimation of
power spectra: A method based on time averaging over short, modified
periodograms. IEEE Trans. Audio Electra-acoustics. 15:70-73.

Zhang, M., R. L. Crocker, R. Mankin, K. Flanders, R. Hickling, and J. Brandhorst-
Hubbard. 2000. Inferring activities and incidental soil-borne sounds of white
grubs. In: Proceedings of the XXI International Congress of Entomology, Book 1,
p. 238, Iguazzu Falls, Brazil.

Zhang, M., R. L. Crocker, R. W. Mankin, K. L. Flanders, and J. L. Brandhorst-
Hubbard. 2003. Acoustic identification and measurement of activity patterns of
white grubs in soil. Journal of Economic Entomology 96: 1704-1710.

182

CONCLUSION

In conclusion disturbance has an effect on ecosystems physical, biological
and acoustical characteristics. Native forest and old field succession ecosystems
have greater water soil aggregate stability and therefore do not slake when
immersed in water (Chapter 1). This results in less noise. Acoustical methods
are apparently an effective method to measure soil’s water-aggregate stability.
Tillage has a negative effect on water-aggregate stability of soils (Amezketa
1999, Six et al. 1998, Pikul et al. 2009). Tilled soils had the most slaking and
therefore the greatest sound power spectral density (PSD).

The conclusions reached in Chapter 1 are:

1. The results of the acoustical research indicate that soil aggregates
from no—till sites have higher resistance to slaking and generate
less sound (PSD) than those from conventional tillage systems.

2. Native soils (non-disturbed soil) had the least slaking and less PSD
when hydrated rapidly.

3. Acoustical methods appear to have potential for estimating water
soil aggregate stability and degree of slaking.

4. Furthermore, the impacts of tillage on aggregate slaking can be
potentially quantified using acoustical technology.

5. Our acoustic methods are faster and simpler than most of the
conventional methods of determining soil aggregate stability, but

more research need to be done in order to determine the

183

quantifiable correlation between the acoustical methodologies and
the conventional methods.

6. Tillage reduces water-soil aggregate stability of soil; this in part is
caused by the decrease in organic carbon in tilled soils (Six et al.
2000 and Zaher et al. 2005). Acoustical methods provide
comparable results to the conventional methods of water-aggregate
stability. Acoustical methods are rapid providing quantifiable
results.

In Chapter 2 it is concluded that Nematode Community Structure is a
useful tool to determine ecosystem function, biodiversity and stability, and that
the KBS/LTER forest and old field succession ecosystems tend to have higher
biodiversity, maturity and stability than agricultural ecosystems. Among the
agricultural ecosystems, the nc-input (organic) was more closely related to the
native ecosystems than the no—till and conventional till ecosystems. Nematode
community structure methods successfully separated the ecosystems. Soil
nematode biodiversity, stability and maturity tended to be highest in the
deciduous forest and the old field succession ecosystems at the Kellogg
Biological Station, Long Term Ecological Research site (KBS/LTER). The lowest
soil ecosystems nematode biodiversity, stability and maturity was found in the
conventional or the no-till ecosystem, with the no-input (organic) having values in
closer to the native ecosystems compared to the conventional tilled and no-till
ecosystem (Chapter 2). It is thought that the chemical inputs and the crops used,

has an effect on the nematode communities. Freckman and Ettema (1993), and

184

Neher et al. 2005 also found that nematode community structure methods
effectively separated native and agricultural ecosystems with different
disturbance levels.

In Chapter 3, the greatest sound diversity and intensity was found in the
native deciduous forest and old field successions ecosystems. The agricultural
ecosystems did not have a high sound PSD in any of the recording dates and
times. In the native ecosystems, sounds had temporal dimension. In August the
insect sounds tended to increase as the darkness increased during the evening
(Chapter 3). In part this might be an acoustic insect’s adaptation to escape bird
predation. There was an attempt to record the sounds of soil invertebrates by
placing a hydrophone near the soil surface. No significant difference was found
between the treatments at the soil surface. Most of the insects sounds and call
were from above ground and arboreal communities. Following an understanding
of the importance of activation events to increase the activity of soil organism, an
soil/compost experiment was done. This experiment measured the acoustical
activity of natural soil/compost invertebrates after being activated by an addition
of water. The soil/compost with water added had significantly greater sound
intensity than the sterilized soil/compost without water added the sterilized
compost with water added and the soil/compost with living invertebrates but
without water added as an activation event (Chapter 4).

l was concluded that agriculture and disturbance does have an effect on
ecosystems physical and acoustical characteristics, biological soil diversity,

maturity and evenness, and on above ground insect acoustical insect sound

185

diversity and intensity. Natural ecosystems perform and preserve many
ecosystem functions. Agricultural ecosystems with the least disturbance have
higher biodiversity and water soil aggregate stability than the more disturbed
ones. Acoustical and nematode methods can be effective tools to study and

compare ecosystems.

186

REFERENCES

Amezketa, E. 1999. Soil Aggregate Stability: A Review. Journal of Sustainable
Agriculture 14: 83-151.

Freckman, D.W. and CH. Ettema. 1993. Assessing nematode communities in
agroecosystems of varying human intervention. Agricultural Ecosystems &
Environment 452239-261.

Neher, D.A., J. Wu, M.E. Barbercheck and O. Anas. 2005. Ecosystem type
affects interpretation of community measures. Applied Soil Ecology 30: 47-64.

Pikul, J.L., G. Chilom, J. Rice, A. Eynard, T.E. Schumacher, K. Nichols, J.M.F.
Johnson, S. Wright, T. Caesar and M. Michael. 2009. Organic matter and water
stability of field aggregates affected by tillage in South Dakota. Soil Science
Society of America Journal 2009 73: 197-206

Six. J., K. Paustian, E.T. Elliott, and C. Combrink. 2000. Soil Structure and
Organic Matter: l. Distribution of Aggregate-Size Classes and Aggregate-
Associated Carbon. Soil Science Society of America Journal 64: 681-689.

Six, J., E. Elliott, K. Paustian, and J. Doran. 1998. Aggregation and soil organic
matter accumulation in cultivated and native grassland soils. Soil Science Society
of America Journal 62:1367-1377.

Zaher, H., J. Caron, and B. Ouaki. 2005. Modeling aggregate internal pressure
evolution following immersion to quantify mechanisms of structural stability. Soil
Science Society of America Journal 69: 1-12.

187

Appendices

188

 

80805.... Em: m .55 8.58:5: >552 -

9... cm .0 o 5 8.9.89 2:. o>o.m o5 .5: 880580.. 8m; xomm ..
o>o.m ..on 8.... m 5 5.95 5.2. 5.3.8 .88 o... 8....m. -
8508 mm .0.. Eu. ooow 5 5.5.8 .58 new ..8 9.. 82.550 -

A582.

.6 .E ooom c. .88 .o 855 $9 ”.88 .98; .58.... .6 _E ooom c. .88 .o 8.55 mom... ”5.5.8 50.8 5.38.. -
5.5.8 .88 o... 5.3 ..8 on. x.E can 893 82.550 9... 2:. 5.5.8 .88 5.59 Son. -

88. o5 :. 5:8 ..8 9... 98. can 5.62. 9.. So 26...... -

8.2.5 m .0. Sim ooom .m 83......8 05 82.550 9.. 2c. woos. 82.350 5.. -

E82. 3.88 on... 82.58 m 9.... o>o.m oz. .5... 58.5 o... .6 oE8 new ..8 :6 Son. -

058 8c... oov o... 55.5 5.5.8 58.5 new _.8 o... Son. -

EmoE om. 8.... o>o.m 99 m 55.5 ..8 o... 88 .5... 29:8 o... :. 58o... m. 8.52. .o .o. m h. -

.o>o.m 8o... oov m .o>> -

can; .6 ._ rm. 5.95 5.; .933 m 2:. ..8 .o 8 2: 9.. em; as. 9.8»... -

558 m c. ..8 .o co 8? Sn. -
=8 x... -

 

mm052mma<

«.352... 823.5 .8 .F 52.2.9...

189

9. 8... 8... 8... 8... 8... 8... 8... 8... w 8... m... 2 2.82.8.8:

8 No... 8.... mm... mm... .... 8.... 8... 8... 8... 8..., m. 8.2.2.823...
o: 8... 8... 8... 8... 8... 8... 8... 8... m... m... N. 88.86
8 8... 8... 8... 8... 8... 8... 8... 8... m... m... 8.. 85.28....
o: m... m... m... n... m... m... 8... 8... 8... 8... 8 88.2.88.
8 8... 8... m... m... 8... 8... .3 8.. 8... 8... 8 88.238...
8 8... 8... m... m... 8... 8... 8... 8... 8... 8... 8 8.82.8.
8 8... 8... m... m... 8... 8... 8.. 8.. 8... 8... 8 «8.888.
8 m... m... 8... 8... 8... 8... 8... 8... 8... 8... 8 82.28..
o: 8... 8... m... 8.. 8.. m... 8... 8... 8... 8... w 8 825.28..
2. 8... 8... 8... 8... 8... 8... 8.. 8.. 8... 8... 2... 888.3...
8 8... 8... 3.. ..N m... 8.. 8... 8... 8... 8... m. £288.23...
2. 8... 8... 8... 8... 8... 8... 8... 8... mm... 8... .. 8.28.8.
9. 8... 8... 8... 8... 8... 8... R... 8.... 8... 8... 2 85.2.2.8...
2. 8... 8... 8... 8... m... m... 8.. 8.. 8... 8... .3 88.8....
.. .3 8..... ...~ 8.... o... 88... 8... ..8... m... 8.... 8 88.238
8 8... 8... 8... 8... 8... 8... 8... 8... 8... 8... 8 8.9.2.22...
8 8... 8... 8... 8... 8... 8... 8.. 8.. 8... 8... 8 885.283....
.. 8.... 8..... .3. 8..... m... 8.... 8... ..8... .... n... N. 8.80.2.2
2. m... m... n... m... .3 n... m... 8.. .... .... 2... 8.22.822...
2. m... m... 8... 8... 8... 8... 8... 8... 8... 8... 8 .=E.~....o....._E<
8 n... 8.. 8... 8... 8... 8... 8... 8... 8.. 8... N. 8.82...
a .8. m... :85. .. :85. .m :85. .. =85. .. :85. 2.... 9.682833
8..... ..FEB 88..-... __....oz 8.8.88-2... .8... B..~\.~\.

 

..N 83...... 83.9 a-.. 82. ..mmcom m... .2 8.8.. an d... .38.

m... .9... 886.... 9.... .8. 8.9.5.5 cc... .8. 8.9....8 A... 8.9.3.0.. .8 8.9.9:... .3. 8.90.8.8...
.505 0.580.. .0.. 8...... ..E:.oo 2.30 9.... Noom .mm .onEm...mw mmv. .m 8.9.... .6. .0. =0. .o 88. So:
28.58.. up... :98. .0.. 88.8.82 .0 .883: .30. m... .o. Gm. m.o..m. 8.2.86 2... 88.2 ”N 898......

 

190

 

O:
O:

O:
O:
O:
O:
O:
O:

O:
O:
O:
O:
O:
O:

O:
O:
O:
0:

ma

8.0
mm.“
006
mmév
mmé
cod
cod
hoN
mmé
00.0
mmé
mmé
moé
mud
06.0
vaH
moé
hoN
cod
cod
cod
cod
cod
mmé

ammé
8.0
mmé
cod

amhmdwfi

nmmé
cod
cod
hQN
mmé
cod
mmé
gamma
hQN
mméu
cod
nod.“
nod
hQN
00.0
00.0
8.0
cod
cod
mmé

cod

ood
cod
o~.m
~w.w~
mn.~
cod
nmfi
cod
emd
00.0
00.0
mm.m
cod
med
cod
cod
cod
mmé
00.0
00.0
cod
00.0
cod
cod

good
cod
cod
8.8

anNNH

good
cod
nod
00.0
8.3
00.0
cod
mumda
cod
cod.“
cod
cod
ood
mmé
006
00.0
cod
00.0
cod
00.0

cod

cod
nod
mmd
mwdu
no.0
cod
cod
cod
mw.m
cod
mmé
omé
cod
mad
cod
mud
cod
cod
cod
006
00.0
cod
006
00.0

A50.0

cod
hQN
8.3”
mmmfim
shod
00.0
8.0
cod
cod
ood
mmé

330.3

cod
coda
ood
mm.mu
ooé
00.0
006
00.0
006

, cod

cod
cod

dew

cod
cod
cod
mima
mw.m
00.0
cod
mad
cod
00.0
00.0
84‘
cod
cod
oo.~
omfi
h~.m
00.0
00.0
00.0
cod
cod
cod
oo.~

30.3.“ waé
cod om.m
cod 36
00.0 mm.“
amoodm owd
moodm cod
cod mm.m
cod omé
cod and
cod ems.“
cod vvd
cod 8.0
@306 00.0
00.0 cod
09$ wwé
oo.~ cod
cod.“ mmé
cod cod
cod cod
cod mwd
00.0 .36.
00.0 mm.m
cod :6
cod mmd
oo.~ mwd

nwné

hmé
3.0
mwé
QNNéN
good
mm.m
mad
weds
hmd
wed
cod
nood
cod
omK
cod
mmé
cod
cod
mmd
31V
afim
mad
mmd
mmd

m;

m;
2

NE;

E
m;
3
S
#0
Ni
2
m;
2
mod
3
#0
v0
2
m;
m;
a
co
co»...

«Eoczéx
20323:.
m__oEo:8_..uo:ux

ancOng
85:52::
3:26;...
35:33:95.3.
«3.3233803
35:233.
£232.:
32:32:
2.09.2. «3595..
35:28..
355.38..
3:22.235...
«:80...
82555.6.—
3830.523
35:23.2
SEE->833.)—
2.502222

«Eon—33.82 .

332mg.—

mzsucoifl .

3338.3

«6235.3 ‘
nus—5:8 ~ 5:52?

191

0c
0...
O:

O...
O...
O...
O...
O:
0:
O:

0:

0c
0:
0:

§

.0 $00

00:28....

00.0 00.0
00.0 05.0..
05. F 05. _.
00.0 00.0
00.0 00.0
00.0 00.0
00.0 00.0
00.0. 00.0.~
00.0 00.0
00.0 00.0
00.0 00.0
00.0 00.0
00.0 00.0
00.0 00.0
cm. Z. om00.0m
00.0 00.0
0N.N. 05. ..v
00.0 00.0
00.0 00.0
05. E. 0005. 5.
m0 30%.
=..F.>:OM

00.0 00.0
00.0 00.0..
00.0 00.0
00.0 00.0
00.0 00.0
00.0 00.0
00.0 00.0
00.5 05.0..
00.0 00.0
00.0 00.0
00.0 00.0
00.0 00.0
00.0 00.0
00.0 00.0
00... :05. :
0_..N 00.0
00.5 00.0.
05... 05...
05. .. 05. ..
00.0 900.0
md 335
30050.05

00.0 00.0
00... 05. E.
00.0 00.0
00.0 00.0
00.0 00.0
00.0 00.0
00.0 00.0
00.0.. 05. ..N
00.0 00.0
00.0 00.0
00.0 00.0
00.0 00.0
00.0 00.0
00.0 00.0
0.0 _. nm050¢
00.0 00.0
00.0 00.0 F
00.0 00.0
00.0 00.0
00.0 nmomd
m6 cam:
...._..02

00.0
00.0
00.0
00.0
00.0
00.0
00.0
0...?
00..V
00.0
00.0
00.0
00.0
05.0
00.00
05...
00.0..
00.0
00.0
05...
m6

5.3330 230

00.0
00.0w
00.0
00.0
00.0
00.0
00.0
00.50
0N0
00.0
00.0
00.0
00.0
05.0
m0~€0w
05...
00.0w
00.0
00.0
:05...
:00:

05.0 05.0 E
05.0 05.0 m.
00.0 00.0 E
05.0 05.0 0..
00.N 006 m:
00.0 00.0 N:
05.0 05.0 N:
0.... 0.25.. No
0.... 0 _. .N .b
05.0 05.0 00
03... 0v... mg
05.0 05.0 00
0:... 03... 00
05.0 05.0 00
0N.N nomN N.
00.0 00.0 N.
00.N 00.0 N: .N.
00.0 00.0 3
00.0 00.0 E
0.5 mowdw Np
md :50: 2.30
.33". .30

32222883.:
Eo:Q2o£:QE
qufiEo
25:83.0
3:33.383.
«33.10.30
«333.330
«30.330
2.8.655
2.3530
«.5330
«2553.63.

33.8.2331< .

a3..oE.o.oo..oq<
«35:233..
23:33.83 .v.
33..o:o:o.2.q<
382%....
2.8.6?
3.320<

a. :98... .333
3......

 

95:33... 2.. 525.2. 3:28.... o: no: 60:30:00 00.: 5.? 30:98.33". .23.: 2.31.0. m.u>..¢.:.
3:30:00 .x. 00 :0 3mm... 9... 0:: m... .a 3322.... 2.... .33.; a-.. :02. 23:00 a... .5. 353.12 .0... .532
w... 3...: 223:3: 2.... ..o. mm..o>.::.o 0:: .3. «9.3.53 .2. 3.3.3.8.. .0. «233:3. .3. «0.05.3.3...
:320 0.....0... .0. «0:5.» ::.3_oo 2.30 2.... 5ch .~.. 30:8qu 00! .a 029:3 ..o» ..0. ..0m .0 002:.
:5... .5832. 3:: :98. .2. 3.3.2.82 .o .8053: .30. m... .0. E0. 29:0 0.3.35.0 3:: 235. ”n 53:39.

 

192

 

O:
0:
0:

0:
0c
0:
0C
0:
O:
O:

0:
0c
0:
0:
0c
0:
0:
O:
O:

O:
O:
0:
0c

ood
ovdv
cod
00.0
00.0
cod
ovd
ovd
00.0
00.0
omN.‘
one
ood
00.0
00.0
00.0
05w
cod
cod
oodm
cod
00.0
omd
DEF

. cad

and
cod

cod
om.mn
cod
good
cod
ooh
09m
cod
00.0
00.0
oodm
oné
cod
00.0
006
cod
Eur
cod
oo.o
oodm
cod
cod

anon.»

059
and;
om.m
cod

ow. Pm
and
006
one
cod
cm. 3
cod
cud
oo.o
cod
and?
9N
ood
00.0
00.0
omd
cod
00.0
00.0
ome
cod
cod
omé
cod
2N
CNN
cod

on. ..m
and
oo.o
non...
ood
oo.m_‘
00.0
00.3
cod
ood
09mm
00m
cod
00.0
006
end
cod
ood
cod
on. 5
00.0
00.0
mofim
cod
and
cod
ood

cod
and
cod
cod
ood
cod
oné
cod
cod
ood
09m
cod
cod
cod
cod
cod
cod
cod
00.0
on...
cod
cod
090—.
cod
2d
owd
00.0

cod
8.2.
00.0
good
cod
cod
on...
cod
cod
cod
cod
cod
cod
cod
00.0
00.0
00.0
00.0
006
one
00.0
00.0
amoodm
cod
and
00.0..
00.0

0562.

cos
ood
cod
cod

oodmr

cod
and
cod
cod
0 —. .N
cod
comm
050—,
00.0
cod
00.0
056
00.0
2.0
cod
cod
on. E.
00.0
ooN
ovdv
cod

and:
052
cod
9802
00.0
owdvm
cod
on. S.
cod
cod
05.0
cod
09mm
059‘
00.0
cod
cod
and
cod
05.0
cod
cod
momNV
cod
00.0w
00.0w
ood

ood
cod
and
91:
and
on...
cod
oo._‘
006
and
09—.
cod
oNN.‘
cod
and
omé
cod
cod
ooé
and
05.0
00.9
ova
cod
ooN
cod
and

go
8.0
2.0

mowfim

and
o F .N
cod
9:.
ow...
and
omN
ood
oodm
cod
and
3N
cod
cod
cod
05.0
and
00.0
pond
cod
cod
cod
05.0

N:
B
_b

m...

N

3333339

2
m:
m:

E

vod
m:
m:
N;
vod
Na

9.:
no

«328325;

2.5582ch

mataEuccum
«83:39.0
583.53% 2.50
macoco‘bocucz
33553:
«35:23:
2893.8:
2.5595
«Stamina:
03.555033:
uEacootooaoE
«5:..th
«$32.33:
«30:810..
aEObAEE.‘
25.333
Eocqo=o>o§ot
«3053.09.31
«3.5020
w:E.§Eot=m

assucqgam .

m:u..:oE=um
3253an
«3323.5
2.530an

3:52.00 0 52.8%?

193

 

0c
0:
0:
O:
0:
0:
CC

0c
0:
O:
O:

0c
0:
O:
O:

O:

cod
cod
cod
and?
cod
09mm
cod
cod
00.0
00.0
cod
and
012.
00.0
ofim
cod
ood
oodw
cm. 5.

cod
00.0
ood
omdw
00.0
0903
cod
good
cod
ood
ood
05.0
noodm
cod
omd
cod
006
now. For
on. EV

cod
ood
cod
cod
cod
owdw
00m
cod
cod
cod
cod
on...
o F .mm
00.0
cod
cod
00.0
omdr
cod

cod
cod
cod
and
cod
oodm
and
good
cod
ood
cod
oh?
nmoodm
cod
00m
cod
00.0
2000.9.
omdv

cod
cod
and
0N6
cod
06mm
cod
00.0
00.0
00.0
00.0
cod
om.N_.
ood
oué
cod
on...
05w...
DYE.

cod
cod
omd
05.0
cod
cod:
cod
good
cod
ood
cod
cod
non. 3
00.0
on...
00.0
on._.
moodw
Show

owdw on. «m
00.0 cod
omd Oman
ONE. omdm
on... omé
05mm and:
cod cod
ovd 00006
cod cod
cad 00m...
ow... one
ONNN coder
oonN nmoodvv
and om.m
cube 050v
cod cod
cod cod
on. 2. 905.9
0Q? om.m©

006
00.0
and
and
cod
ONE.
cod
00.0..
one
cod
cod
om.N_.
omdv
cod
0nd
ooN
00.0
006
end

8.2.
8.8
Ed
8.2
8.0
8.8
88
8:8
8.“
85
03.
8.8
«8.89
8.0
Ed
8.....
8.o
890
8.8

m:
n:
N0

N0
NE:
3

an
m;
m:
B
E
3
mg.“—
mg

mg.
up

2005.3;
E0020 0.: ..X
«$0003.5—
0050:0025
«2003003....
«05:03....
«535305003...
8892».
802388

«$0023.08 .

«acocsbom
«E003!
000.5332
0.0020005;
«0502.61
«350.80.:«tm
«03:30.21

«acocagflm _

«500E
00:03:00 0 50000.0(

 

194

 

 

o: oo.o oo.o oo.o oo.o 8.0 So mm.m mm.m oo.o 8.0 3 butane—35
.. moda amoooN ncé amooofi ofio 38.3 3.: mmm.mm oo.o nooo mu— «5.3053235
32358.
.. a a a an m m; oat—uaEacoutu
... oo.o nooo oo.o nooo oo.o nooo mm.m nmmm.m and mnfié m; uncfimEucetuu
o: mm.m mm.m no.o no.o oo.o oo.o oo.o oo.o oo.o oo.o Na 383220
o: oo.o mm.mH oo.oH mm.m~ mvo mm.o~ oHéH mm.mo on: mo.mN No 2522300
c: oo.o oo.o oo.o oo.o oo.o oo.o NNd no.o om.~ om.~ 3 «Eococao
o: oo.o oo.o oo.o oo.o oo.o oo.o mm.m mm.m oo.o oo.o mo 38559.23
0: no.o no.o mmd no.oH oo.o oo.o mm.m mm.m 8.0 8.0 mo onEE_n_o2ou<
... mm.mH mmmdm ooAN mmm.mn oodH amnoéo No.3 moo.oo om.~ nom.~ mu— «3:25.223
.. mm.m gamma nvé amoooa n13 moo.mm No.2 ammmfim oo.o nooo Q non—ocuco_o3<
o: oo.o oo.o oo.o oo.o noé noé oo.o oo.o oo.o oo.o 3 3.02.333
0: no.o mm.ma Nmé no.o ofim mmoa ow: oo.oo No.3 mm.m~ 3 2:502
o: NNé no.o mm.m mm.m noé noé oo.o oo.o oo.o oo.o 3 32230.5(
0: omdm no.o~ mm.m mm.m oo.o oo.o Nmé no.o mm.m mm.m an mo_mao..u<
.m.m cams. .m.m cams. .u.m cams. .m.m cams. .m.m :82 2:6 5:3
.0
.x 53383 :95“.

mm ___.r.>:ou “5.9.3-08 5.32 2.3 ._umo ooo~\m~\m

.8553: 55

.5255: 00:205.. 0: no: 6052250 oo.o 55 32.9555"... .952: 55 ..8 :3 «523:. 3:03:50 on.
no .5 .523 95 E5 2: 5 «3.5.5.5. 9:. .329 3-0 82. ”59:5 55 .2 .5812 6.: .35. 55 5.5
0.52:5: 2:. .5. 3525.5 can .5. 3.5353 5: $5355; .5 3539:: .3. m20>255a£ 3.5.5

339: ..2 5:3» .5550 230 on... doom .3 525.03%. mm: 5 .5355» .5» ..8 .5» 5 32: 50.:

525.5... ago .553 .53 «255.52 5 53:5: .82 55 .5.— Ammo Echo 22:55 can 252: “v 5952?.

 

195

O:
O:
O:

O:
O:
O:

O:

O:

O:

O:

O:

O:

O:
O:

O:

O:

O:
O:

oo.o
3.x“
oo.o
OmK
mmd
cod
oo.o
mm.m
06.0
00.0
oo.o
Be...»
00.0
oo.o
NNé
mm.m
00.0
00.0
oo.o
cod
made
mmfia
oo.o
mm.m
oo.o
oo.o
ONmH

oo.o
8.0m
oo.o
amoodm
mmd
00.6
00.0
ammd
oo.o
oo.o
oo.o
amooda
oo.o
oo.o
no.o
mm.m
oo.o
oo.o
oo.o
oo.o
amoodn
mm.mu
oo.o
mm.m
good
oo.o
mm.mm

mafia
mwd
mm.m
mod
mhfi
No.0
mm.m
oo.o
oo.o
mvéma
oo.o
mud
8.0
00.0
and
and
00.0
oo.o
mm.mo
oo.o
moKN
hvé
oo.o
oo.o
mm.m
no.o
mde

oo.om
8.0a
mm.m
nmmmda
no.3”
nod
mm.m
000.0
8.0
mm.mma
oo.o
mhmdfi
oo.o
oo.o
mm.mfi
mm.mn
oo.o
oo.o
mm.mo
oo.o
mmm.m~.
oo.ofi
oo.o
oo.o
nmmm.m
no.o
mm.m¢

boa
Bud
:N
mad
an...
00.0
had
oo.o
oo.o
mm.“
8.0
H04»
00.0
00.0
NNé
oo.o
oo.o
had
boa
oo.o
moéw
”no.3”
hmé
8.0
mad
mm.m
med

No.3”
8..:
mm.m
009mm
om.~
oo.o
nod
A50.0
oo.o
nmé
oo.o
nmmmd
8.0
30.0
50.0
00.0
oo.o
mud
boa
oo.o
nmnodm
oo.om
mm...
oo.o
nmmmdfi
mm.m
mmd

om.-
oo.o...
oo.o
No.3
mmfi
Nwdmfi
QWoN
o~.m~
oo.o
$063
00.0
N~é
mm.m
oo.o
mm.m
00.0
5962
oo.o
oNNw
oo.o
m~.~m
mm.m
oo.o
oo.o
NNé
Ned
mad

8.0m
8.0a
oo.o
ammmdw
mmda
no.9;
nodm
amoodm
oo.o
oo.omN
8.6
00590
mm.m
oo.o
mm.m
00.0
5.2:
00.0
oo.om
oo.o
encode
mm.m
oo.o
oo.o
mmmdfi
00.0.“
50.3

8.0
8.0
OWN
omfi
med
oo.o
mm.m
emfi
om.~
mm.m
oo.o
good
oo.o
emé
oo.o
oo.o
Ned
om.N
oo.o
mh.m
oo.o
mmé
oo.o
8.0
00.0
00.0
nod

00.0
00.0
OWN
nomfi
no.o
oo.o
mm.m
3va
OWN
mm.m
oo.o
8.0
00.0
no.o
cod
oo.o
oo.m
om.~
oo.o
mad
good
OWNH
oo.o
8.0
086
8.0
59$

#0
2
2
m;
B
2
3

ma
N”.
3
3
S
3
S
00
m;
E
«0
2
m:
S
00
00
#0
m0
Q ~N:

000.300.000.300
3:000:00
38.0.0350...—
3:30.39...
360...
3:30.323
3.0.0.2300;
3300:0025
36.0.0330
39.30.3050:
3.2.3035.
3:000:05.
2033:005.
35:00.5.
05.305035.
38.233035.
080000.303:
35:300..
3.60250.—
2030.63.80:
3500.33.31
300.2003.—
3.0.0055

000.08.03.00 .

00283300
3.5.208
3:30.35

82.28 0 5.2.2.2

 

196

 

O:

O:
O:
O:
O:
O:
O:
O:
O:

O:

oo.o
oo.o
oo.o
NH.N
modm
oo.o
oo.o
8.0
00.0
00.0
mmdm

«.93

00.0
oo.o
oo.o
mmfi
mm.mw
oo.o
oo.o
8.0
00.0
oo.o
oo.om

mm.mm

oo.o
oo.o
oo.o
mad
mmAN
mm.m
oo.o
mm.m~
oo.o
oo.o
had

mmda

oo.o
oo.o
oo.o
coda
mmdc
mm.m
oo.o
mm.m~
00.0
00.0
59¢

$.mm

oo.o
oo.o
50A
mas
NwKH
oo.o
oo.o
oo.o
mm.m
hodv
de

mo.mfi

oo.o
oo.o
hoé
mwMH
8.9.x.
oo.o
oo.o
oo.o
mm.m
node
mmd

mmda

no.o
oo.o
mm.m
madfi
mafia
oo.o
mm.m
wad
oo.o
mm.m

modwm

HQ:

no.o
oo.o
mm.m
no.9”
oo.om
oo.o
mm.m
OOdN
oo.o
mm.m

hmdmm

oo.o~

oo.o
mm.m
oo.o
mmé
Hedfi
oo.o
oo.o
oo.o
oo.o
thH
mm.m

oo.ow

oo.o
mm.m
oo.o
mw.m
mhdfi
oo.o
oo.o
oo.o
oo.o

mwfia

mm.m

mwdm

m; meoczsx .
2 23¢me .x
3 «82.8.5:
35292.5
N; 3553...

m; 35:53:30.3.
S 35322.22.
3 ms=uE_n_osucm_>._.

mu «3%...
£983.95...

3 £332.:
£2358.

3 32:32:.
3 oat—«.32.:

tuzczccu e 53:39.?

197

 

 

 

Buzzwpc:

may 55. 3
manages”.
338.23%: 0
8955.2

89 5:2 39a
338...;
3255.322
mmEE_m_o._ mmcmm
man: 202
mmpcucocoz
mmu_c>mou_o_wz
23:0: 98..
8252533
825528:
mmutocao=u>u§wx
mmpEE ton.
wmutouocgoo
8252865590

IIIIflDEflIIIIIIflIIIII

mmEEfiEan< I

332065.23. I

3955336. I

mmu_:_:m:< I
gem”.

 

___.r.>:o.U. nommném ___._.-oz.

commmuuzm

 

___

 

 

Noom

.3. 53.538 umEEmm mm; :8 9P .53
8 c8 :53 B 52.35% < .Emiamxv
cozfiw .mo_mo_o_m 30:8. 8 mEEm‘Amooo
w>c E 9.52 :8 ho ”:88? E mwuemEmz
cams. .mmocmncanm 2E5 3295: E
mESw>woom x_w ho comzquoo m 5383.

 

 

 

“$3”.

.0

.0m

- 00H

.03

- cow

- omN

- oo.o.

-omm

 

 

 

 

nos ;0 3000! led sapozewaN 4o JaqwnN ueaw

 

 

198

 

 

 

8.53:5 I
3.225? I
Enzfl I
82.32,. I
onufiasniaa I
3.282.“; I
oQEEEaEnE I
8:18.95»... I
0858... I
oauzucoifihn I
SEEEEE I
302826 I

ufisfizuo: I
082823»: I
o8t91§u§Eo= I
on“??? I
3.39.3.8 I
onEEfiBumE I
83.393815 I
Sagas I
ofifisosutu I
8:32:38 I
oflfifigu §
Sat-roan I
Surfing I
8:53.353 I
8..-.3931} I
932829} I
«8...!!! I
3:51: I

 

___.r.>:oU 083.05 ___._.02

.8:m.>:mm

 

Room .Nr LonEmomo nmaEmw
8: :8 896:2 195%? um meegmooo
9: E cozanmE >__E£ muBmEmz .o x_ucmqa<

 

 

 

#95”. d

 

- com

- 00¢

- com

- cow

- oooH

 

-oomfi.

 

 

I!°S 1° 33001 19" 53!”!me J0 “flu-"N

 

 

199

 

 

 

 

Baggy?
mmu_E_m_2ucw_>.r -
5282: I
322039; I
82923. I
manning”. I
mmnszmcmfiuzo I
825822 I
mfiégsmEmE I
825:2me I
338: U
«8282me I
meEfiB‘amcma a
35896 U
83202 I
$255582 I
$2588: I
826538: I
28:8?3 I
52833.. I
mgr—.8630: I
wantozao=u>u§mr I
mmEEEEoo I
SEEES I
32.83935 I
mmwzocaegfia I
mfiégofiiu I
mmuszmcoUtu I
$28.2qu g
wmuszwcocsm I
8952828 I
w8_E_m_wu.oa< I
~§22822g< I
3238222 I
u8_c_=?< I
32232 I

 

___h>c8 7. 883-05 __Poz

 

.woow .mN .mnEQamw umaEmw mm;
:8 5952.2 .mmbamv. .m mESm>m8o 9,:
5 8:353 aim“. $0552 .5 528.2

 

 

 

.uu:m.>:mm $20". .68

 

w§

I ooow

u soon:

 

 

 

nos 40 3300; Jed sapoxauau "

 

 

200

 

 

 

—I "LL
~1ndUIo N
I-IILLON '2
'aons '2
"153105 I
- l
bgndum
-IILL0N 2
5'2

”15910:! I
:IIIISW

:lllJ.0N 52%
‘RPOSJ 'I
:I'IIISON

:IIuogiz '8
”RNA 'ZI

 

I-ILL s
~Indu IN
-I_I_LON '2

ans 2
—IsaJo=I I
-:|I'ndsoN
'IIU.0N '2
— can '2
4.91031

'IILLON 'E
-':>3ns '2
'JSBJOA 'I
'lllloN 'E
-':):>ns 'z
"'JSSJozl 'I

‘llLLON 'E
-'33ns '2
'153105 'I

-IILL 'S
-Indu o

__I, IJIIIII _++__ III. le Illil [III I- - .-+-+

 

l

-15910:l 'I

 

 

 

fl 8 N
|!°S I0 33001 Jed sapozauaN J0 Jaqwm

5..
0.

-IIu. 's
-JndUION v
-IILL 's
-IndUION v

-IILL 's
—1ndUION iv

I N "7
'IILLON 'E
-':>:>ns 'z

'17
ewauosu M

smpqeuJopewons

aeprupqeuu

Sm39|d

Snwl2|015€U2d

anoIeudaona

eIJeInogng)

snDEIdOIIuo

anoIeud93

salaqonv

 

 

201

Appendix 8. Bacterievore nematodes abundance comparison in two natural and three agricultural ecosystems at KBS/LTER, Michigan in
September 25, 2007 (no-input = bio-based). Bars represent mean and error bars represent 95% Confidence intervals from the mean.

 

 

 

 

lIW—III—III

 

 

FIILL Auoj 'g

~1ndu10N
I.- MW “E
DDS 'Z

-RJO:I 'I
-II!L' M103 '5
.3hdu10N'
-llLL°N 'E
~'33ns '2
491051
-l|!.L' AUOI) 'S
-1ndUION 'iv
-|lu.0N '8
- '33ns 'z
PRJOJ 'I
‘IILL'AUOZJ 'S
~3ndUI0N'v
'llLLON 'E
-'33ns 'z
-1910:l 'I
'IILL' M102) '5
-Indu U10 N'v
~llLL0N '8
- 'I’IDD S 2
#531051
~l|!.L' M103 '5
-1nduo v

- SHCIOIEIldBD

Nb anoIeude3n3
aEMHOEmmfiqmo
ewauzmg
smeldOlH-II)

5nll9PI/U33

.7 ‘J y__-§:I
'llLL‘IIUOb 's
~1ndu10N 'v
-IILL0N '2
- '33ns '_z
#1594031

IPJCE’S’E-E I

-1$J0_-J “I
-|lLL'/\U03 'S
punduloN '1,
:[ILLON 'E
3ns 'Z
:RJOJ 'I
-l|LL' M103 '5
-JndUI°N 'v
'llLLON '£
- '33 5'2
‘19-'03 'I
-||LL'/\U03 'S
-1ndUION'b
-ll!L0N '2
-‘-'33ns 'z

BUJBUOUI'IQ

snIepIudwv

5aner

saIaqonv

 

 

100-

1.
L!)
l\

0
Ln

E
l

—%

I-lI-o

4n

U—EH

——E—-«
I
T
T—1—

Ln O
N

49105 'I

I!°S J0 33001 Jed SieanAIPUI J0 4:1qu ueau

 

Appendix 9. Means and 95% confdence intervals for the mean, for the number of bacterial feeding nematodes found in five ecosystems at

 

202

KBS/LTER, Michigan, in the December 12, 2007 soil sampling (no-input = bio-based). The first half of the Bacterial feeders is included in this

graph.

 

 

 

.ll] endllh'bdi ___ ___ LL44“ dill“- L __ _._- q...

 

"as go 3300: 13d sapowwau

-5|HU'MI1839 'S
- 353% % ewauosnM
-|lLL Au03 s

- ndu o
-I|I_LoNII' ‘7 snleude3oM L
S’

-[l! 0N;%' eunaIIafiaIs

-IIIII':° S
#-
-l_|LL°uI5 :yb 9U931l95935

EI'HIIGI‘SRI S
$551532 smpqeu‘d
~353J0:l_ 'I

II‘HéGUo 5

-lll5I_.’% EIIEPIIIFIQEIl‘d
r1910? zI

-llLL'/I U03 '5
tq'ndou 0'9

- I'I
'153J°:l 'I

:5?ng i8? ’75 snuIIeloIeuIsud
:gssamgai I '
:Illfila'AUOOD E

~IIILON _E‘ Sm33ld

I3|l31! pqeua

o 5 snuIIeIOJfieued

:¥HU'AUO'S

-||u_o snIaqOJfieued
- 33n

49105:! zI

-|l!J.' M103 '5

-1ndu o '9

155% g BJBJSMIUON
-1$JO:I 'IE

1| 'AUO

 

:IILLOUIgQ SSH! PqEIlJosal/Il
-159J0:l SI?

 

l l I
O
In
t\

0- .-.—o+lb ___ ___- .uhb

1250 "
500 -
-250 -

0
m
N

1000

-500 '-

 

 

203

Michigan, in the December 12, 2007 soil sampling (no-input = bio-based). The second half of the bacterial feeders is included in this

Appendix 10. Means and 95% confidence intervals for the number of indiVidual nematodes found in five ecosystems at KBS/LTER,
graph.

 

 

“05 1° 3000'! Jad sewaN

 

441%}? _+-I-clu IHlemb +-I-_-_ Ihlhhith _nIn-I- _+___ -9- +._ 41»- LIL“ 4.4+. ++—-

.-||I 'AUOQV 9

..In quN

:lII-LnoN N9 ewauostVI
_Z

:goalnod L

t”! 'Aqu '9

kIn qu

tlI! 5lI 03,” VeepIIIpqeua

-$3J°d L

:“nIéUIIUOQ 9

IN I
-IIILn_0N ‘32 saneIOIeusIId
:IsalnozI L

-|l! Auoo '9

-In quN'

-|l!.L°N '2 snioaIcI
..oons 'z

-IsaIO:I'I

-II! 'Aqu g
Hn'u

:Il!Ln°N. M2 snuIIeIouseued
II! IIIIIOQ '9

Km UION

-_||!J.n°N “vi: saneIoIUOpo

I- cons '

-Sa-I°.-I “I

-ll! 'AUOQ'S

rm UION 'v

-IllL°N 8 wasAuuow

H3003 ‘Z

I-SGJOJ ”I

I-ll!&Ulo IIqu 9

-In

-Il!J.nolIsI N2 JasefioIqu

_Z

:Saln'od L

:illIIu’I”°Q 9

-||!J.°N '9 snoeIdoIILIQ

-$aln0:l “I

-II! 'Aqu 'g

-III III IN 'v

~|l!J.n°Si\l.'z€ anoIeudaQ

45310:! 'L

-II! 'AUOQ '9

-In u '

-|l!J.°N'€ Bwauoung

-‘ S'Z

-$aJO:I ”I

'III Aqu 'g

:In uoN'9

lILnSO eepIuIIeIV

-$aInOS;I L

-”n!é‘qu AUOQV 9

-Il!J.°SNz M2 sapIoIaquoV

-$910:l I

-Il! 'AUOQ '9

-In quN'

-||!J.°N'€ saIaquoV

..'oons 'z

 

43am 'I
I

 

T

002 002 DOI

 

o-J'b

OOI'

 

 

204

Appendix 11. Bacterievore nematode abundance comparison for five ecosystems in the Kellogg Biological Station (KBS/LTER).

Michigan in September 25, 2008 (no-input = bio-based). Errors represent 95% confidence intervals from the mean.

.59: 05 E0: gator: cocouccoo $3 E0859 Ema ohm. €023-05 n Sages mEEEmm Bow
.mm .mnEofiow 05 E 659525. .mmlrdwmx Hm 2.29308 o>c E 325950 85953 $0052.? m:o_o>_Emo .N_. xficoaa<

 

mzcucocoz
___Héoo .m 5282 .v ___»oz .m .85 .N use”. .H
_ _ _ . _

 

I1 9 n .0

E9

 

 

 

 

 

 

(K

 

IoH

 

ImH

l
Ln
nos J0 3300! Jed sapoaauaN Io Jaqwm

 

 

 

o~

 

 

 

 

205

 

 

 

 

"IILL'AUOQ 's

-:IndUI0N '17

'IILLON '2 snInuauoud
- 'oons '2

459105 '1

 

-III_L'Auo:) 's

-IndUI0N '17

“'IILLON '2 SIIIIIIIDUOMN
- 'oons 'z

bISeJog '1

 

- IILL'Auoo 's

- IndUION 'I7

-III_I_oN 'g snuououow
r 'aans 'z

—- 159mg '1

 

 

MI—

II._I.II __IIJI __I__

 

m

 

r IILL'AUOQ 's

- IndUION 'v

P IILLON '2 snLIDUODIN
- 'oons 'z

- ZISBJO:| '1

-III.L'Au03 's

-IndUION '17

"IILLON '2 srwmoasa
- 'aons 'z

'153JO:I '1

 

 

Ln
N

r f l

O Ln 0 L“
N 1-1 H I

(III-___ -I-————

5

nos Io 33001 .Iad sapoaewau IeanAIpuI Io .Iaqtunu ueew

 

 

206

Appendix 13. Carnivorous nematodes abundance comparison in five ecosystems at KBS/LTER, Michigan, in the December 12,

2007 sampling (no-input = bio-based). Error represent 95% confidence interval of the mean.

.cmoE 9: E0: miZoE. oocouccoo $3 Emmoam: 8.5 333.05 n 59:65 2% @5383 woom .mm .mnEmEow 9: .m $952.2
.Ewkdwmvc cozfiw _mo_mo_o_m 30:8. 9: E mEQm>mooo o>c c_ comtmano wocmucznm wuofiEoc mzouozEmo .3 xficoaa<

 

W
M. M W
m u Q
I. Q 0 0
u. U. u u
d n D D
l U. U.
M. n n n
2 S S S
.SuV .C.._uV mun? m2"?
3 ”ID "la ”I’D ”I.
ONE? OWE? OWE?v OWYZ
um.NS.0=uINSmu:u NSMUI va:
.AUOHJ .AU0nJ .Am.0nJ .AUWnJ
”_Mflwflflduaua udHDBHMHDB
”11".11"m."3.61”m"3.61"11"3$
—_——— —P——— ___—- ___——

 

IOHI

.3

AN

-om

|
|
l
|
:3
nos J0 93001 Jed sapoaauaN Io JaqumN

 

 

 

 

 

 

 

207

.588 9: So: 295:: oocouzcoo $3 E0352 .otm .Anommnofi n 59:65 Emu mEaEmm Bow .3 .onEofiow or: am 595:2
_Emhdwmxv :0:me _mo_mo_o_m 80:3. 9: .m mEQm>mooo 02» E comtmano oocmucznm muoumEoc 990295“. .3 xficmaq<

 

 

 

. I I &
%%) 09% . %s%% 0.2% 90% m
, . 0
«w,» «W» avov , so,» as» w
.o» .5 a.» 3 .no 2 .9 3 .0., 3 M
50%/vb .e v2.%o.m¢ .e .a %)./3% .o eov). «van .o .e %)./202% .eaew

“.wav/«v asses an 4,3229% ’24 29,/0&2 “sow/@2922 x04 o%¢%%2 m.
hI - - n u — p — n - n n p P — p - - — p — - - J
m...
IIII III I I II I I -o m
__ + 2m 2. $2.1 w

m -2
-2 a
..
fr
.8 m
.8 w.
m

.. -om

 

 

 

 

 

 

208

 

 

 

- IIu'Auoo '5
II 'ZlndUION 'v
- IIIJ.0N '2
- 'oons 'z
mama 'I

I - IIu'Auoo 's
v-IIII -1I‘|dUION 'I7
- II!.L°N '2
- 'oons z
I 4910a 'I

 

- IIIJ'Auoo 's
-IlndUI°N 'v
- IIIJ.0N '2
- 'oons 'z
mama 'I

 

. IIIJ'Auoo 's
~3ndUI°N '17
- II!J.0N '2
- 'oons 'z
asuoa 'I

r
I
I
I
I
+
.—E—.
I
4}: - IILL'M-IOD 's
I] -IndUION "I7
1 ~ IILLON '2
"B - 'DDI'IS 'z
| 49105 '1
FE . IIIL'AUOQ '2
II r-zndu10N “b
.—E - IILLON '2
£ - ms '2
I -ZI$-I°:I 'I

 

I-E - IIIJ'AUOQ 's

-I ~IndUI0N "I7
I - IILLON '2
r} - 'oons 'z
| -IISBJ°:I 'I
<5

 

 

 

700 -

600 -

500 -

400 -

"' 300 ~
200 -

100 -
-100 -
-200 -

IIos J0 3300 Jad sapoaauaN

SIIIIBLUIEIOIIDUQMJ

snLIouaIkIOLnoN

snuoumda1

snuouaIAIIa

EJOLICIOJBLIIILIdIC]

anouaIeudv

sepIououeIaudv

 

 

209

Appendix 16. Fungivorous nematode abundance comparison in five ecosystems at the Kellogg Biological Station (KBS/LTER),
Michigan at the December 12, 2007 sampling date (no-input = bio-based). Error represents 95% confidence intervals from the mean.

.cmoE
05 So: mfiEoE. 8.53:8 $3 aficwflae .otm €33-05" .3565 can mEaEum meow mm __onEoEow 05 «a c8225.
ENE—Be: cozfiw 306205 32.8. 05 «a mESgwooo o>c c_ cowtmnEoo 352.2% 263.5: «32029.3. .2 x_uco&<

 

44.. 4.9.23 “.13 4.2.44...
bar; “mm .524... 1. a...\ .
. SMEIM... in.“ “.1426 f Mme... 3%va

 

210

mm .BEofiom 9: E9: 3.6289. c.8223.

Il+

 

.32....2 A. flaws...” ea.

  

Emhdwmv: 23.3w .3305 32.9. 05 «a «Eggnog 9e 5 335.5: 2

.cmmE o£ th $295 8.83:8 $3 3:32am. ..otm €83.03: I 59:65 2.58 :8 Sou
_em: .9 5282

0>

211

 

 

 

 

 

III I II :III I“ +--I-_Jfihmp_¢_

 

-IInI 'uAuog s
”It ’7 &19qude
soon 'Z
45310:! 'I
II-I Auo:) g

N'tv

n UION
aoponew 'x

-III 'AUOI) '5
-'.ln UION ”a
- I o _' sntpuaIAI
-15910:l 'I
-lI!Jd'UIO AUOD b5
:IIIJ.0N0_ 'NEb
pqsaJ :I
-ll! 'uAu03 b5
—;Inu
:llliON°_ 'E
Jon
EJSBI :I I
I ma
_II-II' 9. S
'l_|!.l.0N_'£
- sons 2
-159J0:I'I
-|l! 'u_/\U03 'S

UOI _N
_ -|_l!J.0N_ s

-153J05:l 'I
:wi'hluxo AUOD {75
-|_I!J.°N° 'E
'ISDE‘JOfl ZI
:5l uAléO _ S
-I_II.LON 'E
- 33"5 'Z
-359J0:I 'I
we
-M1UI0 _ND'IrS
-ll!J.°N '9
sons '2
-JSBJO:I 'I
'uAuog 5
Egg” 0% 'b anqdoIprIuJeI-I
[1531052 I
'll! 'uAu03 s
E"“I ”I I
.l 105 Z anouaIAIooIIeH
HSBJOd 'I

” Ill 'AUOD 'S

ewauoIIaInas
snIpuaI A103
sanuaIIsd
smpuaI MEJd
sanuaI Amied
snIUIua w

au AfiopIoIa w

smeIIoais)
45310:! 'I

- "LE M103 '5

-3n UION '

- I o aeunewauooug

 

3153105 'I

 

500 -

I
8
V

0_ fi+__ .._.___ -

300-
200 ‘-
100 "

nos J0 3000! lad sanctum

-100-'

 

Appendix 19. Herbivore nematodes In five ecosystems at the Kellogg Biological Station (KBS/LTER), Michigan recovered from the

 

212

December 12, 2007 soil sample (no-input = bio-based). Error represents 95% confidence interval from the mean.

 

 

 

-IIU.'/\I-I03 's

-1ndUION '17

- IIIJ.°N 'E (&19qude

-'3:)ns '2

-ZISJO:I 'I

'IILL' AUG: 5

-IndUION v

-l|!J.0N E snuauaMI

-':>:>ns 2

”$1051

-IIIJ. MOD 5

HNdUION “I;

-||LL0N '8 SI'IIIDUALIJOIpuaIAI
'z

-l|!J.' M101) '5

-3ndu10N 9

-llI_L0N 'E SIIIIDUSIISd
- sons 2

-1910:I'I

'llli'N-IOD'S
-1ndUI°N'b

-ll!.L0N'£ SIIIIDUGIMEJd
-':>:)ns'z

'RJOA'I

-III_L'Auo:>'s

~IndUION b

-l_|!.l.°N E snuauaIAIEJed

- IIIL'AUOD 's

-IndUION 'lv

- IIIJ.°N “s EIoudOIIaAaIuBH
- 'oons '2

459105 'I

- IIIL'AUOD 's

-IlndUION 'v

-ll!.L0N '2 snuDuaIAIoaIIaH
- 'aans 'z

‘JSEUOJ 'I

- Illi'AUOD 's

-IndUION 'v

- lluoN '2 aepnewauoaua
- 'oons '2

49105 'I

III--- JIM ..___- +4.;  same _I.....I.III ___,._ HEW“; _+___

 

 

 

300-
200-
100-

0.1...

-100-

-200-

-300-

l
o
o
VI-

500 -

IIos JO 93001 Jed sapomuaN

Appendix 20. Herbivore nematodes in five ecosystems at the Kellogg Biological Station (KBS/LTER), Michigan recovered from the

September 25, 2008 soil sample (no-input = bio-based). Error represents 95% confidence interval from the mean.

 

 

213

 

 

 

 

 

ILiqLihL#PJLI

suafiund smuafiund
-JSBJ :l

:IIIaaug rs
$39}: *7

WI

Lunguauoxmed

155
:l'llzifi‘t? 55
- %gé ' snwgewoposw

3mm
who? I?
flasné-
arms
5??ng IEEE eweumqm

ERJ :l
Lag; Auo

smopAwm

snuIIelAJopng
619

_ :uilhou

491$

Hue/g

0

gig: snngewopIda

Pg
455 snlapoqaua
#591 :|

WI

 

Ssnoguaumog
Craig
1"” “game
:usgooéd (fig sanelAJoa
rm:
g aepIuIIelAJoa
-191n0;l
lleuAuog
snwge'eoJodV
4.910%
Illa/mo
Wé snuawgelawodv
-153.IO:I
l Auo _
;fidcg§qgs 1’ snuIIeMJopIudLuv
~19! :l

 

30-

20-

10-

0- ___-..

-10..

I!°S J0 3000'! 13“ samewau

 

 

214

Appendix 21. Omnivore nematodes in five ecosystems at the Kellogg Biological Station (KBS/LTER), Michigan recovered from the

September 25, 2007 soil sample (no-input = bio—based). Error represents 95% confidence interval from the mean.

 

 

 

 

 

l

l

 

 

 

II.“ +____ film} ——-l=

'IILL'AUOD '5
' JHdUION 'b
-|l|J.0N 'E

- sons '2
-:IsaJO=I 'I

' HUI/“103 'S
- 1ndu10N 'v I
-ll!J.°N "E

- 'oans 'z

- 15310:] '1

”IILL'AUOD 'S
‘ lndUION 'b
-|l'J.°N 'E

- 'aans 'z

' 15310:! "I

- Iiu'Auoa 's ,
~ IndUION 'v
rlllJ.0N 's

- 'aans 'z

- 153105 'I

- IIIL'Auoo '5
~ 1ndUI°N 'v
- IlLLON '2

- sons '2

- 153105 'I

'IlLL'AUOD 'S
-1ndUION ”a
“IlliON 'E

- sons '2
-15310:l 'I
'llLL'N-IOI) 'S
.mduloN '9
*llLLON '8

h sons '2
'159J0d 'I

'IILL'AUOZ) 'S
~1ndUI°N '17
”IILLON 'E
- sons '2

srwiewoposew
anopAuIIE'I ‘
auau01qe1
snuiewopng
snaIuauInaa
99”“!qu
BJIPUOIaa

aepIuIeIamodv

 

- 159105 '1

 

8

8

I l
a 2

0—

I
O
H

l!°S J0 33001 Jed sapoaau'au

Appendix 22. Omnivore nematodes in five ecosystems at the Kellogg Biological Station (KBS/LTER), Michigan recovered from the

December 12, 2007 soil sample (no-input = bio-based). Error represents 95% confidence interval from the mean.

 

 

215

.59: 05 E0: 3295 353:8 .38 3:30.39 .otm €023.05 H 59.35 mEEmm __om woom .mw EBEmfiow
05 Eat 8.980.. 59:25. .Ewhdmmv: cozauw _ao_uo_o_m 82.3. 9: 5 3.22308 o>c :_ 33580: 20>EEO .8 50:03.4.

 

 

 

 

u» A. T;

216

.cmmE 05 .2 _mEQE 8:03:00 $8 9: E3039. 23 SEM .Aummmnéfi n 52:05 Emu mEaEmm Bow .NF 52:83.
5 5952.2 .EwEmmxv 53m _Saoaa 82.3. a £8352 85622:: Ea $8552-82 .vm 5282

 

217

doom .8 .09E060w 5 E0952.)— .Ammhdwmxv c285 _mo_uo_o_m
80:3. «0 0E0~m>0oo0 0>c E0: AOE0z-o_:3 00620E0c 03052025 new 00.99923 .moumocoogo .3 5309?

 

 

218

 

.:00E 0:. 80.. 0.0>.0.E 00:03:00

$00 0.0 0.0..m. NOON :. .00:>> 0:0 meow :. 0:000>ow .moom :_ :50 0.0.5 000.0 0:... .:0m.:0..2 0.0500 29.0....
..mmhdwmxv 50.00001 .00_mo.00m. E.0..-m:o.. :0305 .00_mo.o_m 80:3. .0 0.0.00: .00 080.00.: :_ 00.0; .8 50:03.0.

 

JBBLIM-LOOZ
ueanos-9ooz
UJoa-sooz

IILL'AUOD
paseq—OIEI
1ndUIM0'l
llLL'AUOD

IILLON

paseq-OIg
lndUIMO'I
II!J.0N

Paseq-OIEI
1ndUIM0'I
IILL'AUOD

IILLON

 

 

 

 

219

 

 

 

 

 

 

 

 

 

 

 

 

 

0 0.000: 0.000 000.0 0000.0 00.000. 000.00 00.000.00-20 08.05.0000
0 0.0000. 0.000 000.00 00000.0 00.0000 000.00 00:35-23 00230000
0 0.0000 0.000 00.00 00000.. 00.0000 000.00 .00.___0-oz 0023.080
0 0.0000. 0.000 000.00. 000000 00.0000 000.00 .E___0.>000 300.5080
0 0.0000. 0.000 00000 $000.. 00.0000 8.00 00.000.00.20 53.60-0000
0 0.0000 0.000 000.00 00000.0 00.0000 000.00 00.300033 00000000000
0 0.00000 0000 000.00 00000.0 00.0000 000.00 .0:___..-oz 00000000000
0 0.00000 0.000 000.00 00000.. 00.0000 000.00 :0.___0.>000 00000000000
0 0.00000 0000 0:000 00.000 00.0000 000.00 00.00.000-20 0.00-0000
0 0.00000 0000 000.000 $000.0 00.0000 000.000 00:35-33 030-0000
0 0.00000 0.000 000.000 00000.0 00.0000 000.000 0000.2 080.0000
0 0.00000 0.000 000.000 00000.0 0.0000 000.0: :b___0.>000 0.00.0000
0.00.3 0.00.2. 059. 0.0030. .0000 wmv:
0200.300. 059. Sam .55 059. mm mm :02... :00...— .:0Eu00..r 0.0.9.00;

 

 

 

 

 

 

 

 

 

 

..:00E 0.... 00 Em. 0.0..0 0.00:0.0 0:0 0:00E.
003-003 E0... mw....=mmv. 00 00.30.3000 .30. .0. 0.00 .00 0.2.0.5 0:0 080.00.... :. 00.0; KN 50:00.2

 

 

220

050.000 000225. 0000.0 020220 00008. 0_ 10:00 .00 00000000.

 

221

080.0% c0950.:

800.0 020220 000:9. 0_ 0.0000 0:00 __00 .00 0000002

 

222

0.005500
0_ 0008 000.5000 00x 00022.2 .0090 0000.0 _8_00_o_m 00200 0.0 .0 008.00. 000.8 0.00520 __00 .00 000000,.

 

223

0.005500 :.
00000 .:0E.00.. wmv. 600.50.: .3me :0_.0.w .00_mo_0_m 80:3. 0:. .0 3:00.03 500...: .0.:0E0_0 __0w .5 50:02.0.

 

224

   

MTIHIIGAIWUTIITWWIVIWIHIWilllalfllAllm
3 1293 03062 9202