Responses of Cave-Roosting Bats to Complex Environmental Gradients: An
Assessment across Assemblage, Species and Population Levels
by
Kendra Phelps, M.S.
A Dissertation
In
Zoology
Submitted to the Graduate Faculty
of Texas Tech University in
Partial Fulfillment of
the Requirements for
the Degree of
DOCTOR OF PHILOSOPHY
Approved
Dr. Tigga Kingston
Chair of Committee
Dr. Nancy McIntyre
Dr. William Resetarits
Dr. Jodi Sedlock
Dr. Richard Strauss
Mark Sheridan
Dean of the Graduate School
August, 2016
Copyright 2016, Kendra Phelps
Texas Tech University, Kendra Phelps, August 2016
ACKNOWLEDGMENTS
This dissertation has been a long (long!) process, and I thank all those that helped me
finish it. Most importantly, I am most grateful for the invaluable contributions of my
dissertation committee, Drs. Tigga Kingston, Nancy McIntyre, Bill Resetarits, Jodi
Sedlock and Rich Strauss. Thank you for your insightful comments and
encouragement, without which my dissertation could not have been finished.
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TABLE OF CONTENTS
ACKNOWLEDGMENTS .................................................................................... ii
ABSTRACT .......................................................................................................... vi
LIST OF TABLES .............................................................................................. vii
LIST OF FIGURES ............................................................................................. ix
CHAPTERS
I. INTRODUCTION ............................................................................................. 1
Dissertation Overview ....................................................................................... 3
References ......................................................................................................... 5
II. CORRELATES OF CAVE-ROOSTING BAT DIVERSITY AS AN
EFFECTIVE TOOL TO IDENTIFY PRIORITY CAVES .............................. 8
Abstract ............................................................................................................. 9
Introduction ..................................................................................................... 10
Methods ........................................................................................................... 12
Study sites ................................................................................................. 12
Bat surveys ................................................................................................ 13
Quantifying explanatory factors ................................................................ 14
Statistical analysis ..................................................................................... 20
Results ............................................................................................................. 26
Discussion ....................................................................................................... 32
References ....................................................................................................... 37
Appendix A ..................................................................................................... 42
Appendix B ..................................................................................................... 47
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III. CAVE-ROOSTING BAT SPECIES, BUT NOT ASSEMBLAGES,
EXHIBIT ECOLOGICAL THRESHOLDS ACROSS COMPLEX
ENVIRONMENTAL GRADIENTS.................................................................. 51
Abstract ........................................................................................................... 52
Introduction ..................................................................................................... 53
Methods ........................................................................................................... 56
Study area .................................................................................................. 56
Cave surveys ............................................................................................. 56
Environmental gradients ........................................................................... 58
Bat sampling.............................................................................................. 62
Ecological species traits ............................................................................ 62
Data analysis ............................................................................................. 65
Results ............................................................................................................. 68
Assemblage-level responses to environmental gradients .......................... 68
Species responses to environmental gradients .......................................... 73
Comparison of ecological traits ................................................................ 74
Identification of indicator species ............................................................. 75
Discussion ....................................................................................................... 79
References ....................................................................................................... 84
Appendix A ..................................................................................................... 90
Appendix B ..................................................................................................... 92
Appendix C ..................................................................................................... 99
IV. INCREASED HUMAN DISTURBANCE IS ASSOCIATED WITH AN
ATYPICAL PHYSIOLOGICAL RESPONSE IN A CAVE-ROOSTING
BAT..................................................................................................................... 123
Abstract ......................................................................................................... 124
Introduction ................................................................................................... 124
Methods ......................................................................................................... 127
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Texas Tech University, Kendra Phelps, August 2016
Study sites ............................................................................................... 127
Bat sampling............................................................................................ 129
Physiological markers ............................................................................. 130
Statistical analysis ................................................................................... 131
Results ........................................................................................................... 132
Leukocyte profiles................................................................................... 132
Body condition ........................................................................................ 134
Ectoparasite loads.................................................................................... 135
Discussion ..................................................................................................... 138
References ..................................................................................................... 141
Appendix A ................................................................................................... 146
Appendix B ................................................................................................... 148
V. EXECUTIVE SUMMARY .......................................................................... 150
Introduction ................................................................................................... 151
Key findings .................................................................................................. 153
Future directions............................................................................................ 159
Appendix A ................................................................................................... 160
Appendix B ................................................................................................... 165
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ABSTRACT
Cave-roosting bat populations are declining globally, with human disturbance
at caves identified as the leading cause for these declines. Cave-roosting bats face a
multitude of human pressures, namely, hunting, cave tourism and exploitation of cave
resources (e.g., harvesting of guano for fertilizer and cave swiftlet nests for use in
soups), disturb roosting bats and limestone mining results in the complete destruction
of caves. Effects of cave disturbances are exacerbated by logging activities that
fragment and destroy forested habitats, since many cave-roosting species are
dependent upon intact forests as suitable foraging sites. Over a quarter of all bat
species are dependent upon caves for protection from inclement weather and predators
and as a stable environment to rear their young. As a result, cave-roosting bats exhibit
strong roost fidelity, and cave disturbance is consequently an inescapable stressor for
many bat species. Such threats jeopardize the viability of cave-dependent bats.
Furthermore, drastic shifts in abundance and composition of cave-roosting bat
assemblages could have a cascading effect on other cave-dependent wildlife. As
keystone species, bats provide vital ecological services in cave ecosystems; the
deposition of guano by aggregations of roosting bats is the primary energy source in
cave ecosystems. Therefore, the aim of my dissertation is to assess the response of
cave-roosting bats to increasingly human-dominated landscapes in order to make
empirically based recommendations to promote the conservation of cave-roosting bats.
My goals were to identify environmental and human disturbance drivers that influence
cave-roosting bats at multiple ecological levels, specifically assemblage, species and
population levels, on Bohol Island in the central Philippines. With 35 bat species,
Bohol Island is a biodiversity hotspot in the Philippines.
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In an effort to prioritize caves to conserve cave-roosting bat assemblages, I
identified environmental and human disturbance factors that serve as correlates of
assemblage diversity and species composition in caves. My results allowed for the
development of a systematic approach for the identification of priority caves using
rapid cave surveys and freely available open-source data. I demonstrate that
assemblage-level metrics can mask the response of individual species to human
disturbance. My results show the need to consider species-specific responses when
making management decisions, and to focus on species with greatest sensitivity when
planning conservation interventions. To understand the underlying mechanisms by
which individual species respond to cave disturbance, I assessed the physiological
response of Hipposideros diadema (Diadem roundleaf bat), a common cave-roosting
bat species found throughout Southeast Asia, to a disturbance gradient on Bohol
Island. My results are contradictory to the findings of most studies of human
disturbance on wildlife health, and indicate that increasing human disturbance was
correlated with a decreased physiological response, specifically lower neutrophil-tolymphocyte ratios and ectoparasite loads. Results are summarized for the Department
of Natural Resources of the Philippines to assist with meeting mandates set forth in the
National Caves and Cave Resources Management and Protection Act (Republic Act
No. 9072) and to promote bat conservation in the Philippines.
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LIST OF TABLES
2.1
Framework for quantifying human disturbance at each cave,
based on a modified version of the karst disturbance index
developed by van Beynen and Townsend (2005) ..................................... 18
2.2
First six components derived from principal component analysis
of environmental and human disturbance factors ..................................... 24
2.3
Results of mvabund analysis of deviance (anova.manyglm)
(Wang et al., 2014) testing the significance of predicator
variables (PC1 – PC6) on assemblage composition and species
abundance.................................................................................................. 28
3.1
Principal component analysis of the original dataset of 18
environmental variables measured at 56 caves on Bohol Island,
Philippine .................................................................................................. 60
3.2
Results of Threshold Indicator Taxa ANalysis (TITAN) of
assemblage-level thresholds along multiple environmental
gradients on Bohol Island.......................................................................... 70
3.3
Results of the Chi-square test (χ2) on categorical traits and
Kruskal-Wallis tests (H) on continuous traits performed
between species groups (z- and z+) distinguished by Threshold
Indicator Taxa ANalysis (TITAN) and ecological traits........................... 70
4.1
Effects of cave disturbance and complexity on physiological
health of H. diadema ............................................................................... 137
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LIST OF FIGURES
2.1
Caves (n = 62) surveyed on Bohol Island, Philippine............................... 13
2.2
NMDS ordination biplot of site-species correlation matrix: a)
sites (numbered) and b) species (abbreviations) with predictor
variables superimposed. Site (cave) proximity represents
similarity of species composition (a), while species proximity
demonstrates presence at shared sites (b). The direction of each
plotted arrow indicates the direction of an increase in the
gradient for the corresponding variable, while the length is
proportional to the correlation between the predictor variable
and the ordination. Solid arrows in bold are significantly
correlated with the ordination (p < 0.05), while dashed arrows
are not. Cave and species codes are listed in Appendix A and B ............. 29
2.3
Number of species conserved by protecting 10% of caves (n =
6) based on three prioritization schemes: Scheme 1 - lowest
score on PC1 (surface-level disturbance; long dash line),
Scheme 2 - highest scores on PC2 (cave complexity; long dash
line) and Scheme 3 - combination of the prior schemes (low
surface-level disturbance and high cave complexity scores;
dash-dot line) in comparison to random selection (solid black
line; dotted gray lines indicate 95% confidence intervals) and
selection of caves with the highest species richness (short dash
line) ........................................................................................................... 31
3.1
Threshold Indicator Taxa ANalysis (TITAN) of assemblage-level
thresholds along multiple environmental gradients. Summed zscores for all species in a response group (Sum(z)) at candidate
change-points along each complex environmental gradient. Black
solid lines represent the cumulative frequency distribution of
change-points after 500 replications for the negative indicator
species (z-) group and red dashed lines for the positive indicator
species (z+) group. Peaks in these lines indicate a congruence
among species, and define the assemblage-level threshold for each
response group. Connected circles represent the summation of zscores at each candidate change-point, with closed circles
representing sum(z-) and open circles representing sum(z+) values. ............ 71
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3.2
Threshold Indicator Taxa ANalysis (TITAN) summary plots of
species-specific change-points along multiple environmental
gradients. Only species that exhibited a significant response to a
respective gradient are shown (IndVal < 0.05), with each species’
corresponding threshold represented by circles. Black circles
correspond to negative (z-) indicator taxa (with corresponding
species labels on the left axes; see Appendix A for details) and red
circles correspond to positive (z+) indicator taxa (with
corresponding species labels on the right axes). Circles are sized in
proportion to the magnitude of each species’ response (i.e., zscores), with overlapping horizontal lines representing the 95%
confidence limits after 500 bootstrap replicates ........................................... 76
4.1
Caves (n = 29) surveyed on Bohol Island, the Philippines ..................... 128
4.2
Change in neutrophil-to-lymphocyte ratios with increased cave
disturbance .............................................................................................. 133
4.3
Two-dimension contour plot depicting the interaction relationship
between cave complexity and cave disturbance on body condition
(measured as scaled mass index) for H. diadema (n = 714). Scale
bar represents body condition, with increasingly darker shades
indicative of better body condition ............................................................ 135
5.1
Geographic distribution of caves (n = 62) surveyed on Bohol
Island, Philippines .................................................................................... 152
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Texas Tech University, Kendra Phelps, August 2016
CHAPTER I
INTRODUCTION
The Philippines has been identified as one of the highest conservation priorities
in the world (Myers et al., 2000). This is partly due to its exceptionally high bat
diversity, of which over a quarter of bat species are endemic (Heaney, 2004; Heaney
et al., 2010). Of the 78 bat species documented in the Philippines, roughly half are
dependent upon solution caves formed in the karst landscapes, which cover 35000 km2
or 10% of the Philippines (Day and Urich, 2000; Restificar et al., 2006). Caves are
ideal roosting sites for bats because they are permanent structures that provide
protection from weather and predators and have stable microclimatic conditions
favorable for rearing young (Kunz, 1982). Moreover, caves provide a range of
roosting opportunities through structural complexity (e.g., chambers, passages,
cavities and crevices) and an array of favorable temperature and humidity regimes,
which accommodates individual species’ preferences and allows for cohabitation by a
multitude of species (Furey and Racey, 2016). Consequently, caves can harbor some
of the largest aggregations of bat species in the world (Hutson et al., 2001). One
example, Monfort Cave on Samal Island, Philippines, was recognized by the Guinness
World Records for housing the largest colony of fruit bats (Rousettus
amplexicaudatus) in the world, with a colony size estimated at 1.8 million individuals
(roosting density of 452.3 individuals/m2) (Carpenter et al., 2014).
Cave-roosting bats provide vital ecological and economic ecosystem services,
specifically pollination, seed dispersal and pest suppression (Kunz et al., 2011).
Frugivorous bats play essential roles in tropical forest regeneration and maintenance
due to their seed dispersal capabilities (Muscarella and Fleming, 2007). Other caveroosting bats species pollinate economically significant plants. For example, the lesser
dawn bat (Eonycteris spelaea), a colonial cave-dependent species, is the primary
pollinators of petai and durian (Bumrungsri et al., 2013), which generates revenues
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Texas Tech University, Kendra Phelps, August 2016
exceeding $137 million in Thailand alone (Petchmunee, 2008). Insectivorous bat
species provide ecosystem services that have economic significance through the
predation of costly, crop-damaging insects. Leelapaibul et al. (2005) estimated that a
colony of 2.6 million wrinkle-lipped free-tailed bats (Chaerephon plicatus) could
consume roughly 17.5 tons of insects, primarily pests of rice crops, on a nightly basis.
Most importantly, cave-roosting bats supply the primary energy source in cave
ecosystems. Guano produced by roosting bats supports communities of cave
invertebrates dependent entirely upon guano as a food source (Gnaspini and Trajano,
2000).
Tragically, human threats to cave-roosting bats are many-fold. Limestone
mining is the primary threat, resulting in the direct and irreversible loss of roosting
sites (Clements et al., 2006; Kingston, 2010). Human pressures are magnified further
by overexploitation through unregulated hunting for bushmeat and inclusion in
medicinal remedies (e.g., asthma) (Mildenstein et al., 2016). Cave tourism has
increased rapidly in popularity in recent years, which has led to modifications of caves
(e.g., paved paths and stairs, installation of lightening systems) to accommodate
tourists without the consideration of the effects on cave-roosting bats (Zhang et al.,
2009). Frequent and regular visits by tourists can cause temperature and humidity
levels to fluctuate, as well as increase CO2 concentrations (Song et al., 2000), causing
bats to abandon roosts (Mann et al., 2002). Harvesting of guano for fertilizer, cave
swiftlet nests for bird’s nest soup and mineral formations (e.g., stalactites) disturbs
roosting bats (Clements et al., 2006; Suyanto and Struebig, 2007; Kingston, 2010).
Furthermore, outside the roost, cave-roosting bats are under additional pressure from
the loss of foraging sites. Land conversion to accommodate expanding urban areas and
agriculture, in addition to timber harvesting, has resulted in extensive habitat
destruction and deforestation around caves (Hutson et al., 2001; Kingston, 2010). Oldgrowth primary forest once covered 96% of the Philippines but has since be reduced to
6% (Heaney et al., 2000). Thus, cave-roosting species are especially vulnerable to
current deforestation rates (Lane et al., 2006). Collectively, threats to cave-roosting
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Texas Tech University, Kendra Phelps, August 2016
bats occur at both the roost and in the surrounding landscape, which may have
detrimental and long-lasting consequences for the continued viability of cave-roosting
bats in the Philippines.
In recent years, long overdue attention has been given to the protection and
potential restoration of priority caves in the Philippines. The Republic Act No. 9072
(National Caves and Cave Resources Management and Protection Act), enacted in
2001, is intended to protect and safeguard the country’s caves and cave resources.
However, there has been little enforcement of the Act due to a lack of countrywide
cave inventories (Urich et al., 2001) or targeted methods to identify priority caves to
ensure cost-effective management of cave-dependent wildlife in the country. Without
conservation efforts, cave-roosting bats, and the cave wildlife dependent upon them,
are in jeopardy of future declines. However, no studies to date have explicitly
identified drivers that shape the response of cave-roosting bats to environmental
conditions, particularly complex gradients of human disturbance.
Dissertation Overview
The aim of my dissertation is to assess the response of cave-roosting bats to
increasingly human-dominated landscapes in order to make empirically based
recommendations to promote the conservation of cave-roosting bats. My goals were to
identify environmental drivers that influenced cave-roosting bats at multiple
organizational levels, specifically assemblage, species and population levels, on Bohol
Island in the central Philippines. With 35 bat species, Bohol Island is a biodiversity
hotspot in the Philippines (Heaney et al., 2010; Sedlock et al., 2014; Phelps et al., in
press). Nearly half of these species are dependent upon caves, making them vulnerable
to human disturbances at caves and foraging sites in the surrounding landscape.
In an effort to prioritize caves to conserve cave-roosting bat assemblages, I
identified environmental factors that influence assemblage diversity and composition
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Texas Tech University, Kendra Phelps, August 2016
in caves (Chapter II). Based on surveys of 56 caves subjected to varying levels of
human disturbance, I found surface-level disturbance in the landscape surrounding the
caves and cave complexity to be the primary drivers of cave-roosting bat assemblages.
My results allowed for the development of guidelines for the identification of priority
caves using rapid cave surveys and freely available open-source spatial data. In
Chapter III, I demonstrate that assemblage-level metrics can mask the response of
individual species to human disturbance. I identified species-specific thresholds, also
referred to as change-points, in species’ occurrence frequency and relatively
abundance along complex environmental gradients. My results demonstrate the need
to consider species-specific responses when making management decisions, and to
focus on species with the lowest thresholds when planning conservation interventions.
Furthermore, I used indicator species analysis to identify bat species that exhibited
significant responses to each environmental gradient, such species may be useful to
rapidly assess conditions present at a cave. Lastly, to understand the underlying
mechanisms by which individual species respond to cave disturbance, I assessed the
physiological response of Hipposideros diadema (Diadem roundleaf bat), a common
cave-roosting bat species found throughout Southeast Asia, to a disturbance gradient
on Bohol Island (Chapter IV). I compared physiological markers in peripheral blood,
specifically differential white blood cell counts and neutrophil-to-lymphocyte ratios,
as well as ectoparasite loads and body condition under differing levels of cave
disturbance. Contrary to my expectations, increasing human disturbance was
correlated with a decreased physiological response, specifically lower neutrophil-tolymphocyte ratios and ectoparasite loads. However, body condition was not influenced
by cave disturbance, but rather increased significantly in caves with greater
complexity. My results are contradictory to the findings of most studies of human
disturbance on wildlife health, and may indicate that some cave-roosting bat species
are capable of acclimating to (or are at least tolerant of) cave disturbance.
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Carpenter, E.-S., Gomez, R., Waldien, D.L., Sherwin, R.E., 2014. Photographic
estimation of roosting density of Geoffroy’s Rousette Fruit Bat Rousettus
amplexicaudatus (Chiroptera: Pteropodidae) at Monfort Bat Cave, Philippines. J.
Threat. Taxa 6, 5838–5844. doi:10.11609/JoTT.o3522.5838-44.
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Southeast Asia: imperiled arks of biodiversity. Bioscience 56, 733–742.
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Furey, N.M., Racey, P.A., 2016. Conservation ecology of cave bats, in: Voight, C.C.,
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Gnaspini, P., Trajano, E., 2000. Guano communities in tropical caves, in: Wilkens, H.,
Culver, D.C., Humphreys, W.F. (Eds.), Ecosystems of the World: Subterranean
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Heaney, L.R., Dolar, M.L., Balete, D.S., Esselstyn, J.A., Rickart, E.A., Sedlock, J.L.,
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Kingston, T., 2010. Research priorities for bat conservation in Southeast Asia: a
consensus approach. Biodivers. Conserv. 19, 471–484. doi:10.1007/s10531-0089458-5.
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Kunz, T.H., de Torrez, E.B., Bauer, D., Lobova, T., Fleming, T.H., 2011. Ecosystem
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free-tailed bat (Tadarida plicata Buchannan, 1800) in central Thailand:
insectivorous bats potentially act as biological pest control agents. Acta
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Mann, S.L., Steidl, R.J., Dalton, V.M., 2002. Effects of cave tours on breeding Myotis
velifer. J. Wildl. Manage. 66, 618–624. doi:10.2307/3803128.
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medicine, in: Voigt, C.C., Kingston, T. (Eds.), Bats in the Anthropocene:
Conservation of Bats in a Changing World. Springer International Publishing, pp.
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Sedlock, J.L., Jose, R.P., Vogt, J.M., Paguntalan, L.M.J., Cariño, A.B., 2014. A survey
of bats in a karst landscape in the central Philippines. Acta Chiropterol. 16, 197–
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CHAPTER II
CORRELATES OF CAVE-ROOSTING BAT DIVERSITY AS AN
EFFECTIVE TOOL TO IDENTIFY PRIORITY CAVES
In press at Biological Conservation
Kendra Phelpsa,b,*, Reizl Josec, Marina Labonitec, Tigga Kingstona,b
a
Department of Biological Sciences, Texas Tech University, Lubbock, USA
b
Southeast Asian Bat Conservation Research Unit, Lubbock, USA
c
Research & Development, Bohol Island State University, Bilar, Philippines
*Corresponding author: Department of Biological Sciences, Texas Tech University,
MS 43131, Lubbock, TX 79409, USA. Tel.: +1 (806) 742-2731.
E-mail address: [email protected]
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Texas Tech University, Kendra Phelps, August 2016
Abstract
Cave ecosystems are subterranean biodiversity hotspots, but limited knowledge
of the distribution of diversity among caves hampers their conservation. Surveys of
surrogate taxa (e.g., keystone species) can identify hotspots of biodiversity when
knowledge about an ecosystem is lacking. Bats are keystone species in cave
ecosystems because their guano is the primary energy source supporting diverse
assemblages of cave-dependent wildlife. However, directly measuring bat diversity is
time-consuming and requires expert knowledge; instead, we suggest the use of
correlates of bat diversity that can be derived from readily accessible data (e.g., landuse maps) and straightforward methods not requiring expert knowledge (e.g., cave
surveys, interviews) as a foundation for prioritizing caves. To identify easily
measurable correlates of bat diversity, we compared assemblage composition and
species abundances of 21 bat species captured in 56 caves on Bohol Island,
Philippines, along gradients in environmental factors and human disturbance. Modeland distance-based methods indicated that surface-level disturbance (i.e., percent nonforested habitat, degree of urbanization and road development) along with cave
complexity (i.e., available roosting area, structural heterogeneity, number of entrances
and temperature range) were the most influential factors governing cave-roosting bat
assemblages, thus representing correlates of bat diversity. Prioritization schemes based
on these correlates select combinations of caves with greater species richness than
both random selection and selection of caves based on observed richness from
intensive bat surveys. The use of easy-to-measure environmental and disturbance
correlates of bat diversity is an effective tool to prioritize caves to protect caveroosting bats and the cave-dependent wildlife they support.
Keywords: Cave disturbance, Site prioritization, Mvabund, Keystone species,
Chiroptera, Conservation planning
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Texas Tech University, Kendra Phelps, August 2016
Introduction
Setting conservation priorities for the allocation of limited financial resources
is a difficult task faced by conservation organizations, including government agencies
responsible for the protection and management of native biodiversity. Perhaps the
most widely-adopted approach to priority-setting is to identify hotspots of biodiversity
based on the number of endemic species and degree of human threat (Myers et al.,
2000). Many subterranean ecosystems, such as caves, warrant designation as
biodiversity hotspots given the high proportion of endemic species (Whitten, 2009),
especially invertebrate taxa (Deharveng and Bedos, 2012), they support. Many cave
endemic species are relatively small, often poorly studied and are rarely considered in
conservation priority setting, yet their habitats are under intense human pressure
(Whitten, 2009, Deharveng and Bedos, 2012). Mining for nonrenewable sediment
deposits (e.g., limestone and phosphate) is the most severe threat to cave ecosystems
(Culver and Pipan, 2009; Furey and Racey, 2016), at times resulting in the complete
destruction of caves (Calò and Parise, 2006). Dumping of household waste and
agricultural practices that increase sedimentation and pesticide contamination pollute
cave ecosystems (Watson et al., 1997; Culver and Pipan, 2009; van Beynen et al.,
2012). In the absence of designated walkways, foot traffic from tourists can compact
cave substrates and trample floor-dwelling cave wildlife (Watson et al., 1997).
Moreover, tourists unintentionally alter microclimatic conditions, leading to marked
fluctuations in carbon dioxide levels, air temperature and relative humidity (Furey and
Racey, 2016), making caves uninhabitable for some cave-dependent species (Russell
and MacLean, 2008). Unfortunately, cave ecosystems are often simultaneously
subjected to multiple threats (Culver and Pipan, 2009; Furey and Racey, 2016).
To formulate effective conservation strategies to protect cave ecosystems, it is
fundamental to characterize the biodiversity dependent upon caves, but taxonomic
information about many cave taxa (e.g., invertebrates) is lacking (Elliott, 2005). In
such instances, surrogate taxa (e.g., keystone, umbrella and indicator species) (Caro,
2010) can be used to identify hotspots of biodiversity (Martikainen et al., 1998; Suter
10
Texas Tech University, Kendra Phelps, August 2016
et al., 2002). Although the use of surrogate taxa to prioritize ecosystems is debated
(Andelman and Fagan, 2000; Sætersdal and Gjerde, 2011), species that play keystone
roles in structuring trophic networks within ecosystems should serve as centerpieces
for prioritization decisions (Mills et al., 1993; Power et al., 1996). Because cave
ecosystems lack primary producers, cave-roosting bats are keystone species in cave
ecosystems as they support diverse assemblages of cave-dependent wildlife via the
deposition of guano (Gnaspini and Trajano, 2000; Kunz et al., 2011). Guano is the
primary energy source sustaining bottom-up dynamics in caves by supporting
coprophagous invertebrates, which in turn are prey for larger vertebrate predators
(e.g., cavefishes, salamanders) (Gnaspini and Trajano, 2000). For example, grotto
salamanders (Eurycea spelaea), top cave predators, depend on invertebrates that
congregate on guano piles formed by roosting gray bats (Myotis grisescens) to such an
extent that body condition varies with the presence of this migratory bat species
(Fenolio et al., 2014). Bat species differ in their diets (e.g., insects, nectar, fruit), and
as a result, their guano varies in physiochemical and nutritional properties (e.g., pH,
organic matter, minerals) (Studier et al., 1994; Emerson and Roark, 2007). Conserving
diverse aggregations of cave-roosting bats therefore supports a diversity of
coprophagous invertebrates and their predators (Ferreira et al., 2007; Trajano and
Bichuette, 2010). Some of the largest and most diverse aggregations of bat species in
the world roost in caves (Hutson et al., 2001), with roughly 40% of all threatened bat
species using caves as roosts (IUCN, 2015). Yet cave bat populations continue to
decline globally, with cave disturbance identified as the leading cause of these
declines (Hutson et al., 2001). Ultimately, the loss of cave-roosting bats will have
cascading effects on cave ecosystems.
Prioritizing caves based on in-depth knowledge of the compositions of cave bat
assemblages would effectively conserve imperiled bat species and diverse cave
ecosystems. However, identifying caves that house large, diverse aggregations of bats
requires expert knowledge of bat species and can be time-consuming and expensive.
Instead, we suggest use of correlates that reflect increased bat diversity and abundance
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Texas Tech University, Kendra Phelps, August 2016
that are readily accessible, easy-to-use and require minimal training to measure
accurately. In the present study, we identify correlates of cave-roosting bat diversity
using easily acquired data and straightforward methods. We then test their efficacy as
a tool for identifying priority caves to promote bat conservation. First, we compared
species composition and abundance of cave bat assemblages on Bohol Island,
Philippines, with a suite of environmental and human disturbance factors that can be
measured in just a few hours to identify which factors correlated with increased bat
diversity. Then, we developed cave prioritization schemes based on the identified
correlates of bat diversity and tested the performance of each scheme for selecting
caves that conserve the greatest species richness in comparison to random selection
and selection of caves based on our knowledge of observed richness. All prioritization
schemes outperformed random selection, and the scheme that combined measures of
surface-level disturbance and cave complexity conserved the most species. Surfacelevel disturbances can be measured using open-source data and cave complexity using
rapid cave surveys and interviews with local residents. We demonstrate that basic
resources available to government agencies, and other conservation organizations
charged with protecting biodiversity, can be used as an effective prioritization tool to
protect diverse bat assemblages and the cave-dependent wildlife they support.
Methods
Study sites
Bohol Island is located in the central Visayas region of the Philippines and covers an
area of approximately 4100 km2, largely composed of limestone karst (Urich et al.,
2001). Preliminary reconnaissance work and informal conversations with local
residents, cavers and other researchers identified numerous caves on Bohol Island. We
selected 62 caves subject to a gradient of human disturbance, including undisturbed
caves within protected areas, disturbance from local residents who extract cave
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Texas Tech University, Kendra Phelps, August 2016
resources and more severe disturbance from active tourism and mining operations
(Fig. 2.1). Final selection of caves was made based on knowledge of the presence of
cave-roosting bats. Caves were visited between July 2011 and June 2013, with the
exception of January – May 2012.
Fig. 2.1. Caves (n = 62) surveyed on Bohol Island, Philippines (see insert). See
Appendix A for detailed information about each cave.
Bat surveys
Bats were captured using mist nets placed in strategic locations at cave entrances and
inside passages for two consecutive nights at each cave, with all other potential exits
blocked with vegetation. Various-sized nets (i.e., 3, 6, 9 and 12 m) were erected in the
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Texas Tech University, Kendra Phelps, August 2016
same location for both nights and opened just prior to sunset then continuously
monitored, on average, for 5.5 h per night. Captured individuals were identified to
species using morphological characteristics (Ingle and Heaney, 1992), then weighed
(g) and forearm measured (mm). Prior to release, a 3 mm wing biopsy punch of the
right plagiopatagium was taken to mark individuals to avoid including recaptures in
analyses (Faure et al., 2009). Because all cave exits were blocked by mist nets and/or
vegetation, sampling effort was calculated as the number of hours the net(s) was open
(nh) for both nights. Sampling effort was not calculated as net-meter-hours (nmh)
because caves supporting comparable populations of bats could vary in size and
number of entrances and, consequently, would skew, either inflate or dilute, the
capture rate if expressed in net-meter-hours. Raw count data were corrected for
differing sampling efforts among caves, yielding adjusted capture rates
(individuals/nh) for each species at each cave.
For each species, except Cynopterus brachyotis and Eonycteris spelaea, at least one
adult individual was prepared as a voucher specimen and deposited in the Natural
Science Research Laboratory of the Museum of Texas Tech University. All methods
followed guidelines set forth by the American Society of Mammalogists (Sikes, et al.,
2011) and were approved by the Texas Tech University Animal Care and Use
Committee (ACUC 10015-04, 13031-04).
Quantifying explanatory factors
Environmental factors
Each cave was mapped using standard cave survey methods (Ellis, 1976), with most
surveys taking less than 2 h. A series of survey stations, the first station being located
at the main entrance, was established along the length of the cave based on changes in
height or width of the cave passage or presence of side passages. We made great
efforts to survey all passages in each cave, with only four instances in which a passage
was not surveyed due to safety concerns. At each station, distance to the previous
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Texas Tech University, Kendra Phelps, August 2016
station and height and width of the cave passage were measured using a tape measure.
If passage height was unreachable, a laser rangefinder was used or height was visually
estimated. Number of cave entrances, including vertical entrances, was also recorded.
Additionally, ambient temperature (C˚), wind speed (kmh) and relative humidity (%)
were measured at each station using a hand-held weather station (Kestrel 3000 Pocket
Weather Meter).
We estimated available roosting area and structural heterogeneity of each cave
using measurements from cave surveys. Following methods detailed by Brunet and
Medellín (2001), total surface area of a cave was based on a series of elliptical
cylinders represented by length, height and width measurements taken between survey
stations. Surface area was calculated using the formula C = 2πL ([a2 + b2] / 2)1/2,
where L is the length between survey stations and a and b represent height and width
of the passage between survey stations, respectively. Surface area was summed across
survey stations and halved to approximate the roosting area available to bats in each
cave. Structural heterogeneity refers to the complexity of chambers and passages
within a cave and was calculated by dividing the total cave length by the greatest
length between any two survey stations (Arita, 1996). With increasing structural
heterogeneity, a cave is assumed to have a greater diversity of available roosting sites.
Single tunnel caves have a structural heterogeneity of 1.0, with an increasing value
indicating increasing structural heterogeneity.
Variation in microclimatic conditions (i.e., temperature, humidity) has been
shown to influence roost selection by cave-roosting bats (Brunet and Medellín, 2001;
Avila-Flores and Medellín, 2004). Therefore, we included measures of variation in
microclimate using measurements of ambient temperature and relative humidity taken
during cave surveys. Specifically, we calculated the range in values by subtracting the
minimum value from the maximum value. Wind speed was not included in our
analysis since most caves had no measurable wind movement or lacked any variation
in wind speed.
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Texas Tech University, Kendra Phelps, August 2016
Human disturbance factors
We modified the karst disturbance index developed by van Beynen and
Townsend (2005) to include a framework of potential cave disturbances that may
impact cave-roosting bat assemblages and incorporated additional human disturbance
factors by interviewing local residents. Human disturbance was assessed at the
surface- and subsurface-level, with multiple indicators of disturbance at each level
(Table 2.1). We assigned each indicator a score ranging from 0 to 3 depending on the
extent and severity of the human disturbance: 0 – none, 1 – localized and not severe, 2
– highly disturbed and widespread, 3 – severely disturbed (van Beynen and Townsend,
2005).
Surface-level disturbances - Cave locations were georeferenced using a global
positioning system unit and plotted using Google Earth Pro, a freely available online
resource for satellite imagery, to gauge surface-level disturbance within a 1 km radius
of the main cave entrance. We included visual observations of surface-level
disturbances as a supplement when satellite images from Google Earth Pro (2011 –
2015) were obscured by dense cloud cover. We selected a 1 km radius since many bat
species remain near the roost if quality foraging habitat (i.e., intact forest) is present
locally (Kingston, 2013); thus, disturbances at this scale could make a cave unsuitable
due to the increase in energetic costs of commuting greater distances to available
foraging habitat. Within 1 km radius of each cave, we estimated the percentage of
non-forested habitat (%), number of human residents (i.e., number of habitations *
average household size of 4.79 individuals on Bohol Island; Philippine Statistics
Authority, 2013) and road size as indicators of human development surrounding the
cave (Table 2.1).
Subsurface-level disturbances – Information regarding human disturbance at
the subsurface level was obtained through visual observations during cave surveys and
one-on-one interviews with local residents. Percentage of the cave area impacted by
mining, typically from the extraction of limestone and/or phosphate, was estimated
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Texas Tech University, Kendra Phelps, August 2016
visually during cave surveys, along with household waste and vandalism/graffiti.
Furthermore, modifications of the cave structure to accommodate humans (e.g.,
tourists, miners) were observed and scored based on increasing invasiveness.
One-on-one interviews using a structured questionnaire composed of closedformat questions were conducted to collect data on subsurface-level disturbances by
residents living in close proximity to caves (1 – 2 km). We interviewed, on average,
10 individuals from different households living near each cave, which took typically
less than an hour but duration varied depending on the number of interviewers. Local
residents (n = 559) were asked about the frequency and purpose of visits (e.g.,
tourism, guano or swiftlet nest collection, bat hunting and/or religious purposes) to the
cave, both by themselves and others in the barangay (village or subunit of a city).
Interviewee responses to these questions allowed us to score resource extraction, bat
hunting and visitation frequency. Specifically, we summed the number of responses
indicating that the resident or someone they knew visited the cave to extract resources
or hunt bats and divided by the total number of responses. Scores for both factors were
confirmed with visual observations of human activities indicative of resource
extraction (e.g., sacked guano, shovels and buckets) and bat hunting (e.g., nets strung
between poles to catch bats, burned palm fronds and/or rubber to suffocate roosting
bats) during cave surveys. Visitation frequency was based on greatest visitation
frequency (i.e., daily, weekly, monthly or yearly/never) indicated by two or more
interviewees.
17
Table 2.1. Framework for quantifying human disturbance at each cave, based on a modified version of the karst disturbance index
developed by van Beynen and Townsend (2005).
Scale
Indicator
Score
3
2
1
0
> 66%
34 – 66%
< 33%
None
Urbanization
Large city
(> 1000 residents)
Small town (100 1000 residents)
Rural settlements
None
(within 1 km)
Road size
Major highway
Two-lane road
Single-lane trail
None
Mining*
Large-scale operation
(> 10%)
Moderate-scale
operation (1 – 10%)
Small-scale operation
(< 1%)
None
Cave development
Lighting systems,
paved walkways
Primitive walkways,
stairs
Signage or marked
trail to cave
None
Resource extraction+
> 66%
34 – 66%
< 33%
None
Bat hunting+
> 66%
34 – 66%
< 33%
None
Trash dumping*
Widespread areas
(> 66%)
34 – 66%
Isolated areas
(< 33%)
None
Vandalism/graffiti*
Widespread areas
(> 66%)
34 – 66%
Isolated areas
(< 33%)
None
Visitation frequency++
Daily
Weekly
Monthly
Never/Yearly
Subsurface-level
Disturbances
Texas Tech University, Kendra Phelps, August 2016
Non-forested habitat
18
Surface-level
disturbances
*Scoring was based on percentage of cave area impacted by the indicator.
+Scoring was based on percentage of interviewees that responded that they or someone they knew extracted resources or hunted bats
in the cave.
++Scoring was based on the highest visitation frequency that two or more local residents indicated during interview.
Texas Tech University, Kendra Phelps, August 2016
19
Texas Tech University, Kendra Phelps, August 2016
Additional factors – In addition to georeferencing cave localities, the center of
the nearest barangay was georeferenced and plotted online using Google Earth Pro.
The Euclidean distance between the cave and barangay center was measured (km), as
well as the distance from the cave to nearest access point (km) (e.g., foot trail, road).
Geospatial data delineating protected areas, including watershed forest reserves,
protected landscapes and seascapes and national monuments, identified under the
National Integrated Protected Areas System were obtained via the freely accessible
website maintained by the Department of Environment and Natural Resources of the
Philippines (http://www.bmb.gov.ph/) and plotted with cave localities in Google Earth
Pro. Caves that were located within the boundaries of a protected area were scored
binarily: 1 (cave within boundaries of protected area) or 0 (outside boundaries).
Statistical analyses
Prior to statistical analyses, thorough data exploration was carried out to check
for outliers, heterogeneity of variance, deviations from normality and other potential
issues pointed out by Ieno and Zuur (2015). Continuous data were tested for normality
using the Shapiro-Wilk test, with non-normally distributed data log10-transformed to
improve normality. Of the 62 caves included in this study, six were removed from
analyses due to missing data (e.g., microclimate values were not measured due to
equipment malfunction). All analyses were conducted in R version 3.1.2 (R Core R
Core Team, 2014), with results considered significant at p < 0.05.
Principal component analysis (PCA) was used to reduce the dimensionality of
the dataset. All environmental and human disturbance factors (transformed if
improved normality) were used as inputs to function princomp in base package stats
(R Core R Core Team, 2014). The PCA, based on a correlation matrix, extracted six
components with eigenvalues > 1.0 (Kaiser, 1960). Combined, the first six
components explained 73.9% of the variance in caves (Table 2.2). All meaningful
loadings (i.e., loadings > 0.30) (Harlow, 2014) were included in the interpretation of
20
Texas Tech University, Kendra Phelps, August 2016
principal components (PCs). Confidence intervals are based on resampling of 999
iterations with replacement.
The first component (PC1) was positively associated with surface-level
disturbances (i.e., percent non-forested habitat, degree of urbanization and road
development) and negatively associated with distance to access. All environmental
factors, with the exception of humidity, loaded positively on the second component
(PC2). Caves with increasingly positive scores on PC2 are characterized by increasing
complexity, those having greater available roosting area, structural heterogeneity,
number of entrances and range in temperature. The third component (PC3) was
positively associated with mining and household waste but negatively associated with
a wide range in microclimatic conditions. Thus, high scores on PC3 describe caves
with stable microclimates that experience human disturbances that include mining
and/or dumping of household waste. The fourth component (PC4) was positively
associated with cave development and visitation frequency but negatively associated
with resource extraction. Caves with high scores on PC4 were frequently visited and
had greater development of the cave (e.g., installed lighting, walkways) but were not
exploited for swiftlet nests, speleothems or any other resource. Resource extraction,
range in humidity, protected area and visitation frequency was positively associated
with the fifth component (PC5), representing caves likely found within protected areas
that are subjected to resource extraction and frequent visitation with stable humidity
levels. The sixth component (PC6) was positively associated with bat hunting,
household waste and protected area but negatively associated with mining. Caves with
high scores on PC6 were likely located within the boundaries of a protected area but
experienced bat hunting as the primary form of human disturbance. However,
confidence intervals for all PCs, with the exception of PC1, are rather wide and may
influence interpretation of these PCs.
To examine variation in species composition among assemblages (n = 56), we
used a multivariate generalized linear model with principal components (PC1 – PC6)
as predictor variables using the function manyglm in the package mvabund (Wang et
21
Texas Tech University, Kendra Phelps, August 2016
al., 2014). Negative binomial regression structure was specified in our models due to
overdispersion of the species composition data. The function anova.manyglm in
mvabund was used to test for significant effects of predictor variables on assemblage
composition, with post-hoc univariate tests used to determine the responses of
individual species. We report responses of species with unadjusted p-values.
Diagnostic plots were checked to ensure that model assumptions were met.
To visualize the variation in bat assemblage composition in relation to our
predictor variables, we conducted indirect gradient analysis using non-metric
multidimensional scaling (NMDS) on the site-species matrix. Ordinations based on
Manhattan dissimilarity coefficients were conducted using the function metaMDS in
package vegan (Oksanen et al., 2015). We superimposed computed vectors for
predictor variables onto the ordination plot using the function envfit in vegan to
determine which variables have the greatest influence on assemblage structure. The
significance of the fitted vectors was assessed after 999 permutations, with resulting
correlation values (r2) indicating the strength of the association between each predictor
variable and the configuration of assemblage composition in the NMDS ordination.
Identification of significant correlates of composition structure and species
abundance of cave-roosting bats allowed us to prioritize caves for bat conservation. To
test the performance of our proposed prioritization schemes based on identified
correlates in comparison to random selection, we compared the number of species
conserved when selecting roughly 10% of caves (i.e., 6 caves) in our study. Based on
principal component scores, we selected six caves based on three prioritization
schemes: Scheme 1 - caves with the highest negative scores on PC1 (surface-level
disturbance); Scheme 2 - caves with the highest positive scores on PC2 (cave
complexity); and Scheme 3 - a combination of Schemes 1 and 2 (caves with a
combination of both negative scores on PC1 and positive scores on PC2). Next, we
randomly selected six caves without replacement from a pool of all 56 caves,
summing species richness during each of the 999 repetitions. We adjusted species
richness for varying sampling effort at each cave and compared with unadjusted
22
Texas Tech University, Kendra Phelps, August 2016
species richness, the results did not differ so we elected to report unadjusted species
richness. Observed cumulative species richness from six caves selected based on our
prioritization schemes were then compared with the distribution of cumulative species
richness of random selection. In addition, we also compared the observed cumulative
species richness of our prioritized caves with that of the six most species-rich caves in
our study.
23
Table 2.2. First six components derived from principal component analysis of environmental and human disturbance factors.
Eigenvalues
Explained variance (%)
Interpretation
PC1
PC2
PC3
PC4
PC5
PC6
2.33
30.20
1.54
13.21
1.38
10.65
1.21
8.16
1.00
5.56
Surface-level
disturbance
Cave complexity
Mining
Cave
development
1.06
6.21
Resource
extraction in
protected areas
Bat hunting
PC loadings
0.10 [0.01 - 0.22]
0.21 [0.08 - 0.28]
0.13 [0.11 - 0.21]
0.11 [0.01 - 0.22]
0.10 [0.02 - 0.19]
0.35 [0.32 - 0.38]
0.35 [0.30 - 0.38]
0.33 [0.27 - 0.37]
0.21 [0.12 - 0.29]
0.28 [0.21 - 0.32]
‫־‬0.07 [‫־‬0.19 - ‫־‬0.01]
0.17 [0.05 - 0.26]
0.27 [0.20 - 0.31]
0.28 [0.20 - 0.32]
0.07 [0.01 - 0.19]
‫־‬0.29 [‫־‬0.33 - ‫־‬0.23]
0.33]
‫־‬0.32 [‫־‬0.36 - ‫־‬0.25]
0.50 [0.16 - 0.51]
0.39 [0.08 - 0.45]
0.37 [0.09 - 0.45]
0.35 [0.04 - 0.48]
0.05 [0.02 - 0.41]
‫־‬0.16 [‫־‬0.25 - ‫־‬0.02]
‫־‬0.17 [‫־‬0.27 - ‫־‬0.02]
‫־‬0.25 [‫־‬0.34 - ‫־‬0.06]
0.13 [0.02 - 0.40]
‫־‬0.01 [‫־‬0.27 - 0.02]
0.28 [0.03 - 0.43]
0.19 [0.02 - 0.35]
0.05 [0.01 - 0.31]
0.15 [0.02 - 0.32]
‫־‬0.05 [‫־‬0.35 - ‫־‬0.01]
0.11 [0.01 - 0.23]
0.23 [0.03 - 0.32]
‫־‬0.26 [‫־‬0.31 - ‫־‬0.17]
0.03 [0.01 - 0.25]
‫־‬0.04 [‫־‬0.46 - ‫־‬0.02]
‫־‬0.19 [‫־‬0.41 - ‫־‬0.02]
‫־‬0.16 [‫־‬0.42 - ‫־‬0.01]
‫־‬0.35 [‫־‬0.52 - ‫־‬0.03]
‫־‬0.47 [‫־‬0.54 - ‫־‬0.06]
‫־‬0.09 [‫־‬0.23 - ‫־‬0.01]
‫־‬0.15 [‫־‬0.27 - ‫־‬0.02]
‫־‬0.14 [‫־‬0.30 - ‫־‬0.01]
0.41 [0.03 - 0.46]
0.02 [0.01 - 0.41]
0.25 [0.03 - 0.50]
0.25 [0.02 - 0.42]
0.30 [0.02 - 0.43]
0.24 [0.02 - 0.35]
0.29 [0.03 - 0.49]
0.02 [0.01 - 0.28]
0.08 [0.01 - 0.27]
‫־‬0.10 [‫־‬0.35 - ‫־‬0.01]
0.11 [0.01 - 0.38]
‫־‬0.09 [‫־‬0.40 - ‫־‬0.01]
0.08 [0.01 - 0.36]
0.17 [0.02 - 0.49]
‫־‬0.13 [‫־‬0.53 - ‫־‬0.02]
‫־‬0.05 [‫־‬0.20 - ‫־‬0.01]
‫־‬0.06 [‫־‬0.24 - ‫־‬0.01]
‫־‬0.09 [‫־‬0.23 - ‫־‬0.02]
‫־‬0.06 [‫־‬0.44 - ‫־‬0.02]
0.48 [0.04 - 0.49]
‫־‬0.47 [‫־‬0.62 - ‫־‬0.03]
‫־‬0.20 [‫־‬0.49 - ‫־‬0.02]
‫־‬0.09 [‫־‬0.41 - ‫־‬0.01]
0.23 [0.01 - 0.37]
0.48 [0.04 - 0.61]
0.28 [0.02 - 0.35]
‫־‬0.02 [‫־‬0.26 - ‫־‬0.01]
0.22 [0.02 - 0.46]
‫־‬0.26 [‫־‬0.38 - ‫־‬0.02] ‫־‬0.12 [‫־‬0.35 - ‫־‬0.02]
‫־‬0.23 [‫־‬0.37 - ‫־‬0.01] 0.06 [0.01 - 0.35]
‫־‬0.14 [‫־‬0.43 - ‫־‬0.02] 0.07 [0.02 - 0.58]
0.27 [0.02 - 0.45] ‫־‬0.07 [‫־‬0.45 - ‫־‬0.01]
0.50 [0.03 - 0.57] ‫־‬0.18 [‫־‬0.54 - ‫־‬0.02]
‫־‬0.01 [‫־‬0.25 - ‫־‬0.01] 0.02 [0.01 - 0.28]
0.17 [0.01 - 0.28] 0.10 [0.01 - 0.29]
[0.01
-0 [[ -0.06 [‫־‬0.26 - ‫־‬0.02]
‫־‬0.04 [‫־‬0.22
- ‫־‬0.02]
0.10 [0.01 - 0.44] ‫־‬0.42 [‫־‬0.46 - ‫־‬0.12]
0.06 [0.01 - 0.43] 0.15 [0.02 - 0.39]
0.36 [0.02 - 0.56] ‫־‬0.26 [‫־‬0.50 - ‫־‬0.02]
0.19 [0.02 - 0.57] 0.58 [‫־‬0.59 - ‫־‬0.03]
0.10 [0.01 - 0.46] 0.37 [‫־‬0.46 - ‫־‬0.02]
0.00 [0.01 - 0.31] ‫־‬0.07 [‫־‬0.34 - ‫־‬0.01]
0.34 [0.02 - 0.64] ‫־‬0.26 [‫־‬0.59 - ‫־‬0.08]
‫־‬0.10 [‫־‬0.35 - ‫־‬0.01] 0.02 [0.01 - 0.39]
0.15 [0.01 - 0.30] 0.16 [0.01 - 0.34]
0.41 [0.02 - 0.50] 0.31 [0.02 - 0.49]
Texas Tech University, Kendra Phelps, August 2016
24
Available roosting area (m2)
Spatial heterogeneity
No. entrances
Temperature range (oC)
Humidity range (%)
Non-forested habitat
Urbanization
Road size
Mining
Cave development
Resource extraction
Bat hunting
Trash dumping
Vandalism/graffiti
Visitation frequency
Distance to barangay (km)
Distance to access (km)
Protected area
Loadings [95% confidence intervals] of environmental and human disturbance factors on each principal component based on
resampling of 999 iterations with replacement. Loadings greater than 0.30 explain a moderate percentage of the variance (Harlow,
2014) and are in bold.
Texas Tech University, Kendra Phelps, August 2016
25
Texas Tech University, Kendra Phelps, August 2016
Results
We surveyed 62 caves across Bohol Island from July 2011 - June 2013 to
collect data on environmental and human disturbance factors simultaneously with bat
composition (Appendix A). After 655 net-hours, we captured 7522 bats of 23 species
(Appendix B). These captures represent 69.6% of the 33 bat species reported on Bohol
Island (Sedlock et al., 2014), in addition to the first published records of Chaerephon
plicatus and Taphozous melanopogon on Bohol Island, though T. melanopogon was
acoustically recorded previously by Sedlock et al. (2014). Of the bat species on Bohol
Island that were not captured during our study, all are forest-roosting species (e.g.,
Kerivoula, Murina, Pteropus, Acerodon) except Hipposideros bicolor, which remains
taxonomically unresolved and of uncertain distribution in the Philippines.
Because of missing environmental factor values from six caves, we included a
subset of 56 caves in our analyses (Appendix A). From this subset of caves, we
captured 6825 bats, excluding recaptures, of 21 species, representing the families
Emballonuridae, Hipposideridae, Megadermatidae, Molossidae, Pteropodidae,
Rhinolophidae and Vespertilionidae. Hipposideros diadema was the most abundant
species, representing 31% of all captures, followed by Miniopterus schreibersii
(14.8%) and Miniopterus australis (14.2%).
Model-based analysis using mvabund revealed surface-level disturbance (PC1,
p = 0.003) and cave complexity (PC2, p = 0.020) as significant variables shaping the
assemblage composition of cave-roosting bats on Bohol Island (Table 2.3).
Abundance for a majority of species (61.9%) declined with increasing surface-level
disturbance, with two species significantly influenced. Emballonura alecto and
Hipposideros pygmaeus responded negatively to surface-level disturbances, with
decreasing abundance in caves with a lower percentage of intact forest cover and
located near major roadways and urban areas. Conversely, Myotis macrotarsus and
Rousettus amplexicaudatus responded positively to surface-level disturbance. Over
three-quarters (76.2%) of bat species exhibited increases in abundance with increasing
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Texas Tech University, Kendra Phelps, August 2016
cave complexity, with six species significantly influenced. Several Rhinolophus
species, along with Hipposideros diadema and Miniopterus tristis, responded
positively to increasing cave complexity. Abundance of these species increased in
caves with greater roosting area, structural heterogeneity, number of entrances and
range in temperature. Only one species, Hipposideros obscurus, responded negatively
to increasing cave complexity.
No other factors exhibited a significant influence on assemblage composition
in the mvabund analysis (p > 0.14). Mining (PC3) explained the least amount of
deviance in assemblage composition (Dev = 23.07, p = 0.539) and had no significant
influence on any individual species’ abundance, with the exception of Hipposideros
ater which exhibited a marginal response (p = 0.04) (Table 2.3). However, several
species exhibited a significant response in abundance to at least one of the remaining
three principal components (PC4 – 6, Table 2.3). T. melanopogon had higher
abundance in caves with high levels of cave development (PC4); those in which
lighting systems, paved walkways and/or other modifications to the cave environment
had been made to accommodate human activities (e.g., mining, tourism). Conversely,
H. diadema was lower in abundance in such caves. Two species, C. brachyotis and M.
macrotarsus, responded negatively to increasing resource extraction and human
visitation frequency in caves within the boundaries of protected areas. M. australis
responded negatively to bat hunting (PC6), yet R. amplexicaudatus had highest
abundances in caves with greatest hunting pressure. This is likely the result of R.
amplexicaudatus being a species targeted for human consumption.
27
Table 2.3. Results of mvabund analysis of deviance (anova.manyglm) (Wang et al., 2014) testing the significance of predictor
variables (PC1 – PC6) on assemblage composition and species abundance.
Deviance
p-value
Surface-level disturbance
(PC1)
39.68
0.003
Myotis macrotarsus
Rousettus amplexicaudatus
Emballonura alecto
Hipposideros pygmaeus
Hipposideros diadema
Miniopterus tristis
Rhinolophus arcuatus
Rhinolophus philippinensis
Rhinolophus rufus
Hipposideros obscurus
54.45
0.020
Mining
(PC3)
23.07
0.539
Cave development
(PC4)
74.04
0.144
Resource extraction
in protected areas
(PC5)
31.75
0.509
Bat hunting
(PC6)
33.88
0.488
28
Cave complexity
(PC2)
Positive
Negative
Hipposideros ater
Taphozous melanopogon
Hipposideros diadema
Cynopterus brachyotis
Myotis macrotarsus
Rousettus amplexicaudatus
Miniopterus australis
Significant p-values for predictor variables are in bold and only species with p-values < 0.05 in unadjusted post-hoc univariate tests
are shown.
Texas Tech University, Kendra Phelps, August 2016
Predictor Variable
Texas Tech University, Kendra Phelps, August 2016
Non-metric multidimensional scaling (NMDS) allowed us to visualize sites
(caves) and species in a two-dimensional plot (Fig. 2.2; stress = 0.15). Plotting vectors
using envfit (distance-based method) gave similar results to mvabund (model-based
method), and corroborated that surface-level disturbance (PC1) and cave complexity
(PC2) were the most influential variables governing assemblage composition (r 2 =
0.101, p = 0.048; r2 = 0.180, p = 0.007, respectively). Species situated in the upper left
of the NMDS plot (Fig. 2.2) tend to occupy complex caves that experience greater
surface-level disturbances. Bat hunting (PC6) exhibited a marginal influence on
assemblage composition (r2 = 0.105, p = 0.053), with R. amplexicaudatus abundance
correlated with increasing hunting pressure.
a
29
Texas Tech University, Kendra Phelps, August 2016
b
Fig. 2.2. NMDS ordination biplot of site-species correlation matrix: a) sites
(numbered) and b) species (abbreviations) with predictor variables superimposed. Site
(cave) proximity represents similarity of species composition (a), while species
proximity demonstrates presence at shared sites (b). The direction of each plotted
arrow indicates the direction of an increase in the gradient for the corresponding
variable, while the length is proportional to the correlation between the predictor
variable and the ordination. Solid arrows in bold are significantly correlated with the
ordination (p < 0.05), while dashed arrows are not. Cave and species codes are listed
in Appendix A and B.
Both model-based and distance-based methods pointed to surface-level
disturbances (PC1) and cave complexity (PC2) as the most influential variables
shaping bat assemblages in caves on Bohol Island. Selection of six caves based solely
30
Texas Tech University, Kendra Phelps, August 2016
on low levels of surface-level disturbance (Scheme 1) resulted in the protection of 13
species (61.9% of all captured species, Fig. 2.3), high cave complexity (Scheme 2)
also protected 13 species (61.9%) and the combination of both factors (Scheme 3)
protected 15 species (71.4%). Random selection of six caves based on our scripted
algorithm resulted in the protection of 12 species, on average, with a range between 9
– 15 species (95% confidence intervals, Fig. 2.3). All prioritization schemes protected
more species than those protected on average when caves were selected randomly (13
- 15 vs. 12 species, respectively), thereby indicating that prioritization based on any
one of our proposed schemes will outperform random selection of caves. However,
when compared to the cumulative species richness from six caves with the greatest
number of captured species in our study (14 species), only Scheme 3 protected more
species (15 species; Fig. 2.3).
Fig. 2.3. Number of species conserved by protecting 10% of caves (n = 6) based on
three prioritization schemes: Scheme 1 - lowest score on PC1 (surface-level
disturbance; long dash line), Scheme 2 - highest scores on PC2 (cave complexity; long
31
Texas Tech University, Kendra Phelps, August 2016
dash line) and Scheme 3 - combination of the prior schemes (low surface-level
disturbance and high cave complexity scores; dash-dot line) in comparison to random
selection (solid black line; dotted gray lines indicate 95% confidence intervals) and
selection of caves with the highest species richness (short dash line).
Discussion
We demonstrate, for the first time, that cave-roosting bat assemblages are
significantly influenced by both environmental factors and human disturbance of
caves. Assemblage diversity responded negatively to increasing levels of surface-level
disturbance, specifically decreased forest cover with increased urbanization and road
development, but responded positively to increasing cave complexity, including all
measured environmental factors with the exception of humidity. Similar patterns were
also detected in abundances for a majority of the 21 species in our study. Thus, we
considered surface-level disturbance and cave complexity to represent correlates of
cave bat diversity. Using these identified correlates to prioritize 10% of the 56 caves in
our study (n = 6) resulted in the protection of more cave-roosting bat species than
random selection of caves as well as more species than the cumulative species richness
of the top six species-rich caves in our study. This demonstrates that correlates of bat
diversity can be a useful tool for prioritizing caves to promote the conservation of
cave-roosting bats. As keystone species in cave ecosystems, the protection of diverse
assemblages of bats will provide an umbrella of protection for other cave-dwelling
wildlife, particularly coprophagous invertebrates dependent upon bat guano as a
primary energy source (Gnaspini and Trajano, 2000; Kunz et al., 2011).
Interestingly, changes in assemblage composition and species abundances of
cave-roosting bats varied depending on the scale: surface-level (landscape surrounding
the cave in a 1 km radius) or subsurface-level (subterranean). At the surface level,
decreasing forest cover, combined with increasing urbanization and development of
roadways, contributed to significant differences in assemblage composition of cave32
Texas Tech University, Kendra Phelps, August 2016
roosting species in our study. Not surprisingly, 13 species (62%) also responded
negatively to increasing surface-level disturbances surrounding the cave, likely
because such surface-level disturbances were correlated with increased accessibility to
the cave (i.e., decreased distance to roads, trails and foot paths). Cave-roosting bats
exhibit high roost fidelity (Lewis, 1995), so any reduction in forest cover surrounding
a cave may result in increased commuting costs to reach suitable foraging grounds or
risk foraging in suboptimal habitats (Kingston, 2013). Forest loss and increasing
pressures from urbanization and development pose serious threats to bat populations
worldwide (Jung and Threlfall, 2016; Meyer et al., 2016), such disturbances should be
used as a gauge for prioritizing caves to improve bat conservation.
At the subsurface-level, human disturbance, of any form, did not significantly
shape assemblage composition. Most interestingly, mining did not influence
compositional structure and only significantly affected the abundance of one species in
our study. Mining operations on Bohol Island tend to be on a smaller scale than is
typical in Southeast Asia (Clements et al., 2006), with most extraction conducted
manually by workers rather than by machinery, which may have contributed to our
results. Other forms of human disturbance had mixed effects on the abundances of
individual species. For example, cave development had a positive effect on the
abundance of T. melanopogon; however, H. diadema were significantly less abundant
in these caves. Bat hunting had differing effects on the abundance of R.
amplexicaudatus and M. schreibersii. Hunting pressure was highest in caves with
large aggregations of R. amplexicaudatus, a species targeted for human consumption
throughout its range in Southeast Asia (Csorba et al., 2008). Though assemblage
composition was not influenced by human disturbance at the subsurface-level,
population abundances of most species declined in caves with increasing levels of
subsurface-level disturbances.
Conversely, at the subsurface-level, all of the measured environmental factors,
with the exception of humidity, exerted a significant influence on the composition of
bat assemblages. Numerous studies have shown a positive correlation between
33
Texas Tech University, Kendra Phelps, August 2016
assemblage attributes (e.g., species richness and diversity, overall abundance) and
available roosting area (Brunet and Medellín, 2001; Luo et al., 2013), total cave length
(Arita, 1996; Cardiff, 2006), entrance dimensions (Cardiff, 2006) and degree of
chamber and passage complexity (Arita, 1996; Brunet and Medellín, 2001; Cardiff,
2006). Microclimatic conditions have also been shown to influence roost selection by
cave bats, particularly temperature (Avila-Flores and Medellín, 2004; Furey and
Racey, 2016). Our study shows that environmental factors collectively promote a
richer assemblage composition and increase abundances of a majority of species.
Results from our study combined with the findings of other studies demonstrate
clearly that the protection of large, complex caves will conserve a greater diversity of
cave-roosting bats.
Caves are critical roosting sites for many bat species, including many
threatened species, and need to be protected to maintain high bat diversity. For
instance, caves and other subterranean habitats (e.g., abandoned mines, military
fortifications) support 77% of the bat fauna in China, including 30 species classified as
nationally endangered or vulnerable (Luo et al., 2013). Similarly, 80% of the bat
species in Puerto Rico roost in caves (Rodríguez-Durán, 2009). Our results
demonstrate that species composition and abundance of cave-roosting bat assemblages
are shaped by both human disturbance at the surface-level and environmental factors
at the subsurface-level and are therefore correlates of bat diversity. When we
prioritized caves based on these easily measurable correlates, all of our prioritization
schemes (1-3) lead to the protection of more species of cave-roosting bats than were
protected, on average, when randomly selecting caves, indicating that correlates of bat
diversity identified by this study are effective at prioritizing caves. However, only
Scheme 3, which prioritized caves based on both surface-level disturbance and cave
complexity, resulted in the cumulative protection of more species than were observed
across our six most species-rich caves. Based on our findings, to safeguard diverse
assemblages of cave-roosting bats and the ecosystems they support, we advocate for
34
Texas Tech University, Kendra Phelps, August 2016
the protection of caves with combined high cave complexity and minimal surfacelevel human disturbance.
Both our model- and distance-based methods pointed to cave complexity as
having the strongest effect on assemblage composition and species abundances,
followed by surface-level human disturbance. Therefore, we suggest that identification
of candidate caves for protection follow a systematic process. Prioritization efforts
should first focus on cave complexity, which can be assessed using standard cave
surveys that can be completed in less than 2 h and require minimal training. Prior to
conducting cave surveys, it is wise to contact local officials to confirm the presence of
cave-roosting bats to avoid wasting time and financial resources. In some countries,
cave maps, from which cave complexity can be measured, have already been created
by speleological clubs (e.g., Shepton Mallet Caving Club, Texas Speleological
Society) and government agencies (e.g., Department of Environment and Natural
Resources of the Philippines), and these often include information about the presence
of cave-roosting bats. Prioritization should then be refined by gauging levels of human
disturbance in the landscape surrounding the cave, selecting those caves with minimal
landscape disturbance. This can be evaluated from open-access sources, such as
Google Earth and nonprofit- or government-managed land cover repositories (e.g.,
U.S. Geological Society Landsat Mission, National Mapping and Resource
Information Authority of the Philippines). Finally, while our systemic process allows
for a subset of candidate caves to be identified out of potentially thousands of caves,
we strongly recommend candidate caves be investigated by researchers with expert
knowledge to characterize diversity of bat species and other cave-dependent wildlife.
We used only mist nets to characterize bat diversity because they are lightweight, easy
to acquire and inexpensive in comparison to harp traps and acoustic detectors. While
we had high capture success using mist nets alone (i.e., capturing all cave-roosting bat
species on Bohol Island with the exception of one species), we encourage the use of
harp traps and acoustic detectors by researchers to increase the detectability of highfrequency echolocating bat species (Flaquer et al., 2007; Francis, 1989), which in turn
35
Texas Tech University, Kendra Phelps, August 2016
could influence abundance-based analyses. Although beyond the scope of our study, to
maximize returns on limited financial resources available for conservation initiatives,
final decisions on priority caves should consider the social, political and economic
culture of the region (McBride et al., 2007).
In light of increasing human pressures on caves, many countries have put forth
policies recently to conserve caves in order to maintain healthy cave ecosystems
diverse in cave-dependent wildlife (e.g., Philippines, Slovenia, Puerto Rico), yet tools
for prioritizing caves are lacking. Our study fills this need by identifying correlates of
cave bat diversity that are readily available (i.e., open-source data) or require little
time and training (i.e., cave surveys, local resident interviews), providing an effective,
easy-to-use tool to prioritize caves. Our prioritization schemes are directly applicable
to other Southeast Asian countries since taxonomic congruency among species exists
within the region; in addition, cave-roosting bats face similar human threats
throughout (Kingston, 2010). We suggest that correlates of bat diversity can be used as
a foundation for prioritizing caves globally, but we caution that differences in
evolutionary history and ecological niches of bats, and the history of land-use change,
in other regions warrant validation and parameterization of our correlative approach
beyond Southeast Asia. Ultimately, adoption of prioritization schemes based on
correlates of bat diversity could not only ensure protection of cave-roosting bat species
but the subterranean biodiversity hotspots they support.
Acknowledgments
We thank the Department of Environment and Natural Resources of the
Philippines for permission to conduct this study (permit no. 2011-04, 2013-02). We
are especially grateful to all those that assisted with fieldwork, most of whom were
undergraduate students, as well as Bohol Island State University for lodging and
transportation during portions of this project. The study was supported by U.S.
Department of State – Fulbright Fellowship, Bat Conservation International, American
36
Texas Tech University, Kendra Phelps, August 2016
Philosophical Society, The Explorers Club, American Society of Mammalogists,
National Speleological Society, Cave Research Foundation, John Ball Zoo, Sigma Xi
and Texas Tech Association of Biologists. This manuscript benefited from interactions
through the Southeast Asian Bat Conservation Research Unit (SEABCRU) network
supported by the National Science Foundation (grant no. 1051363). We thank Jodi
Sedlock and Nancy McIntyre for comments on an earlier version of this manuscript,
Marina Fisher-Phelps for preparing our map and Julie Parlos for molecular
confirmation of some species identifications.
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potential microclimate impacts of cave modifications. J. Environ. Manag. 87,
474-483. doi:10.1016/j.jenvman.2007.01.012.
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41
Appendix A. Environmental and human disturbance factor values for caves on Bohol Island, Philippines included in this study. Caves
No. entrances
Temperature
range (˚C)
Humidity range
(%)
Urbanization
Road size
Mining
Development
Resource
extraction
Bat hunting
Household waste
Vandalism/
graffiti
Visitation
frequency
Distance to
barangay (km)
Distance to
access (km)
Protected area
1
110.07
1.50
1
5.20
5.70
2
2
1
0
1
2
1
0
0
1
1.09
1.00
0
Atbang Cave*
2
36.91
2.82
1
-----
-----
0
0
1
0
1
0
0
0
0
0
1.72
0.50
1
Bakongkong Cave
3
252.52
4.28
4
2.30
17.00
2
2
2
0
1
3
1
0
0
1
0.19
0.22
1
Batongay Cave
4
212.46
4.19
4
2.90
10.30
3
2
2
0
3
0
0
1
1
2
1.53
0.02
0
Bayang Cave
5
710.69
7.79
2
3.20
10.70
3
2
2
0
0
2
1
0
1
1
0.80
0.25
0
Benito Cave
6
190.76
3.29
1
2.90
13.60
1
1
1
0
0
3
0
1
0
2
0.89
1.00
0
Bodiong Cave*
7
178.08
3.13
1
-----
-----
2
1
1
0
0
3
3
0
0
0
2.13
0.50
1
Bogtong Park Cave
8
71.77
3.28
1
0.60
3.80
2
3
2
2
1
2
2
3
0
1
0.49
0.15
0
Burial Cave
9
16.30
1.00
1
0.00
0.00
0
0
0
0
1
1
0
0
0
3
1.71
0.70
1
Calompanan Cave
10
253.07
3.37
1
1.80
3.70
1
1
0
0
1
3
3
2
1
1
1.87
1.50
1
Cang Ihong Cave
11
149.32
3.11
2
1.40
5.40
2
2
1
2
2
3
1
1
3
3
0.85
1.00
1
Canlusong Cave
12
243.98
2.52
2
2.00
8.19
0
1
0
0
0
3
1
1
0
2
0.85
1.50
0
Cantijong Cave
13
531.96
9.50
3
5.20
0.00
2
1
1
1
2
3
2
1
1
2
0.76
0.05
0
Non-forested
habitat
Spatial
heterogeneity
Agaw-gaw Cave
Texas Tech University, Kendra Phelps, August 2016
42
Code
for
Fig.
2.1
Available
roosting area
(m2)
followed by * indicated caves not included in data analysis due to missing values.
No. entrances
Temperature
range (˚C)
Humidity range
(%)
Urbanization
Road size
Mining
Development
Resource
extraction
Bat hunting
Household
waste
Vandalism/
graffiti
Visitation
frequency
Distance to
barangay (km)
Distance to
access (km)
Protected area
Cantumocad Cave
14
248.55
3.09
1
0.50
5.20
3
3
3
1
1
2
0
0
2
2
0.56
0.05
0
Casampong Cave
15
279.24
4.89
1
1.50
0.00
0
1
1
1
0
3
0
1
0
0
1.23
1.00
0
Catalina Cave
16
505.26
4.27
1
3.70
5.50
0
0
0
0
0
3
0
0
0
1
2.25
3.00
1
Claise Cave
17
394.23
3.79
2
2.20
11.20
1
1
1
2
0
1
1
0
2
2
0.96
0.10
0
Dagohoy Cave
18
29.76
3.99
1
1.30
18.70
3
3
2
0
0
3
1
1
1
1
0.31
0.00
0
Dakong-Buho Cave
19
667.03
3.09
3
2.80
7.70
1
0
0
0
0
3
0
0
0
0
1.40
1.50
0
Duguilan Cave
20
494.52
3.01
1
4.80
10.80
0
0
1
0
0
3
0
0
0
2
2.65
0.81
1
Guimba Cave
21
524.33
6.19
5
0.60
4.70
0
1
0
2
0
3
1
1
1
1
1.17
1.50
0
Hinagdanan Cave
22
355.10
3.80
1
1.30
3.10
3
3
3
0
3
0
0
1
2
3
0.77
0.00
0
Inorok Cave
23
92.05
2.98
1
0.90
5.50
2
2
1
0
0
3
0
0
0
2
0.91
0.30
0
Ka Anoy Cave*
24
562.29
2.09
2
5.10
-----
1
0
0
2
1
3
0
1
1
0
1.23
1.50
1
Ka Dodong Cave
25
459.76
5.76
1
3.30
2.70
0
0
0
0
0
3
0
0
2
1
1.90
3.00
1
Ka Goryo Cave
26
121.14
2.18
1
0.60
0.00
2
1
1
0
0
3
0
2
1
1
0.25
0.20
0
Ka Iska Cave
27
85.42
1.85
2
0.80
4.70
0
0
0
0
1
1
0
0
1
3
1.71
0.70
1
Ka Martin Cave*
28
-----
-----
2
-----
-----
0
0
0
0
0
0
0
0
0
0
2.24
1.50
0
Non-forested
habitat
Spatial
heterogeneity
43
Code
for
Fig.
2.1
Available
roosting area
(m2)
Appendix A cont.
No. entrances
Temperature
range (˚C)
Humidity range
(%)
Urbanization
Road size
Mining
Development
Resource
extraction
Bat hunting
Household
waste
Vandalism/
graffiti
Visitation
frequency
Distance to
barangay (km)
Distance to
access (km)
Protected area
29
81.07
2.69
1
3.55
17.20
1
2
2
0
0
3
1
0
0
3
0.52
0.51
1
Kabjawan Cave
30
362.37
2.77
5
2.40
9.40
2
2
1
2
1
3
1
2
3
3
1.12
1.00
0
Kabyawan Cave
31
112.36
1.46
1
0.80
0.00
3
2
2
1
1
2
2
2
1
1
0.91
1.00
0
Kalanguban Cave
32
181.05
4.51
1
0.40
0.00
0
0
0
0
0
3
0
0
0
2
1.26
1.50
1
Kamagahi Cave
33
131.08
2.16
1
0.60
9.50
3
2
2
0
1
3
1
1
0
2
1.93
1.00
0
Kamira Cave
34
488.46
5.02
1
1.70
0.20
3
2
2
0
1
0
0
0
1
3
0.87
0.10
0
Kang Mana Cave
35
44.91
4.63
2
1.70
7.30
3
2
2
2
1
2
1
1
2
3
0.63
0.20
0
Kasabas Cave
36
70.95
1.00
1
0.00
0.00
2
1
2
2
1
3
0
1
1
2
1.11
0.50
0
Kokok Cave
37
115.26
5.81
1
2.60
5.30
3
2
2
0
0
3
2
0
0
2
1.22
0.15
1
Lagbas Cave
38
273.48
5.16
2
1.10
6.30
1
1
1
3
1
3
2
3
2
3
0.73
0.25
0
Lahos-Lahos Cave
39
152.25
4.94
3
2.00
11.60
3
3
2
0
1
2
1
2
2
1
1.50
0.05
0
Lahug 2 Cave*
40
306.56
3.32
2
-----
-----
1
0
1
0
1
1
0
1
1
1
0.85
1.00
0
Lahug Cave
41
117.50
5.40
1
1.40
2.90
3
2
2
3
1
3
1
1
1
2
1.15
0.25
0
Langgam Cave
42
1545.53
15.53
5
6.80
16.10
3
2
1
1
2
2
1
0
2
2
0.91
0.30
0
Loboc Tourist Cave
43
112.67
5.25
2
4.80
19.60
2
3
3
0
3
0
0
3
1
0
0.28
0.30
1
Non-forested
habitat
Spatial
heterogeneity
Kabera Cave
Texas Tech University, Kendra Phelps, August 2016
44
Code
for
Fig.
2.1
Available
roosting area
(m2)
Appendix A cont.
No. entrances
Temperature
range (˚C)
Humidity range
(%)
Urbanization
Road size
Mining
Development
Resource
extraction
Bat hunting
Household
waste
Vandalism/
graffiti
Visitation
frequency
Distance to
barangay (km)
Distance to
access (km)
Protected area
44
125.44
3.60
2
1.50
7.10
0
0
1
0
1
3
0
0
0
0
1.36
0.75
1
Lujang Cave
45
85.02
3.93
2
3.50
8.40
1
1
1
0
1
1
0
0
0
0
1.57
0.15
0
Lungon Cave
46
55.75
6.60
2
2.00
15.70
3
2
3
3
1
3
0
2
1
1
0.48
0.02
0
Manlawe Cave
47
155.13
3.53
2
1.30
3.90
2
2
2
0
1
2
0
1
1
2
1.61
0.15
0
Mesias Cave
48
74.52
3.33
1
1.80
13.40
3
3
2
0
3
1
0
1
2
2
0.75
0.10
0
Mohon Cave
49
185.81
3.11
1
0.80
2.80
0
0
3
0
0
2
0
0
0
0
1.16
0.00
0
Nangka Cave
50
148.36
2.91
1
0.50
1.50
2
1
2
3
3
3
2
3
3
3
0.81
0.05
0
Odiong Cave
51
347.59
4.01
1
2.40
9.30
1
2
1
1
1
2
1
2
2
1
1.03
1.00
0
Pig-ot Cave
52
258.23
7.05
2
2.40
0.60
3
2
3
2
2
2
1
2
3
0
0.27
0.10
0
Polito Cave
53
173.62
1.53
1
3.80
9.80
0
1
0
0
0
3
0
0
1
3
1.42
1.40
1
Popog Cave
54
3116.42
8.66
3
3.30
3.60
2
2
2
3
3
3
1
2
2
3
0.57
0.25
0
Pou Cave
55
326.73
8.08
2
3.20
11.80
2
1
1
2
1
3
1
1
2
1
0.91
0.10
0
Seminary Cave
56
1043.49
6.46
3
6.90
6.10
3
3
3
0
3
2
3
3
2
3
0.23
0.03
0
Tagjaw Cave
57
258.81
1.05
1
0.60
15.70
2
2
2
2
0
3
0
0
1
2
0.46
0.45
0
Tambo 1 Cave
58
272.41
3.12
1
10.50
8.20
2
2
2
3
3
3
0
1
1
3
1.06
0.05
0
Tambo 2 Cave
59
358.62
3.83
1
12.70
9.50
2
2
2
3
3
3
0
2
2
3
1.32
0.03
0
Non-forested
habitat
Spatial
heterogeneity
Logarita Cave
Texas Tech University, Kendra Phelps, August 2016
45
Code
for
Fig.
2.1
Available
roosting area
(m2)
Appendix A cont.
Spatial
heterogeneity
No. entrances
Temperature
range (˚C)
Humidity range
(%)
Urbanization
Road size
Mining
Development
Resource
extraction
Bat hunting
Household
waste
Vandalism/
graffiti
Visitation
frequency
Distance to
barangay (km)
Distance to
access (km)
Protected area
Tamboco Cave*
60
--------2
--------1
2
2
0
0
1
0
0
0
2
0.30
0.10
0
Tangob Cave
61
66.82
1.00
1
0.00
0.00
0
0
0
0
1
1
0
0
0
3
1.71
0.70
1
Tinugdan Cave
62
56.41
1.94
2
5.90
10.60
1
1
1
0
2
2
0
0
0
1
1.02
1.50
1
Non-forested
habitat
Code
for
Fig.
2.1
Available
roosting area
(m2)
Appendix A cont.
Texas Tech University, Kendra Phelps, August 2016
46
Appendix B. Raw count data for 23 species of bats captured from 62 caves across Bohol Island, Philippines from July 2011 – June
Rousettus amplexicaudatus
Taphozous melanopogon
RHVI
ROAM
TAME
0
26
0
19
0
Atbang Cave
0
0
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Bakongkong Cave
0
0
0
0
0
0
27
0
0
0
7
8
0
0
0
0
1
0
0
0
0
0
0
Batongay Cave
0
0
0
68
0
0
49
0
0
0
51
129
0
0
0
0
10
0
0
3
0
0
129
Bayang Cave
0
1
0
0
0
0
55
0
0
0
5
3
0
0
0
1
4
0
0
18
0
0
0
Benito Cave
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
47
0
0
0
0
0
0
Bodiong Cave
0
0
0
0
1
5
0
0
42
0
0
0
0
18
0
0
151
0
0
12
55
0
0
Bogtong Park Cave
0
0
0
0
0
0
7
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
Burial Cave
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
55
Calompanan Cave
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
0
47
RHRU
R. virgo
RHPH
0
R. rufus
RHMA
0
R. philippinensis
Rhinolophus arcuatus
RHAR
0
R. macrotis
Ptenochirus jagori
0
M. macrotarsus
MYM
A
PTJA
0
Myotis horsfieldii
MYHO
8
M. tristis
MITR
12
M. schreibersii
MISP
31
Miniopterus australis
MIAU
0
Megaderma spasma
MESP
0
H. pygmaeus
HIPY
0
H. obscurus
HIOB
47
H. diadema
HIDI
0
H. coronatus
HICO
0
Hipposideros ater
HIAT
98
Eonycteris spelaea
EOSP
0
Emballonura alecto
EMAL
0
Cynopterus brachyotis
CYBR
0
Chaerephon plicatus
Agaw-gaw Cave
Cave
Texas Tech University, Kendra Phelps, August 2016
CHPL
2013.
CYBR
EMAL
EOSP
HIAT
HICO
HIDI
HIOB
HIPY
MESP
MIAU
MISP
MITR
MYHO
MYM
A
PTJA
RHAR
RHMA
RHPH
RHRU
RHVI
ROAM
TAME
Cang Ihong Cave
0
0
0
0
0
0
11
0
1
1
0
16
0
0
0
0
0
0
0
2
0
0
0
Canlusong Cave
0
0
1
0
0
0
66
0
5
0
34
38
8
0
0
0
32
1
2
15
0
0
0
Cantijong Cave
0
0
0
96
0
0
130
0
0
0
0
1
9
0
0
0
0
0
0
0
0
83
0
Cantumocad Cave
0
0
0
0
0
0
166
0
0
0
0
11
0
0
0
0
0
0
0
0
0
0
0
Casampong Cave
0
0
0
57
0
0
25
0
0
0
0
1
32
0
0
0
0
0
2
11
0
31
0
Catalina Cave
0
0
2
0
0
0
32
0
8
0
69
0
0
3
0
0
14
0
0
1
0
0
0
Claise Cave
0
0
0
150
0
0
64
0
0
0
20
0
0
0
0
0
1
0
0
0
0
1
0
Dagohoy Cave
0
0
0
0
8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
201
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
1
0
Duguilan Cave
0
0
0
50
0
0
61
0
10
0
14
58
13
0
0
0
14
0
0
41
0
0
0
Guimba Cave
0
0
0
0
0
0
167
0
0
0
0
8
0
0
0
0
0
0
0
0
0
0
0
Hinagdanan Cave
0
0
0
0
0
0
0
0
0
0
152
60
0
0
0
0
0
0
0
0
0
0
0
Inorok Cave
0
0
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Ka Anoy Cave
0
0
0
0
0
0
51
0
4
0
73
72
0
0
0
0
0
0
0
7
0
0
0
Ka Dodong Cave
0
0
0
0
1
0
13
0
15
0
91
86
0
0
0
0
33
0
6
13
0
0
0
Ka Goryo Cave
0
0
0
0
0
0
32
0
1
0
1
67
0
0
0
0
0
0
0
0
0
0
0
Ka Iska Cave
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
85
Ka Martin Cave
0
0
0
0
0
0
21
0
0
2
2
0
0
0
0
25
31
0
0
2
1
0
0
Cave
48
Dakong-Buho Cave
Texas Tech University, Kendra Phelps, August 2016
CHPL
Appendix B cont.
HIAT
HICO
MESP
MIAU
MISP
MITR
MYHO
MYM
A
PTJA
RHAR
RHMA
RHPH
RHRU
RHVI
ROAM
TAME
0
0
5
0
153
0
51
0
0
0
0
1
0
0
0
0
0
0
0
0
0
Kabjawan Cave
0
0
0
5
0
0
6
0
0
0
8
3
12
0
0
0
4
0
1
76
0
5
0
Kabyawan Cave
0
0
0
95
0
0
27
0
0
0
0
0
0
0
0
0
0
0
0
8
0
72
0
Kalanguban Cave
0
0
0
0
0
0
12
0
0
0
4
0
0
0
0
0
0
0
1
0
0
0
0
Kamagahi Cave
0
0
0
0
0
0
0
4
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
Kamira Cave
0
0
0
0
0
0
5
6
14
0
7
13
0
0
0
15
0
0
0
0
0
0
0
Kang Mana Cave
0
0
0
0
0
0
36
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
Kasabas Cave
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
Kokok Cave
0
0
0
0
3
0
51
0
0
0
1
17
0
0
0
0
0
0
0
0
0
0
0
Lagbas Cave
0
0
0
0
0
0
241
0
0
0
1
0
0
0
0
0
10
0
1
9
0
0
0
Lahos-Lahos Cave
0
0
0
0
0
0
62
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Lahug 2 Cave
0
0
1
0
0
0
2
1
0
0
14
1
0
0
0
5
0
0
0
1
0
0
0
Lahug Cave
0
0
0
0
0
0
0
0
0
0
61
1
0
6
0
0
0
0
0
0
0
0
0
Langgam Cave
0
0
0
5
0
0
53
0
0
0
47
26
0
0
0
0
5
0
1
2
0
93
0
Loboc Tourist Cave
0
0
0
0
0
0
5
8
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
Logarita Cave
0
0
0
0
0
0
35
0
1
0
12
42
0
0
0
0
0
0
0
3
0
0
0
Lujang Cave
0
0
0
0
3
0
138
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
HIPY
EOSP
0
HIOB
EMAL
0
HIDI
CYBR
Kabera Cave
Cave
49
Texas Tech University, Kendra Phelps, August 2016
CHPL
Appendix B cont.
CYBR
EMAL
EOSP
HIAT
HICO
HIDI
HIOB
HIPY
MESP
MIAU
MISP
MITR
MYHO
MYM
A
PTJA
RHAR
RHMA
RHPH
RHRU
RHVI
ROAM
TAME
Lungon Cave
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
Manlawe Cave
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Mesias Cave
0
0
0
0
0
0
19
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
Mohon Cave
0
0
20
0
0
0
1
0
5
0
6
5
0
0
0
0
0
0
0
4
0
0
0
Nangka Cave
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
9
0
0
0
0
0
0
1
Odiong Cave
0
0
0
16
0
0
4
0
1
0
0
1
0
0
0
1
0
0
0
1
0
75
0
Pig-ot Cave
0
0
0
0
0
0
49
0
1
0
37
69
0
0
38
0
0
0
1
0
0
0
0
Polito Cave
0
0
10
0
0
0
25
0
1
0
2
11
0
0
0
0
0
0
0
1
0
0
0
Popog Cave
0
0
0
4
0
0
15
0
0
0
37
22
55
0
9
0
10
1
1
2
0
52
0
Pou Cave
0
0
0
0
15
0
78
0
0
0
227
22
0
0
0
0
13
20
0
0
0
0
0
Seminary Cave
0
0
0
28
0
0
52
0
0
0
0
13
0
0
26
0
0
0
0
0
0
115
0
Tagjaw Cave
0
0
0
0
0
0
62
0
2
0
24
190
0
3
0
1
0
0
0
1
0
0
0
Tambo 1 Cave
0
0
0
0
0
0
31
0
0
2
15
72
0
0
0
0
0
2
0
23
0
0
0
Tambo 2 Cave
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
0
0
0
0
Tamboco Cave
0
0
0
10
0
0
47
0
0
0
0
1
0
0
0
0
0
0
0
3
0
31
0
Tangob Cave
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
52
Tinugdan Cave
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Cave
50
Texas Tech University, Kendra Phelps, August 2016
CHPL
Appendix B cont.
Texas Tech University, Kendra Phelps, August 2016
CHAPTER III
CAVE-ROOSTING BAT SPECIES, BUT NOT ASSEMBLAGES,
EXHIBIT ECOLOGICAL THRESHOLDS ACROSS COMPLEX
ENVIRONMENTAL GRADIENTS
In preparation for PLoS ONE
Kendra Phelpsa,b,*, Reizl Josec, Marina Labonitec, Tigga Kingstona,b
a
Department of Biological Sciences, Texas Tech University, Lubbock, USA
b
Southeast Asian Bat Conservation Research Unit, Lubbock, USA
c
Research & Development, Bohol Island State University, Bilar, Philippines
*Corresponding author: Department of Biological Sciences, Texas Tech University,
MS 43131, Lubbock, TX 79409, USA. Tel.: +1 (806) 742-2731.
E-mail address: [email protected]
51
Texas Tech University, Kendra Phelps, August 2016
Abstract
Ecological thresholds, or transition points defined by abrupt responses by
species or assemblages to environmental gradients, are an attractive concept with
practical value for designing cost-effective and efficient management plans.
Thresholds disclose a specific tipping point that, once breached, can have irreversible
consequences for species persistence and assemblage structure. Moreover, ecological
thresholds can be used to identify species with the greatest sensitivity to changes in an
environmental gradient. Bats are keystone species in cave ecosystems yet are under
tremendous pressure from inescapable human threats, specifically landscape and cave
disturbances (i.e., reduced forest cover, urbanization, hunting, cave tourism). Informed
management plans are needed to prioritize caves to promote bat conservation;
therefore, our aim is to compare cave-roosting bat assemblages across complex
environmental gradients measured across 56 caves in the Philippines to identify
species-specific thresholds and assess congruence among species to identify
assemblage thresholds. Furthermore, we identify credible indicator species that exhibit
strong associations with specific gradients and tested for differences in ecological and
morphological traits between species groups with shared responses (i.e., negative or
positive). We detected no clearly defined assemblage thresholds, with the exception of
surface-level disturbance. This was due largely to the varied responses exhibited by
individual species and lack of congruity in sensitivity thresholds. Species with
concordant response directions were used to assess species-specific ecological and
morphological traits that may shape their response to an environmental gradient. Few
traits were useful for discriminating the direction of a species response, with some
exceptions. Species that responded positively to increased levels of mining and
hunting had greater body mass, whereas species that negatively responded to mining
and hunting emitted higher peak call frequencies and flew at slower speeds (lower
Vmr), respectively. Credible indicator species were identified that characterized each
gradient, but combinations of indicator species could prove to be a more powerful tool
to proactively monitor or prioritize caves.
52
Texas Tech University, Kendra Phelps, August 2016
Keywords: Change-points, Indicator species, TITAN, Ecological traits, IndVal,
Chiroptera
Introduction
Ecological thresholds are critical transition points along environmental
gradients that lead to abrupt, at times non-linear, changes in system dynamics across
space or time (Groffman et al., 2006; Huggett, 2005; Luck, 2005). Often times,
thresholds are defined by discrete points along a continuous gradient at which a
species shifts drastically in relative frequency of occurrence and abundance (Baker
and King, 2010; Feld et al., 2014; Berger et al., 2016) and/or an abrupt shift in the
distribution of multiple species in a community (King and Baker, 2010). Detection of
ecological thresholds can improve the effectiveness of wildlife management efforts
(Johnson, 2013). Butcher et al. (2010) reported that thresholds in minimum patch size
between 15.0 and 20.1 ha significantly influenced the reproductive success of the
endangered golden-cheeked warblers (Setophaga chrysoparia), even more so than
prey biomass and cowbird parasitism. However, to identify threshold changes within a
complex landscape of interacting environmental pressures can be a challenge (Foley et
al., 2015). Yet, it is necessary, since shifts in ecological thresholds can provide
wildlife managers with guidance on the environmental dynamics that are likely to
drive a species, or potentially a community, over a critical tipping point with
irreversible outcomes (Groffman et al., 2006; Johnson, 2013; Foley et al., 2015).
Ecological thresholds can also define the sensitivity or resilience of a species
to environmental pressures, which can provide guidance to improve the effectiveness
of wildlife management initiatives (Huggett, 2005). While this practice is typically
applied to the conservation of species of concern (e.g., threatened or rare species),
information about the ecological threshold of an invasive or pest species can also be
used to devise cost-effective strategies that target these thresholds to proactively
manage problematic species (Choquenot and Parkes, 2001; Huggett, 2005; Foley et
53
Texas Tech University, Kendra Phelps, August 2016
al., 2015). Furthermore, a strong association between an individual species response
(e.g., based on changes in occurrence and/or abundance) and a specific point along an
environmental gradient can be used to identify indicator species. Indicator species can
be monitored to inform managers if an intervention is necessary to minimize the
likelihood of crossing a critical community threshold (Sasaki et al., 2011). Moreover,
indicator species with similar responses to an environmental gradient may share
similar ecological traits, which could provide greater understanding of how traits can
link a species to a particular environment (Caro and O’Doherty, 1999; Williams et al.,
2010). For instance, the response of bee species to isolation from natural habitat and
agricultural intensification varied based on nesting guilds, with above-ground nesters
more negatively affected by landscape changes than below-ground nesters (Williams
et al., 2010). Such traits (e.g., geographic range, trophic level, reproductive rate) may
predispose species to increased vulnerability to population declines or extinction
(Purvis et al., 2000; Jones et al., 2003; Johnson, 2013). The ability to distinguish sets
of characteristics that define indicator species (i.e., indicator traits) in one region could
be applied to identify and monitor species with these same traits in other regions with
comparable gradients (Manne and Williams, 2003).
Cave-roosting bat populations are declining globally, with human disturbance
at caves identified as the leading cause for these declines (Hutson et al., 2001;
Mickleburgh et al., 2002). Cave-roosting bats face a multitude of human pressures,
namely, hunting for human consumption, frequent visits to tourism caves and
exploitation of caves for natural resources (e.g., harvesting of guano for fertilizer and
cave swiftlet nests for use in soups), which disturb roosting bats. Effects of cave
disturbances are exacerbated by unabated logging activities that fragment and destroy
forested habitats, since many cave-roosting species are dependent upon intact forests
as suitable foraging sites (Lane et al., 2006). Roughly a quarter of all bat species are
dependent upon caves (IUCN, 2016) for protection from inclement weather and
predators and as a stable environment to rear their young (Kunz, 1982). As a result,
cave-roosting bats exhibit strong roost fidelity (Lewis, 1995), and cave disturbance is
54
Texas Tech University, Kendra Phelps, August 2016
consequently an inescapable stressor for many bat species. Furthermore, drastic shifts
in abundance and composition of cave-roosting bats could have a ripple effect on other
cave-dependent wildlife. Guano deposited by aggregations of roosting bats is the
primary energy resource in cave ecosystems (Gnaspini and Trajano, 2000), as a result
bats are keystone species in cave ecosystems. Cave-roosting bats are under intense
pressure yet, as keystone species, bats provide vital ecological services in cave
ecosystems. Bats are thus, as a result, ideal candidates for exploring the existence of
and explicitly defining ecological thresholds across environmental gradients that
typify cave disturbance.
Due to increasing concerns over the continued viability of cave-roosting
assemblages globally, it is essential to identify thresholds in species responses that
could result in a cascade of effects on cave ecosystems as a whole. Our goal is to
assess if ecological thresholds exist for cave-roosting bat species and assemblages to
proactively manage cave ecosystems. We compared cave-roosting bat assemblages
along complex environmental gradients in the Philippines to: (1) identify thresholds
for species responding significantly to environmental gradients typifying cave
ecosystems globally; (2) assess congruence among species responses to identify
thresholds defining significant shifts in assemblage composition; (3) identify indicator
species that reliably represent conditions along the environmental gradient; and (4)
compare differences in ecological traits (e.g., body mass, wing morphology) between
species with similar responses (i.e., those whose abundance and occurrence are
negatively or positively correlated with a gradient). Specifically, we assessed the
effects of six complex gradients, representing both environmental conditions and
human disturbances common to cave ecosystems, on species and assemblages of caveroosting bats on Bohol Island in the central Philippines. We predicted that certain
environmental gradients would have a stronger influence on assemblage composition,
specifically that cave-roosting bat assemblages would demonstrate the strongest
response to surface-level disturbance as this has been shown to shape assemblage
composition in our previous work (Phelps et al., in press). Moreover, we investigated
55
Texas Tech University, Kendra Phelps, August 2016
the direction and magnitude of species responses to each environmental gradient. We
predicted that the occurrence and abundance of cave-roosting bat species would vary
markedly across environmental gradients owing to the ecological divergence among
species, and that species exhibiting sensitivity or tolerance to an environmental
gradient could serve as indicator species for prioritizing caves. Furthermore, we
anticipated that ecological traits would differ between sensitive and tolerant species,
allowing for generalizations of potential indicator species across the region based on
shared traits.
Methods
Study area
Over 10% of the Philippine archipelago is covered by tropical limestone karst,
which is characterized by a high density of solution caves (Restificar et al., 2006). In
this study we focused on Bohol Island (4100 km2), located in the central Philippines.
Karst is estimated to cover approximately 60% of the total surface area of the island.
Human modification of the landscape has steadily increased over the past century,
with wet-rice cultivation and slash-and-burn agriculture replacing an estimated 97% of
the native forests (Urich et al., 2001; Salomon, 2011). Bohol Island is a popular
tourism destination in Southeast Asia, attracting roughly a half-million visitors
annually to its unique karst landscape (e.g., Chocolate Hills) and cave exploration
adventures (i.e., spelunking) (Bohol Tourism Office, 2016).
Cave surveys
We selected 62 caves on Bohol Island that are exposed to gradients of human
disturbance. The gradients included undisturbed caves in remote forested landscapes,
caves visited by local residents to extract resources (e.g., guano, water) and more
severe disturbance from active tourism and mining operations. Caves were surveyed
56
Texas Tech University, Kendra Phelps, August 2016
continuously between July 2011 and June 2013, with the exception of January – May
2012, following standard cave survey methods (Ellis, 1976). Beginning at the cave
entrance, a series of survey stations were established along the entire length of the
cave. Stations were placed chiefly based on changes in height and width of the cave
passage or the existence of side passages or chambers. The measurements taken
between stations included length, height and width of the passage (m) using a tape
measure and microclimatic conditions (i.e., ambient temperature (°C) and relative
humidity (%)) using a handheld weather station. We calculated surface area (m2) and
spatial heterogeneity of each cave following methods detailed by Brunet and Medellín
(2001) and Arita (1996), respectively. Total surface area of the cave was halved to
approximate the surface area suitable for roosting bats (Brunet and Medellín, 2001).
We estimated spatial heterogeneity, which represents the complexity of passages and
chambers within each cave, by dividing the total cave length by the greatest length
between any two survey stations (Arita, 1996). With increasing values, a cave is
assumed to have increasing spatial complexity and, consequently, greater diversity of
available roosting sites for bats. Variation in microclimatic conditions of each cave
was explained by the range in values (subtracting minimum value from the maximum
value) measured during cave surveys. The number of entrances, including vertical
entrances, was also recorded.
In addition to standard cave surveys, we modified the karst disturbance index
developed initially by van Beynen and Townsend (2005) to quantify the magnitude
and severity of human disturbances that may impact cave-roosting bats at each cave.
Details are provided in Phelps et al. (in press), but briefly, a series of factors that
typify different forms of human disturbance commonly observed in caves throughout
Southeast Asia were scored on an ordinal scale ranging from 0 (no disturbance
observed) to 3 (severely disturbed). Human disturbance was assessed at the surfacelevel within a 1 km radius surrounding the main cave entrance. We estimated forest
cover (%) and road size (e.g., national highway, road, trail or none; provides
information about ease of access to the cave) based on Google Earth Pro imagery
57
Texas Tech University, Kendra Phelps, August 2016
and/or visual observations. If a barangay (village, neighborhood) was located within
the 1 km radius of the cave, government statistics (Philippine Statistics Authority,
2013) were used to estimate the number of residents as a proxy for the degree of
urbanization (i.e., > 1000 residents, 100 – 1000 residents, < 100 residents or none),
otherwise the number of houses visible on Google Earth Pro imagery was used to
estimate the number of residents, with the assumption that a household included 4.9
residents. Human disturbance at the subsurface-level was assessed based on visual
observations during caves surveys and one-on-one interviews with local residents.
Disturbances at the subsurface-level included phosphate and limestone mining,
modifications of the cave to accommodate humans (e.g., tourists, miners), hunting of
bats for food and trash dumping (but see Phelps et al. in press for full details). Lastly,
cave localities were georeferenced in Google Earth Pro to measure the straight-line
distance (km) to the nearest barangay, nearest point of access to the cave (e.g., trails,
roads) and to determine if the cave was located within a designated protected area
(DENR, 2015). Of the 62 caves surveyed, six caves were dropped from further
analysis due to missing values for one or more environmental variables.
Environmental gradients
Environmental gradients encompass a gradual change in a suite of correlated
environmental variables across space. We employed principal component analysis
(PCA) to condense our dataset of 18 environmental variables measured at 56 caves
into principal component axes that explain the greatest variation in environmental
gradients among our surveyed caves. Based on a correlation matrix, the PCA
identified six axes each representing a complex environmental gradient that
cumulatively explained 73.9% of the variance in caves (Table 3.1). Axes were
interpreted based on the environmental variables with loadings (i.e., correlations) >
0.30, which is considered a medium effect size based on Cohen (1988) and
recommended by Harlow (2014). Based on our interpretation of the loadings, we
58
Texas Tech University, Kendra Phelps, August 2016
identified the following complex environmental gradients: i) surface-level
disturbances; ii) cave complexity; iii) mining; iv) cave development; v) resource
extraction; and vi) hunting (Table 3.1). For example, caves with high scores on the
environmental gradient representing surface-level disturbance had less forest cover,
but greater urbanization and road size, in a 1 km radius of the cave, and points of
access, such as trails, were nearer to the cave in comparison to caves with lower
scores.
59
Table 3.1. Principal component analysis of the original dataset of 18 environmental variables measured at 56 caves on Bohol Island,
Philippines.
Eigenvalues
Explained variance (%)
Interpretation
PC1
PC2
PC3
PC4
PC5
PC6
2.33
30.20
1.54
13.21
1.38
10.65
1.21
8.16
1.00
5.56
Surface-level
disturbance
Cave complexity
Mining
Cave
development
1.06
6.21
Resource
extraction in
protected areas
Bat hunting
PC loadings
0.10 [0.01 - 0.22]
0.21 [0.08 - 0.28]
0.13 [0.11 - 0.21]
0.11 [0.01 - 0.22]
0.10 [0.02 - 0.19]
0.35 [0.32 - 0.38]
0.35 [0.30 - 0.38]
0.33 [0.27 - 0.37]
0.21 [0.12 - 0.29]
0.28 [0.21 - 0.32]
‫־‬0.07 [‫־‬0.19 - ‫־‬0.01]
0.17 [0.05 - 0.26]
0.27 [0.20 - 0.31]
0.28 [0.20 - 0.32]
0.07 [0.01 - 0.19]
‫־‬0.29 [‫־‬0.33 - ‫־‬0.23]
0.33]
‫־‬0.32 [‫־‬0.36 - ‫־‬0.25]
0.50 [0.16 - 0.51]
0.39 [0.08 - 0.45]
0.37 [0.09 - 0.45]
0.35 [0.04 - 0.48]
0.05 [0.02 - 0.41]
‫־‬0.16 [‫־‬0.25 - ‫־‬0.02]
‫־‬0.17 [‫־‬0.27 - ‫־‬0.02]
‫־‬0.25 [‫־‬0.34 - ‫־‬0.06]
0.13 [0.02 - 0.40]
‫־‬0.01 [‫־‬0.27 - 0.02]
0.28 [0.03 - 0.43]
0.19 [0.02 - 0.35]
0.05 [0.01 - 0.31]
0.15 [0.02 - 0.32]
‫־‬0.05 [‫־‬0.35 - ‫־‬0.01]
0.11 [0.01 - 0.23]
0.23 [0.03 - 0.32]
‫־‬0.26 [‫־‬0.31 - ‫־‬0.17]
0.03 [0.01 - 0.25]
‫־‬0.04 [‫־‬0.46 - ‫־‬0.02]
‫־‬0.19 [‫־‬0.41 - ‫־‬0.02]
‫־‬0.16 [‫־‬0.42 - ‫־‬0.01]
‫־‬0.35 [‫־‬0.52 - ‫־‬0.03]
‫־‬0.47 [‫־‬0.54 - ‫־‬0.06]
‫־‬0.09 [‫־‬0.23 - ‫־‬0.01]
‫־‬0.15 [‫־‬0.27 - ‫־‬0.02]
‫־‬0.14 [‫־‬0.30 - ‫־‬0.01]
0.41 [0.03 - 0.46]
0.02 [0.01 - 0.41]
0.25 [0.03 - 0.50]
0.25 [0.02 - 0.42]
0.30 [0.02 - 0.43]
0.24 [0.02 - 0.35]
0.29 [0.03 - 0.49]
0.02 [0.01 - 0.28]
0.08 [0.01 - 0.27]
‫־‬0.10 [‫־‬0.35 - ‫־‬0.01]
0.11 [0.01 - 0.38]
‫־‬0.09 [‫־‬0.40 - ‫־‬0.01]
0.08 [0.01 - 0.36]
0.17 [0.02 - 0.49]
‫־‬0.13 [‫־‬0.53 - ‫־‬0.02]
‫־‬0.05 [‫־‬0.20 - ‫־‬0.01]
‫־‬0.06 [‫־‬0.24 - ‫־‬0.01]
‫־‬0.09 [‫־‬0.23 - ‫־‬0.02]
‫־‬0.06 [‫־‬0.44 - ‫־‬0.02]
0.48 [0.04 - 0.49]
‫־‬0.47 [‫־‬0.62 - ‫־‬0.03]
‫־‬0.20 [‫־‬0.49 - ‫־‬0.02]
‫־‬0.09 [‫־‬0.41 - ‫־‬0.01]
0.23 [0.01 - 0.37]
0.48 [0.04 - 0.61]
0.28 [0.02 - 0.35]
‫־‬0.02 [‫־‬0.26 - ‫־‬0.01]
0.22 [0.02 - 0.46]
‫־‬0.26 [‫־‬0.38 - ‫־‬0.02] ‫־‬0.12 [‫־‬0.35 - ‫־‬0.02]
‫־‬0.23 [‫־‬0.37 - ‫־‬0.01] 0.06 [0.01 - 0.35]
‫־‬0.14 [‫־‬0.43 - ‫־‬0.02] 0.07 [0.02 - 0.58]
0.27 [0.02 - 0.45] ‫־‬0.07 [‫־‬0.45 - ‫־‬0.01]
0.50 [0.03 - 0.57] ‫־‬0.18 [‫־‬0.54 - ‫־‬0.02]
‫־‬0.01 [‫־‬0.25 - ‫־‬0.01] 0.02 [0.01 - 0.28]
0.17 [0.01 - 0.28] 0.10 [0.01 - 0.29]
[0.01
-0 [[ -0.06 [‫־‬0.26 - ‫־‬0.02]
‫־‬0.04 [‫־‬0.22
- ‫־‬0.02]
0.10 [0.01 - 0.44] ‫־‬0.42 [‫־‬0.46 - ‫־‬0.12]
0.06 [0.01 - 0.43] 0.15 [0.02 - 0.39]
0.36 [0.02 - 0.56] ‫־‬0.26 [‫־‬0.50 - ‫־‬0.02]
0.19 [0.02 - 0.57] 0.58 [‫־‬0.59 - ‫־‬0.03]
0.10 [0.01 - 0.46] 0.37 [‫־‬0.46 - ‫־‬0.02]
0.00 [0.01 - 0.31] ‫־‬0.07 [‫־‬0.34 - ‫־‬0.01]
0.34 [0.02 - 0.64] ‫־‬0.26 [‫־‬0.59 - ‫־‬0.08]
‫־‬0.10 [‫־‬0.35 - ‫־‬0.01] 0.02 [0.01 - 0.39]
0.15 [0.01 - 0.30] 0.16 [0.01 - 0.34]
0.41 [0.02 - 0.50] 0.31 [0.02 - 0.49]
Texas Tech University, Kendra Phelps, August 2016
60
Available roosting area (m2)
Spatial heterogeneity
No. entrances
Temperature range (oC)
Humidity range (%)
Non-forested habitat
Urbanization
Road size
Mining
Cave development
Resource extraction
Bat hunting
Trash dumping
Vandalism/graffiti
Visitation frequency
Distance to barangay (km)
Distance to access (km)
Protected area
Loadings [95% confidence intervals] of environmental and human disturbance factors on each principal component based on
resampling of 999 iterations with replacement. Loadings greater than 0.30 explain a moderate percentage of the variance (Harlow,
2014) and are in bold.
Texas Tech University, Kendra Phelps, August 2016
61
Texas Tech University, Kendra Phelps, August 2016
Bat sampling
We captured bats using mist nets placed across cave entrances and inside cave
passages for two consecutive night at each cave. Captured individuals were identified
to species based on morphological characteristics (Ingle and Heaney, 1992), with age
and reproductive status assessed following Anthony (1988) and Racey (2009). Lastly,
captured individuals were weighed (g), forearm length measured (mm), and then
marked by taking a wing punch prior to release to eliminate recaptured individuals
from our analyses. All methods followed guidelines set forth by the American Society
of Mammalogists (Sikes and Gannon, 2011) and were approved by the Texas Tech
University Animal Care and Use Committee (ACUC 10015-04, 13031-04). For each
species, with the exception of Cynopterus brachyotis and Eonycteris spelaea, between
one to seven adults were collected as voucher specimens under a permit (no. 2011-04,
2013-02) issued by the Department of Environment and Natural Resources of the
Philippines and deposited at the Natural Science Research Laboratory of the Museum
of Texas Tech University.
Trapping effort varied among caves, but mist nets were opened at sunset and
continuously monitored until closed, on average, after 5.5 h. Raw count data was
corrected for differing trapping effort (i.e., number of hours the net(s) were open),
yielding adjusted capture rates (individual/net-hour) for each species at each cave.
Corrected abundance data were log10(x+1) transformed prior to analyses. Following
recommendations of Baker and King (2010), species captured in fewer than 3 caves
were excluded.
Ecological species traits
To explore the effects of ecological and morphological traits on the responses
of cave-roosting bats to multiple environmental gradients, we selected 10 traits (five
categorical, five quantitative) that reflect the ecological and evolutionary diversity
among the species in our study and may influence a species’ sensitivity or tolerance to
62
Texas Tech University, Kendra Phelps, August 2016
the environmental gradients on Bohol Island (Appendix A). Roughly one-quarter of all
bat species in the Philippines are endemic (Heaney et al., 2010), an geographic
construct typically viewed to increase a species sensitivity to disturbance in
comparison to non-endemic, widespread species (Pimm, 1998). Species were
categorized as Philippine endemics or regionally widespread based on current
knowledge of their geographic distribution in Southeast Asia (Francis, 2008; Heaney
et al., 2010). Landscape degradation and fragmentation can alter the availability and
spatial distribution of food resources, thus putting unequal pressure on species based
on their dietary preferences and foraging space. Dietary niche was categorized into
two broad categories, insectivorous or phytophagous (including nectarivorous and
frugivorous species) (Reiter and Tomaschewski, 2003; Francis, 2008; IUCN, 2016).
Foraging space was categorized as clutter (species that exploit prey on or close to
vegetation in forest interiors with dense background obstacles), semi-clutter (species
capable of commuting across open space but forage at forest edges) and open (species
that forage in uncluttered space, such as above the forest canopy) ( Kingston et al.,
2003; Kingston, 2013). We assumed that a species’ dependence on caves as roosting
sites would increase its sensitivity to cave disturbance. Species were assigned as
obligatory cave-roosting species if they have only been documented roosting in caves
or facultative cave-roosting species if they have been reported to use roosts other than
caves (e.g., human-made structures, tree hollows) (Francis, 2008; IUCN, 2016). Bat
species that roost colonially in large aggregations may be a target for increased
hunting pressure (Mickleburgh et al., 1992; Mildenstein et al., 2016), and may be
more vulnerable to cave disturbance since the loss of one roost site would affect a
significant proportion of the population (McCracken, 1988). We categorized colony
size as small (< 100 individuals), medium (100 – 1000 individuals) and large (> 1000
individuals). Categorical traits were refined based on personal observation during this
study, if necessary. For example, colony size for Taphozous melanopogon has been
reported to range from 10 to 15000 individuals across Southeast Asia, but we selected
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Texas Tech University, Kendra Phelps, August 2016
a medium colony size because we never observed more than 500 individuals
congregated together during our cave surveys.
Wing morphology, specifically aspect ratio and relative wing loading, can limit
a species’ ability to efficiently maneuver through the landscape and catch prey
(Norberg and Rayner, 1987). To estimate aspect ratio and wing loading from alcoholfixed specimens collected during our study, we had to first estimate wing area (m2)
and wingspan (m). Following Blood and McFarlane (1988), wing area of one wing
was calculated using the formula WA = (FA x D5) + 0.5 (D5 x D3), where FA is the
forearm length (m), D5 is the length of the fifth digit (m) and D3 is the length of the
third digit (m). The wingspan of one wing was estimated using the formula S = FA +
D3 (Norberg and Rayner, 1987). Given that both measurements excluded the body and
tail membrane, we added 10% to both measurements to account for the body and
between 10-15% was added to wing area to account for the tail membrane depending
on the family as recommended by Norberg and Rayner (1987). Aspect ratio and
relative wing loading (wing loading independent of mass; Norberg, 1988) were
calculated following equations provided in Norberg and Rayner (1987) and Norberg
and Fenton (1988), respectively: A = B2/S, where A is aspect ratio, B is wing span (m)
and S is wing area (m2); and C = (Mg/S)/M1/3, where C is relative wing loading, Mg is
weight (mass times gravitational acceleration, g) and S is wing area (m2). Flight speed
can limit the distance a species can commute between roosting sites and foraging sites;
therefore, we calculated Vmr (maximum range speed, often referred to as commuting
speed) based on a formula provided in Norberg and Rayner (1987; Eqn. 4). Body mass
was averaged across all adult, non-reproductive individuals captured during our study
to report mean body mass for each species.
Similar to wing morphology, echolocation call design dictates how bat species
are able to capture prey while flying (Bogdanowicz et al., 1999). Bats specialized to
forage in structurally complex habitats (i.e., higher frequency) would be poorly suited
to detect and capture prey in open habitats, thus increasing their vulnerability to the
reduction in forest cover (Kingston, 2013). We expected that species with relatively
64
Texas Tech University, Kendra Phelps, August 2016
high peak frequencies would be more negatively affected by surface-level disturbance
since this would decrease the habitat structure leading to lower foraging success.
Three-second recordings of echolocation calls were taken from captured bats while in
the hand (i.e., Hipposideros, Rhinolophus) and during release using a time expansion
detector (Pettersson D240X), with outputs recorded to a digital audio recorder, and
analyzed using BatSound4. We identified the frequency at which most energy was
expended during the call sequence based on the power spectrum; we considered this
the peak frequency (kHz). We compared and supplemented our data using published
echolocation records from Bohol Island (see Sedlock et al., 2014). All quantitative
traits were normalized by a log10-transformation prior to statistical analysis.
Data analysis
To identify species responses to each of our identified environmental gradients,
we used the Threshold Indicator Taxa ANalysis — TITAN, a function that combines
change-point analysis and indicator species analysis (Baker and King, 2010). TITAN
identifies abrupt, non-linear changes (i.e., change-points) in occurrence frequency and
relative abundance along environmental gradients for each species (Appendix C), and
assesses congruence among species change-points as an indication of assemblage
thresholds (Baker and King, 2010).
To identify species change-points (i.e., thresholds) across a continuous
environmental gradient, TITAN uses indicator value (IndVal) scores derived from
indicator species analysis (Dufrêne and Legendre, 1997). IndVal scores represent the
strength-of-association between a species and a defined group, with groups
traditionally set a priori across categories of environmental conditions (e.g., levels of
forest fragmentation) (Dufrêne and Legendre, 1997). However, group classification in
TITAN is initially unknown and is determined instead based on abrupt changes in the
relative frequency of occurrence and abundance of a species across a continuous
environmental gradient (Baker and King, 2010). To do so, sample observations are
65
Texas Tech University, Kendra Phelps, August 2016
first ordered across the continuous gradients, and then split into two groups at
candidate change-points identified at midpoints between values along the gradient.
Two IndVal scores are produced at each candidate change-point that signifies the
strength-of-association in species abundance weighted by occurrence on each side of
the change-point. A larger IndVal score on the left side of the candidate change-point
indicates a negative response, while larger scores on the right side indicate a positive
response (Baker and King, 2010). Greater differences in species association with a
specific side of a candidate change-point results in a larger IndVal score, with the
largest score at a change-point and the direction of the response (i.e., negative or
positive) retained for comparison with all other candidate change-points along the
gradient. The change-point with the maximum IndVal score among all change-points
is considered the ecological threshold for that species, with the direction of response at
the identified threshold used to assign species into negative or positive groups.
Randomized permutation of group membership is used to estimate the probability of
obtaining an IndVal scores equal to or greater than the observed IndVal score
corresponding to the identified threshold. We considered a species to be significantly
associated with a response group if IndVal < 0.05. Bootstrapping procedures are used
to estimate uncertainty around the threshold location and consistency in the
directionality in species response (i.e., negative or positive). Furthermore, we assessed
species responses as credible indicators of an environmental gradient using diagnostic
indices incorporated in TITAN, specifically purity and reliability, based on 500
bootstrap replicates (Baker and King, 2010). Purity is the proportion of bootstrap
replicates that assign a species to the same response group as initially observed, and
reliability is the proportion of bootstrap change-points with IndVal scores with p <
0.05 (Baker and King, 2010). We considered species with purity > 0.90 and reliability
> 0.75 to be credible indicator species for a particular environmental gradient.
Congruence in change-point location among species in a response group
(negative or positive) is used as evidence of assemblage thresholds, with an
assemblage threshold for each response group. IndVal scores are used to identify
66
Texas Tech University, Kendra Phelps, August 2016
assemblage thresholds, but first scores are standardized to z-scores to facilitate crossspecies comparison (Baker and King, 2010). Standardization is carried out by
subtracting the mean of the randomized permutations from the observed IndVal
scores, and dividing by the standard deviation. Then, species are grouped according to
the direction of their response: z- group has a negative response and z+ group a
positive response. Finally, the IndVal scores for the z- and z+ scores at each point
along the gradient are summed. The negative and positive assemblage thresholds
correspond to the value on the environmental gradient where the sum(z-) or sum(z+)
scores show a distinct peak, respectively. Strong responses by multiple species to the
same gradient value will result in a distinctly sharp peak with large sum(z) scores,
whereas weak responses result in lower sum(z) values without a distinct peak (Baker
and King, 2010). Uncertainty around the estimated assemblage thresholds are assessed
by bootstrapping the original data and recalculating the change-points, and are
expressed as confidence limits (i.e., quantiles of the change-point distribution). We
performed TITAN analysis using published R scripts (see Baker and King, 2010).
TITAN classifies species with similar responses to an environmental gradient
into two groups: i) species with negative z-scores and significant IndVal scores <0.05
(z- group) and ii) species with positive z-scores and significant IndVal scores <0.05
(z+ group). We used TITAN results to test for potential associations between
ecological traits and species responses to the environmental gradients (i.e., do species
groups identified by TITAN differ in their traits). Since our data were unbalanced with
differing number of species in each group for some environmental gradients, we tested
each trait independently by using a series of Chi-square (χ2) tests and Kruskal-Wallis
tests. Chi-square tests were performed to assess differences in categorical traits (i.e.,
endemism, dietary niche, foraging space, roost dependence and colony size), while
differences in quantitative traits (i.e., aspect ratio, relative wing loading, body mass,
Vmr and peak call frequency) were assessed using Kruskal-Wallis tests. All p-values
for Chi-square tests were computed by permutation tests using 999 Monte Carlo
simulations. If significant differences were detected by the Kruskal-Wallis tests (p <
67
Texas Tech University, Kendra Phelps, August 2016
0.05), post hoc Dunn pairwise comparison tests were implemented using the package
dunn.test (Dinno, 2016). All analyses were performed using R 3.1.2 (R Core Team,
2014).
Results
From the 56 caves surveyed across Bohol Island from July 2011 – June 2013,
we captured 6825 bats, excluding recaptures, of 21 species, representing the families
Emballonuridae, Hipposideridae, Megadermatidae, Molossidae, Pteropodidae,
Rhinolophidae and Vespertilionidae (see Phelps et al. in press for details about each
cave). Hipposideros diadema was the most abundant species, representing 31% of all
captures, followed by Miniopterus schreibersii (14.8%) and Miniopterus australis
(14.2%). However, two species, Chaerephon plicatus and Cynopterus brachyotis,
were excluded from further analyses since they were captured in fewer than five
caves.
Assemblage-level responses to environmental gradients
TITAN estimated two assemblage-level thresholds associated respectively with
the low and high ends of each environmental gradient by identifying peaks in the
cumulative frequency distribution of the sum(z-) and sum(z+) groups (Table 3.2; Fig.
3.1). The distribution of sum(z-) and sum(+) groups showed relatively strong peaks in
response to surface-level disturbance and cave complexity (Fig. 3.1). Principal
component scores for surface-level disturbance ranged from -4.625 to 4.939,
indicating that the thresholds for the sum(z-) group (-1.938) and the sum(z+) group
(2.658) are quite distinct from one another along the gradient. Furthermore,
confidence limits for the bootstrap distributions of each group do not overlap. On the
other hand, PC scores for cave complexity ranged from -2.914 to 3.640, yet the
thresholds of the sum(z-) group (-0.176) and sum(z+) group (0.552) are both centrally
68
Texas Tech University, Kendra Phelps, August 2016
located along the gradient and had slightly overlapping confidence limits. Conversely,
no obvious peaks in threshold responses were observed for sum(z-) and sum(+) groups
along gradients of mining, cave development, resource extraction and bat hunting,
indicating that evidence of a distinct assemblage-level threshold to these
environmental gradients is not apparent (Fig. 3.1). Therefore, these thresholds should
be interpreted with caution. Uncertainty in threshold estimates are also reflected in the
large, overlapping confidence limits associated with each gradient (Table 3.2).
69
Table 3.2. Results of Threshold Indicator Taxa ANalysis (TITAN) of assemblage-level thresholds along multiple environmental
gradients on Bohol Island.
Environmental Gradient
Surface-level Disturbance
Cave Complexity
70
Cave Development
Resource Extraction
Bat Hunting
Obs.
sum(z-)
Confidence Limits
0.05
0.10
0.50
0.90
0.95
-1.938
-3.523
-3.303
-2.071
-1.031
-0.656
sum(z+)
2.658
0.370
0.628
2.658
2.675
2.675
sum(z-)
-0.176
-2.012
-1.843
-0.805
0.155
0.274
sum(z+)
0.552
0.024
0.274
0.616
2.141
2.224
sum(z-)
0.380
-1.851
-1.851
-0.152
0.448
0.827
sum(z+)
0.012
-0.235
-0.087
0.179
1.473
1.628
sum(z-)
-0.500
-1.279
-1.279
-0.660
0.958
1.143
sum(z+)
1.498
-0.222
0.058
0.863
1.715
1.885
sum(z-)
-0.428
-1.368
-1.368
-0.752
0.699
0.843
sum(z+)
1.394
-0.137
-0.012
1.121
1.394
1.394
sum(z-)
0.208
-1.078
-1.078
-0.245
0.333
0.437
sum(z+)
0.597
0.067
0.147
0.519
1.402
1.402
Assemblage thresholds (Obs.) are determined by the summation of z-scores for all species in the negative (z-) and positive (z+)
response groups, with the largest aggregate z-scores for each group used to identify threshold values along each environmental
gradient. Confidence limits correspond to the frequency distribution of identified assemblage thresholds after 500 bootstrap replicates.
Texas Tech University, Kendra Phelps, August 2016
Mining
Response
Group
Summed z-scores for all species in a response group (Sum(z)) at candidate change-points along each complex environmental gradient.
Black solid lines represent the cumulative frequency distribution of change-points after 500 replications for the negative indicator
species (z-) group and red dashed lines for the positive indicator species (z+) group. Peaks in these lines indicate a congruence among
Texas Tech University, Kendra Phelps, August 2016
71
Figure 3.1. Threshold Indicator Taxa ANalysis (TITAN) of assemblage-level thresholds along multiple environmental gradients.
species, and define the assemblage-level threshold for each response group. Connected circles represent the summation of z-scores at
each candidate change-point, with closed circles representing sum(z-) and open circles representing sum(z+) values.
Texas Tech University, Kendra Phelps, August 2016
72
Texas Tech University, Kendra Phelps, August 2016
Species responses to environmental gradients
In general, cave-roosting bat species exhibited threshold responses to at least
one environmental gradient (Fig. 3.2). Approximately 80% of all species exhibited
significant responses to more than one gradient, with only Emballonura alecto,
Ptenochirus jagori, Miniopterus tristis and Rhinolophus rufus having thresholds along
only one environmental gradient. Conversely, T. melanopogon exhibited significant
responses with defined threshold values along five (out of six) environmental
gradients. However, T. melanopogon did not identify with a single response group
(either z+ or z-) across all gradients, but instead displayed mixed responses of
sensitivity (z- group) to one environmental gradient (e.g., surface-level disturbance)
but tolerance (z+ group) to another (e.g., cave development).
Along the gradient of surface-level disturbance, TITAN identified eight out of
19 species (42.1%) with significant IndVal scores (p > 0.05) (Fig. 3.2) (Appendix B).
Four species composed a group of negative indicators (z-), those species that decrease
in relative frequency of occurrence and abundance, with threshold values for these
species ranging from -3.52 to 1.32. Another four species were positive indicators (z+),
species that increase in occurrence and abundance, with threshold values ranging from
1.06 to 2.68. Cave complexity represents a gradient in cave dimensions and
microclimate; therefore, caves with high values along this gradient would provide
more roosting opportunities due to greater surface area, spatial variability, temperature
range and entrances. As such, nine out of 19 species (47%) responded positively to
increasing cave complexity, with threshold values ranging between 0.27 – 2.22.
Conversely, only two species were significantly associated with low cave complexity,
with threshold values between -0.85 and -2.01. Along the gradient of mining, five
species increased with high mining activity, while another five species decreased (Fig.
3.2). Regardless of individual species responses, either negative or positive, most
species had similar threshold values (0.00 – 0.50). Few species exhibited significant
responses to cave development, with only two species as negative indicators (z-) and
two as positive indicators (z+). Similarly, resource extraction elicited few species
73
Texas Tech University, Kendra Phelps, August 2016
responses, with only a quarter of species having distinct threshold values. Four out of
19 species declined in occurrence and abundance in caves with high hunting activity,
while another four species increased. Not surprising, Eonycteris spelaea and Rousettus
amplexicaudatus, two species commonly hunted for meat, were abundant in these
caves. It is apparent that hunting activities are highest when these two species are
present; however, two non-target species had a positive response to hunting (Fig. 3.2).
Another four species were negative indicators (z-) of hunting, decreasing in
occurrence and abundance at thresholds ranging from -0.79 to 0.62 on the gradient of
hunting.
Comparison of ecological traits
Our investigation into the link between species response groups (z+ and z-)
identified for each environmental gradient and species ecological traits revealed few
significant differences (Table 3.3). Along the gradient of mining, the positive indicator
group (z+), which included P. jagori, T. melanopogon, Rhinolophus philippinensis,
Rhinolophus macrotis and Megaderma spasma, differed in body mass (H = 4.811, p =
0.028) and peak call frequency (H = 5.000, p = 0.025) from the negative indicator
group (z-), composed of Hipposideros pygmaeus, Hipposideros obscurus,
Hipposideros ater, Rhinolophus arcuatus and M. australis. Specifically, species
sensitive to mining activities (i.e., negative indicator group) had significantly smaller
body mass (Z = -2.193, p = 0.014) and emitted a significantly higher peak frequency
(Z = 2.236, p = 0.014) based on Dunn post hoc pairwise tests. Similarly, along the
gradient of hunting, body mass also significantly differed between the positive
indicator group (z+), including Myotis macrotarsus, R. amplexicaudatus, E. spelaea
and T. melanopogon, and the negative indicator (z-) group, composed of M. australis,
R. arcuatus, R. macrotis and Myotis horsfieldii (H = 5.333, p = 0.021). Species
comprising the positive indicator group were significantly larger, based on body mass,
than those that were sensitive to hunting disturbance (Z = -2.309, p = 0.011).
Moreover, Vmr significantly differed between indicator groups along the gradient of
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Texas Tech University, Kendra Phelps, August 2016
hunting (H = 5.333, p = 0.021), with the positive species group (z+) having greater
flight speed that allows for greater commuting distances (Z = -2.309, p = 0.011).
Identification of indicator species
In addition to the identification of change-points in species occurrence and
abundance along environmental gradients, TITAN provides information regarding the
purity and reliability of certain species as indicator species corresponding to each
gradient. Specifically, species with purity > 0.90 and reliability > 0.75 were
considered credible indicator species. At least one indicator species was identified for
each of our environmental gradients (Appendix B), with the most indicator species
along the gradient of cave complexity (n = 10). Of the 19 species included in our
study, 16 species (84.2%) were identified as indicator species. Several species (n = 5)
were indicators for more than one environmental gradient (Appendix B).
75
gradients. Only species that exhibited a significant response to a respective gradient are shown (IndVal < 0.05), with each species’
Texas Tech University, Kendra Phelps, August 2016
76
Fig. 3.2. Threshold Indicator Taxa ANalysis (TITAN) summary plots of species-specific change-points along multiple environmental
corresponding threshold represented by circles. Black circles correspond to negative (z-) indicator taxa (with corresponding species
labels on the left axes; see Appendix A for details) and red circles correspond to positive (z+) indicator taxa (with corresponding
species labels on the right axes). Circles are sized in proportion to the magnitude of each species’ response (i.e., z-scores), with
overlapping horizontal lines representing the 95% confidence limits after 500 bootstrap replicates.
Texas Tech University, Kendra Phelps, August 2016
77
Table 3.3. Results of the Chi-square test (χ2) on categorical variables and Kruskal-Wallis tests (H) on continuous variables performed
between species groups (z- and z+) distinguished by Threshold Indicator Taxa ANalysis (TITAN) and ecological traits.
Environmental Gradients
Surface-level
Disturbance
Cave
Complexity
Mining
Cave
Development
Resource
Extraction
Bat Hunting
p-value
χ2
p-value
χ2
p-value
χ2
p-value
χ2
p-value
χ2
p-value
Endemism
1.143
0.285
3.704
0.110
0.058
0.809
N/A
N/A
0.444
0.505
N/A
N/A
Dietary Niche
1.406
0.407
0.148
0.700
0.875
0.349
N/A
N/A
1.875
0.171
2.667
0.429
Foraging Space
3.200
0.202
0.178
0.915
2.100
0.349
N/A
N/A
5.000
0.082
3.000
0.642
Roost Dependence
2.667
0.103
3.704
0.135
1.667
0.196
1.333
0.248
2.222
0.136
0.000
1.000
Colony Size
2.057
0.358
1.333
0.792
N/A
N/A
3.333
0.415
5.000
0.082
3.000
0.655
H
p-value
H
p-value
H
p-value
H
p-value
H
p-value
H
p-value
Aspect Ratio
0.750
0.387
0.692
0.405
0.500
0.479
2.400
0.121
1.800
0.179
0.333
0.564
Wing Loading
0.240
0.624
3.086
0.157
0.000
1.000
3.857
0.123
3.000
0.083
3.000
0.083
Body Mass
1.333
0.282
0.077
0.782
4.811
0.028
1.191
0.275
1.333
0.248
5.333
0.021
Vmr
0.240
0.624
0.692
0.405
3.153
0.076
0.600
0.484
0.221
0.655
5.333
0.021
Peak Frequency
0.150
0.699
0.000
1.000
5.000
0.025
1.333
0.248
1.500
0.221
2.000
0.157
78
Continuous Traits
For χ2 tests, p-values are based on permutations tests using 999 Monte Carlo simulations. Significant results are in bold.
Texas Tech University, Kendra Phelps, August 2016
χ2
Categorical Traits
Texas Tech University, Kendra Phelps, August 2016
Discussion
Our study provides evidence of the existence of ecological thresholds for
individual species and assemblages of cave-roosting bats along multiple
environmental gradients. However, with the exception of surface-level disturbance,
uncertainty surrounding many of the identified thresholds and lack of an overall
congruent response among species indicates that considerable caution should be taken
when applying a threshold value to this assemblage. The majority of species exhibited
significant responses to one or more environmental gradients, yet there was no
evidence indicating that cave-roosting bat species respond to environmental gradients
at the same change-point or in the same direction (i.e., increase, decrease), with
responses varying considerably. Moreover, with a few exceptions, ecological traits did
not vary significantly between significant response groups identified through threshold
analysis. In spite of these varied responses, we were able to identify species that
demonstrated strong, consistent response patterns to specific conditions along each
gradient; these species were considered credible indicator species.
No previous studies have identified thresholds for bat assemblages even
though bats are under intense pressure from a multitude of human disturbances
globally (Mickleburgh et al., 2002). We predicted that certain environmental gradients
would elicit strong responses at the assemblage-level. Our study demonstrates that
cave-roosting bat assemblages exhibit a clear threshold to surface-level disturbance,
but only species-specific responses to all other environmental gradients. This finding
has management implications, as it points to landscape disturbances surrounding the
cave as being potentially more influential on assemblage composition than cave
disturbance. Alarmingly, species sensitive to surface-level disturbances showed a
marked decrease in overall occurrence and abundance at relatively low levels of
surface-level disturbance. Thus, efforts to prioritize and manage cave systems should
also consider the surrounding landscape, and make efforts to restore the forested habits
in close proximity to cave entrances. Although elucidating assemblage thresholds to
surface-level disturbance is vitally important for effective management practices, they
79
Texas Tech University, Kendra Phelps, August 2016
do not reveal the mechanisms that drive changes in cave-roosting bat assemblages.
One possibility may be that the reduction in forest cover surrounding the cave,
coupled with expanding urbanization, may exert strong effects on food availability
locally and increase commuting costs to reach suitable foraging areas.
A lack of congruent responses among bat species resulted in detections of
weak assemblage thresholds to all other environmental gradients. Identification of
thresholds for assemblages can be problematic since they require commonality among
a suite of species, such as ecological traits, for distinct thresholds to be detected.
Similarly, Lindenmayer et al. (2005) failed to identify thresholds for bird and reptile
assemblages across a gradient in landscape cover of native eucalyptus and exotic
radiate pine in southeastern Australia. The authors suggest that identifying
assemblage-level responses to thresholds is unlikely because of the diversity of
individual species traits and the possibility that even ecologically similar species will
respond differently to landscape change (Lindenmayer et al., 2005). Though we report
threshold values for assemblages of cave-roosting bats, we do not advocate that a
single threshold value can explain the response of an entire cave-roosting bat
assemblage. Rather, our results suggest there is a common level where the risk of
losing sensitive species is likely greater, but also that interspecific differences allude to
varying susceptibility to complex environmental gradients. Instead, it may be more
informative to examine the response of individual species within the assemblage (King
and Baker, 2010).
Estimation of species-specific thresholds is arguably the key output from
TITAN, providing insights that are not captured by aggregate community metrics
(e.g., species richness, functional diversity) (Baker and King, 2010; King and Baker,
2010). Our study clearly demonstrates that individual bat species have varied
responses to each of our measured environmental gradients. Although surface-level
disturbance was the catalyst for change-points in assemblage composition, our results
indicate that the largest proportion of species responded significantly to cave
complexity. However, along each environmental gradient there was a suite of species
80
Texas Tech University, Kendra Phelps, August 2016
that responded positively (i.e., increasing in occurrence frequency and relative
abundance) and another suite of species responding negatively (i.e., decreasing in
occurrence and abundance). Distinguishing between species response patterns makes
sense from a broad management viewpoint; as there is limited evidence that all species
in an assemblage will respond to environmental gradients in the same manner
(positive or negative) or at the same threshold. In general, studies aimed at detecting
congruent responses to thresholds in environmental gradients instead report mixed
species responses and varied, wide-ranging threshold values (Suarez-Rubio et al.,
2013, Ávila-Gómez et al., 2015; Berger et al., 2016), making it difficult to identify
valid generalizations to inform management strategies.
Although there was a lack of congruence among species responses in general,
some species exhibited strong associations with specific environmental gradients and
so were deemed credible indicator species. Interestingly, nearly half of all bat species
in our study were considered positive indicators of complex caves. Our results point to
the fact that the protection of more complex caves, that is caves with greater surface
area, spatial variability, number of entrances and range in temperature, would promote
increasing frequency of occurrence and abundance of a greater proportion of the caveroosting bat assemblage. Similarly, Phelps et al. (in press) reported cave complexity to
be a significant driver of assemblage composition in cave-roosting bats. Fewer
indicator species were observed for all other environmental gradients. However,
combinations of indicator species with similar responses can increase the certainty of
cave assessments across environmental gradients. For example, relatively high
abundances of R. philippinensis and R. arcuatus would indicate a complex cave with a
low incidence of mining and hunting, since both species respond positively to cave
complexity and negatively to mining and hunting. Collections of indicator species
with a congruence in response direction and/or ecological threshold provide greater
confidence that site conditions are assessed correctly (De Cáceres et al., 2012), and
may be the most powerful tool for proactively monitoring or prioritizing sites.
81
Texas Tech University, Kendra Phelps, August 2016
Overall, our results do not suggest that shared ecological traits influence the
responses of cave-roosting bat species, at least in regards to the traits we examined. A
lack of trait effect may be attributed to the small number of bat species compared, with
only 19 species occurring at great enough frequency to include in our analyses. The
ability to distinguish sets of traits associated with differing responses to environmental
gradients may be possible in larger, more diverse assemblages since there are likely to
have greater trait differences. However, there were a few traits associated with
response direction in our study, namely body mass, Vmr and peak call frequency, but
significant differences between species groups only existed along gradients in mining
and bat hunting. Body mass was a distinguishing ecological trait across both gradients,
with both positive response groups being significantly heavier than either negative
response groups. Our results are not surprising since body mass has been shown to be
strongly correlated with numerous physiological and behavioral differences among
species that may influence their response. For example, heavier bird species tend to
initiate flight in response to approaching humans at a further distance than lighter
species (Blumstein et al., 2005). Moreover, ecological traits selected for our study are
not exhaustive, and future studies should include additional traits that may be more
informative (e.g., home range size). Few studies consider ecological trait differences
associated with species thresholds, which is unfortunate since identifying shared traits
allow generalizations to species management beyond the focal study site.
In conclusion, interspecific differences in change-points emphasize the
importance of focusing largely on identifying thresholds for individual species, rather
than assemblages, when planning management intervention. This was clearly
demonstrated in our threshold analysis, with most species differing in the direction and
magnitude of their response to each of our six complex environmental gradients. Yet
ecological and morphological traits did not differ between those species that
responded positively or negatively to our gradients, with a few exceptions. Several
species of cave-roosting bats were identified as credible indicator species, most of
which are widely distributed species throughout Southeast Asia. Our results have
important implications for policies recently put forth the Philippines to prioritize caves
82
Texas Tech University, Kendra Phelps, August 2016
as a means to protect cave-dependent wildlife, including bat species. As keystone
species in cave ecosystems, cave-roosting bats are ideal taxa to rapidly gauge the
environmental conditions of a cave and the surrounding landscape. Doing so would
inform proactive efforts to prioritize caves for protection as well as identify caves in
greatest need of forest restoration in the surrounding landscape. As we have
demonstrated, methods that identify ecological thresholds, such as TITAN, are a
valuable tool to prioritize sites in greatest need of management. As funding is often
limited, effective conservation measures should focus on species individually,
particularly those species with the lowest threshold values or those that are species of
conservation concern. However, considerations of sample size, minimum split size and
frequency of taxa occurrence are important when using a classification model such as
TITAN. Small sample sizes will likely result in unreliable change-point estimates, but
Baker and King (2010) advise that minimum sample size will be dataset specific.
Furthermore, Baker and King (2010) suggest a minimum split size > 3 – 5 to compute
z-scores more accurately, and taxa with < 3 – 5 occurrences not be included because
these taxa are too infrequent to provide interpretable z-score estimates. While our
TITAN analyses met these requirements, increased samples sizes would likely result
in more reliable estimates with narrower confidence limits for both assemblage and
species-level thresholds.
Acknowledgments
We thank the Department of Environment and Natural Resources of the
Philippines for permission to conduct this study (permit no. 2011-04, 2013-02). We
are especially grateful to all those involved with fieldwork, as well as Bohol Island
State University for lodging and transportation during portions of this project. The
study was supported by U.S. Department of State – Fulbright Fellowship, Bat
Conservation International, American Philosophical Society, The Explorers Club,
American Society of Mammalogists, National Speleological Society, Cave Research
Foundation, John Ball Zoo, Sigma Xi and Texas Tech Association of Biologists. This
83
Texas Tech University, Kendra Phelps, August 2016
manuscript benefited from interactions through the Southeast Asian Bat Conservation
Research Unit (SEABCRU) network supported by the National Science Foundation
(grant no. 1051363).
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Appendix A. Scientific names and ecological traits of 19 cave-roosting bat species captured on Bohol Island, Philippines.
Code
for Fig.
3.2
Endemic
Dietary niche
Foraging
space
Wing
loading
(N m2)
Vmr
(m/s)
Peak
frequency
(kHz)
Body
mass
(g)
Eonycteris spelaea
EOSP
No
Phytophagous
5.6
46.7
6.9
N/A
62.1
Ptenochirus jagori
PTJA
Yes
Small
6.8
54.7
7.1
N/A
74.2
ROAM
Obligate
Large
7.0
59.3
7.7
N/A
83.7
Emballonura alecto
Open
Facultative
Small
7.6
38.1
3.9
49.4
6.2
Taphozous melanopogon
Insectivorous
Open
Facultative
Medium
8.4
51.5
5.4
29.1
23.0
No
Insectivorous
Clutter
Facultative
Small
6.1
35.7
3.9
138.6
5.2
HIDI
No
Insectivorous
Clutter
Facultative
Medium
6.2
34.7
5.2
69.3
42.8
Hipposideros obscurus
HIOB
Yes
Insectivorous
Clutter
Obligate
Small
6.0
43.9
4.6
142.4
9.1
Hipposideros pygmaeus
HIPY
Yes
Insectivorous
Clutter
Obligate
Small
6.3
35.5
3.7
95.5
4.0
MESP
No
Insectivorous
Clutter
Facultative
Small
6.3
43.0
5.4
N/A
22.9
Miniopterus australis
MIAU
No
Insectivorous
Open
Obligate
Medium
7.5
41.8
4.1
65.9
6.0
Miniopterus schreibersii
MISC
No
Insectivorous
Open
Obligate
Medium
7.3
39.6
4.3
48.5
9.8
Miniopterus tristis
MITR
No
Insectivorous
Open
Obligate
Medium
7.6
43.5
5.0
34.6
18.4
Roost
dependence
Colony
size
Aspect
ratio
Semi-clutter
Obligate
Large
Phytophagous
Semi-clutter
Facultative
No
Phytophagous
Semi-clutter
EMAL
No
Insectivorous
TAME
No
Hipposideros ater
HIAT
Hipposideros diadema
Scientific name
Pteropodidae
Rousettus amplexicaudatus
Emballonuridae
Megadermatidae
Megaderma spasma
Miniopteridae
Texas Tech University, Kendra Phelps, August 2016
90
Hipposideridae
Appendix A. cont.
Code
for Fig.
3.2
Endemic
Dietary niche
Foraging
space
Roost
dependence
Colony
size
Aspect
ratio
Myotis horsfieldii
MYHO
No
Insectivorous
Clutter
Facultative
Small
Myotis macrotarsus
MYMA
No
Insectivorous
Clutter
Obligate
Rhinolophus arcuatus
RHAR
No
Insectivorous
Clutter
Rhinolophus macrotis
RHMA
No
Insectivorous
Rhinolophus philippinensis
RHPH
No
Rhinolophus rufus
RHRU
Yes
Scientific name
Wing
loading
(N m2)
Vmr
(m/s)
Peak
frequency
(kHz)
Body
mass
(g)
5.9
38.2
4.2
47.6
6.4
Small
6.3
37.8
4.7
N/A
12.8
Obligate
Small
5.8
37.5
4.3
68.7
7.7
Clutter
Obligate
Small
5.9
34.9
4.1
50.0
6.7
Insectivorous
Clutter
Obligate
Small
5.9
31.1
4.2
31.2
10.0
Insectivorous
Clutter
Obligate
Small
5.8
36.6
5.4
39.5
29.3
Vespertilionidae
Rhinolophidae
Texas Tech University, Kendra Phelps, August 2016
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See methods section for details regarding the selected ecological traits.
Appendix B. Species-specific results from Threshold Indicator Taxa ANalysis (TITAN) for cave-roosting bat species in response to
multiple environmental gradients on Bohol Island, Philippines.
Environmental Gradient: Surface-level Disturbance
Change-point Analysis
Species
+/-
z
Obs.
5%
95%
IndVal
p-value
Purity
Reliability ≤0.05
+
+
+
+
+
+
+
+
+
+
+
-
9.27
2.68
2.72
1.93
1.86
1.60
1.59
1.56
1.37
1.36
1.18
4.88
3.31
4.36
1.90
1.66
1.60
1.07
1.04
2.68
2.63
2.66
-0.51
-0.96
-1.34
-0.66
1.06
2.66
-1.09
0.24
-1.53
1.32
-3.17
-3.52
1.52
1.98
-1.94
1.45
1.97
-1.03
-3.17
-3.52
-1.03
-3.17
-1.09
-2.47
-3.30
-1.20
-3.17
-3.52
-2.92
-3.52
-3.52
-1.34
-3.30
-3.30
-3.52
2.68
2.68
2.68
2.68
2.68
2.66
2.20
2.68
2.68
2.57
2.68
-0.96
1.52
1.87
2.58
1.74
2.07
2.68
1.52
60.00
47.90
35.78
45.10
16.67
49.89
11.76
12.41
48.36
13.16
20.75
26.67
40.48
34.46
39.59
16.22
36.95
16.51
11.11
0.004
0.032
0.044
0.064
0.096
0.072
0.160
0.042
0.104
0.212
0.120
0.004
0.004
0.012
0.048
0.132
0.068
0.108
0.252
0.95
0.93
0.64
0.89
0.79
0.83
0.87
0.82
0.54
0.59
0.83
1.00
0.99
0.84
0.74
0.79
0.88
0.62
0.78
0.81
0.79
0.53
0.74
0.49
0.59
0.40
0.47
0.40
0.27
0.57
0.89
0.97
0.72
0.57
0.31
0.67
0.37
0.28
Texas Tech University, Kendra Phelps, August 2016
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Myotis macrotarsus
Rousettus amplexicaudatus
Rhinolophus philippinensis
Miniopterus schreibersii
Ptenochirus jagori
Hipposideros diadema
Megaderma spasma
Rhinolophus macrotis
Miniopterus australis
Hipposideros obscurus
Eonycteris spelaea
Emballonura alecto
Hipposideros pygmaeus
Taphozous melanopogon
Rhinolophus arcuatus
Hipposideros ater
Rhinolophus rufus
Miniopterus tristis
Myotis horsfieldii
Indicator Species Analysis
Appendix B cont.
Environmental Gradient: Cave Complexity
Change-point Analysis
Species
+/-
z
Obs.
5%
95%
IndVal
p-value
Purity
Reliability ≤0.05
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-
7.68
5.79
5.72
4.68
4.48
3.84
3.21
3.17
3.10
2.78
2.46
1.23
1.23
0.33
5.35
2.84
2.54
1.07
0.96
0.77
2.22
1.46
0.59
0.27
0.55
0.77
1.65
0.27
1.46
2.22
-1.84
1.65
-1.38
-0.85
-2.01
-1.38
0.55
-0.42
-0.42
0.16
0.36
0.27
-0.75
-0.90
-1.51
-0.30
-2.01
-0.52
-1.61
-1.61
-1.84
-1.38
-1.41
-2.01
-2.01
-1.38
-1.84
1.88
2.30
2.22
2.30
1.88
2.30
2.14
2.30
1.88
2.30
2.30
2.22
2.22
2.14
-0.44
0.36
0.59
0.62
2.22
55.03
61.02
54.95
30.22
49.01
34.46
51.02
21.30
56.08
15.48
60.05
36.73
17.08
9.30
26.32
28.29
20.14
11.11
10.30
0.004
0.004
0.004
0.004
0.004
0.008
0.016
0.032
0.012
0.088
0.050
0.124
0.096
0.492
0.004
0.028
0.044
0.320
0.220
1.00
1.00
1.00
0.99
1.00
0.98
0.97
0.97
0.98
0.94
0.84
0.54
0.54
0.64
1.00
0.99
0.88
0.72
0.69
1.00
0.98
0.99
0.95
0.97
0.90
0.91
0.75
0.92
0.53
0.72
0.30
0.31
0.28
0.93
0.73
0.58
0.27
0.31
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Rhinolophus arcuatus
Rhinolophus philippinensis
Rousettus amplexicaudatus
Miniopterus tristis
Rhinolophus rufus
Eonycteris spelaea
Miniopterus australis
Rhinolophus macrotis
Hipposideros diadema
Myotis macrotarsus
Miniopterus schreibersii
Hipposideros pygmaeus
Hipposideros ater
Emballonura alecto
Hipposideros obscurus
Taphozous melanopogon
Ptenochirus jagori
Megaderma spasma
Myotis horsfieldii
Indicator Species Analysis
Appendix B cont.
Environmental Gradient: Mining
Change-point Analysis
Species
+/-
z
Obs.
5%
95%
IndVal
p-value
Purity
Reliability ≤0.05
+
+
+
+
+
+
+
+
+
+
-
3.88
2.87
2.85
2.20
2.00
1.85
1.59
0.98
0.36
0.02
2.83
2.30
2.04
2.00
1.99
1.98
1.80
1.43
0.08
0.18
0.15
1.63
1.31
0.01
0.18
-0.07
0.01
1.63
1.41
-1.34
0.18
-1.85
0.38
1.37
-1.85
0.68
0.38
0.53
-1.85
-0.16
-0.83
-1.85
-0.51
-0.90
-0.38
-1.85
-1.60
-1.34
-1.85
-1.85
-1.85
-1.85
-1.85
-1.85
-1.34
-0.67
-1.85
0.94
1.63
1.63
1.41
1.31
1.31
0.83
1.47
1.47
1.47
0.38
0.41
1.47
1.47
1.41
1.37
0.94
0.43
1.41
27.36
15.38
34.02
20.24
10.71
17.05
13.33
22.33
32.67
9.71
28.32
15.62
61.88
43.08
52.93
40.11
34.57
11.76
16.31
0.004
0.032
0.060
0.032
0.188
0.060
0.092
0.164
0.268
0.280
0.032
0.092
0.080
0.044
0.072
0.028
0.046
0.204
0.436
0.94
0.95
0.77
0.72
0.92
0.81
0.77
0.79
0.52
0.52
0.98
0.96
0.77
0.90
0.88
0.87
0.90
0.77
0.62
0.86
0.61
0.52
0.58
0.39
0.48
0.33
0.48
0.29
0.19
0.80
0.63
0.60
0.73
0.75
0.68
0.67
0.25
0.31
Texas Tech University, Kendra Phelps, August 2016
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Rhinolophus philippinensis
Megaderma spasma
Ptenochirus jagori
Taphozous melanopogon
Myotis macrotarsus
Miniopterus tristis
Rhinolophus macrotis
Rousettus amplexicaudatus
Rhinolophus rufus
Myotis horsfieldii
Hipposideros ater
Hipposideros obscurus
Hipposideros diadema
Miniopterus australis
Miniopterus schreibersii
Rhinolophus arcuatus
Hipposideros pygmaeus
Emballonura alecto
Eonycteris spelaea
Indicator Species Analysis
Appendix B cont.
Environmental Gradient: Cave Development
Change-point Analysis
Species
z
Obs.
5%
95%
IndVal
p-value
Purity
Reliability ≤0.05
12.22
1.98
1.77
1.47
1.06
0.93
0.88
0.87
0.74
0.08
2.34
2.11
1.69
1.12
1.50
-0.35
0.63
1.14
-1.05
0.54
0.75
0.45
0.86
1.72
-0.65
1.50
-0.65
1.14
0.68
-1.03
-1.23
-0.85
-1.05
-1.10
-1.28
-1.10
-1.28
-1.28
-1.28
-1.28
-1.28
-1.28
1.89
1.50
1.72
1.50
1.32
1.32
1.14
1.89
1.89
1.72
0.54
1.89
0.54
1.15
57.05
24.37
45.37
15.24
27.50
30.03
9.72
10.90
39.12
13.99
18.58
62.16
13.46
28.26
0.004
0.048
0.060
0.152
0.132
0.136
0.168
0.188
0.216
0.188
0.024
0.040
0.076
0.096
0.99
0.85
0.63
0.87
0.62
0.63
0.71
0.68
0.71
0.53
0.94
0.74
0.91
0.55
0.94
0.63
0.53
0.38
0.35
0.39
0.26
0.33
0.53
0.23
0.67
0.63
0.54
0.29
0.72
0.68
0.64
0.54
0.50
-0.75
-1.23
1.89
1.27
1.14
-1.03
-1.28
-1.28
-1.28
-1.28
1.50
0.87
1.72
0.96
1.01
10.05
12.53
35.29
17.02
15.22
0.344
0.088
0.148
0.408
0.128
0.58
0.62
0.63
0.46
0.41
0.21
0.31
0.31
0.19
0.18
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95
Taphozous melanopogon
Eonycteris spelaea
Miniopterus schreibersii
Megaderma spasma
Rhinolophus arcuatus
Rhinolophus rufus
Myotis macrotarsus
Hipposideros obscurus
Miniopterus australis
Ptenochirus jagori
Hipposideros ater
Hipposideros diadema
Myotis horsfieldii
Rousettus amplexicaudatus
Rhinolophus macrotis
Emballonura alecto
Hipposideros pygmaeus
Rhinolophus philippinensis
Miniopterus tristis
+/+
+
+
+
+
+
+
+
+
+
-
Indicator Species Analysis
Appendix B cont.
Environmental Gradient: Resource Extraction
Change-point Analysis
Species
+/-
z
Obs.
5%
95%
IndVal
p-value
Purity
Reliability ≤0.05
+
+
+
+
+
+
+
-
3.78
4.15
3.57
2.67
1.05
0.63
0.53
2.01
1.79
1.37
1.34
1.32
1.28
1.28
1.22
1.14
1.13
0.98
0.28
0.41
1.33
1.39
1.00
1.39
0.53
-0.45
0.84
-0.43
-0.08
-1.37
0.84
-1.37
0.72
-0.31
-1.03
0.47
0.72
-0.91
-0.01
-1.37
-1.34
-0.26
-1.37
-1.34
-0.45
-0.91
-1.37
-1.34
-1.34
-1.03
-1.37
-1.37
-0.99
-1.37
-1.19
-1.37
-1.37
1.39
1.39
1.39
1.39
1.39
1.25
1.33
0.96
1.25
1.33
1.39
0.97
0.53
0.72
0.96
1.00
0.52
0.72
1.00
19.05
67.16
35.40
19.68
16.61
28.80
10.00
27.27
46.05
39.07
57.21
28.26
14.27
18.60
23.57
13.84
13.51
16.28
12.07
0.004
0.008
0.040
0.060
0.196
0.244
0.424
0.036
0.056
0.096
0.116
0.112
0.076
0.156
0.128
0.140
0.200
0.276
0.436
0.98
0.74
0.83
0.92
0.53
0.70
0.58
0.92
0.73
0.89
0.70
0.48
0.78
0.85
0.86
0.69
0.73
0.72
0.65
0.78
0.71
0.63
0.58
0.30
0.43
0.21
0.74
0.58
0.68
0.50
0.26
0.39
0.53
0.59
0.32
0.29
0.37
0.34
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Megaderma spasma
Hipposideros pygmaeus
Hipposideros ater
Myotis horsfieldii
Emballonura alecto
Rhinolophus rufus
Rhinolophus macrotis
Eonycteris spelaea
Miniopterus schreibersii
Miniopterus australis
Hipposideros diadema
Rhinolophus arcuatus
Myotis macrotarsus
Rhinolophus philippinensis
Rousettus amplexicaudatus
Hipposideros obscurus
Taphozous melanopogon
Miniopterus tristis
Ptenochirus jagori
Indicator Species Analysis
Appendix B cont.
Environmental Gradient: Bat Hunting
Change-point Analysis
Species
+/-
z
Obs.
5%
95%
IndVal
p-value
Purity
Reliability ≤0.05
+
+
+
+
+
+
-
4.48
4.59
3.81
3.41
1.75
0.51
2.82
2.28
5.07
1.77
1.56
1.39
1.33
1.29
1.02
1.01
0.74
0.65
0.57
0.18
0.59
0.46
0.46
0.60
1.40
0.62
0.52
-0.75
-0.79
-1.08
0.21
0.52
-0.54
0.21
-0.21
0.15
0.38
-0.53
0.09
-1.08
-0.31
-0.54
-1.08
-0.96
-0.75
-0.79
-1.08
-1.08
-1.08
-1.08
-1.08
-0.79
-0.54
-0.79
-0.73
-1.08
-1.08
0.46
1.40
1.40
1.40
0.80
1.40
0.80
0.58
0.13
0.16
0.35
0.58
0.60
0.27
0.44
0.59
0.80
0.38
0.62
23.81
19.13
39.27
35.28
56.33
16.69
55.40
31.82
24.51
16.92
25.73
30.58
34.54
12.40
17.00
14.74
13.24
10.00
39.16
0.008
0.016
0.004
0.008
0.068
0.144
0.012
0.024
0.004
0.054
0.048
0.096
0.116
0.096
0.128
0.156
0.292
0.328
0.236
1.00
0.80
0.88
0.85
0.79
0.50
0.97
0.94
0.95
0.92
0.86
0.69
0.78
0.79
0.65
0.64
0.61
0.74
0.52
0.91
0.60
0.84
0.78
0.53
0.27
0.92
0.78
0.69
0.61
0.49
0.44
0.52
0.34
0.32
0.37
0.28
0.29
0.30
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Taphozous melanopogon
Myotis macrotarsus
Rousettus amplexicaudatus
Eonycteris spelaea
Hipposideros diadema
Hipposideros obscurus
Miniopterus australis
Rhinolophus arcuatus
Rhinolophus macrotis
Myotis horsfieldii
Ptenochirus jagori
Hipposideros pygmaeus
Rhinolophus rufus
Emballonura alecto
Rhinolophus philippinensis
Miniopterus tristis
Hipposideros ater
Megaderma spasma
Miniopterus schreibersii
Indicator Species Analysis
Change-point analysis: +/- indicates the direction of each species response (increasing/decreasing) to an environmental gradient,
standardized z-scores, observed change points (Obs.) with bootstrap confidence intervals (5 and 95%) based on 500 bootstrap
replicates. Indicator species analysis: indicator value scores (IndVal) with corresponding p-values and estimates of purity (i.e., mean
proportion of correct response direction (z- or z+) assignments) and reliability (i.e., mean proportion of p ≤ 0.05) based on 500
replicates. Species in bold are valid indicator species with purity >0.90 and reliability >0.75 for the respective environmental gradient.
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Appendix C. Scatterplots of corrected log10 abundance of cave-roosting bat species plotted along each complex environment gradient.
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Appendix C cont.
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Appendix C cont.
101
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Appendix C. cont.
102
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Appendix C. cont.
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Texas Tech University, Kendra Phelps, August 2016
CHAPTER IV
INCREASED HUMAN DISTURBANCE IS ASSOCIATED WITH AN
ATYPICAL PHYSIOLOGICAL RESPONSE IN A CAVE-ROOSTING
BAT
In preparation for the Proceedings of the Royal Society, B
Kendra Phelpsa,b,* and Tigga Kingstona,b
a
Department of Biological Sciences, Texas Tech University, Lubbock, USA
b
Southeast Asian Bat Conservation Research Unit, Lubbock, USA
*Corresponding
author: Department of Biological Sciences, Texas Tech University, MS
43131, Lubbock, TX 79409, USA. Tel.: +1 (806) 742-2731.
E-mail address: [email protected]
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Abstract
Human disturbance represents a physiological stressor for most wildlife species,
triggering a cascade of physiological responses. Leukocyte profiles are commonly used to
measure stress response, with an increased ratio of neutrophils-to-lymphocytes indicating
an elevated response. Short-term responses are beneficial, but prolonged stress responses
can have detrimental effects on individual health, including decreased body condition and
increased risk of parasite infections. Cave ecosystems are frequently subjected to a
multitude of human pressures (e.g., mining, tourism), yet no studies have examined the
stress response of cave-dwelling wildlife to human disturbance. To assess the response of
cave-roosting bats to disturbance, we compared physiological health markers of
Hipposideros diadema (n = 714) from 29 caves along a disturbance gradient in the
Philippines. Contrary to expectations, greater levels of disturbance were associated with
decreased neutrophil-to-lymphocyte ratios and ectoparasite loads based on separate
generalized linear models, which were largely driven by the response of non-reproductive
individuals. Body condition did not vary with cave disturbance, but was better in more
complex caves. Our results are contradictory to the findings of most studies of human
disturbance on wildlife health. We suggest that bats in highly disturbed caves may have
acclimatized to chronic human disturbance in contrast to bats in less-disturbed caves.
Keywords: Stressor, Chiroptera, Cave disturbance, Leukocyte profile, Body condition,
Ectoparasites
Introduction
Human disturbance can act as a stressor on wildlife species, with detrimental
effects on physiological health and fitness, and ultimately, species persistence (Wikelski
and Cooke, 2006). Stressors can trigger a physiological response via activation of the
hypothalamic-pituitary-adrenal axis (HPA), resulting in a cascade of physiological
processes spearheaded by spikes in circulating concentrations of glucocorticoids (Reeder
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and Kramer, 2005). Increased glucocorticoid concentrations lead to parallel changes in
leukocyte profiles (also known as white blood cell differentials). Specifically, neutrophil
proportions increase in the peripheral bloodstream to defend against injury and infection,
whereas proportions of lymphocytes decrease due to immunosuppressive properties of
glucocorticoids (Davis et al., 2008). Although this response to physiological stress is
beneficial in general, problems can arise when the stress response is sustained over an
extended period or occurs repeatedly (Reeder and Kramer, 2005). Prolonged stress
responses can have detrimental effects on physiological health, including reduced growth
rates and increased risk of infection. Sustained stress responses are energetically
expensive and necessitate the reallocation of energy to maintain homeostasis or, at
minimum, basic physiological processes (e.g., digestion, cellular regeneration) (Eberhardt
et al., 2013; Applebaum et al., 2014). Consequently, constraints on energy can result in
downstream effects on physiological health, including suppression of the immune system,
resulting in increased vulnerability to ectoparasite infections and a reduction in body
mass (Dhabhar, 1997; Rauw, 2012). Both of these can reduce fitness and contribute to
reduced population viability, threatening the persistence of populations or even entire
species (Wikelski and Cooke, 2006; Davis et al., 2008). Therefore, an integrated
assessment of the physiological effects of human disturbance on wildlife should include
downstream health measures, including body condition and ectoparasite loads, in addition
to measures of physiological stress (i.e., leukocyte profiles) (Johnstone et al., 2012).
Physiological stress markers have been used successfully to uncover the
consequences of human disturbance on birds (Mazerolle and Hobson, 2002; Suorsa et al.,
2004), mammals (Creel et al., 2002; Allen et al., 2011) and amphibians and reptiles
(Romero and Wikelski, 2002; Homan et al., 2003). Overall, studies largely report that
wildlife exposed to human disturbance (e.g., tourism, habitat fragmentation, urbanization)
exhibit elevated stress responses, including increased glucocorticoid concentrations and
skewed leukocyte profiles. However, a growing number of studies report contradictory
findings (Mazerolle and Hobson, 2002; Homan et al., 2003; Partecke et al., 2006; French
et al., 2008; Viblanc et al., 2012). Allen et al. (2011) reported lower glucocorticoid levels
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and lower ectoparasite loads in Brazilian free-tailed bats (Tadarida brasiliensis) roosting
under interstate bridges than in counterparts roosting in caves. Similarly, king penguins
(Aptenodytes patagonicus) subjected to chronic human disturbance (~ 50 years) exhibited
reduced stress responses (i.e., attenuated heart rates) in comparison to penguins exposed
to lower intensity disturbance (Viblanc et al., 2012). Conceivably, individuals exposed to
frequent, long-term stressors, such as urban development and tourism, are able to
habituate to human disturbance, resulting in down-regulation of the stress response.
Another possible explanation is that chronic exposure to human disturbance could result
in the directional selection of human-tolerant phenotypes (e.g., bolder, less fearful
individuals) (Viblanc et al., 2012; Tablado and Jenni, 2015).
Bats spend half their lives at the roost (Kunz, 1982). For many species, caves
represent critical sites for roosting and rearing young as well as shelter from weather and
predators (Kunz, 1982). The largest and most diverse aggregations of bat species in the
world roost in caves, yet cave disturbance is the primary threat to cave-roosting bats
globally (Hutson et al., 2001; Furey and Racey, 2016). Cave-roosting bats exhibit the
highest level of roost fidelity (Lewis, 1995); thus, human disturbance at the roost has the
potential to be an inescapable physiological stressor. However, no studies have assessed
the impact of cave disturbance on the physiological health of cave-roosting bats. Bats are
keystone species in cave ecosystems since they provide the primary energy source (i.e.,
guano) in an otherwise barren food web (Gnaspini and Trajano, 2000). Therefore, to
prevent the loss of keystone species and the syntopic cave-dwelling species dependent
upon them, it is necessary to understand how cave disturbance influences physiological
health of cave-roosting bats.
To understand the underlying mechanisms by which cave-roosting bats respond to
cave disturbance, we investigated the physiological health of individual Hipposideros
diadema (diadem leaf-nosed bat), specifically leukocyte profiles, body condition and
ectoparasite loads, along a gradient of cave disturbance in the Philippines. H. diadema is
an abundant cave-roosting bat species distributed widely across Southeast Asia and
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northern Australia, the largest distribution of any species in Hipposideridae (Csorba et al.,
2008). We hypothesized that H. diadema would exhibit physiological stress responses to
increased human disturbance at caves, including bat hunting, mining and cave tourism,
stressors typical of cave ecosystems in Southeast Asia. Specifically, we predicted that
increased cave disturbance would be associated with increased proportions of neutrophils
and decreased proportions of lymphocytes, leading to increased neutrophil-to-lymphocyte
ratios. Furthermore, we predicted bats in more disturbed caves would have greater
ectoparasite loads and lower body condition. Our results suggest that cave disturbance
significantly influenced physiological health, with the exception of body condition.
However, contrary to our predictions, increased cave disturbance was associated with an
atypical physiological response, specifically decreased neutrophil-to-lymphocyte ratios
and ectoparasites loads. Such findings may indicate that bats in highly disturbed caves are
more tolerant of long-term cave disturbance, whereas bats in less disturbed caves exhibit
physiological stress in response to short-term disturbance.
Methods
Study sites
Our study was conducted on Bohol Island, located in the central region of the
Philippine archipelago (Fig. 4.1), from July 2011 to June 2013, with the exception of
January – May 2012, as part of a larger study on the effects of cave disturbance on caveroosting bats at the assemblage and population levels (see Phelps et al., in press). Bohol
Island is roughly 4100 km2 and composed largely of limestone, which is characterized by
a high density of caves (Urich et al., 2001). Exploitation of caves is increasing steadily on
Bohol Island; many caves have been developed for tourism, mined for limestone and/or
phosphate and stripped of natural resources, including bats and guano (Sedlock et al.,
2014; Urich et al., 2001).
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Figure 4.1. Caves (n = 29) surveyed on Bohol Island, the Philippines (see insert). See
Appendix A for detailed information about each cave.
We included 29 caves occupied by H. diadema in our study (Fig. 4.1). We
selected caves across a gradient of human disturbance, from undisturbed caves in
protected areas to moderately disturbed caves exploited by local residents, and caves
subject to high levels of disturbance resulting from active mining and tourism (Appendix
A). We quantified disturbance at each cave based on a modified version of the karst
disturbance index developed by van Beynen and Townsend (2005) to focus on cave
disturbances that may affect cave-roosting bats (Phelps et al., in press). Briefly, a series
of factors that typify cave disturbance, both at the surface and subsurface level, were
scored on an ordinal scale based on severity and extent. At the surface level, we gauged
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the amount of forest cover and degree of urbanization (i.e., number of residents and road
size) within a 1 km radius around the main cave entrance based on visual observations
and supplemented with Google Earth Pro imagery (2012 – 2015). Human disturbance at
the subsurface-level included mining, cave development (e.g, installation of lights and
walkways), resource extraction (e.g., guano, bird nests), bat hunting, dumping of
household waste and vandalism. Visual observations and one-on-one interviews with
local residents were used to assess the frequency, nature and intensity of subsurface-level
disturbances. Scores for each factor ranged from 0 (no disturbance) to 3 (severely
disturbed), and were summed then divided by the maximum total score possible (i.e., 30)
to give a final disturbance score between 0 and 1 (Appendix A). Caves with higher scores
are considered more disturbed, often due to simultaneous pressure from multiple forms of
human disturbance.
In addition to the assessment of cave disturbance, we mapped caves using
standard survey methods (Ellis, 1976) to quantify the available roost area (i.e., upper onehalf of the total surface area suitable for bats to roost, following equation provided in
Brunet and Medellín, 2001), spatial heterogeneity (i.e., complexity in passages and
chambers, following equation provided in Arita, 1996) and number of entrances (see
Phelps et al., in press for specific details) (Appendix A). We used principal component
analysis to combine cave features into a single value of cave complexity based on the
first component, which explained 78% of variation among the 29 caves. We considered
cave complexity a proxy for roost conditions, with larger values indicative of caves with
greater roost area and spatial heterogeneity that may provide more refuge from cave
disturbances.
Blood sampling
We captured bats by placing mist nets across cave entrances and in passages for
two consecutive nights at each cave. Captured H. diadema were weighed (± 0.5 g) and
forearm length measured (± 0.5 mm). We assessed age and reproductive status following
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Anthony (1988) and Racey (2009) prior to blood collection to prevent sampling juveniles
and pregnant or lactating females. We sampled only adults, specifically non-reproductive
females and both non-reproductive and reproductive males (scrotal swelling observed).
On average, we sampled 25 individual bats per cave, but sample numbers ranged from 4
– 60 individuals depending largely on the population size in a particular cave.
For blood collection, the brachial artery was sterilized with an alcohol wipe then
lanced using a 26-gauge needle and blood was collected in a heparinized capillary tube.
Blood samples were less than 75 μl per individual, well under the maximum of 240 μl
recommended for an individual weighing 40 g (mean body mass of H. diadema in this
study) (Smith et al., 2010). Individuals were processed and released within 2 hours of
initial capture, the minimum time before capture and handling stress alters leukocyte
profiles (Davis et al., 2008). All methods followed guidelines of the American Society of
Mammalogists (Sikes and Gannon, 2011) and were approved by the Animal Care and
Use Committee of Texas Tech University (ACUC 10015-04, 13031-04). Fieldwork was
approved by the Department of Environment and Natural Resources of the Philippines.
Physiological markers
Leukocyte Profiles - Immediately following blood collection, we transferred a
drop of blood onto 1 - 3 microscope slides then smeared using the wedge method and airdried in the field. Slides were fixed in absolute methanol for 1 minute and stained using
Wright-Geimsa stain following manufacturer instructions (Fisher Scientific, Hema 3 Stat
Pack) one to two days later in the laboratory. The proportion of each leukocyte
(neutrophil, lymphocyte, eosinophil, monocyte and basophil) was determined by
examining > 100 leukocytes per slide using 400x magnification on a compound
microscope. Slides were independently examined by two individuals without prior
knowledge of the cave from which the sample was collected. We determined leukocyte
proportions by taking the mean count of each leukocyte for the two examiners.
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Body Condition – Body condition of individual bats was calculated according to
the scaled mass index (SMI) (Peig and Green, 2009) as:
𝑏𝑠𝑚𝑎
𝐿
𝑆𝑀𝐼 = 𝑀𝑖 ( 0⁄𝐿 )
𝑖
where Mi and Li are the body mass (g) and forearm length (mm) measurements of
individual i respectively; L0 is the arithmetic mean of the population; bsma is the scaling
exponent derived from the standardized major axis regression of M on L. Higher SMI
values are indicative of individuals in better body condition, with an average SMI of 41.7
but ranging from 22.1 to 62.5. We used R version 3.1.2 (R Core Team, 2014) with
package smatr (Warton et al., 2012). The SMI is a size-corrected body condition index
that assumes individuals with higher SMI values have greater energy (fat) stores (Peig
and Green, 2009).
(iii) Ectoparasite Loads – We visually screened all body surfaces of individual H.
diadema for up to one minute to get a count of ectoparasites, including ticks (i.e.,
Argasidae, Macronyssidae, Spinturnicidae), mites (i.e., Demodecidae, Myobiidae,
Sarcoptidae, Trombiculidae), fleas (i.e., Ischnopsyllidae) and bat flies (i.e., Nycteribiidae
and Streblidae). To describe ectoparasite loads, we summed the number of ectoparasites
observed and categorized intensity of infection into four categories: Category 0 (no
ectoparasites observed), Category 1 (1 – 25 ectoparasites), Category 2 (26 – 50
ectoparasites) and Category 3 (> 50 ectoparasites).
Statistical Analysis
A separate generalized linear model was created for each response variable of the
leukocyte profile (using a quasi-binomial distribution) and body condition (using a
Poisson distribution) with disturbance score, cave complexity and the interaction between
disturbance and complexity as predictor variables. We used a cumulative link model for
analysis of ectoparasite loads since this is an ordinal variable, but predictor variables
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remained the same as described above. Furthermore, because sex and reproductive class
can influence physiological health (Reeder and Kramer, 2005), we created an additional
set of generalized linear models for each sex-reproductive class: non-reproductive
females (Female – NR, n = 238), non-reproductive males (Male – NR, n = 241) and
reproductive males (Male – R, n = 233). Analyses were conducted in R version 3.1.2
using packages stats (R Core Team, 2014) and ordinal (Christensen, 2015). Output from
the generalized linear models were visualized using the package visreg (Breheny and
Burchett, 2016).
Results
Leukocyte profiles
We compared the relationship between leukocyte profiles and cave disturbance to
test our prediction that leukocyte profiles would vary along the gradient of cave
disturbance. We found that there were significant shifts in leukocyte profiles of H.
diadema (n = 714; Table 4.1). A decreased proportion of neutrophils was associated with
an increase in cave disturbance (t = -3.42, p < 0.001), whereas the proportion of
lymphocytes increased (t = 4.16, p < 0.001). Consequently, increased cave disturbance
was associated with a significant decrease in neutrophil-to-lymphocyte ratios (t = -3.76, p
< 0.001; Fig. 4.2). We predicted that increasing cave complexity, a proxy for roost
conditions, would result in an increased proportion of neutrophils and decreased
proportion of lymphocytes resulting in decreased neutrophil-to-lymphocyte ratios.
However, leukocyte profiles did not vary in relation to cave complexity nor was there an
interaction between cave disturbance and complexity (Table 4.1).
Comparison of leukocyte profiles across the disturbance gradient for each sexreproductive class revealed that non-reproductive individuals, both females and males,
had similar physiological responses (Appendix B). Specifically, non-reproductive males
had significantly decreased proportions of neutrophils (t = -2.29, p = 0.02) with increased
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cave disturbance, with non-reproductive females demonstrating a similar but only
marginally significant trend (t = -1.76, p = 0.08). Furthermore, non-reproductive males
and females both exhibited significant increases in the proportion of lymphocytes with
increased cave disturbance (t = 2.82, p = 0.005; t = 2.06, p = 0.04, respectively). Neither
cave complexity nor the interaction between cave disturbance and complexity had any
association with significant shifts in proportions of neutrophils or lymphocytes for nonreproductive individuals. Conversely, proportions of neutrophils in reproductive males
were negatively influenced by the interaction between cave disturbance and complexity (t
= -2.65, p = 0.01). Similar trends also existed for neutrophil-to-lymphocyte ratios, with
non-reproductive individuals having lower ratio values with increased disturbance
(Appendix B), whereas reproductive males had lower ratio values in response to the
interaction between cave disturbance and complexity (t = -2.96, p = 0.003).
Fig. 4.2. Change in neutrophil-to-lymphocyte ratios with increased cave disturbance
Visualization of the generalized linear model for the effect of cave disturbance on
neutrophil-to-lymphocyte ratios. The black line indicates the change in ratio value with
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increased cave disturbance while holding other predictor variables constant, and shaded
bands represent 95% confidence intervals.
Body Condition
We predicted that cave disturbance would negatively influence body condition
(based on scaled mass index) of H. diadema, whereas increasing cave complexity would
positively influence body condition. Body condition did not differ along the gradient of
cave disturbance (t = -1.46, p = 0.14; Table 4.1), but significantly increased in response
to increasing cave complexity (t = 5.65, p < 0.001). The interaction between cave
disturbance and cave complexity had a significant negative effect on body condition (t = 3.98, p < 0.001) indicating that H. diadema inhabiting more complex yet less disturbed
caves were in better body condition than counterparts inhabiting less complex and more
disturbed caves (Fig. 4.3).
Across sex-reproductive classes, body condition for males, both non-reproductive
and reproductive, was negatively influenced by the interaction of cave disturbance and
complexity (Appendix B). Males had lower body condition scores in complex caves with
high levels of cave disturbance. Conversely, body condition for non-reproductive females
was negatively influenced by cave disturbance (t = -2.92, p = 0.004), with females
exhibiting significantly lower body condition scores with increased cave disturbance.
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Fig. 4.3. Two-dimension contour plot depicting the interaction relationship between cave
complexity and cave disturbance on body condition (measured as scaled mass index) for
H. diadema (n = 714). Scale bar represents body condition, with increasingly darker
shades indicative of better body condition.
Ectoparasite loads
We predicted that cave disturbance would alter host-parasite interactions, with
increased cave disturbance being associated with increased ectoparasite loads. However,
ectoparasite loads of H. diadema (n = 714) decreased with increased cave disturbance (t =
-4.38, p < 0.001; Table 4.1), therefore individuals inhabiting more disturbed caves had
fewer ectoparasites. We assessed the relationship between ectoparasite loads and cave
complexity to test the prediction that ectoparasite loads would decrease with increasing
cave complexity since there would potentially be less contact between infected
individuals in complex caves. Ectoparasite loads were not influenced by cave complexity
(t = -0.99, p = 0.32) or the interaction with cave disturbance (t = 0.89, p = 0.38).
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Ectoparasite loads on non-reproductive females were not influenced by any
predictor variables (Appendix B); however, ectoparasite loads on males, both nonreproductive and reproductive, decreased in response to increased cave disturbance (t = 2.41, p = 0.02; t = -2.41, p < 0.001, respectively).
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Table 4.1. Effects of cave disturbance and complexity on physiological health of H. diadema.
Response
Variable
Cave Disturbance
Cave Complexity
Disturbance * Complexity
t-value
p-value
Estimate (SE)
t-value
p-value
Estimate (SE)
t-value
p-value
Neutrophils (N)
-0.67 (0.20)
-3.42
< 0.001
-0.02 (0.09)
0.023
0.82
-0.11 (0.17)
-0.67
0.50
Lymphocytes (L)
0.79 (0.19)
4.16
< 0.001
-0.11 (0.09)
-1.30
0.20
0.21 (0.16)
1.35
0.18
N:L
-0.76 (0.20)
-3.76
< 0.001
0.07 (0.09)
0.73
0.46
-0.17 (0.17)
-1.01
0.31
Body Condition
-0.05 (0.34)
-1.46
0.14
0.09 (0.02)
5.65
< 0.001
-0.11 (0.03)
-3.98
< 0.001
Ectoparasite
Load
-1.16 (0.26)
-4.38
< 0.001
-0.12 (0.12)
-0.99
0.32
0.18 (0.21)
0.89
0.38
Results of generalized linear models (cumulative link model for ectoparasite load) for each physiological health measure, specifically
differential leukocyte proportions, neutrophils-to-lymphocytes ratio (N:L), body condition and ectoparasite load. Significant values
based on p-values < 0.05 are indicated in bold.
Texas Tech University, Kendra Phelps, August 2016
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Estimate (SE)
Texas Tech University, Kendra Phelps, August 2016
Discussion
Based on several measures of physiological health, we demonstrate for the first
time that cave disturbance represents a physiological stressor to a common, widespread
cave-roosting bat species, but in a manner contrary to our expectations. Increased levels
of human disturbance at and around caves resulted in a reduced physiological response in
H. diadema; specifically, the proportion of neutrophils decreased whereas the proportion
of lymphocytes increased, resulting in lower neutrophil-to-lymphocyte ratios. Bats
captured in less-disturbed caves exhibited elevated stress responses with increased
neutrophil-to-lymphocyte ratios. Our results are driven largely by the response of nonreproductive individuals, both males and females, while leukocyte profiles for
reproductive males were not influenced by cave disturbance. Furthermore, ectoparasite
loads were lower in caves that are more disturbed, with the strongest response exhibited
by males. Patterns in our results are incongruent with most studies of human disturbance
on physiological health of wildlife species. On the other hand, cave complexity, or the
interaction of cave complexity and disturbance, had no influence on physiological health,
with the exception of body condition. This pattern was consistent for non-reproductive
individuals in general; however, physiological measures for reproductive males were
more influenced by the interaction of cave complexity and disturbance.
Contrary to our expectations, neutrophil-to-lymphocyte ratios were lowest in H.
diadema captured in highly disturbed caves and were not influenced by cave complexity.
This suggests that the relationship between human disturbance and the physiological
response of cave-roosting bats does not follow the same pattern demonstrated by other
studies (Smith, 2011). For example, Smith (2011) reported northern long-eared bats
(Myotis septentrionalis) captured on active military installments (i.e., training base,
bombing range) had significantly higher neutrophil-to-lymphocyte ratios than
counterparts captured in undisturbed forests. Yet, a growing number of studies report
wildlife species are able to downregulate stress response in the face of continuous, longterm disturbances. French et al. (2008) showed that tree lizards (Urosaurus ornatus)
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inhabiting urban environments exhibited lower baseline corticosterone levels than lizards
from semi-natural and natural environments. One hypothesis put forth by the authors was
that urban lizards acclimatized to repeated stressors in human-modified habitats. In
regards to our study, H. diadema may have acclimatized to high levels of cave
disturbance in contrast to sporadic, shorter duration stressors in less-disturbed caves.
Perhaps acute disturbances, such as occasional visits to the cave to collect guano by local
farmers, who may also occasionally hunt bats, are more stressful than chronic
disturbances, such as continuous visitation by tourists. In addition, long-term exposure to
human disturbance may drive selection of more human-tolerant phenotypes, resulting in
populations of wildlife species less sensitive to human disturbance (Viblanc et al., 2012;
Tablado and Jenni, 2015). However, we were unable to determine the duration of
exposure to cave disturbance and time elapsed since exposure, which may have
influenced our results.
Skewed proportions of leukocytes may alternatively arise in response to an ongoing parasitic infection. We observed an inverse relationship between cave disturbance
and ectoparasite loads, with individuals in disturbed caves being infected with fewer
ectoparasites (i.e., lower ectoparasite loads) than individuals from less disturbed caves.
This pattern of higher ectoparasite loads in less disturbed caves coincides with increased
neutrophil-to-lymphocyte ratios. When individuals are more stressed, as indicated by
increased neutrophil-to-lymphocyte ratios, they may reduce the amount of time spent
grooming or investment in immune functioning, thus making individuals in less-disturbed
caves more susceptible to infection. On the other hand, H. diadema from highly disturbed
caves also had higher proportions of lymphocytes, suggesting increased investment in
immune functioning that might in turn combat ectoparasite infections. Yet another
possibility may be that cave disturbance could influence host quality in a manner not
detected by our selected physiological markers, making H. diadema in disturbed caves
less suitable for maintaining higher ectoparasite loads.
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Body condition is a commonly used indicator of physiological health since it
provides insight into the nutritional state of an individual (Peig and Green, 2009) and has
been demonstrated to decline in response to human-driven stressors (Suorsa et al., 2003;
Janin et al., 2011). We did not find differences in body condition along the gradient of
cave disturbance, but rather body condition was influenced positively by the interaction
of cave complexity and disturbance. This pattern was driven largely by the response of
male individuals, whereas body condition for females was negatively influenced by cave
complexity. This indicates that body condition may not be an ideal indicator to gauge the
effects of human disturbance on cave-roosting bats, especially since there was a disparity
in physiological response between sexes.
In summary, this study is the first investigation into the physiological response of
cave-dwelling wildlife to cave disturbance. H. diadema is a common, widespread bat
species known to roost in caves throughout Southeast Asia, so our findings have broad
applicability in the region. Contrary to our expectations, increasing levels of cave
disturbance resulted in reduced neutrophil-to-lymphocyte ratios and ectoparasite loads in
H. diadema. This may imply that cave-roosting bats are capable of acclimatizing to longterm, chronic human disturbances, and that short-term, acute disturbances may be more
detrimental on the physiological health and, potentially, on continued persistence of bat
populations in low disturbance caves. Further studies are needed to get a clearer picture
of the effects of acute and chronic disturbance, such as quantifying glucocorticoid
concentrations in both blood and fur samples to provide insight into stress responses over
multiple timescales. Furthermore, incongruence in responses between sexes and
reproductive classes (of males) indicates a clear need to consider the effects of cave
disturbance on each group separately.
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Acknowledgements
We thank the Department of Environment and Natural Resources of the
Philippines for permission to conduct this study. We are grateful to the Bohol Island State
University for providing lodging and transportation during portions of this project. We
appreciate the undergraduate students at Bohol Island State University and Texas Tech
University for help during fieldwork and for analyzing blood smears, respectively. We
thank the Jodi Sedlock, Liam McGuire, Elizabeth Farley-Dawson and the Introduction to
Publishing course at Texas Tech University for improving earlier versions of the
manuscript and M. Fisher-Phelps for creating our map. The study was funded by U.S.
Department of State – Fulbright Fellowship, Bat Conservation International, American
Philosophical Society, The Explorers Club, American Society of Mammalogists, National
Speleological Society, Cave Research Foundation, John Ball Zoo, Sigma Xi and Texas
Tech Association of Biologists.
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Appendix A. Caves occupied by H. diadema on Bohol Island, Philippines included in this study. Code refers to the numbering system
used in Fig. 4.1. Cave disturbance scores are calculated based on summing 10 factors that were scored on an ordinal scale of 0 to 3
based on extent and severity of the specific human disturbance then dividing by the maximum score possible (i.e., 30), with high
scores indicating caves with high levels of human disturbance. Cave complexity is based on the scores of the first component in a
Road size
Mining
Cave
development
Resource
extraction
Bat hunting
Household waste
Vandalism/
graffiti
Visitation
frequency
Cave
complexity
Available roost
area (m2)
Spatial
heterogeneity
No. entrances
0.40
3
2
2
0
0
2
1
0
1
1
0.90
710.69
7.79
2
Bogtong Park Cave
2
0.60
2
3
2
2
1
2
2
3
0
1
-1.47
71.77
3.28
1
Cang Ihong Cave
3
0.67
2
2
1
2
2
3
1
1
3
3
-0.89
149.32
3.11
2
Cantijong Cave
4
0.53
2
1
1
1
2
3
1
1
1
2
1.57
531.96
9.50
3
Cantumocad Cave
5
0.57
3
3
3
1
1
2
0
0
2
2
-1.28
248.55
3.09
1
Casampong Cave
6
0.23
0
1
1
1
0
3
0
1
0
0
-0.84
279.24
4.89
1
Catalina Cave
7
0.13
0
0
0
0
0
3
0
0
0
1
-0.68
505.26
4.27
1
Claise Cave
8
0.37
1
1
1
2
0
1
1
0
2
2
-0.41
394.23
3.79
2
Guimba Cave
9
0.33
0
1
0
2
0
3
1
1
1
1
1.87
524.33
6.19
5
Ka Anoy Cave
10
0.30
1
0
0
2
1
3
0
1
1
0
-0.57
562.29
2.09
2
Ka Dodong Cave
11
0.20
0
0
0
0
0
3
0
0
2
1
-0.41
459.76
5.76
1
Ka Goryo Cave
12
0.37
2
1
1
0
0
3
0
2
1
1
-1.65
121.14
2.18
1
Non-forested
habitat
1
Cave
disturbance
score
Bayang Cave
Cave
Code
for
Fig.
4.1
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Urbanization
principal component analysis that combines available roost area, spatial heterogeneity and number of entrances.
Road size
Mining
Cave
development
Resource
extraction
Bat hunting
Household waste
Vandalism/
graffiti
Visitation
frequency
Cave
complexity
Available roost
area (m2)
Spatial
heterogeneity
No. entrances
0.57
3
2
2
1
1
2
2
2
1
1
-1.83
112.36
1.46
1
Kalanguban Cave
14
0.17
0
0
0
0
0
3
0
0
0
2
-1.05
181.05
4.51
1
Kamira Cave
15
0.40
3
2
2
0
1
0
0
0
1
3
-0.53
488.46
5.02
1
Kang Mana Cave
16
0.63
3
2
2
2
1
2
1
1
2
3
-0.68
44.91
4.63
2
Kokok Cave
17
0.47
3
2
2
0
0
3
2
0
0
2
-0.85
115.26
5.81
1
Lagbas Cave
18
0.67
1
1
1
3
1
3
2
3
2
3
-0.26
273.48
5.16
2
Lahos-Lahos Cave
19
0.57
3
3
2
0
1
2
1
2
2
1
0.05
152.25
4.94
3
Lahug 2 Cave
20
0.23
1
0
1
0
1
1
0
1
1
1
-0.63
306.56
3.32
2
Langgam Cave
21
0.53
3
2
1
1
2
2
1
0
2
2
5.31
1545.53
15.53
5
Logarita Cave
22
0.17
0
0
1
0
1
3
0
0
0
0
-0.81
125.44
3.60
2
Mesias Cave
23
0.57
3
3
2
0
3
1
0
1
2
2
-1.46
74.52
3.33
1
Mohon Cave
24
0.17
0
0
3
0
0
2
0
0
0
0
-1.36
185.81
3.11
1
Odiong Cave
25
0.47
1
2
1
1
1
2
1
2
2
1
-0.95
347.59
4.01
1
Pig-ot Cave
26
0.67
3
2
3
2
2
2
1
2
3
0
0.14
258.23
7.05
2
Popog Cave
27
0.77
2
2
2
3
3
3
1
2
2
3
4.78
3116.42
8.66
3
Pou Cave
28
0.50
2
1
1
2
1
3
1
1
2
1
0.46
326.73
8.08
2
Seminary Cave
29
0.83
3
3
3
0
3
2
3
3
2
3
1.56
1043.49
6.46
3
Non-forested
habitat
13
Cave
disturbance
score
Kabyawan Cave
Cave
Code
for
Fig.
4.1
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Urbanization
Appendix A cont.
Appendix B. Effects of cave disturbance and complexity on physiological health of H. diadema grouped by sex-reproductive class.
Results of separate generalized linear models (cumulative link model for ectoparasite load) for each physiological health measure by
sex-reproductive group: NR = non-reproductive individuals (either female or male), R = reproductive individuals (exclusively males).
Significant values based on p-values < 0.05 are marked in bold.
Response
Variable
(N)
Lymphocytes
(L)
Cave Disturbance
Estimate
(SE)
Cave Complexity
t-value p-value
Estimate
(SE)
Disturbance * Complexity
t-value p-value
Estimate
(SE)
t-value p-value
Female – NR
-0.56 (0.32)
-1.76
0.08
-0.06 (0.14)
-0.40
0.67
0.01 (0.25)
0.05
0.96
Male – NR
-0.80 (0.35)
-2.29
0.02
-0.33 (0.21)
-1.58
0.12
0.74 (0.40)
1.88
0.06
Males - R
0.29 (0.42)
0.67
0.49
0.29 (0.16)
1.80
0.08*
-0.80 (0.30)
-2.65
0.01
Female – NR
0.64 (0.31)
2.06
0.04
-0.01 (0.13)
-0.09
0.93
0.06 (0.24)
0.25
0.80
Male – NR
0.95 (0.34)
2.82
0.005
0.25 (0.20)
1.21
0.23
-0.64 (0.38)
-1.68
0.09
Males - R
-0.20 (0.41)
-0.50
0.62
-0.40 (0.15)
-2.62
0.01
0.91 (0.28)
3.21
0.002
Texas Tech University, Kendra Phelps, August 2016
148
Neutrophils
SexReproductive
Class
Appendix B cont.
Response
Variable
N:L
Ectoparasite
Load
Cave Disturbance
Estimate
(SE)
Cave Complexity
t-value p-value
Estimate
(SE)
Disturbance * Complexity
t-value p-value
Estimate
(SE)
t-value p-value
Female – NR
-0.61 (0.32)
-1.89
0.06
-0.03 (0.14)
-0.18
0.86
-0.03 (0.26)
-0.10
0.92
Male – NR
-0.90 (0.36)
-2.52
0.01
-0.31 (0.22)
-1.40
0.16
0.72 (0.40)
1.79
0.08
Males - R
0.25 (0.43)
0.58
0.56
0.37 (0.17)
2.23
0.03
-0.93 (0.31)
-2.96
0.003
Female – NR
-0.16 (0.06)
-2.92
0.004
0.04 (0.03)
1.50
0.13
-0.01 (0.04)
-0.15
0.88
Male – NR
-0.03 (0.06)
-0.52
0.60
0.15 (0.04)
3.83
< 0.001 -0.22 (0.07)
-3.09
0.002
Males - R
0.08 (0.07)
1.10
0.27
0.08 (0.03)
2.92
0.004
-0.11 (0.05)
-2.27
0.02
Female – NR
-0.44 (0.43)
-1.03
0.31
-0.08 (0.22)
-0.35
0.73
-0.02 (0.39)
-0.05
0.96
Male – NR
-1.37 (0.57)
-2.41
0.02
-0.01 (0.38)
-0.02
0.99
-0.02 (0.70)
-0.03
0.97
Males - R
-2.34 (0.62)
-3.80
< 0.001 -0.36 (0.21)
-1.74
0.08
0.68 (0.35)
1.93
0.05
Texas Tech University, Kendra Phelps, August 2016
149
Body Condition
SexReproductive
Class
Texas Tech University, Kendra Phelps, August 2016
CHAPTER V
EXECUTIVE SUMMARY
To be submitted to the Department of Environmental and Natural Resources (DENR)
of the Philippines
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Texas Tech University, Kendra Phelps, August 2016
Introduction
Caves ecosystems are unique and biologically diverse, yet are threatened by
many human activities. Mining for phosphate and limestone can lead to the complete
destruction of caves, while harvesting of bat droppings (guano), swiftlet nests, and
other cave resources can jeopardize the integrity of cave ecosystems. In addition, the
hunting of cave wildlife, such as bats, fishes, and crabs, can result in the loss of cave
biodiversity. Realizing the fragility of cave ecosystems, the National Caves and Cave
Resources Management and Protection Act (Republic Act No. 9072) was legislated in
2001. The Republic Act 9072 mandates the Department of Environment and Natural
Resources (DENR) to “formulate, develop, and implement a national program for the
management, protection, and conservation of caves and cave resources”. With over
1,800 caves identified in the Philippines, the task of prioritizing caves to protect is a
challenge. To aid the DENR efforts to prioritize caves, our goal was to identify
readily accessible correlates of high bat diversity in caves in order to develop a
systematic approach to identify key caves. This document summarizes the findings of
our project aimed at effectively prioritizing caves to ensure the conservation of cave
bats, which may have an indirect effect on other cave wildlife that depend upon bat
guano as a food resource. This project was a collaboration between Bohol Island State
University – Bilar and Texas Tech University, with permission from the DENR
Region VII.
The main objectives of our project were to:
1) Quantify human disturbance at caves and in the surrounding landscape,
2) Measure the dimensions and structural complexity of caves,
3) Document bat diversity in caves, and
4) Develop a method to prioritize caves that can be completed using cave
surveys/maps, interviews with local residents, and satellite imagery
(Google Earth).
To complete these objectives, we visited 62 caves across Bohol Island from
July 2011 to June 2013 (see Fig. 5.1). At each cave, we used standard cave survey
methods currently employed by the DENR to measure passage and chamber
151
Texas Tech University, Kendra Phelps, August 2016
dimensions (length, height and width). During cave surveys, we documented evidence
of human disturbance, such as discarded nets used to capture bats, campfire remains,
equipment for mining or harvesting guano, and tourism development of the cave
(walkways, lights and modified entrances). Google Earth images were used to gauge
human disturbance surrounding the cave entrances. For two consecutive nights, we
captured bats as they exited the cave, providing information about bat diversity in
each cave and the distribution of bats on Bohol Island. We also interviewed local
residents (n = 559) living near each cave to determine how often the cave is visited
and the purpose of visits. In addition, we also asked questions about their knowledge
and perceptions of bats and the Cave Act (Republic Act 9072).
Fig. 5.1. Geographic distribution of caves (n = 62) surveyed on Bohol Island,
Philippines. See Appendix A for locality information and species diversity of each
cave.
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Texas Tech University, Kendra Phelps, August 2016
Key Findings
This summary synthesizes the project findings and translates them into
conclusions and recommendations to assist in achieving the mandates of the Cave Act
(Republic Act No. 9072).
Key Finding 1: Cave bat diversity was influenced positively by cave complexity
(overall cave size) and negatively by human disturbance in the surrounding karst
landscape. Complex caves housed up to 11 bat species, but caves in disturbed
landscapes rarely supported more than six bat species. Due to their influence on bat
diversity, both cave complexity and surface-level disturbance can be considered
correlates of bat diversity useful for prioritizing caves to promote bat conservation.
Recommendation 1: As correlates of high cave-roosting bat diversity, cave
prioritization should focus on cave complexity and surface-level disturbance to
protect a high diversity of bat species.
Key Finding 2: Correlates of bat diversity can be easily measured. Prioritizing
caves based on cave complexity and surface-level disturbance can be done using
readily accessible, easy-to-use methods that require minimal training. We used
standard cave surveys to assess cave complexity, although this can also be done using
cave maps prepared previously by the DENR and speleological expeditions, and
visual observations during cave surveys coupled with freely available satellite
imagery (Google Earth) to gauge human disturbance in the surrounding karst
landscape.
Recommendation 2: We suggest that identification of candidate caves for protection
follow a systematic approach. First, conduct standard cave surveys to assess cave
complexity, a combination of overall surface area (length, height, and width),
variability in passages and chambers, and number of entrance. However, cave
complexity can also be assessed using prepared cave maps, specifically measurements
of cave length, height, and width can be used to estimate surface area of a cave and
number of passages/chambers and entrances can be counted. Caves with the greatest
surface area and number of passages/chambers and entrances should be considered
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Texas Tech University, Kendra Phelps, August 2016
candidate caves. Next, use visual observations and/or satellite imagery (Google Earth,
National Mapping and Resource Information Authority of the Philippines) to gauge
surface-level disturbance surrounding the cave, specifically amount of forest cover,
number of residents, and size of roads. Caves with the greatest forest cover but distant
from residents and roads should be considered candidate caves. This approach will
allow the DENR to narrow down potentially thousands of caves to a handful of
candidate caves. Finally, candidate caves with both high cave complexity and low
surface-level disturbance should be prioritized, and the diversity of bats and other
cave wildlife should be further investigated to ensure the most diverse cave
ecosystems are protected.
Key Finding 3: Cave disturbance is widespread across Bohol Island, with
extraction of cave resources (e.g., wildlife, guano, swiftlet nests) the most
prevalent disturbance. Sixty-one caves (out of 62) were exposed to some form of
human disturbance, including caves located within the boundaries of protected areas.
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Texas Tech University, Kendra Phelps, August 2016
Recommendation 3: Harvesting of cave resources is largely unsustainable and, over
time, can degrade the cave ecosystem.
-
Bat guano. Harvesting of guano is practiced by many farmers during the
rice-planting season. Guano harvesting is a mild form of human
disturbance, and bats may tolerate the presence of humans during the
collection process. However, we recommend that guano be harvested only
occasionally throughout the year (< 5 times) and only after nightfall when
the bats have left the cave1. Farmers should be informed that excessive
guano harvesting will disturb the bats, causing them to abandon the roost
and there will no longer be a free source of organic fertilizer.
1
Additional recommendations can be found in: IUCN SSC (2014) Guidelines for
minimizing the negative impacts to bats and other cave organisms from guano harvesting,
ver. 1.0. IUCN, Gland.
-
Hunting. Hunting of bats should be strictly forbidden as this activity is the
most severe disturbance and can have negative effects on the viability of
cave-roosting bat populations.
-
Mining & Cave Tourism. Both mining and tourism may compromise the
integrity of cave ecosystems; these activities need to have increased
regulation and enforcement. None of the mines was operating under a
government-approved permit, and the Mines and Geosciences Bureau
were unaware of any mining activities occurring on Bohol Island.
Key Finding 4: Bohol Island has a rich diversity of cave bats, but local residents
are unaware of this diversity. We captured 23 bat species in caves on Bohol Island
(Table 5.1), including two new species records for the island and six endemic species
(see Appendix A). This brings the total species richness of the island to 35 species
(45% of all bat species in the Philippines), of which, more than half of these bat
species rely on caves as critical roost sites. However, most local residents (85%) think
there are fewer than 10 bat species on Bohol Island. Residents are also unware that
bats provide numerous ecological and economic services. Most local residents (72%)
responded that bats produce guano (fertilizer) but were unaware that bats pollinate
plants, consume insects, and help regenerate forests by spreading seeds.
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Texas Tech University, Kendra Phelps, August 2016
Most commonly captured species: Diadem Roundleaf bat (Hipposideros diadema) and
Common bent-wing bat (Miniopterus schreibersii).
Table 5.1. Raw capture data for bat species occupying 62 caves across Bohol Island.
Species
Common Name
Hipposideros diadema
Diadem leaf-nosed bat
Miniopterus schreibersii
Common bent-winged bat
Miniopterus australis
Little bent-winged bat
Eonycteris spelaea
Lesser dawn bat
Rousettus amplexicaudatus
Geoffrey’s rousette fruit bat
Rhinolophus arcuatus
Arcuate horseshoe bat
Taphozous melanopogon
Black-bearded tomb bat
Rhinolophus rufus
Large rufous horseshoe bat
Chaerephon plicatus
Wrinkle-lipped free-tailed bat
Hipposideros pygmaeus
Philippine pygmy roundleaf bat
Miniopterus tristis
Greater bent-winged bat
Myotis macrotarsus
Pallid large-footed myotis
Ptenochirus jagori
Greater musky fruit bat
Rhinolophus virgo
Yellow-faced horseshoe bat
Emballonura alecto
Small Asian sheath-tailed bat
Hipposideros ater
Dusky leaf-nosed bat
Myotis horsfieldii
Horsfield’s bat
Hipposideros obscurus
Philippine forest roundleaf bat
Rhinolophus macrotis
Big-eared horseshoe bat
Rhinolophus philippinensis
Large-eared horseshoe bat
Megaderma spasma
Lesser false vampire bat
Hipposideros coronatus
Large Mindanao leaf-nosed bat
Cynopterus brachyotis
Lesser short-nosed fruit bat
TOTAL
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Endemic No. Captured
X
X
X
X
X
X
6
2,238
1,086
1,056
682
585
380
322
285
201
170
137
73
60
56
40
36
31
28
24
16
9
5
2
7522
Texas Tech University, Kendra Phelps, August 2016
A
B
C
D
E
F
B
Endemic species captured: A - Greater musky fruit bat (Ptenochirus jagori); B - Large
Mindanao roundleaf bat (Hipposideros coronatus); C - Yellow-faced horseshoe bat
(Rhinolophus virgo); D - Large rufous horseshoe bat (R. rufus); E - Philippine pygmy
roundleaf bat (Hipposideros pygmaeus); F - Philippine forest roundleaf bat (H. obscurus).
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Texas Tech University, Kendra Phelps, August 2016
Recommendation 4: Given the high diversity of cave-roosting bats on Bohol Island,
conservation efforts should focus on conserving this diversity by protecting priority
caves. Conservation efforts should also focus on caves that house large populations of
ecologically important species, including fruit bats (Eonycteris spelaea, Rousettus
amplexicaudatus) and colonial, insectivorous bats (Chaerephon plicatus), in addition
to threatened species (e.g., Hipposideros coronatus, H. pygmaeus, Rhinolophus rufus,
R. virgo). Furthermore, educational campaigns to improve the awareness of bat
diversity on Bohol Island are needed. Campaigns should incorporate information
about the ecological and economic services provided by bats (i.e., pollination, forest
regeneration, insect suppression) to improve awareness of bats as key players in
maintaining a healthy ecosystem (see Appendix B for an example).
Key Finding 5: While a majority of residents were aware of the Cave Act
(Republic Act 9072), some indicated they have not changed their activities within
caves in response to the Act. Interestingly, 64% of respondents believe the Act is
effective at protecting caves from human disturbance.
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Texas Tech University, Kendra Phelps, August 2016
Recommendation 5: While local
residents are largely aware of the Act,
residents may not be aware that
permits are required to collect cave
resources and that it is forbidden to
hunt cave wildlife or remove
speleothems. Increased
correspondence with barangay
captains about the permitting process
and penalties may increase awareness among local residents, and perhaps deter local
residents from disturbing caves.
Future Directions
1) Assess the effectiveness of our proposed prioritization method for
identifying caves that house a diverse aggregation of bats on Bohol
Island,
2) Evaluate our proposed method for prioritizing caves on other karst-rich
Philippine islands and regionally within Southeast Asia,
3) Determine if conserving caves with a high diversity of bat species has
indirect effects on other cave-dependent wildlife.
Acknowledgments
We thank the DENR Region VII, PENRO-Bohol and CENRO-Bohol for their
constant support and permission to conduct this study (wildlife gratuitous permit no.
2011-04, 2013-02). This study was conducted in collaboration between Texas Tech
University and Bohol Island State University – Bilar. We extend our appreciation to
Dr. Marina Labonite and Ms. Reizl Jose for serving as local collaborators and the
numerous volunteers that assisted with fieldwork.
159
Taphozous melanopogon
Rousettus amplexicaudatus
R. virgo
R. rufus
R. philippinensis
R. macrotis
Rhinolophus arcuatus
Ptenochirus jagori
M. macrotarsus
Myotis horsfieldii
M. tristis
M. schreibersii
Miniopterus australis
Megaderma spasma
H. pygmaeus
H. obscurus
H. diadema
H. coronatus
Hipposideros ater
Eonycteris spelaea
Barangay
Emballonura alecto
Municipality
Cynopterus brachyotis
Cave Name
Chaerephon plicatus
Appendix A. Locality information and species captured for caves (n = 62) surveyed across Bohol Island, Philippines from July 2011 - June 2013.
Anda
Badiang
X
Ka Iska
Anda
Badiang
X
Tangob
Anda
Badiang
Kamagahi
Balilihan
Baucan Norte
X
Inorok
Balilihan
Datag Norte
X
Ka Anoy
Batuan
Calongpanan
Batuan
Cang Ihong
Batuan
Aloha
Behind the
Clouds
Cabacnitan
Ka Dodong
Bilar
Cambigsi
Bakongkong
Bilar
Dagohoy
Tagjaw
Bilar
Owac
Atbang
Bilar
Riverside
Logarita
Bilar
Riverside
Agaw-gaw
Bilar
Rizal
Polito
Bilar
Roxas
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Texas Tech University, Kendra Phelps, August 2016
160
Burial
Tinugdan
Bilar
Villasuerta
Kabjawan
Candijay
Tawid
Bodiong
Carmen
Alegria
X
Kokok
Carmen
Montevideo
X
Bayang
Catigbian
Bong Bong
Catalina
Catigbian
Hagbuaya
Pig-ot
Clarin
Candajec
Lahug
Dagohoy
Poblacion
Kamira
Danao
Magtangtang
Hinagdanan
Bingag
Kang Mana
Dauis
Garcia
Hernandez
Getafe
Dakong-Buho
Guindalman
Biabas
Bogtong Park
Inabanga
Lapacan Sur
Tamboco
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Cagwang
X
Salog
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Taphozous melanopogon
Rousettus amplexicaudatus
R. virgo
R. rufus
R. philippinensis
R. macrotis
Rhinolophus arcuatus
X
Ptenochirus jagori
X
M. macrotarsus
X
Myotis horsfieldii
X
M. tristis
Megaderma spasma
H. pygmaeus
H. obscurus
H. diadema
X
M. schreibersii
Villa Aurora
Miniopterus australis
Bilar
Texas Tech University, Kendra Phelps, August 2016
161
Mohon
H. coronatus
Hipposideros ater
Eonycteris spelaea
Barangay
Emballonura alecto
Municipality
Cynopterus brachyotis
Cave Name
Chaerephon plicatus
Appendix A cont.
Nabuad
Jagna
Odiong
X
X
Casampong
Loboc
Buenavista
X
X
X
Guimba
Loboc
Oy
X
X
Loboc Tourist
Loboc
X
Ka Goryo
Loon
Cantumocad
Loon
Upper Bonbon
Cantam-is
Baslay
Cantumocad
Claise
Loon
Nagtuang
X
X
Popog
Mabini
Marcelo
X
X
Tambo 1
Mabini
Tambo
Tambo 2
Mabini
Tambo
Lahug II
Pilar
Lundag
Kalanguban
Sagbayan
Kabasacan
Cantijong
San Isidro
Cansagi Sur
Taphozous melanopogon
Rousettus amplexicaudatus
R. virgo
X
R. rufus
X
R. philippinensis
Ptenochirus jagori
R. macrotis
Inabanga
Odiong
Rhinolophus arcuatus
Pou
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Texas Tech University, Kendra Phelps, August 2016
162
Lapacan Sur
M. macrotarsus
X
Inabanga
Myotis horsfieldii
M. schreibersii
X
Dagohoy
M. tristis
Miniopterus australis
Megaderma spasma
H. pygmaeus
H. obscurus
H. diadema
H. coronatus
Hipposideros ater
Eonycteris spelaea
Barangay
Emballonura alecto
Municipality
Cynopterus brachyotis
Cave Name
Chaerephon plicatus
Appendix A cont.
Bayawahan
Sevilla
Lobgob
Lujang
Sevilla
Lujang
Duguilan
Magsaysay
Lahos-Lahos
Sevilla
Sierra
Bullones
Sierra
Bullones
Sierra
Bullones
Sierra
Bullones
Tagbilaran
Cabawan
X
Mesias
Tagbilaran
Cabawan
X
X
Seminary
Tagbilaran
Taloto
X
X
Lagbas
Trinidad
San Isidro
X
Nangka
Trinidad
San Isidro
X
Canlusong
Ka Martin
Benito
Kasabas
Bugsoc
X
X
X
Taphozous melanopogon
X
Rousettus amplexicaudatus
X
R. virgo
R. macrotis
Rhinolophus arcuatus
Ptenochirus jagori
X
R. rufus
Sevilla
Manlawe
X
R. philippinensis
Kabera
X
X
X
X
X
X
X
Canlangit
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Nan-od
X
X
X
X
X
X
X
X
Nan-od
X
X
X
X
X
X
X
X
X
X
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163
Tomoc
M. macrotarsus
X
San Miguel
Myotis horsfieldii
M. schreibersii
X
Langgam
M. tristis
Miniopterus australis
Megaderma spasma
H. pygmaeus
H. obscurus
H. diadema
H. coronatus
Hipposideros ater
Eonycteris spelaea
Barangay
Emballonura alecto
Municipality
Cynopterus brachyotis
Cave Name
Chaerephon plicatus
Appendix A cont.
Cave Name
Municipality
Barangay
164
Batongay
Trinidad
Sto. Tomas
Lungon
Valencia
Anas
Kabyawan
Valencia
Nailo
X
X
X
Megaderma spasma
H. pygmaeus
X
X
X
X
X
X
Texas Tech University, Kendra Phelps, August 2016
X
Taphozous melanopogon
Rousettus amplexicaudatus
R. virgo
R. rufus
R. philippinensis
R. macrotis
Rhinolophus arcuatus
Ptenochirus jagori
M. macrotarsus
Myotis horsfieldii
M. tristis
M. schreibersii
X
Miniopterus australis
X
H. obscurus
H. diadema
H. coronatus
Hipposideros ater
Eonycteris spelaea
Emballonura alecto
Cynopterus brachyotis
Chaerephon plicatus
Appendix A cont.
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Texas Tech University, Kendra Phelps, August 2016
Appendix B. Education poster distributed to barangay officials during cave visits.
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