Population Connectivity Predicts Vulnerability to White-Nose Syndrome in the Chilean Myotis ( Myotis chiloensis ) - A Genomics Approach

Abstract

Despite its peculiar distribution, the biology of the southernmost bat species in the world, the Chilean myotis ( Myotis chiloensis ), has garnered little attention so far. The species has a north-south distribution of c. 2800 km, mostly on the eastern side of the Andes mountain range. Use of extended torpor occurs in the southernmost portion of the range, putting the species at risk of bat white-nose syndrome (WNS), a fungal disease responsible for massive population declines in North American bats. Here, we examined how geographic distance and topology would be reflected in the population structure of M. chiloensis along the majority of its range using a double digestion RAD-tag method. We sampled 66 individuals across the species range and discovered pronounced isolation-by-distance. Furthermore, and surprisingly, we found higher degrees of heterozygosity in the southernmost populations compared to the north. A coalescence analysis revealed that our populations may still not have reached secondary contact after the Last Glacial Maximum. As for the potential spread of pathogens, such as the fungus causing WNS, connectivity among populations was noticeably low, especially between the southern hibernatory populations in the Magallanes and Tierra del Fuego, and more northerly populations. This suggests the probability of geographic spread of the disease from the north through bat-to-bat contact to susceptible populations is low. The study presents a rare case of defined population structure in a bat species and warrants further research on the underlying factors contributing to this.

Full text

INVESTIGATION
Population Connectivity Predicts Vulnerability to
White-Nose Syndrome in the Chilean Myotis (Myotis
chiloensis) - A Genomics Approach
Thomas M. Lilley,*
,
1
,
2
Tiina Sävilammi,
†
Gonzalo Ossa,
‡
,
§
Anna S. Blomberg,
†
Anti Vasemägi,**
Veronica Yung,
††
David L. J. Vendrami,
‡‡
and Joseph S. Johnson
§§
*Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland, †Department of Biology, University of Turku,
Finland, ‡ConserBat EIRL, San Fabian, Chile, §Programa para la Conservación de los Murciélagos de Chile, Santiago, Chile,
**Department of Aquatic Resources, Swedish University of Agricultural Sciences, Uppsala, Sweden, ††Sección Rabia,
Subdepartamento de Enfermedades Virales, Instituto de Salud Pública, Santiago, Chile, ‡‡Department of Animal Behavior,
University of Bielefeld, Germany, and §§Department of Biological Sciences, Ohio University, Athens, Ohio
ORCID IDs: 0000-0001-5864-4958 (T.M.L.); 0000-0001-9836-3843 (T.S.); 0000-0002-6754-4948 (A.S.B.); 0000-0001-9409-4084 (D.L.J.V.);
0000-0003-2555-8142 (J.S.J.)
ABSTRACT Despite its peculiar distribution, the biology of the southernmost bat species in the world, the Chilean
myotis (Myotis chiloensis), has garnered little attention so far. The species has a north-south distribution of
c. 2800 km, mostly on the eastern side of the Andes mountain range. Use of extended torpor occurs in the
southernmost portion of the range, putting the species at risk of bat white-nose syndrome, a fungal disease
responsible for massive population declines in North American bats. Here, we examined how geographic distance
and topology would be reflected in the population structure of M. chiloensis along the majority of its range using a
double digestion RAD-seq method. We sampled 66 individuals across the species range and discovered pro-
nounced isolation-by-distance. Furthermore, and surprisingly, we found higher degrees of heterozygosity in the
southernmost populations compared to the north. A coalescence analysis revealed that our populations may still not
have reached secondary contact after the Last Glacial Maximum. As for the potential spread of pathogens, such as
the fungus causing WNS, connectivity among populations was noticeably low, especially between the southern
hibernatory populations in the Magallanes and Tierra del Fuego, and more northerly populations. This suggests the
probability of geographic spread of the disease from the north through bat-to-bat contact to susceptible populations
is low. The study presents a rare case of defined population structure in a bat species and warrants further research on
the underlying factors contributing to this. See the graphical abstract here. https://doi.org/10.25387/g3.12173385
KEYWORDS
Population
genetics
population
connectivity
population
structure
chiroptera
disease spread
Transmission of infectious diseases has garnered attention as one
of the greatest risks to human, agriculture and wildlife health over
the last decade (Cangelosi et al. 2004; Semenza and Menne 2009).
Previous research demonstrates that the emergence of previously
unknown diseases often results from a change in the ecology of
the host, pathogen, and/or their environment (Scholthof 2007).
An example of this is white-nose syndrome (hereafter WNS), an
epizootic disease that emerged in North America in 2006 (Blehert
et al. 2009). The disease is caused by the fungus, Pseudogymnoascus
destructans, which infects insectivorous bats during the hiberna-
tion period at latitudes where prey are not widely available
during winter (Lorch et al. 2011). Populations of highly suscep-
tible species, especially from the genus Myotis, have declined
by .90% in areas affected by WNS (Frick et al. 2015). The
opportunistic pathogen can utilize alternative carbon sources
(Raudabaugh and Miller 2013) and can persist in the cold, humid
environment within hibernacula in the absence of bat hosts
(Lorch et al. 2013; Hoyt et al. 2014).
Copyright © 2020 Lilley et al.
doi: https://doi.org/10.1534/g3.119.401009
Manuscript received December 21, 2019; accepted for publication April 21, 2020;
published Early Online April 22, 2020.
This is an open-access article distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work is properly cited.
Supplemental material available at figshare: https://doi.org/10.25387/g3.12173385.
1
Present address: Finnish Museum of Natural History, P. Rautatiekatu 13, University
of Helsinki, PL 17, 00014 Helsinki, Finland.
2
Corresponding author: Luonnontieteellinen keskusmuseo, Helsingin yliopisto,
P. Rautatiekatu 13, PL17 00100 Helsinki, Finland. E-mail: thomas.lilley@helsinki.fi
Volume 10 | June 2020 | 2117

P. destructans is native to Eurasia, where it has a large geographic
range, with transmission to North America likely facilitated by
humans (Warnecke et al. 2012; Leopardi et al. 2015). In North
America, bats suffering from WNS were first detected in the state
of New York during the winter of 2006–2007 (Blehert et al. 2009).
The fungus has since spread across North America, with records
of prevalence in 33 U.S. states and 7 Canadian provinces. So far,
P. destructans has been detected on 17 species of bats, with more
species likely to follow. While human-assisted transmission of
P. destructans likely has contributed to this spread, the ecology and
behavior of cave-hibernating bats in North America also makes them
efficient vectors over large geographic areas (Wilder et al. 2015).
Because WNS affects bats during extended bouts of torpor, at low
temperatures where the fungus is able to grow and infect the host,
there has been speculation over how far into the southern North
America the disease will spread (Verant et al. 2012; Meierhofer et al.
2019). Although bats inhabiting lower latitudes may suffer less from
WNS, P. destructans conidia may be able to survive on the body
of bats for extended periods of time, even at temperatures up to
37°(Campbell et al. 2020). This could facilitate the movement of
WNS across Mesoamerica and the tropics, to arrive to high southern
latitudes where bats may be susceptible (Holz et al. 2019; Turbill and
Welbergen 2019).
Of species known to harbor the WNS fungus, Tadarida brasi-
liensis is of particular interest. As a long-range migratory species, with
movements spanning thousands of kilometres (Cockrum 1969; Glass
1982), T. brasiliensis may be an important vector for spreading
P. destructans into the southern hemisphere (Ommundsen et al.
2017; McCracken et al. 2018). Ecological niche models predict suit-
able habitat for the proliferation of P. destructans in South America,
highlighting the need to understand vectors such as T.brasiliensis
as well as human transmission (Escobar et al. 2014). However, once
P. destructans arrives in South America, its spread will not necessarily
resemble that seen in North America, as it is likely to be influenced by
differing geology and species ecology.
The Chilean myotis (Myotis chiloensis) is the most Southerly
distributed species of bat in the world, together with the southern
big-eared brown bat (Histiotus magellanicus, Koopman 1967; Gardner
2007). Myotis chiloensis has a vast north-south distribution that
includes forested areas on both sides of the Andes from the northern
shore of Navarino Island to the southern border of the Atacama
desert in Chile (Ossa and Rodriguez-San Pedro 2015). Most of
the distribution range of M. chiloensis overlaps with the distribution
of T. brasiliensis, from the north, where M. chiloensis is not believed to
hibernate, to 45°S of latitude, where M. chiloensis possibly hibernate
and may therefore be susceptible to WNS (Bozinovic et al. 1985).
However, there is no information available on the population structure
of M. chiloensis, precluding an understanding of how P. destructans
could be transported from the northern edge of its range to more
southern, and vulnerable, populations. The connectedness of individ-
uals across the range of the species will determine the speed and
intensity of potential spread. Population structuring in bats is often
relatively low because of their efficient mode of dispersal, flight (Laine
et al. 2013). An ability to disperse more efficiently results in decreased
population differentiation (Bohonak 1999) to the extent that some
bat species are panmictic across their range (Burland and Wilmer
2001; Laine et al. 2013). Such high dispersal would likely result in
rapid spread of P. destructans. However, bats in the genus Myotis
show instances of pronounced population structure which may
hinder the spread of the fungus. For instance, the Gibraltar Strait,
which separates the Iberian Peninsula from the Maghreb in Morocco
by a minimum gap of 14 km of the open sea, represents a barrier for
gene flow for M. myotis (Castella et al. 2000). Chile is littered with
such potential barriers to gene flow, such as the Atacama Desert,
glaciers, ice fields, the Andes Mountains, and the Magellan Strait,
which in turn can hinder the potential spread of P. destructans.
Furthermore, populations may still be affected by the Last Glacial
Maximum, which covered a large part of Patagonia under ice until c.
10000 years bp (Sérsic et al. 2011; Mansilla et al. 2018).
With tourism in southern Chile expected to increase (e.g.,.http://
www.conaf.cl/parques-nacionales/visitanos/estadisticas-de-visitacion/),
and migratory species such as T. brasiliensis capable of carrying
spores across large distances, there is a serious need to better
understand the population structure of Patagonian hibernatory
bat species before WNS spreads to the region. The lack of knowledge
on the extent of migration and mixing, and life history traits in
general, means that research in this area is now urgent and essential
(Ossa and Rodriguez-San Pedro 2015; Ossa 2016; Ossa et al. 2019).
Studying population ecology through molecular genetic methods
allows for the identification of more accurate population bound-
aries, which is important when assessing conservation in response
to threats of disease and dramatic declines in population size
(Moritz 1994). This study will therefore aim to describe popu-
lation structure and isolation-by-distance in M. chiloensis across
the range of the species. By studying M. chiloensis along 2400 km
of latitudinal gradient using genome-wide SNP markers, we aimed to
test if geography and the Last Glacial Maximum influence genetic
isolation patterns.
MATERIALS AND METHODS
Sample collection and DNA extraction
To describe population genetic structure in M. chiloensis, we obtained
wing tissue samples of 66 bats from two sources. A portion were
obtained from live bats captured in the field during November and
December 2017 (i.e., austral spring) from two localities: Chicauma,
Metropolitana region (33 °S70°W) and Karukinka Reserve, Tierra
del Fuego (64 °S78°W) respectively (Capture permit: 4924_2017,
Figure 1 A, Table S1). We used disposable biopsy punches (5 mm,
MLT3335, Miltex Instrument Co, Plainsboro, New Jersey) to collect
tissue samples from the plagiopatagium of captured, live bats. The
sampled bats were released at the capture site. Additional samples
were obtained from dead bats submitted to the Public Health Institute
of Chile for rabies testing. Submitted bats included latitude and
longitude locations of origin (Figure 1 A, Table S1). Tissue samples
from the bats submitted for rabies testing were obtained from the
plagiopatium using sterile scalpels. To determine if P. destructans had
already spread to Chile, we swabbed the nose and wings of all bats in
the field and at the Public Health Institute of Chile with a sterile
polyester swab (Puritan 25-806 1PD, Guildford ME, USA) which was
stored at -20°until analysis.
We divided samples into four populations according to their
geographic origin, with sub-regions within each group to assist in
further analyses. Sampling locations are presented in Figure 1 A
and details of populations and samples are provided in Table 1 and
Table S1. Tissue samples were stored in 1.5 ml tubes with 95% EtOH
and stored at -20°until further analysis. Fungal spore samples were
stored in 1.5 ml tubes and stored at -20°until further analysis. We
extracted DNA from tissue samples using QIAmp DNA Mini Kits
(Qiagen, Hilden Germany) and stored DNA at -80°. DNA from
fungal swabs was extracted using QIamp DNA Micro Kits (Qiagen,
Hilden Germany).
2118 | T. M. Lilley et al.

The amount of DNA in the final solution of each sample was
tested and quantified using the Thermo Scientific Nanodrop spec-
trophotometer, giving a result for the amount of DNA in ng/mL.
Samples were frozen between DNA extraction and analysis.
Identification and quantification of P. destructans
Quantification of P.destructans load by qPCR was completed as
described previously in Johnson et al. (2015) with the exception of
using 1 ml sample in the reaction, Roche Fast Start Essential DNA
Probe Master, and a Roche Lightcycler 480 instead of a BioRad
iCycler.
RAD sequencing
We sequenced 66 individuals in total. Duplicate samples from
30 individuals were additionally sequenced to estimate repeat-
ability and error rate of the called genotypes. DNA was prepared
for genotyping-by-sequencing using a double digestion RAD-seq
method as described in Elshire et al. (2011). PstI-BamHI-digested
libraries were prepared by the Center of Evolutionary Applications
(University of Turku; see Lemopoulos et al. 2017 and references
therein for further details) and sequenced in an Illumina HiSeq2500
run (100 bp single-end reads and pooling 96 barcoded samples on a
lane) at Finnish Functional Genomics Centre (Turku Bioscience).
Yield comparison
As the amount of DNA available may often be very limited in studies
where preserved samples from the rabies laboratory are utilized, we
wanted to estimate the effect of initial DNA concentration to the
resulting read coverage. We compared the total read coverages of the
replicate samples with Pearson’s correlation.
The resulting fastq reads, separated by barcode for each sample,
were quality-controlled with FastQC v. 0.11.8 and low-quality bases
were trimmed with ConDeTri v. 2.3 with parameter minlen = 30 (min.
length of a trimmed read) followed by adapter-trim with Cutadapt
v. 1.10 for Illumina universal adapters from the end of the reads
(identified with FastQC quality control in some of the samples).
Then, reads were mapped against M. lucifugus genome BioProject ID:
PRJNA16951, using BWA mem 0.7.17 with parameters -B 3 -O 5 -k
15. After mapping the reads against the reference, we used SAMtools
v. 1.4 and the associated bcftools for calling genotypes for SNPs
and for filtering SNPs based on minimum of 40% of the samples
genotyped, at least eight alternative alleles detected and SNP
quality $20 (bcftools filter). We further filtered the SNPs based
on exactly two alleles detected and excluded SNPs with particularly
low (#5) or unusually high ($125) mean coverage based on visual
inspection of the mean coverage distribution.
Population genetic analysis
For the final SNP dataset, which only included unduplicated samples,
we performed principal component analysis implemented in prcomp
function of R stats package v. 3.5.2, and calculated Euclidean genetic
coordinates for each individual from PC1 and PC2. The four pre-
determined ancestral populations based on the sampling regions
of each individual were confirmed by hierarchical clustering of
the Euclidean distance, calculated from PC1 and PC2. Individuals
clustering to a neighboring population were assumed to have dis-
persed from their natal populations and were reassigned for the later
analysis. We further investigated the relative contributions of the
ancestral populations inhabiting regions 1-4 in the present-day
nucleotide variation using ADMIXTURE (Alexander et al. 2009)
analysis for the A-samples, run with four expected populations
(parameter K = 4) based on prior knowledge of the population
structure, and with quasi-Newton convergence acceleration method.
We were particularly interested in identifying possible hybrid
individuals.
After assigning each individual to the final regions, we calculated
Nei’s pairwise F
ST
using hierfstat v. 0.04.22 function pairwise.fst
for A-samples for SNP’s with no missing values. To assess the
significance of genetic differentiation, we compared the actual
obtained F
ST
estimates to the null distributions of F
ST
values under
panmixia, obtained from a hundred random permutations of alleles.
Latitude and longitude coordinates of the sampling locations were
used to calculate pairwise geographic distances between individuals
in kilometres using Haversine method assuming a spherical earth,
implemented in function distm in the R package geodist v. 1.5.10.
We estimated Isolation-by-distance with two methods: a Mantel
test with complete permutations and a linear model geographic
distance genetic distance. We used pairwise F
ST
as a measure for
the genetic distances and mean of between-individual distances as
geographic coordinates for populations inhabiting each of the four
regions. To study the population structure in more detail, we repeated
the isolation-by-distance analysis for the genetic vs. geographic
distances from the most extreme individual (sample ID 700) using
a Mantel test with all possible permutations, and a linear model to
identify individuals with unusually high or low genetic differentia-
tion. We then studied the systematic differentiation of the individ-
uals sampled in different regions by assessing the differences of the
residual distributions of each of the four populations from zero using
t-tests and Bonferroni-correction of the P-values.
We also calculated mean observed heterozygosity within the
variable loci in Hardy-Weinberg equilibrium (FDR$0.05) in each
of the four study regions and compared the observed values to the
mean expected heterozygosities using inbreeding coefficient F,
calculated as the difference between expected and observed hetero-
zygosity divided by expected heterozygosity (Serre 2006). The 95%
confidence intervals for the heterozygosity estimates were found by
randomly sampling the variable loci for 1000 times and extracting
the distributions of bootstrap means. The deviations of Fstatistics
from zero were detected using single-sample Wilcoxon tests. The
significances of the regional differences in the observed and expected
heterozygosity distributions, and in the Fstatistics, were tested both by
inspecting the overlaps in the bootstrapped confidence intervals and
using analysis of variance and Tukey’spost hoc tests. The significances of
within-region differences between the expected and observed heterozy-
gosities were tested both by comparing the bootstrapped confidence
intervals and by pairwise t-tests and Bonferroni-corrected P-values.
Finally, the fraction of SNPs unique to any one region, and the number
of SNPs shared between all regions, were calculated from the obser-
vations of variable and non-variable loci within regions.
Demographic modeling
To formally test whether any of the studied populations experienced
secondary contact following glaciation, we implemented a demo-
graphic analysis using the software fastsimcoal2 (Excoffier et al. 2013)
in combination with the folded site frequency spectra (SFS) calculated
from our data using easySFS.py utility (available from https://github.com/
isaacovercast/easySFS). Specifically, we evaluated support for two
alternative models (Supplemental Material, Figure S6). The first
model, representing our null hypothesis of no secondary contact
among populations, specified four distinct lineages (region 1, region 2,
region 3 and region 4) corresponding to the populations inhabiting
Volume 10 June 2020 | Population Structure in M. chiloensis |2119

the four geographic regions sampled in the present study. These diverged
from each other at the time points T1, T2 and T3 as presented in Figure
S6A, and exchanged no migrants. Region 1 was used as the lineage from
which the other three populations emerged, as this was determined to be
the population which is closest to the ancestral M. chiloensis population.
The second model, representing our alternative hypothesis of secondary
contact among populations, was identical to the first model with the
exception that symmetric migration was present between population pairs
region 1-2 and region 3-4, and asymmetric migration was implemented
from region 2 to region 3 (Figure S6B). In addition to identifying the
model that was best supported by our data, we also estimated divergence
times (T1, T2 and T3) and effective population sizes (region 1, region 2,
region 3 and region 4) for the four modeled lineages, as well as migration
rates (Mig12, Mig21, Mig34, Mig43 and Mig32).
We performed 50 independent fastsimcoal2 runs for each model,
with 100,000 simulations and 40 cycles of the likelihood maximization
algorithm. We then calculated Akaike’sInformationCriteria(AIC)from
the fastsimcoal2 runs which yielded the highest maximum likelihood for
each model and used these values for model comparison. Finally, we
extracted parameter estimates from the best run of the most supported
model and calculated 95% confidence intervals based on 100 parametric
bootstrap replicates, as described in Excoffier et al. (2013).
Data availability
The RAD sequencing reads were deposited at NCBI SRA under
BioProject ID PRJNA596389. R scripts are available at https://
github.com/tiinasa/mchilorad. Supplemental material and graphical
abstract available at figshare: https://doi.org/10.25387/g3.12173385.
RESULTS
Filtering
Ninety-one of the 96 samples (66 individuals and 30 duplicates) had
reads matched with a barcode. Of the obtained genotypes, 54846 were
biallelic and used in the later analysis, while we excluded 88882 non-
variable (homozygous to alternative) variants and 1708 variants with
more than 2 alleles. After filtering, the mean SNP coverage ranged from
0.5 to 257.0 (Figure S1). We excluded tags with ,5or.125 mean
coverage, leaving 47079 tags. The average rate of missing SNPs among
the final unique samples was 6.2%, ranging from 0 to 24%.
Replicate samples
The correlation between input DNA concentration and the resulting
mean per-sample read coverage was only moderate (cor = 0.3394,
t
61
= 2.8178, P= 0.0065, Figure S2 A, Figure S3 A-B). In contrast, we
found strong and negative association between mean read coverage
after sequence assembly and the number of missing genotype calls
(cor = -0.9763, t
61
= -35.223, P,2.2e-16, Figure S2 B). Furthermore,
read coverages were very similar between the technical replicate
samples (cor = 0.8736, t
26
= 9.1542, P= 1.292e-09, Figure S2 C),
allowing us to omit “B”samples (replicated). The removal of the
biological replicates was conducted to minimize the possible SNP
calling bias induced by some samples having approximately twice the
amount of sequence data compared to the others, if replicate samples
had been combined. Identical genotype calls ranged from 85.6 to
96.9% with an average of 94.2% identical genotype calls (Figure S3 C),
depending heavily on read coverage (cor = 0.9275, t
26
= 12.641, P=
1.312e-12 and cor = 0.8484, t
26
= 8.1716, P= 1.186e-08 in “A”and “B”
samples, respectively; Figure S2 E-F). Although the correlation be-
tween the initial DNA concentration and identical genotype calls
between biological replicates was significant (cor = 0.5990, t
26
= 3.814,
P,0.001; Figure S2 D), this seemed to mainly be due to two outlier
observations with both very low concentration and repeatability.
Identification and quantification of P. destructans
Besides our control samples, no samples showed signs of amplifica-
tion of the multicopy intergenic spacer region of the rRNA gene
complex of P. destructans by 38 cycles, which is generally considered
Figure 1 Sampling locations and group-
ings for genetic sampling of Myotis
chiloensis in Chile (A). The two most
important principal components calcu-
lated from allele frequencies explain
15.8% of the total nucleotide variation
(B). Shades of red, green blue and purple
refer to different sub-regions (communa)
within the regions (Please see Table S1).
2120 | T. M. Lilley et al.

as a cut-off for the presence of the pathogen DNA in the samples
when using a qPCR-approach (Muller et al. 2013; Johnson 2014).
Therefore, we can conclude that the M. chiloensis individuals sampled
in this study did not carry P. destructans.
Population genetic analysis
Principal component analysis on 66 individuals and 5538 SNPs
without any missing values from non-duplicated samples (Figure
1 B) revealed a clear structuring of individuals according to the
sampling location indicative of strong population structure. Based on
hierarchical clustering of the two most important principal compo-
nents (Figure S4), we confirmed the four pre-determined populations
based on the natural hierarchical structuring of the data. Based on the
clustering, the sub-population assignment of one individual, sample
number 679, changed from Biobio to Maule (which was the most
common region assignment among the three nearest neighbors for
that individual). The estimation of ancestry of each sampled individual
by examining the relative contributions of ancestral populations
inhabiting regions 1-4 in the present-day revealed particularly pure
ancestral lines in the northern and southern parts of the range, with
hybridization occurring in the central part of the range (Figure 2).
Pairwise F
ST
-value estimates between the populations sampled
from the four geographical locations ranged from 0.04 (between
regions 2 and 3) to 0.113 (between the most distant regions 1 and 4,
Table 2). All estimated F
ST
values were found significantly larger
(P,0.01) than the permuted F
ST
distributions, with the 95% confidence
intervals of the null distributions between 0 and 0.0205. Both the Mantel
test approach and linear modeling between genetic distances (Euclidean
distances calculated from PC1 and PC2) and geographical distances
(latitude/longitude coordinates converted to distances in kilometres)
strongly suggested that we reject the null hypothesis of geographic
and genetic distances being unrelated. For the between-population
comparisons with F
ST
, we calculated Mantel statistic r = 0.9497
(P= 0.05) and R-squared estimate of 0.8773 (t
1,4
= 6.0630, P= 0.004)
(Figure S5). Also, for the between-individual distances, the observed
Mantel statistic r = 0.944 (P= 0.001), and R-squared estimate 0.943
(t
1,61
= 31.9940; P,0.0001) from the linear modeling, suggesting
that genetic and geographic distances are strongly positively associ-
ated (Figure 3). More detailed exploration of the between-individual
linear model revealed that the distribution of the residuals in regions
1(t
20
= -3.0679, Bonferroni P= 0.024) and 4 (t
11
¼-10.523, Bonferroni
P=,0.0001) were marginally smaller than 0, while the residuals
of individuals in regions 2 (t
15
= 3.5896; Bonferroni P= 0.0027)
and 3 (t
13
= 5.9325, Bonferroni P= 0.0002) were significantly larger
than 0 (Figure 3).
Of the total of 47079 SNPs, 43903 were found to be in Hardy-Weinberg
equilibrium. Analyses of variance revealed statistically significant differ-
ences between populations in observed heterozygosities [F
3,106450
=760.6,
P,2e-16], in expected heterozygosities [F
3,106450
=871.4, P,0.0001],
and in the F statistics [F
3,106450
=55.29, P,0.0001]. Both based on
confidence intervals and pairwise comparisons, observed and expected
heterozygosity distributions were consistently higher (non-overlapping
95% confidence intervals and adjusted P,0.05) in the southern
regions than in north, except for the two northernmost regions
(region 1 vs. region 2), where a statistically significant difference
was not observed (Table 1, Table S2). Similarly, observed heterozy-
gosities were consistently lower than those estimated from allele
frequencies (non-overlapping 95% confidence intervals and adjusted
P,0.05), which may be caused by within-region population
structure (Table 1, Table S2). Finally, pairwise comparisons of the
inbreeding coefficient distributions indicated that the excess of homo-
zygotes was greater in the north than in the south when compared to
neutral expectation based on allele frequencies. This was supported
with statistically significant differences (non-overlapping confidence
intervals and adjusted P,0.05) observed in all comparisons except in
region pairs 1-2 and 3-4 (Table 1, Table S2). This indicated that the
northern populations are inbreeding more than the southern pop-
ulations (Table 1, Table S2).
A large proportion of SNPs, 25.3%, were polymorphic in all
four regions. The fraction of SNPs unique only to one region
decreased from going north to south: while 8.6% of the SNPs were
unique to region 1, only 2.5% unique SNPs were found in region 4.
We did not find support for secondary contact using demographic
modeling with Fastsimcoal2. Instead, the null model that included no
Figure 2 Biogeographical ancestry (admixture) analysis based on nucleotide polymorphisms. Each vertical bar represents an individual, ordered by
latitude. Red, green, blue and purple colors indicate the relative genetic contributions of ancestral populations inhabiting regions 1-4, respectively.
Volume 10 June 2020 | Population Structure in M. chiloensis |2121

migration among populations (Figure S6A) received the highest AIC
support (Table S3). Parameter priors and estimates, together with
their 95% confidence intervals, from the best model are reported in
Table S4.
DISCUSSION
Our results present the first assessment of population structure in the
widely distributed bat species, M. chiloensis, using individual-based
approach with genome-wide markers. We found that geographic
distance within the range of the species are reflected in its population
structure. Although we found a clear and robust population structure
among sampling sites, population structure is correlated with geo-
graphical distance, even though populations are separated by ice
fields, mountain ranges and stretches of open water. This has
implications for the protection of populations that may be susceptible
to WNS. Our results also show that the highest genetic variability in
the species is at the southern extent of its range.
Strong population structure is rarely seen in bats, even across large
geographical scales in genera such as Myotis, with shorter dispersal
distances (Castella et al. 2000; Atterby et al. 2010; Laine et al. 2013). In
fact, geographical distance often correlates significantly with genetic
distance in bats. This is partially due to autumn migration and
swarming behavior in Myotis species, which brings together bats
from broad geographic areas to breed and promote recombination
(Burns et al. 2014; Burns and Broders 2015). Furthermore, powered
flight allows effective dispersal, which is often male biased (Arnold
2007; Angell et al. 2013). This is reflected in low fixation indices in
widespread bat species, such as M. daubentonii, where individuals
separated by thousands of kilometres in Europe show low fixation
indices (Laine et al. 2013). While fixation indices cannot be compared
directly across species, especially when different methodological ap-
proaches are used, they do give an indication of the connectivity of
populations.
Although geographic and genetic distances were found to have
a strong positive correlation in our study, we can concur that the
southernmost population shows a higher degree of isolation com-
pared to the geographic distance to its closest comparative population
to the north. An F
ST
of 0.075 between regions 3 and 4 using whole-
genome data are already higher than F
ST
values recorded for M.
daubentonii across Europe using microsatellites (Laine et al. 2013),
and in our data, the geographic distance is only c. 1000 km. Our data,
with tens of thousands of SNP’s also allowed a more precise estimate
of fixation compared to a handful of microsatellites. However, due
to a limited number of individuals, it did not allow us to examine
dispersal as a function of sex, which in bats is often a male driven
function (Arnold 2007; Laine et al. 2013; Angell et al. 2013). Our
sampling may also have missed some connecting populations in
between regions 3 and 4, which couldforinstancebelocatedonthe
eastern slopes of the Andes. However, our test for relative contri-
butions of ancestral populations revealsbatsinregion4,inthe
Magallanes and Cameron (Tierra del Fuego), have no mixing of
ancestral populations with the other regions.
In the northern hemisphere, approximately 18000 years ago, at the
end of the Late Pleistocene, the ice sheets began to recede as the global
Figure 3 The correlation between geo-
graphic and genetic distances between
individuals. Geographic distances are mea-
sured in kilometres, and genetic distances
as Euclidean principal component distances
from the reference individual (sample 700).
Dashed lines represent 95% confidence
interval for the linear model. The violin plot
highlights the differences between model
residuals for each study region. Residual
distributions that differ significantly from
zero after Bonferroni correction for mul-
tiple testing are marked with

(P,0.05)
and

(P,0.0001).
2122 | T. M. Lilley et al.

climate became warmer. The biota migrated northwards following
their optimal environments (Huntley and Webb 1989). This expan-
sion of refugial populations has been associated with genetic variation
decreasing south-to-north in some species: a trend attributed to a
series of bottlenecks when the biota spread from the leading edge of
the refugial population, leading to a loss of alleles and decreasing
genetic diversity (Hewitt 1996, 1999). Contrary to what one could
expect based on these latitudinal shifts in diversity in the northern
latitude, genetic diversity in M. chiloensis appears to increase with
increasing latitude, from north to south. We presumed this counter-
intuitive pattern of heterozygosity within the species may be related to
the glaciation history of South America, where populations isolated by
glacial events could have been able to hybridize. For instance, in some
terrestrial European and Scandinavian vertebrates, the intraspecific
genealogical lineages, which formed in separate refugia, were
found to have come to secondary contact in the Fennoscandian
area (Tegelström 1987; Jaarola and Tegelström 1995; Nesbø et al.
1999; Knopp and Merilä 2009).
Myotis chiloensis is described as a vicariant species with respect
to other closely related Myotis species (M. albescens,M. nigricans,
M. levis) from South America, Ruedi et al. (2013) estimated this
isolation event at 5.5 My in late Miocene. This same time period saw
the beginning of a number of glaciations events in Patagonia with
variable intensity and duration (Rabassa et al. 2011). Glacial episodes
isolated the Patagonian forest from around middle Miocene well into
the late Quaternary, the Last Glacial Maximum (Rabassa et al. 2011,
Figure S7). During the glaciations, the forests on the Pacific coast were
most like completely suppressed, possibly with isolated small refugia.
On the Atlantic side, the forest was fragmented from 36°S southwards
(Sérsic et al. 2011). Finally, in Tierra del Fuego the forest was probably
displaced toward the current submarine shelf (Ponce et al. 2011). As
the ice retreated refugial populations may have come into secondary
contact in the southern part of current range, which could explain the
high heterozygosity as well as the small fraction of unique SNP’sof
these populations. However, our coalescence analysis rejected the
secondary contact model, favoring the null model suggesting our
study populations are still largely separated after the Last Glacial
Maximum. Indeed, the F
ST
-values are high, suggesting isolation of
the populations. By contrast, our analysis for the relative contri-
butions of ancestral populations suggests mixing of populations.
One potential explanation for this apparent discrepancy is that we
derived site frequency spectra from a rather small number of individ-
uals, which may carry a signal of migration that is not strong enoughto
allow a model that includes secondary contact to be favored over a
simpler model without migration.
The spread of P. destructans via one host to another was very rapid
in North America (Blehert et al. 2009; Frick et al. 2010). This was
facilitated in part by the ecology of North American cave-hibernating
bats and the availability of suitable environment for the fungus to
propagate: limestone caves found throughout the Appalachian region
in eastern North America (Lorch et al. 2013). Furthermore, as a
consequence of down-regulation of metabolism during extended
torpor bouts, attempted immune responses fall short, and may even
contribute to mortality in hosts infected with P. destructans (Field
et al. 2015; Lilley et al. 2017, 2019). In addition to these, the massive
population declines associated with WNS in affected species (Turner
et al. 2011) were magnified by the panmictic population struc-
ture across eastern North America in the most affected species,
M. lucifugus (Miller-Butterworth et al. 2014; Vonhof et al. 2015).
Our results for M. chiloensis from austral South America suggest
the southernmost population in region 4, may be less likely to be
infected via their northerly conspecifics, because of reduced contact
between the populations. Our results indicated no mixing of
ancestry in the southernmost individuals in our study, suggesting
the M. chiloensis in the Magallanes and Cameron (Tierra del Fuego)
are isolated from their mainland counterparts. To our knowledge, this
is also the only population to use extended torpor, a prerequisite for
the propagation of P. destructans and the onset of WNS (Ossa et al.
2020). Tierra del Fuego, the southern tip of Patagonia and the
continent of South America, experiences extended low winter tem-
peratures comparable to areas in North America where WNS is
manifested. Our genetic analysis for the presence of P. destructans on
the sampled bats suggests the fungal pathogen does not exist within
the distribution range of our focal species at present. Even if the
fungus were to enter the region, variability in host behavior and
environmental characteristics may be the primary factors pro-
tecting hosts from the pathology related to WNS (Zukal et al. 2014,
2016). Most strikingly large cave hibernacula with suitable, stable
environmental conditions favoring the environmental persis-
tence of the pathogen in the absence of the hosts, are very scarce
and separated by hundreds of kilometers in most of the southern
range of M. chiloensis.
The observed distribution of M. chiloensis is vast, covering a range
of forested habitats from arid Sclerophyllous to sub-Antartic (Ossa
and Rodriguez-San Pedro 2015). In this respect, taking into consid-
eration our results on clear population segregation begs to propose
thequestiononthespeciesstatusofM. chiloensis as a whole.
Indeed, M. chiloensis also appears to vary phenotypically along its
n■Table 1 Individuals and samples per region
Numeric
region
Number of
individuals (A)
Number of
duplicates (B)
male /
female
Mean observed
heterozygosity (95%
confidence interval)
Mean expected
heterozygosity (95%
confidence interval)
Inbreeding
coefficient F
Unique
SNPs
1 20 6 16/4 0.2550 (0.2530-0.2570) 0.2793 (0.2777-0.2809) 0.0581 (0.0537-0.0624) 8.6%
2 19 6 9/10 0.2536 (0.2514-0.2557) 0.2765 (0.2749-0.2780) 0.0529 (0.0484-0.0571) 4.4%
3 14 9 7/7 0.2872 (0.2848-0.2896) 0.2993 (0.29762-0.3009) 0.0252 (0.0204-0.0301) 3.6%
4 13 9 5/8 0.3248 (0.3221-0.3275) 0.3335 (0.3316-0.3352) 0.0210 (0.0153-0.0268) 2.5%
tot. 66 30
n■Table 2 Nei’s pairwise F
ST
and geographic distances (in italics)
between populations inhabiting the four geographic regions. The
significance level (P<0.01) of the F
ST
statistics is denoted with

F
ST
and mean geographic distance (km)
Region 4 Region 3 Region 2 Region 1
Region 4 970.9 1550.3 2266.9
Region 3 0.075

588.7 1326.6
Region 2 0.089

0.041

757.0
Region 1 0.113

0.072

0.040

Volume 10 June 2020 | Population Structure in M. chiloensis |2123

distribution range (Mann 1978; Ossa 2016). It has been proposed that
the species was composed of three sub-species: M. atacamensis
(Larsen et al. 2012); M. ch. arescens from 29°Sto39°S; and M. ch.
chiloensis from 39°Sto53°S (Mann 1978). That classification was due
according to their coat color changes in relation to exposure to solar
radiation and ambient temperature, which is correlated to latitude
(Budyko 1969) and levels of precipitation in their habitat. They vary
from a lighter pelage to a dark brown color on a gradient from the
northern part of their range to the south (Galaz et al. 2006). This
promotes the theory, isolation by adaptation, as a driver of population
genetic structure. The genetic adaptations of an individual to their
local environment separates populations and leads to a reduced gene
flow (Orsini et al. 2013). However, the isolation by adaptation theory
negates the fact that there is a possibility of a barrier so that gene flow
is inhibited by climate or adaptations to the local environment. It is
possible to therefore state that isolation-by-dispersal limitation, and
moreover isolation-by- distance, are the more probable causes of the
observed results in the present study. Indeed, the reluctance of the
species to cross barriers, for instance the Andes, can clearly be seen by
examining population 3, where six individuals sampled from Aysén
(437, 442, 443, 167, 256, 260) are visibly isolated on the PCA plot
form individuals on the other side of the Andes, in Coyhaique, under
150 km away. This also depicts the fine scale resolution our indi-
vidual-based SNP-based approach allows. However, further studies
should focus deeper on the taxon status of different population of the
species currently recognized as M. chiloensis.
The results highlight the importance to assess the population
structure which may limit the spread of white-nose syndrome
disease. Whether P. destructans or another epizootic in the future
could spread depends largely on the population structure and con-
nectedness of hosts (Lilley et al. 2018).
ACKNOWLEDGMENTS
We thank the Rufford Foundation ((Rufford Small Grant (10502-1
and 23042-2), H2020 Marie Skłodowska-Curie Actions (706196), and
Ohio University Research Council for funding the work. We thank
Servicio Agricola y ganadero Diproren for the capture permits in
Tierra del Fuego (Res Ex: 1253/2016 and 4924/2017), Juan Carlos
Aravena from the Instituto de la Patagonia for his help, the personnel
from WCS Chile for allowing us to conduct research at Karukinka
Natural Reserve as well as for their help with field work. We thank
Michelle Lineros and Tania Gatica from the National Health Institute
for their help to obtain the tissue samples. We thank Austin Waag for
assistance with field work and Satu Mäkelä and Meri Lindqvist for
assistance with lab work.
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