multi-scale factors related to abundance of bats and insect prey in savannas, woodlands, and ...

177 Pages·2017·3.76 MB·English

MULTI-SCALE FACTORS RELATED TO ABUNDANCE OF BATS AND INSECT
PREY IN SAVANNAS, WOODLANDS, AND FORESTS IN THE OZARK
HIGHLANDS, USA
_______________________________________________________________________
A Dissertation
Presented to
The Faculty of the Graduate School
At the University of Missouri-Columbia
________________________________________________________________________
In Partial Fulfillment
Of the Requirements for the Degree
Doctor of Philosophy
________________________________________________________________________
by
KATHRYN MARIE WOMACK

Dr. Frank R. Thompson III, Dissertation Supervisor
MAY 2017
The undersigned, appointed by the dean of the Graduate School, have examined the
dissertation entitled
MULTI-SCALE FACTORS RELATED TO ABUNDANCE OF BATS AND INSECT
PREY IN SAVANNAS, WOODLANDS, AND FORESTS IN THE OZARK
HIGHLANDS, USA
Presented by Kathryn Marie Womack
a candidate for the degree of Doctor of Philosophy
and hereby certify that, in their opinion, it is worthy of acceptance.

____________________________________________
Professor Frank R. Thompson III

____________________________________________
Professor Matthew Gompper

____________________________________________
Dr. Sybill K. Amelon

____________________________________________
Professor Lori Eggert

_____________________________________________
Professor Rose-Marie Muzika
This would have not been possible without the love and support from my village. Special
thanks to Niki, Ed, Jaymi, and Cara.
ACKNOWLEDGEMENTS
I would like to thank the many individuals without whose efforts this study would
not have been possible. First, I would like to thank my advisor, Frank Thompson for all
his support, advice, and patience throughout the years. I would also like to extend a
special thanks to Sybill Amelon for taking a chance on me over a decade ago, and who
brought me to Missouri. I would like to my dissertation committee: Matt Gompper, Sybill
Amelon, Lori Eggert, and Rose-Marie Muzika. I would not have completed as many sites
without the help and willingness to run trap at sites for 3 nights from Sarah Bradley,
Nettie Sittingup, Linda Mills, Megan York-Harris, Clarissa Starbuck, Risa Wright, and
Sybill Amelon. In addition, I could not have completed this project without the ArcGIS
and statistical assistance from Bill Dijak and Jaymi LeBrun. Tom Bonnot and Julianna
Jenkins supported me tremendously in helping me trouble shoot errors in R code, and
provided an environment for me to increase my confidence in my statistical analysis
skills. I would like to thank my other lab mates: Liz Matseur and Melissa Roach for being
a support system, and providing encouragement during the final hours of the degree. I
could not have finished with as much grace without their encouragement. Lastly, I would
like to thank my family, both born with and chosen, for taking my 4 a.m. phone calls, and
believing in me when I did not believe in myself. I love you and I hope you feel that this
degree is shared among us. This research was funded by the USDA Forest Service
Northern Research Station.

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Table of Contents
ACKNOWLEDGEMENTS…………………………………………………...………...ii
LIST OF ILLUSTRATIONS…………………………………………………….….…vii
DESCRIPTION OF CHAPTERS………………………………………………..……xv
DISSERTATION ABSTRACT………………………………………………….…....xvi
CHAPTER 1: INTRODUCTION……………………………………………………….1
LITERATURE CITED ……………………………………………………..…………6
CHAPTER 2: PERFORMANCE OF HIERARCHICAL ABUNDANCE MODELS
ON SIMULATED BAT CAPTURE DATA…….……………………………………...8
ABSTRACT…………………………………………………………………………8
1 INTRODUCTION…………………………………………………………………...9
2 METHODS……………………………………………………………………….14
2.1 Model descriptions and assumptions………………………………..…...14
2.2 Data simulation……………………………………………….……..…...14
2.3 Model performance………………………………………………….…...17
3 RESULTS……………………………………………………………………..…17
3.1 Scenario 1………………………………..……………………………....17
3.2 Scenario 2…………………………………………….……..…………...18
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3.3 Scenario 3………………………………………………….………….....18
3.4 Scenario 4………………………………………………….………….....19
4 DISCUSSION…………………………………………………………………….19
LITERATURE CITED……………………………………………………………..…23
CHAPTER 3: RESTORATION AND HABITAT FACTORS RELATED TO
INSECT ABUNDANCE ACROSS A GRADIENT OF SAVANNAS, WOODLANDS,
AND NON-MANAGED FORESTS IN THE OZARK HIGHLANDS, USA…….….38
ABSTRACT………………………………………………………………..………..38
1 INTRODUCTION…………………………………………………………………..39
2 METHODS…………………………………………………………………..……45
2.1 Study areas……………………………………………………….…..45
2.2 Study design……………………………………………………….…46
2.3 Insect surveys………………………………………………………...47
2.4 Insect processing protocol…………………………………………...48
2.5 Vegetation surveys and covariates………………………………...…49
2.6 Environmental, management, and temporal covariates……………..49
2.7 Data analysis……………………………………………………...…50
3 RESULTS………………………………………………………………...……….52
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3.1 Active plots……………………………..…………………….……....52
3.2 Passive plots…………………………………………….………........54
3.3 Pitfall traps……………………………………….……………….....55
4 DISCUSSION……………………………………………………………………...55
LITERATURE CITED………………………………………………………….…….62
CHAPTER 4: BAT ABUNDANCE IN RELATIONSHIP TO HABITAT FACTORS
AT MULTIPLE SCALES ACROSS SAVANNAS, WOODLANDS, AND FORESTS
IN THE MISSOURI OZARKS…………………………………………………...……92
ABSTRACT………………………………………………………………………....92
1 INTRODUCTION…………………………………………………………………..93
2 METHODS………………………………………………………………………..98
2.1 Study areas..………………………………………………….………98
2.2 Experimental design……………………………………………...…..99
2.3 Mist net survey and bat capture protocol……………………..……100
2.4 Site scale measurements and covariates……………………………101
2.5 Patch scale covariates…………………………………………...…104
2.6 Landscape scale covariates……………………………………..….104
2.7 Data analysis…………………………………………….….............105
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3 RESULTS……………………………………………………………………..…106
3.1 Northern long-eared bats……………..………………………………...107
3.2 Tri-colored bats……………………………………….……..……..…...108
3.3 Evening bats…………………………………………….………...….....109
3.4 Eastern red bats…………………………………….………………......110
4 DISCUSSION……………………………………………………………….........111
5 MANAGEMENT IMPLICATIONS…………………………………………….........117
LITERATURE CITED………………………………………………………….…...119
CHAPTER 5: CONCLUSION…………………………………………………..……152
VITA………………………………………………………………………………...…156

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List of Illustrations
LIST OF TABLES
Chapter 2 Tables Page
1. Model scenarios and parameters used to generate simulated datasets to evaluate
the performance of n-mixture and removal models fit in the UNMARKED
package. We varied the number of sites, number of visits to a site, the known
population size (Ń) and the probability of detection (p) to create four
scenarios………………………………………………………………..……...…27

2. Mean estimated abundance (N), standard error (SE), relative bias (RB), mean
absolute error (MAE) and mean absolute percent error (MA%E) in abundance
estimates by the n-mixture and removal model from simulated data for different
numbers of sites and Ń=70, p=0.5, and 3 visits to a site………………...………29

3. Mean estimated abundance (N), standard error (SE), relative bias (RB), mean
absolute error (MAE) and mean absolute percent error (MA%E) in abundance
estimates by the n-mixture and removal model from simulated data for different
numbers of visits and Ń=70, p=0.5, and 80 sites………………………………...30

4. Mean estimated abundance (N), standard error (SE), relative bias (RB), mean
absolute error (MAE) and mean absolute percent error (MA%E) in abundance
estimates by the n-mixture and removal model from simulated data for different
known population sizes and detection probabilities and 3 visits to a site…...…...31

5. Mean estimated abundance (N), standard error (SE), relative bias (RB), mean
absolute error (MAE) and mean absolute percent error (MA%E) in abundance
estimates by the n-mixture and removal model from simulated when detection
probability changed from 0.5 to 0.1 after individuals first capture (p, p ) and
1
known population =70, number of sites=80, and 3 visits to a site……………….33

Chapter 3 Tables
1. Hypotheses and candidate models used for effects on insect response variables
based on passive and active plot surveys across a gradient of savannas,
woodlands, and non-managed forests in the Ozarks of Missouri, 2014-2016. Site
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was used as a random effect and year (YR) and Julian date (JUL) were included
as fixed effects in all models.…………………………………………………….66

2. Descriptions of model covariates used in generalized linear mixed models with a
negative binomial distribution to predict abundances of insects across a gradient
of actively managed savanna-woodlands and non-managed forests in the Ozark
Highlands of Missouri, 2014-2016. ………………………………………..…....77

3. Response variables used in generalized linear mixed models with a negative
binomial distribution examining the effects of restoration management, habitat,
and climate covariates on insect abundance across a gradient of savanna,
woodland, and forests in the Ozarks of Missouri, 2014-2016. Response variables
represent the mean captures at plots over the 3 survey days. We combined malaise
and panel trap yields for each plot for analysis. ……………………..…………..70

4. Descriptive statistics for covariates measured at plots where active methods were
used to sample insects in a study of relationships between insect abundances and
savanna woodland restoration in the Ozarks of Missouri, 2014-2016. ………….71

5. Descriptive statistics for covariates measured at plots where passive methods were
used to sample insects in a study of relationships between insect abundances and
savanna woodland restoration in the Ozarks of Missouri, 2014-2016..………….72

6. Support for generalized linear mixed models of different insect responses
including the number of model parameters (k), log likelihood (LogLik), Akaike’s
Information Criterion for small sample size (AICc), delta AICc (∆AICc), and
AICc weight (w). Models were fit to data from 179 active plots collected across
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a gradient of savanna, woodland, and forest in the Missouri Ozarks in summers,
2014-2016. ………………………………………………………………………73

7. Support for post hoc and a priori candidate generalized linear mixed models for
mean Tricopteran response variable including the number of model parameters
(k), log likelihood (LogLik), Akaike’s Information Criterion for small sample size
(AICc), delta AICc (∆AICc), and AICc weight (w). Models were fit to data from
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179 active plots collected across a gradient of savanna, woodland, and forest in
the Missouri Ozarks in summers, 2014-2016……..……………………………..76
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