baker kelly thesis - texas tech university

Local and Landscape Factors Influencing

Diversity and Fitness in Odonates at Playa Wetlands

by

Kelly S. Baker, B.S.

A Thesis

in

Biology

Submitted to the Graduate Faculty

of Texas Tech University in

Partial Fulfillment of

the Requirements for

the Degree of

MASTER OF SCIENCE

Approved

Dr. Nancy E. McIntyre

Chair of Committee

Dr. Kevin Mulligan

Dr. Richard E. Strauss

Peggy Gordon Miller

Dean of the Graduate School

August, 2011

Texas Tech University, Kelly Baker, August 2011

ii

ACK�OWLEDGME�TS

The completion of this work would not have been possible without the help and

support of many different people.

First and foremost, I would like to thank my committee chair Dr. Nancy

McIntyre. In addition to providing lab space and resources, Dr. McIntyre has generously

given advice and direction from the inception of this project to its close. It has been a

blessing to work under the counsel of such a kind and scholarly advisor. I would also

like to thank my other committee members, Dr. Richard Strauss and Dr. Kevin Mulligan,

both of whose advice and help has been invaluable. Dr. Strauss was chiefly responsible

for the statistical component of chapter II; without his input, this project would not have

been a success. Similarly, I am indebted to Dr. Mulligan for his ArcGIS expertise, as his

help was also instrumental in the completion of chapter II. Furthermore, I would like to

acknowledge Dr. Bryan Reece for his assistance in developing the oviposition chambers,

as well as his significant role in data collection for the biodiversity chapter. I would like

to thank Chris Van Nice for his ArcGIS guidance and Steve Collins for his input

throughout all stages of this project.

Over the course of the last several years, I have received funding from three

different organizations. I would like to extend the sincerest of thanks to AT&T for

providing the AT&T Chancellor’s Fellowship, Texas Tech Graduate School and Sandy

Land Underground Water Conservation District for providing the Water Conservation

Research Fellowship, and lastly, the Department of Biological Sciences at Texas Tech

University for its constant and faithful financial investment in its graduate students.


iii

Through the efforts of Dr. John Zak and Dr. Llewellyn Densmore (as well as countless

others), the biology graduate students have been blessed with liberal TA and RA funding.

On a personal note, I would like to thank my loving husband Joshua who

provided not only an abundance of moral support, but also field and lab assistance when

necessary. Also, I am deeply appreciative of my parents, Mark and Brenda Klinkerman,

who have always been a source of strength and encouragement. Finally, I would like to

thank God, the loving Creator, in whom I find joy and hope.


TABLE OF CO�TE�TS

ACK�OWLEDGME�TS ii

LIST OF TABLES vi

LIST OF FIGURES vii

CHAPTER

I. I�TRODUCTIO� 1

Literature Cited 5

II. LOCAL A�D LA�DSCAPE-LEVEL VARIABLES IMPORTA�T

I� THE DETERMI�ATIO� OF ODO�ATE OCCURRE�CE A�D

RICH�ESS 7

Abstract 7

Introduction 8

Methods 10

Results 16

Discussion 21

Literature Cited 27

Tables and Figures 30

III. ASSOCIATIO�S BETWEE� ADULT FEMALE BODY SIZE

A�D FIT�ESS I� ODO�ATES 67

Abstract 67

Introduction 68

Methods 69

Results 74


Discussion 76

Literature Cited 84

Tables and Figures 88


vi

LIST OF TABLES

2.1. Correct classification table for landscape-level multiple logistic regression 30

2.2. Correct classification table for local-level multiple logistic regression 31

2.3. Correct classification table for landscape-level PLS discriminant analysis 32

2.4. Correct classification table for local-level PLS discriminant analysis 33

2.5. Landscape-level variables by number 34

2.6. Local-level variables by number 35

2.7. Landscape-level multiple logistic regression coefficients 36

2.8. Local-level multiple logistic regression coefficients 43

2.9. Landscape-level PLS discriminant analysis coefficients 45

2.10. Local-level PLS discriminant analysis coefficients 52

2.11. Landscape-level PLS multiple regression coefficients 54

2.12. Local-level multiple logistic regression coefficients 55

3.1. Results of t-tests for fitness relationships in female E. civile 88

3.2. Correlation results related to overall E. civile fitness 89

3.3. Post-hoc Tukey test results for the relationship between HCW of females

caught early in the season (June 23-July 6) and females caught late in the

season (September 2-8) 90

3.4. Data for egg-laying female E. civile for 2009 and 2010 91

3.5. Egg-length data for female E. civile clutches in 2010 98

3.6. Data for non-egg-laying female E. civile for 2009 and 2010 102


vii

LIST OF FIGURES

2.1. Distribution of actual species richness (a) and log richness (b) 56

2.2. Landscape-level multiple logistic regression results by species 57

2.3. Local-level multiple logistic regression results by species 59

2.4. Landscape-level PLS discriminant analysis results by species 61

2.5. Local-level PLS discriminant analysis results by species 63

2.6. Landscape-level PLS multiple regression variable weights 65

2.7. Local-level PLS multiple regression variable weights 66

3.1. Distribution of clutch size 108

3.2. Distribution of time to hatch 109

3.3. Distribution of hatch duration 110

3.4. Distribution of hatch success 111

3.5. Distribution of mean egg length 112

3.6. HCW of egg-laying vs. non-egg-laying females 113

3.7. Pearson correlation of HCW and mean egg length 114

3.8. Pearson correlation of HCW and hatch success 115

3.9. Pearson correlation of HCW and hatch duration 116

3.10. Pearson correlation for number of eggs and mean egg length 117

3.11. Spearman correlation of hatch duration and mean egg length 118

3.12. Pearson correlation of number of eggs and hatch success 119

3.13. Pearson correlation of mean egg length and hatch success 120

3.14. Pearson correlation of hatch duration and hatch success 121


1

CHAPTER I

I�TRODUCTIO�

Anthropogenic changes to the environment are inevitable and continual,

especially with the increasing human population. Building homes, growing crops, raising

livestock, and the pursuit of hobbies all come with environmental costs. This can be seen

acutely in the landscape of the southernmost portion of the Great Plains of North

America, the Southern High Plains.

In the most simplistic terms, the ecosystem of the Southern High Plains is

cropland and grassland. In Texas, this region is highly agricultural: around 46% of the

Southern High Plains is composed of cropland (chiefly cotton, corn, sorghum, and winter

wheat), with agriculture being the primary economic driver of the region (Haukos and

Smith 1994, Smith 2003). Grasslands in the Southern High Plains comprise indigenous

grasslands and Conservation Reserve Program (CRP) grasslands (former cropland

restored to grassland). Indigenous grasslands account for approximately 37% of the

Southern High Plains whereas CRP makes up about 12% (Haukos and Smith 1994). The

main source of above-ground freshwater is playa wetlands, comprising approximately 2%

of the landscape.

Playas are shallow, ephemeral ponds with highly variable hydroperiods.

Typically, playas are less than 1.5 meters in depth (Smith 2003: 9) with an average

surface area of 6.3 hectares (ranging from less than one hectare to more than 260

hectares) (Guthery and Bryant 1982). Playa hydroperiods are defined by alternating

intervals of wet and dry, but no specification exists as to the lengths of these intervals.

Some playas will fill and dry several times in one year whereas others may go several


2

years before either filling or drying out (Smith 2003: 8-9). The soils in a playa wetland

are distinct from those of the surrounding Great Plains uplands (Mulligan and Fish 2004),

in that they are hydric soils with high clay content. When wet, the hydric soil expands

and helps retain water in the wetland basin. There are approximately 30,000 playas in the

Great Plains (Osterkamp and Wood 1987). However, the exact number of the playa lakes

on the Southern High Plains is difficult to discern because of the varying hydroperiod of

playas, road construction through these wetlands, and their continuing loss to agricultural

pursuits.

Playas have significant ecological and economic value. Ecologically, these

wetlands are a focal point for regional biodiversity (Haukos and Smith 1994).

Economically, playas provide water to support human life through direct consumption

and especially irrigation-based agriculture relying upon the Ogallala Aquifer, as playas

are the primary method of Ogallala recharge (Playa Lakes Joint Venture website,

accessed 23 October 2008:

http://www.pljv.org/assets/Media/News%20Release_Playas%20Recharge%20Ogallala_0

70927.pdf).

While anthropogenic environmental change is a well-established issue, scientists

are continually discovering new and different ways in which human activity effects

ecosystems. Several recent studies have emerged revealing that playas are affected by

the varying land cover forms within their watersheds. Sedimentation occurs at a much

higher rate in cropland watersheds than in grassland playas (Luo et al. 1997, Tsai et al.

2007). Sedimentation alters the hydroperiod of a playa by increasing evaporation and

infiltration (by pushing water beyond the boundary of the clay hydric soil). Additionally,


3

cropland ecosystems are often more heterogeneous than are grassland ecosystems,

increasing the difficulty of dispersal for terrestrial organisms (Gray et al. 2004). Finally,

because of the fertilizers, herbicides, and pesticides used on agricultural fields, the water

chemistry of cropland playas is altered relative to that of grassland playas.

Because of the biological and economical worth of playas to the Southern High

Plains region, it is timely to invest in research aimed at understanding and preserving the

well-being of these wetlands. From a broad perspective, the objectives of my thesis were

to examine how anthropogenic environmental changes have impacted odonates

(dragonflies and damselflies), a local amphibious invertebrate group championed as an

indicator taxon for playa health (Hernandez et al. 2006).

My second chapter explores the factors governing odonate biodiversity at playa

wetlands. As discussed above, the literature confirms that interactions occur between a

playa and the upland ecosystem. Therefore, an aquatic or amphibious organism

inhabiting a playa (such as odonates) must deal not only with immediate variables (such

as water temperature, hydroperiod, depth, etc.) but also with larger-scaled factors (such as

land use/land cover) that may have both direct influences and indirectly act through

proximal variables. Up to this point, the extent to which these interactions affect aquatic

or amphibious organisms has been drastically understudied. In this chapter, I sought to

determine which factors (from suites of local and landscape-level variables) most

influence the occurrence of odonates at playa wetlands on the Southern High Plains.

My third chapter builds on the work of Dr. Bryan Reece. In lab experiments,

Reece found that odonate larval growth and development are influenced by different

environmental variables, such as pH and temperature, which are essentially determined


4

by the playa’s surrounding ecosystem. For example, pH is determined by chemical

inputs from the surrounding ecosystem, whereas temperature is dependent upon playa

depth, which is affected by sedimentation (which itself is ultimately affected by

surrounding land use). These factors affect odonate growth and development by

influencing the rate at which they occur. Odonates have the ability to sense habitat

quality as they mature. If the habitat quality is poor, the larvae may grow and develop

faster in order to escape adverse conditions. The assumption is that faster growth and

development cause a tradeoff of smaller adult body size (De Block and Stoks 2005,

Mikolajewski et al. 2005). Therefore, researching the relationship between adult female

body size and fitness is the next logical step in exploring the mechanistic effects of

environmental variables on these organisms.

The two separate studies of my thesis are linked conceptually in examining how

aspects of the environment affect the distribution and life history of organisms. The

second chapter utilizes a GIS approach to analyze a long-term dataset on odonate

diversity. The third chapter explains a lab-based study on one focal odonate species.

Together, they provide more information on how the ecology of playa organisms is

influenced by anthropogenic modifications to the Southern High Plains landscape.


5

Literature Cited

De Block, M., and R. Stoks. 2005. Fitness effects from egg to reproduction: Bridging

the life history transition. Ecology 86:185-197.

Gray, M.J., L.M. Smith, and R.I. Leyva. 2004. Influence of agricultural landscape

structure on a Southern High Plains, USA, amphibian assemblage. Landscape

Ecology 19:719-729.

Guthery, F.S., and F.C. Bryant. 1982. Status of playas in the Southern Great Plains.

Wildlife Society Bulletin 10:309-317.

Haukos, D.A., and L.M. Smith. 1994. The importance of playa wetlands to biodiversity

of the Southern High Plains. Landscape and Urban Planning 28:83-98.

Hernandez, K.M., B.A. Reece, and N.E. McIntyre. 2006. Effects of anthropogenic land

use on Odonata in playas of the Southern High Plains. Western North American

Naturalist 66:273-278.

Luo, H.R., L.M. Smith, B.L. Allen, and D.A. Haukos. 1997. Effects of sedimentation on

playa wetland volume. Ecological Applications 7:247–52.

Mikolajewski, D.J., Brodin, T., Johansson, F., and G. Joop. 2005. Phenotypic plasticity

in gender specific life-history: effects of food availability and predation. Oikos

110: 91-100.

Mulligan, K.R., and E.B. Fish. 2004. Mapping Playa Lake Basins on the Llano Estacado,

Texas, GIS. The Language of Geography, ESRI Map Book, vol. 19.


6

Osterkamp, W.R., and W.W. Wood. 1987. Playa-lake basins on the Southern High

Plains of Texas and New Mexico; Part I, Hydrologic, geomorphic, and geologic

evidence for their development. Geological Society of America Bulletin 99:215-

233.

Smith, L.M. 2003. Playas of the Great Plains. University of Texas Press, Austin, TX.

Tsai, J.-S., L.S. Venne, S.T. McMurry, and L.M. Smith. 2007. Influences of land use

and wetland characteristics on water loss rates and hydroperiods of playas in the

Southern High Plains, USA. Wetlands 27:683-692.


7

CHAPTER II

LOCAL A�D LA�DSCAPE-LEVEL VARIABLES IMPORTA�T I� THE

DETERMI�ATIO� OF ODO�ATE OCCURRE�CE A�D RICH�ESS

Abstract

Because odonates (dragonflies and damselflies) are good indicators of wetland

health on the Southern High Plains of Texas, there is value in being able to predict their

occurrence as well as richness. In order to utilize the limited amount of fresh water

available in this region, odonates must adapt to a range of environmental conditions.

However, odonate species are certainly not ubiquitously distributed at all wetland sites.

This study shows that odonates discriminate among wetland sites using both landscape

and local-level variables.

From 2003 to 2010, 411 playa visits (encompassing 104 different wetlands) were

recorded. Local-level variables collected on-site, as well as landscape-level variables

determined using NAIP aerial imagery, ArcGIS, and FRAGSTATS, were used as

independent variables in a series of statistical tests: multiple logistic regression, PLS

(partial least squares) discriminant analysis, and PLS multiple regression. Water quality

variables (pH, concentration of phosphate, concentration of nitrate, dissolved oxygen, and

turbidity) were the most precise at determining odonate presence at the local scale.

Measures of habitat fragmentation and land-use dominance (Shannon evenness index,

largest-patch dominance, and total edge) emerged as important variables at the landscape

scale.


8

Introduction

Elucidating the relative importance of local versus landscape-level variables on

the abundance and distribution of species has emerged as a recent focus in landscape

ecology and conservation biology (e.g. Rubbo and Kiesecker 2005, Van Buskirk 2005,

Thogmartin and Knutson 2007). Treating study sites as isolated, independent points is

unrealistic, as constant exchanges occur between any given site and its surroundings. In

this study, I used the playa wetland ecosystem in West Texas (USA) to examine which

factors (from suites of local and landscape-scaled variables) have the most influence in

governing regional biodiversity of a focal group of amphibious animals.

In the most simplistic terms, the ecosystem of the Southern High Plains of North

America at the landscape level is typically either cropland or grassland. The Southern

High Plains is highly agricultural, the primary economic driver of the region. Around

46% of this region is composed of cropland, chiefly cotton, corn, sorghum, and winter

wheat (Haukos and Smith 1994, Smith 2003). Grasslands in the Southern High Plains

comprise indigenous grasslands and Conservation Reserve Program (CRP) grasslands

(former cropland restored to grassland). Indigenous grasslands account for

approximately 37% of the Southern High Plains whereas CRP makes up about 12%

(Haukos and Smith 1994). The main source of above-ground fresh water in this region is

playa wetlands. Playas are shallow, ephemeral ponds found in arid or semi-arid

environments worldwide (Smith 2003). There are approximately 30,000 playas in this

area (Osterkamp and Wood 1987), comprising approximately 2% of the land surface in

the Southern High Plains (Smith 2003). These runoff-fed wetlands are typically less than

1.5 m in depth and are naturally fishless; most have been modified by inclusion of


9

irrigation pumps, dugout pits, or by surrounding urban development or agriculture (Smith

2003).

Several recent studies have emerged revealing that playa wetlands are affected by

the varying land cover forms within their watersheds. For example, sedimentation occurs

at a much higher rate in cropland watersheds than in grassland playas (Luo et al. 1997,

Tsai et al. 2007). Sedimentation alters the hydroperiod of a playa by increasing

evaporation and infiltration (by pushing water beyond the boundary of the clay hydric

soil). Additionally, cropland ecosystems are often more heterogeneous than are grassland

ecosystems, increasing the difficulty of dispersal for terrestrial organisms (Gray et al.

2004). Finally, because of the fertilizers, herbicides, and pesticides used on agricultural

fields, the water chemistry of cropland playas is altered relative to that of grassland

playas.

Based on these interactions between a playa and the upland ecosystem, an aquatic

or amphibious organism inhabiting a playa must deal not only with immediate variables

(such as water temperature, hydroperiod, depth, etc.) but also with larger-scaled factors

(such as land use/land cover) that may have both direct influences and indirectly act

through proximal variables. The extent to which these interactions affect aquatic or

amphibious organisms is drastically understudied. Here, my objectives were to

determine which factors (from suites of local and landscape-level variables) most

influence species occurrence and to use these factors to predict species presence at other

playas.


10

Methods

Study Organisms

Being recognized as an indicator species for playa ecological well-being

(Hernandez et al. 2006), odonates (Insecta: Odonata, dragonflies [suborder Anisoptera]

and damselflies [suborder Zygoptera]) are particularly well-suited for this study.

Dragonflies (a generic term for odonates that includes both suborders) are amphibious

invertebrates with an aquatic larval stage and a terrestrial adult stage. This amphibious

quality is useful because it means that these animals can be used to reflect changes in

both the aquatic and terrestrial aspects of playa wetlands, thereby integrating both local

and landscape influences. Dragonflies are also a diverse group of invertebrates, with

several dozen sympatric species in the Southern High Plains (Reece and McIntyre

2009b). They are often top predators at playas, particularly as larvae (if fish or large

amphibians are not present). Top predators accumulate any toxins or contaminants

present in the environment and therefore may display an exaggerated effect to ecosystem

inputs relative to other trophic levels. Furthermore, odonates have been shown to

respond to surrounding land cover in terms of adult diversity (Reece and McIntyre

2009a), as well as larval growth, development, and survivorship (Reece 2009).

Sample Sizes

From 2003 to 2010, the McIntyre lab made 411 visits to 104 different playas

located throughout 15 counties in the Southern High Plains of Texas (Bailey, Briscoe,

Castro, Crosby, Dawson, Deaf Smith, Floyd, Hale, Hockley, Lamb, Lynn, Lubbock,

Parmer, Randall, and Swisher). Ten of these playas were long-term sites (4 cropland, 4


11

grassland, and 2 urban) that were visited monthly from May through August starting in

2006. The other 94 playas were visited at least once during the eight-year sampling

period. However, because of considerable missing values in local-level variables due to

different sampling practices over the course of the study as well as dry conditions (when

no water measurements could be made), the sample size for local-level analyses was

drastically reduced. To determine which most-complete subset of observations and

variables to include, I followed Strauss and Atanassov (2006). All possible complete

sub-matrices were considered, and the one was chosen that maintained (as best as

possible) the statistical properties of the original matrix while maximizing the number of

included local-level variables and observations. The final sample size for local-level

analyses was 51 observations with 9 variables (explained in the following two sections).

Local-Level Variables

During each of the 411 playa visits, several local-level environmental variables

that may influence odonate presence were measured. Local-level variables change

frequently and characterize the immediate, small-scale environment. The measured

local-scale variables were Julian date, percent of basin area filled with water, pH, water

temperature, water depth, dissolved oxygen (DO), turbidity, nitrate (NO3) concentration,

and phosphate (PO4,) concentration, relative humidity, average wind speed, percent cloud

cover, time of day, and air temperature. However, the last five variables were excluded

from all subsequent local-level tests due to the fact that they do not impact actual species

presence or absence.


12

Each of the water measurements (pH, temperature, depth, DO, turbidity, NO3, and

PO4,) was taken from two sites within the playa, with sites separated by at least 10 m;

these measures were then averaged and the average values included in subsequent

analyses. Measurements of pH, temperature, and DO were collected using a HACH

sension 156 meter with sension dissolved oxygen electrode and sension platinum series

pH electrode (Loveland, Colorado, USA). Turbidity was measured in the field using a

HACH 2100P turbidimeter calibrated with Formazin standard at least monthly. Nitrate

and phosphate concentrations were determined using a HACH DR/2400

spectrophotometer via the cadmium reduction method 8171 (for concentrations from 0.1-

10.0 ppm NO3) or method 8039 (for concentrations from 0.3-30.0 ppm NO3) and the

PhosVer3 (ascorbic acid) method 8048 (for concentrations of 0.02-2.5 ppm PO4).

Percent basin area filled with water was determined visually in the field.

Landscape-Level Variables

Landscape-level variables remain fairly stable over time and describe patterns

occurring at a large scale. The landscape-level variables in this study are assumed to

have remained constant from 2003-2010. I used several sources to derive landscape-level

information. NAIP (National Agricultural Imagery Program) 2004 and 2008 aerial

imagery of all counties in this study were downloaded from the Geospatial Data Gateway

(made available by the United States Department of Agriculture and the National

Resources Conservation Service, http://datagateway.nrcs.usda.gov/GDGOrder.aspx).

Playa coordinates were collected as latitude/longitude coordinates at field visits. I

initially imported all NAIP imagery and playa coordinates into ArcGIS version 9.2 using


13

the WGS_1984 projection. Later, all layers were converted to UTM coordinates so as to

facilitate measurements of data. Once the playa coordinates were added to ArcGIS, a

2.5-km-radius buffer was created around each playa point. This radius was selected

because it incorporates a significant amount of upland ecosystem and it is beyond the

dispersal range of most non-migratory odonates studied to date (Bick and Bick 1963,

Conrad et al. 1999, Angelibert and Giani 2003). Next, each buffer zone was digitized at

a 1:15,000 scale into the following six mutually exclusive land-cover categories: wetland

(playas and any man-made aquatic structures), grassland (native and CRP), cropland

(both active and fallow), dairies/CAFOs (concentrated animal feeding operations, i.e.,

feedlots), built (roads, buildings, etc.), and open space (expanses of land in urban areas

lacking buildings, i.e., parks, golf courses, etc.).

After digitization was complete, feature layers were converted into raster grids to

be compatible with FRAGSTATS, a landscape ecology freeware program

(http://www.umass.edu/landeco/research/fragstats/fragstats.html). The square raster cell

size was set at 17 meters per side, the smallest integer possible without compromising

accuracy. FRAGSTATS calculates various metrics of landscape composition and

configuration (McGarigal et al. 2002). The following standard metrics were chosen to

represent a spectrum of compositional and configurational assays to characterize the area

surrounding each playa: land-cover class area (CA), number of patches per land-cover

type (NP), largest patch index (LPI), total edge (TE), mean (AREA_MN) and standard

deviation (AREA_SD) of patch area, the Shannon evenness index of land-cover diversity

(SHEI), mean Euclidean nearest neighbor (ENN_MN) distance for wetlands, contagion

of land covers (CONTAG), and patch richness (PR). Details on how each of these


14

metrics is mathematically calculated may be found online at:

http://www.umass.edu/landeco/research/fragstats/documents/Metrics/Metrics%20TOC.ht

m. FRAGSTATS output was used as input predictor landscape-level variables for

subsequent statistical analyses.

Dependent Variables

It is logistically unfeasible to quantify individual odonate species abundance

because of their extreme vagility. Thus, at each playa visit, adult odonate presence was

recorded, resulting in individual species presence/absence (estimated as detected/non-

detected) data per playa per visit, as well as an overall species richness count per playa

per visit. Only adults were included in analyses because larvae are extremely difficult to

identify to species, particularly in very young instars.

Although 33 different odonate species were observed over the course of this

analysis, only a subset of the observed species was used in subsequent analyses. The

very rare species (Argia apicalis, Brachymesia gravida, Celithemis eponina, Drythemis

fugax, Enallagma basidens, Erythemis vesiculosa, Erythrodiplax umbrata, Ischnura

barberi, I. damula, I. posita, I. ramburii, and Libellula subornata were observed fewer

than five times out of 411 visits) and the almost ubiquitous species (there were more than

100 observations of Anax junius, Enallagma civile, and Sympetrum corruptum), as well

as known migratory species (Anax junius, Pantala flavescens, P. hymenaea, Tramea

lacerata, and T. onusta), provide little in terms of predictive value. The most common

species were excluded because their ubiquity indicates a broad tolerance of most regional

environmental conditions, and the least common species were excluded because low


15

sample sizes would preclude detection of which local or landscape-scaled environmental

variables were important in determining their occurrence. Therefore, only the moderately

abundant (observed 10-59 times), non-migratory species (N = 12) were examined:

Erythemis simplicicollis, I. denticollis, I. hastata, Lestes alacer, Lestes australis,

Libullula luctuosa, Libellula pulchella, Libellula saturata, Orthemis ferruginea,

Pachidiplax longipennis, Perithemis tenera, and Plathemis lydia. This group included

both dragonflies (N = 8 species) and damselflies (N = 4).

Statistical Analyses

The highly correlated nature of the landscape-level variables, as well as the large

number of predictor variables compared to observations for local-level analyses, reduced

the usefulness of customary statistical methods (such as PCA or multiple regression).

Partial least squares (PLS) analyses are relatively new to ecology, but they have been

used in other scientific disciplines (e.g. chemistry) for some time (Carrascal et al. 2009).

In ecology, PLS methods are valuable because they account for collinear variables and

potential overfitting situations, as is the case here.

In order to achieve the objectives of this study, three different statistical analyses

were used (multiple logistic regression, PLS discriminant analysis, and PLS multiple

regression) at two scales (local and landscape), for a total of six separate analyses.

Although multiple logistic regression and PLS discriminant analyses were used to answer

the same question, namely how well the occurrence of odonates can be determined based

on environmental variables, these are not redundant tests. Multiple logistic regression

approaches the question from a non-linear perspective whereas discriminant analysis


16

takes a linear perspective. Because I did not know the nature of the relationship at the

onset of the study, there was not a suitable reason to choose one test over the other;

therefore, both were included. Finally, PLS multiple regression was used to determine

which suite of variables best predicted overall species richness.

In the PLS multiple regression, the dependent variable was overall species

richness per playa per visit. This overall richness included all 33 species. However, due

to the highly right-skewed nature of this variable (i.e., most species were encountered

infrequently), log-richness was regressed in place of the actual richness (Figure 2.1).

One limitation of collecting presence/absence data is the inherent fact that

observed absences may not be true absences. It is quite possible that a given species may

be present at a site but not observed (and therefore recorded as absent). The statistical

methods I used, however, assume that false negatives do not occur. The problem of

pseudoabsences in ecology has only recently started to be recognized (MacKenzie et al.

2005), and analytical techniques to cope with errors of omission are still in their infancy.

All analyses were performed using special-purpose functions written for

MATLAB version 7.10.0.499 (R2010a). Multiple logistic regression utilized the

MATLAB function glmfit from the Statistics Toolbox (version 7.3). The PLS analyses

made use of functions pls and plsda from the PLS Toolbox (Eigenvector Research Inc.,

version 6.2.1).

Results

In this section, the overall trends emerging from each analysis are presented,

separated by test and by scale. Complete results for each analysis can be found in


17

Appendices 2.1-2.6. As a general rule for the PLS discriminant analyses, any variable

with a coefficient greater than or equal to 0.50 or less than or equal to -0.50 was

considered important. In both the multiple logistic regressions and PLS multiple

regressions, any variable with a scaled weight greater than or equal to 0.50 was

considered important. If any variable is discussed that differs from this general rule, it

will be explicitly stated in the text. Interpretation of Figs. 2.2-2.5 is thus based on the

relative length (indicating strength of influence) and direction (positive, negative) of each

bar (with each bar representing an individual environmental variable). Finally, at the end

of this section, a simple comparison of the performance of the multiple logistic regression

and PLS discriminant analysis is presented.

Multiple Logistic Regression at the Landscape Level - Figure 2.2

The Shannon evenness index (SHEI) (observed range: 0.13 – 0.76) was the most

important variable in the multiple logistic regression analysis at the landscape level for all

of the 12 species considered. However, the response to SHEI varied by species. Six of

the 12 species responded positively to SHEI, meaning that increasing evenness across the

landscape (i.e., increasingly equal amounts of area per land-use category) predicted

species presence. Conversely, the other seven species responded negatively to SHEI,

meaning that increasing SHEI indicated their absence (and indicating that these species

may prefer large areas of a single land-use). Furthermore, the largest patch index (LPI)

of dairy (observed range: 0.00 – 4.91%) and urban open space (observed range: 0.00 –

8.03%) emerged as significant variables for many species. For both of these variables,

the responses were divided fairly equally as to whether the species responded positively


18

or negatively to increasing LPI. This type of response should be expected. Both dairies

and open space tend to appear as small, isolated patches of land that are markedly

different from the main land use in the area, thus likely triggering the strong species

reaction seen. Either the species seeks out the isolated patches of dairy or open space

(positive response), or the species prefers the main land-use type and actively avoids the

smaller, interspersed patches of dairy or open space (negative response).

Multiple Logistic Regression at the Local Level - Figure 2.3

The most important variables, in descending order of importance, for the multiple

logistic regression at the local level were pH (observed range: 6.07 - 9.73), concentration

of phosphate (observed range: 0.01 – 2.50 ppm), amount of dissolved oxygen (DO)

(observed range: 0.22 – 16.18 mg/l), and concentration of nitrate (observed range: 0.00 –

23.65 ppm). For all four of these variables, positive and negative reactions were

observed. In terms of pH, most species (8 out of the 11 exhibiting a strong response)

avoided sites with high alkalinity; a smaller portion (3 of 11) occurred largely at alkaline

sites. Of the ten species responding strongly to phosphate, five tended to be absent as

phosphate concentrations rose within the observed range for this study, whereas the other

five tended to become increasingly present under these conditions. Six species had DO

emerge as important, four of which exhibited positive responses (i.e., were present) as the

amount of oxygen in the water increased. Five species reacted to the concentration of

nitrate in the playa basin. Three of these species had negative regression coefficients,

meaning that these three species preferred ranges of nitrates near the minimum observed

nitrate level; the opposite is true of the other two species.


19

PLS Discriminant Analyses at the Landscape Level - Figure 2.4

The total edge (TE) of grassland (observed range: 0 – 67,337 m), of cropland

(observed range: 0 – 57,868 m), and of wetland (observed range: 1,258 – 48,773 m) were

the decisive factors in the PLS discriminant analysis at the landscape level. All species

responded strongly to the TE of grassland, nine to the TE of cropland, and six to the TE

of wetland. In all cases, responses were positive, meaning that as the TE increased (i.e.,

surrounding landscape became more fragmented), species presence increased.

PLS Discriminant Analyses at the Local Level - Figure 2.5

The PLS discriminant analysis at the local level had one key variable emerge as

predictive: turbidity (observed range: 2.50 – 5560.00 NTU). All species responded

strongly and positively to this variable, indicating that their presence increased at playas

with highly turbid water.

PLS Multiple Regression at the Landscape Level - Figure 2.6

In determining overall species richness at the landscape level, TE of cropland, TE

of grassland, and, to a lesser extent, TE of wetland (weight = 0.324) emerged as

important factors.

PLS Multiple Regression at the Local Level - Figure 2.7

Turbidity was clearly the most important variable in predicting overall species

richness at the local level. To a much lesser extent, Julian date (observed range: 137 -


20

232 days, weight = 0.2745) and percent basin filled (observed range: 30-100%, weight =

0.1324) also contributed to richness.

Comparison of Multiple Logistic Regression and PLS Discriminant Analysis

Pseudoabsences may account for many of the incorrectly predicted presences in

the classification tables. By averaging the correct classification rates for each test (Tables

2.1 – 2.4), I arrived at a mean correct classification rate per analysis. Superior

performance was indicated by a higher average. At the landscape level, the multiple

logistic regression had a mean classification rate of 0.75 ± 0.10, whereas PLS

discriminant analysis had a mean classification rate of 0.79 ± 0.07, indicating that the

performance of both tests was high and comparable. At the local level, the multiple

logistic regression had a mean classification rate of 0.81 ± 0.08, whereas PLS

discriminant analysis had a mean classification rate of 0.68 ± 0.10, indicating that

multiple logistic regression out-performed PLS discriminant analysis, but both were

better than random assignment. Neither test functioned decisively better than the other in

terms of elucidating patterns. However, due to ease of interpretation at the landscape-

level (see Discussion below) as well as a much higher predictive ability at the local level,

I believe that multiple logistic regression is a better descriptive model for determining

odonate occurrence at playa wetlands.


21

Discussion

Landscape-Level Results

In both multiple logistic regression and PLS discriminant analysis, the landscape-

level variables (i.e., composition and configuration of land-use types within 2.5 km of a

playa) were roughly as correct at classifying odonate occurrence (for the 12 moderately

abundant species examined) as were the local-level (i.e., within-playa) variables. This

adds support to the current trend in landscape ecology and conservation biology to

broaden the consideration of study sites to include not only immediate variables, but also

larger scaled (landscape) influences. For odonates, it is clear that colonization decisions

are complex, including more than just immediate wetland characteristics.

Although different variables emerged as important for the landscape-level

multiple logistic regression analysis and the landscape-level PLS discriminate analysis,

those variables that were important in both cases indicate a response to the amount of

landscape fragmentation. In the landscape-level multiple logistic regression analysis,

SHEI emerged as the most important variable for all species. High SHEI values are

indicative of landscapes that are divided equally among land-use categories in terms of

area (these landscapes are typically more fragmented), whereas low SHEI values are

descriptive of landscapes dominated by one or two land-use categories (typically less

fragmented). Half of the species preferred sites with high SHEI values, and the other half

preferred low SHEI values. Based on this response, it is not possible to make a

generalization as to the effect of landscape fragmentation on odonates. However, it is

clear that fragmentation affects odonates, but the nature of the effect (whether positive or

negative) is determined by species, not by the overall taxonomic order. In the PLS


22

discriminate analysis, the TE of grassland, TE of cropland, and TE of wetland were the

important variables. In all species where these variables were important, the response

was positive. However, the interpretation of these positive responses is not

straightforward because TE is somewhat ambiguous. A large TE value is descriptive of a

fragmented landscape in that increasing TE amounts correspond to greater numbers of

small isolated patches (consider the surface area to volume ratio). This can be beneficial

to species desiring to live in these patches (i.e., more patches implies more suitable

habitat), thereby eliciting a positive response. However, it can also be beneficial to

species seeking to avoid these patches (i.e., small, isolated patches are better than one or

two large patches), also eliciting a positive response. Although it is possible that edges in

general offer some type of benefit (potentially improved foraging or larger shrubs for

protection from wind/predators), I believe that these TE variables emerged as important

because they somehow capture odonates’ reaction to landscape fragmentation. However,

because of the ambiguous interpretation, further research is needed to determine the exact

influence TE has on odonates.

Class area (CA), a direct measure of land-use amount, did not prove to be

important in either the multiple logistic regression analysis or the PLS discriminant

analysis, indicating that perhaps odonates do not choose their habitat based simply on

amount of preferred surrounding land-use type, but instead discriminate between sites

based upon other aspects of the surrounding landscape that are apparently related to the

degree of spatial heterogeneity (from SHEI and TE results). Aspects of wetland density,

size, and spacing were not as important in dictating whether an odonate species would be

encountered at a given site, because contagion (CONTAG), patch richness (PR), nearest-


23

neighbor distance among playas (ENN_MN), number of patches (NP), and the mean and

standard deviation of playa size (AREA_MN, AREA_SD) were not useful in

distinguishing between sites where a given species was recorded versus a playa where it

had not been sighted.

Local-Level Results

The local-level multiple logistic regression had numerous variables emerge with

strong coefficients. The top four in importance were pH, concentration of phosphate,

DO, and nitrate concentration. Generalizing these results, it appears that water quality

was the discriminating factor for odonate occurrence. Interestingly, water quality is

closely tied to surrounding land use (see Introduction). Although the local-level multiple

logistic regression performed more precisely than either of the landscape-level analyses,

it must be noted that all of the important local-level variables are ultimately influenced at

the landscape level. Determining exactly how and to what extent different land-use types

contribute to water quality measures requires further investigation. Understanding these

relationships in greater detail is necessary before any land management practices can be

implemented to alter the influence of surrounding land use on playa wetlands. In the PLS

discriminant analysis at the local scale, turbidity was overwhelmingly the most important

variable. Every species in the analysis had a positive discrimination coefficient for this

variable, suggesting that species presence is linked with turbid playas. Turbid water

offers increased camouflage for developing larvae, which are subject to predation by

larger invertebrates, amphibians, and even larger conspecifics. Turbidity can be linked

with the amount of surrounding cropland, because as sedimentation increases, the water


24

within the playa becomes more turbid. However, grazed grassland playas may also be

turbid as the sediments become churned up by livestock.

Because of the amphibious nature of odonates, the importance of the presence of

water is assumed. While relevant for L. saturata, O. ferruginea, and P. tenera in the

multiple logistic regression, percent basin filled did not emerge as an especially important

variable. The nature of the data matrix explains this occurrence. In order to be included

in these local-level analyses, a given observation must have possessed data for every

variable (i.e., have no missing data). At dry sites, water measurements were not

available. Therefore, these observations were excluded from the analyses, resulting in a

matrix that only includes sites where some amount of water was present. In fact, of the

51 observations in the local-level analyses, only three sites had percent basin filled entries

of less than 50%, the lowest of which was 30%. Therefore, upon closer examination, the

importance of water to odonate presence cannot be determined with these analyses, but

the biological necessity of water to odonates is tacit. Future studies examining the

critical value for percent basin filled triggering odonate presence could be enlightening.

Based on this study, it appears as if that critical amount may be less than 30%.

Five additional variables were included in initial local-level tests run for each of

the three statistical analysis methods. These additional variables were time of day, air

temperature, percent relative humidity, average wind speed, and percent cloud cover. In

these initial tests, time of day (observed range: 8:50 a.m. to 6:30 p.m.) and air

temperature (observed range: 19.7ºC to 34.7ºC) emerged as important variables. For both

of these variables, coefficients were positive. In terms of time of day, all species

examined exhibited increased observed presence later in the afternoon as compared to


25

early morning. For air temperature, odonates are more active at higher air temperatures

compared to lower air temperatures, which logically corresponds to the time of day, as

temperatures increase later in the day. The importance of these two variables on

observed odonate presence has significant implications for data collection in odonate

studies. Neither of these variables affected species presence or absence at a site, but

rather the observed presence or absence of the species. Therefore, to improve odonate

occurrence data accuracy, researchers should schedule observation times later in the

afternoon on days with high temperatures so that odonates will be more active and

therefore more easily observed.

PLS Multiple Regression Results

Species richness per observation ranged from 0 to 21 species. For all 411

observations, mean species richness was 3.20 ± 0.28 species. However, when only

considering the 91 observations that had a percent basin filled greater than 0%, the mean

species richness rose to 4.52 ± 0.70 species. The most species rich site was an urban

playa within the city of Lubbock.

According to the PLS multiple regression, species richness at the landscape level

was principally determined by TE of grassland and TE of cropland, and by turbidity at

the local level. Interestingly, at both scales the PLS multiple regression results

corresponded extremely well to the PLS discriminant analysis results.


26

Final Thoughts

It is not surprising that certain variables were influential whereas others were not

in determining playa odonate occurrence and richness. The importance of landscape as

opposed to simply local-scale variables is consistent with other multiscaled studies on

other taxa in different regions and ecosystems (e.g. Hecnar and M'Closkey 1998, Melles

et al. 2003, Dauber et al. 2005). For odonates of the Southern High Plains, the inherently

ephemeral nature of playas may mean that the species here must be highly adaptable to

both within-playa and landscape factors. Indeed, the odonate species of the Southern

High Plains are all widely distributed in the U.S., evidence of their generalist habits.

Anthropogenic alterations to the Southern High Plains landscape are relatively

recent; all playas were once surrounded by relatively homogeneous grasslands. Land

conversion (due primarily to agriculture and urbanization) has created patterns that would

not have existed for the vast majority of time of odonate species’ history on the Southern

High Plains. Therefore, species’ responses to these patterns are likely at their start and

will continue to be shaped over time.

The implications of these patterns are that predicting odonate biodiversity

responses to future landscape changes (e.g. due to land conversion or climate change)

will be problematic. Surrounding land use as well as immediate wetland variables such

as pH are known to influence larval odonate growth, development, and survival (Reece

2009). Effects on adults, such as distribution, abundance, and fitness, still remain

challenges to be addressed.


27

Literature Cited

Angelibert, S., and N. Giani. 2003. Dispersal characteristics of three odonate species in

a patchy habitat. Ecography 26:13-20.

Bick, G.H., and J.C. Bick. 1963. Behavior and population structure of the damselfly,

Enallagma civile (Hagen) (Odonata: Coegnagrionidae). Southwestern Naturalist

8:57-84.

Carrascal L.M., I. Galván and O. Gordo. 2009. Partial least squares regression as an

alternative to current regression methods used in ecology. Oikos 118:681-690.

Conrad, K.F., K.H. Willson, I.F. Harvey, C.J. Thomas, and T.N. Sherratt. 1999.

Dispersal characteristics of seven odonate species in an agricultural landscape.

Ecography 22:524-531.

Dauber, J., T. Purtauf, A. Allspach, J. Frisch, K. Voigtländer, and V. Wolters. 2005.

Local vs. landscape controls on diversity: a test using surface-dwelling soil

macroinvertebrates of differing mobility. Global Ecology and Biogeography

14:213-221.

Gray, M.J., L.M. Smith, and R.I. Leyva. 2004. Influence of agricultural landscape

structure on a Southern High Plains, USA, amphibian assemblage. Landscape

Ecology 19:719-729.

Haukos, D.A., and L.M. Smith. 1994. The importance of playa wetlands to biodiversity

of the Southern High Plains. Landscape and Urban Planning 28:83-98.

Hecnar, S.J., and R.T. M'Closkey. 1998. Species richness patterns of amphibians in

southwestern Ontario ponds. Journal of Biogeography 25:763-772.


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Hernandez, K.M., B.A. Reece, and N.E. McIntyre. 2006. Effects of anthropogenic land

use on Odonata in playas of the Southern High Plains. Western North American

Naturalist 66:273-278.

Luo, H.R., L.M. Smith, B.L. Allen, and D.A. Haukos. 1997. Effects of sedimentation on

playa wetland volume. Ecological Applications 7:247–52.

MacKenzie, D.I., J.D. Nichols, J.A. Royle, K.H. Pollock, L.L. Bailey, and J.E. Hines.

2005. Occurpancy Estimation and Modeling: Inferring Patterns and Dynamics of

Species Occurrence. Academic Press, San Diego, CA.

McGarigal, K., S.A. Cushman, M.C. Neel, and E. Ene. 2002. FRAGSTATS: Spatial

Pattern Analysis Program for Categorical Maps. Computer software program

produced by the authors at the University of Massachusetts, Amherst, MA. URL:

http://www.umass.edu/landeco/research/fragstats/fragstats.html.

Melles, S., S. Glenn, and K. Martin. 2003. Urban bird diversity and landscape

complexity: Species–environment associations along a multiscale habitat gradient.

Conservation Ecology 7(1):5. URL: http://www.consecol.org/vol7/iss1/art5/.

Osterkamp, W.R., and W.W. Wood. 1987. Playa-lake basins on the Southern High

Plains of Texas and New Mexico; Part I, Hydrologic, geomorphic, and geologic

evidence for their development. Geological Society of America Bulletin 99:215-

233.

Reece, B.A. 2009. Diversity, distribution, and development of the Odonata of the

Southern High Plains of Texas. Ph.D. dissertation, Texas Tech University,

Lubbock, TX.


29

Reece, B.A., and N.E. McIntyre. 2009a. Community assemblage patterns of odonates

inhabiting a wetland complex influenced by anthropogenic disturbance. Insect

Conservation and Diversity 2:73-80.

Reece, B.A., and N.E. McIntyre. 2009b. New county records of Odonata of the playas

of the Southern High Plains, Texas. Southwestern Naturalist 54:96-99.

Rubbo M.J., and J.M. Kiesecker. 2005. Urbanization and amphibian breeding.

Conservation Biology 19:504-511.

Smith, L.M. 2003. Playas of the Great Plains. University of Texas Press, Austin, TX.

Strauss, R.E., and M.N. Atanassov. 2006. Determining best subsets of specimens and

characters in the presence of large amounts of missing data. Biological Journal of

the Linnean Society 88:309-328.

Thogmartin, W.E., and M.G. Knutson. 2007. Scaling local species-habitat relations to

the larger landscape with a hierarchical spatial count model. Landscape Ecology

22:61-75.

Tsai, J.-S., L.S. Venne, S.T. McMurry, and L.M. Smith. 2007. Influences of land use

and wetland characteristics on water loss rates and hydroperiods of playas in the

Southern High Plains, USA. Wetlands 27:683-692.

Van Buskirk, J. 2005. Local and landscape influence on amphibian occurrence and

abundance. Ecology 86:1936-1947.


30

Tables and Figures

TABLE 2.1. Correct classification table for landscape-level multiple logistic regression.

Correct Classification Table

Landscape-Level Multiple Logistic Regression

Species Correctly

Predicted Absent

Incorrectly

Predicted Absent

Correctly

Predicted

Present

Incorrectly

Predicted

Present

Correct

Classification Rate

Erythemis simplicicollis 245 148 7 11 0.62

Ishnura denticollis 221 172 0 18 0.58

Ishnura hastata 192 191 3 25 0.53

Lestes alacer 281 72 15 43 0.79

Lestes australis 379 0 8 24 0.98

Libellula luctuosa 374 10 2 25 0.97

Libellula pulchella 266 98 0 47 0.76

Libellula saturata 220 179 2 10 0.56

Orthemis ferruginea 331 41 14 25 0.87

Pachydiplax longipennis 386 0 2 23 1.00

Perithemis tenera 308 69 17 17 0.79

Plathemis lydia 208 174 12 17 0.55

* total number of observations = 411 Average: 0.75


31

TABLE 2.2. Correct classification table for local-level multiple logistic regression.


Local-Level Multiple Logistic Regression

Species Correctly

Predicted Absent

Incorrectly

Predicted Absent

Correctly

Predicted

Present

Incorrectly

Predicted

Present

Correct

Classification Rate




Lestes alacer 30 11 0 10 0.78











32

TABLE 2.3. Correct classification table for landscape-level PLS discriminant analysis.


Landscape-Level PLS Discriminant Analysis

Species Correctly

Predicted Absent

Incorrectly

Predicted Absent

Correctly

Predicted

Present

Incorrectly

Predicted

Present

Correct

Classification Rate




Lestes alacer 227 126 0 58 0.69











33

TABLE 2.4. Correct classification table for local-level PLS discriminant analysis.


Local-Level PLS Discriminant Analysis

Species Correctly

Predicted Absent

Incorrectly

Predicted Absent

Correctly

Predicted

Present

Incorrectly

Predicted

Present

Correct

Classification Rate




Lestes alacer 41 0 2 8 0.96











34

TABLE 2.5. Landscape-level variables by number (for use in interpreting Figures 2.2, 2.4, 2.6).

Landscape Level Variables, by �umber

1 CA - Wetland 13 LPI - Wetland 25 AREA_MN - Wetland 37 ENN_MN - Wetland

2 CA - Cropland 14 LPI - Cropland 26 AREA_MN - Cropland 38 SHEI

3 CA - Grassland 15 LPI - Grassland 27 AREA_MN - Grassland 39 CONTAG

4 CA - Built 16 LPI - Built 28 AREA_MN - Built 40 PR

5 CA - Dairy 17 LPI - Dairy 29 AREA_MN - Dairy

6 CA - Open Space 18 LPI - Open Space 30 AREA_MN - Open Space

7 NP - Wetland 19 TE - Wetland 31 AREA_SD - Wetland

8 NP - Cropland 20 TE - Cropland 32 AREA_SD - Cropland

9 NP - Grassland 21 TE - Grassland 33 AREA_SD - Grassland

10 NP - Built 22 TE - Built 34 AREA_SD - Built

11 NP - Dairy 23 TE - Dairy 35 AREA_SD - Dairy

12 NP - Open Space 24 TE - Open Space 36 AREA_SD - Open Space


35

TABLE 2.6. Local-level variables by number (for use in interpreting Figures 2.3, 2.5, 2.7).

Landscape Level Variables, by �umber

1 Julian Date 4 Water Temperature 7 Turbidity

2 Percent Basin Filled 5 Water Depth 8 Concentration of Nitrate

3 pH 6 DO 9 Concentration of Phosphate


36

TABLE 2.7. Landscape-level multiple logistic regression coefficients. Any value greater than or equal to 0.50 or less than

or equal to -0.50 has been highlighted (see Results).

Landscape-Level Multiple Logistic Regression Coefficients

1 2 3 4 5 6 7

Species

CA -

Wetland

CA -

Cropland

CA -

Grassland

CA -

Built

CA -

Dairy

CA –

Open Space

�P -

Wetland

Erythemis simplicicollis 0.16 0.17 0.19 0.18 0.22 0.14 0.12

Ishnura denticollis 0.17 0.18 0.20 0.11 0.17 0.12 0.12

Ishnura hastata -0.06 -0.07 -0.01 -0.05 -0.03 -0.03 -0.09

Lestes alacer -0.12 -0.14 -0.10 -0.12 -0.10 -0.10 -0.09

Lestes australis -0.17 -0.17 -0.18 -0.21 -0.24 -0.22 -0.21

Libellula luctuosa 0.07 0.12 0.03 0.07 -0.10 0.07 0.13

Libellula pulchella 0.22 0.20 0.29 0.24 0.29 0.48 0.22

Libellula saturata 0.06 0.07 0.12 0.05 0.01 0.18 0.12

Orthemis ferruginea -0.19 -0.16 -0.19 -0.18 -0.35 -0.14 -0.19

Pachydiplax longipennis -0.14 -0.18 -0.16 -0.14 -0.14 -0.20 -0.17

Perithemis tenera -0.13 -0.19 -0.22 -0.12 -0.18 -0.22 -0.15

Plathemis lydia 0.19 0.14 0.20 0.13 0.10 0.12 0.26


37

TABLE 2.7. Continued.


8 9 10 11 12 13 14

Species �P - Cropland �P - Grassland �P - Built �P - Dairy �P - Open Space

LPI -

Wetland

LPI -

Cropland

E. simplicicollis 0.15 0.21 0.21 0.15 0.19 0.12 0.14

I. denticollis 0.15 0.24 0.15 0.31 0.16 0.13 0.19

I. hastata -0.07 -0.04 -0.03 -0.08 -0.11 0.15 -0.02

L. alacer -0.08 -0.15 -0.16 -0.08 -0.14 -0.14 -0.10

L. australis -0.18 -0.21 -0.15 -0.38 -0.20 -0.15 -0.17

L. luctuosa 0.09 0.04 0.08 -0.76 -0.04 0.01 0.06

L. pulchella 0.18 0.26 0.20 0.41 0.50 0.22 0.24

L. saturata 0.12 0.09 0.11 -0.19 0.06 0.21 0.10

O. ferruginea -0.19 -0.13 -0.15 -0.75 -0.19 -0.14 -0.10

P. longipennis -0.21 -0.18 -0.23 -0.22 -0.15 -0.13 -0.14

P. tenera -0.15 -0.18 -0.18 -0.44 -0.17 -0.15 -0.13

P. lydia 0.09 0.11 0.12 -0.27 -0.03 0.46 0.10


38



15 16 17 18 19 20 21 22

Species

LPI -

Grassland

LPI –

Built

LPI -

Dairy

LPI –

Open Space

TE -

Wetland

TE -

Cropland

TE -

Grassland TE - Built

E. simplicicollis 0.15 0.17 -1.41 0.57 0.16 0.16 0.22 0.22

I. denticollis 0.19 0.21 -0.12 0.57 0.16 0.17 0.13 0.13

I. hastata -0.08 -0.36 -2.96 -1.01 -0.06 -0.05 -0.10 -0.03

L. alacer -0.16 -0.11 -1.61 -0.59 -0.10 -0.11 -0.14 -0.15

L. australis -0.20 -0.17 4.12 -1.52 -0.25 -0.23 -0.23 -0.14

L. luctuosa 0.04 -0.33 4.45 -0.72 0.03 0.02 0.10 0.06

L. pulchella 0.26 0.24 -2.99 -4.29 0.18 0.23 0.27 0.21

L. saturata 0.04 0.12 3.15 -0.46 0.07 0.10 0.11 0.15

O. ferruginea -0.19 -0.22 5.07 -0.94 -0.16 -0.20 -0.19 -0.15

P. longipennis -0.15 -0.13 -0.11 -0.23 -0.17 -0.18 -0.16 -0.19

P. tenera -0.09 -0.20 0.31 -0.30 -0.20 -0.20 -0.18 -0.20

P. lydia 0.14 -0.29 0.77 1.01 0.14 0.12 0.12 0.14


39



23 24 25 26 27

Species TE - Dairy TE - Open Space AREA_M� - Wetland AREA_M� - Cropland AREA_M� - Grassland

E. simplicicollis 0.19 0.20 0.17 0.19 0.16

I. denticollis 0.16 0.13 0.14 0.15 0.12

I. hastata -0.10 -0.10 -0.09 -0.07 -0.05

L. alacer -0.18 -0.08 -0.08 -0.09 -0.06

L. australis -0.17 -0.18 -0.18 -0.20 -0.19

L. luctuosa 0.06 0.04 0.05 0.08 0.06

L. pulchella 0.23 0.20 0.23 0.19 0.21

L. saturata 0.12 0.11 0.05 0.09 0.11

O. ferruginea -0.20 -0.21 -0.11 -0.15 -0.22

P. longipennis -0.14 -0.12 -0.12 -0.12 -0.17

P. tenera -0.23 -0.16 -0.12 -0.21 -0.14

P. lydia 0.17 0.15 0.20 0.17 0.15


40



28 29 30 31 32

Species

AREA_M� -

Built

AREA_M� -

Dairy

AREA_M� –

Open Space AREA_SD - Wetland AREA_SD - Cropland


I. denticollis 0.11 0.18 0.13 0.19 0.19

I. hastata 0.00 0.05 -0.10 -0.12 -0.07

L. alacer -0.13 0.00 -0.14 -0.08 -0.08

L. australis -0.18 -0.47 -0.23 -0.22 -0.19

L. luctuosa 0.13 -0.15 -0.20 -0.04 0.11

L. pulchella 0.23 0.48 0.41 0.16 0.24

L. saturata 0.07 -0.03 0.07 0.00 0.06

O. ferruginea -0.12 -0.46 0.00 -0.23 -0.20

P. longipennis -0.15 -0.13 -0.28 -0.21 -0.10

P. tenera -0.16 -0.25 -0.26 -0.16 -0.13

P. lydia 0.10 0.06 0.20 0.04 0.19


41



33 34 35 36 37

Species

AREA_SD -

Grassland

AREA_SD –

Built

AREA_SD –

Dairy

AREA_SD –

Open Space E��_M� - Wetland


I. denticollis 0.14 0.11 0.11 0.10 0.14

I. hastata 0.01 -0.04 0.17 0.00 -0.10

L. alacer -0.02 -0.11 -0.05 -0.09 -0.11

L. australis -0.19 -0.15 -0.42 0.13 -0.19

L. luctuosa 0.03 0.08 -0.13 0.28 0.05

L. pulchella 0.18 0.22 0.42 0.80 0.17

L. saturata 0.10 0.07 -0.05 0.17 0.08

O. ferruginea -0.22 -0.17 -0.41 -0.24 -0.17

P. longipennis -0.15 -0.14 -0.17 -0.14 -0.20

P. tenera -0.14 -0.16 -0.16 -0.07 -0.18

P. lydia 0.19 0.11 0.14 -0.09 0.21


42



39 40

Species CO�TAG PR

E. simplicicollis 0.00 0.08

I. denticollis 0.01 0.03

I. hastata 0.10 0.26

L. alacer -0.01 -0.01

L. australis -0.11 -0.09

L. luctuosa -0.06 0.36

L. pulchella 0.18 0.18

L. saturata -0.08 0.06

O. ferruginea -0.07 -0.11

P. longipennis -0.02 0.01

P. tenera 0.01 0.18

P. lydia -0.01 0.53


43

TABLE 2.8. Local-level multiple logistic regression coefficients. Any value greater than or equal to 0.50 or less than or

equal to -0.50 has been highlighted (see Results).

Local-Level Multiple Logistic Regression Coefficients

1 2 3 4 5 6

Species Julian Date Percent Basin Filled pH Water Temperature Water Depth DO

Erythemis simplicicollis 0.38 0.19 -2.55 -0.14 0.23 0.63

Ishnura denticollis 0.33 0.07 1.67 -0.08 0.41 -0.01

Ishnura hastata 0.65 0.43 -1.47 0.21 0.38 1.41

Lestes alacer 0.19 0.04 -2.49 0.23 -0.20 0.94

Lestes australis -0.10 0.13 1.87 0.38 -1.26 -1.54

Libellula luctuosa -0.11 -0.16 0.67 -1.31 -0.89 0.09

Libellula pulchella 0.20 0.41 -2.21 -0.29 0.08 0.32

Libellula saturata 0.58 0.54 -2.31 -0.27 0.41 0.10

Orthemis ferruginea 0.48 0.76 -2.00 1.04 0.19 0.45

Pachydiplax longipennis 0.43 0.37 -1.97 0.62 0.45 0.58

Perithemis tenera 0.72 0.70 -0.32 0.78 0.60 0.14

Plathemis lydia 0.11 0.38 -2.04 0.47 -0.01 -0.68


44


Local-Level Multiple Logistic Regression Coefficients

7 8 9

Species Turbidity Nitrates Phosphates

E. simplicicollis 0.15 0.41 0.71

I. denticollis 0.26 -2.09 -0.64

I. hastata 0.44 -0.17 -1.73

L. alacer 0.11 0.19 0.88

L. australis -0.13 0.39 0.30

L. luctuosa -0.45 2.22 0.09

L. pulchella 0.09 -0.30 1.54

L. saturata 0.49 -0.59 1.06

O. ferruginea 0.23 0.03 -1.29

P. longipennis 0.41 0.60 -1.49

P. tenera 0.58 -2.03 -1.26

P. lydia 0.06 0.13 1.67


45

TABLE 2.9. Landscape-level PLS discriminant analysis coefficients. Any value greater than or equal to 0.50 or less than or

equal to -0.50 has been highlighted (see Results).

Landscape-Level PLS Discriminant Analysis Coefficients

1 2 3 4 5 6 7

Species

CA -

Wetland

CA -

Cropland

CA -

Grassland

CA –

Built

CA –

Dairy

CA –

Open Space

�P –

Wetland

Erythemis simplicicollis -0.20 -0.04 -0.11 0.22 -0.01 0.09 0.10

Ishnura denticollis 0.05 0.04 0.18 -0.04 0.17 0.12 0.15

Ishnura hastata -0.19 0.19 0.10 0.31 0.15 0.01 0.05

Lestes alacer -0.03 0.14 -0.10 0.29 0.09 0.22 0.02

Lestes australis -0.19 0.24 0.16 -0.20 -0.12 0.34 -0.03

Libellula luctuosa 0.15 -0.08 0.08 0.12 0.07 0.01 0.16

Libellula pulchella -0.18 0.14 0.21 -0.05 0.31 0.05 0.06

Libellula saturata 0.03 -0.17 -0.37 0.14 0.13 0.03 0.10

Orthemis ferruginea -0.15 0.18 0.39 -0.11 0.06 0.21 -0.14

Pachydiplax longipennis -0.02 0.13 -0.15 -0.34 -0.19 0.02 -0.16

Perithemis tenera -0.15 -0.42 -0.03 0.41 -0.07 0.01 0.09

Plathemis lydia 0.05 0.15 -0.25 -0.05 0.08 -0.09 -0.19


46



8 9 10 11 12 13 14

Species

�P –

Cropland

�P –

Grassland

�P –

Built

�P –

Dairy

�P –

Open Space

LPI –

Wetland

LPI –

Cropland

E. simplicicollis 0.14 0.28 -0.16 0.09 -0.14 0.05 -0.07

I. denticollis 0.12 -0.03 0.28 -0.41 0.05 0.56 -0.09

I. hastata -0.26 -0.03 0.27 -0.10 0.05 0.14 0.19

L. alacer -0.13 0.15 -0.04 0.11 0.10 0.16 0.04

L. australis 0.15 0.02 -0.13 0.07 0.09 -0.31 0.22

L. luctuosa 0.07 0.17 0.19 0.31 -0.21 -0.21 -0.10

L. pulchella -0.19 0.00 0.23 0.34 -0.12 -0.03 -0.18

L. saturata 0.14 0.20 -0.48 -0.13 0.49 0.36 0.26

O. ferruginea 0.11 0.08 0.47 -0.33 0.00 0.06 -0.01

P. longipennis 0.20 -0.17 0.07 -0.13 0.04 -0.28 -0.23

P. tenera -0.16 -0.01 0.01 0.17 -0.06 -0.08 0.37

P. lydia 0.24 -0.14 0.22 0.06 0.29 0.12 0.06


47



15 16 17 18 19 20 21

Species

LPI –

Grassland

LPI –

Built

LPI –

Dairy

LPI –

Open Space

TE –

Wetland

TE –

Cropland

TE –

Grassland

E. simplicicollis 0.23 -0.09 -0.07 0.06 0.44 0.84 0.87

I. denticollis -0.14 -0.19 0.14 -0.08 0.49 0.67 0.84

I. hastata 0.20 0.11 -0.01 -0.04 0.27 0.72 0.99

L. alacer -0.06 -0.08 0.20 -0.38 0.50 0.85 0.85

L. australis -0.08 -0.06 0.19 -0.13 0.44 0.44 1.22

L. luctuosa 0.02 0.04 0.05 0.20 0.43 1.05 1.18

L. pulchella -0.13 -0.09 0.10 0.20 0.58 0.76 1.23

L. saturata -0.19 -0.07 -0.46 0.10 0.87 0.34 0.97

O. ferruginea -0.10 0.05 0.23 0.16 0.45 0.53 0.78

P. longipennis 0.06 0.01 -0.12 -0.14 0.66 0.99 1.19

P. tenera -0.42 -0.27 -0.06 -0.10 0.64 0.43 1.33

P. lydia -0.02 0.01 -0.24 0.27 0.85 0.60 1.37


48



22 23 24 25 26

Species TE - Built TE - Dairy TE - Open Space AREA_M� - Wetland AREA_M� - Cropland

E. simplicicollis 0.33 -0.14 -0.14 -0.04 0.19

I. denticollis 0.23 0.17 -0.17 0.05 0.03

I. hastata 0.02 0.09 -0.13 -0.06 0.10

L. alacer 0.03 0.15 0.04 -0.07 -0.06

L. australis 0.17 -0.31 0.58 -0.03 -0.17

L. luctuosa -0.01 0.16 0.03 -0.11 -0.12

L. pulchella 0.38 -0.13 -0.10 0.01 0.04

L. saturata 0.07 -0.10 0.06 0.37 0.09

O. ferruginea 0.33 0.26 0.04 -0.11 -0.05

P. longipennis 0.17 0.19 -0.11 0.11 0.17

P. tenera -0.23 0.00 0.14 -0.26 -0.05

P. lydia 0.03 -0.05 0.28 -0.16 -0.14


49



27 28 29 30

Species AREA_M� - Grassland AREA_M� - Built AREA_M� - Dairy AREA_M� - Open Space

E.

simplicicollis -0.08 0.16 0.13 0.29

I. denticollis -0.06 0.28 0.50 0.01

I. hastata -0.03 -0.07 -0.34 -0.05

L. alacer 0.00 0.28 -0.23 0.05

L. australis -0.10 -0.39 0.45 -0.33

L. luctuosa -0.29 -0.36 0.22 -0.06

L. pulchella -0.32 -0.21 -0.19 -0.11

L. saturata 0.06 -0.05 -0.03 0.11

O. ferruginea 0.32 0.16 0.06 0.18

P. longipennis -0.28 0.22 -0.20 0.16

P. tenera 0.20 -0.33 0.06 -0.26

P. lydia -0.13 0.21 -0.03 0.04


50



31 32 33 34 35

Species

AREA_SD –

Wetland

AREA_SD –

Cropland

AREA_SD –

Grassland

AREA_SD –

Built

AREA_SD –

Dairy

E. simplicicollis -0.35 -0.14 -0.11 -0.08 0.14

I. denticollis -0.42 -0.02 -0.20 -0.30 0.40

I. hastata 0.17 -0.01 -0.01 -0.02 -0.01

L. alacer -0.19 0.44 -0.52 0.23 0.11

L. australis 0.22 0.35 -0.08 0.00 -0.47

L. luctuosa -0.01 -0.18 0.13 -0.12 -0.14

L. pulchella 0.02 -0.24 -0.24 -0.01 -0.01

L. saturata 0.14 0.06 0.07 -0.01 -0.07

O. ferruginea 0.18 -0.09 -0.13 -0.14 0.19

P. longipennis -0.03 -0.02 0.04 0.11 0.06

P. tenera 0.01 -0.11 0.04 0.01 0.19

P. lydia 0.18 -0.32 -0.11 0.12 -0.11


51



36 37 38 39 40

Species AREA_SD - Open Space E��_M� - Wetland SHEI CO�TAG PR

E. simplicicollis 0.09 0.13 0.04 -0.06 -0.10

I. denticollis -0.13 0.26 0.00 -0.04 0.13

I. hastata 0.14 -0.20 -0.02 0.53 0.33

L. alacer -0.11 0.39 -0.20 -0.09 -0.05

L. australis 0.25 0.07 -0.11 -0.17 0.33

L. luctuosa 0.09 -0.03 0.07 0.16 0.00

L. pulchella 0.13 0.06 0.14 0.02 -0.14

L. saturata 0.05 -0.31 0.01 -0.08 0.04

O. ferruginea 0.04 -0.10 -0.25 0.12 -0.23

P. longipennis -0.06 -0.07 0.17 -0.06 -0.02

P. tenera -0.42 -0.37 -0.37 0.18 -0.15

P. lydia -0.07 0.16 -0.21 0.22 -0.22


52

TABLE 2.10. Local-level PLS discriminant analysis coefficients. Any value greater than or equal to 0.50 or less than or equal

to -0.50 has been highlighted (see Results).

Local-Level PLS Discriminant Analysis Coefficients

1 2 3 4 5 6

Species Julian Date Percent Basin Filled pH Water Temperature Water Depth DO

Erythemis simplicicollis 0.48 0.30 -0.15 0.29 0.23 0.06

Ishnura denticollis 0.50 0.05 0.22 -0.26 0.12 0.10

Ishnura hastata -0.12 0.40 0.14 -0.27 0.01 0.08

Lestes alacer 0.07 0.06 -0.06 0.21 0.04 -0.03

Lestes australis 0.51 -0.09 -0.13 0.04 -0.22 0.03

Libellula luctuosa -0.22 0.01 0.36 0.43 -0.27 -0.03

Libellula pulchella 0.43 0.36 0.01 0.01 0.14 0.05

Libellula saturata 0.29 0.30 -0.29 0.40 -0.39 0.48

Orthemis ferruginea 0.13 -0.24 -0.07 0.28 0.08 0.04

Pachydiplax longipennis 0.07 -0.05 0.24 0.15 -0.08 -0.13

Perithemis tenera 0.33 0.05 0.28 -0.25 -0.03 0.09

Plathemis lydia 0.14 -0.13 -0.38 -0.24 -0.17 -0.07


53

TABLE 10. Continued.

Local-Level PLS Discriminant Analysis Coefficients

7 8 9

Species Turbidity �itrates Phosphates

E. simplicicollis 1.13 -0.14 0.54

I. denticollis 0.81 0.32 -0.07

I. hastata 1.27 0.16 0.15

L. alacer 1.23 -0.46 0.44

L. australis 1.12 -0.07 0.00

L. luctuosa 0.92 0.22 -0.03

L. pulchella 1.11 -0.13 0.28

L. saturata 1.34 -0.17 0.32

O. ferruginea 0.70 -0.18 -0.02

P. longipennis 1.21 0.10 -0.12

P. tenera 1.21 -0.34 0.23

P. lydia 1.19 -0.08 0.21


54

TABLE 2.11. Landscape-level PLS multiple regression coefficients. Any value greater

than or equal to 0.50 or less than or equal to -0.50 has been highlighted

see Results).

Landscape-Level Variables Weight Landscape-Level Variables Weight

1 CA - wetland 0.00 21 TE - grassland 0.73

2 CA - cropland 0.01 22 TE - built 0.18

3 CA - grassland 0.01 23 TE - dairy 0.01

4 CA - built 0.00 24 TE - open space 0.08

5 CA - dairy 0.00 25 AREA_MN - wetland 0.00

6 CA - open space 0.00 26 AREA_MN - cropland 0.01

7 NP - wetland 0.00 27 AREA_MN - grassland 0.01

8 NP - cropland 0.00 28 AREA_MN - built 0.00

9 NP - grassland 0.00 29 AREA_MN - dairy 0.00

10 NP - built 0.00 30 AREA_MN - open space 0.00

11 NP - dairy 0.00 31 AREA_SD - wetland 0.00

12 NP - open space 0.00 32 AREA_SD - cropland 0.00

13 LPI - wetland 0.00 33 AREA_SD - grassland 0.00

14 LPI - cropland 0.00 34 AREA_SD - built 0.00

15 LPI - grassland 0.00 35 AREA_SD - dairy 0.00

16 LPI - built 0.00 36 AREA_SD - open space 0.00

17 LPI - dairy 0.00 37 ENN_MN - wetland 0.01

18 LPI - open space 0.00 38 SHEI 0.00

19 TE - wetland 0.32 39 CONTAG 0.00

20 TE - cropland 0.57 40 PR 0.00


55

TABLE 2.12. Local-level multiple logistic regression coefficients. Any value greater

than or equal to 0.50 or less than or equal to -0.50 has been highlighted

(see Results).

Local-Level Variables Weight

1 Julian Date 0.27

2 Percent Basin Filled 0.13

3 pH 0.01

4 Water Temperature 0.04

5 Water Depth 0.04

6 DO 0.01

7 Turbidity 0.98

8 Concentration of Nitrate 0.00

9 Concentration of Phosphate 0.00


56

(a) Distribution of Species Richness

0 5 10 15 200

20

40

60

80

100

120

140

160

180

Fre

qu

ency

Richness

(a) Distribution of Species Richness

0 5 10 15 200

20

40

60

80

100

120

140

160

180

Fre

qu

ency

Richness

0 0.5 1 1.5 2 2.5 30

20

40

60

80

100

120

Fre

qu

ency

log(Richness)

(b) Distribution of log(Richness)

0 0.5 1 1.5 2 2.5 30

20

40

60

80

100

120

Fre

qu

ency

log(Richness)

(b) Distribution of log(Richness)

FIGURE 2.1. Distribution of actual species richness (a) and log richness (b).


57

0 5 10 15 20 25 30 35 40-6

-5

-4

-3

-2

-1

0

1

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent

Landscape: Erythemis simplicicollis(a)

0 5 10 15 20 25 30 35 40-6

-5

-4

-3

-2

-1

0

1

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent

Landscape: Erythemis simplicicollis

0 5 10 15 20 25 30 35 40-6

-5

-4

-3

-2

-1

0

1

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent


0 5 10 15 20 25 30 35 40-7

-6

-5

-4

-3

-2

-1

0

1

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent

Landscape: Ishnura denticollis(b)

0 5 10 15 20 25 30 35 40-7

-6

-5

-4

-3

-2

-1

0

1

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent

Landscape: Ishnura denticollis

0 5 10 15 20 25 30 35 40-7

-6

-5

-4

-3

-2

-1

0

1

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent


0 5 10 15 20 25 30 35 40-3

-2

-1

0

1

2

3

4

5

6

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent

Landscape: Ishnura hastata(c)

0 5 10 15 20 25 30 35 40-3

-2

-1

0

1

2

3

4

5

6

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent

Landscape: Ishnura hastata

0 5 10 15 20 25 30 35 40-3

-2

-1

0

1

2

3

4

5

6

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent


0 5 10 15 20 25 30 35 40-2

-1

0

1

2

3

4

5

6

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent

Landscape: Lestes alacer(d)

0 5 10 15 20 25 30 35 40-2

-1

0

1

2

3

4

5

6

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent

Landscape: Lestes alacer

0 5 10 15 20 25 30 35 40-2

-1

0

1

2

3

4

5

6

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent


0 5 10 15 20 25 30 35 40-2

-1

0

1

2

3

4

5

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent

Landscape: Lestes australis(e)

0 5 10 15 20 25 30 35 40-2

-1

0

1

2

3

4

5

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent

Landscape: Lestes australis

0 5 10 15 20 25 30 35 40-2

-1

0

1

2

3

4

5

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent


0 5 10 15 20 25 30 35 40-5

-4

-3

-2

-1

0

1

2

3

4

5

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent

Landscape: Libellula luctuosa(f)

0 5 10 15 20 25 30 35 40-5

-4

-3

-2

-1

0

1

2

3

4

5

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent

Landscape: Libellula luctuosa

0 5 10 15 20 25 30 35 40-5

-4

-3

-2

-1

0

1

2

3

4

5

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent


FIGURE 2.2. Landscape-level multiple logistic regression results by species. See Table

2.5 for list of landscape-level variables by number.


58

0 5 10 15 20 25 30 35 40-5

-4

-3

-2

-1

0

1

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent

Landscape: Libellula pulchella(g)

0 5 10 15 20 25 30 35 40-5

-4

-3

-2

-1

0

1

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent

Landscape: Libellula pulchella

0 5 10 15 20 25 30 35 40-5

-4

-3

-2

-1

0

1

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent


0 5 10 15 20 25 30 35 40-6

-5

-4

-3

-2

-1

0

1

2

3

4

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent

Landscape: Libellula saturata(h)

0 5 10 15 20 25 30 35 40-6

-5

-4

-3

-2

-1

0

1

2

3

4

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent

Landscape: Libellula saturata

0 5 10 15 20 25 30 35 40-6

-5

-4

-3

-2

-1

0

1

2

3

4

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent


0 5 10 15 20 25 30 35 40-1

0

1

2

3

4

5

6

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent

Landscape: Orthemis ferruginea(i)

0 5 10 15 20 25 30 35 40-1

0

1

2

3

4

5

6

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent

Landscape: Orthemis ferruginea

0 5 10 15 20 25 30 35 40-1

0

1

2

3

4

5

6

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent


0 5 10 15 20 25 30 35 40-1

0

1

2

3

4

5

6

7

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent

Landscape: Pachydiplax longipennis(j)

0 5 10 15 20 25 30 35 40-1

0

1

2

3

4

5

6

7

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent

Landscape: Pachydiplax longipennis

0 5 10 15 20 25 30 35 40-1

0

1

2

3

4

5

6

7

Variable

Logis

tic r

egre

ssio

n c

oef

fici

ent


0 5 10 15 20 25 30 35 40-1

0

1

2

3

4

5

6

7

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent

Landscape: Perithemis tenera(k)

0 5 10 15 20 25 30 35 40-1

0

1

2

3

4

5

6

7

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent

Landscape: Perithemis tenera

0 5 10 15 20 25 30 35 40-1

0

1

2

3

4

5

6

7

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent


0 5 10 15 20 25 30 35 40-6

-5

-4

-3

-2

-1

0

1

2

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent

Landscape: Plathemis lydia(l)

0 5 10 15 20 25 30 35 40-6

-5

-4

-3

-2

-1

0

1

2

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent

Landscape: Plathemis lydia

0 5 10 15 20 25 30 35 40-6

-5

-4

-3

-2

-1

0

1

2

Variable

Logis

tic

reg

ress

ion c

oef

fici

ent


FIGURE 2.2. Continued.


59

1 2 3 4 5 6 7 8 9-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

Variable

Logis

tic

reg

ress

ion

coeff

icie

nt

Local: Erythemis simplicicollis(a)

1 2 3 4 5 6 7 8 9-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

Variable

Logis

tic

reg

ress

ion

coeff

icie

nt

Local: Erythemis simplicicollis

1 2 3 4 5 6 7 8 9-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

Variable

Logis

tic

reg

ress

ion

coeff

icie

nt


1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Ishnura denticollis(b)

1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Ishnura denticollis

1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent


1 2 3 4 5 6 7 8 9-2

-1.5

-1

-0.5

0

0.5

1

1.5

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Ishnura hastata(c)

1 2 3 4 5 6 7 8 9-2

-1.5

-1

-0.5

0

0.5

1

1.5

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Ishnura hastata

1 2 3 4 5 6 7 8 9-2

-1.5

-1

-0.5

0

0.5

1

1.5

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent


1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Lestes alacer(d)

1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Lestes alacer

1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent


1 2 3 4 5 6 7 8 9-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Lestes australis(e)

1 2 3 4 5 6 7 8 9-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Lestes australis

1 2 3 4 5 6 7 8 9-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent


1 2 3 4 5 6 7 8 9-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Variable

Logis

tic

reg

ress

ion

coeff

icie

nt

Local: Libellula luctuosa(f)

1 2 3 4 5 6 7 8 9-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Variable

Logis

tic

reg

ress

ion

coeff

icie

nt

Local: Libellula luctuosa

1 2 3 4 5 6 7 8 9-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

Variable

Logis

tic

reg

ress

ion

coeff

icie

nt


FIGURE 2.3. Local-level multiple logistic regression results by species. See Table 2.6 for

list of local- level variables by number.


60

1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Libellula pulchella(g)

1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Libellula pulchella

1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent


1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Libellula saturata(h)

1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Libellula saturata

1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent


1 2 3 4 5 6 7 8 9-2

-1.5

-1

-0.5

0

0.5

1

1.5

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Orthemis ferruginea(i)

1 2 3 4 5 6 7 8 9-2

-1.5

-1

-0.5

0

0.5

1

1.5

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Orthemis ferruginea

1 2 3 4 5 6 7 8 9-2

-1.5

-1

-0.5

0

0.5

1

1.5

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent


1 2 3 4 5 6 7 8 9-2

-1.5

-1

-0.5

0

0.5

1

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Pachydiplax longipennis(j)

1 2 3 4 5 6 7 8 9-2

-1.5

-1

-0.5

0

0.5

1

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Pachydiplax longipennis

1 2 3 4 5 6 7 8 9-2

-1.5

-1

-0.5

0

0.5

1

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent


1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Perithemis tenera(k)

1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent

Local: Perithemis tenera

1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

Variable

Log

isti

c re

gre

ssio

n c

oef

fici

ent


1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Variable

Logis

tic

regre

ssio

n c

oef

fici

ent

Local: Plathemis lydia(l)

1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Variable

Logis

tic

regre

ssio

n c

oef

fici

ent

Local: Plathemis lydia

1 2 3 4 5 6 7 8 9-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

Variable

Logis

tic

regre

ssio

n c

oef

fici

ent




61

0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

crim

inan

t co

effi

cien

t


0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

crim

inan

t co

effi

cien

t

Landscape: Erythemis simplicicollis

0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

crim

inan

t co

effi

cien

t


0 5 10 15 20 25 30 35 40-0.5

0

0.5

1

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.5

0

0.5

1

Variable

Dis

cri

min

ant

coeff

icie

nt

Landscape: Ishnura denticollis

0 5 10 15 20 25 30 35 40-0.5

0

0.5

1

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt

Landscape: Ishnura hastata

0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt

Landscape: Lestes alacer

0 5 10 15 20 25 30 35 40-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt

Landscape: Lestes australis

0 5 10 15 20 25 30 35 40-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Variable

Dis

cri

min

ant

coeff

icie

nt

Landscape: Libellula luctuosa

0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Variable

Dis

cri

min

ant

coeff

icie

nt


FIGURE 2.4. Landscape-level PLS discriminant analysis results by species. See Table

2.5 for list of landscape-level variables by number.


62

0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt

Landscape: Libellula pulchella

0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.5

0

0.5

1

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.5

0

0.5

1

Variable

Dis

cri

min

ant

coeff

icie

nt

Landscape: Libellula saturata

0 5 10 15 20 25 30 35 40-0.5

0

0.5

1

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt

Landscape: Orthemis ferruginea

0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Variable

Dis

cri

min

ant

coeff

icie

nt

Landscape: Pachydiplax longipennis

0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt

Landscape: Perithemis tenera

0 5 10 15 20 25 30 35 40-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt


0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt

Landscape: Plathemis lydia

0 5 10 15 20 25 30 35 40-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt




63

1 2 3 4 5 6 7 8 9-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Variable

Dis

crim

inan

t co

effi

cien

t


1 2 3 4 5 6 7 8 9-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Variable

Dis

crim

inan

t co

effi

cien

t

Local: Erythemis simplicicollis

1 2 3 4 5 6 7 8 9-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Variable

Dis

crim

inan

t co

effi

cien

t


1 2 3 4 5 6 7 8 9-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt


1 2 3 4 5 6 7 8 9-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt

Local: Ishnura denticollis

1 2 3 4 5 6 7 8 9-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt


1 2 3 4 5 6 7 8 9-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt


1 2 3 4 5 6 7 8 9-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt

Local: Ishnura hastata

1 2 3 4 5 6 7 8 9-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt


1 2 3 4 5 6 7 8 9-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt


1 2 3 4 5 6 7 8 9-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt

Local: Lestes alacer

1 2 3 4 5 6 7 8 9-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Variable

Dis

cri

min

ant

coeff

icie

nt


1 2 3 4 5 6 7 8 9-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Variable

Dis

cri

min

ant

coeff

icie

nt


1 2 3 4 5 6 7 8 9-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Variable

Dis

cri

min

ant

coeff

icie

nt

Local: Lestes australis

1 2 3 4 5 6 7 8 9-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Variable

Dis

cri

min

ant

coeff

icie

nt


1 2 3 4 5 6 7 8 9-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt


1 2 3 4 5 6 7 8 9-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt

Local: Libellula luctuosa

1 2 3 4 5 6 7 8 9-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Variable

Dis

cri

min

ant

coeff

icie

nt


FIGURE 2.5. Local-level PLS discriminant analysis results by species. See Table 2.6 for

list of local-level variables by number.


64

1 2 3 4 5 6 7 8 9-0.2

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65

0 5 10 15 20 25 30 35 400

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FIGURE 2.6. Landscape-level PLS multiple regression variable weights. See Table 2.5

for list of landscape-level variables by number.


66

FIGURE 2.7. Local-level PLS multiple regression variable weights. See Table 2.6 for list

of local-level variables by number.

1 2 3 4 5 6 7 8 90

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67

CHAPTER III

ASSOCIATIO�S BETWEE� ADULT FEMALE BODY SIZE A�D

FIT�ESS I� ODO�ATES

Abstract

Many odonate species (dragonflies and damselflies) have been shown to vary

intraspecifically in body size due to environmental variables (e.g. water quality,

surrounding land use, food availability, photoperiod, etc.). These size differences are

hypothesized to impact fitness. The most ubiquitous yet one of the least-studied odonate

species on the Southern High Plains of Texas is the damselfly Enallagma civile. Until

now, no study has examined the effects of size differences of female E. civile on fitness,

and few have documented reproductive life history information. Between June 2009 and

June 2010, I captured 561 actively mating Enallagma civile females in the field.

Although roughly only a fourth of captured females subsequently laid eggs in the lab,

among those that did, clutch size averaged 249.46 eggs (range of 1-1047), and overall

there was a high mean hatch success rate of 75.80%. Contradictory to my original

hypothesis, female body size (in terms of head capsule width, a non-labile trait in adults)

was not significantly associated with fitness metrics. Even among egg-laying females,

body size had no impact on fitness. Hatch success was positively associated with egg

length (indicating that egg size may be an assay of egg quality) and negatively related to

hatch duration. This study provides baseline life history information about E. civile, as

well as contributing to the growing (but still incomplete) body of literature on the effects


68

of body size on fitness in odonates. Finally, this study provides several interesting areas

for future research.

Introduction

Body size in odonates (Insecta: Odonata, dragonflies [suborder Anisoptera] and

damselflies [suborder Zygoptera]), as in many animals, can be influenced by many

environmental factors, including food availability or temperature during maturation (see

Reece 2009). Differences in these factors between locations or times mean that a cohort

of odonates can exhibit differences in size, with some individuals being larger than

conspecifics. The implications of a difference in body size within a population are not

well-understood, but one implication may be on fitness.

In odonates, female fitness may be positively influenced by body size because

larger females would have the resource accrual to support the production and weight of

larger clutches. However, the literature is conflicting on this topic. Some studies have

indeed shown a positive relationship between mass and fitness (e.g. Harvey and Corbet

1985, Banks and Thompson 1987, Koenig and Albano 1987, Gribbin and Thompson

1990, Harvey and Walsh 1993, Cordero 1995; see Sokolovska et al. 2000 for a meta-

analysis) whereas others have shown a negative relationship, or none at all (Fincke 1986,

1988; Anholt 1991; Richardson and Baker 1997). However, mass is only one assay of

body size, and it is far more plastic than some other size metrics. Therefore, in light of

these contradictory studies on a labile assay of body size, it appears that our

understanding of the effect of female body size on fitness for odonates is still incomplete.


69

My objectives were twofold. First, I sought to test the hypothesis that larger

female odonates would have higher fitness (lay more eggs, have higher hatch success)

than would smaller females. Second, I gathered baseline reproductive and fitness

information (including clutch size, time to hatch, hatch duration, mean egg length, and

hatch success) on a widespread and abundant but surprisingly little-studied model

species, thereby providing information that may be of comparative value in trying to

detect overall trends in odonate life-history patterns from species-specific idiosyncrasies.

Methods

Study Species

Enallagma civile, or the familiar bluet damselfly (Coenagrionidae), is distributed

from southern Canada to Central America and from the Atlantic to Pacific Oceans

(Westfall and May 1996). In the Southern High Plains of Texas, E. civile is undoubtedly

the most common odonate (Reece and McIntyre 2009). The familiar bluet is a non-

territorial species with males that engage in scramble competition for mates (Corbet

1999: 432). On average, the life span from egg hatching to adult emergence is 21 days in

the field (Booker 2002); the adult life span is typically less than a week (Bick and Bick

1963). Although one of the most common species, E. civile remains one of the least-

studied, especially in terms of basic reproductive and fitness information.

Study Site

All samples were collected from a single locality to ensure that there was no

confounding between-site variability in damselfly size. All size differences were,


70

therefore, assumed to be due chiefly to genetics rather than environment. All individuals

were collected from the northwestern portion of Canyon Lake Number 5 in Mae

Simmons Park in Lubbock County, Texas (33.57779 N, -101.82596 W). Canyon Lake

Number 5 is an impoundment of the Brazos River. Grass grows to the edge of much of

the site and is periodically mowed by the city of Lubbock, but a variety of aquatic reeds

and plants such as cattails are also present at the site, providing additional perching and

oviposition material. Fish (most notably carp and bass) are present. Canyon Lake

Number 5 was chosen because of its prolific population of E. civile, as well as its

location. The site had to be close to the lab to minimize transportation time, and thus

stress and fatality, of female bluets.

Collection Methods

E. civile begin to hatch in Lubbock Co. in mid to late spring. Tandem pairs of

reproducing bluets can generally be collected from May through September. In 2009,

females were collected on 16 different days spanning from late June through early

September (June 23-24; July 3-4, 6-7, 13-16; August 27-29, 31; and September 2, 8). In

2010, females were collected on 17 days but over a shorter period (May 28-June 1, June

7-20) in order to concentrate sampling over a smaller window of time, thereby reducing

any potential biases from time stress (see Discussion), which may cause a difference to

be seen between cohorts that emerge early in the summer versus nearer to autumn.

Individuals collected over the four-month period in 2009 likely belonged to at least three

cohorts (assuming 21 days from egg hatching to adult emergence; Booker 2002), whereas

the individuals collected in 2010 likely belonged to the same generation.


71

Samples were collected during mid-day, between 1100-1530 hours. Aerial nets

were used to collect the damselflies. Only females caught in tandem were collected to try

to ensure that collected females had been inseminated and had eggs to lay. Females were

individually placed into a small, clear specimen envelope inside a shaded container in the

field and were then transported back to the lab. In the lab, each individual was placed

into a separate oviposition chamber. The chamber was a 1-qt glass Mason jar filled with

approximately one inch of pond water. An oviposition apparatus (wooden rod wrapped

in damp paper towels) was placed in the chamber at approximately a 45o angle. The

oviposition apparatus allowed the females to perch while in the chamber and mimicked

the vegetation stems in which female E. civile naturally lay their eggs.

Each female was left undisturbed in the chamber for approximately 36-48 hours,

with the lab maintained at 27oC and a 16:8 hr light:dark cycle. After this time, the female

was removed from the chamber and placed into a labeled specimen envelope for

preservation. Next, the oviposition apparatus was checked for eggs. If eggs were

present, the section of paper towel containing the eggs was cut out and placed into a Petri

dish filled with pond water. A Zeiss Stemi 2000 dissecting microscope was used to count

the number of eggs laid by each individual damselfly. Furthermore, for samples

collected in 2010, 10 eggs per clutch were chosen at random and measured for length.

(In clutches with fewer than 10 eggs, as many as possible were measured.) The Petri

dishes with eggs were incubated under UV lights with a 16:8 hr light:dark cycle until the

clutch hatched. Once hatching commenced, each Petri dish was checked daily to

quantify the number of larvae, which were removed daily to reduce cannibalism of later-

hatching individuals. After the last active day of larval emergence, the dish was checked


72

for an additional 14 days to ensure that no other larvae emerged. If a larva was found

after several dormant days, the 14-day count restarted. After a two-week period with no

more hatching, the sample was considered complete.

Body Size and Fitness Measurements

There are several standard descriptors of odonate body size, including total body

length, mass, and head capsule width. Considerable problems arise when using either

total body length or mass, however. Odonates can telescope their abdomen, manipulating

their overall body length and effectively impeding accurate measurement. Mass is highly

variable and directly influenced by the individual’s recent activity (i.e., feeding or bowel

movements). Furthermore, mass is influenced by an individual’s age and physical

condition (Anholt 1991). Head capsule width is a fixed measurement in adults that is not

subject to any immediate environmental conditions and so was used as an assay of overall

body size.

I measured fitness in E. civile in terms of clutch size and hatching success.

Ideally, I would have included number of clutches into fitness, but the logistical

constraints (i.e., lab-rearing, collection of all eggs per clutch, etc.) of doing so are

immense. Furthermore, I would have liked to follow larvae through all developmental

stages until reaching adult maturity, but high lab-related mortality precluded this.

Therefore, due to logistical and natural constraints, I measured fitness as clutch size and

hatching success.

After oviposition, each adult female was euthanized via refrigeration at 4oC for at

least 48 hrs, and then the head capsule width (HCW) was measured from the outermost


73

edge of one eye to the outermost edge of the other eye, using a micrometer in the

dissecting microscope. Each individual was measured once, and a sub-sample of 20

individuals for each set (egg-laying and non-egg-laying for 2009 and 2010) was chosen at

random and re-measured to gauge accuracy. All re-measurements had less than 5%

differences among them.

Analyses

SAS (Statistical Analysis Systems software) version 9.2 was used for all analyses.

Two-tailed t-tests and Pearson/Spearman correlations were employed to measure how

female body size affects fitness. Data were checked for normality using a Shapiro-Wilks

test. In all but one case, the data were normally distributed. In the one case with non-

normal data, a Spearman correlation was used (instead of Pearson). Furthermore, t-tests

assume that both populations being tested have equal variance. Folded F-tests for

equality of variance were run prior to each test and showed that in all cases both groups

being tested had satisfied the assumption of equal variance.

Hatch success measures the percentage of eggs that hatch into larvae. Because

this variable is a proportion, it was transformed with the arcsine square root function for

inclusion in correlation statistics (Sokal and Rohlf 1981: 427). All data are included as

Appendices 3.1-3.3.


74

Results

Life History Information

Between 2009 and 2010, I caught 561 actively mating females (187 in 2009, 374

in 2010). The average HCW of mating female E. civile was 3.73 ± 0.01 mm (n = 530),

median = 3.75 mm (medians are also reported because most data were strongly right-

skewed). Theoretically, because all females were caught in tandem, all females should

have been able to lay eggs. However, of the females captured in 2009 and 2010, only

27.3% (153 out of 561) laid eggs. Of those females who laid eggs, the mean (+ standard

error) number of eggs laid was 249.5 ± 17.74 eggs (n = 151), with a range of 1-1047 eggs

and a median of 212.0 (Figure 3.1). Time to hatch measures the amount of time in days

from when the eggs were counted and when the first larva was observed. The mean time

to hatch was 11.9 days ± 0.21 (n = 116), median = 12.0 days. The range of time to hatch

was from 10-34 days (Figure 3.2). However, over 98% of the data fell within the range

of 10-14 days. There are two outliers at 17 and 34 days; excluding the outliers, the mean

drops to 11.6 ± 0.08 days. Hatch duration measures the number of days between the

observation of when the first larva hatched and the last. The mean hatch duration ranged

from 1-25 days (Figure 3.3), with a mean of 7.1 days ± 0.43 (n = 115) and median of 6.0

days. Hatch success refers to the percentage of eggs that hatched into larvae. The mean

hatch success was 75.8% ± 2.14% (n = 122), median = 83.5%. Between 2009 and 2010,

there were 8 females who laid eggs but had none emerge into larvae. Excluding these 8

individuals, the mean hatch success rises to 81.1% ± 0.01 (n = 114) (Figure 3.4).

The size of eggs within a clutch and among clutches varied. In 2010, the overall

range of egg lengths spanned from 0.60 mm to 0.90 mm (n = 807 total number of eggs


75

measured). The mean egg length was 0.76 ± 0.00 mm (median = 0.76 mm), and the

range of mean egg lengths was from 0.71-0.83 mm (n = 82 number of clutches measured)

(Figure 3.5). Within a single clutch, the largest difference between minimum and

maximum lengths was 0.23 mm (0.60-0.83 mm and 0.67-0.90 mm, respectively). Most

clutches expressed such variation in egg lengths, but there were 9 clutches that had no

variation (all 10 eggs measured were the same length).

Effects of Body Size on Fitness

There was not a significant difference detected in HCW between egg-laying and

non-egg laying females (means = 3.74 ± 0.01 mm and 3.72 ± 0.01 mm, respectively; p-

value = 0.294) (Table 3.1, Figure 3.6). Likewise, when considering only egg-laying

females, HCW had no detectable effect on clutch size. Females laying more than 500

eggs had the same HCW as females laying fewer (Table 3.1). Similarly, females who

laid fewer than 100 eggs had the same HCW as females laying more (Table 3.1).

Furthermore, female body size does not appear to affect egg size nor hatch

success because there were no significant correlations between HCW and mean egg

length (Table 3.2, Figure 3.7) or between HCW and hatch success (Table 3.2, Figure 3.8)

when excluding the eight outliers (clutches in which none of the eggs hatched into

larvae). Finally, there was no significant correlation between HCW and hatch duration

(Figure 3.9).

A post-hoc Tukey comparison of the 2009 data (collected June – September) of

HCW between all (egg-laying and non-egg laying) females collected early in the summer

(June 23-July 7; n = 42) and females collected later in the summer (September 2-8; n =


76

23) found that there was no significant difference between the two groups (Table 3.3). In

2010, I collected all females in a much shorter amount of time (less than three weeks),

thereby reducing any effects seasonality may have on female body size. Thus, I am

confident that my results are not skewed due to time stress (see Discussion).

General Fitness Information

In E. civile, there was no tradeoff seen between clutch size and egg length, as

there was no significant correlation between the number of eggs laid and the mean egg

length (Table 3.2, Figure 10) nor between hatch duration and mean egg length (Table 3.2,

Figure 3.11). Furthermore, the number of eggs laid was not correlated to hatch success

(Table 3.2, Figure 3.12).

However, there was a strong positive correlation between mean egg length and

hatch success (Table 3.2, Figure 3.13), indicating that the size of the eggs laid by the

female may be related to the survivorship of the larvae. Survivorship of larvae may also

be related to the length of time it takes to emerge. Hatch success was negatively

correlated with hatch duration (Table 3.2, Figure 3.14), suggesting that females whose

clutches take longer to hatch tend to have fewer larvae emerge.

Discussion

Life History Information

Although E. civile is widely distributed across North America, little is known

about its life-history characteristics. To my knowledge, this is the first study to measure

HCW of mating females, clutch size, time to hatch, hatch duration, mean egg length, and


77

hatch success in this species. As such, this study provides key reproductive and fitness

information for E. civile.

The percentage of egg-laying females in this study was low (27.3%). Along with

unnatural laboratory conditions, behavioral mating patterns may help explain this

phenomenon. E. civile copulation includes three distinct behavioral phases: exploratory,

underwater oviposition, and terminal (Corbet 1999: 26). During the first and third stages,

females are in tandem. During the second, the female voluntarily submerges herself

beneath the surface of the water, at which point the male releases her. The majority

(92%) of E. civile females lay their eggs during the second phase. In my study, all

females were caught in tandem. There was no way to determine during which behavioral

stage (first or third) the individuals were caught. Because the exploratory stage lasts

significantly longer than the terminal stage, I can assume that most individuals were

caught during the exploratory stage, still having eggs to oviposit. However, some

individuals were likely caught in stage three. Furthermore, in nature, the males assist in

the underwater submersion stage by remaining in tandem as the female initially

submerges herself in water (Paulson 2009: 82). The change in the mating sequence could

have influenced female oviposition decisions.

In this study, female HCW was slightly smaller than formerly reported. In 2008,

Córdoba-Aguilar reported the mean HCW of female E. civile as 3.80 mm (n = at least 3

individuals). In contrast, I found that the average HCW of female E. civile was 3.73 ±

0.01 mm (n = 530). The difference between these two averages may be attributed to

sample size, or possibly variation in sites/environmental conditions. The relatively

narrow HCW size range I found may be due to constraints imposed by urban land use or


78

any other unique factors at the single study site I used; further research would be needed

to determine whether this is so.

Despite high consistency in time to hatch, there was considerable variation in

hatch duration (1-25 days). This could indicate that more than one strategy is being

employed within this species. Some clutches had all eggs hatch into larvae in a single

day. By hatching all at once, a clutch could take advantage of optimal environmental

conditions. Other clutches in my study had eggs that hatched into larvae over a period of

20+ days. By extending the hatch duration, it is possible that survivorship may increase

because the clutch is less vulnerable to unforeseen, changeable conditions (weather,

predators, etc.), and there is a lower concentration of individuals, which reduces

intraspecific competition and risk of cannibalism.

I expected to find a negative correlation between mean egg length and clutch size

(as a female lays more eggs, the size of the eggs would decrease because of the finite

resources available to the female to allocate to either clutch size or to egg size).

Surprisingly, however, there was not a relationship between these variables. If such a

relationship existed, it could indicate that there was an “average” reproductive mass that

could be reached either through egg size or through number of eggs. However, no such

“average” reproductive mass appears to exist.

Hatch success was affected by mean egg length and hatch duration. Females with

high hatch success tended to have clutches that consist of eggs with larger mean lengths,

and their eggs hatched into larvae more quickly. Notably, hatch success was independent

of female body size.


79

Effects of Body Size on Fitness

There have been a number of studies on the effect that body mass has on odonate

fitness, with variable effects seen depending on species, sex, and territoriality (see review

in Anholt 2008), but none using the less-labile metric of head capsule width. For

example, larger females (in terms of mass) have been shown to produce larger clutch

sizes in Plathemis lydia (Koenig and Albano 1987) and Pyrrhosoma nymphula (Gribbin

and Thompson 1990). Furthermore, more massive females also produce more clutches in

some species (Cordero 1991, Leung and Forbes 1997, Marden and Rowan 2000, De

Block and Stoks 2005) but not in others (Koenig and Albano 1987, Michiels and Dhondt

1989, Anholt 1991). For males, smaller mass may actually be associated with higher

lifetime reproductive success in some species (Anholt 1991, Carchini et al. 2000).

Finally, responses also differ with respect to territoriality: greater mass is associated with

higher reproductive success in territorial species (Sokolovska et al. 2000), but no such

effect has been seen in non-territorial species (Banks and Thompson 1985, Stoks 2000).

My investigation for an affect of female body size in terms of a metric other than mass on

fitness (in terms of clutch size and hatch success) in Enallagma civile adds to the attempt

at understanding the complex phenomenon of life-history tradeoffs.

It appears that fitness (in terms of both clutch size and hatch success) in E. civile

is influenced by a variable other than HCW. Although it has been speculated that in

odonates the fixed non-mass size (head capsule width) of an individual “must set some

upper limit to clutch size” (Anholt 2008: 168), I found that HCW was not associated with

clutch size. The females who laid eggs were the same size as those who did not lay

eggs. And among females who laid eggs, females with the largest and smallest clutches


80

had the same HCW as all other egg-laying females. Similarly, I failed to detect an effect

of HCW on hatching success (no correlation between HCW and hatch success), as both

large and small females experienced similar rates of larval emergence.

In light of my results, the ultimate effects of body size variation in E. civile may

be minimal. Barring indirect influences (i.e., resource accrual), if this species is

physically able to survive a set of environmental conditions, E. civile can inhabit the area

without a reduction in fitness and exhibit variation in body size. In conjunction with its

high tolerance level for several environmental variables, the independence of body size

and fitness may help to explain the ubiquitous nature of E. civile.

Time Stress

Time stress has been documented in odonate larvae of several species (Stoks et al.

2008). Species experiencing time stress can sense as winter approaches (cued by

photoperiod, changes in temperature, or other environmental factors), and either

accelerate their growth and development to reach maturity before winter or completely

halt their growth and development and wait as larvae for spring. If they accelerate their

growth and development to reach maturity before winter, the tradeoff may be a smaller

adult body size (Stoks et al. 2008). Therefore, if time stress exists in this species, I would

expect that as the summer progresses, female size would decrease, as has been shown in

other species (Stoks et al. 2008). However, no significant effect of time (early vs. late in

the season) was seen in 2009 (Table 3.3), and the sampling regime in 2010 was

compressed into a much smaller window to minimize this potential effect.


81

In conclusion, it is notable that regardless of sampling period length, fitness was

independent of HCW. Furthermore, this is the first indication that time stress does not

exist in E. civile. Naturally, more research must be conducted on this topic before any

definite conclusions about time stress in this species can be made.

Things to Consider in the Future

As a result of this study, new life history and fitness information for E. civile has

been collected. However, as is typical, there is still much to be discovered. Future

research is needed in several areas. First, this study assumed that observed size

differences were due largely to genetics rather than environment (e.g. land use) because

all individuals were collected from the same site. Environmental factors are known to

influence larval odonate size (Reece 2009). It is possible that environmental differences

between sites (such as land use) will amplify size differences between adult females and

thus amplify fitness differences as well. Until quite recently, the Southern High Plains

were a nearly homogeneous grassland landscape. Therefore, the pressures of

anthropogenic activities on the landscape are relatively recent. Potential effects of

surrounding land use on a playa and its wildlife are, evolutionarily speaking, thus novel

and recent. The lack of a significant effect detected of body size on fitness in E. civile

may be due to these recent changes. Future research is needed to tease apart the relative

contributions of genetics versus environment on fitness. Second, in this study, I limited

the definition of fitness to clutch size and hatch success. Fitness also includes lifetime

reproductive success, or total number of clutches per female. Future research could

examine the number of times a female mates and the differences in clutch size/hatch


82

success per copulation. In fact, some studies (Fincke 1988, De Block and Stoks 2005)

found that the number of clutches laid was more important for determining lifetime

fitness than was clutch size. Female bluets typically leave their natal wetland to forage in

the surrounding uplands, whereas males tend to stay by the water, patrolling for females

(Bick and Bick 1963). When a female returns to the water to reproduce, she encounters

males that engage in scramble competition for an opportunity to copulate with her; it is

unknown whether she exerts some kind of mate choice. Following oviposition, she

normally retreats from the males, again going away from the wetland to forage.

Accumulating resources is a risky business, and because the adult familiar bluet lifespan

is just over a week (Bick and Bick 1963), the possibility for multiple copulations is low.

Therefore, having some kind of mechanism to maximize fitness on a per-clutch basis

(such as egg size or clutch size) is likely to be selected for. Third, a more comprehensive

understanding of female fitness and life history must include information about how

females reach reproductive maturity. It is probable that there are body size differences

between reproductive and non-reproductive females; females may have to attain a certain

critical mass before reproducing. If so, what is that critical mass, and how long, on

average, does it take for a female to attain that size? Fourth, I did not consider the male’s

role in the reproductive process and his potential influence on fitness. Male size could

influence fitness through his ability to acquire a mate and the condition and amount of

sperm. Finally, I found a large amount of variation in the hatch duration. Future research

could explore the environmental and/or genetic cues that account for this variation.

This study adds to the previous publications on the effects of female body size on

fitness in odonates. Obviously, the relationship between these two variables is not


83

straightforward, as evidenced by the accumulating amount of research. Previously

proposed as a potential new angle for gaining insight into odonate fitness (Anholt 2008:

168), this study establishes HCW as independent of fitness in E. civile.


84

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Fincke, O.M. 1988. Sources of variation in lifetime reproductive success in a

nonterritorial damselfly (Odonata: Coenagrionidae). Pp. 24-43 in: Reproductive

Success (T.H. Clutton-Brock, ed.). University of Chicago Press, Chicago, IL.

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nymphula (Sulzer) (Zygoptera: Coenagrionidae). Odonatologica 19:347-357.

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success of male dragonflies. Animal Behaviour 33:561-565.

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Koenig, W.D. and S.S. Albano. 1987. Lifetime reproductive success, selection, and the

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87

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88

Tables and Figures

TABLE 3.1. Results of t-tests for fitness relationships in female E. civile.

t-test Results Year Variable 1 n1 Variable 2 n2 t-value p-value

2009 & 2010 HCW non-egg-laying 384 HCW egg-laying 146 -1.05 0.294

2009 & 2010

HCW laying

< 500 eggs 127

HCW laying

> 500 eggs 17 -1.38 0.169

2009 & 2010

HCW laying

< 100 eggs 47

HCW laying

> 100 eggs 97 1.08 0.281

2009 HS smallest quartile

of mean egg length 20

HS largest quartile

of mean egg length 20 2.23 0.032

HS = Hatch Success

HCW = Head Capsule Width


89

TABLE 3.2. Correlation results related to overall E. civile fitness. All data was checked

for normality and equality of variance. Unless otherwise noted, correlations

are Pearson correlations.

Correlation Results Variables n r p-value

Hatch Success and Mean Egg Length 81 0.242 0.034

Hatch Duration and Hatch Success 114 -0.186 0.048

Mean Egg Length and HCW 80 0.108 0.342

Mean Egg Length and Hatch Duration* 78 -0.097 0.397

Number of Eggs and HCW 144 0.067 0.428

Hatch Success and Number of Eggs (without outliers) 114 0.055 0.561

Mean Egg Length and Number of Eggs 81 -0.041 0.720

Hatch Success and HCW (without outliers) 110 -0.032 0.744

HCW and Hatch Duration 111 0.031 0.747

* Spearman correlation


90

TABLE 3.3. Post-hoc Tukey test results for the relationship between HCW of females caught early in the season (June 23 –

July 6) and females caught late in the season (September 2-8).

Tukey Test Results

Year Variable 1 n1 Group Variable 2 n2 Group Critical Value

2009 HCW early in season 42 A HCW late in season 23 A 2.826


91

TABLE 3.4. Data for egg-laying female E. civile for 2009 and 2010.

Data for Egg-Laying Females

ID # Caught Counted HCW (mm) # Eggs # Larvae Time to Hatch Hatch Duration Hatch Success

1 6/23/09 6/25/09 3.600 119 . . . .

2 6/24/09 6/26/09 3.675 7 . . . .

3 6/24/09 6/26/09 3.750 8 . . . .

4 6/24/09 6/26/09 3.750 3 . . . .

6 7/3/09 7/5/09 3.825 55 . . . .

7 7/4/09 7/6/09 3.900 11 . . . .

8 7/4/09 7/6/09 3.825 35 . . . .

9 7/4/09 7/6/09 3.300 37 . . . .

10 7/4/09 7/6/09 3.750 3 . . . .

11 7/6/09 7/8/09 3.750 8 . . . .

12 7/6/09 7/8/09 3.450 22 . . . .

13 7/7/09 7/8/09 3.825 31 . . . .

14 7/7/09 7/8/09 4.050 80 . . . .

15 7/13/09 7/14/09 . 130 . . . .

17 7/14/09 7/16/09 3.675 1 . . . .

18 7/14/09 7/16/09 3.750 8 . . . .

19 7/14/09 7/16/09 3.825 267 . . . .

20 7/14/09 7/16/09 3.600 286 . . . .

21 7/14/09 7/16/09 3.750 136 . . . .

22 7/14/09 7/16/09 . 1 . . . .

23 7/15/09 7/17/09 3.675 553 . . . .

24 7/15/09 7/17/09 3.600 223 . . . .

25 7/15/09 7/17/09 3.525 390 . . . .

26 7/15/09 7/17/09 3.750 427 . . . .


92




27 7/16/09 7/17/09 3.750 17 . . . .

29 8/27/09 8/29/09 3.825 132 . . . .

30 8/27/09 8/29/09 3.750 249 . . . .

31 8/27/09 8/29/09 . 171 . . . .

32 8/27/09 8/29/09 3.675 450 320 12 4 0.71

33 8/27/09 8/29/09 3.675 44 . . . .

34 8/28/09 8/30/09 3.750 103 60 12 12 0.58

35 8/28/09 8/30/09 3.525 287 254 11 4 0.89

36 8/28/09 8/30/09 3.600 428 380 11 8 0.89

37 8/28/09 8/30/09 3.675 34 0 . . 0.00

38 8/28/09 8/30/09 . 521 467 11 14 0.90

39 8/28/09 8/30/09 3.600 475 447 12 7 0.94

40 8/28/09 8/30/09 3.750 25 20 12 2 0.80

41 8/28/09 8/30/09 3.750 378 354 11 9 0.94

42 8/29/09 8/31/09 3.750 . . 12 . .

43 8/29/09 8/31/09 . 158 144 11 4 0.91

45 8/29/09 8/31/09 3.750 340 226 11 5 0.66

46 8/29/09 8/31/09 3.900 451 379 11 9 0.84

47 8/29/09 8/31/09 3.600 293 250 11 7 0.85

48 8/31/09 9/2/09 3.750 162 105 11 8 0.65

49 8/31/09 9/2/09 3.600 338 266 10 9 0.79

50 8/31/09 9/2/09 3.900 59 41 11 1 0.69

51 8/31/09 9/2/09 3.750 117 56 11 7 0.48

52 8/31/09 9/2/09 3.600 418 376 11 13 0.90


93




53 8/31/09 9/2/09 3.825 414 381 11 12 0.92

54 8/31/09 9/2/09 3.975 86 60 11 8 0.70

55 8/31/09 9/2/09 4.050 289 242 11 6 0.84

56 9/2/09 9/4/09 4.050 28 0 . . 0.00

57 9/2/09 9/4/09 3.600 358 291 10 8 0.81

58 9/2/09 9/4/09 3.750 475 393 11 9 0.83

59 9/2/09 9/4/09 3.750 283 246 10 7 0.87

60 9/2/09 9/4/09 3.675 424 297 11 25 0.70

61 9/2/09 9/4/09 3.750 41 36 11 4 0.88

62 9/2/09 9/4/09 3.900 326 275 10 8 0.84

63 9/2/09 9/4/09 3.900 1027 878 10 12 0.85

64 9/2/09 9/4/09 3.600 378 345 10 10 0.91

65 9/2/09 9/4/09 3.600 376 317 10 12 0.84

66 9/8/09 9/10/09 3.600 534 447 10 7 0.84

67 9/8/09 9/10/09 3.900 176 162 10 8 0.92

68 9/8/09 9/10/09 3.675 112 100 10 4 0.89

69 9/8/09 9/10/09 3.900 12 0 . . 0.00

70 9/8/09 9/10/09 3.750 291 257 11 4 0.88

71 5/28/10 5/30/10 3.825 22 0 12 . 0.00

72 5/28/10 5/30/10 3.900 525 444 13 5 0.85

73 5/28/10 5/30/10 3.750 267 242 13 4 0.91

74 5/28/10 5/30/10 3.675 93 86 . 5 0.92

75 5/31/10 6/2/10 3.750 385 342 12 8 0.89

76 6/1/10 6/3/10 3.900 207 187 11 7 0.90


94




77 6/1/10 6/3/10 3.975 12 1 11 1 0.08

78 6/1/10 6/3/10 3.900 501 191 12 7 0.38

79 6/1/10 6/3/10 3.825 327 293 12 8 0.90

80 6/1/10 6/3/10 3.975 229 212 12 4 0.93

81 6/2/10 6/3/10 3.750 46 43 12 5 0.93

82 6/2/10 6/3/10 3.750 44 39 13 4 0.89

83 6/2/10 6/3/10 3.750 31 23 13 2 0.74

84 6/2/10 6/3/10 4.050 600 485 34 16 0.81

85 6/7/10 6/9/10 3.600 386 305 11 9 0.79

86 6/7/10 6/9/10 3.750 268 227 11 4 0.85

87 6/7/10 6/9/10 3.450 58 50 12 6 0.86

88 6/7/10 6/9/10 3.750 378 307 12 8 0.81

89 6/7/10 6/9/10 3.675 198 166 13 2 0.84

90 6/8/10 6/10/10 3.675 523 481 11 4 0.92

91 6/8/10 6/10/10 3.750 326 294 12 4 0.90

92 6/8/10 6/10/10 3.525 96 64 12 3 0.67

93 6/9/10 6/11/10 3.975 1047 734 11 24 0.70

94 6/9/10 6/11/10 3.600 535 426 11 6 0.80

95 6/9/10 6/11/10 3.900 367 249 11 8 0.68

96 6/9/10 6/11/10 3.825 49 0 11 . 0.00

97 6/9/10 6/11/10 3.675 51 36 12 5 0.71

98 6/9/10 6/11/10 3.675 488 426 12 4 0.87

99 6/9/10 6/11/10 3.750 467 428 12 5 0.92

100 6/9/10 6/11/10 3.825 367 336 14 8 0.92


95




101 6/9/10 6/11/10 3.975 918 715 17 15 0.78

102 6/9/10 6/11/10 3.825 303 257 . 7 0.85

103 6/10/10 6/12/10 . 18 8 11 5 0.44

104 6/10/10 6/12/10 3.675 502 486 12 6 0.97

105 6/10/10 6/12/10 3.600 239 187 12 12 0.78

106 6/10/10 6/12/10 3.675 207 185 12 6 0.89

107 6/10/10 6/12/10 3.750 287 239 12 4 0.83

108 6/10/10 6/12/10 3.750 488 357 13 7 0.73

109 6/10/10 6/12/10 3.750 61 52 13 5 0.85

110 6/11/10 6/13/10 3.750 553 453 12 3 0.82

111 6/11/10 6/13/10 3.750 37 29 12 7 0.78

112 6/11/10 6/13/10 3.675 263 200 12 12 0.76

113 6/11/10 6/13/10 3.450 151 122 13 7 0.81

114 6/11/10 6/13/10 3.450 297 252 13 5 0.85

115 6/11/10 6/13/10 3.525 182 114 14 8 0.63

116 6/12/10 6/14/10 3.450 54 45 12 4 0.83

117 6/12/10 6/14/10 3.750 238 189 12 4 0.79

118 6/12/10 6/14/10 3.600 257 206 12 11 0.80

119 6/12/10 6/14/10 3.675 387 284 12 20 0.73

120 6/12/10 6/14/10 3.675 578 484 12 7 0.84

121 6/12/10 6/14/10 3.900 316 287 12 8 0.91

122 6/12/10 6/14/10 4.050 212 166 12 8 0.78

123 6/12/10 6/14/10 3.675 36 26 12 4 0.72

124 6/12/10 6/14/10 3.675 449 351 13 6 0.78


96




125 6/13/10 6/15/10 3.750 73 59 12 6 0.81

126 6/13/10 6/15/10 3.675 76 71 12 4 0.93

127 6/13/10 6/15/10 3.900 24 0 12 . 0.00

128 6/13/10 6/15/10 3.825 13 12 12 2 0.92

129 6/13/10 6/15/10 3.825 10 8 12 6 0.80

130 6/13/10 6/15/10 3.600 164 92 . 20 0.56

131 6/14/10 6/16/10 3.600 450 366 11 23 0.81

132 6/14/10 6/16/10 3.900 200 179 11 4 0.90

133 6/14/10 6/16/10 3.750 357 222 11 16 0.62

134 6/14/10 6/16/10 3.750 560 506 11 10 0.90

135 6/16/10 6/18/10 3.525 358 269 12 4 0.75

136 6/16/10 6/18/10 3.675 244 192 12 10 0.79

137 6/16/10 6/18/10 3.750 59 58 12 3 0.98

138 6/16/10 6/18/10 3.750 185 172 12 5 0.93

139 6/16/10 6/18/10 3.600 41 30 12 3 0.73

140 6/18/10 6/20/10 3.675 349 309 11 3 0.89

141 6/18/10 6/20/10 3.525 602 446 11 10 0.74

142 6/18/10 6/20/10 . 21 18 11 2 0.86

143 6/18/10 6/20/10 3.600 181 150 12 3 0.83

144 6/18/10 6/20/10 3.750 102 98 12 2 0.96

145 6/18/10 6/20/10 3.975 8 0 13 . 0.00

146 6/18/10 6/20/10 3.825 148 131 13 7 0.89

147 6/18/10 6/20/10 3.750 . . . 5 .

148 6/19/10 6/21/10 3.975 968 891 11 7 0.92


97




149 6/19/10 6/21/10 3.750 15 0 11 . 0.00

150 6/19/10 6/21/10 3.975 435 314 11 6 0.72

151 6/19/10 6/21/10 3.900 1 1 12 1 1.00

152 6/19/10 6/21/10 3.600 155 130 12 3 0.84

153 6/19/10 6/21/10 3.600 165 128 12 6 0.78

154 6/19/10 6/21/10 3.675 791 594 12 12 0.75

155 6/19/10 6/21/10 3.600 199 170 . 3 0.85

156 6/20/10 6/22/10 3.825 304 269 11 4 0.88

157 6/20/10 6/22/10 3.675 364 313 12 4 0.86


98

TABLE 3.5. Egg-length data for female E. civile clutches in 2010.

Egg Length Data (in mm)

ID # # Eggs Egg 1 Egg 2 Egg 3 Egg 4 Egg 5 Egg 6 Egg 7 Egg 8 Egg 9 Egg 10

Mean Egg

Length

76 207 0.50 0.50 0.50 0.40 0.50 0.50 0.50 0.50 0.50 0.55 0.74

77 12 0.50 0.50 0.50 0.45 0.50 0.50 0.50 0.50 0.50 0.50 0.74

78 501 0.55 0.50 0.50 0.50 0.50 0.50 0.50 0.45 0.50 0.50 0.75

79 327 0.55 0.55 0.55 0.55 0.50 0.50 0.50 0.50 0.50 0.55 0.79

80 229 0.50 0.55 0.55 0.50 0.50 0.55 0.50 0.50 0.55 0.50 0.78

81 46 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.45 0.50 0.50 0.74

82 44 0.50 0.50 0.50 0.50 0.45 0.50 0.50 0.50 0.50 0.50 0.74

83 31 0.50 0.55 0.55 0.55 0.50 0.50 0.50 0.55 0.50 0.55 0.79

84 600 0.40 0.50 0.50 0.50 0.50 0.50 0.50 0.55 0.50 0.55 0.75

85 386 0.55 0.50 0.50 0.55 0.50 0.50 0.50 0.50 0.50 0.55 0.77

86 268 0.50 0.55 0.50 0.50 0.50 0.50 0.50 0.50 0.55 0.55 0.77

87 58 0.50 0.55 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.76

88 378 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.55 0.76

89 198 0.50 0.55 0.50 0.50 0.50 0.55 0.50 0.50 0.50 0.50 0.77

90 523 0.55 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.76

91 326 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.75

92 96 0.55 0.55 0.50 0.55 0.50 0.50 0.50 0.60 0.45 0.50 0.78

93 1047 0.50 0.50 0.50 0.45 0.50 0.50 0.50 0.50 0.50 0.50 0.74

94 535 0.50 0.50 0.50 0.55 0.50 0.50 0.40 0.50 0.50 0.50 0.74

95 367 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.75

96 49 0.45 0.50 0.50 0.50 0.50 0.50 0.50 0.45 0.45 0.50 0.73

97 51 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.45 0.50 0.50 0.74

98 488 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.75


99

TABLE 3.5. Continued



Mean Egg

Length

99 467 0.50 0.50 0.50 0.55 0.50 0.55 0.50 0.50 0.50 0.50 0.77

100 367 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.60 0.50 0.50 0.77

101 918 0.45 0.60 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.76

102 303 0.50 0.50 0.50 0.50 0.55 0.50 0.50 0.50 0.50 0.50 0.76

103 18 0.50 0.50 0.55 0.55 0.50 0.60 0.55 0.50 0.50 0.50 0.79

104 502 0.50 0.50 0.50 0.50 0.50 0.50 0.55 0.50 0.50 0.50 0.76

105 239 0.50 0.50 0.55 0.50 0.50 0.55 0.60 0.55 0.55 0.50 0.80

106 207 0.50 0.50 0.50 0.55 0.50 0.50 0.50 0.50 0.50 0.50 0.76

107 287 0.55 0.50 0.50 0.50 0.50 0.50 0.50 0.55 0.50 0.50 0.77

108 488 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.55 0.50 0.50 0.76

109 61 0.50 0.50 0.50 0.55 0.55 0.50 0.50 0.50 0.50 0.55 0.77

110 553 0.55 0.50 0.45 0.55 0.50 0.50 0.50 0.55 0.50 0.50 0.77

111 37 0.50 0.50 0.55 0.45 0.50 0.50 0.55 0.55 0.50 0.50 0.77

112 263 0.50 0.50 0.45 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.74

113 151 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.75

114 297 0.50 0.50 0.50 0.50 0.55 0.50 0.50 0.50 0.50 0.50 0.76

115 182 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.45 0.74

116 54 0.45 0.50 0.50 0.45 0.50 0.45 0.50 0.40 0.50 0.45 0.71

117 238 0.50 0.50 0.50 0.55 0.50 0.50 0.50 0.50 0.45 0.50 0.75

118 257 0.50 0.50 0.50 0.50 0.55 0.50 0.50 0.50 0.50 0.50 0.76

119 387 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.75

120 578 0.50 0.55 0.50 0.50 0.45 0.50 0.50 0.50 0.50 0.50 0.75

121 316 0.50 0.50 0.50 0.50 0.50 0.50 0.55 0.55 0.50 0.50 0.77


100




Mean Egg

Length

122 212 0.50 0.55 0.50 0.50 0.55 0.50 0.50 0.50 0.50 0.50 0.77

123 36 0.55 0.50 0.50 0.50 0.55 0.50 0.50 0.50 0.50 0.50 0.77

124 449 0.50 0.55 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.76

125 73 0.55 0.50 0.55 0.50 0.50 0.50 0.50 0.50 0.55 0.50 0.77

126 76 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.75

127 24 0.50 0.45 0.45 0.50 0.50 0.50 0.50 0.50 0.50 0.55 0.74

128 13 0.55 0.55 0.50 0.50 0.55 0.50 0.50 0.50 0.50 0.60 0.79

129 10 0.45 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.74

130 164 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.55 0.45 0.50 0.75

131 450 0.55 0.50 0.50 0.50 0.55 0.55 0.50 0.50 0.50 0.50 0.77

132 200 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.75

133 357 0.55 0.55 0.50 0.50 0.55 0.60 0.55 0.50 0.50 0.50 0.80

134 560 0.55 0.60 0.50 0.50 0.60 0.50 0.50 0.55 0.55 0.55 0.81

135 358 0.50 0.50 0.50 0.50 0.50 0.55 0.50 0.50 0.50 0.50 0.76

136 244 0.55 0.50 0.50 0.50 0.50 0.50 0.55 0.50 0.60 0.55 0.79

137 59 0.50 0.50 0.55 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.76

138 185 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.60 0.50 0.50 0.77

139 41 0.55 0.50 0.50 0.50 0.50 0.45 0.50 0.50 0.50 0.50 0.75

140 349 0.50 0.50 0.50 0.50 0.55 0.50 0.50 0.50 0.55 0.50 0.77

141 602 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.75

142 21 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.55 0.50 0.76

143 181 0.50 0.50 0.55 0.50 0.50 0.50 0.55 0.50 0.50 0.50 0.77

144 102 0.50 0.50 0.60 0.50 0.60 0.50 0.55 0.50 0.55 0.50 0.80


101




Mean Egg

Length

145 8 0.55 0.50 0.50 0.45 0.45 0.50 . . . . 0.74

146 148 0.60 0.60 0.55 0.50 0.50 0.50 0.55 0.50 0.50 0.50 0.80

147 . 0.55 0.50 0.55 0.50 0.50 0.50 0.50 0.50 0.55 0.55 0.78

148 968 0.60 0.55 0.50 0.50 0.50 0.50 0.50 0.50 0.55 0.50 0.78

149 15 0.50 0.55 0.50 0.50 0.55 0.55 0.50 0.50 0.55 0.50 0.78

150 435 0.50 0.50 0.55 0.50 0.50 0.50 0.50 0.50 0.55 0.50 0.77

151 1 0.55 . . . . . . . . . 0.83

152 155 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.75

153 165 0.45 0.55 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.75

154 791 0.50 0.50 0.50 0.50 0.50 0.50 0.45 0.50 0.50 0.50 0.74

155 199 0.50 0.50 0.50 0.55 0.50 0.60 0.50 0.55 0.50 0.50 0.78

156 304 0.50 0.50 0.50 0.50 0.45 0.50 0.50 0.50 0.50 0.50 0.74

157 364 0.50 0.50 0.50 0.50 0.50 0.55 0.50 0.50 0.50 0.50 0.76


102

TABLE 3.6. Data for non-egg-laying female E. civile for 2009 and 2010.

Data for �on-Egg-Laying Females

ID# Caught HCW (mm) ID# Caught HCW (mm) ID# Caught HCW (mm)

1001 7/6/09 3.600 1025 7/7/09 3.600 1049 7/14/09 3.825

1002 7/6/09 3.600 1026 7/7/09 3.675 1050 7/14/09 3.600

1003 7/6/09 3.525 1027 7/7/09 3.900 1051 7/14/09 3.600

1004 7/6/09 3.525 1028 7/7/09 3.825 1052 7/14/09 3.600

1005 7/6/09 3.600 1029 7/7/09 3.600 1053 7/14/09 3.525

1006 7/6/09 3.750 1030 7/7/09 3.600 1054 7/14/09 .

1007 7/6/09 3.750 1031 7/13/09 3.750 1055 7/14/09 3.600

1008 7/6/09 3.900 1032 7/13/09 3.750 1056 7/14/09 3.600

1009 7/6/09 3.900 1033 7/13/09 3.600 1057 7/14/09 3.675

1010 7/6/09 3.525 1034 7/13/09 3.900 1058 7/14/09 .

1011 7/6/09 3.600 1035 7/13/09 3.675 1059 7/15/09 .

1012 7/6/09 3.600 1036 7/13/09 3.600 1060 7/15/09 .

1013 7/6/09 3.600 1037 7/13/09 3.525 1061 7/15/09 3.750

1014 7/6/09 3.900 1038 7/13/09 3.600 1062 7/15/09 3.600

1015 7/6/09 3.675 1039 7/13/09 . 1063 7/15/09 3.600

1016 7/7/09 3.600 1040 7/13/09 . 1064 7/15/09 .

1017 7/7/09 3.900 1041 7/13/09 . 1065 7/15/09 3.450

1018 7/7/09 3.750 1042 7/13/09 3.900 1066 7/16/09 3.975

1019 7/7/09 3.900 1043 7/13/09 3.450 1067 7/16/09 3.750

1020 7/7/09 3.750 1044 7/13/09 . 1068 7/16/09 3.525

1021 7/7/09 3.750 1045 7/14/09 3.675 1069 7/16/09 3.525

1022 7/7/09 3.900 1046 7/14/09 3.600 1070 7/16/09 3.975

1023 7/7/09 3.900 1047 7/14/09 3.600 1071 7/16/09 3.900

1024 7/7/09 . 1048 7/14/09 . 1072 7/16/09 3.750


103




1073 7/16/09 3.825 1097 8/29/09 3.825 1121 9/8/09 3.600

1074 7/16/09 . 1098 8/29/09 3.825 1122 5/28/10 3.900

1075 8/27/09 3.750 1099 8/29/09 3.750 1123 5/28/10 3.900

1076 8/27/09 3.750 1100 8/29/09 3.750 1124 5/28/10 3.825

1077 8/27/09 3.900 1101 8/29/09 3.825 1125 5/28/10 3.975

1078 8/27/09 3.825 1102 8/29/09 3.525 1126 5/28/10 3.750

1079 8/27/09 . 1103 8/29/09 3.750 1127 5/31/10 3.750

1080 8/27/09 3.750 1104 8/29/09 3.900 1128 5/31/10 3.825

1081 8/27/09 3.675 1105 8/29/09 3.975 1129 5/31/10 3.525

1082 8/27/09 3.600 1106 8/31/09 3.825 1130 5/31/10 3.825

1083 8/27/09 3.900 1107 8/31/09 3.825 1131 5/31/10 3.975

1084 8/27/09 3.750 1108 8/31/09 3.675 1132 5/31/10 3.900

1085 8/28/09 3.825 1109 8/31/09 3.600 1133 5/31/10 3.525

1086 8/28/09 3.750 1110 8/31/09 3.675 1134 5/31/10 3.900

1087 8/28/09 3.825 1111 8/31/09 3.825 1135 5/31/10 3.750

1088 8/28/09 3.750 1112 8/31/09 3.750 1136 6/1/10 3.525

1089 8/28/09 3.750 1113 8/31/09 3.825 1137 6/1/10 3.825

1090 8/28/09 3.750 1114 9/2/09 3.900 1138 6/1/10 3.975

1091 8/28/09 3.750 1115 9/2/09 3.675 1139 6/1/10 3.675

1092 8/28/09 3.675 1116 9/2/09 3.750 1140 6/1/10 3.600

1093 8/28/09 4.050 1117 9/2/09 3.900 1141 6/1/10 3.750

1094 8/28/09 3.900 1118 9/8/09 4.050 1142 6/1/10 3.600

1095 8/28/09 3.900 1119 9/8/09 3.975 1143 6/1/10 3.750

1096 8/29/09 3.450 1120 9/8/09 3.750 1144 6/1/10 3.600


104




1145 6/1/10 3.600 1169 6/2/10 3.825 1193 6/7/10 3.750

1146 6/1/10 3.675 1170 6/2/10 3.825 1194 6/7/10 3.750

1147 6/1/10 3.900 1171 6/2/10 3.825 1195 6/7/10 3.600

1148 6/1/10 3.750 1172 6/2/10 3.900 1196 6/7/10 3.675

1149 6/2/10 3.750 1173 6/2/10 3.900 1197 6/7/10 3.675

1150 6/2/10 3.825 1174 6/2/10 3.900 1198 6/7/10 3.900

1151 6/2/10 3.675 1175 6/2/10 3.825 1199 6/7/10 3.750

1152 6/2/10 3.750 1176 6/7/10 3.900 1200 6/7/10 3.675

1153 6/2/10 3.975 1177 6/7/10 3.450 1201 6/8/10 3.675

1154 6/2/10 3.675 1178 6/7/10 3.600 1202 6/8/10 3.675

1155 6/2/10 4.050 1179 6/7/10 3.675 1203 6/8/10 3.900

1156 6/2/10 4.050 1180 6/7/10 3.825 1204 6/8/10 3.900

1157 6/2/10 3.750 1181 6/7/10 3.600 1205 6/8/10 3.600

1158 6/2/10 4.050 1182 6/7/10 . 1206 6/8/10 3.375

1159 6/2/10 3.825 1183 6/7/10 3.675 1207 6/8/10 3.825

1160 6/2/10 3.675 1184 6/7/10 3.525 1208 6/8/10 3.675

1161 6/2/10 3.600 1185 6/7/10 3.900 1209 6/8/10 3.750

1162 6/2/10 3.825 1186 6/7/10 3.750 1210 6/8/10 3.300

1163 6/2/10 3.825 1187 6/7/10 3.900 1211 6/8/10 3.750

1164 6/2/10 3.825 1188 6/7/10 3.900 1212 6/8/10 3.675

1165 6/2/10 3.825 1189 6/7/10 3.525 1213 6/8/10 3.675

1166 6/2/10 . 1190 6/7/10 3.900 1214 6/8/10 3.675

1167 6/2/10 3.825 1191 6/7/10 4.050 1215 6/8/10 3.675

1168 6/2/10 3.825 1192 6/7/10 3.600 1216 6/8/10 3.675


105




1217 6/8/10 3.600 1241 6/9/10 3.675 1265 6/10/10 3.750

1218 6/8/10 3.900 1242 6/9/10 3.750 1266 6/10/10 3.600

1219 6/8/10 3.750 1243 6/9/10 3.750 1267 6/10/10 3.600

1220 6/8/10 3.750 1244 6/9/10 3.825 1268 6/10/10 3.525

1221 6/8/10 3.750 1245 6/9/10 3.525 1269 6/10/10 3.525

1222 6/8/10 3.450 1246 6/9/10 3.525 1270 6/10/10 3.525

1223 6/8/10 3.825 1247 6/9/10 3.825 1271 6/10/10 3.750

1224 6/8/10 4.050 1248 6/9/10 3.600 1272 6/10/10 3.450

1225 6/8/10 3.675 1249 6/9/10 3.750 1273 6/10/10 3.900

1226 6/8/10 3.600 1250 6/9/10 3.825 1274 6/10/10 .

1227 6/9/10 3.600 1251 6/9/10 3.750 1275 6/10/10 4.050

1228 6/9/10 3.600 1252 6/10/10 3.900 1276 6/10/10 .

1229 6/9/10 3.600 1253 6/10/10 3.750 1277 6/10/10 3.600

1230 6/9/10 3.825 1254 6/10/10 3.600 1278 6/10/10 3.675

1231 6/9/10 . 1255 6/10/10 3.600 1279 6/11/10 3.525

1232 6/9/10 3.750 1256 6/10/10 3.825 1280 6/11/10 3.675

1233 6/9/10 3.600 1257 6/10/10 3.750 1281 6/11/10 3.750

1234 6/9/10 3.825 1258 6/10/10 3.450 1282 6/11/10 3.900

1235 6/9/10 3.600 1259 6/10/10 3.675 1283 6/11/10 3.525

1236 6/9/10 3.975 1260 6/10/10 3.675 1284 6/11/10 3.750

1237 6/9/10 3.600 1261 6/10/10 3.675 1285 6/11/10 3.900

1238 6/9/10 3.750 1262 6/10/10 3.525 1286 6/11/10 3.600

1239 6/9/10 3.600 1263 6/10/10 3.675 1287 6/11/10 3.675

1240 6/9/10 3.825 1264 6/10/10 3.825 1288 6/11/10 3.675


106




1289 6/11/10 3.750 1313 6/12/10 3.750 1337 6/14/10 3.450

1290 6/11/10 . 1314 6/12/10 3.450 1338 6/14/10 3.825

1291 6/11/10 3.675 1315 6/12/10 3.600 1339 6/14/10 3.900

1292 6/11/10 3.600 1316 6/12/10 3.600 1340 6/14/10 4.125

1293 6/11/10 3.750 1317 6/12/10 3.825 1341 6/14/10 3.975

1294 6/11/10 3.600 1318 6/12/10 3.600 1342 6/14/10 3.675

1295 6/11/10 3.975 1319 6/12/10 3.600 1343 6/16/10 3.750

1296 6/11/10 3.825 1320 6/13/10 3.450 1344 6/16/10 3.450

1297 6/11/10 3.525 1321 6/13/10 3.825 1345 6/16/10 3.675

1298 6/11/10 3.450 1322 6/13/10 3.975 1346 6/16/10 3.600

1299 6/11/10 . 1323 6/13/10 3.600 1347 6/16/10 3.750

1300 6/11/10 3.750 1324 6/13/10 3.675 1348 6/16/10 3.600

1301 6/11/10 3.600 1325 6/13/10 3.750 1349 6/16/10 3.750

1302 6/11/10 3.600 1326 6/13/10 3.750 1350 6/16/10 4.050

1303 6/11/10 . 1327 6/13/10 3.600 1351 6/16/10 3.750

1304 6/11/10 . 1328 6/13/10 3.600 1352 6/16/10 .

1305 6/11/10 3.900 1329 6/13/10 3.900 1353 6/16/10 3.825

1306 6/12/10 3.750 1330 6/13/10 3.750 1354 6/16/10 3.525

1307 6/12/10 3.825 1331 6/13/10 3.750 1355 6/16/10 3.675

1308 6/12/10 3.750 1332 6/13/10 3.600 1356 6/16/10 3.600

1309 6/12/10 3.750 1333 6/13/10 3.525 1357 6/16/10 3.525

1310 6/12/10 3.450 1334 6/13/10 3.750 1358 6/16/10 3.750

1311 6/12/10 3.675 1335 6/13/10 3.900 1359 6/16/10 3.450

1312 6/12/10 3.975 1336 6/13/10 3.450 1360 6/16/10 3.675


107



ID# Caught HCW (mm) ID# Caught HCW (mm)

1361 6/16/10 3.600 1385 6/19/10 3.825

1362 6/18/10 3.450 1386 6/19/10 3.750

1363 6/18/10 3.675 1387 6/19/10 4.125

1364 6/18/10 3.825 1388 6/19/10 3.600

1365 6/18/10 . 1389 6/19/10 3.525

1366 6/18/10 3.675 1390 6/19/10 3.675

1367 6/18/10 3.525 1391 6/19/10 3.525

1368 6/18/10 3.450 1392 6/19/10 3.675

1369 6/18/10 3.750 1393 6/20/10 3.600

1370 6/18/10 3.600 1394 6/20/10 3.675

1371 6/18/10 4.125 1395 6/20/10 3.600

1372 6/18/10 4.200 1396 6/20/10 3.450

1373 6/18/10 3.525 1397 6/20/10 3.750

1374 6/18/10 3.525 1398 6/20/10 3.825

1375 6/18/10 3.525 1399 6/20/10 3.600

1376 6/18/10 3.750 1400 6/20/10 3.600

1377 6/18/10 3.600 1401 6/20/10 3.750

1378 6/18/10 3.675 1402 6/20/10 3.450

1379 6/18/10 3.525 1403 6/20/10 3.750

1380 6/18/10 4.050 1404 6/20/10 3.750

1381 6/19/10 3.750 1405 6/20/10 3.900

1382 6/19/10 3.750 1406 6/20/10 3.825

1383 6/19/10 3.750 1407 6/20/10 3.600

1384 6/19/10 3.750 1408 6/20/10 3.750


108

Distribution of Clutch Size

1-100 101-200 201-300 301-400 401-500 501-600 601-700 701-800 801-900 901-1000 1001-11000

10

20

30

40

50

60

50

23 22 23

1512

1 1 2 2

Clutch Size (number of eggs)

�u

mb

er o

f C

lutc

hes

FIGURE 3.1. Distribution of clutch size.


109

Distribution of Time to Hatch

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 340

10

20

30

40

50

60

10

50

12

2 1 1

40

Time to Hatch (days)

�u

mb

er o

f C

lutc

hes

FIGURE 3.2. Distribution of time to hatch.


110

Distribution of Hatch Duration

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 250

5

10

15

20

25

3

22

14

236

8

1110

5

14

4

1 1 1 1 1 1 12 2

Hatch Duration (days)

�u

mb

er o

f C

lutc

hes

FIGURE 3.3. Distribution of hatch duration.


111

Distribution of Hatch Success

0.01-10 10.01-20 20.01-30 30.01-40 40.01-50 50.01-60 60.01-70 70.01-80 80.01-90 90.01-1000

5

10

15

20

25

30

35

40

45

50

55

1

25

8

4048

27

221

Hatch Success (percent)

�u

mb

er o

f C

lutc

hes

FIGURE 3.4. Distribution of hatch success.


112

Distribution of Mean Egg Length

0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.80 0.81 0.82 0.830

5

10

15

20

14

1615

18

1 1 1 1

45

6

Mean Egg Length (mm)

�u

mb

er o

f C

lutc

hes

FIGURE 3.5. Distribution of mean egg length.


113

Distribution of HCW of Egg-Laying vs. �on-Egg-Laying Females

3.30 3.38 3.45 3.53 3.60 3.68 3.75 3.83 3.90 3.98 4.05 4.130.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.05

Non-Egg-Laying

Egg-Laying

0.03

0.08

0.04

0.20

0.16

0.13

0.100.11

0.10

0.030.05

0.13

0.17

0.22

0.29

0.030.030.010.01

HCW (mm)

Pro

po

rtio

n o

f F

ema

les

FIGURE 3.6. HCW of egg-laying vs. non-egg-laying females.


114

Correlation: HCW vs. Mean Egg Length

3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.10.70

0.72

0.74

0.76

0.78

0.80

0.82

0.84

HCW (mm)

Mea

n

Eg

g L

eng

th (

mm

)

FIGURE 3.7. Pearson correlation of HCW and mean egg length (r = 0.1076, p-value =

0.3420).


115

Correlation: HCW vs. Hatch Success

3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.10.0

0.2

0.4

0.6

0.8

1.0

HCW (mm)

Ha

tch

S

ucc

ess

(pro

po

rtio

n)

FIGURE 3.8. Pearson correlation of HCW and hatch success (r = -0.0315, p-value =

0.7440).


116

Correlation: HCW vs. Hatch Duration

3.4 3.5 3.6 3.7 3.8 3.9 4.0 4.10

5

10

15

20

25

HCW (mm)

Ha

tch

Du

rati

on

(d

ay

s)

FIGURE 3.9. Pearson correlation of HCW and hatch duration (r = 0.0310, p-value =

0.7469).


117

Correlation: �umber of Eggs vs. Mean Egg Length

0 100 200 300 400 500 600 700 800 900 1000 11000.70

0.72

0.74

0.76

0.78

0.80

0.82

0.84

�umber of Eggs

Mea

n

Eg

g L

eng

th (

mm

)

FIGURE 3.10. Pearson correlation for number of eggs and mean egg length (r = -0.0405,

p-value = 0.7198).


118

Correlation: Hatch Duration vs. Mean Egg Length

0 5 10 15 20 250.70

0.72

0.74

0.76

0.78

0.80

0.82

0.84


Mea

n

Eg

g L

eng

th (

mm

)

FIGURE 3.11. Spearman correlation of hatch duration and mean egg length (r = -0.0972,

p-value = 0.3971).


119

Correlation: �umber of Eggs vs. Hatch Success

0 100 200 300 400 500 600 700 800 900 1000 11000.0

0.2

0.4

0.6

0.8

1.0

�umber of Eggs

Ha

tch

S

ucc

ess

(pro

po

rtio

n)

FIGURE 3.12. Pearson correlation of number of eggs and hatch success (r = 0.0551, p-

value = 0.5606).


120

Correlation: Mean Egg Length vs. Hatch Success

0.70 0.72 0.74 0.76 0.78 0.80 0.82 0.840.0

0.2

0.4

0.6

0.8

1.0

Mean Egg Length (mm)

Ha

tch

Su

cces

s (p

orp

ort

ion

)

FIGURE 3.13. Pearson correlation of mean egg length and hatch success (r = 0.2423, p-

value = 0.0337).


121

Correlation: Hatch Duration vs. Hatch Success

0 5 10 15 20 250.0

0.2

0.4

0.6

0.8

1.0


Ha

tch

Su

cces

s (p

orp

ort

ion

)

FIGURE 3.14. Pearson correlation of hatch duration and hatch success (r = -0.1858,

p-value = 0.0478).

baker kelly thesis - texas tech university

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