Faculty of Science

ECOLOGICAL, MORPHOLOGICAL
EXTINCTION RISK IN BATS

AND

2022 | MATHIEU ADRIEN LIZEE

B.Sc. HONOURS THESIS - BIOLOGY

GEOGRAPHIC

PREDICTORS

OF

ECOLOGICAL, MORPHOLOGICAL AND GEOGRAPHIC PREDICTORS OF
EXTINCTION RISK IN BATS

by

MATHIEU ADRIEN LIZEE

A THESIS SUBMITTED IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF

BACHELOR OF SCIENCE (HONS.)
in the
DEPARTMENT OF BIOLOGICAL SCIENCES
(General Biology)

This thesis has been accepted as conforming to the required standards by:
Matthew Reudink (Ph.D.), Thesis Supervisor, Dept. Biological Sciences
Nancy J. Flood (Ph.D.), Co-supervisor, Dept. Biological Sciences
Tom Dickinson (Ph.D.), External examiner, Dept. Biological Sciences
Dated this 1st day of June, 2022, in Kamloops, British Columbia, Canada
© Mathieu Adrien Lizee, 2022

ABSTRACT
Bats comprise a diverse and speciose order (Chiroptera) with over 1,400 species inhabiting
every continent on earth except Antarctica. Globally, bat species are experiencing unprecedented
declines due to factors such as habitat loss and degradation, climate change, direct persecution,
and disease. Unfortunately, many bat species are found in parts of the world that are difficult to
access (e.g., Africa, Amazonia), roost deep in caves or hollow trees, and are most commonly active
at night, all of which pose challenges to data collection. As a result, little data on the population
abundance of many species of bats is available. It is thereby difficult to assess the conservation
risk of data deficient bat species, as conservation status is based largely upon changes in population
abundance. In this project, we conducted a phylogenetically controlled analysis to examine 835
species of bats with an International Union for the Conservation of Nature (IUCN) listing status
with respect to 20 morphological (e.g., body size and forearm length), ecological (e.g., mean
annual temperature and precipitation of the species’ range), and geographic (e.g., range size,
latitude) variables. Our analysis suggests that species with primary diets of nectar/pollen, those
that inhabit islands, and those with large forearms and hindfeet are at a higher risk of extinction.

Thesis Supervisor: Professor Matthew Reudink

ii

ACKNOWLEDGMENTS
This research took place on the traditional lands of the Tk’emlúps te Secwépemc within
Secwépemc’ulucw, the traditional and unceded territory of the Secwépemc.

Thank you to my supervisors Dr. Matthew Reudink and Dr. Nancy Flood for their guidance
throughout the course of my thesis. I would also like to thank everyone in the BEAC lab, Dr. Eric
Bottos, and Dr. Sean Mahoney for their support and contributions to my project.

iii

TABLE OF CONTENTS
ABSTRACT………………………………………………………………….…………..............ii
ACKNOWLEDGEMENTS…………………………………………..…….…………..............iii
LIST OF FIGURES.......................................................……………………..…………...............v
LIST OF TABLES......…………………………………………………………..……................vi
INTRODUCTION………………………………………………..…………….………..............1
METHODS……………………………………………………………...……….……….............4
STUDY SPECIES…………..……………………………………..……..............4
DATA COLLECTION…………..………….………………………..…..............4
CONSERVATION RISK……………….....………………….….........................6
PHYLOGENETIC METHODS…………..………………………………...........6
STATISTICAL ANALYSIS..........…………………………………....................6
RESULTS………………………………………………………….......……………....................7
DISCUSSION……………………............…………………………………………...................17
LITERATURE CITED................................................................................................................20

iv

LIST OF FIGURES
Figure 1. – Box plot showing the mean (small red box), IQR (box), 1.5x IQR (whiskers), and
outliers (points outside of the 1.5x IQR) of the conservation category, based for island/mainland
classification (N= 835). The IUCN conservation category is higher for species endemic to island
habitats……………………………………………………………………………………...……10
Figure 2. – Box plot showing the mean (small red box), IQR (box), 1.5x IQR (whiskers), and
outliers (points outside of the 1.5x IQR) of the conservation category, based for habitat type (N=
835). Bat species which primarily inhabit caves have the highest conservation status………….11
Figure 3. – Correlation between conservation category and area size (range in km) of N=835 bat
species range. As a bat species range size decreases, it appears conservation category increases
significantly..………………………………………………………………………….....………12
Figure 4. – Box plot showing the mean (small red box), IQR (box), 1.5x IQR (whiskers), and
outliers (points outside of the 1.5x IQR) of the conservation category, concerning primary diet of
a bat species (N= 835)………………………………………………………………...……….…13
Figure 5. – Correlation between conservation category and hindfoot size (mm) of N=835 bat
species.

As

hindfoot

size

increases,

conservation

category

generally

increases.………………………………………………………………………………………....14
Figure 6. – Correlation plot between conservation category and minimum temperature of the
coldest month (°C) of N=835 bat species.…………………………………………………...……15
Figure 7. – Correlation plot between conservation category and forearm length (mm) of N=835
bat species. Conservation category appears to increase with forearm length……………………..16

v

LIST OF TABLES
Table 1. All Variables used for analysis in this study listed with a brief description. All data was
sourced from the International Union for Conservation of Nature (IUCN)…………………….... 5
Table 2. Top AIC models demonstrating the effect of different Climatic and Morphological
variables on conservation risk category. Significant results (P < 0.05) are bolded…………...…….8

vi

INTRODUCTION
Understanding extinction risk and the traits that predispose organisms to population decline
is paramount for enacting effective conservation measures. Predicting vulnerability to extinction is
especially important for species that are rare on the landscape or challenging to survey, as data on
population abundance may be sparse or unavailable (Welch & Beaulieu 2018). The order
Chiroptera, which encompasses all bat species, is particularly limited in data concerning population
trends for many species. This lack of information is largely due to many species of bats being
nocturnal and/or living in regions that are sparsely populated and difficult to survey (e.g., the
Amazon basin and central Africa). Additionally, members of order Chiroptera often roost in
locations like caves and hollow trees that are difficult to access and survey, creating additional
challenges for data collection.
Approximately 200 of the over 1400 bat species recognized by the International Union for
the Conservation of Nature (IUCN) are designated as vulnerable, endangered, or critically
endangered (IUCN 2022). Bat species worldwide have been experiencing population declines due
to stressors such as habitat loss and fragmentation, the destruction of roosts and hibernacula,
disease, and hunting (Tuttle 2013). Deforestation is particularly problematic as species richness
and abundance in bats is positively related to forest cover (García-Morales et al. 2016). In addition,
climate change is negatively impacting population abundance of bat species via its effect on their
access to food, rate and duration of energy use, reproduction, hibernation, and developmental
timing (Sherwin et al. 2012).
In addition to habitat loss and climate change, the recent emergence and rapid spread of
White Nose Syndrome has been having devastating effects on bat populations, particularly in
North America where infected populations have decreased by 60-98% (Peuchmaille et al. 2011).

1

White Nose Syndrome, a fungal disease that affects hibernating bats with an observed mortality
rate of 90-100% has killed millions of individuals (Hoyt et al. 2021).
Chiroptera is the 2nd largest order within the Class Mammalia; it includes over 1400 species
that are widely dispersed globally (Arnaout et al.2022). Bat species are incredibly diverse in their
behavior and ecology. However, it remains unclear whether bats exhibiting a particular behavior
(e.g., foraging behavior), or ecology (e.g., diet, habitat), are more susceptible to population
declines and at a higher risk of extinction than others. This information is particularly important
as it can provide guidance for focussing conservation efforts on species that have not been assessed
by the IUCN due to a lack of data on population abundance and trends. Jones et al. (2003) examined
the traits associated with extinction risk of over 300 bat species and found smaller wing aspect
ratios as well as smaller geographic ranges to be associated with a higher risk of extinction. They
also noted that extinction risk is not distributed randomly throughout the Order Chiroptera; closely
related species have a similar risk of extinction, and, at a broader taxonomic level, extinction risk
differs between the suborders Megachiroptera and Microchiroptera. More members of
Megachiroptera face a high risk of extinction than members of Microchiroptera.
In a recent study, Welch & Beaulieu (2018) examined predictors of extinction risk in bats
and found that island endemism and small geographic ranges were associated with a higher risk
of extinction. Using this information, the authors constructed a model to assess predicted
extinction risk in comparison to the IUCN (International Union for Conservation of Nature) listed
conservation category. This model was shown to be effective in predicting the risk of extinction
with over 90% of bat species having the same threat level as the IUCN listed status. Like Jones
et al. (2003), Welch & Beaulieu (2018) found that extinction risk is non-randomly distributed
through the order, with closely related species sharing similar extinction risk, and suggested that

2

the degree of relatedness to other species may be a useful tool in inferring extinction risk for data
deficient species.
Previous work on predicting extinction risk in bats has faced two major limitations. Neither
the Jones et al. (2003) and Welch & Beaulieu (2018) analyses incorporated behavioural and
ecological factors that may play an important role in influencing susceptibility to extinction; these
include variables such as diet, habitat type, and climatic variables (e.g., annual precipitation, mean
temperature of range annually, isothermality, etc.) associated with where the species is found. The
lack of habitat information is particularly problematic as species richness and abundance in bat
species decline in response to habitat loss (Muylaert et al. 2016). Furthermore, forests are being
lost disproportionately in comparison to other habitats due to deforestation (Frick et al. 2019),
which may put forest-dwelling bats at higher risk. Incorporating factors such as habitat, diet, and
climate may be key in constructing models that effectively predict extinction risk in data deficient
bat species.
To assess the influence of biological, behavioural, and ecological factors on extinction risk
in bats, we compiled data with ono 20 morphological, ecological, and geographic variables for 835
bat species that have been assessed by the IUCN. We then constructed a series of phylogenetically
controlled models to assess the combination of factors that best predict the current IUCN threat
category for data-rich species. Using this model, future work will then be able to extrapolate these
findings to data-deficient species to generate a predicted extinction risk classification.

3

METHODS
Study Species
Order Chiroptera, which makes up 20% of all mammals consists of over 1400 species of
bats that are widely distributed globally (Arnaout et al. 2022). This study concentrated on 835
species for which IUCN listing status, as well as information on morphology, behaviour, and
ecology was available (IUCN 2022).

Data collection
To examine the possible relationship between various geographic, ecological, and
morphological traits (Table 1) and extinction risk in bat species, data on over 1200 of the 1400
species of bats were obtained from the International Union for Conservation of Nature (IUCN
2022).

4

Table 1. All Variables used for analysis in this study listed with a brief description. All data was
sourced from the International Union for Conservation of Nature (IUCN).
Variable:
Average Weight (g)
Hindfoot (mm)

Description:
The average weight of an individual, measured in grams
Length of hindfoot measured in millimetres

Altitudinal range lower (m)
Altitudinal range upper (m)
Primary diet

Lowest point of altitudinal range measured in metres
Highest point of altitudinal range measured in metres
The food source (insects, nectar/pollen, fruit, other) most
frequently consumed by individuals.
The range size of the species, measured in kilometers
The range size of the species, measured in meters

Area (km)
Area (m)
Island/mainland
classification
Conservation category

Classification if a species is either endemic to an island or is
found on mainland environments.
Conservation category is listed from 1-5 and is characterized
based upon the IUCN criteria.
Habitats
The most frequent habitat type for a species
(Artificial/Terrestrial, Forest, Caves, Open, Savana, Aquatic)
Temp seasonality (C°)
The standard deviation in temperature annually multiplied by
100. Described in degrees Celsius.
Isothermality (C°)
A quantification for the oscillation in temperatures from day to
night relative to the annual oscillation of summer and winter
Maximum temperature of The maximum temperature of the coldest month in a species
warmest month (C°)
range, measured in degrees Celsius.
Minimum temperature of The minimum temperature of the coldest month in a species
coldest month (C°)
range, measured in degrees Celsius.
Temperature of annual range The annual temperature range of a species range, measured in
(C°)
degrees Celsius.
Annual precipitation (mm)
The precipitation that accumulates annually in a species range,
measured in mm.
Forearm average (mm)
The average length of the forearm measured in millimetres
Head body average (mm)
X centroid (absolute value)

The average length of the bat species measured nose to base of
tail, in millimetres
The x centroid of the species range, as an absolute value.

Y centroid (absolute value)

The x centroid of the species range, as an absolute value.

5

Conservation Risk
Conservation risk as assessed by the IUCN classifies species into categories that are rank
on scale of 1-5: 1 (least concern), 2 (near threatened), 3 (vulnerable), 4 (endangered) and 5
(critically endangered). Conservation risk is determined by the IUCN based on characteristics
concerning population size, species geographic range size, and extinction probability analysis
(IUCN 2022). Numerical thresholds associated with these characteristics result in the designation
of each species into one of the above categories of extinction risk (1-5) (IUCN 2022).

Phylogenetic Methods
To control for, and examine the influence of, phylogenetic effects on predicting extinction
risk, we obtained a full mammalian phylogeny from Upham et al. (2019). Using this phylogeny,
we pruned our phylogenetic tree to construct a phylogeny for the 835 bat species for which we had
sufficient data for analysis. This phylogeny was paired with characteristics available from the
IUCN to examine the predictors of extinction risk while controlling for non-independence across
species due to common ancestry.

Statistical Analysis
All statistical analyses were conducted in R 3.5.3 (R Core Team 2017) and all figures were
created using the ggplot package (Wickham 2016). To examine the factors predicting extinction
risk in bats, we constructed a series of phylogenetic generalized least squares (PGLS) models using
the nlme package (Pinheiro et al. 2018) to control for the effects of common ancestry (Symonds &
Blomberg 2014). We constructed a series of full models to examine the effects of morphological,
behavioural, and ecological variables, while not allowing highly correlated (r > 0.7) variables to

6

remain in the same model (e.g., temperature seasonality, minimum temperature of range, and Y
centroid of range). Next, we used the stepAIC function in the mass package (Venables & Ripley
2002), to evaluate model fit for all potential combinations of predictor variables (Burnham and
Anderson 2003). The model with the lowest AIC value was then selected as the top model. This
was repeated for each correlated variable which produced 6 separate models and using the ANOVA
function, we examined the significance of each predictor variable in the top models.

RESULTS
Because several morphological and climatic variables were highly correlated, we ran a total
of six full models. Each model included a different variable from the correlated group, followed
by a stepwise model reduction procedure to identify a best fit model. Regardless of the full starting
model, each reduced top model included significant effects of habitat, diet, island/mainland
classification, and range size (area in km). Bat species inhabiting caves, islands, and those with
small ranges and a primary diet of nectar/pollen were more likely to have a higher IUCN listing
status (Tables 2, Figures 1-6.). Bat species that primarily consume pollen/nectar appear to be at the
highest conservation risk in contrast with other diets such as insects (Table 2, Figure 4). Of the
body size metrics we examined, weight was not an important predictor of conservation status;
however, hindfoot length (mm) and, to a lesser extent, forearm length (mm), were found to increase
with conservation category. Of the climatic variables, only temperature of the coldest month was
a significant predictor of conservation status (Table 2). Ranges with higher minimum temperatures
were associated with greater conservation category.

7

Table 2. Top AIC models demonstrating the effect of different Climatic and Morphological
variables on conservation risk category. Significant results (P < 0.05) are bolded.
Model/Correlate Group

Variable
Habitats Artificial/Terrestrial
Habitats Caves
Habitats Forest
Model 1 - Using variables
Habitats Open
Average Weight for the
Habitats Savana
morphological variable
Primary Diet Insects
and Minimum
Primary Diet Nectar/Pollen
Temperature of the coldest
Primary Diet Other
month as the climatic
Island/Mainland Classification
variable (AIC = 2993.03)
Minimum Temp. of Coldest Month
Annual Precipitation
Area (km)
Habitats Artificial/Terrestrial
Habitats Caves
Habitats Forest
Model 2 - Using variables
Habitats Open
Average Weight for the
Habitats Savana
morphological variable
Island/Mainland Classification
and Temperature
Primary Diet Insects
Seasonality as the climatic
Primary Diet Nectar/Pollen
variable (AIC = 2994.36)
Primary Diet Other
Average Weight
Area (km)
Habitats Artificial/Terrestrial
Habitats Caves
Habitats Forest
Model 3 - Using variables
Habitats Open
Average Weight for the
Habitats Savana
morphological variable
Primary Diet Insects
and Y centroid as the
Primary Diet Nectar/Pollen
climatic variable (AIC =
Primary Diet Other
2994.36)
Average Weight
Island/Mainland Classification
Area (km)
Habitats Artificial/Terrestrial
Habitats Caves
Habitats Forest
Model 4 - Using variables
Habitats Open
Forearm Length for the
Habitats Savana
morphological variable
Primary Diet Insects
and Temperature
Primary Diet Nectar/Pollen
Seasonality as the climatic
Primary Diet Other
variable (AIC = 2992.21)
Forearm Avg (mm)
Island/Mainland Classification
Area (km)

Beta Coefficent Std. Error
0.1140
0.3757
0.9526
0.3827
0.3242
0.3725
0.4029
0.3850
0.4791
0.3957
-0.4368
0.5740
-1.0058
0.3131
-0.0153
0.4375
-0.3789
0.1073
0.0012
0.0006
-0.0001
0.0000
<-0.000001
<0.0001
0.1170
0.3759
0.9421
0.3828
0.3325
0.3727
0.4043
0.3843
0.4823
0.3958
-0.3638
0.1019
-0.6421
0.5714
-0.9842
0.3133
-0.2006
0.4323
-0.0005
0.0003
<-0.000001
<0.0001
0.1170
0.3759
0.9421
0.3828
0.3326
0.3727
0.4042
0.3843
0.4823
0.3958
-0.6421
0.5714
-0.9841
0.3133
-0.2006
0.4323
-0.0005
0.0003
-3.9340
0.1019
<-0.000001
<0.0001
0.1241184
0.3811
0.9529
0.3877
0.3317
0.3776
0.4145
0.3887
0.4721
0.4004
-0.3811438
2.3868
-0.8619
2.4566
-0.0145
2.4625
0.0052
0.0026
-0.3893
0.1022
<-0.000001
<0.0001

T-Value
0.30
2.49
0.87
1.05
1.21
-0.76
-3.21
-0.04
-3.53
2.18
-1.69
-7.20
0.31
2.46
0.89
1.05
1.22
-3.57
-1.12
-3.14
-0.46
-1.45
-7.17
0.31
2.46
0.89
1.05
1.22
-1.12
-3.14
-0.46
-1.45
-3.57
17.17
0.20
2.36
0.75
0.96
1.07
-0.21
-0.28
0.11
2.03
-3.78
-7.20

P-Value
0.76
0.013
0.38
0.30
0.23
0.45
0.0014
0.97
0.0004
0.03
0.09
<0.0001
0.76
0.014
0.37
0.29
0.22
0.0004
0.26
0.0017
0.64
0.15
<0.0001
0.76
0.014
0.37
0.29
0.22
0.26
0.0017
0.64
0.15
0.0004
<0.0001
0.84
0.019
0.45
0.34
0.29
0.83
0.78
0.91
0.043
0.0002
<0.0001

8

Habitats Artificial/Terrestrial
Habitats Caves
Model 5 - Using variables Habitats Forest
Head/Body Size Average Habitats Open
for the morphological Habitats Savana
variable and Temperature Primary Diet Insects
Seasonality as the climatic Primary Diet Nectar/Pollen
variable (AIC = 2994.49) Primary Diet Other
Island/Mainland Classification
Area (km)
Primary Diet Insects
Primary Diet Nectar/Pollen
Primary Diet Other
Model 6 - Using variables
Habitats Artificial/Terrestrial
Hindfoot Size Average
Habitats Caves
for the morphological
Habitats Forest
variable and Temperature
Habitats Open
Seasonality as the climatic
Habitats Savana
variable (AIC = 2983.2)
Island/Mainland Classification
Hindfoot (mm)
Area (km)

0.1214
0.9433
0.3329
0.4129
0.4856
-0.5671
-0.9602
-0.1300
-0.3720
<-0.000001
-0.2192
-0.9446
0.0833
0.1679
0.9813
0.3684
0.4611
0.5121
-0.3653
0.0258
<-0.000001

0.3761
0.3830
0.3729
0.3845
0.3961
0.5694
0.3130
0.4298
0.1018
<0.0001
0.5733
0.3108
0.4307
0.3736
0.3804
0.3703
0.3820
0.3932
0.1011
0.0071
<0.0001

0.32
2.46
0.89
1.07
1.23
-1.00
-3.07
-0.30
-3.65
-7.18
-0.38
-3.04
0.19
0.45
2.58
0.99
1.21
1.30
-3.61
3.63
-7.18

0.75
0.014
0.37
0.28
0.22
0.32
0.002
0.76
0.0003
<0.0001
0.70
0.002
0.85
0.65
0.01
0.32
0.23
0.19
0.0003
0.0003
<0.0001

9

Figure 1. – Box plot showing the mean (small red box), IQR (box), 1.5x IQR (whiskers), and
outliers (points outside of the 1.5x IQR) of the conservation category, based for island/mainland
classification (N= 835). The IUCN conservation category is higher for species endemic to island
habitats.

10

Figure 2. – Box plot showing the mean (small red box), IQR (box), 1.5x IQR (whiskers), and
outliers (points outside of the 1.5x IQR) of the conservation category, based for habitat type (N=
835). Bat species which primarily inhabit caves have the highest conservation status.

11

Figure 3. – Correlation between conservation category and area size (range in km) of N=835 bat
species range. As a bat species range size decreases, it appears conservation category increases
significantly.

12

Figure 4. – Box plot showing the mean (small red box), IQR (box), 1.5x IQR (whiskers), and
outliers (points outside of the 1.5x IQR) of the conservation category, concerning primary diet of
a bat species (N= 835).

13

Figure 5. – Correlation between conservation category and hindfoot size (mm) of N=835 bat
species. As hindfoot size increases, conservation category generally increases.

14

Figure 6. – Correlation plot between conservation category and minimum temperature of the
coldest month (°C) of N=835 bat species.

15

Figure 7. – Correlation plot between conservation category and forearm length (mm) of N=835
bat species. Conservation category appears to increase with forearm length.

16

DISCUSSION
Worldwide, bats are critical to ecosystem function through their role in myriad of ecological
interactions, from pollination and seed dispersal to controlling insect populations. Unfortunately,
many bat species are currently in decline, with 15% of the assessed bat species listed as threatened,
endangered, or critically endangered, though 18% of bat species remain data deficient on the IUCN
red list (IUCN 2022). With limited data on population abundance for many bat species and so many
species remaining data deficient, it is difficult to establish protections and enact effective
management for many potentially at-risk species. In this study, we conducted the largest and most
comprehensive analysis to date on the predictors of extinction risk in bats, examining 835 bat
species assessed by IUCN with the largest suite of biological, ecological, and geographic predictors
yet tested. Our findings suggest that important predictors of extinction risk in 835 species of bats
are range size (area in km), island endemism, habitat type, primary diet, body size (hindfoot length,
forearm length), and minimum temperature of the coldest month. The strongest predictor in our
study was range size and followed by island endemism. This finding is consistent with previous
findings by Welch & Beaulieu (2018) as well as Jones et al. (2003), who found that both range size
and island endemism were the strongest predictors of extinction risk in bats.
Small geographical range size is a key driver of population declines not only in bats, but
thousands of other species, including those among plants (Staude et al. 2019), insects (RochaOrtega et al. 2020), birds (Harris & Pimm 2007) and other types of mammals (Collen et al. 2011,
Staude et al. 2019). Species with smaller ranges are at a higher risk of extinction as the threats
causing population decline, such as disease, natural disasters, lack of resources, etc., is more likely
to affect the entire range of the species (Collen et al. 2016). Furthermore, island endemism is
closely linked to range size, as species that are endemic to islands tend to have small range sizes
(Jones et al. 2003). Threatened species that are endemic to islands also have lower genetic diversity
17

than related, nonthreatened, mainland species (Jamieson 2007). Therefore, genetic factors may be
playing a role in the extinction risk of island species through processes such as inbreeding
depression. Regardless of the mechanisms driving population declines in island endemics, our
results reinforce the importance of continued efforts to protect these species.
Our results suggest that conservation measures should be enacted to protect cave habitats
for bat species, as cave-dwelling species have more species at risk than any other habitat type.
Cave-dwelling being of disproportionately high conservation concern may be a result of the
impacts of White Nose Syndrome, as this disease disproportionately affects bats roosting in caves.
For species that currently lack an IUCN listing status due to insufficient population abundance
data, our results suggest that cave-dwelling species should be prioritized for population surveys
and assessment.
Globally, pollinating species of both birds and mammals have been in decline (Regan et al.
2015). These declines are caused primarily by extensive habitat loss in recent years (Regan et al.
2015). Many other drivers of pollinator decline are currently recognized by the Intergovernmental
Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). These threats include
pests and pathogens, ineffective management of pollinating species, pesticide use, land
management, invasive species, GMOs, and climate change (Dicks et al 2021). Moreover,
pollinating mammal species are more strongly affected by hunting than pollinating bird species
(Ripple et al. 2016). These drivers of pollinator declines may provide insight into to why pollinating
bat species appear to be at a high risk of extinction.
Larger bat species are commonly harvested for bushmeat in many parts of the world,
including Southeast Asia, Africa, and South America (Ripple et al. 2016). Persecution of larger
bat species may explain why bat species with larger hindfeet and forearms appear to be at a higher
risk of extinction, as they are disproportionately affected by hunting. Moreover, larger species
18

typically occur in lower population densities, a factor related to elevated extinction risk (Cardillo
et al. 2005).
Finally, we found species living in habitats with higher minimum temperatures during the
coldest month of the year were at a greater risk of extinction. As the impacts of deforestation are
most significant in the tropics, where temperatures are high year-round, the effects of deforestation,
habitat loss, and reduced resource availability may be disproportionately high in these areas (Wang
2013). This, in turn, may be putting species inhabiting warmer climates at a higher risk of
extinction. However, this may be related to deforestation and habitat loss as deforestation has been
shown to result in increased temperatures of local climates (Prevedello et al. 2019).
Though our research highlights the importance of understanding the drivers behind
population declines, the next step will be to construct a predictive model based on these findings
to assign data deficient bat species to a putative conservation category. By doing so, we will be
able to identify bat species in most urgent need of population assessment. By identifying these
potentially at-risk species, we may be able to avoid extinctions and mitigate threats by producing
effective conservation measures before it’s too late.

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