using maximum entropy species distribution modeling for

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Master's Theses Graduate School

Spring 2017

Using Maximum Entropy Species DistributionModeling For Long-term Conservation PlanningOf Three Federally Listed Bats In North AmericaMitchell L. MeyerFort Hays State University, [email protected]

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USING MAXIMUM ENTROPY SPECIES DISTRIBUTION MODELING FOR LONG-

TERM CONSERVATION PLANNING OF THREE FEDERALLY LISTED BATS IN

NORTH AMERICA

being

A Thesis Presented to the Graduate Faculty

of the Fort Hays State University

in Partial Fulfillment of the Requirements for

the Degree of Masters of Science

by

Mitchell L. Meyer

B.S. University of Central Missouri

Date_____________________ Approved________________________

Major Professor

Approved________________________

Chair, Graduate Council

i

This thesis for

The Master of Science Degree

By

Mitchell L. Meyer

Has Been Approved

___________________________________

Chair, Supervisory Committee

___________________________________

Supervisory Committee

___________________________________


__________________________________


_________________________________

Chair, Department of Biological Sciences

ii

PREFACE

This thesis is formatted in the style of Diversity and Distributions: A Journal of

Conservation Biogeography.

Keywords: biodiversity, bats, climate change, MaxEnt, species distribution modeling

iii

ABSTRACT

We are currently in a sixth mass extinction event in which the extinction rate is

higher than it has ever been. This mass extinction event is caused by human influence on

the environment. Biodiversity is worth conserving because of its many uses to humans.

Bats are a diverse group of mammals that humans rely on for pest control services. The

gray bat, northern long-eared bat, and Indiana bat are on the Threatened and Endangered

Species List and are in need of conservation.

I built species distribution models using occurrence records, climate data, and

Maximium entropy (MaxEnt) modeling technique. I predicted the historical range and

projected the range into 2050 and 2070 in best- and worst-case future climate scenarios.

The presence of each bat was most influenced by precipitation, which influences water

availability, prey abundance, mortality, and natality. Future projected ranges became

more fragmented, shifted north from the historical range, or both; and less of the

historical range remained in the future.

Fragmentation and shifting ranges due to changing climate could have a negative

effect on each bat. I recommend conserving forested corridors especially around cave

sites used by each bat species. Forested corridors will be important for dispersal when

the range shifts or to connect fragmented areas. The models produced in this study

provide a guide for conservation management efforts for each bat. Conservation efforts

should strive to maintain or increase bat populations because of the economic and

environmental benefits they provide.

iv

ACKNOWLEDGMENTS

I give an eternal thank you to my advisor, Dr. Rob Channell, for adopting me into

your research lab. Your guidance was needed in one of the most difficult times of my

life. I learned a lot from you, educationally and about myself. Thank you to the rest of

my graduate committee, Dr. Greg Farley, Dr. Mitchell Greer, and Dr. William Stark for

being a part of this project and my graduate career. Also, thank you to the rest of the

faculty in the Department of Biological Sciences for guidance, instruction, and

encouragement.

I thank Dr. Elmer Finck and Mr. Curtis Schmidt for providing me the opportunity

to work on the Myotis septentionalis project, on which I gained much experience and

knowledge. Thank you to the Kansas Department of Wildlife Parks and Tourism and the

Department of Biological Sciences for funding during my graduate career.

I thank those who helped collect and process my data, especially Allison

Hullinger, Dr. Yass Kobayashi, Geneva McKown, Ethan Oltean, Taylor Rasmussen,

Adam Rusk, and Elizabeth Schumann. Thank you to all the people I met and befriended;

especially Elizabeth Bainbridge, Kasandra Brown, Diedre Kramer, Brendon

McCampbell, Kaitlin Moore, Samantha Pounds, and Elizabeth Tharman; I am forever

grateful for everyone’s friendship and support. Angelica Sprague and Holly Wilson were

there most; thank you for being there for me during graduate school and every aspect of

my life. Finally, thank you to my family, especially Mom, Dad, Stacey and Lori. You all

realized my potential, even when I could not, and have always pushed me to succeed.

v

TABLE OF CONTENTS

GRADUATE COMMITTEE APPROVAL ......................................................................... i

PREFACE ........................................................................................................................... ii

ABSTRACT ....................................................................................................................... iii

ACKNOWLEDGMENTS ................................................................................................. iv

TABLE OF CONTENTS .....................................................................................................v

LIST OF TABLES ............................................................................................................ vii

LIST OF FIGURES ......................................................................................................... viii

INTRODUCTION ...............................................................................................................1

Climate, Biodiversity, and Importance of Bats ........................................................1

Climate and Bats ......................................................................................................3

Gray Bat ...................................................................................................................3

Northern Long-eared Bat .........................................................................................4

Indiana Bat ...............................................................................................................5

Project Purpose and Objectives ...............................................................................5

METHODS ..........................................................................................................................7

Occurrence Records .................................................................................................7

Climate Data ............................................................................................................8

Maximum Entropy Species Distribution Modeling .................................................9

RESULTS ..........................................................................................................................12

Gray Bat Results ....................................................................................................12

Northern Long-eared Bat Results ..........................................................................13

vi

Indiana Bat Results ................................................................................................14

DISCUSSION ....................................................................................................................16

Bats and Drought ...................................................................................................16

Gray Bat .................................................................................................................17

Northern Long-Eared Bat ......................................................................................18

Indiana Bat .............................................................................................................18

Conclusions ............................................................................................................19

REFERENCES ..................................................................................................................21

TABLES ............................................................................................................................33

FIGURES ...........................................................................................................................41

vii

LIST OF TABLES

1 Bioclim variables included in MaxEnt climate models (Hijmans et al., 2005). .........32

2 Permutation importance values for each bioclim variable for the MaxEnt model of

the gray bat Myotis grisescens. Higher values indicate a larger influence on the

model. .........................................................................................................................33


the northern long-eared bat (Myotis septentrionalis) Higher values indicate a larger

influence on the model. ..............................................................................................34


the Indiana bat (Myotis sodalis). Higher values indicate a larger influence on the

model. .........................................................................................................................35

5 Summary statistics for the MaxEnt models of the gray bat (Myotis grisescens),

northern long-eared bat (Myotis septentrionalis), and Indiana bat (Myotis sodalis). .36

6 Areas (km2) of the predicted historical ranges produced by the MaxEnt model of the

gray bat (Myotis grisescens), northern long-eared bat (Myotis septentrionalis), and

Indiana bat (Myotis sodalis). ......................................................................................37

7 Areas (km2) of the projected future ranges produced by the MaxEnt model of the

gray bat (Myotis grisescens), northern long-eared bat (Myotis septentrionalis), and

Indiana bat (Myotis sodalis). ......................................................................................38

8 Percent of the historical range that remained in each future climate scenario

produced by MaxEnt model of the gray bat (Myotis grisescens), northern long-eared

bat (Myotis septentrionalis), and Indiana bat (Myotis sodalis). .................................39

viii

LIST OF FIGURES

1 MaxEnt model of the gray bat (Myotis grisescens) under historical climate

conditions. Areas in black indicate a high probability of occurrence, areas in grey

indicate a low probability of occurrence. ...................................................................40

2 MaxEnt model of the northern long-eared bat (Myotis septentrionalis) under

historical climate conditions. Areas in black indicate a high probability of

occurrence, areas in grey indicate a low probability of occurrence ...........................41

3 MaxEnt model of the Indiana bat (Myotis sodalis) under historical climate


indicate a low probability of occurrence. ...................................................................42

4 MaxEnt model of the gray bat (Myotis grisescens) under a best-case emission

scenario for 2050. Areas in black indicate a high probability of occurrence, areas in

grey indicate a low probability of occurrence. ...........................................................43

5 MaxEnt model of the gray bat (Myotis grisescens) under a best-case emission



6 MaxEnt model of the gray bat (Myotis grisescens) under a worst-case emission



ix

7 MaxEnt model of the gray bat (Myotis grisescens) under a worst-case emission



8 MaxEnt model of the northern long-eared bat (Myotis septentrionalis) under a best-

case emission scenario for 2050. Areas in black indicate a high probability of

occurrence, areas in grey indicate a low probability of occurrence. ..........................47

9 MaxEnt model of the northern long-eared bat (Myotis septentrionalis) under a best-



10 MaxEnt model of the northern long-eared bat (Myotis septentrionalis) under a worst-



11 MaxEnt model of the northern long-eared bat (Myotis septentrionalis) under a worst-



12 MaxEnt model of the Indiana bat (Myotis sodalis) under a best-case emission



13 MaxEnt model of the Indiana bat (Myotis sodalis) under a best-case emission



x

14 MaxEnt model of the Indiana bat (Myotis sodalis) under a worst-case emission



15 MaxEnt model of the Indiana bat (Myotis sodalis) under a worst-case emission



1

INTRODUCTION

We are currently in a sixth mass extinction event in which the extinction rate is

higher than it has ever been (Barnosky et al., 2011). Traditional natural resource

management strategies were established assuming climate would remain stable (Heller

and Zavaleta, 2009); however, climatic conditions are changing due to human influence

(Huston and Marland, 2003; Thomas and Trenberth, 2003). Climate change trends

include increased mean global temperatures, prolonged and more frequent droughts, and

more frequent intense extreme precipitation events (IPCC, 2014). Climate change due to

human influence is a contributing factor to biodiversity loss (Huston and Marland, 2003)

and biodiversity will continue to decline with current climate change trends (Thomas et

al. 2004). Biodiversity will also be affected by changing species compositions through

range contractions (Hong-Wa and Arroyo, 2012), expansions, and shifts (Chen et al.,

2011; Lundy et al., 2010; Parmesan and Yohe, 2003). Fluctuating species’ ranges further

contributes to biodiversity loss by introducing new threats to ecosystems, e.g. invasive

species. (Samson and Knopf, 1993).

Biodiversity provides direct extractive uses (e.g. timber harvest), direct non-

extractive uses (e.g. recreational use), and indirect uses (e.g. pest control services;

Edwards and Abivardi, 1998). Bats benefit humans directly by providing crop security in

the form of pest control (Puig-Montserrat et al., 2015; Wagner et al., 2014; Cleveland et

al. 2006). Because climate change will continue to reduce crop yields (Challinor et al.,

2014; Lobell et al., 2011) and increase uncertainty already present in natural resource

2

strategies (Nichols et al., 2011), crop security will remain uncertain. Bats can help

reinforce crop security and because of this, they have a high economic value (Puig-

Montserrat et al., 2015; Wagner et al., 2014; Cleveland et al., 2006) estimated at $22.9

billion/year in the United States for pest control alone (Boyles et al., 2011). Without

bats, agricultural losses have been estimated at more than $3.7 billion/year (Boyles et al.,

2006). Pest control by bats also benefits the environment and public health by reducing

the need of pesticides (Cleveland et al., 2006). Because bats are taxonomically stable,

high in the food chain, and long-lived, they are effective bioindicators (Jones et al.,

2009), and therefore, useful in assessing ecosystem health and the effects of climate

change.

Bats are the second most diverse group of mammals with more than 1110 species

described (Simmons, 2005). Historically, large scale bat population declines have been

attributed to habitat destruction (Thomson, 1982). More recently, bat mortality has been

influenced by the presence of windfarms (Zimmerling and Francis, 2016), continued

habitat loss through urbanization (Russo and Ancillotto, 2015), and white-nose syndrome

(Blehert et al., 2009). White-nose syndrome is caused by a fungal pathogen

(Pseudogymnoascus destructans) that was introduced in 2006 to a cave in New York that

has been responsible for wide-spread declines in bat populations in North America

(Blehert et al., 2009). The distribution of white-nose syndrome is a result of its

transmission among colony roosting bats (Cryan et al., 2010). White-nose syndrome is

projected to spread further in North America (Ingersoll et al., 2016), resulting in

increased mortality among bats.

3

Climate influences bat activity; during increased precipitation and low

temperatures bats typically do not fly (Erikson and West, 2002). Climate change has also

been attributed to a range expansion in a European bat species (Lundy et al, 2010).

Precipitation has a strong influence on bat reproduction and survival (Adams, 2010),

natality is decreased during droughts because water and prey resources are limited

(Adams, 2010). Bat mortality is increased during droughts because of water resource

limitation (Adams and Hayes, 2008); droughts also decrease prey abundance (Jonsson et

al., 2015; Zhu et al., 2014), which could contribute to increased competition among bats.

It is probable that prolonged droughts due to climate change will have a negative effect

on bat populations. These droughts limit prey and water resources for extended times

that bat populations cannot survive.

Slow reproductive rates (Pontier et al., 1993) coupled their sensitivity to human

induced environmental stressors makes bats a prime target for conservation. Three

species of bats are in need of conservation in North America, which are listed on the

United States Threatened and Endangered Species List: the gray bat (Myotis grisescens),

the northern long-eared bat (M. septentrionalis), and the Indiana bat (M. sodalis). A

subspecies of the Townsend’s big eared bat (Corynorhinus townsendii ingens) is also

listed as endangered but is represented by few populations, not the entire species, and is

not covered in this project.

Gray Bat

The gray bat occurs in the central and southeastern portion of the United States

(Figure 1). The diet of the gray bat consists of insects and arachnids (Best et al., 1997).

4

It roosts in colonies, with aggregations of 300 to 150,000 individuals (Tuttle, 1976). The

gray bat uses caves year-round (Tuttle and Stevenson, 1977), but uses different caves

depending on the season (Hall and Wilson, 1966). Storm drains also serve as roost sites

for the gray bat (Hays and Bingman, 1964). Roosts are commonly within 1 km of major

water bodies and are rarely farther than 4 km (Tuttle, 1976). These bodies of water

provide a drinking source and foraging habitat (Tuttle, 1976). The gray bat flies under

the forest canopy (Tuttle, 1976).

On 28 April 1976, the gray bat was listed as a federally endangered species

(United States Fish and Wildlife Service, 1976). Population declines are attributed to

large scale human disturbance at roost caves (Tuttle, 1979) and pollution (Clark et al.,

1978). More recently, white-nose syndrome has infected gray bat populations (Powers et

al., 2016). Although this disease has had a detrimental effect on other bat species it does

not seem to have reduced gray bat populations (Powers et al., 2016).

Northern Long-eared Bat

The northern long-eared bat occurs in most of the central and northeastern

portions of the United States, extending into the southeastern portion of Canada (Figure

2). Northern long-eared bats require forested areas for foraging (Ford et al., 2005). Their

diet consists of insects and arachnids (Feldhammer et al., 2009). The northern long-eared

bat roosts solitarily or in colonies (Foster and Kurta, 1999) that typically do not exceed

70 individuals (Foster and Kurta, 1999; Menzel et al., 2002). In winter, the northern

long-eared bat roosts in caves (Whitaker and Rissler, 1992) and mines (Whitaker, 1992).

In the summer, this species roosts in snags and live trees in forested areas (Lacki and

5

Schwierjohann, 2001; Timpone et al., 2010). On 2 April 2015, the northern long-eared

bat was listed as a federally threatened species (United States Fish and Wildlife Service,

2015). This federal listing was due to population declines associated with the onset of

white-nose syndrome (Reynolds et al. 2016).

Indiana Bat

The Indiana bat occurs in the east-central and northeastern portions of the United

States and extends into the southeastern portion of Canada (Figure 3). The diet of the

Indiana bat consists of insects (Feldhammer et al., 2009). Foraging occurs under the

forest canopy (Humphrey et al., 1977; LaVal et al., 1977) and is not restricted to areas

associated with water (LaVal et al., 1977). The Indiana bat roosts in large colonies up to

100,000 individuals (United States Fish and Wildlife Service, 1999). The Indiana bat is

migratory, using cave roosts in winter and trees in forested areas the rest of the year

(Thomson, 1982). On 11 March 1967, The Indiana bat was listed as a federally

endangered species (United States Fish and Wildlife Service, 1967). Historically,

population declines have been attributed to human disturbance (Thomson, 1982); recent

population declines have been caused by white-nose syndrome (Thogmartin et al., 2012).

Project Purpose and Objectives

The purpose of this project was to determine the historical and projected future

distributions in response to climate for two federally endangered (gray bat and Indiana

bat) and one federally threatened (northern long-eared bat) species in North America.

The objectives of this project were to develop species distribution models using climate

variables, identify which variable had most influenced the distribution of the species, and

6

make recommendations for management. I hope to contribute to the long-term

management goals regarding these species.

7

METHODS

I built species distribution models (SDM) for three Myotis bat species in North

America: the gray bat (M. grisescens) the northern long-eared bat (M. septentrionalis),

and Indiana bat (M. sodalis) by using historical occurrence records and bioclimatic

(bioclim) variables. I predicted the historical range and projected the ranges into the

years 2050 and 2070 under a best and worst-case emission scenario for climate change. I

also quantified the percent of the historical range that remained in each future projected

range for each future climate scenario.

Occurrence Records

I obtained occurrence records from Global Biodiversity Information Facility

(GBIF, 2012) (received from www.gbif.org in September 2016) and VertNet (received

from www.vertnet.org in September 2016). I included records that had latitude and

longitude coordinates and those with adequate locality information to georeference using

GEOLocate Web Application (Rios and Bart, 2010). Additional occurrence records for

M. septentrionalis were obtained from an IACUC-approved field study (protocol number

15-0002) in 2015 and 2016 by Fort Hays State University. Duplicate records, records

with the same latitude and longitude coordinates, were removed. I vetted occurrences by

reviewing spatial relationships in ArcGIS version 10.3.1 and removed outlying

occurrences, i.e. any records that occurred in the oceans or great lakes.

8

Climate Data

I retrieved 19 bioclim variables (Table 1) for historical, best-case, and worst-case

future climate scenarios for 2050 and 2070 from WorldClim (Hijmans et al., 2005).

Bioclim variables are derived from global temperature and precipitation data (Hijmans et

al., 2005). Historical bioclim variables are averaged from 1960 to 1990 (Hijmans et al.,

2005). Future bioclim variables for 2050 are averaged from the projected values for 2041

to 2060, while 2070 are averaged from the projected values for 2061 to 2080 (Hijmans et

al., 2005). Future bioclim variables were derived from projected greenhouse gas

emissions.

Representative concentration pathways (RCP) differ in amounts of greenhouse

gas emissions over time (Hijmans et al., 2005). I used best- and worst-case future climate

scenarios represented by RCPs of 2.6 W/m2 and 8.5 W/m2, respectively. The RCP of 2.6

W/m2 is characterized by low greenhouse gas emissions over time that are expected to

rise and slightly decline (van Vuuren et al., 2011). The RCP of 8.5 W/m2 is characterized

by high greenhouse gas emissions over time caused by high human population growth

(van Vuuren et al., 2011). In the worst case climate scenario, greenhouse gas emissions

experience a sharp increase over time and do not decline (van Vuuren et al., 2011).

The climate data was a raster: geographically projected grid cells that were

homogenous for a physical measurement. The spatial resolution was 2.5 arcminutes

(approximately 5 km2). I downloaded the future climate scenarios from the Beijing

Climate Center Climate System Model (BCC-CSM1) because it includes both best- and

9

worst-case climate scenarios, and its performance is similar to other climate models

(Tongwen et al., 2014).

Maximum Entropy Species Distribution Modeling

I used climate envelopes, the associations of an organisms current geographic

distribution with current climate (Thomas et al., 2004), to predict species ranges

(Hijmans and Graham, 2006). Species distribution models predict occurrence to un-

sampled sites or into past or future climate scenarios (Elith and Leathwick, 2009).

Maximim entropy (MaxEnt) is an algorithmic technique (Phillips et al., 2006; Phillips et

al., 2004) used for species distribution modeling with many other applications, such as

studying climate change effects, invasive species management, and mapping disease

spread and risk (Miller, 2010; Phillips and Dudík, 2007) and has high predictive accuracy

(Elith et al., 2006). MaxEnt uses presence-only modelling (Phillips et al., 2006), this is

advantageous because absence data are rarely available and when available could

represent the organism was present but undetected (Graham et al., 2004).

Occurrence records are often biased because sampling efforts are most likely to be

in areas with easier access, e.g. closer to roads (Phillips et al., 2009). Sampling effort

bias causes inaccurate predictions (Kadmon et al., 2004). MaxEnt uses background

points with similar bias as the occurrence data to increase model performance (Phillips et

al., 2009). Many bioclim variables are highly correlated; MaxEnt is an appropriate

method because it is not sensitive to multicollinearity (Evangelista et al., 2011). I used

MaxEnt version 3.3.3k to build the species distribution models.

10

I cross-validated each model by randomly dividing the occurrence data into two

parts: training data (90%) and test data (10%). I used the training data to build the model

and the test data to test the model (Hijmans, 2012). Because the species is known to

occur at the locations in the test data, I could compare the models’ predictions (derived

from the training data) to the observed occurrences from the test data. If the test data

occurrences were in places predicted by the model, the model had high performance; if

the test data occurrences were in areas the model predicted them not to be, the model had

low performance. To evaluate model performance, I used the area under the curve

(AUC) value of the receiver operating characteristic (ROC) curve produced from the

MaxEnt program. AUC values range from 0 to 1 (Phillips et al., 2006): values >0.9 are

considered excellent; 0.7-0.9 are considered good; and <0.7 are considered uninformative

(Swets, 1988).

Each model predicted to new environmental conditions that were not encountered

during model training. Values become inflated when they are predicted outside of what

an organism has been observed (Phillips et al., 2006). To eliminate the inflation of

predicted values, I used a clamping procedure to set an upper and lower limit of suitable

conditions during model training (Phillips et al., 2006).

To assess the importance of each bioclim variable in the MaxEnt model, I used a

jackknife procedure and the permutation importance values produced by the MaxEnt

program. The jackknife procedure omits each variable, constructs a model without that

variable, then constructs a model using only the omitted variable (Baldwin, 2009). The

models created in the jackknife procedure are then compared to the model that includes

11

every variable. MaxEnt returns permutation importance values, for each variable, that

estimate the importance of each variable on the final model (Songer et al., 2012).

MaxEnt returns a geographical representation of the species ecological niche

made of grid cells based on input variables. Each grid cell has a probability of

occurrence for the species. For each distribution, I used a threshold to establish the

minimum probability that indicated presence for each grid cell (Urbani et al., 2015).

Using a fixed cumulative value 10 logistic threshold, I converted the grid cells to binary

values that represented presence (1) or absence (0) (Urbani et al., 2015). Using the fixed

cumulative value 10 logistic threshold assumes 10% of the occurrence records were

misidentified or incorrectly georeferenced (Raes et al., 2009). This fixed cumulative

value is frequently used in species distribution modeling (Bosso et al., 2013); it is

conservative and recommended for studies with large datasets in which data are collected

without standardized methods over long time spans (Rebelo and Jones, 2010). Errors

associated with GPS devices, the GEOLocate web application, or incorrectly collected

data should not have a significant negative effect on the model. Because the climate data

had high spatial autocorrelation, climate did not vary drastically across large geographic

space at this grain and would not contribute to error in the MaxEnt model.

12

RESULTS

Gray Bat

I obtained 330 unique occurrence records for the gray bat. The MaxEnt model

performance was high (AUC of 0.915), indicating this model can be used for prediction.

The most influential variable for the gray bat model was precipitation of driest month.

This variable had the highest permutation importance value (26.5). In the jackknife

procedure, the model performed worse without this variable than it did without any other

variable.

The historical range predicted by the MaxEnt model for the gray bat (Figure 1)

had an area of 823,020 km2, and was located in central and southeastern portion of the

United States. In each future climate scenario, there was a range expansion predicted to

the western portion of the United States from Mexico, through Canada and into Alaska.

In a best-case climate scenario in 2050 (Figure 4) and 2070 (Figure 5) the areas were

3213100 km2 and 3097150 km2, respectively. The eastern portion of the range did not

become more fragmented but it shifted north relative to the historical range. The western

portion of the range was larger and less fragmented than the eastern portion in 2050 and

2070. In a worst-case climate scenario for 2050 (Figure 6) and 2070 (Figure 7) the areas

were 2631130 km2 and 2498100 km2, respectively. The eastern portion of the range

became highly fragmented and shifted north relative to the historical range. The western

portion of the range remained larger and less fragmented than the eastern portion of the

range.

13

When projected into the future, the historical range decreased with each future

climate scenario. In a best-case climate scenario for 2050, 42% of the historical range

remained, while for 2070, 48% of the historical range remained. In a worst-case

emission scenario for 2050, 23% of the historical range remained; while for 2070, 17% of

the historical range remained.


I obtained 500 unique occurrence records for the northern long-eared bat. The

MaxEnt model had moderately high performance (AUC of 0.886), indicating this model

can be used for prediction. The most influential variable for the northern long-eared bat

model on the model was precipitation of warmest quarter. This variable had the highest

permutation importance value (20.6) and in the jackknife procedure, this variable

performed well when it was the only variable included.

The historical range produced by the MaxEnt model for the northern long-eared

bat (Figure 2) had an area of 2414430 km2. The historical range was located in the

central and northeastern portion of the United States, into the southeastern portion of

Canada, with small fragments in western Canada and Alaska. In a best-case emission

scenario for 2050 (Figure 8) and 2070 (Figure 9) range areas were 2110910 km2 and

2517100 km2, respectively. For both years, the range shifted north and became

increasingly fragmented relative to the historical range while large areas became

climatically suitable in the western part of North America. In a worst-case emission

scenario for 2050 (Figure 10) and 2070 (Figure 11) the areas were 2415000 km2 and

14

2384780 km2, respectively. The ranges became more fragmented, shifted north, and

larger areas existed in the western part of North America relative to the historical range.



remained; while for 2070, 76% of the historical range remained. In a worst-case climate

scenario for 2050, 57% of the historical range remained; while for 2070, 40% of the

historical range remained.

Indiana Bat

I obtained 383 unique historical occurrence records for the Indiana bat. The

MaxEnt model had moderately high performance (AUC of 0.845), indicating this model

can be used for prediction. The most influential variable for the Indiana bat model was

precipitation seasonality. This variable had the highest permutation importance value

(29).

The historical range produced by the MaxEnt model for the Indiana bat (Figure 3)

had an area of 1562840 km2. The historical range was located in the central and

northeastern portion of the United States into the southeastern portion of Canada. In each

future climate scenario, there is range expansion predicted in the western portion of the

North America, from Mexico through Canada and into Alaska. This range expansion

consists of small fragmented areas. In a best-case climate scenario for 2050 (Figure 12)

and 2070 (Figure 13) the areas were 1888430 km2 and 2258400 km2, respectively. In

2050 and 2070, the areas in the west did not differ drastically. The range in the eastern

part of the United States was projected to become highly fragmented and shifted north for

15

2050 and 2070, relative to the historical range. In a worst-case climate scenario for 2050

(Figure 14) and 2070 (Figure 15) the areas for each range were 2263230 km2 and

2352950 km2, respectively; both ranges shifted north and became more fragmented. The

western portion of the range increased in size in the western United States from 2050 to

2070.



remained; while for 2070, 76% of the historical range remained. In a worst-case climate

scenario for 2050, 23% of the historical range remained; for 2070, 17% of the historical

range remained.

16

DISCUSSION

Bats and Drought

Precipitation had the most influence on predicting presence of each bat species.

During times of decreased precipitation, bats have low survival and low reproductive

output (Amorim et al., 2015; O’Shea et al., 2010). Prolonged droughts due to climate

change might limit resources important for survival. Insect abundance decreases with

decreases in precipitation (Zhu et al., 2014; Janzen and Schoener, 1968); prolonged

droughts could influence species occurrence of bats because it negatively impacts prey

abundance. Decreased precipitation could also limit water sources for drinking by bats.

Limited water sources might increase mortality because, during lactation, bats need to

increase water intake for milk production (Adams and Hayes, 2008).

Precipitation of driest month was the most influential variable for determining

presence of the gray bat. Because gray bats are restricted in distribution by roost distance

to major water bodies (Tuttle, 1976), prolonged droughts due to climate change might

reduce the presence of available water and further limit where the gray bat can occur.

Precipitation of warmest quarter was the most influential variable for determining

presence of the northern long-eared bat. Prolonged droughts from climate change might

negatively affect gestation, parturition, and lactation as these stages of reproduction occur

in warmer months (Caceres and Barclay, 2000). Precipitation seasonality had the most

influence for the presence of the Indiana bat. Prolonged droughts might restrict drinking

sources important during winter when Indiana bats arouse to drink (Boyles et al., 2006).

17

Gray Bat

Each projected range of the gray bat was larger than the historical range predicted

by the MaxEnt model. This suggests that there could be a larger area for the gray bat to

inhabit in the future. However, in the models for each future climate scenario there was a

decrease in the total area from 2050 to 2070. Also, less of the historical range remains in

the future climate scenarios. The gray bat might be negatively affected by climate

change because of the reduction in size of the historical range over time, increase in

fragmentation, and shifts in the climatic envelope.

The gray bat will need to disperse to new areas for continued survival. As

suitable areas shift north, forested corridors will be necessary to allow for dispersal,

because the gray bat flies under the forest canopy (Tuttle, 1976). Suitable habitat

corridors would be necessary because energetic demands of long distance flight increase

the chance of mortality in the gray bat (Tuttle and Stevenson, 1977). Management should

focus on creating and maintaining forested corridors that make connections among caves

and large water bodies, with an emphasis on conserving already existing corridors used

by the gray bat. It is unlikely that the gray bat will disperse to the projected range in the

western United States in each future climate scenario, unless habitat corridors can be

maintained that lead from east to west that include suitable habitat. It is also unlikely that

the forests in the western range expansions are suitable because they are coniferous and

the gray bat inhabits deciduous forests in the eastern United States. However, the

expanded range in the east could be used as introduction habitat.

18


The areas of the projected ranges by the MaxEnt model for the northern long-

eared bat were similar to the predicted historical range. Every projected range for the

northern long-eared bat shifts north and is more fragmented than the historical range,

with more areas projected in the northwest region of North America. This shifting and

fragmented range indicates the northern long-eared bat might be negatively affected by

climate change.

Corridors will be necessary for the northern long-eared bat to move among

suitable areas because future projected ranges were highly fragmented. Northern long-

eared bats use forested corridors in fragmented landscapes (Yates and Muzika, 2006). I

recommend the management of forested corridors from the southern portion to the

northern portion of the projected range to coincide with a north shifting range over time.

I also recommend conserving forested areas currently occupied by the northern long-

eared bat.

Indiana Bat

Each projected range became more fragmented than the historical range.

However, each projected range was larger than the historical range and over half of the

historical range remained in each future climate scenario. This could mean a large for the

Indiana bat to inhabit in the future. Despite the large projected ranges, some populations

will be impacted by the fragmentation and shifting of the projected range and, thus, the

Indiana bat might be negatively impacted by climate change.

19

The expanded range in the western United States projected in each future-climate

scenario was highly fragmented and disjunct from the eastern portion. Dispersal to the

expanded western range is unlikely because of the disjunction of the eastern and western

ranges. It is also unlikely that the forests in the western range expansions are suitable

because they are coniferous and the Indiana bat inhabits deciduous forests in the eastern

United States. Because the range is projected to shift north, I recommend the

management of suitable habitat in the northern portion of the Indiana Bat range. Suitable

forested habitat should be conserved to provide for corridors to allow for movement and

foraging sites. Forested areas associated with cave sites should be conserved to provide

appropriate habitat for the Indiana bat.

Conclusions

Researchers have claimed that changing climate influences bat behavior (Erikson

and West, 2002), natality (Adams, 2010), survival (Frick et al., 2010) and distribution

(Lundy et al., 2010). Overall, for each species covered in this project, the future

projected ranges become more fragmented and shift north from the historical ranges.

Each species must disperse to new areas for continued survival. Understanding where

suitable climactic conditions occur currently and in the future, will help to identify places

suitable for habitat management. Because of climatological influence on bats

populations, MaxEnt SDM’s will provide useful in management for each of the species

modeled in this study. The models produced in this study provide a guide for

conservation management efforts for each bat. Conservation efforts should strive to

20

maintain or increase bat populations because of the economic and environmental benefits

they provide.

21

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33

TABLES

Table 1. Bioclim variables included in MaxEnt climate models (Hijmans et al., 2005).

1 Annual Mean Temperature

2 Mean Diurnal Range (Mean of Montly (Max Temperature - Min Temperature))

3 Isothermality (Mean Diurnal Range/Temperature Annual Range) (* 100)

4 Temperature Seasonality (Standard Deviation * 100)

5 Max Temperature of the Warmest Month

6 Min Temperature of the Coldest Month

7 Temperature Annual Range (Max Temperature of Warmest Month - Min

Temperature of Coldest Month)

8 Mean Temperature of Wettest Quarter

9 Mean Temperature of Driest Quarter

10 Mean Temperature of Warmest Quarter

11 Mean Temperature of Coldest Quarter

12 Annual Precipitation

13 Precipitation of Wettest Month

14 Precipitation of Driest Month

15 Precipitation Seasonality (Coefficient of Variation)

16 Precipitation of Wettest Quarter

17 Precipitation of Driest Quarter

18 Precipitation of Warmest Quarter

19 Precipitation of Coldest Quarter

34

Table 2. Permutation importance values for each bioclim variable for the MaxEnt model

of the gray bat Myotis grisescens. Higher values indicate a larger influence on the model.

Variable Permutation Importance

Precipitation of Driest Month 26.5

Precipitation Seasonality (Coefficient of Variation) 16.7

Mean Diurnal Range (Mean of Monthly (Max Temperature

- Min Temperature))

10.7

Max Temperature of Warmest Month 7.2

Annual Precipitation 5.1

Mean Temperature of Driest Quarter 4.3

Annual Mean Temperature 3.9

Isothermality (Mean Dirunal Range/Temperature Annual

Range)(*100)

3.6

Mean Temperature of Coldest Quarter 3.4

Precipitation of Wettest Quarter 3

Precipitation of Warmest Quarter 3

Temperature Seasonality (Standard Deviation * 100) 2.5

Mean Temperature of Wettest Quarter 1.8

Mean Temperature of Warmest Quarter 1.6

Precipitation of Driest Quarter 1.6

Min Temperature of Coldest Month 1.4

Temperature Annual Range (Max Temperature of Warmest

Month - Min Temperature of Coldest Month)

1.4

Precipitation of Coldest Quarter 1.3

Precipitation of Wettest Month 1

35


of the northern long-eared bat (Myotis septentrionalis). Higher values indicate a larger

influence on the model.


Precipitation of Warmest Quarter 20.6

Mean Temperature of Warmest Quarter 12

Precipitation of Wettest Quarter 9.8



Range)(*100)

8.5

Annual Precipitation 8





- Min Temperature))

3.6

Precipitation Seasonality (Coefficient of Variation) 3.1



2.3

Mean Temperature of Driest Quarter 2



Precipitation of Wettest Month 0.7



Precipitation of Driest Quarter 0

36


of the Indiana bat (Myotis sodalis). Higher values indicate a larger influence on the

model.


Precipitation Seasonality (Coefficient of Variation) 29

Annual Precipitation 18.3




7.9






Range)(*100)

3.4

Precipitation of Driest Quarter 2.9

Precipitation of Warmest Quarter 1.7


- Min Temperature))

1.7

Mean Temperature of Driest Quarter 1.7


Precipitation of Wettest Month 0.7

Precipitation of Wettest Quarter 0.2



Mean Temperature of Warmest Quarter 0

37

Table 5. Summary statistics for the MaxEnt models of the gray bat (Myotis grisescens),

northern long-eared bat (Myotis septentrionalis), and Indiana bat (Myotis sodalis).

Species Scenario Year Test

AUC

StDev

AUC

Training

Points

Test

Points

Myotis

grisescens

Best-

Case

2050 0.915 0.0218 153 17

2070 0.915 0.0218 153 17

Worst-

Case

2050 0.915 0.0218 153 17

2070 0.915 0.0218 153 17

Myotis

septentrionalis

Best

Case

2050 0.886 0.0255 304 33

2070 0.886 0.0255 304 33

Worst-

Case

2050 0.886 0.0255 304 33

2070 0.886 0.0255 304 33

Myotis sodalis

Best-

Case

2050 0.845 0.0282 193 21

2070 0.845 0.0282 193 21

Worst-

Case

2050 0.845 0.0282 193 21

2070 0.845 0.0282 193 21

38

Table 6. Areas (km2) of the predicted historical ranges produced by the MaxEnt model of

the gray bat (Myotis grisescens), northern long-eared bat (Myotis septentrionalis), and

Indiana bat (Myotis sodalis).

Species Area of Historical Range (km2)

Myotis grisescens 823020

Myotis septentrionalis 2414430

Myotis sodalis 1562840

39

Table 7. Areas (km2) of the projected future ranges produced by the MaxEnt model of

the gray bat (Myotis grisescens), northern long-eared bat (Myotis septentrionalis), and

Indiana bat (Myotis sodalis).

Species Scenario Year Area (km2)

Myotis grisescens

Best-Case 2050 3213100

2070 3097150

Worst-Case 2050 2631130

2070 2498100

Myotis septentrionalis

Best Case 2050 2110910

2070 2517100

Worst-Case 2050 2415000

2070 2384780

Myotis sodalis

Best-Case 2050 1888430

2070 2263230

Worst-Case 2050 2258400

2070 2352950

40

Table 8. Percent of the historical range that remained in each future climate scenario

produced by MaxEnt model of the gray bat (Myotis grisescens), northern long-eared bat

(Myotis septentrionalis), and Indiana bat (Myotis sodalis).

Species Scenario Year Remnant historical range (%)

Myotis grisescens

Best-Case 2050 42

2070 48

Worst-Case 2050 23

2070 17

Myotis septentrionalis

Best Case 2050 67

2070 76

Worst-Case 2050 57

2070 40

Myotis sodalis

Best-Case 2050 54

2070 76

Worst-Case 2050 64

2070 55

41

FIGURES

Figure 1. MaxEnt model of the gray bat (Myotis grisescens) under historical climate


indicate a low probability of occurrence.

42

Figure 2. MaxEnt model of the northern long-eared bat (Myotis septentrionalis) under

historical climate conditions. Areas in black indicate a high probability of occurrence,

areas in grey indicate a low probability of occurrence.

43

Figure 3. MaxEnt model of the Indiana bat (Myotis sodalis) under historical climate



44

Figure 4. MaxEnt model of the gray bat (Myotis grisescens) under a best-case emission

scenario for 2050. Areas in black indicate a high probability of occurrence, areas in grey


45

Figure 5. MaxEnt model of the gray bat (Myotis grisescens) under a best-case emission



46

Figure 6. MaxEnt model of the gray bat (Myotis grisescens) under a worst-case emission



47

Figure 7. MaxEnt model of the gray bat (Myotis grisescens) under a worst-case emission



48

Figure 8. MaxEnt model of the northern long-eared bat (Myotis septentrionalis) under a

best-case emission scenario for 2050. Areas in black indicate a high probability of

occurrence, areas in grey indicate a low probability of occurrence.

49


best-case emission scenario for 2070. Areas in black indicate a high probability of


50


worst-case emission scenario for 2050. Areas in black indicate a high probability of


51


worst-case emission scenario for 2070. Areas in black indicate a high probability of


52

Figure 12. MaxEnt model of the Indiana bat (Myotis sodalis) under a best-case emission



53

Figure 13. MaxEnt model of the Indiana bat (Myotis sodalis) under a best-case emission



54

Figure 14. MaxEnt model of the Indiana bat (Myotis sodalis) under a worst-case emission



55

Figure 15. MaxEnt model of the Indiana bat (Myotis sodalis) under a worst-case emission



using maximum entropy species distribution modeling for

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