University of Florida Thesis Or Dissertation

FORAGING ECOLOGY OF THE FLORIDA BONNETED BAT, Eumops floridanus

ELYSIA WEBB

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2018

To my family—especially to my grandparents, who have encouraged, molded, and inspired me more than they will ever know

ACKNOWLEDGMENTS

I owe an interminable amount of gratitude to a great deal of people. First, I would like to thank my adviser Dr. Holly Ober for offering me this position and investing her time into helping me grow as a scientist and a thinker. This project also would not have been possible without the assistance of Dr. Elizabeth Braun de Torrez. Liz was my role model and it meant so much that she was around to answer my questions, as well as show me the ropes in the field and in the lab. I’m grateful to Dr. Robert McCleery for allowing me space in his lab and giving me a community to fit myself into. I also could not have accomplished one of these chapters without the generosity of Dr. Jiri Hulcr, who gave me access to a lab and the supplies I needed.

This project would have been impossible without the generous support of Bat

Conservation International and Florida Fish and Wildlife Conservation Commission. I am indebted not only for financial resources, but for time spent, equipment lent, housing provided, and a host of other reasons. Brendan Myers, Troy Hershberger, and Rob

Aldredge of Avon Park Air Force Range were very helpful in providing access and assistance for my field work. Thank you for not letting me get blown up by active ordnance, be blinded by lasers, or get burned up in that one wildfire. I would also like to thank my field technician Shalana Gray. Her indomitable enthusiasm made field season

2017 one for the books; I could only wish that time had passed us more . . . Despacito.

I have had an amazing host of teachers from the very beginning of my educational career, but a few stand out—thank you Mrs. Ruetschle, Mrs. Schlegel, Mrs.

Dunn, Mrs. LeVesseur, Mr. Schumaker, Ms. Thiel, Mr. Revis, Mrs. Nartker, Mr.

Campbell, and Dr. Buckley for preparing me for this point. I could only hope that

someday all students have access to the same quality of education that I was privileged enough to have.

I would also like to thank my husband Ian. Ian has been my absolute rock since starting grad school. Many times, he believed in me when I no longer had faith in myself and encouraged me to keep pressing forward. I cannot imagine a more supportive partner.

Of course, none of this would have been possible without my family: my sister

Kayla who is the manifestation of the word “strength” and helps me be strong, too; my grandparents who instilled in me a love of hiking, camping, catching crawdads, and rolling rocks; and my mother. My mother has made incredible sacrifices for her children.

She lined the walls of our house with bookshelves and filled them all three rows deep.

She taught me to love reading and learning. Her belief in my greatness has never wavered, and she always pushed me to succeed. This is just as much her accomplishment as it is my own.

TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 8

LIST OF FIGURES ...... 9

LIST OF ABBREVIATIONS ...... 10

ABSTRACT ...... 11

CHAPTER

1 INTRODUCTION ...... 13

Background ...... 13 Objectives ...... 13

2 MOVEMENT PATTERNS AND HABITAT SELECTION OF Eumops floridanus ..... 15

Background ...... 14 Methods ...... 18 Movement Patterns ...... 21 Habitat Selection ...... 21 Results ...... 23 Movement Patterns ...... 23 Habitat Selection ...... 25 Discussion ...... 25 Movement Patterns ...... 25 Habitat Selection ...... 28 Management Implications ...... 30

3 DIET CHARACTERIZATION OF Eumops floridanus, WITH ANALYSIS OF SEASONAL AND GEOGRAPHIC VARIATION ...... 35

Background ...... 34 Methods ...... 37 Guano Collection ...... 37 Molecular Sequencing ...... 41 Data Analysis ...... 42 Results ...... 44 Diet Characterization ...... 44 Seasonal Variation ...... 45 Geographic Variation ...... 46 Discussion ...... 47

Diet Characterization ...... 47 Seasonal Variation ...... 50 Geographic Variation ...... 51 Management Implications ...... 52

4 CONCLUSIONS ...... 60

APPENDIX LIST OF UNIQUE PREY TAXA OF Eumops floridanus ...... 61

REFERENCE LIST...... 72

BIOGRAPHICAL SKETCH ...... 81

LIST OF TABLES

Table Page

2-1 Foray loop length and maximum distance from roost of E. floridanus at Babcock-Webb Wildlife Management Area, FL, by sex and season...... 31

2-2 Home range sizes of E. floridanus roosting at Babcock-Webb Wildlife Management Area, FL ...... 32

3-1 Number of guano samples collected from E. floridanus, by site and sex...... 54

3-2 Frequency of occurrence (FOQ) in percentage of insect families in the diet of E. floridanus...... 55

3-4 Insect orders and families consumed by Eumops spp...... 59

A-1 List of unique prey taxa of E. floridanus identified to order or higher specificity...... 62

LIST OF FIGURES

Figure Page

2-1 Foray loop length of E. floridanus roosting at Babcock-Webb Wildlife Management Area, FL, by month and sex ...... 31

2-2 Maximum distance from roost of E. floridanus roosting at Babcock-Webb Wildlife Management Area, FL, by month and sex ...... 32

2-3 Home ranges (95% MCP) of E. floridanus roosting at Babcock-Webb Wildlife Management Area, FL were smaller for males than females...... 33

2-4 Three-dimensional heat map of a female E. floridanus at BWWMA...... 33

2-5 Frequencies of each land class in observed and randomly generated foray loops of E. floridanus roosting in Babcock-Webb Wildlife Management Area, FL...... 34

3-1 Diagram of E. floridanus guano collection setup, showing (A) buckets, (B) ratchet strap, (C) funnels, bat houses, and the posts supporting the bat houses at BWWMA...... 53

3-2 Map of study sites in southern Florida for E. floridanus guano collection...... 54

3-3 Taxonomy of OTUs identified to family in the diet of E. floridanus...... 57

3-4 Box and whisker plot of number of pest taxa (as defined in the Appendix) detected per guano sample of E. floridanus...... 58

3-5 Prey accumulation curves comparing richness in the diet of E. floridanus at three locations: BWWMA, APAFR, and GMA...... 58

LIST OF ABBREVIATIONS

APAFR Avon Park Air Force Range—a military installation in Central Florida.

BWWMA Fred C. Babcock/Cecil M. Webb Wildlife Management Area—public lands near Punta Gorda, Florida managed by the Florida Fish and Wildlife Conservation Commission.

FWC Florida Fish and Wildlife Conservation Commission—the public agency responsible for managing wildlife in Florida

GMA Greater Miami Area—the metropolitan area associated with the city of Miami.

GPS Global Positioning System—a radionavigation system that triangulates location using satellites.

USFWS United States Fish and Wildlife Service—the public agency responsible for managing threatened and endangered species (as designated by the Endangered Species Act) in the United States.

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

FORAGING ECOLOGY OF THE FLORIDA BONNETED BAT, Eumops floridanus

Elysia Webb

December 2018

Chair: Holly Ober Major: Wildlife Ecology and Conservation

The Florida bonneted bat (Eumops floridanus) is an endangered species of free- tailed bat (Chiroptera: Molossidae) endemic to Florida. Little is known about its biology and ecology: specifically, movement patterns and diet have been recognized as research gaps. I studied bat movement with global positioning system (GPS) technology, determining that individuals are wide-ranging. Females generally have greater nightly movements than males, while individuals in December travel further than those in April or August. Management of this species should take into consideration these intersexual and seasonal differences. An analysis of habitat selection revealed use of agricultural areas such as pastures, row crops, and tree crops more than would be expected based on their relative abundances. My finding that bats roosting in natural areas foraged in agricultural lands located far distances away suggests that the species may require landscape mosaics containing multiple land classes. Characterization of diet using metabarcoding showed that E. floridanus frequently consumes moths

(Lepidoptera), with beetles (Coleoptera) and grasshoppers/crickets (Orthoptera) also consumed frequently. A year-long dataset showed that dietary niche breadth of E. floridanus is widest during the winter and spring. Niche overlap analysis among three

sites showed that diets are similar in composition of insect genera. Lastly, I identified a number of economically-important insect species in the diet of E. floridanus, including crop pests, which could provide a compelling reason for the general public to promote conservation of the species.

CHAPTER 1 INTRODUCTION

Background

The Florida bonneted bat (Eumops floridanus) is a neotropical free-tailed bat

(Chiroptera: Molossidae) endemic to Florida. It was listed as a federally endangered species in 2013 due to concerns over present and projected anthropogenic habitat destruction, sea level rise and increased storm severity due to climate change, and small intrinsic growth potential due to low fecundity and slow reproduction (USFWS

2013). The United States Fish and Wildlife Service (USFWS) highlighted the many aspects of the species’ biology and ecology that were unknown at the time of listing: foraging and dispersal distances; home range size; foraging habitat preference; and detailed diet information were specifically cited as knowledge gaps (USFWS 2013).

Objectives

In this study, I explored two aspects of the foraging ecology of E. floridanus, specifically movement and diet. Together these two topics provide insight into where bats exist on the landscape and why they select these locations.

Pertaining to movement, I sought to characterize E. floridanus movement across the landscape, including the total distance individuals travel in a single night and how far their travels take them from their roosts, which has important implications for assessing the whereabouts of potential roosts and management of foraging habitat surrounding roosts. I also attempted to characterize the home range size of E. floridanus. Next, I sought to understand the degree of variability of flight paths, by determining whether movement patterns or home range size differed according to season or sex.

Furthermore, I analyzed habitat selection of E. floridanus to determine which land use types were selected and which were avoided.

In terms of diet analysis, I had three objectives: Broadly, I wanted to describe the prey taxa consumed by E. floridanus, as no detailed published records exist. The second objective was to determine the extent of seasonal variation in diet. My third objective was to determine how diet varies among three populations of E. floridanus from contrasting parts of its range, as well as between individuals in urban and natural areas. Collectively, these analyses will increase knowledge of the composition and breadth of the diet of E. floridanus, which is important in understanding the species’ ecological niche.

CHAPTER 2 MOVEMENT PATTERNS AND HABITAT SELECTION OF Eumops floridanus

Background

Movement matters: animals move to avoid predators, find mates, seek shelter, and to acquire food resources necessary to fuel their biological functions (Dingle &

Drake 2007). Learning about animal movement is a crucial step in understanding each species’ relationship with the environment and other biota. Studying movement ecology, including habitat preference, also helps inform management decisions, such as promoting metapopulation connectivity or establishing critical habitat of threatened and endangered species (Cooke 2008).

The neotropical bat Eumops floridanus (Chiroptera: Molossidae), the Florida bonneted bat, is a rare species endemic to central and southern Florida (Bailey et al.

2017). In 2013, it was listed as federally endangered under the Endangered Species Act in response to current and predicted habitat destruction, small population size, and inadequate regulatory measures to halt its decline, among other factors (USFWS 2013).

In the early 1980s, the species was briefly believed extinct (Belwood 1992). As a high- flying molossid, the species was nearly impossible to capture with traditional capture techniques. No free-flying individuals were captured until 2006, despite extensive efforts

(Smith 2010, Braun de Torrez et al. 2017).

Fine-scale movement patterns of this species are unknown, yet an understanding of this is critical for the development of conservation and management strategies. The miniaturization of geolocation technology means that E. floridanus can now be tracked accurately without the use of airplanes to assess factors such as the distances individuals fly as well as movements of individuals in relation to landcover. Tracking

animals through the application of archival global positioning system transmitters (GPS transmitters) is more repeatable and accurate than the traditional approach to tracking bat movements: ground-based triangulation using very high frequency (VHF) radiotelemetry (Coelho et al. 2007). Notably, GPS units provide more accurate locational data than VHF radio telemetry because the former determines locations through satellites whereas the latter is prone to human error in recording bearings and is influenced by topography (Tomkiewicz et al. 2010). As the third largest bat in the United

States (Harvey et al. 2011) with an average weight of 43.8 g and 40.5 g for nonreproductive males and females, respectively, E. floridanus individuals are presumably large enough to carry 1.5 g GPS units with limited burden on their flight energetics.

Assessing movement patterns of E. floridanus is critical to the development of management and conservation plans. A better understanding of the species’ habitat selection could inform land acquisition decisions, improve management recommendations to enhance habitat quality, and provide guidance in designing mitigation strategies to conserve the species. Characterizing the species’ home range size and maximum distance traveled per night can inform decisions about the size of effective roost buffers required to minimize excessive disturbance to foraging habitat.

This information could also aid in the discovery of new roosts, since knowing the average maximum distance an individual flies in a night could help define the search radius for a roost after an acoustic detection of E. floridanus. Understanding the maximum nightly distance moved from roosts could also be useful in determining the likelihood of gene flow between populations, establishing roosting and foraging corridors

to promote genetic diversity, and encouraging the colonization of new, suitable habitat

(FWC 2013; USFWS 2013).

The goal of this study was to characterize movement patterns of E. floridanus to better understand habitat needs of the species, and ultimately inform management recommendations. Specific objectives were to quantify bat foray loop length, describe maximum displacement from roosts while foraging, calculate home range size, and determine habitat selection. Additionally, I examined differences in movement patterns between sexes and among seasons. I predicted that foraging distances and home ranges would be similar to values reported for other Eumops, known to travel over 20 km from their roosts in a night (Siders et al. 1999; Tibbitts et al. 2002). I predicted that male E. floridanus would have shorter foray loops than females, given their harem social structure and the need to defend territory and females from other males (Morrison and Morrison 1981; Ober et al. 2017a). I predicted that individuals would have seasonal variation in movement—in colder months, foraging bouts of both sexes would be shorter than in warmer months, as individuals would reduce their energetic expenditure during colder weather rather than over-invest in foraging effort for little reward (O’Donnell

2000). I also predicted that E. floridanus would use agricultural lands more than expected and developed lands less than expected, given their relative availability, based on landscape use patterns previously determined through occupancy modeling with acoustic data (Bailey et al. 2017). Lastly, I predicted that E. floridanus would use open water more than expected, given its relative availability, as the species has been acoustically detected flying over open water (Marks & Marks 2012) and individuals have

been captured over canals or near freshwater bodies (E. Webb unpublished data;

USFWS 2013).

Methods

I conducted this study at Babcock-Webb Wildlife Management Area (BWWMA) in

Charlotte County of southwest Florida (26.858573, -81.961591). The area receives

128.8 cm of annual precipitation (Climate Punta Gorda 2018). The vegetation communities of BWWMA include mesic and hydric pine flatwoods with a mosaic of freshwater marshes, ponds, and hardwood hammocks (Ober et al. 2017a). Eumops floridanus was first acoustically detected at BWWMA in 2006; from 2007-2012, 13 artificial bat roosts were erected for the species. These bat roosts consisted of paired one- or three-chambered houses atop a pole. Most of these artificial roosts have been used by E. floridanus at some point during the previous 10 years, though with varying consistency (USFWS 2013).

I used archival global positioning system (GPS) units (Pinpoint-50 and Pinpoint-

10, Lotek Wireless Inc., Ontario, Canada) to track fine-scale movements of E. floridanus. These GPS units were data loggers which do not transmit data, only store it.

Because I needed to recover each unit to obtain the data, I restricted data collection to

BWWMA, where more than 200 E. floridanus are individually marked with passive integrated transponder (PIT) tags and six artificial roosts have PIT tag readers

(Biomark, Boise, ID IS1001 antenna and data logger). I used information collected by the PIT tag readers to identify individuals with high roost fidelity, and therefore higher likelihood of recapture and GPS unit recovery. I only attached GPS units to individuals with ≥90% roost fidelity during the six weeks preceding capture. GPS units were attached only to adult males and adult non-pregnant, non-lactating females. Individuals

that met these criteria were selected so that GPS units weighed ≤ 5% of their body mass (44.0g and 30.0g for the PinPoint-50 units and PinPoint-10 units, respectively

(Aldridge and Brigham 1988)).

I sampled bats to collect foraging movement data during eight capture sessions between April 2015 and August 2017, as part of a collaborative, ongoing effort with

FWC. The sampling interval was every four months. To capture individuals as they emerged from their roost, I placed three or four triple-high stacked mist nets (Avinet,

Inc., Dryden, New York) around each occupied artificial bat house in a triangle or square configuration, using 2.4m x 9m or 2.4m x 12m mist nets. I continuously monitored the nets from sunset until three hours past sunset. I extracted each bat from the mist net and placed each in a cloth bag for processing. I recorded standard morphological measurements and inserted PIT tags (12mm, 134.2kHz FDXB tags,

Biomark Inc., Boise, ID) into untagged individuals to enable their unique identification. I categorized adults and sub-adults by the degree of fusion of the phalangeal cartilage

(Racey 1974). I determined reproductive status of males by testes length and the status of the individual’s gular-thoracic gland (Ober et al. 2017b) and classified female reproductive status as “non-reproductive,” “pregnant,” “lactating,” or “post-lactating.” I released all captured individuals at their site of capture within two hours to minimize stress on the animals.

I attached GPS units to selected individuals via collars, which I created from inelastic shoelace and held closed with absorbable medical-suture (Violet braided suture 4-0, Securos Surgical, Castle Cary, UK). Attachment via collars is preferable to direct attachment to bat bodies via surgical glue due to the tendency of molossids to

quickly remove transmitters glued directly to their skin (O'Mara, Wikelski and Dechmann

2014). In previous attempts, E. floridanus scratched at glued VHF transmitters until they were removed, removing skin and causing wounds (J. Gore pers. comm.). An additional benefit of the use of collars is that tracking devices attached to collars remain attached to bats longer than tracking devices applied with surgical glue (O'Mara et al. 2014). The suture eventually dissolves or breaks, allowing the collar and GPS unit to fall off if the bat cannot be recaptured. All bat capture and handling procedures were conducted in accordance with the guidelines for use of mammals in research set forth by the

American Society of Mammalogists (Sikes et al. 2011) and all methods were approved by the Institutional Animal Care and Use Committee at the University of Florida

(#201609497) and a permit from USFWS (#TE 23583B-2).

I programmed each GPS unit to begin recording data two nights after collar attachment to presumably allow the bats to adjust to the units and resume normal foraging behavior. Early on, I programmed each GPS unit to record a fix every 20 minutes from sunset to 0300, and later changed the inter-fix interval to 10 minutes. I selected the 0300 cutoff time because I determined that most E. floridanus return to their day roost by this time each morning, based on preliminary data from GPS units and data from PIT tag readers; this programming emphasized capturing the longer movements at the beginning of the night and conserving GPS battery to collect more nights of data (E. Braun de Torrez pers. comm.). The firmware of the GPS units was upgraded several times during the study, extending the battery life of the units and increasing the volume of data collected per individual as the study progressed. The firmware updates also allowed the collection of altitudinal data beginning in December

2016. The GPS units had enough battery life to collect data for 2-12 full nights of foraging, depending on firmware version, number of missed fixes, fix interval, and sunset time. I attempted to recapture each collared bat after the data collection period was complete to recover the GPS units and the data (recapture occurred median 9.5 nights after initial attachment).

I downloaded location data from each GPS unit recovered, using PinPoint v

2.11.1.4 software (Lotek Wireless Inc., Ontario, Canada). To identify outlier fixes highly likely to be erroneous, I calculated the centroid of all fixes and standardized the distances of the fixes from the centroid. I removed fixes that were >3 SD away from the centroid. In total, I removed 15 fixes (0.49% of the data). All analyses were conducted with statistical software R (v 3.3.2) with RStudio interface (v 1.0.136).

Movement Patterns

For each foray loop, I calculated two metrics: the maximum distance each bat traveled from its respective roost and the total path length. I then compared these two metrics between sexes and among seasons using a two-way ANOVA test coupled with

Tukey’s HSD tests. I square-root transformed maximum distance from roost and total path length to meet the assumption of homogeneity of variances. I also calculated the home range size of each bat using 95% minimum convex polygons (MCPs).

Habitat Selection

To analyze habitat selection, I used a random path selection function in which I compared observed foray loops to foray loops generated from parameters of observed paths and Brownian movement. I created the random foray loops using the simm.bb function from the R package “adehabitatLT,” which allowed me to specify a start and end fix for each random foray loop. Path selection functions are better suited for central-

place foragers than point selection functions, as point selection functions implicitly assume that the only factor influencing habitat selection is habitat type (Rosenberg and

McKelvey 1999). Path selection functions can account for the influence that the “central place” (in this instance, the bats’ roosts) has on habitat selection.

I created two criteria for both observed and randomly generated foray loops.

First, each foray loop had to both start and end at the roost. Thus, I manually added the roost’s coordinates as the first fix in a foray loop if a collared individual left its roosts before the nightly data collection period began. Likewise, if the unit’s battery died before the bat returned to its roost, I added the roost’s coordinates as the last fix in that foray loop. Second, I required each foray loop to be ≥1 km in length when rounded to the nearest 0.1 km to remove the “noisy” fixes around the roosts generated by the signal interference of a collared bat inside its roost.

I generated 100 random foray loops per bat. Like the observed foray loops, these random foray loops started and ended at the given roost. For each bat, the set of 100 random foray loops was created by randomly selecting one of the bat’s observed paths and creating a random path with a similar path length (±1 km) and the same number of fixes as that observed path. This selection-generation process was repeated until 100 random paths existed, each based on one of the bat’s observed paths.

I compared land class values between observed and random foray loops using landcover data from Florida Natural Areas Inventory’s (FNAI) Cooperative Land Cover map v3.2 with a 500 m resolution to account for GPS error. I condensed the original 44 land classes into 9 composite land classes: marine, open freshwater, unforested wetlands, forested wetlands, uplands crops and pastures (referred to hereafter as

‘agriculture’), tree farms and orchards (referred to hereafter as ‘tree crops’), flatwoods, and developed. I extracted land class values for observed and random foray loops using the extract function from the “raster” package.

I then compared the observed paths to the random paths to determine if E. floridanus uses habitats in proportion to their availability (proportional habitat use, or

PHU), assessed by comparing the proportion of fixes within each habitat to the proportion of random points within each habitat. I tested the hypothesis of PHU using the phuassess function from the “phu” package (Fattorini et al. 2017).

Results

Movement Patterns

I attached collars to 37 individuals. I recovered 26 of the GPS units via recapturing the collared individual or recovering the collar after the suture broke and the collar fell to the ground. Twenty units successfully recorded movement, resulting in

3053 initial fixes. An average of 5 foray loops were recorded per individual, with 100 foray loops in total across all bats.

There was a significant difference in foray loop length among months (F2,94 =

7.810, p < 0.01), as well as between sexes (F1,94 = 17.609, p < 0.01) (Table 2-1). In addition, there was a significant interaction between month and sex (F2,94 = 5.477, p <

0.01). Post hoc analyses using Tukey HSD tests showed that females had significantly longer foray loop lengths in December (푥̅ ± 푆퐷 = 45.8 ± 22.5푘푚) than males (푥̅ ± 푆퐷 =

19.9 ± 16.8푘푚)(p=0.0002), but there were no differences in foray loop lengths between sexes in April (p= 0.13) or August (p= 92) (Figure 2-1).

Similarly, I found a significant effect of sex (F1,94 = 10.975, p < 0.01) and month (F2,94

= 4.435, p = 0.014) on maximum distance bats traveled from their roosts (Figure 2-2,

Table 2-1). Additionally, there was a significant interaction between month and sex (F2,94

= 3.664, p =0.029). Post hoc analyses using Tukey HSD tests showed that individuals flew farther from their respective roosts in December than in August (p=0.012), though no other pairwise comparison was significant (Dec-Ap p=0.26; Ap-Aug p=0.56).

Females traveled greater distances from their roosts than males (p=0.0028, Table 2-1).

The greatest difference between sexes again occurred during December, when the mean of the maximum distances females were located from their roost (푥̅ ± 푆퐷 =

17.8 km ± 9.40) exceeded that of males (푥̅ ± 푆퐷 = 8.71 km ± 7.96) (p=0.0012).

Males and females also had significantly different home range sizes, with female home ranges significantly larger than males (F1,14 = 6.625, p=0.022, Table 2-2, Figure 2-

3). There was no significant interaction between sex and month (F2,14 = 0.849, p= 0.45).

Average female home range exceeded males in every season. Home range size varied significantly by month (F2,14 = 8.050, p= 0.0047). Home ranges in April and August did not significantly differ from each other (p=0.99), while home ranges in December were significantly larger than in both April (p=0.0059) and August (p=0.016).

I recorded altitude data from 10 individuals—7 in December 2016 and 3 in April

2017. All 7 individuals tracked during December obtained a maximum altitude of ≥300 m above the ground, while the individuals tracked during April obtained maximum altitudes of only 37.1, 50.4, and 186.3 m, respectively. The greatest altitude recorded for all 10 individuals was for a female in December, who reached a maximum of 545.5 m (Figure

2-4).

Habitat Selection

Eumops floridanus spent the greatest proportion of time in flatwoods (37.0%), agriculture lands (28.0%), and unforested wetlands (14.9%). However, the high usage of flatwoods and unforested wetlands was because the artificial roosts were located in a region dominated by these two land cover types so bats had to pass over these areas to and from their roost locations. When taking into account the central place foraging tendencies of the species, I found that E. floridanus uses agriculture lands (p=0.0004) and treecrops (p=0.0490) more than expected given their availability, with no preference for one of these over the other (p=0.1563) (Figure 2-5). Forested and unforested wetlands, developed lands, and freshwater were used in proportion to their availability in the landscape. Uplands, marine, and flatwoods were used less than expected, given their relative availabilities (p=0.0192, p=0.0386, and p<0.0001, respectively). Again, the comparison among avoided land cover types was not significant. In order of most- to least-preferred, the land classes were:

agriculture = tree crop > forested wetlands = unforested wetlands

= freshwater = developed > uplands = marine = flatwoods

Discussion

Movement Patterns

Attempts to determine movement patterns for bats in the genus Eumops are scarce due to the difficulty of capturing these high-flying bats (Braun de Torrez et al.

2017) and tracking their long-distance movements via VHF radio telemetry (Gore et al.

2015). Molossids, particularly Eumops, are among the fastest flying bats, which considerably complicates efforts to track them with VHF technology (Findley 1972;

McCracken 2016). The few records of movement patterns in other North American

Eumops species are consistent with the obtained values in this study; E. floridanus individuals were documented up to 39.0 km from their roosts in this study, which is similar to distances reported for other mastiff bats. The greater western mastiff bat, E. perotis is known to forage long distances from its roost, with two individuals tracked to roosts 28.0 and 29.1 km away from their respective capture sites (Siders et al. 1999).

Similarly, the mean 95% MCP home range reported in this study (145.6 km2 after tracking individuals 2-12 days) is comparable to movements of three Underwood’s mastiff bats (E. underwoodi) that had 95% MCP home ranges of 99.6, 160.4, and 473.7 km2 after tracking for up to two weeks in the Sonoran Desert (Tibbitts et al. 2002).

The differences between male and female travel patterns (path lengths, distances traveled from roosts, and home range sizes) may underscore life history differences in a species with polygynous social structure (Ober et al. 2017a). For females, nightly foraging could approximate a balance between seeking to maximize caloric intake while minimizing energy expenditure. Males presumably must balance foraging needs against the need to defend the harem from other males and advertise to females (Morrison and Morrison 1981), possibly resulting in the shorter distances from roosts I observed relative to females in this study. Alternatively, the difference between male and female movement patterns could be a result of dimorphic wing morphology.

Females have wing morphology that may be adapted to faster flight, so they may incur lower energetic cost to travel greater distances than males (Ober et al. 2017b). A larger sample size might reveal additional differences in movements between “dominant” males and “subordinate” males within harems, as defined by Ober et al. 2017a. While female movement could also vary based on reproductive status, analysis for this study

was not possible due to restrictions on disturbing pregnant or lactating females by attaching tracking devices.

The pattern of males foraging closer to their roosts than females has been observed in other species of bat that form harems (Morrison and Morrison 1981,

Fleming and Heithaus 1986). The long movements bats made in December relative to

April or August suggest possible resource scarcity near roost structures in December relative to other months (Vaughan and Vaughan 1986). For conservation and management, identifying what resources and prey items are most important to E. floridanus during this time of year and ensuring that these resources are not compromised may be critical to maintaining and growing E. floridanus populations.

Alternatively, the long foraging bouts observed in December could be a response to colder on-average temperatures, which suppress insect activity (O’Donnell 2000). In

Punta Gorda, Florida (the town closest to BWWMA), the mean lowest temperature in

December is 12.2° C, much lower than the mean lows of 16.1° C in April and 23.3° C in

August (Climate Punta Gorda 2018). Molossid species are intermediate in their ability to respond to cool temperatures; they have been described as a “transition” between tropical and temperate bat families (Wimsatt 2008). Eumops perotis, for example, never hibernates and uses daily torpor in the winter but not during the rest of the year (Leitner

1966). Based on data collected by PIT-tag readers on bat houses where E. floridanus roosted, these bats will sometimes fail to emerge from their roosts if the minimum daily temperature is below 13° C (J. Gore, unpublished data). Following cool nights when no foraging occurs, E. floridanus may need to forage more extensively (travel longer distances) on subsequent warm nights.

The altitude values I report are the first for this species and greatly exceed previous predictions of 9 m (Barbour and Davis 1969). Other molossids forage at high altitudes as well, with Tadarida brasiliensis commonly foraging 400-500 m above the ground (McCracken et al. 2008). In future acoustic surveys for E. floridanus, researchers should take note of how such great foraging heights may influence detection probability—at such altitudes, they would not be detected at all using traditional, ground-based acoustic detectors. Furthermore, conservation efforts must recognize that because the aerosphere is an important foraging resource for E. floridanus, the difficulties of managing this species are compounded, as the aerosphere suffers from tragedy of the commons due to diffuse ownership (Voigt et al. 2018).

Habitat Selection

I found that E. floridanus prefers agricultural lands including row crops, pasture, orchards, and tree farms. This is consistent with other studies demonstrating molossid selection for foraging over agriculture (Noer et al. 2012), as well as acoustic studies of

E. floridanus showing an increased occupancy probability as amount of agriculture increased (Bailey et al. 2017a). From a conservation perspective, it is encouraging that the species can make use of land impacted by humans, especially considering that agricultural land uses are prevalent throughout the species’ range. The species’ habitat selection patterns are another argument in favor of the preservation of agroecosystems in Southern Florida, which will face mounting pressure for development as the human population of Florida continues to grow. By 2060, 2.7 million acres of existing agricultural land in Florida is predicted to be urbanized, impacting preferred foraging habitat for E. floridanus (Zwick and Carr 2006).

Contrary to my prediction, E. floridanus did not avoid developed land. However, some molossid species are described as “urban exploiters” persistent in the face of urbanization because of their ability to roost in human structures, commute long distances to exploit fragmented resource patches, and forage despite artificial light pollution (Schoeman 2016). While developed lands were used in proportion to their availability, developed lands made up an average of only 2.9% of an individual’s foray loop. At the very least, urban areas are livable for E. floridanus, as demonstrated by their persistence in Miami, Florida (USFWS 2013).

The three land classes avoided by E. floridanus were uplands, marine, and, unexpectedly, flatwoods. Flatwoods is a dominant land class at BWWMA, and many of the known natural roosts for this species are in live pine trees or pine snags in flatwoods

(E. Webb unpublished data, E. Braun de Torrez unpublished data, Angell & Thompson

2015, Braun de Torrez et al. 2016). Flatwoods was the most-used land class, as the majority of roosts were within this land class and individuals flew over flatwoods en route to row crops, pasture, orchards, and tree farms, which perhaps provided more abundant or preferred prey items.

The avoidance of marine land classes (marine, coastal uplands, mangrove swamps, maritime hammocks, estuaries, tidal flats, and salt marshes), while statistically significant, may not be biologically significant. Eighteen individuals did not use marine land classes at all (marine land classes were only accessible to the colonies on the western end of BWWMA), while the two remaining individuals had relatively high usage frequencies of 13.7 and 14.3%, respectively. A larger sample size would help illuminate the extent to which E. floridanus uses marine land classes.

Management Implications

I found that E. floridanus move long distances each night, and the distance each bat moves varies according to season and sex. In general, females flew longer distances than males, and all bats moved longer distances during December than during April or August. I also found that bats use row crops, pasture, orchards, and tree farms more than expected, given their availability.

These observations on habitat selection and movement patterns can inform management of this species. Surveying agricultural lands within the geographic range of

E. floridanus for additional populations may prove fruitful. Management actions should be implemented at the landscape scale, as E. floridanus has wide-ranging movements that take bats far from their roosts. Additionally, it should be noted that this species may require multiple kinds of land classes to be successful. Many bat roosts are in “natural” flatwoods areas, even though my research shows that these bats preferentially forage elsewhere in agricultural land classes. Further studies should corroborate whether bats in other portions of the species geographic range also forage in agricultural lands, and examine what effect, if any, extensive foraging in agricultural lands has on this species.

Table 2-1. Foray loop length and maximum distance from roost of E. floridanus at Babcock-Webb Wildlife Management Area, FL, by sex and season. Mean path length ± sd Mean maximum distance Variable Group (km) from roost ± sd (km) Sex Male 20.26 ± 15.05 9.62 ± 6.97 Female 36.27 ± 23.35 14.32 ± 8.62 Season April 26.26 ± 21.22 11.31 ± 5.87 August 20.82 ± 13.16 9.29 ± 5.38 December 38.53 ± 23.96 15.28 ± 9.85

Figure 2-1. Foray loop length of E. floridanus roosting at Babcock-Webb Wildlife Management Area, FL, by month and sex. Vertical bars represent 95%CIs.

Figure 2-2. Maximum distance from roost of E. floridanus roosting at Babcock-Webb Wildlife Management Area, FL, by month and sex. Vertical bars represent 95%CIs.

Table 2-2. Home range sizes of E. floridanus roosting at Babcock-Webb Wildlife Management Area, FL, calculated according to 95% Minimum Convex Polygon (MCP), categorized by sex and month. Variable Group 95% MCP home range ± sd (km2) Male 50.2 ± 46.0 Sex Female 209.1 ± 160.4 April 65.1 ± 98.6 Season August 74.6 ± 60.0 December 260.2 ± 156.2

Figure 2-3. Home ranges (95% MCP) of E. floridanus roosting at Babcock-Webb Wildlife Management Area, FL were smaller for males than females.

Figure 2-4. Three-dimensional heat map of a female E. floridanus at Babcock-Webb Wildlife Management Area, FL in December 2016. Altitude is not to scale with latitude and longitude. This individual's roost is in the lower left corner of the figure.

Figure 2-5. Frequencies of each land class in observed and randomly generated foray loops of E. floridanus roosting in Babcock-Webb Wildlife Management Area, FL.

CHAPTER 3 DIET CHARACTERIZATION OF Eumops floridanus, WITH ANALYSIS OF SEASONAL AND GEOGRAPHIC VARIATION

Background

Flight has enabled bats to exploit many types of food resources, including fruit, nectar, vertebrates, and insects. Exploring the diet of insectivorous bats has been challenging, however, particularly in species with hawking foraging strategies. Such bats rapidly acquire and process prey items in flight at night, making direct observation infeasible (Melcón et al. 2007). Traditional techniques for analyzing bat diet includes sacrificing animals for gut content analysis or dissecting guano pellets, both of which rely on visual identification of chitinous insect fragments that have survived mastication and digestion (Belwood & Fenton 1976, Kunz et al. 1995). Manual dissection of prey remains in guano is prone to bias, as soft-bodied prey items are not well-represented, and subjective assessments of relative proportions of prey may not accurately reflect original proportions (Rabinowitz & Tuttle 1982). Additionally, prey identification is possible only at low taxonomic resolution, usually order or family (Razgour et al. 2011).

However, the molecular technique called metabarcoding has enabled higher taxonomic prey resolution from guano samples and avoids subjectivity of traditional approaches

(Zeale et al. 2011). Metabarcoding uses high-throughput sequencing (HTS) to sequence insect DNA on a next-generation sequencing platform.

Analyzing prey species of insectivorous bats is important as it enables identification of ecosystem services provided by bats, such as disease vector control and agricultural pest suppression (Kunz et al. 2011). Prey identification also enables characterization of the dietary niche of each bat species, making it possible to assess dietary niche breadth. Niche breadth, in turn, can be negatively correlated with

sensitivity to habitat disturbance (Swihart et al. 2003). Prey identification also allows detailed analyses of interseasonal, intersexual, and geographic variation of diet within and among species (Clare et al. 2014, Mata et al. 2016).

While metabarcoding of bat guano is a relatively recent development, it has already been used to assess the diets of several threatened and endangered taxa in the

United States, including the Ozark big-eared bat (Corynorhinus townsendii ingens),

Indiana bat (Myotis sodalis), northern long-eared bat (Myotis septentrionalis), and gray bat (Myotis grisescens) (Bussche et al. 2016, Divoll et al. 2018, Cravens et al. 2018).

Understanding the niche breadth of federally endangered species such as E. floridanus is important, as the species is at risk of extinction in part because of past and ongoing habitat loss (USFWS 2013).

Attempts to analyze the diet of the most recently designated endangered bat species, the Florida bonneted bat (Eumops floridanus), have been scarce. The lone published analysis is of a single 200 mL fecal sample collected from a felled roost. The results of the analysis was limited to one sentence—“By volume, Coleoptera (55%),

Diptera (15%), and Hemiptera (10%) were the main orders of insects identified”

(Belwood 1981). Additional investigation is clearly warranted, now that the species is designated as federally endangered (USFWS 2013).

Understanding the diet of E. floridanus is an important step in understanding the species’ ecological niche, which can help assess overall risk of extinction (Williams et al. 2006, Boyles et al. 2007). Determining diet variation among populations of the species can help determine if it is rigid or flexible in selecting prey species (see

Maibeche et al. 2015). Also, identifying ecosystem services provided by E. floridanus

could provide an important tool in promoting conservation of these bats among the general public. Many people believe that bats are threatening and frightening, and are unaware of the services bats provide that benefit humans (Kunz et al. 2011).

The objectives of this study were to: 1) provide a detailed record of prey species consumed by E. floridanus; 2) describe seasonal variation of diet within a single population of bats by examining niche breadth and overlap across an entire year; 3) describe the geographic variation of diet from three bat populations distributed throughout the species’ range by examining niche breadth and overlap within a two- month period; and 4) examine E. floridanus diet relative to other members of its genus.

Methods

Guano Collection

I analyzed seasonal variation in diet by collecting guano from a population of E. floridanus at Fred C. Babcock / Cecil M. Webb Wildlife Management Area (BWWMA) in

Charlotte County of southwest Florida (26.858573, -81.961591). The area receives

(USFWS 2013). To date, 13 roosts have been constructed, the majority of which consist of a paired set of houses atop a single pole (FWC 2013). Since April 2014, mist-netting has occurred around each roost every four months to capture individuals as they emerge at sunset (Ober et al. 2017a). All individuals captured in these multi-day survey efforts were implanted with a passive integrated transponder (12 mm, 134.2 kHz FDXB

pit-tags, Biomark Inc., Boise, ID, USA) to enable unique identification. Many roosts have been outfitted with pit-tag readers, which register when individuals enter or exit the roost

(Biomark Boise, ID IS1001 antenna and data logger) (Bailey et al. 2017a). I examined data from pit-tag readers the day prior to each data collection session to determine the number of bats roosting in each house.

I collected guano monthly during a 12-month period extending from November

2016 to October 2017, ensuring that the sampling interval was 3-5 weeks. During each sampling trip, I attempted to collect guano from the six houses used by the greatest number of bats. Colony size within each of the houses varied throughout the sampling period; four of the houses had consistent colonies of ≥10 individuals during the sampling period and were sampled each of the twelve months. In addition, I sampled whichever additional two houses contained the greatest number of bats.

During these sampling trips, I deployed collection containers within a window of time from two hours before sunset until two hours after sunset, and collected them between two hours before sunrise and two hours after sunrise. This timing allowed me to collect guano shortly after its excretion, minimizing DNA degradation due to weathering and solar exposure. I hung two metal, 10-cm diameter hooked buckets

(Figure 3-1, A) onto a ratchet strap (Figure 3-1, B) encircling the post of each house. I also used wire to attach mesh funnels (Figure 3-1, C) to the buckets to increase the surface area of collection. If only one of the paired houses was occupied, I placed both buckets underneath the occupied house. Each bucket had small holes drilled in the bottom to allow the flow-through of condensation or precipitation. I lined the buckets with coffee filters to prevent guano loss through the holes. Each collecting apparatus

was mounted at least 1.5 m from the ground to minimize the likelihood of disturbance of buckets from terrestrial animals.

In the morning, I removed the buckets and used isopropyl alcohol-cleaned, flame-sterilized tweezers to transfer five guano pellets to a vial of 95% ethanol

(Bohmann et al. 2011, Nsubuga et al. 2004). If there was sufficient guano, I collected a second, backup sample from each roost. Between 24 and 48 hours after collection, I transferred the guano from the ethanol to vials with dehydrating silica beads (Silica Gel

Desiccant, Indicating, 4-10 Mesh, Grade 48, Fisher Scientific) and stored the samples at

-20° C. Mesh funnels and coffee filters were replaced for each sampling trip. Buckets were cleaned in a 10% sodium hypochlorite solution and rinsed with water before reuse to destroy residual DNA (Prince & Andrus 1992).

To sample geographic variation in diet, I actively collected guano from free-flying individuals in two regions outside of BWWMA (Figure 3-2). I sampled two study regions.

The first, Avon Park Air Force Range (APAFR), is a fire-maintained pine flatwood system at the northernmost extent of the species’ range. The second is the Greater

Miami Area (GMA), which is a large metropolitan area at the southernmost extent of the species’ range. To minimize the conflation of geographic and seasonal diet variation, I conducted all sampling for this objective within an 8-week period from May to July 2017.

I placed two to four sets of 7.3‐m triple-high mist net poles (Bat Conservation and

Management, Carlisle, PA, USA) at each netting site, using 9-m or 12-m mist nets (38‐ mm mesh, Avinet, Freeville, NY, USA). I used an acoustic lure (BatLures; Apodemus

Field Equipment, Netherlands) to attract E. floridanus to the mist nets by broadcasting pre-recorded conspecific social calls from a roost in Florida Panther National Wildlife

Refuge (Braun de Torrez et al. 2017). I netted for bats from sunset to 0200, weather permitting, checking nets every 10 minutes. I placed all captured E. floridanus in clean cloth bags until processing. During processing, I reported standard morphological measurements as well as age, sex, and reproductive status, and I documented pelage patterns, which are often unique to individuals in this species (L. Smith pers. comm.).

Recaptured individuals—identified by wing punch holes—were matched with previous capture ID based on these unique pelage patterns. I retained bats up to two hours post- capture to facilitate guano collection. I stored guano as outlined in the previous section on seasonal variation in diet. Bat capture and handling followed American Society of

Mammalogists guidelines for research on live animals (Sikes et al. 2016) and permits from the University of Florida Institutional Animal Care and Use Committee (no.

201609497) and USFWS (no. TE 23583B).

At APAFR, I had 17 captures of 13 unique individuals: two subadult females were recaptured once, and one subadult male was recaptured twice. I obtained 14 guano samples, with two recaptured individuals each contributing two samples. All E. floridanus captured at APAFR were in mesic pine land types near freshwater. At GMA, I had 11 captures of 11 unique individuals, obtaining guano samples from 9 individuals.

These individuals were captured at the Granada Golf Course in Coral Gables, Kendall

Indian Hammocks County Park in Kendall, and Zoo Miami in South Miami Heights. In addition, a male E. floridanus was observed using a bat house at Zoo Miami during the data collection period, allowing for passive guano collection by staking a plastic sheet underneath the bat house overnight; this provided a tenth guano sample from GMA.

Lastly, I used the guano samples passively collected at BWWMA from May-July

2017 (as described in the previous section on seasonal variation) for geographic comparison of a third site located centrally within the geographic range of the species.

Because I collected guano passively at BWWMA, sex of the individual who produced each guano pellet was unknown (Table 3-1).

I attempted to sequence prey from 94 guano samples—70 from BWWMA for the seasonal variation analysis (and part of the geographic variation analysis), 14 from

APAFR, and 10 from GMA.

Molecular Sequencing

I utilized high-throughput sequencing (HTS) to determine prey species from guano. I purified DNA from all guano samples using DNeasy PowerSoil Kits (QIAGEN,

Hilden, Germany), following manufacturer’s instructions with a modification of the first incubation step from 5 minutes to 1 hour (Brown et al. 2015). I included a negative control in each group of samples processed. I then used two different sets of primer pairs to amplify the target region of the CO1 gene: ZBJ-ArtF1c/ZBJ-ArtR2 (“ZBJ”) (Zeale et al. 2011), and LCO1-1490/CO1-CFMRa (“ANML”) (Jusino et al. 2017). I used ZBJ because it has been used more frequently in HTS studies of bat guano (Bohmann 2011,

Alberdi et al. 2012, Clare et al. 2014, Rolfe et al. 2014), and also used ANML because it was shown to have greater efficacy than ZBJ in analyzing a mock community of insect taxa (Jusino et al. 2017).

For both ZBJ and ANML, I used a two-step polymerase chain reaction (PCR).

The first PCR consisted of creating a 25 μL reaction of 0.5 μL molecular-grade water,

12.5 μL KAPA HiFi HotStart ReadyMix (Kapa Biosystems, Wilmington, MA, USA), 1 μL of BSA (diluted at 10 mg/mL) (Sigma-Aldrich, St. Louis, MO, USA), 0.5 μL of forward

and reverse primer, and 10 μL of purified fecal DNA. Thermocycler parameters for the

PCR consisted of denaturation at 95° C for 5 min; 25 cycles of: 98° C for 20 s, 52° C for

30 s, and 72° C for 1 min; and then 72° C for 7 min.

Following the first PCR, the products were cleaned of unincorporated nucleotides with Agencourt AMPure XP beads (Beckman Coulter, Brea, CA, USA) as outlined by

Illumina’s 16S Metagenomic Sequencing Library Preparation, with the substitution of molecular-grade water for Tris pH 8.5 and 25 μL of XP beads per reaction. I ran a negative control with each batch of 24 samples.

In the second PCR, I added unique combinations of multiplex identifier (MID) tags to each sample, with a 45 μL reaction consisting of 15.5 μL molecular-grade water,

25 μL HotStart ReadyMix, 1.5 μL BSA, 1.5 μL each of forward and reverse MID tags, and 5 μL post-cleanup product. Thermocycler parameters for the second PCR consisted of denaturation at 95° C for 5 min; 12 cycles of: 98° C for 20 s, 71° C for 30 s, and 72° C for 1 min; and then 72° C for 7 min. I verified amplification success by running a subset of samples on a 1% agarose gel. Again, I used Agencourt AMPure XP beads to purify the PCR product. I quantified DNA concentration and then pooled samples in an equimolar amount and sent them for processing on an Illumina MiSeq sequencing platform, which identifies more operational taxonomic units (OTUs) than Ion Torrent

PGM; Illumina MiSeq recovers additional taxa, especially Coleoptera spp. (Divoll et al.

2018).

Data Analysis

I used custom Python scripts to unzip the fastq files, and then used cutadapt to remove the primers and adapters (Martin 2011). I removed sequences with a Phred score <25 (Divoll et al. 2018). I used PEAR (Paired-End reAd merger) to merge

overlapping paired-end reads. I then discarded sequences with <175 bp. I collapsed sequences with 100% similarity and used those as seeds to collapse the remaining sequences into operational taxonomic units (OTUs) based on 98.5% similarity. I then discarded OTUs with <5 total reads across all samples.

I performed taxonomic assignment of OTUs with statistical software R (v 3.3.2) as in Divoll et al. 2018. OTUs were cross-referenced with the Barcode of Life Database

(BOLD) (BOLD version 3, January 2017, >4 M specimens; Ratnasingham & Hebert,

2007) via the “bold” package. For each OTU, I obtained the top 40 matches from BOLD;

I then filtered the matches to only retain taxa from local geographic regions (the United

States, Canada, Mexico, the Dominican Republic, Jamaica, and Cuba). I filtered further by discarding matches <98%, resulting in potential matches with 2bp or less variation from a reference taxon in BOLD.

I manually vetted the remaining list of taxa to determine which occur in Florida

(Heppner 2007). I assigned taxonomic ID based on the remaining taxa which were geographically feasible. For OTUs that matched to two or more plausible taxa, I assigned ID based on the taxonomic group with the lowest possible resolution that the

IDs had in common—genus, family, or order.

Molecular data obtained from HTS of guano samples is not appropriate to determine relative proportions of prey consumed, so such estimates were not attempted. I measured seasonal and geographic dietary variation of prey species richness by creating binary matrices of the insect genera present in each sample. I then created distance matrices based on the binary matrices to use a perMANOVA (function

“adonis”, package vegan) to test the hypotheses of no significant difference of diet

among months (seasonal variation) or among sites (geographic variation). If I found no seasonal variation among months, that would suggest that E. floridanus has rigid dietary needs that do not respond to shifting prey availability or life history events of the species such as reproduction. Conversely, significant variation among months would indicate a more generalist prey selection strategy likely influenced by shifting prey availability or response to seasonal changes in dietary needs of individuals. Similarly, no significant difference among sites would indicate dietary specialization of the entire species on certain prey taxa, whereas significant differences among sites would indicate more flexible selection.

I rendered the family-level diversity of prey OTUs with a multi-level pie chart

(Ondov et al. 2011). I assessed niche breadth during each of the four seasons and at each of the three sites, as well as niche overlap among the three sites, using the indicspecies package (De Cáceres et al. 2011). Finally, I calculated accumulation curves to determine if estimates of dietary richness approached an asymptote in each site used to assess geographic variation in diet, by randomizing the samples 100 times.

This allowed me to compare dietary richness among the three sites, despite differences in sampling intensity. I also calculated the number of “unseen” species in each of the three sites using the Chao equation (function “specpool”, package vegan) (Chao 1987).

Results

Diet Characterization

Of the 94 samples collected, 92 samples matched to at least one OTU, with the remaining two samples failing to sequence. I obtained 24 million raw sequences, from which 10,244 OTUs from the ZBJ primers and 5,818 OTUs from the ANML primers met the quality and minimum number of reads requirements and matched to a sample in the

Bold database. Lepidoptera was the most prevalent insect order when considering frequency of occurrence, with OTUs identified in 100% of samples. In descending order of frequency of occurrence, other insect orders identified were: Coleoptera (97.8%),

Orthoptera (96.7%), Diptera (64.1%), Ephemeroptera (35.9%), Hemiptera (22.8%),

Blattodea (14.1%), Hymenoptera (8.7%), Dermaptera (6.5%), Neuroptera (4.3%), and

Odonata (1.1%). The families with the highest frequencies of occurrence were both

Lepidoptera: Erebidae (100%) and Noctuidae (100%) (Table 3-2).

The importance of Lepidoptera in E. floridanus diet was even more apparent when considering the number of OTUs. Of the 6,007 OTUs identified to at least family, the vast majority were Lepidopteran (87%; Figure 3-3). Within Lepidoptera, Noctuidae comprised 27% of the total OTUs, while Erebidae comprised 22%. Coleoptera and

Orthoptera represented only 7% and 4% of total OTUs, respectively, while

Ephemeroptera and Diptera were each 0.3%.

I documented consumption of a number of economically important prey species

(Appendix A), including the fall armyworm (Spodoptera frugiperda), lesser cornstalk borer (Elasmopalpus lignosellus), tobacco hornworm (Manduca sexta), and the black cutworm (Agrotis ipsilon). Pests were frequently consumed, with 100% of samples containing one or more pest species. Each guano sample contained a mean of 9.6 pest species, with up to 17 pest taxa represented in a single sample (Figure 3-4).

Seasonal Variation

The PERMANOVA showed that a seasonal grouping of spring (March, April,

May), summer (June, July, August), fall (September, October, November), and winter

(December, January, February) was significant based on insect genera occurrence (p <

0.001). Using these seasonal designations, I found that E. floridanus had the greatest

niche breadth in the winter (0.491), followed by spring (0.490), summer (0.487), and fall

(0.483). The greatest niche overlap was between winter and spring (0.779), while the smallest overlap was between fall and spring (0.701). Other overlaps were as follows: fall-summer (0.743); fall-winter (0.735); winter-summer (0.724); and spring-summer

(0.767).

Geographic Variation

The PERMANOVA showed that the a priori sample groupings of prey genera based on geographic region was significant (p < 0.001). Niche breadths at the three sites were similar. The E. floridanus at BWWMA had the largest niche breadth (0.500), exceeding APAFR (0.487) and GMA (0.487). The pairwise niche overlaps of the three sites were similar: The greatest was between APAFR and GMA (0.769), while that between BWWMA and GMA was the smallest (0.722). The niche overlap of APAFR and

BWWMA was intermediate, at 0.750.

None of the prey accumulation curves reached an asymptote, indicating our sampling of geographic variation among bat populations was not adequate to fully characterize bat diets in these locations (Figure 3-5). The curves show that bat diets at

BWWMA had the highest expected prey richness of the three regions, and accumulated prey genera faster than the other two regions. Overall, bats at BWWMA consumed the greatest number of insect genera (182 detected in 18 samples). I detected 139 insect genera in 14 guano samples at APAFR and 137 genera in 10 samples at GMA. An estimation of missing, undetected prey taxa predicted that APAFR had the greatest number of undetected taxa (53) compared to BWWMA (39) and GMA (33).

Discussion

Diet Characterization

Moths were the most prevalent component of the diet of E. floridanus, with

Lepidoptera identified in 100% of samples. Eumops floridanus consumed a variety of moths, with 28 different families identified. These results contrast sharply with the lone published record of E. floridanus diet, which did not include Lepidoptera (Belwood

1981). Coleoptera and Orthoptera were the second- and third-most frequent orders, with prevalence of 97.8% and 96.7%, respectively. Diptera, Ephemeroptera, Hemiptera, and Blattodea were consumed at intermediate frequencies (14% - 65%). Hymenoptera,

Dermaptera, Neuroptera, and Odonata were negligible, with each identified in less than

10% of all samples. It is worth noting that some taxa identified (such as Ephemeroptera:

Ephemeridae, Diptera: Culicidae, and Hemiptera:Aphididae) may not have been consumed directly by bats, but instead could be relict DNA present in the digestive tracts of predaceous insects consumed by E. floridanus. For example, members of

Coleoptera: Dytiscidae and Coleoptera: Hydrophilidae, predaceous beetles, prey on mosquito larvae and other small, aquatic insects; these predaceous beetles were highly prevalent, and thus could influence the results with their diets (Wilson 1923, Shaalan et al. 2009, Cobbaert et al. 2010).

Eumops floridanus consumed over two dozen economically important insect species, particularly moths, including those that affect row crops, ornamental plants, tree crops, and even apiculture (Heppner 2007). Each of the guano samples contained one or more pest taxa, with the average sample containing nearly 10 different pest species. This demonstrates that E. floridanus regularly consumes insects of economic importance. Garnering public support is important in the conservation of endangered

species, yet animals such as bats often lack public support due to fear (Knight 2008).

Knowledge of the ecosystem services of endangered species such as Eumops floridanus may help build the public’s willingness to support their conservation.

In comparing the diet of E. floridanus to other Eumops spp., it is important to note that all other records of Eumops diet were based on visual identification of insect fragments in guano or stomach contents, and were thus at the resolution of order or family rather than genus or species (Ross 1961, Ross 1967, Easterla & Whitaker 1972,

Bowles et al. 1990; Table 3-4). Lepidoptera has been shown to be an important component of the diet of other Eumops species. A study on the diet of E. perotis determined that Lepidoptera was the most prevalent prey order (100%), with a volume of 79.9% (Easterla & Whitaker 1972). Similar conclusions were reached for E. bonariensis in Mexico, where Lepidoptera represented 55% of the diet, and, interestingly, Coleoptera was the second-most common prey order (Bowles et al. 1990).

Although I could not directly calculate the relative volume of prey consumed for E. floridanus, I also concluded that Lepidoptera and Coleoptera were important components of E. Floridanus diet, as these were the orders with the highest frequency of occurrences, and also the orders with the majority of OTUs. In contrast, a second study of E. perotis estimated that Lepidoptera only composed 10% of that species’ diet, and that Hymenopterans composed 58% of its diet by volume (Ross 1961). In the present study of E. floridanus, few Hymenoptera OTUs were recovered—order prevalence was only 8.7%. It is unlikely that my methodology failed to identify

Hymenopteran DNA that was present, because although the ZBJ primer set is known to

have an amplification bias against Hymenoptera DNA, the ANML set is effective at amplifying it (Jusino et al. 2017).

Previous research that reported diets of other Eumops species to the resolution of insect family indicates some similarity with diet composition of E. floridanus. Five other Eumops species (E. perotis, E. bonariensis, E. glaucinus, E. hansae, and E. underwoodi) reportedly consume some of the same Coleopteran families as E. floridanus (Carabidae, Chrysomelidae, Curculionidae, Hydrophilidae, and

Scarabaeidae), as well as a Lepidopteran family (Sphingidae), and Orthopteran families

(Acrididae and Gryllidae) (Ross 1961, Ross 1967, Easterla & Whitaker 1972, Bowles et al. 1990, Anderson 1997, Aguirre et al. 2003; Table 3-4).

Bats and moths are engaged in an evolutionary arms race, leading to the evolution of ears in moths, which allows them to hear and potentially evade echolocating bats (Hristov & Conner 2005). The allotonic frequency hypothesis predicts that incidence of eared insects should be highest in the diet of bats whose echolocation calls are dominated by frequencies outside the 20–60 kHz range (Schoeman & Jacobs

2003). Large molossids such as Eumops spp. with their low echolocation frequencies are predicted to have greater access to tympanate prey than many other bat species

(Mora & Torres 2008). The low-frequency echolocation calls of E. floridanus (10-25 kHz;

Braun de Torrez et al. 2016) may enable them to overcome the defenses of moths from tympanate prey families, which includes prey families documented in this study such as

Noctuidae, Geometridae, Pyralidae, Notodontidae, and Erebidae: Arctiinae (Fenton &

Fullard 1979). The use of low frequency echolocation calls reduces the amount of time that a tympanate moth has to escape. Another way that bat species can reduce the

interval between initial prey detection and capture is through fast flight (Fenton & Fullard

1979). Eumops floridanus has a high aspect ratio, high wing loading, and long wingtips, which enables rapid flight (Norberg & Rayner 1987, Ober et al. 2017b). By echolocating at frequencies below eared moths’ optimum range of sensitivity and possessing morphological adaptations for rapid flight, E. floridanus can exploit these moths as a food resource despite their ability to hear the bats (Fenton & Fullard 1979).

Seasonal Variation

The composition of the four seasonal groupings of E. floridanus diet were significantly different, showing that bat diet varies seasonally. Eumops floridanus had a broader dietary niche breadth in the winter and spring than in the summer and fall. The increase in dietary breadth in the cooler months could be a response to increased inter- and intraspecific competition for limited resources, causing E. floridanus to be more opportunistic in selecting prey (Heng et al. 2018). As insect prey becomes more abundant in the warmer months, E. floridanus can be more selective, leading to the narrower niche breadths observed in the summer and fall. This is similar to two

Mormoopid bats in Mexico (Pteronotus davyi and P. personatus) that increased their dietary breadth during the dry season, which is when prey is presumably scarce

(Salinas‐Ramos et al. 2015), but in contrast to the seasonal variation of dietary breadth of Mystacina tuberculate, which was greater in the summer than the winter (Czenze et al. 2018).

The difference in diet throughout the year illustrates the need to move beyond examining species’ ecological needs exclusively during the summer “maternity season”

(Weller et al. 2009). The ecological needs of bat species are not static throughout each

year, but rather shift in response to resource abundance and life history changes

(Salinas-Ramos et al. 2015, Czenze et al. 2018).

Geographic Variation

The diet of E. floridanus varied geographically, as shown through comparisons of niche breadth and overlap. Niche breadth at BWWMA was slightly larger than that at

APAFR and GMA (0.500 compared to the latter two at 0.487). Although this could be due to the smaller number of samples available from GMA and APAFR (n = 10 and 14, respectively) relative to BWWMA (n = 18), the shape of the accumulation curves and the lack of overlap in confidence intervals associated with larger sample sizes suggest these patterns would hold even if larger sample sizes were available.

Niche overlap values of insect genera occurrence was lowest between GMA and

BWWMA, and greatest between GMA and APAFR. This was unexpected, as BWWMA and APAFR are both surrounded by natural areas dominated by pine flatwoods and would thus be expected to have more similar prey availability than the metropolitan

GMA which has little natural land remaining. Also, BWWMA is slightly closer to APAFR

(103 km) than GMA (128 km). These unanticipated results suggest that comparisons of diet breadth and overlap using data collected using two different methods may have been problematic. Each sample collected at GMA and AFAFR represented the diet of a single animal, whereas each sample collected at BWWMA (consisting of 5 pellets collected in bulk beneath bat houses) likely represented the diet of multiple individuals.

Future diet research should use the same data collection technique when assessing variation among sites.

Management Implications

The relatively high degree of niche overlap among the three sites suggests that populations of E. floridanus prey on a similar variety of insect taxa across the species’ geographic range. Eumops floridanus could be adapted to pursue certain insect species or genera, and preferentially consume those even if other taxa are more available.

Future studies should examine E. floridanus prey selection relative to insect availability at different sites to determine how closely variation in E. floridanus tracks changes in prey availability.

Eumops floridanus likely faces competition for prey from the other Molossid species confirmed to exist in sympatry with it: Tadarida brasiliensis. Like E. floridanus,

T. brasiliensis is a frequent consumer of moths (McWilliams 2005) that flies at high altitudes. Tadarida spp. also echolocate with low frequencies and fly swiftly to minimize the response time available to typanate prey (Rydell & Arlettaz 1994). However, E. floridanus is more than three times larger than T. brasiliensis (males: 40.7 g vs 11.7 g; females: 40.5 g vs. 12.9 g) (Wilkins 1989, Ober et al. 2017b). This larger body translates to a larger skull and jaw size as well. It is possible that the niches of these two species separate based on prey size, with E. floridanus accessing larger moths than T. brasiliensis.

Eumops floridanus provides ecosystem services to humans by consuming agricultural pests. Insectivorous bats exert a top-down control on arthropods, therefore limiting herbivory by insects (Kalka et al. 2008). Future efforts would be well-placed in attempting to quantify the economic impact of E. floridanus via crops saved from damage and pesticide applications made unnecessary.

C A

Figure 3-1. Diagram of E. floridanus guano collection setup, showing (A) buckets, (B) ratchet strap, (C) funnels, bat houses (at top), and the posts supporting the bat houses, at BWWMA.

Figure 3-2. Map of study sites in southern Florida for E. floridanus guano collection.

Table 3-1. Number of guano samples collected from E. floridanus, categorized by location and sex. Location Female Male Total APAFR 8 6 14 GMA 3 7 10 BWWMA Unknown Unknown 18

Table 3-2. Frequency of occurrence (FOQ) in percentage of insect families in the diet of E. floridanus.

Lepidoptera FOQ Diptera FOQ Coleoptera FOQ Hemiptera FOQ Orthoptera FOQ Erebidae 100 Sciaridae 32.6 Dytiscidae 75.0 Aphididae 7.6 Gryllidae 89.1 Noctuidae 100 Culicidae 25.0 Scarabaeidae 67.4 Cercopidae 5.4 Acrididae 32.6 Crambidae 98.9 Chironomidae 8.7 Carabidae 58.7 Pentatomidae 4.3 Gryllotalpidae 9.8 Geometridae 92.4 Empididae 7.6 Hydrophilidae 56.5 Coreidae 2.2 Tettigoniidae 2.2 Tortricidae 72.8 Tipulidae 7.6 Cerambycidae 35.9 Cydnidae 2.2 Gelechiidae 70.7 Faniidae 6.5 Silphidae 30.4 Rhyparochromidae 1.1 Blastobasidae 69.6 Cecidomyiidae 5.4 Curculionidae 7.6 Depressariidae 69.6 Drosophilidae 5.4 Chrysomelidae 1.1 Pyralidae 67.4 Rhagionidae 5.4 Cicindelidae 1.1 Gracillariidae 56.5 Anthomyiidae 3.3 Ptilodactylidae 1.1 Sphingidae 33.7 Psychodidae 3.3 Coleophoridae 25.0 Tabanidae 3.3 Euteliidae 23.4 Dolichopodidae 1.1 Bombycidae 18.5 Phoridae 1.1 Limacodidae 15.2 Syrphidae 1.1 Saturniidae 15.2 Tachinidae 1.1 Uraniidae 14.1 Yponomeutidae 13.0 Glyphipterigidae 9.8 Pterophoridae 8.7 Scythrididae 8.7 Bucculatricidae 7.6 Oecophoridae 6.5 Nolidae 5.4 Elachistidae 4.3 Lasiocampidae 4.3 Nepticulidae 4.3 Mimallonidae 2.2

Table 3-2. Continued.

Hymenoptera FOQ Ephemeroptera FOQ Blattodea FOQ Neuroptera FOQ Odonata FOQ Braconidae 5.4 Ephemeridae 34.8 Blaberidae 13.0 Chrysopidae 4.3 Libellulidae 1.1 Vespidae 3.3 Caenidae 1.1 Ectobiidae 1.1 Apidae 1.1

Figure 3-3. Taxonomy of OTUs identified to family (n=6007) in the diet of E. floridanus across all 92 samples (collected at BWWMA once per month from November 2016 to October 2017, and at APAFR and GMA during summer 2017).

Figure 3-4. Box and whisker plot of number showing number of pest taxa (as defined in Appendix A) detected per guano sample of E. floridanus across all sites.

Figure 3-5. Prey accumulation curves comparing richness in the diet of E. floridanus at three locations: BWWMA, APAFR, and GMA.

Table 3-4. Insect orders and families consumed by Eumops spp. (only overlapping families are listed for E. floridanus).

Order Family Eumops Eumops Eumops Eumops Eumops Eumops perotis1,2,3 bonariensis4 glaucinus6 hansae5 underwoodi2 floridanus7 Coleoptera Carabidae X X Cerambycidae X X Chrysomelidae X X Curculionidae X X Hydrophilidae X X Scarabaeidae X X X X Tenebrionidae X Hemiptera X X X X Cicadellidae X Cicadidae X Fulgoridae X X Lygaeidae X Miridae X Hymenoptera X X Halictidae X Formicidae X Megachilidae X Apidae X X Lepidoptera X X X X X Sphingidae X X Odonata X X Orthoptera X X X X X Acrididae X X X Gryllidae X X X X Tettigoniidae X X 1Ross 1961; 2Ross 1967; 3Easterla & Whitaker 1972; 4Bowles 1990; 5Anderson 1997; 6Aguierre et al. 2003; 7this study

CHAPTER 4 CONCLUSIONS

Eumops floridanus forage widely, highlighting the need for landscape-scale management. Based on investigations of nightly movement patterns a single population of bats (at BWWMA), I found that females travel farther and have larger home ranges than males. Management of this species must take these intersexual differences into consideration. As females are a limiting factor in population growth, management recommendations should provide for females as an umbrella for males rather than averaging movement patterns of both sexes.

I found that bats roosting in BWWMA travel farther distances in December than in April or August. Also, their diet varies seasonally, with the greatest dietary niche breadth occurring in winter. Taken together, the GPS movement and metabarcoding diet data suggest that bats may be forced to travel further during colder months to acquire necessary resources and become more generalist in prey selection at this time.

This contrasted to the warmer summer and fall months, when prey is presumably abundant enough that E. floridanus can be more selective in only consuming preferred prey items closer to roosts. Eumops floridanus may therefore be more susceptible to disruptions in its insect food supply in the winter than in the summer.

If the wide-ranging movements of E. floridanus in December and wider dietary niche breadths in December are truly indicative of resource scarcity, then the winter months likely cause added physiological stress to individuals as they try to meet their caloric needs. This stress of resource scarcity should give pause to the current recommendation that winter is the best time to exclude E. floridanus from human houses in situations where the bats are causing human-wildlife conflict.

Habitat selection analysis showed significant selection for foraging over agricultural areas, including row crops and tree crops. The suitability of these land classes for foraging is supported by the presence of known crop pests in the diet of bats at not only BWWMA, but also the other two sites, APAFR and GMA. Uplands, marine, and pine flatwoods land classes were all used less than expected by bats from

BWWMA, given their relative occurrence on the landscape: these results can help shape future survey efforts to identify occurrence of this species in other regions.

Surveying agricultural landscapes near suitable roosting habitat could be more effective than surveying contiguous blocks of more “natural” land classes.

Eumops floridanus diet is marked with an abundance of moths, though beetles and crickets had high frequencies of occurrence as well. The proclivity of E. floridanus to consume moths is supported by the allotonic hypothesis: given its low-frequency echolocation and morphological adaptations for speed, it is well-adapted to the pursuit of moths. Given its similarity with Tadarida brasiliensis in terms of foraging style, echolocation characteristics, and wing morphology, future studies to determine the extent of partitioning between the two species could identify ways to help the rare and endangered E. floridanus out-compete the widespread and abundant T. brasiliensis.

APPENDIX UNIQUE PREY TAXA OF Eumops floridanus

Table A-1. List of unique prey taxa of E. floridanus identified to order or higher specificity. Pest species are indicated. No. Order Family Subfamily, genus, or species Pest 1 Blattodea Blaberidae Pycnoscelus surinamensis x 2 Blattodea Ectobiidae Blatella germanica x 3 Coleoptera Carabidae Calosoma 4 Coleoptera Carabidae Calosoma sayi

5 Coleoptera Carabidae Cicindela 6 Coleoptera Carabidae Pterostichus ebeninus

7 Coleoptera Carabidae Selenophorus ellipticus 8 Coleoptera Cerambycidae Acanthocinus nodosus

9 Coleoptera Cerambycidae Acanthocinus obsoletus 10 Coleoptera Cerambycidae Monochamus titillator

11 Coleoptera Chrysomelidae Chrysomela scripta 12 Coleoptera Curculionidae Hylobius pales

13 Coleoptera Dytiscidae Copelatus chevrolati 14 Coleoptera Dytiscidae Cybister fimbriolatus 15 Coleoptera Dytiscidae Thermonectus

16 Coleoptera Hydrophilidae Hydrophilus ovatus 17 Coleoptera Hydrophilidae Hydrophilus triangularis 18 Coleoptera Ptilodactylidae Ptilodactyla serricollis 19 Coleoptera Scarabaeidae Dyscinetus morator x 20 Coleoptera Scarabaeidae Phyllophaga latifrons 21 Coleoptera Scarabaeidae Strategus splendens 22 Coleoptera Silphidae Necrodes surinamensis 23 Dermaptera 24 Diptera Anthomyiidae 25 Diptera Cecidomyiidae 26 Diptera Chironomidae Orthocladiinae 27 Diptera Chironomidae Tanytarsus

28 Diptera Culicidae Coquillettidia perturbans 29 Diptera Culicidae Culex 30 Diptera Culicidae Culex nigripalpus 31 Diptera Culicidae Psorophora ciliata 32 Diptera Culicidae Psorophora columbiae 33 Diptera Dolichopodidae Chrysotus 34 Diptera Drosophilidae 35 Diptera Empididae Rhamphomyia 36 Diptera Faniidae Fannia

Table A-1. Continued No. Order Family Subfamily, genus, or species Pest 37 Diptera Limoniidae 38 Diptera Phoridae Megaselia 39 Diptera Psychodidae Psychoda alternata 40 Diptera Rhagionidae 41 Diptera Sciaridae Corynoptera 42 Diptera Syrphidae Sphaerophoria 43 Diptera Tabanidae Hybomitra 44 Diptera Tabanidae Tabanus atratus 45 Diptera Tachinidae Lespesia aletiae 46 Diptera Tipulidae Tipula furca 47 Diptera Tipulidae Tipula tricolor

48 Ephemeroptera Caenidae Caenis amica 49 Ephemeroptera Ephemeridae Hexagenia limbata 50 Hemiptera Aphididae Aphis craccivora 51 Hemiptera Aphididae Aphis gossypii 52 Hemiptera Cercopidae Prosapia bicincta 53 Hemiptera Coreidae 54 Hemiptera Cydnidae Pangaeus bilineatus 55 Hemiptera Pentatomidae Loxa 56 Hemiptera Pentatomidae Nezara viridula 57 Hemiptera Rhyparochromidae Ozophora 58 Hymenoptera Apidae Apis mellifera 59 Hymenoptera Braconidae Neothlipsis

60 Hymenoptera Vespidae Polistes fuscatus 61 Lepidoptera Blastobasidae Holcocera chalcofrontella 62 Lepidoptera Blastobasidae Pigritia laticapitella 63 Lepidoptera Bombycidae Apatelodes torrefacta 64 Lepidoptera Bucculatricidae Bucculatrix staintonella 65 Lepidoptera Coleophoridae Coleophora ladonia 66 Lepidoptera Crambidae Anageshna primordialis

67 Lepidoptera Crambidae Argyria lacteella 68 Lepidoptera Crambidae Carectocultus perstrialis 69 Lepidoptera Crambidae Compacta 70 Lepidoptera Crambidae Condylorrhiza vestigialis 71 Lepidoptera Crambidae Crambus girardellus 72 Lepidoptera Crambidae Crambus praefectellus

73 Lepidoptera Crambidae Crambus quinquareatus 74 Lepidoptera Crambidae Crambus satrapellus

Table A-1. Continued No. Order Family Subfamily, genus, or species Pest 75 Lepidoptera Crambidae Desmia maculalis 76 Lepidoptera Crambidae Desmia ploralis 77 Lepidoptera Crambidae Elophila icciusalis 78 Lepidoptera Crambidae Eudonia strigalis 79 Lepidoptera Crambidae Fissicrambus profanellus 80 Lepidoptera Crambidae Herpetogramma phaeopteralis x 81 Lepidoptera Crambidae Herpetogramma thestealis 82 Lepidoptera Crambidae Microtheoris ophionalis 83 Lepidoptera Crambidae Neodactria murellus

84 Lepidoptera Crambidae Neodactria zeellus 85 Lepidoptera Crambidae Niphograpta albiguttalis 86 Lepidoptera Crambidae Nomophila nearctica x 87 Lepidoptera Crambidae Omiodes 88 Lepidoptera Crambidae Palpita persimils 89 Lepidoptera Crambidae Parapoynx allionealis x 90 Lepidoptera Crambidae Penestola bufalis 91 Lepidoptera Crambidae Pilocrocis ramentalis 92 Lepidoptera Crambidae Pyrausta tyralis 93 Lepidoptera Crambidae Raphiptera argillaceellus 94 Lepidoptera Crambidae Samea ecclesialis 95 Lepidoptera Crambidae Samea multiplicalis 96 Lepidoptera Crambidae Udea rubigalis 97 Lepidoptera Crambidae Urola nivalis 98 Lepidoptera Depressariidae Agonopterix

99 Lepidoptera Depressariidae Antaeotricha decorosella 100 Lepidoptera Depressariidae Psilocorsis cryptolechiella 101 Lepidoptera Depressariidae Psilocorsis quercicella 102 Lepidoptera Depressariidae Psilocorsis reflexella 103 Lepidoptera Elachistidae 104 Lepidoptera Erebidae Anomis erosa x 105 Lepidoptera Erebidae Apantesis phalerata 106 Lepidoptera Erebidae Bleptina inferior 107 Lepidoptera Erebidae Catocala aestivalia 108 Lepidoptera Erebidae Catocala neogama 109 Lepidoptera Erebidae Chytolita morbidalis 110 Lepidoptera Erebidae Cisseps fulvicollis

111 Lepidoptera Erebidae Clemensia albata 112 Lepidoptera Erebidae Coenipeta bibitrix

Table A-2. Continued No. Order Family Subfamily, genus, or species Pest 113 Lepidoptera Erebidae Composia fidelissima

114 Lepidoptera Erebidae Crambidia pallida 115 Lepidoptera Erebidae Dasychira tephra

116 Lepidoptera Erebidae Doryodes bistrialis 117 Lepidoptera Erebidae Ephyrodes cacata 118 Lepidoptera Erebidae Estigmene acrea x 119 Lepidoptera Erebidae Eublemma cinnamomea 120 Lepidoptera Erebidae Eupseudosoma involute 121 Lepidoptera Erebidae Grammia doris 122 Lepidoptera Erebidae Halysidota tessellaris 123 Lepidoptera Erebidae Hypenula cacuminalis 124 Lepidoptera Erebidae Hypercompe scribonia 125 Lepidoptera Erebidae Hyphantria cunea x 126 Lepidoptera Erebidae Hypoprepia fucosa 127 Lepidoptera Erebidae Idia americalis 128 Lepidoptera Erebidae Idia lubricalis 129 Lepidoptera Erebidae Janseodes melanospila 130 Lepidoptera Erebidae Lascoria orneodalis 131 Lepidoptera Erebidae Ledaea perditalis

132 Lepidoptera Erebidae Lesmone formularis 133 Lepidoptera Erebidae Leucanopsis longa

134 Lepidoptera Erebidae Macrochilo louisiana 135 Lepidoptera Erebidae Macrochilo orciferalis

136 Lepidoptera Erebidae Melipotis fasciolaris 137 Lepidoptera Erebidae Melipotis januaris

138 Lepidoptera Erebidae Melipotis jucunda 139 Lepidoptera Erebidae Metalectra richardsi

140 Lepidoptera Erebidae Metallata absumens 141 Lepidoptera Erebidae Metria amella 142 Lepidoptera Erebidae Mocis cubana 143 Lepidoptera Erebidae Mocis disseverans

144 Lepidoptera Erebidae Mocis latipes 145 Lepidoptera Erebidae Mocis marcida 146 Lepidoptera Erebidae Ommatochila 147 Lepidoptera Erebidae Palthis asopialis 148 Lepidoptera Erebidae Physula albipunctilla 149 Lepidoptera Erebidae Pseudanthracia coracias 150 Lepidoptera Erebidae Pyrrharctia isabella x

Table A-1. Continued No. Order Family Subfamily, genus, or species Pest 151 Lepidoptera Erebidae Renia adspergillus 152 Lepidoptera Erebidae Renia discoloralis 153 Lepidoptera Erebidae Renia flavipunctalis

154 Lepidoptera Erebidae Renia salusalis 155 Lepidoptera Erebidae Schrankia macula

156 Lepidoptera Erebidae Selenisa sueroides 157 Lepidoptera Erebidae Simplicia cornicalis 158 Lepidoptera Erebidae Spilosoma congrua 159 Lepidoptera Erebidae Spilosoma dubia 160 Lepidoptera Erebidae Spilosoma virginica x

161 Lepidoptera Erebidae Syllectra erycata 162 Lepidoptera Erebidae Tetanolita mynesalis 163 Lepidoptera Erebidae Zale lunata

164 Lepidoptera Erebidae Zanclognatha laevigata 165 Lepidoptera Euteliidae Marathyssa inficita

166 Lepidoptera Gelechiidae Chionodes 167 Lepidoptera Gelechiidae Metzneria lappella 168 Lepidoptera Gelechiidae Monochroa 169 Lepidoptera Gelechiidae Pseudotelphusa 170 Lepidoptera Gelechiidae Telphusa longifasciella 171 Lepidoptera Geometridae Cabera variolaria 172 Lepidoptera Geometridae Costaconvexa centrostrigaria 173 Lepidoptera Geometridae Cyclophora myrtaria

174 Lepidoptera Geometridae Ectropis crepuscularia 175 Lepidoptera Geometridae Eubaphe meridiana 176 Lepidoptera Geometridae Heterophleps triguttaria 177 Lepidoptera Geometridae Iridopsis pergracilis x 178 Lepidoptera Geometridae Iridopsis vellivolata 179 Lepidoptera Geometridae Lambdina fiscellaria 180 Lepidoptera Geometridae Lomographa vestaliata 181 Lepidoptera Geometridae Macaria 182 Lepidoptera Geometridae Melanophia signataria 183 Lepidoptera Geometridae Nemoria lixaria 184 Lepidoptera Geometridae Orthonama obstipata 185 Lepidoptera Geometridae Oxydia 186 Lepidoptera Geometridae Patalene 187 Lepidoptera Geometridae Phrygionis 188 Lepidoptera Geometridae Pleuroprucha asthenaria

Table A-1. Continued No. Order Family Subfamily, genus, or species Pest 189 Lepidoptera Geometridae Scopula lautaria 190 Lepidoptera Geometridae Sphacelodes vulneraria 191 Lepidoptera Geometridae Synchlora frondaria 192 Lepidoptera Geometridae Synchlora xysteraria 193 Lepidoptera Geometridae Trigrammia quadrinotaria 194 Lepidoptera Glyphipterigidae Glyphipterix

195 Lepidoptera Gracillariidae Parornix 196 Lepidoptera Lasiocampidae Artace cribrarius 197 Lepidoptera Noctuidae Malacosoma disstria x 198 Lepidoptera Limacodidae Tortricidia 199 Lepidoptera Mimallonidae Cicinnus melsheimeri 200 Lepidoptera Nepticulicidae 201 Lepidoptera Noctuidae Acronicta brumosa 202 Lepidoptera Noctuidae Acronicta grisea x 203 Lepidoptera Noctuidae Acronicta increta 204 Lepidoptera Noctuidae Acronicta insularis 205 Lepidoptera Noctuidae Acronicta longa 206 Lepidoptera Noctuidae Acronicta noctivaga 207 Lepidoptera Noctuidae Acronicta oblinita x 208 Lepidoptera Noctuidae Agrotis apicalis 209 Lepidoptera Noctuidae Agrotis gladiaria x 210 Lepidoptera Noctuidae Agrotis ipsilon x 211 Lepidoptera Noctuidae Agrotis venerabilis x 212 Lepidoptera Noctuidae Allagrapha aerea 213 Lepidoptera Noctuidae Anarta trifolii 214 Lepidoptera Noctuidae Anicla infecta x 215 Lepidoptera Noctuidae Anterastria teratophora 216 Lepidoptera Noctuidae Argyrogramma verruca 217 Lepidoptera Noctuidae Argyrostrotis quadrifilaris

218 Lepidoptera Noctuidae Bagisara rectifascia 219 Lepidoptera Noctuidae Bagisara repanda 220 Lepidoptera Noctuidae Bellura 221 Lepidoptera Noctuidae Bellura brehmei 222 Lepidoptera Noctuidae Bellura densa x 223 Lepidoptera Noctuidae Bellura obliqua 224 Lepidoptera Noctuidae Callopistria floridensis x 225 Lepidoptera Noctuidae Callopistria mollissima 226 Lepidoptera Noctuidae Capsula oblonga

Table A-1. Continued No. Order Family Subfamily, genus, or species Pest 227 Lepidoptera Noctuidae Chloridea subflexa 228 Lepidoptera Noctuidae Chloridea virescens 229 Lepidoptera Noctuidae Chrysodeixis includens x

230 Lepidoptera Noctuidae Cirrhophanus pretiosa 231 Lepidoptera Noctuidae Condica concisa 232 Lepidoptera Noctuidae Condica confederata

233 Lepidoptera Noctuidae Condica cupentia 234 Lepidoptera Noctuidae Condica mobilis 235 Lepidoptera Noctuidae Condica sutor

236 Lepidoptera Noctuidae Condica videns 237 Lepidoptera Noctuidae Elaphria chalcedonia 238 Lepidoptera Noctuidae Elaphria deltoides

239 Lepidoptera Noctuidae Elaphria exesa 240 Lepidoptera Noctuidae Elaphria nucicolora 241 Lepidoptera Noctuidae Emarginea percara

242 Lepidoptera Noctuidae Enigmogramma antillea 243 Lepidoptera Noctuidae Enigmogramma basigera 244 Lepidoptera Noctuidae Epidromia pannosa 245 Lepidoptera Noctuidae Epidromia rotundata 246 Lepidoptera Noctuidae Eudryas grata 247 Lepidoptera Noctuidae Euxoa tessellata 248 Lepidoptera Noctuidae Feltia floridensis 249 Lepidoptera Noctuidae Feltia jaculifera x 250 Lepidoptera Noctuidae Feltia subterranea x 251 Lepidoptera Noctuidae Feralia major 252 Lepidoptera Noctuidae Heliocheilus paradoxus 253 Lepidoptera Noctuidae Homophoberia

254 Lepidoptera Noctuidae Hypena baltimoralis 255 Lepidoptera Noctuidae Iodopepla u-album 256 Lepidoptera Noctuidae Lacinipolia laudabilis

257 Lepidoptera Noctuidae Leucania extincta 258 Lepidoptera Noctuidae Leucania incognita

259 Lepidoptera Noctuidae Leucania pilipalpis 260 Lepidoptera Noctuidae Leucania scirpicola

261 Lepidoptera Noctuidae Leucania senescens 262 Lepidoptera Noctuidae Leucania subpunctata 263 Lepidoptera Noctuidae Lithacodia musta 264 Lepidoptera Noctuidae Lithophane antennata x 265 Lepidoptera Noctuidae Lithophane bethunei

Table A-1. Continued No. Order Family Subfamily, genus, or species Pest 266 Lepidoptera Noctuidae Litoprosopus futilis 267 Lepidoptera Noctuidae Macrochilo hypocritalis 268 Lepidoptera Noctuidae Marilopteryx lutina 269 Lepidoptera Noctuidae Morrisonia confuse 270 Lepidoptera Noctuidae Mythimna sequax 271 Lepidoptera Noctuidae Mythimna unipuncta

272 Lepidoptera Noctuidae Nedra ramosula 273 Lepidoptera Noctuidae Orthodes cynica 274 Lepidoptera Noctuidae Orthodes goodelli 275 Lepidoptera Noctuidae Papaipema 276 Lepidoptera Noctuidae Perigea xanthioides 277 Lepidoptera Noctuidae Plagiomimicus spumosum 278 Lepidoptera Noctuidae Ponometia 279 Lepidoptera Noctuidae Rivula propinqualis 280 Lepidoptera Noctuidae Schinia meskeana 281 Lepidoptera Noctuidae Schinia nubila 282 Lepidoptera Noctuidae Schinia septentrionalis 283 Lepidoptera Noctuidae Simyra 284 Lepidoptera Noctuidae Spodoptera albula x 285 Lepidoptera Noctuidae Spodoptera dolichos x 286 Lepidoptera Noctuidae Spodoptera frugiperda x 287 Lepidoptera Noctuidae Spodoptera latifascia x 288 Lepidoptera Noctuidae Spragueia margana 289 Lepidoptera Noctuidae Stiria rugifrons

290 Lepidoptera Noctuidae Tarache 291 Lepidoptera Noctuidae Xestia c-nigrum 292 Lepidoptera Nolidae Garella nilotica

293 Lepidoptera Nolidae Motya abseuzalis 294 Lepidoptera Notodontidae Datana angusii 295 Lepidoptera Notodontidae Datana integerrima x 296 Lepidoptera Notodontidae Datana major 297 Lepidoptera Notodontidae Datana modesta

298 Lepidoptera Notodontidae Datana robusta 299 Lepidoptera Notodontidae Heterocampa astarte 300 Lepidoptera Notodontidae Heterocampa guttivitta x 301 Lepidoptera Notodontidae Macrurocampa marthesia 302 Lepidoptera Oecophoridae Eido trimaculella

303 Lepidoptera Pterophoridae Pleuroprucha insulsaria 304 Lepidoptera Pyralidae Atheloca subrufella

Table A-1. Continued No. Order Family Subfamily, genus, or species Pest 305 Lepidoptera Pyralidae Dioryctria clarioralis x 306 Lepidoptera Pyralidae Elasmopalpus lignosellus x 307 Lepidoptera Pyralidae Ephestiodes 308 Lepidoptera Pyralidae Galleria mellonella x 309 Lepidoptera Pyralidae Macrorrhinia endonephele 310 Lepidoptera Pyralidae Phidotricha erigens 311 Lepidoptera Pyralidae Pococera melanogrammos 312 Lepidoptera Pyralidae Pococera subcanalis

313 Lepidoptera Pyralidae Tulsa finitella 314 Lepidoptera Pyralidae Ufa rubedinella 315 Lepidoptera Saturniidae Anisota senatoria 316 Lepidoptera Saturniidae Antheraea polyphemus 317 Lepidoptera Saturniidae Dryocampa rubicunda x 318 Lepidoptera Scythrididae Areniscythris 319 Lepidoptera Sphingidae Darapsa choerilus 320 Lepidoptera Sphingidae Enyo lugubris x 321 Lepidoptera Sphingidae Manduca sexta x

322 Lepidoptera Sphingidae Paratrea plebeja 323 Lepidoptera Sphingidae Xylophanes tersa 324 Lepidoptera Tortricidae Acleris curvalana 325 Lepidoptera Tortricidae Choristoneura fumiferana x 326 Lepidoptera Tortricidae Choristoneura parallela x 327 Lepidoptera Tortricidae Cydia pomonella x 328 Lepidoptera Tortricidae Epiblema scudderiana

329 Lepidoptera Tortricidae Gypsonoma salicicolana 330 Lepidoptera Tortricidae Larisa subsolana 331 Lepidoptera Tortricidae Olethreutes 332 Lepidoptera Tortricidae Rhopobota finitimana

333 Lepidoptera Tortricidae Rudenia leguminana 334 Lepidoptera Tortricidae Sonia constrictana 335 Lepidoptera Tortricidae Sparganothis 336 Lepidoptera Tortricidae Strepsicrates smithiana 337 Lepidoptera Tortricidae Zeiraphera

338 Lepidoptera Uraniidae 339 Lepidoptera Yponomeutidae Zelleria retiniella 340 Neuroptera Chrysopidae 341 Odonata Libellulidae 342 Orthoptera Acrididae Chortophaga australior 343 Orthoptera Acrididae Chortophaga viridifasciata

Table A-1. Continued No. Order Family Subfamily, genus, or species Pest 344 Orthoptera Acrididae Psinidia fenestralis

345 Orthoptera Acrididae Spharagemon marmorata 346 Orthoptera Acrididae Stenacris vitreipennis 347 Orthoptera Gryllidae Allonemobius 348 Orthoptera Gryllidae Eunemobius carolinus 349 Orthoptera Gryllidae Gryllus fermis 350 Orthoptera Gryllidae Gryllus rubens x 351 Orthoptera Gryllidae Neonemobius cubensis 352 Orthoptera Gryllotalpidae Scapteriscus borellii x 353 Orthoptera Gryllotalpidae Scapteriscus vicinus 354 Orthoptera Tettigoniidae Amblycorypha floridana 355 Orthoptera Tettigoniidae Conocephalus brevipennis Note: Pest status designated per Heppner 2007

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BIOGRAPHICAL SKETCH

Elysia Webb received her Bachelor of Science in Wildlife Conservation and

Biology at Ohio University in Athens, Ohio. She discovered her passion for bats upon receiving an internship to work for Indiana State University on a bat research project.

After working on a second research project with imperiled bat species, she decided to attend graduate school to take charge of her own project. She completed her Master of

Science in Wildlife Ecology and Conservation at the University of Florida in 2018.