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\begin{document}

\title{Trophic resource partitioning drives fine-scale coexistence in cryptic
bat species}



\author[1]{Roberto Novella-Fernandez}%
\author[2]{Carlos Ibáñez}%
\author[3,4]{Javier Juste}%
\author[5]{Beth Clare}%
\author[1]{C. Patrick Doncaster}%
\author[6]{Orly Razgour}%
\affil[1]{University of Southampton}%
\affil[2]{Estación Biológica de Doñana (CSIC)}%
\affil[3]{EBD-CSIC}%
\affil[4]{Estacion Biologica de Donana CSIC}%
\affil[5]{Queen Mary University of London}%
\affil[6]{University of Exeter}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
Understanding the processes that enable species coexistence has
important implications for assessing how ecological systems will respond
to global change. Morphology and functional similarity increase the
potential for competition, and therefore, co-occurring morphologically
similar but genetically unique species are a good model system for
testing coexistence mechanisms. We used DNA metabarcoding and High
Throughput Sequencing to characterise for first time the trophic ecology
of two recently-described cryptic bat species with parapatric ranges,
Myotis escalerai and Myotis crypticus. We collected faecal samples from
allopatric and sympatric regions and locations to describe the diet both
taxonomically and functionally and compare prey consumption with prey
availability. The two bat species had similar diets characterised by
high arthropod diversity, particularly Lepidoptera, Diptera and Araneae,
and a high proportion of prey that is not volant at night, which points
to extensive use of gleaning. Diet overlap at the prey-item level was
lower in locally sympatric than allopatric locations, supporting trophic
shift under fine-scale sympatry. Furthermore, locally sympatric samples
of M. escalerai had a marginally lower proportion of not nocturnally
volant prey, suggesting that the shift in diet may be driven by a change
in foraging mode. Our findings suggest that fine-scale coexistence
mechanisms can have implications for maintaining broad-scale diversity
patterns. This study highlights the importance of including both
allopatric and sympatric populations and choosing meaningful spatial
scales for detecting ecological patterns. We conclude that a combination
of high taxonomic resolution with a functional approach helps identify
patterns of niche shift.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{introduction}}

Understanding the processes that enable species coexistence is a key
theme of ecology with important implications for interpreting diversity
patterns and predicting how systems respond to global change
(Valladares, Bastias, Godoy, Granda, \& Escudero, 2015). Interspecific
competition is thought to have a major influence on community structure
for many taxonomic groups. Niche theory (Chase \& Leibold, 2003; P.
Chesson, 2000; Letten, Ke, \& Fukami, 2017) asserts that species
coexistence is promoted through differential use of resources driven by
functional differences between species, which results in communities
that tend to be assembled by functionally dissimilar species (Schoener,
1974). This has been shown in numerous cases, including fish (Ross,
1986), shorebirds (Bocher et al., 2014) and rodent communities (Codron
et al., 2015). Alternatively, community structure and coexistence,
primarily in sessile organisms, has been often explained through neutral
processes, such as dispersal or stochasticity (The neutral theory of
biodiversity and biogeography; Hubbell, 2001). This framework has been
often used as a null model to evaluate whether observed patterns deviate
from neutral expectations (Alonso, Etienne, \& McKane, 2006; McGill,
Maurer, \& Weiser, 2006). Yet, some studies of mobile organisms have
failed to identify evidence of resource partitioning (e.g. Luiselli,
2008), suggesting that in some cases biotic interactions only play a
minor role in governing community assembly, perhaps because resources
are not limiting (Salinas-Ramos, Ancillotto, Bosso, S\selectlanguage{ngerman}ánchez\selectlanguage{english}-Cordero, \&
Russo, 2020), and therefore neutral processes likely play a more
important role.

Morphologically similar species pose a challenge for understanding
mechanisms of coexistence from a niche theory perspective because they
are more likely to be functionally similar, and therefore less likely to
be able to use resources in a different way, a pre-requisite for
resource partitioning (Weiher \& Keddy, 1999). Consequently,
considerable attention has been given to understanding resource
partitioning among morphologically identical (cryptic) or similar
co-occurring species (e.g. Gabaldon, Montero-Pau, Serra, \& Carmona,
2013; Jiang, Feng, Sun, \& Wang, 2008; Razgour et al., 2011). Many
studies have focused on the trophic dimension, an important aspect of
species' ecological niche (Schoener, 1974). DNA metabarcoding and High
Throughput Sequencing (molecular diet analysis) approaches helped
overcome many of the limitations of traditional morphological methods
(Sousa, Silva, \& Xavier, 2019), opening the door to new opportunities
for studying mechanisms of species coexistence (Arrizabalaga-Escudero et
al., 2018; Kruger et al., 2014; Razgour et al., 2011). However, the
majority of coexistence studies focus on only sympatric populations,
preventing an evaluation of how the presence of a competitor may change
resource use, thus limiting the power of inferences (Salinas\selectlanguage{english}-Ramos et
al., 2020). Moreover, most studies also focus on diet only, disregarding
prey selection relative to prey availability or resource limitation
(Salinas-Ramos et al. 2019). Accounting for prey selection (e.g.
Rytkonen et al., 2019) can provide a more complete picture of consumer
trophic preferences (Lawlor, 1980).

The processes that govern community assembly, including coexistence
mechanisms, vary with spatial scale (Lewis, Bailey, Vandewoude, \&
Crooks, 2015; Snyder \& Chesson, 2004; Viana \& Chase, 2019), yet
spatial scale is rarely considered in coexistence studies (Hart,
Usinowicz, \& Levine, 2017). A better understanding of the scale of
coexistence mechanisms and how different processes interact is important
for both basic and applied ecology (Peixoto, Braga, \& Mendes, 2018).

This study aims to identify whether trophic ecology enables
morphologically similar species to coexist across spatial scales. We
focus on two recently described, morphologically nearly identical,
insectivorous bat species, whose trophic ecology has not been studied to
date, \emph{Myotis crypticus} and \emph{Myotis escalerai.} These bats
are restricted to the Western Mediterranean Basin, where they overlap
across the north of the Iberian Peninsula, but at the fine-scale are
known to co-occur only in a few locations (Juste, Ruedi, Puechmaille,
Salicini, \& Ibanez, 2018). Phylogeographic analysis and species
distribution modelling suggest that their ranges have been shaped by
competition (Razgour, Salicini, Ibanez, Randi, \& Juste, 2015). These
bats therefore provide an excellent case study for understanding
mechanisms of coexistence among morphologically similar species. We use
DNA metabarcoding and High Throughput Sequencing to characterise the
trophic ecology of \emph{M. crypticus} and \emph{M. escalerai} by
analysing their taxonomic and functional diets and their prey selection
relative to prey availability in sympatric versus allopatric populations
at both fine and broad spatial scales. Given their near identical
morphology and echolocation calls, the overall trophic niches of the two
bats are expected to be similar and niche overlap should be high. We
hypothesise that if resource partitioning is the main process
facilitating coexistence, competing species will diverge in their use of
resources in sympatry compared to allopatry (e.g. Klawinski, Vaughan,
Saenz, \& Godwin, 1994). We test the predictions that 1) trophic niche
overlap and diet similarity are higher in allopatric than sympatric
locations; and 2) differences in trophic niche overlap are most
pronounced at the fine spatial scale where individuals of the two
species share the same foraging areas.

\section*{Methods}

{\label{methods}}

\subsection*{Sampling design}

{\label{sampling-design}}

Sampling took place in the Iberian Peninsula, focusing on two sympatric
regions in the north where both \emph{Myotis escalerai} and \emph{Myotis
crypticus} are found (La Rioja-Soria and southern Cantabria), and two
allopatric regions: the south (Andalucia: Jaen and Granada), where
only\emph{M. escalerai} is found, and the north Atlantic coast (northern
Cantabria), where only \emph{M. crypticus} is found. Additionally, we
sampled a single sympatric swarming site in Catalunya, where the two
species use the same cave during the autumn mating season (Fig. 1).
Within each region, 9-24 locations were sampled based on suitable
habitat and accessibility, using monofilament mist nets and a harp trap
placed over water sources, forest paths and cave entrances. The sampling
period extended from June to September 2017, for a total of 68 sampling
nights (Supplementary Table S1 for list of sampling locations). Captured
bats were kept in individual cotton bags for up to 1 hour. We collected
faecal samples from the cotton bags for diet analysis, and biopsy
punches (3 mm) from the wing membrane of the bats to confirm species
identification. Dropping samples and wing biopsies were stored in
absolute and 70\% ethanol, respectively. Bat sampling was carried out
under local permits and ethical approval from the University of
Southampton (study ID: 26627).

We sampled the arthropod community in bat sampling locations using
vegetation sweeping (Barclay, 1991; Swift \& Racey, 2002) to assess bat
prey selectivity relative to prey availability (Jones, 1990; Kunz,
2009). We chose vegetation sweeping because of the expected low flight
and gleaning behaviour of the species based on their morphology and
echolocation calls, and the foraging behaviour of the morphologically
similar better studied member of the cryptic species
complex,\emph{Myotis nattereri} (de Jong, 1995). During bat sampling
nights, we set linear transects in each habitat type in the sampling
location and swept the vegetation along each transect. After 10 sweeps,
we collected the capture into a plastic bag and moved five steps further
without sampling to increase spatial representativeness. Each sampling
unit of 10 sweeps and five steps forward was repeated 5-10 times until
the capture size was considered representative (\textgreater{} 100
individuals). Transect length ranged between 30 and 80 m. Arthropod
specimens captured were separated from vegetation remains in the field
and stored in 70\% ethanol.

\subsection*{DNA extractions and species
confirmation}

{\label{dna-extractions-and-species-confirmation}}

Bat species identity was confirmed in the Estacion Biologica de Donana
Laboratory of Molecular Ecology (LEM, EBD-CSIC, Spain). DNA was
extracted from wing biopsy punches through precipitation with
isopropanol. Part of the hypervariable region of the mtDNA control
region was amplified using the primers CSBC-F
5'-CCTCTTAAATAAGACATCTCGATGG-3' (Wilkinson \& Chapman, 1992) and
HV2-Mna-R 5'-ATGCGTGCGTGTGTAATGTC-3' (Garcia-Mudarra, Ibanez, \& Juste,
2020). Species specific differential amplification patterns for this
primer set were used to confirm species identity through gel
electrophoresis (Garcia-Mudarra et al., 2020).

DNA was extracted from all bat dropping samples using the Qiagen DNA
stool mini kit, following the protocol in Zeale et al. (2011). A total
of 43 sweeping samples from 23 locations with at least three bat
dropping samples were selected (Supplementary Table S1). From those, all
arthropod individuals (\emph{N} = 8366) were first identified
morphologically to taxonomic order. Subsequently, whole specimens, if
smaller than a drosophila, or a specimen part (leg or head) if larger,
were separated out, dried and pooled together for DNA extraction.
Arthropod DNA was extracted using the NucleoSpin DNA Insect kit with up
to 35 mg of sample dry weight in each tube. Larger samples were split
into several tubes. The following modifications were applied to the kit
extraction protocol: In steep 2, vortex for 20 minutes in the MN Bead
Tube Holder on a Vortex-Genie at maximum speed; after steep 3, pipette
550uL of clean supernatant in to a new 2mL Eppendorf, centrifuge again
at 13,000RPM for 2 minutes and continue with steep 4; in steep 6,
centrifuge for 3 minutes; in steep 7, add 50uL of ddH\textsubscript{2}0
and incubate for 3 minutes.

\subsection*{High Throughput
Sequencing}

{\label{high-throughput-sequencing}}

Both dropping and sweeping samples were sequenced in the Bart's and the
London Genome Centre, London, UK. DNA extracts were checked for quality
and concentration on a TapeStation D1000. Two sets of primers were used
together in order to reduce primer taxonomic bias (Alberdi, Aizpurua,
Gilbert, \& Bohmann, 2018), especially given the high diversity of prey
types expected in the diet, ZBJ (Zeale, Butlin, Barker, Lees, \& Jones,
2011) Forward: ZBJ-ArtF1c 5'-AGATATTGGAACWTTATATTTTATTTTTGG-3' and
Reverse: ZBJ-ArtR2c 5'-WACTAATCAATTWCCAAATCCTCC-3', and ANML~(Jusino et
al., 2019) Forward: LCO1490 5'-GGTCAACAAATCATAAAGATATTGG-3'~and
Reverse:~ CO1-CFMRa 5'-GGWACTAATCAATTTCCAAATCC-3'. For the ZBJ amplicon
each 15\selectlanguage{greek}μ\selectlanguage{english}l PCR reaction used 7.5\selectlanguage{greek}μ\selectlanguage{english}l of Multiplex PCR mastermix (QIAGEN,
Germany), 0.25 \selectlanguage{greek}μ\selectlanguage{english}L of each primer (10 \selectlanguage{greek}μ\selectlanguage{english}M), 5 \selectlanguage{greek}μ\selectlanguage{english}L H2O and 2 \selectlanguage{greek}μ\selectlanguage{english}L template
DNA. Negative and positive controls were included in PCR reactions and
later sequencing. The thermal cycling protocol was as follows: 95~\selectlanguage{english}C for
15 min, 34 cycles of 94~\selectlanguage{english}C for 40s, 40~\selectlanguage{english}C for 1 min, 72~\selectlanguage{english}C for 30s,
followed by a final extension of 72~\selectlanguage{english}C for 5 min. ANML regions were
amplified in 15ul reactions following published protocols (Jusino et
al., 2019). All products were visualised on a 1.5\% agarose gel.
Products were tagged using Fluidigm barcodes and checked on a
TapeStation D1000 before pooling and sequencing on an Illumina MiSeq
using paired end (2 x 250 bp) chemistry (Illumina, San Francisco, USA).
We used two technical PCR replicates to reduce biases associated with
PCR stochasticity. This led to each sample being sequenced four times
(combination of two primer and two PCR replicates).

\subsection*{Bioinformatics}

{\label{bioinformatics}}

Sequencing runs were merged using USEARCH (Edgar, 2010) and primers and
adaptors removed using cutadapt (Martin, 2011). Sequences were processed
on the mBRAVE platform (http://www.boldsystems.org/bin) (Ratnasingham \&
Hebert, 2007) setting the following parameters: Min QV = 0 qv, Min
Length = 100 bp, Max Bases with Low QV (\textless{}20) = 75\%, Max Bases
with Ultra Low QV (\textless{}10) = 75\%, ID Distance Threshold = 1.5\%,
Exclude from OTU Threshold = 3\%, Minimum OTU Size = 1, OTU Threshold =
2\%. Sequences were compared with the BOLD reference libraries
SYS-CRLINSECTA and SYS-CRLNONINSECTARTH to established Barcode Index
Numbers (BINs). BINs are a type of Operational Taxonomic Unit (OTU)
integrated in the BOLD system with advantages over traditional OTUs,
such as being unique and stable (Ratnasingham \& Hebert, 2013). We used
BIN identity as a proxy of taxonomic prey item unit.

After obtaining BIN (prey item) composition per sample and run, we
removed singletons, i.e. BINs that only had a single read per run and
sample, because they are likely to be PCR or sequencing errors (Alberdi
et al., 2018). We established the threshold for the minimum number of
reads per sample to retain an identification based on comparing
composition similarity between molecular and morphological ID from
sweeping samples and any prey present in blanks. Additionally, we
controlled for potential contamination during extractions by removing
the BINs present in extraction blanks from other samples in the same
extraction run if present with a high number of reads (\textgreater{}100
in any blank sample) or with less than 10 times more reads in samples
than in blank. We controlled for contamination from the sequencing
process by removing BINs present in sequencing blanks from all the
samples from the same primer following the same criteria as above.

We used two alternative approaches to combine data from PCR replicates.
In the first, the additive criteria, BIN composition from both PCR
replicates of each sample were added together. In the second, the
conservative approach, only BINs that appeared in both PCR replicates
were considered (Alberdi et al., 2018). Under this second criteria only
samples in which the four runs contained a minimum number of reads
(\textgreater{}100) were considered given that a failed sequencing run
in a sample would lead to a null composition for both of the PCR
replicates of a primer (52 samples, Supplementary Table S3). Finally, we
combined taxa recovered from both primers to obtain the prey composition
per dropping sample for downstream ecological analysis. Duplicated BINs
in the same sample coming from different PCR replicates or primers were
removed. A flow chart describing the methods is shown in Supplementary
Fig. S1. Ecological results from both approaches were very similar, thus
we present results based on the additive approach (See Supplementary
Fig. S9 for diet based on the conservative approach).

\subsection*{Characterising the diets of the two bat
species}

{\label{characterising-the-diets-of-the-two-bat-species}}

The contribution of different elements to the diet for a set of samples
was quantified using weighted Percent of Occurrence (wPOO), which
measures the relative occurrence of diet elements (prey items/OTUs/BINs)
in a set of samples considering first their relative proportion per
sample (Deagle et al., 2019). For example, a prey item found in a sample
with 9 other prey items will be interpreted to contribute in the diet
1/10 of what it would if it was the only prey item present.
Contributions to diet based on the two other commonly used metrics:
Percent of Occurrence (POO) and Relative Read Abundance (RRA) (Deagle et
al., 2019), are shown in supplementary materials (Supplementary Fig.
S2). We tested for differences in the number of BINs per sample between
bat species for each of the orders that constitute at least 10\% of the
diet of either one of the bat species (Araneae, Diptera, Lepidoptera,
and Hemiptera), using negative binomial generalised linear models (GLMs;
in R) to fit data structure based on the distribution of model
residuals. We measured order level and prey species level (BIN-level)
diet composition overlap between bat species using Pianka's measure of
niche overlap (O\textsubscript{jk}) (R package: EcoSimR (Gotelli, Hart,
\& Ellison, 2015)). We tested with an ANOSIM test (R package vegan
(Oksanen et al., 2019)) whether Jaccard distance in BIN composition was
greater between than within bat species. The ANOSIM statistic R is~based
on the difference of mean ranks between and within groups, with a range
between -1 and +1. A value of zero indicates that the group does not
explain compositional differences. We visualised ordination of samples
depending on their BIN composition with Non-Metric Multidimensional
Scaling (NMDS, R package vegan: Oksanen et al., 2019). We calculated
Levins' (1968) standardised measure of niche breadth
(B\textsubscript{A}) at the prey species (BIN) level for each bat
species.

\subsection*{Functional diet
assessment}

{\label{functional-diet-assessment}}

Prey items were classified based on the literature (outlined in
Supplementary Table S2) and an expert entomological taxonomist into
three functional categories: non-volant, not actively-volant,
nocturnally volant. Categorisation depended on their mobility and type
of activity, reflecting their likelihood of being captured by gleaning
or aerial hawking (Supplementary Data file S1, Supplementary Table S2).
The categorisation was done at family or finer taxonomic level by
checking the literature for data on daily activity patterns of each
family and presence in nocturnal light traps (Supplementary Table S2 for
criteria used). The non-volant category included wingless arthropod
groups (Araneae, Isopoda and wingless insects such as some members of
Blattodea, Orthoptera). The not actively-volant category included those
able to fly but unlikely to have been captured by the bat through aerial
hawking because they are not active fliers, either at night (diurnal
Diptera), or not active fliers in general (e.g. Hemiptera, some
Blattodea, Orthoptera and Coleoptera). The nocturnally volant category
comprised arthropods with aerial and nocturnal activity, and therefore
likely to be captured by aerial hawking (e.g. non-Ropalocera
Lepidoptera, nocturnal Diptera, Neuroptera, Ephemeroptera, Trichoptera).
This classification represents the likelihood of being captured by
gleaning or aerial hawking rather than direct inference of the capture
mode because nocturnally active aerial prey can also be captured by
gleaning when resting on vegetation and not active nocturnal fliers
could also be captured in the air (e.g. ballooning in spiders). Once all
prey items were classified into functional groups, we obtained the
functional diet of both bat species using weighted percent of occurrence
(wPOO), and compared the percentage of not nocturnally volant (including
both non-volant and not actively volant categories) per sample between
bat species using a linear model.

\subsection*{Trophic niche overlap in allopatry vs sympatry across
spatial
scales}

{\label{trophic-niche-overlap-in-allopatry-vs-sympatry-across-spatial-scales}}

Locations from Andaluc\selectlanguage{ngerman}ía (Mediterranean climate) and northern Cantabria
(Atlantic climate) were classified as regionally allopatric. Locations
from La Rioja and southern Cantabria (climatically Mediterranean to
sub-Atlantic) as regionally sympatric (based on data from Razgour et
al., (2019) and EBD records). At the fine-scale within the sympatric
regions, we classified locations as locally sympatric or allopatric
depending on whether they were within 3 km of records of the other
species based on a conservative estimation of the home-range diameter of
the better studied cryptic congener \emph{M. nattereri} (Boye \& Dietz,
2005). The swarming location in Catalunya was removed from the
fine-scale analysis because bats gather in swarming sites from distances
of up to 60 km from their colonies for the purpose of breeding rather
than foraging (Rivers, Butlin, \& Altringham, 2005), and therefore it is
unclear whether those individuals forage in sympatric areas
(Supplementary Table S1 for sampling locations and their broad and
fine-scale sympatry category).

To identify differential use of certain prey orders and functional
groups, we tested separately for allopatric and sympatric locations
whether 1) the number of BINs per sample for each of the main arthropod
orders, and 2) the percentage of not nocturnally volant functional
groups differed between bat species. We used negative binomial zero
inflated GLMs and a linear model respectively. We run separate models
for broad and fine spatial scales given that both fine scale sympatry
and allopatry treatments are within regional sympatry. Cases where
resource (prey order or functional group) use was different between bat
species when sympatric but not when allopatric were regarded as evidence
of resource partitioning. We measured prey species (BIN) level niche
overlap (O\textsubscript{jk}) between bat species in sympatry and in
allopatry, and tested, using null models (R package ecosimR) whether
overlap was lower or higher than random in sympatry versus allopatry. We
tested whether O\textsubscript{jk} differed between sympatric and
allopatric locations by pooling the diet composition of each bat species
per location and measuring O\textsubscript{jk} between pairs of
locations. At the regional scale we used a Gaussian Hurdle model due to
the high density of zeroes in overlap values. At the local scale we used
a linear model with log transformed values of O\textsubscript{jk} to
meet assumptions of normal distribution. All statistical analysis was
carried out in R (R core team, 2019).

\subsection*{Prey consumption relative to
availability}

{\label{prey-consumption-relative-to-availability}}

For each location, we quantified the relative availability of each
arthropod order and functional group using weighted percent of
occurrence (wPOO) after pooling together sweeping samples from the
different habitats. Similarly, we obtained bat diet composition (wPOO)
per location of each arthropod order and functional group by pooling
diet composition of all individual bats. Then, we subtracted from the
bat diet wPOO the prey availability wPOO to obtain prey arthropod and
functional group selection per location. A higher proportion of a given
arthropod order in the diet than in sweeping samples indicates the bats
may be preferentially consuming this resource, based on prey
availability at the sampled strata. This analysis is based on
assumptions of sampling representativeness (see discussion for detailed
overview of methodological limitations).

\subsection*{Testing primer performance and representation of the DNA
metabarcoding
approach}

{\label{testing-primer-performance-and-representation-of-the-dna-metabarcoding-approach}}

For each arthropod order we described the number and proportion of BINs
identified by each primer. Morphological identification of the arthropod
communities allowed us to compare the performance of the primers and
metabarcoding approaches. We compared the presence of orders in each
sweeping sample based on molecular and morphological identification to
determine whether metabarcoding offers a good estimation of arthropod
community composition.

\section*{Results}

{\label{results}}

We analysed a total of 138 dropping samples for \emph{Myotis
escalerai}and 90 for \emph{Myotis crypticus} from 49 locations, 26 of
which were in the broad-scale allopatric regions and 23 in sympatric
regions. Within the sympatric regions (La Rioja and southern Cantabria),
91 samples were classified as locally allopatric and 28 as locally
sympatric (Fig. 1, Table 1; Supplementary Table S1). We recovered a
total of 2,859,300 reads (Supplementary Table S3 for details) from the
228 dropping samples for the four combinations of PCR replicates and
primers (1,403,636 from ANML1 and 1,455,664 from ZBJ). These reads were
associated into 1461 different BINs. Based on the BINs present in
sequencing blanks, we removed for the ANML primer 6 BINs from the first
run of 56 dropping samples, and 2 BINs for the second run of 10 samples.
For the ZBJ primer, we removed 5 BINs from the first run of 10 samples
and 23 BINs from the second run of 88 samples. Based on BINs present in
extraction blanks, we removed a total of 39 BINs from 16 dropping
samples

\subsection*{\texorpdfstring{Characterising the diet of \emph{M.
escalerai} and \emph{M.
crypticus}}{Characterising the diet of M. escalerai and M. crypticus}}

{\label{characterising-the-diet-of-m.-escalerai-and-m.-crypticus}}

A total 19 arthropod orders were obtained based on the 1461 BINs
(Supplementary Data file S1 for list of prey items obtained for each bat
species). The diets of \emph{M. escalerai} and \emph{M. crypticus} were
characterised by high arthropod diversity, and were composed mostly of
the orders Lepidoptera (\emph{M. escalerai} = 26.6 \%; \emph{M.
crypticus} = 23.7\%), Diptera (24.8\%; 33.2\%), Araneae (20.7\%;
17.2\%), but also included Hemiptera (11.8\%; 6.2\%), Coleoptera (4.8\%;
5.1\%), and Orthoptera (4.3\%; 4.8\%), among others (\textless{}5\%)
(Fig. 2a-b; Supplementary Fig. S2 for diet composition based on POO and
RRA measures). Diet composition at the prey order level was very similar
between bat species (O\textsubscript{JK} = 0.98, above 1000 null
models). However there were differences in the number of BINs per sample
of Diptera, which was lower in \emph{M. escalerai} (5.27 versus 6.75)
(Negative binomial GLM: z\textsubscript{1,226}=-2.03, P = 0.042), and
Hemiptera, which was higher in \emph{M. escalerai} (2.09 versus 1.68)
(Negative binomial GLM: z\textsubscript{1,226}=2.85, P = 0.004
Supplementary Fig. S3).

At the prey species (BIN) level, Levins' niche breadth was similar for
both species, B\textsubscript{A} = 0.17 for \emph{M. escalerai} and
B\textsubscript{A} = 0.19 for \emph{M. crypticus} . Niche overlap
between species was higher than expected by chance (O\textsubscript{JK}
= 0.71, above 95\% of 1000 null models). The samples from the two bat
species showed some differences in prey item composition in NMDS
ordination space (Fig. 3a, Stress: 0.25, k=3, non-metric fit
R\textsuperscript{2}=0.934, Linear fit, R\textsuperscript{2}= 0.532). An
analysis of similarity confirms that distance in prey item composition
among samples is greater between species than within species (ANOSIM R
statistic: 0.10, P= 0.001).

\subsection*{Trophic partitioning in sympatric versus allopatric
locations}

{\label{trophic-partitioning-in-sympatric-versus-allopatric-locations}}

At the arthropod order level, there is no clear pattern of shift from
high similarity in order composition between species to differential use
in sympatry at any of both spatial scales (O\textsubscript{JK} regional
allopatry =0.88, O\textsubscript{JK} regional sympatry = 0.96,
\textgreater{}1000 null models, Fig 2c-d; O\textsubscript{JK} local
allopatry =0.95, O\textsubscript{JK} local sympatry = 0.98,
\textgreater{}1000 null models, Fig 2e-f). When examining the number of
BINs of the main arthropod orders per sample, there were differences
between bat species between the allopatric regions for Araneae and
Hemiptera, which were both higher in \emph{M. escalerai} (\emph{M.
escalerai} = 4.00, 1.65, \emph{M. crypticus} = 2.16, 0.55 respectively),
and for Lepidoptera, which was higher in \emph{M. crypticus} (4.6,
11.94) (Negative binomial GLM: df=1,98, P\textless{}0.05). In the
sympatric region, the higher number of Hemiptera in \emph{M.
escalerai}holds (\emph{M. escalerai} = 2.50, \emph{M. crypticus} 1.50),
and in Lepidoptera there is a shift whereby is \emph{M. escalerai} the
one that consumes a higher number (\emph{M. escalerai} = 6.78, \emph{M.
cryptius}= 4.09, Negative binomial GLM: P\textless{}0.05). At the
fine-scale, within the sympatric region, the only difference found
between the bat species was the higher number of BINs per sample of
Hemiptera (2.67, 1.49) (Negative binomial GLM: z\textsubscript{1,89}=
-2.68, P=0.007) and Lepidoptera (6.70, 3.64) in \emph{M. escalerai} in
allopatric locations (Negative binomial GLM: z\textsubscript{1,89}=
-2.92, P=0.004). There were no differences in arthropod orders consumed
between the bat species in locally sympatric locations (Negative
binomial GLM: P\textgreater{}0.05; Supplementary Fig. S4).

At the prey species (BIN) level, at the broad-scale, trophic niche
similarity between species was lower in allopatric than in sympatric
regions (O\textsubscript{JK} allopatric = 0.35,
O\textsubscript{JK}sympatric= 0.62). Conversely, at the fine-scale,
within the sympatric region, trophic niche overlap between species was
higher in locally allopatric locations (O\textsubscript{JK}= 0.56) than
in locally sympatric locations (O\textsubscript{JK}= 0.37). Despite the
low values of overlap in regionally allopatric and locally sympatric
locations, in all the four cases, observed niche overlap was higher than
1000 null models. When measuring trophic niche overlap between species
using pairs of locations, we observed the same pattern. At the broad
scale we found higher diet overlap in sympatric than allopatric
locations (O\textsubscript{JK} sympatric= 0.107±0.056,
O\textsubscript{JK}allopatric= 0.050±0.04; Gausian hurdle model:
binomial GLM: z\textsubscript{1,316}=4.76, P\textless{}0.05; Gaussian
GLM: t\textsubscript{1,265}=8.26, P\textless{}0.05). In contrast, at the
fine-scale, niche overlap was lower among pairs of locally sympatric
than among locally allopatric locations (O\textsubscript{JK} sympatric=
0.099±0.065, O\textsubscript{JK} allopatric= 0.126±0.057; Linear model:
F\textsubscript{1,73}=6.34, P=0.014; Fig 3b).

\subsection*{Functional diet analysis}

{\label{functional-diet-analysis}}

Both species had a similar high percentage of non-volant (\emph{M.
escalerai} =21.4 \%, \emph{M. crypticus} =19.5\%) and not
actively-volant (44.6\%, 45.8\%) prey items in the diet. Only 34.0\% and
34.7\% of weighted percent of occurrence (wPOO) was composed of
arthropods classified as nocturnally volant (Fig. 4a). There were no
differences in the overall percentage of not nocturnally volant prey
taxa (BINs) per sample between bat species (66\% ±20\%, 66\% ±21\%,
Linear model: F\textsubscript{1,217} \textless{}0.001, P= 0.990; Fig.
4b). When analysing functional diet differences separately in allopatric
versus sympatric regions, we found differences between species in
allopatric regions, whereby \emph{M. crypticus} consumed lower
percentage of prey that were not nocturnally volant (allopatric
regions:\emph{M. escalerai} = 66\% ±19\%, \emph{M. crypticus} =48\%
±25\%; F\textsubscript{1,98}=11.72, P\textless{}0.05; Fig. 4c; sympatric
regions: 65\% ±22\%, 71\% ±17\%; F\textsubscript{1,117}=2.3, P=0.13;
Fig. 4d). At the fine-scale, there were no differences among bats in
locally allopatric locations (\emph{M. escalerai} = 67\% ±22\%, \emph{M.
crypticus} =72\% ±16\%, F\textsubscript{1,89}=1.325, P=0.250; Fig. 4e)
while in locally sympatric locations the percent of prey that were not
nocturnally volant was borderline lower in the diet of \emph{M.
escalerai} (52\% ±17\%) than \emph{M. crypticus} (68\% ±18\%;
F\textsubscript{1,26}=4.03, P=0.055; Fig. 4f).

\subsection*{Prey consumption relative to
availability}

{\label{prey-consumption-relative-to-availability-1}}

In nearly all cases, we could not detect over- or under-selection of
arthropod orders by the bats relative to their availability in sweeping
samples. The distribution of prey order selection values between the
1\textsuperscript{st} and 3\textsuperscript{rd} quartiles overlapped
with zero in all cases, except in the case of \emph{M. escalerai} and
Lepidoptera, where positive selection values could indicate
over-selection (1\textsuperscript{st}-3\textsuperscript{rd} quartile:
+0.43 --- +18.7; Supplementary Fig. S5).

\subsection*{Metabarcoding and primer
performance}

{\label{metabarcoding-and-primer-performance}}

There were compositional differences in the prey orders that each primer
recovered. A large proportion of the BINs identified in dropping samples
were only recovered by one of the primers (Supplementary Fig. S6).
Neuroptera, Orthoptera, Coleoptera were more frequently recovered by
ZBJ, while Plecoptera and Thysanoptera, Dermaptera Mantodea where more
frequently recovered by ANML (Supplementary Fig. S6). Supplementary
Figure S7a-b shows composition for a subset of dropping samples
comparing each primer.

In sweeping samples, 7065 insects were identified morphologically to
order level, with an average of 174.3 individuals per sample (range
7-624). Using molecular tools, we recovered 899,853 reads (Supplementary
Table S2), and identified 813 different BIN items. Some of the rarer
orders were under-represented in the molecular analysis. Specifically,
Opiliones, Dermaptera, and Archaeognatha, appeared in more than 10
sweeping samples each identified morphologically, but were rarely
recovered in the molecular approach, despite being present in the
reference databases (Supplementary Fig. S8).

\section*{Discussion}

{\label{discussion}}

Morphologically almost identical species are likely to compete for
resources, and therefore offer good case studies to understand processes
that drive species coexistence. We find evidence of reduced trophic
niche overlap in recently separated cryptic bat species in sympatric
locations relative to allopatric ones based on DNA metabarcoding and
high throughput sequencing. The functional analysis suggests that the
subtle trophic shift seen may be driven by differential foraging mode.
Our results support niche theory predictions of the role of biotic
interactions in driving species assemblages (Schoener, 1974). Trophic
resource partitioning was only evident at the fine spatial scale, within
areas of range overlap, suggesting that fine-scale mechanisms of
coexistence could have implications for the maintenance of broad- scale
diversity patterns (Godsoe, Murray, \& Plank, 2015).

\subsection*{Trophic ecology of Myotis escalerai and Myotis
crypticus}

{\label{trophic-ecology-of-myotis-escalerai-and-myotis-crypticus}}

Our results reveal that the two bat species have a broad generalist
diet, tend to consume prey relative to their availability and use
gleaning to a high extent. We found high similarity in their trophic
ecology in terms of both order and prey species composition. Both bat
species' diets are mostly composed of Lepidoptera, Diptera and Araneae,
but also include several other prey orders. However, \emph{M.
escalerai}consumes a higher percentage of Hemiptera, while \emph{M.
crypticu} s Diptera. Functionally, the two bat species consume an
equally high proportion of prey items that are not nocturnally volant,
which suggests that both bats predominantly glean prey from vegetation.

The trophic ecology of these two recently described bat species is very
similar to their cryptic sister-species \emph{M. nattereri,} which also
feeds mostly on Lepidoptera, Diptera and Araneae (Hope et al., 2014;
Swift, 1997; Swift \& Racey, 2002; Vaughan, 1997) and is known to catch
a high proportion of its prey through gleaning (Arlettaz, 1996; Hope et
al., 2014; Shiel, McAney, \& Fairley, 1991; Swift, 1997; Swift \& Racey,
2002). Similarly to our study, Shiel et al., (1991) estimated that 68\%
of \emph{M. nattereri} 's diet is made up of arthropod families that are
not active at night. The row of hairs in the uropatagium border is a
characteristic trait of members of the \emph{M. nattereri} species
complex that is thought to be functionally linked with gleaning (Czech,
Klauer, Dehnhardt, \& Siemers, 2009). Although the presence of more
developed hairs in \emph{M. escalerai} is one of the characters which
separates these taxa (Juste et al., 2018), we found no difference in the
extent of gleaning between the two bat species.

\subsection*{Trophic partitioning across spatial
scales}

{\label{trophic-partitioning-across-spatial-scales}}

Despite overall high trophic niche similarity between the two bat
species at the prey order level, we detect a signature of trophic shift
at the prey species (BIN) level, whereby diet overlap is lower in
locally sympatric compared to locally allopatric locations at the
fine-scale. This supports the contribution of trophic partitioning to
species coexistence even when overall trophic niche overlap is high. A
similar trend was seen at the functional level, whereby the proportion
of prey items that are not nocturnally volant is borderline different
between bat species only when locally sympatric. This suggests that the
differentiation in diet composition seen at the prey species level when
locally sympatric may be driven by a shift in foraging strategy (e.g.
Krüger et al., 2014), through \emph{M. escalerai} decreasing its extent
of gleaning. However, our inference is limited by small sample sizes,
which reduced the power of the analysis. At the arthropod order level,
we find differences in the use of some arthropod orders among allopatric
regions, likely due to differences in arthropod availability between the
Mediterranean region, where only \emph{M. escalerai} is found and the
Atlantic region, where \emph{M. crypticus} is present.

Several studies have identified trophic niche shifts from allopatry to
sympatry, for instance between morphologically similar fish (Gkenas,
Magalhães, Cucherousset, Orjuela, \& Ribeiro, 2019; Schmitt \& Coyer,
1983) and reptile species (Huey, Pianka, Egan, \& Coons, 1974; Klawinski
et al., 1994). However, in bats, previous coexistence studies looking at
trophic ecology only focused on sympatric populations, and rarely found
evidence of trophic resource partitioning. A few exceptions are the
gleaning bats \emph{M. nattereri} , \emph{Plecotus auritus}
and\emph{Myotis bechsteini} in a sympatric population in central Europe
(Andreas, Reiter, \& Benda, 2012a), and evidence of low dietary overlap
between sympatric \emph{P. auritus} and \emph{Plecotus
macrobullaris}(Ashrafi, Beck, Rutishauser, Arlettaz, \& Bontadina,
2011).

The observed trophic shift, albeit subtle, suggests that the two bat
species are likely competing for food resources. It has been previously
hypothesised that arthropods are abundant and do not constitute a
limiting resource for bats (Arlettaz, 1999; Krüger et al., 2014).
However, exclusion experiments in both tropical (Kalka, Smith, \& Kalko,
2008) and temperate forests (Böhm, Wells, \& Kalko, 2011) show that bats
can control the abundance of arthropods, and therefore arthropods could
be a limiting resource to competitors (Salinas\selectlanguage{english}-Ramos et al., 2020).

Our study does not refute the possibility that other coexistence
mechanisms, such as habitat or temporal partitioning (Schoener, 1974),
occur among these two species, or the role of environmental variability
in facilitating coexistence (Chesson \& Warner, 1981). Spatial
partitioning is frequently cited as a key mechanism of coexistence in
other bat studies (e.g. Arlettaz, 1999; Emrich, Clare, Symondson,
Koenig, \& Fenton, 2014; Kunz, 1973; Russo et al., 2014). Although in
many cases, contrary to our study, spatial partitioning may be driven by
slight differences in bat morphology (e.g. Salsamendi et al., 2008,
2012), which would affect their performance in different habitats
(Norberg, 1994). However, in our study the two species were caught in
the same sampling sites, some of which were forests, where they are
known to forage, suggesting they may share the same foraging sites.

A better understanding of the spatial scales of species coexistence is
an important advance in our understanding of the maintenance of
diversity (Hart et al., 2017). Our finding that trophic partitioning
only occurs at the fine spatial scale is consistent with other bat
(Peixoto et al., 2018), ant (Albrecht \& Gotelli, 2001), parasitoid
insects (Harvey, Snaas, Malcicka, Visser, \& Bezemer, 2014) and bobcat
(Lewis et al., 2015) studies, showing that interspecific interactions
are more important for shaping community structure at fine rather than
broad spatial scales. However, this pattern is not universal (e.g
Harma\selectlanguage{polish}č\selectlanguage{english}kov\selectlanguage{ngerman}á, Reme\selectlanguage{polish}š\selectlanguage{english}ov\selectlanguage{ngerman}á, \& Reme\selectlanguage{polish}š, \selectlanguage{english}2019). Fine-scale coexistence mechanisms
could prevent competitive effects from scaling-up (Godsoe et al., 2015),
which in our study system could contribute to enabling broad-scale range
overlap across the north of the Iberian Peninsula.

\subsection*{Prey consumption relative to
availability}

{\label{prey-consumption-relative-to-availability-2}}

The diet of a species is a function of both consumer selection and
trophic resource availability within the foraging habitat (Lawlor,
1980). Therefore, considering resource availability allows for a better
inference of species trophic preferences. Previous studies comparing bat
prey consumption with prey availability pointed to selection of certain
prey orders, such as Coleoptera by \emph{Eptesicus fuscus} (Agosta,
Morton, \& Kuhn, 2003), chironomid flies by \emph{Myotis
daubentoni}(Vesterinen et al., 2016) and certain prey traits like moth
size by\emph{Barbastella barbastella} (Andreas, Reiter, \& Benda,
2012b). Similarly, \emph{M. nattereri} was found to over-select
arachnids, Opiliones, Coleoptera, and several Diptera families, and
under-select Hemiptera (Swift \& Racey, 2002). In this study we do not
detect clear trends of over-selection for specific prey orders matching
the generalist broad trophic niche of the studied bats. However, diet
selection results should be interpreted with caution due to the
difficulty of obtaining a representative estimation of arthropod
availability. Any arthropod sampling technique is biased towards certain
types of arthropods (Cooper \& Whitmore, 1990) and the habitats sampled
and their respective sampling effort may not adequately represent where
bats actually forage, especially given that they can use large areas and
arthropod communities change depending on habitat type (Lamarre et al.,
2016) and vertical stratification (Ulyshen, 2011). Our molecular diet
analysis results confirm that the two studied bat species indeed glean
prey from the vegetation, and therefore the arthropod community sampled
using sweep nets likely represents at least part of the prey resources
available to the bats.

\subsection*{Methodological considerations and study
limitations}

{\label{methodological-considerations-and-study-limitations}}

Primer bias towards certain taxonomic groups is a major issue in
metabarcoding studies (Elbrecht et al., 2019). In this study, prey items
were frequently recovered by only one of the primers, and differences
existed in the recovery of the different arthropod orders. This supports
previous studies that suggest that more than one set of primers should
be used when the expected diet covers a broader taxonomical spectrum
(Alberdi et al., 2018). The inclusion in this study of a set of samples
with known composition based on morphological analysis (albeit only at
the order level) gives us some idea of potential biases in the molecular
identification. Opiliones, in particular, were morphologically
identified in several sweep net samples and are known to be present in
the diet of \emph{M. nattereri} (Galan et al., 2018; Swift, 1997; Swift
\& Racey, 2002), but were absent from the molecularly-characterised
diets of the two bats. Thus, their absence in this study is likely the
result of primer amplification bias.

Parameter choice during bioinformatic analysis can modify the diet
composition recovered (Alberdi et al., 2018). The strong match between
the inferences drawn using the additive and the conservative approach of
dealing with PCR replicates (Fig. S9), show that the results are robust
to that choice, and mirror other studies showing that parameters choice
does not change ecological conclusions (Clare, Chain, Littlefair,
Cristescu, \& Deiner, 2016). In the same manner, high similarity in diet
composition based on weighted Percent of Occurrence (wPOO) and Relative
Read abundance (RRA), indicates that the results are also robust to the
measure used.

Because prey development stage cannot be identified using the
metabarcoding approach, some of the prey species (BINs) classified as
nocturnally volant may correspond to non-flying larval stages. This
could be important in Lepidoptera, and could increase the inferred
importance of the gleaned behaviour of both species because larval
stages are known to be consumed by \emph{M. nattereri} (Hope et al.,
2014). More generally, nocturnal aerial activity is not directly
quantifiable, and therefore our classification is subject to a certain
degree of subjectivity. However, because arthropod orders most difficult
to categorise due their diversity, like Coleoptera, are consumed in
similarly low proportion by both bat species, potential classification
biases are expected to be low and standardised across species.
Nevertheless, due to potential classification biases and low sample
sizes in sympatric locations, interpretations of functional prey shift
should be considered with caution.

\subsection*{Conclusions}

{\label{conclusions}}

In line with niche theory predictions, we show that coexistence among
morphologically identical (cryptic) species can be facilitated through
fine-scale mechanisms of resource partitioning, despite high levels of
trophic similarity at the broad-scale, even in sympatric regions. Hence,
this study highlights the importance of using appropriated spatial
scales when studying impacts of biotic interactions on community
assembly (Viana \& Chase, 2019). Our findings that trophic resource
partitioning is only evident at the fine spatial scale, within areas of
range overlap, suggest that fine-scale mechanisms of coexistence could
have implications for the maintenance of broad- scale diversity
patterns. This is the first study to identify a trophic shift between
allopatric and sympatric populations of insectivorous bats, supporting
the role of trophic resource partitioning in enabling species
co-occurrence in the same foraging site. It thereby addresses some of
the key limitations identified in a recent review of interspecific
competition in bats (Salinas-Ramos et al., 2020). We highlight the
importance of using high taxonomic resolution and allopatric populations
at meaningful spatial scales for identifying patterns of niche shift,
and the utility of using a functional approach that better links
mechanistically with species trophic ecology. Understanding mechanisms
of coexistence is essential for predicting species vulnerability under
climate change because range shifts will result in new community
assemblages and competitive interactions (HilleRisLambers, Harsch,
Ettinger, Ford, \& Theobald, 2013). This is particularly relevant in our
study system as both species are restricted to the Mediterranean region,
where climate change is predicted to be particularly severe (Sala et
al., 2000), and both are predicted to experience range shifts and
changes in range overlap under climate change (Razgour et al., 2019).

\section*{Acknowledgements}

{\label{acknowledgements}}

We are grateful to the following people who assisted with fieldwork:
Raul Molleda, Carmen Soria, Daniel Fernandez, Pedro Alonso, Juan Tomas
Alcalde, Xavier Puig and Maria Mas, Jes\selectlanguage{ngerman}ús Nogueras and Sonia Sánchez. We
also thank Jorge Mederos for his help in designing the arthropod
sampling and functional classification, and Juan Luis García-Mudarra for
his assistance with laboratory work and comments on the manuscript.
Permits to carry out the fieldwork were provided by the administrations
of Junta de Andalucia, Gobierno de La Rioja and Cantabria, and Junta de
Castilla y León. RNF was funded through a School of Biological Science
studentship at the University of Southampton. OR was funded through a
Natural Environment Research Council Independent Research Fellowship
(NE/M018660/1). Sampling in Jaen was funded by the MICINN project (CGL
2012-38610).

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\section*{Data Accessibility}

{\label{data-accessibility}}

\begin{itemize}
\tightlist
\item
  Data will be deposited in Dryad and made available upon acceptance.
\item
  Sampling locations are available in Supplementary Information (Table
  S1).
\item
  List of BINs (OTUs) obtained for each bat species, their
  identification and categorisation based on level of volancy are
  presented in Supplementary Data File S1.
\end{itemize}

\section*{Author contribution}

{\label{author-contribution}}

OR conceptualised and designed the study. RNF, JJ and CI collected the
samples. RNF, JJ, CI and ELC carried out the laboratory work. OR, RNF,
ELC and CPD contributed to designing the analysis, and RNF performed it.
RNF drafted the manuscript and all authors contributed to revisions.



\section*{Tables}

{\label{tables}}

\textbf{Table 1} -- Number of bat dropping samples, sweeping samples,
and locations for each bat species by allopatry/sympatry classification
at broad (regional) and fine (local) spatial scales.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
& & Total & Broad-scale allopatric & Broad-scale sympatric & Fine-scale
allopatric & Fine-scale sympatric\tabularnewline
\midrule
\endhead
Dropping samples & \emph{M. escalerai} & 138 & 82 & 56 & 46 &
6\tabularnewline
& \emph{M. crypticus} & 90 & 18 & 72 & 45 & 22\tabularnewline
Sweeping samples & \emph{M. escalerai} & 13 & 5 & 8 & 3 &
5\tabularnewline
& \emph{M. crypticus} & 15 & 2 & 13 & 6 & 7\tabularnewline
Locations & & 49 & 26 & 23 & 14 & 8\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figures}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Fig. 1} -- Sampling locations overlaying summer precipitation
across Spain, with gradient from dry Mediterranean to wet Atlantic.
Ovals encompass the two allopatric regions (Granada and Jaen for\emph{M.
escalerai} and northern Cantabria for \emph{M. crypticus} ). The
rectangle encompasses the sympatric region (La Rioja and the
Mediterranean climatic zone at the south of Cantabria). Insert shows the
sympatric region with locally sympatric (red) versus locally allopatric
(blue and orange) locations. Black star on the sympatric location at the
E side of the main map in Catalunya denotes a swarming site and was
excluded from the analysis.

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/357477/articles/480010-trophic-resource-partitioning-drives-fine-scale-coexistence-in-cryptic-bat-species}

\textbf{Fig. 2 --} Overall diet composition of \emph{M. escalerai} and
\emph{M. crypticus} using\emph{w} eighted Percent of Occurrence (wPOO)
(a, b). Dietary composition by scale of allopatry/sympatry (broad-scale:
c, d, fine-scale: e, f).

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/357477/articles/480010-trophic-resource-partitioning-drives-fine-scale-coexistence-in-cryptic-bat-species}

\textbf{Fig. 3 -- a} ) Non-Metric Multidimensional Scaling ordination of
individual bat samples based on their BIN composition, with \emph{M.
escalerai} samples in yellow and \emph{M. crypticus} in blue\textbf{.
b)} Pianka's measure of niche overlap (O\textsubscript{JK}) between the
two bat species in allopatric versus sympatric locations at the regional
(left) and local (right) scales. Replicates are values of overlap
between pairs of locations of different bat species. Star denotes
significant differences between groups.

\textbf{Hosted file}

\verb`image4.emf`  available at \url{https://authorea.com/users/357477/articles/480010-trophic-resource-partitioning-drives-fine-scale-coexistence-in-cryptic-bat-species}

\textbf{Fig. 4} -- Functional diets of \emph{M. escalerai} and \emph{M.
crypticus} depending on the nocturnal flight behaviour of the prey
species, classified into non-volant (wingless arthropod groups), not
actively-volant (able to fly but unlikely to have been captured through
aerial hawking), nocturnally volant (likely to be captured by aerial
hawking). Overall proportions of functional categories in the bat diets
(a); proportion of not nocturnally volant prey items per dropping sample
in \emph{M. escalerai} and \emph{M. crypticus} overall (b), in
broad-scale allopatric versus sympatric regions (c) and in fine-scale
locally allopatric versus locally sympatric locations (d). Star denotes
significant differences between groups (Linear Model).

\selectlanguage{english}
\FloatBarrier
\end{document}

