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\begin{document}

\title{Spatial and environmental influences on selection in a clock gene coding
trinucleotide repeat in Canada lynx (Lynx canadensis)}



\author[1]{Melanie Prentice}%
\author[2]{Jeff Bowman}%
\author[1]{Dennis Murray}%
\author[3]{Kamal Khidas}%
\author[1,1]{Paul Wilson}%
\affil[1]{Trent University}%
\affil[2]{Ontario Ministry of Natural Resources and Forestry}%
\affil[3]{Canadian Museum of Nature}%


\vspace{-1em}



  
  \date{May 04, 2024}


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\selectlanguage{english}
\begin{abstract}
Clock genes exhibit substantial control over gene expression and
ultimately life-histories using external cues such as photoperiod, and
are thus likely to be critical for adaptation to shifting seasonal
conditions and novel environments as species redistribute their ranges
under climate change. Coding trinucleotide repeats (cTNRs) are found
within several clock genes, and may be interesting targets of selection
due to their containment within exonic regions and elevated mutation
rates. Here, we conduct inter-specific characterization of the NR1D1
cTNR between Canada lynx and bobcat, and intra-specific spatial and
environmental association analyses of neutral microsatellites and our
functional cTNR marker, to investigate the role of selection on this
locus in Canada lynx. We report signatures of divergent selection
between lynx and bobcat, with the potential for hybrid-mediated gene
flow in the area of range overlap. We also provide evidence that this
locus is under selection across Canada lynx in eastern Canada, with both
spatial and environmental variables significantly contributing to the
explained variation, after controlling for neutral population structure.
These results suggest that cTNRs may play an important role in the
generation of functional diversity within some mammal species, and allow
for contemporary rates of adaptation in wild populations in response to
environmental change. We encourage continued investment into the study
of cTNR markers to better understand their broader relevance to the
evolution and adaptation of mammals.%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

In the near future, global climate change will surpass habitat
destruction as the leading threat to biodiversity (Leadley et al.,
2010). Given such changes, the ability of species to persist will rely
on either redistribution, evolution towards new behavioural and
physiological optima, or both. While a number of range redistributions
have already been observed (e.g., Laliberte \& Ripple, 2004; Parmesan,
2006), the few species that are capable of such large-scale dispersal to
track changing environments are unlikely to redistribute to landscapes
containing their complete suite of optimal habitat conditions. For
example, where species redistribute to maintain equilibrium with one
habitat feature (e.g., prey abundance or distribution, temperature),
they might simultaneously become discordant with other critical features
(e.g., novel predators, photoperiod). Thus, it is likely that for most
species, some extent of microevolution will be necessary to allow for
their persistence in a changing climate (Visser, 2008).

With the advent of novel molecular technologies and developments in
bioinformatics, acquisition and analyses of large-scale data has made
the investigation of genomic elements of adaptation possible for both
model and non-model species (Harrison, Pavlova, Telonis-Scott, \&
Sunnucks, 2014). In wild populations, adaptive genomic change will be
dependent on the level of standing genetic variation or the rate
of\emph{de novo} mutations in the existing population (Carlson,
Cunningham, \& Westley, 2014). While standing genetic variation is
considered most important for contemporary adaptation as it is readily
available for selection (Barrett \& Schluter, 2008), genetic variation
in ecologically important traits may be absent in widely dispersed
species, making them targets for extirpation. Specifically, panmictic
species experiencing high rates of dispersal and gene flow may be
limited in the extent to which adaptive genetic mutations can accumulate
and contribute to adaptation. However, while gene flow is predicted to
preclude the local adaptation of wide-ranging species (Lenormand, 2002),
recent empirical evidence provides support for adaptive genetic
divergence in the face of ongoing gene flow (e.g., Andr\selectlanguage{ngerman}é et al., 2011;
Feder, Egan, \& Nosil, 2012; Hemmer-Hansen, Nielsen, Frydenberg, \&
Loeschcke, 2007; Nielsen et al., 2009). This is often attributed to
adaptations along ecological clines, including clines in salinity
(Defaveri, Johnsson, \& Meril\selectlanguage{russian}ӓ, 2013), temperature (Aitken, Yeaman,
Holliday, Wang, \& Curtis-McLane, 2008), or altitude, (Keller, Taverna,
\& Seehausen, 2011). Characterizing this diversity across species
distributions can allow us to better understand the ``adaptive
potential'' of species, and contribute to conservation plans where
needed (Keller et al., 2011; Mer\selectlanguage{ngerman}öndun, Murray, \& Shafer, 2019).

In lieu of high standing genetic variability, some species will rely
largely on \emph{de novo} mutations for adaptation, a mechanism that may
not be effective over short time-frames. Specifically, rates of mutation
are likely to be too low (Fan \& Chu, 2007), most new mutations will be
either neutral or deleterious rather than providing an adaptive benefit
(Eyre-Walker \& Keightley, 2007), and in small or declining populations,
deleterious mutations can accumulate (Willi, Griffin, \& Van Buskirk,
2012). Further, the fate of new mutations depends on generation time and
population size, where species with longer generation times and smaller
population sizes have fewer mutational opportunities (Carlson et al.,
2014). In cases where beneficial mutations do arise, they will have to
persist until they reach high frequencies, during which time the chances
of losing beneficial alleles to genetic drift or demographic
stochasticity can be high (Whitlock, 2000).

Considering the importance of rapid adaptation, coding trinucleotide
repeats (cTNRs) may be important targets of selection due to their
containment within exonic regions of the genome and elevated mutation
rates in comparison to more conventionally studied markers (ranging from
10\textsuperscript{-6} to 10\textsuperscript{-2} for microsatellites
versus 10\textsuperscript{-9} for SNPs; Fan \& Chu, 2007). These repeats
are found within genes that code for biologically relevant functions,
and may facilitate the fine-tuning of gene function and expression
patterns (Gemayel, Vinces, Legendre, \& Verstrepen, 2010; Gemayel, Cho,
Boeynaems, \& Verstrepen, 2012; Haerty \& Golding, 2010). Most
interestingly, however, is the high mutability of cTNRs, which has been
attributed to their inherent instability and may allow for higher rates
of evolution in response to environmental stress (Gemayel et al., 2010,
2012). For example, the presence of variable cTNRs in genes coding for
body morphology has implicated these repeats as the driver of the
rapidly evolved canid skeleton in an evolutionarily short time span of
fifty years (Laidlaw et al., 2007). This provides compelling evidence
for the role of cTNRs in the rapid morphological adaptation of large
mammals.

The candidate gene approach to identifying loci subjected to natural
selection consists of the selection of previously characterized genes
that are hypothesized to play a role in local adaptation, or genes that
may be biologically influenced by environmental heterogeneity (Rellstab,
Gugerli, Eckert, Hancock, \& Holderegger, 2015). This type of approach
is especially appealing for non-model species with uncharacterized
genomes, as genomic information from closely related model species
(e.g., homologous genes) can be used to select genes of known function
and design primers for applications in non-model species of interest
(Primmer, Papakostas, Leder, Davis, \& Ragan, 2013). Further, the
candidate gene approach is beneficial for traits that are not
phenotypically apparent but suspected to be of adaptive significance for
adaptation to climate change (Keller et al., 2011). The environmental
heterogeneity associated with latitude implies that adaptation should
involve a range of traits in species spanning latitudinal gradients. For
example, adaptation to varying temperatures and photoperiods, which are
often used to time life-history events (e.g., breeding and migration),
should be of particular importance to many species (O'Malley, Camara, \&
Banks, 2007). Thus, adaptation of these traits will be critical for
species persistence in changing environments.

Although species can track shifts in temperature via redistribution,
exposure to novel photoperiods will likely result in challenges for
species relying on photoperiodic cues, as photoperiod-dependent
behaviours may not change at the same rate as the change in photoperiod
that occurs during a range shift, resulting in photoperiodic mismatches
(Milligan, Holt, \& Lloyd, 2009). As a result, it is possible that the
rate of evolution in traits controlling photoperiod-dependent behaviours
will be more important than traits involved in temperature sensitivity
(Bradshaw \& Holzapfel, 2006, 2008), and the ability of species to adapt
their behavioural response to new photoperiods, or eliminate
photoperiod-dependent behaviours in favour of opportunism, will become
critical (Bronson, 2009). In these cases, microevolution of genes
responding to photoperiod is likely necessary to ensure species
persistence in the face of climate change (Bellard, Bertelsmeier,
Leadley, Thullier, \& Courchamp, 2012). Species inhabiting seasonal
environments often respond to photoperiod cues via circadian clocks
(Goldman, 2001), making clock genes good candidates for characterizing
the potential genetic responses of species to shifting seasonal
conditions and novel environments (Kondratova, Dubrovsky, Antoch, \&
Kondratov, 2010). cTNRs have been observed in a number of clock genes,
and emerging studies have begun to demonstrate the evolutionary and
adaptive importance of clock gene cTNRs in a range of species (e.g.,
Johnsen et al., 2007; Liedvogel, Szulkin, Knowles, Wood, \& Sheldon,
2009; O'Malley, Ford, \& Hard, 2010; Prentice et al., 2017a).

Our objective was to explore the relationship between space, the
environment, and a putatively adaptive cTNR within the candidate clock
gene, Nuclear Receptor Subfamily 1, Group D, Member 1 (\emph{NR1D1} ),
in Canada lynx (\emph{Lynx canadensis} ). The \emph{NR1D1} gene is a
nuclear receptor that links circadian rhythms to the transcriptional
control of metabolic pathways, and functions as a core component of the
circadian clock. Its influence on numerous pathways, including memory
and learning, behaviour, immune function, metabolism, and mitochondrial
content and function (i.e., exercise capacity), suggest that this gene
likely plays a role in adaptation to new environments (Everett \& Lazar,
2014). Most interestingly, the \emph{NR1D1} gene has demonstrated
involvement in circadian regulation of food entrainment (Delezie et al.,
2016), and cold tolerance (Gerhart-Hines et al., 2013). The cTNR within
the \emph{NR1D1} gene has been previously shown to exhibit signatures of
positive selection in Canada lynx (Prentice et al., 2017a). Lynx are a
suitable species for investigating selection on clock genes because they
show high gene flow at neutral genetic markers across their mainland
range (Row et al., 2012), and thus, spatial patterns in NR1D1 are likely
to be due to selection rather than neutral processes such as dispersal
and gene flow.

Here, we assessed the influence of spatial and environmental patterns on
the variation and distribution of \emph{NR1D1} cTNR alleles in Canada
lynx, while controlling for the influence of background population
structure using a dataset of neutral microsatellite markers. First, we
examine inter-specific differences in the distribution and frequencies
of \emph{NR1D1} alleles between Canada lynx, and the closely related
species, bobcat (\emph{Lynx rufus} ) (Johnson \& O'Brien, 1997).
Although the evolutionary history of Canada lynx and bobcat is unclear,
the largely allopatric distributions of these species suggest that they
are likely to be differentially adapted to their respective
environments. The lynx is largely distributed across the northern
boreal, sub-boreal and western montane forests of North America (Quinn
\& Parker, 1987), occupying a narrower niche than expected if randomly
distributed (Peers, Thornton, \& Murray, 2012). The bobcat is broadly
distributed throughout southern North America in the contiguous USA, and
occupies a broader niche than expected (Peers et al., 2012), suggesting
that it is a habitat generalist (Anderson \& Lovallo, 2003). We
hypothesized that the differing habitats used by the two species will
result in a divergence in selection pressures experienced by lynx and
bobcat, and predicted that we should observe a corresponding divergence
between lynx and bobcat alleles at the \emph{NR1D1} cTNR locus
{[}consistent with the findings of Prentice et al., (2017a){]}. We also
characterized this locus in introgressed lynx and bobcat individuals
(Koen, Bowman, Lalor, \& Wilson, 2014) to explore the possibility of
selection in the lynx-bobcat contact zone. We hypothesized that
selection will favour lynx alleles in bobcats inhabiting lynx habitats
(i.e., individuals at the north end of the bobcat range). We contrasted
genetic differentiation in our candidate cTNR locus and neutral
microsatellite datasets to test for differences in pairwise population
differentiation at a putatively selected locus versus neutral loci. As
differing processes are responsible for influencing neutral versus
functional genetic variation (gene flow and genetic drift versus natural
selection, respectively), we hypothesized that estimates of
differentiation at our putatively selected cTNR locus would be outside
of the range reported for neutral loci, representing population pairs
that were under either more similar (\emph{NR1D1} F\textsubscript{ST}
\textless{} neutral F\textsubscript{ST}) or different (\emph{NR1D1}
F\textsubscript{ST}\textgreater{} neutral F\textsubscript{ST}) selection
pressures than would be expected based on gene flow alone.. Further, we
assessed the association of environmental variables with the observed
genetic structure of the \emph{NR1D1} locus, by assessing relationships
between\emph{NR1D1} alleles and environmental and spatial variables,
while controlling for background genetic structure with our neutral
microsatellite dataset. We hypothesized that environmental heterogeneity
is influencing selection at the \emph{NR1D1} locus, and predicted that
we would detect associations between environmental variables associated
with lynx habitat selection and \emph{NR1D1} alleles, after removing the
effects of background neutral genetic structure.

\textbf{METHODS}

\emph{Sample collection}

The Canada lynx and bobcat analysis was continental, incorporating the
entire range of Canada lynx, and the area of range overlap with bobcat
at the southern extent of Canada (see Koen et al., 2014). Hide samples
(2.5 mm × 2.5 mm) of legally trapped individuals were collected from the
North American Fur Auction. Species assignments were initially made by
the trappers who harvest the animals, and then confirmed by fur graders
at the auction house and our sampling team. In total, we assessed 1,899
Canada lynx samples and 750 bobcat samples, in addition to 2
introgressed lynx and 5 introgressed bobcat samples identified in Koen
et al., (2014) (Table 1, Fig. 1). The samples used in this study were a
subset of those used in Koen et al., (2014), with the addition of
western Canada lynx samples used by Prentice et al., (2017a), and Cape
Breton Island lynx samples used by Prentice et al., (2017b).

\emph{Neutral and functional genetic datasets}

We used an existing data set of 14 neutral microsatellite loci (Fca031,
Fca035, Fca077, Fca090, Fca096, Fca441, Fca391, Fca559, Lc106, Lc109,
Lc110, Lc111, Lc118) (Koen et al., 2014, Prentice et al., 2017a). We
omitted samples missing genotypes from more than 2 loci (Table 1). The
candidate gene cTNR amplified in this study is within the
\emph{NR1D1}gene. Primer design, optimization and amplification of this
marker was conducted in Prentice et al., (2017a). We used the existing
dataset of\emph{NR1D1} genotypes and genotyped bobcat individuals and
additional lynx from New Brunswick, Cape Breton Island, and Newfoundland
according to the same protocol. Sample sizes for analyses of the
\emph{NR1D1}locus are slightly smaller due to missing genotypic data for
some samples (Table 1).

\emph{Inter-specific comparison of the NR1D1 cTNR}

Preliminary analyses for signatures of selection conducted on
the\emph{NR1D1} locus in Canada lynx and bobcat were conducted by
Prentice et al., (2017a). Here, we mapped the distribution and
frequencies of\emph{NR1D1} alleles both across and within species, and
assessed introgressed lynx-bobcat individuals to determine whether these
individuals had lynx, bobcat or mixed genotypes. To facilitate
comparison at the \emph{NR1D1} locus, we also assessed allele frequency
distributions at neutral microsatellites of lynx and bobcat. All bobcat
and introgressed individuals were removed from the remainder of the
analyses.

\emph{Genetic structure of neutral microsatellites and the NR1D1 cTNR}

We first calculated pairwise estimates of Jost's D at both
the\emph{NR1D1} locus and our 14 neutral microsatellite loci with the
software package diveRsity v1.9.90 (Keenan, McGinnity, Cross, Crozier,
\& Prodöhl, 2013) implemented in R (R Core Team, 2016). We assigned
individuals to populations based on previously reported STRUCTURE
results {[}Newfoundland (Row et al., 2012), Quebec lynx located south of
the St. Lawrence River (Koen, Bowman, \& Wilson, 2015), Cape Breton
Island (Prentice et al., 2017b), and mainland Canada (the remaining
distribution of lynx across continental Canada){]}. To estimate whether
pairwise genetic differentiation at the \emph{NR1D1} locus fell within
or outside the expected range of neutrality, we plotted pairwise Jost's
D values estimated for our neutral marker dataset with standard error of
the mean intervals, and then overlaid each pairwise estimate of Jost's D
for the \emph{NR1D1} locus. We used Jost's D here as it uses the
effective number of alleles (Kimura \& Crow, 1964), which is a more
appropriate approach for outlier-based assessments compared to
F\textsubscript{ST} and its analogues, which are dependent on, and can
be biased by, within-population heterozygosity (Hedrick, 1999). Further,
Jost's D has been shown to perform better at estimating the
differentiation in allele frequencies among populations, whereas
F\textsubscript{ST} is preferential for describing the effect of
demography on genetic variation (Meirmans \& Hedrick, 2011).

\emph{Environmental associations of the NR1D1 cTNR}

To determine the effect of the environment on functional genetic
variation at the \emph{NR1D1} cTNR locus across the range of Canada
lynx, we conducted multiple constrained redundancy analyses (RDA)
testing the relationship between functional genetic variation
(\emph{NR1D1} alleles), space, and environmental variables, while
controlling for neutral genetic variation (using alleles at our 14
neutral microsatellites). Coordinates for our fur samples were taken as
centroids of regionally managed areas that differed between province,
territory and state (referred to herein as ``sampling unit'').
Resolution of sampling units varied by necessity due to different
jurisdictional lynx management practices. Lynx from Quebec, Ontario and
British Columbia were characterized by centroids of traplines or
trapping units called ``Unités de gestion des animaux à fourrure''
(UGAF) in the case of Quebec. Samples from Alberta were characterized by
centroids of fur management units. Samples from Manitoba were largely
characterized by centroids of major trapline units, except for samples
for which finer spatial resolution (minor trapline units) was available.
Similarly, lynx from Alaska were characterized by centroids of minor
drainage units when possible, or major drainages when finer scale data
was not available. Yukon lynx were characterized by centroids of
aggregated traplines provided by the Yukon government. In Atlantic
Canada (Newfoundland, Labrador, New Brunswick and Cape Breton Island),
samples were characterized by centroids of each of the four locations.

Our selection of environmental variables was based on previously
published evidence of lynx occurrence and habitat selection across their
range. Within each environmental variable we expect to characterize a
range of variation across the large geographic extent of our study that
might lead to differential selection in locations where lynx are likely
to be present. For example, lynx occupancy modelling suggests that
variables including maximum temperature of the warmest month, snow depth
and ecoregion are influential in predicting lynx presence/absence (Peers
et al., 2012; Peers, Thornton, \& Murray, 2013). Further, throughout
their eastern distribution, lynx occurrence has been correlated with
average annual snowfall, where lynx are unlikely to occur in habitats
with \textless{}270cm of snow per year (Hoving, Harrison, Krohn, Joseph,
\& O'Brien, 2005), suggesting that snowfall patterns may drive
differential selection for lynx throughout their distribution. We
selected 9 variables hypothesized to be important for describing lynx
neutral and functional genetic structure including; (1) human influence,
(2) snow cover, (3) snow depth, (4) mean temperature of the warmest
quarter, (5) mean temperature of the coldest quarter, (6) precipitation
of the wettest quarter, (7) precipitation of the driest quarter, and
proportions of (8) suitable and (9) unsuitable lynx habitat. To estimate
the proportion of suitable and unsuitable lynx habitat, we used a
categorical raster layer of land cover (ESA GlobCover Project, 2009),
which we reclassified into a binary layer of ``lynx habitat
suitability'' representing land cover types that have been shown to be
selected by lynx as suitable habitat {[}including closed
(\textgreater{}40\%) needle leaved evergreen forest (\textgreater{}5m),
open (15-40\%) needle leaved deciduous or evergreen forest
(\textgreater{}5m), and closed to open (\textgreater{}15\%) mixed
broadleaved and needle leaved forest (\textgreater{}5m); supported by
lynx selection for coniferous forest, and avoidance of landscapes
dominated by deciduous forest; Hoving et al., 2005; Koehler, 1990;
Poole, 2003; Walpole, Bowman, Murray, \& Wilson, 2012{]}, and unsuitable
habitat (all other land cover types).

The spatial resolution of our environmental variables ranged between
300m - 1km, with varying degrees of temporal resolution (detailed
descriptions of variables in Table 2). As the effects of neutral
differentiation over space (e.g., IBD) were accounted for using neutral
microsatellite markers, we used latitude and longitude as predictor
variables, to represent unaccounted for environmental variation
correlated with space such as large-scale climate gradients and
photoperiod.

We characterized each sample with environmental data by extracting point
estimates (snow cover, snow depth, and mean temperature of the warmest
and coldest quarters), averages (human influence), and proportions
(suitable and unsuitable lynx habitat) using ArcMap version 10.4.1.
Point estimates were extracted at each sample coordinate. Averages were
taken over the corresponding sampling unit containing each sample with
the exception of Alaska, for which we were unable to obtain geographic
raster layers for the major and minor drainage units and instead
extracted averages over larger game management subunits (which contain
the major and minor drainages). Proportions were extracted for each
sample by estimating the total area classified as either suitable (1) or
unsuitable (0) lynx habitat and dividing these estimates by the total
area of each sampling unit. For the extraction of both averages and
proportion estimates, we resampled our raster's to the layer with the
lowest resolution, and snapped these raster's and the sampling unit
layers together, to align the cells for consistent extraction of
estimates across layers.

We conducted a principal component analysis (PCA) in R (R Core Team,
2016) on our set of environmental variables (excluding latitude and
longitude) to condense our dataset into fewer, uncorrelated variables
describing environmental variation across the landscape. We retained
principal components that explained \textgreater{}80\% of the cumulative
proportion of variation, and assessed the eigenvectors of each retained
component to determine the variables that loaded most strongly. We
calculated the standardized scores of each retained principal component
for subsequent analyses.

We conducted constrained RDA analyses in the R package vegan (Oksanen et
al., 2017), where our dependent variables represented ``functional
genetic diversity'' (\emph{NR1D1} alleles), and our independent
variables included descriptors of the ``environment'' (retained
environmental principal components), ``space'' (latitude and longitude),
and ``neutral genetic structure'' (14 neutral microsatellite loci).
First we conducted an RDA on the full model, to estimate the
significance of, and amount of variation explained by the model
including environment, space and neutral genetic structure. We then
conducted 3 partial RDAs to estimate the significance of, and variation
explained by: (1) spatial variables, (2) environmental variables and (3)
neutral genetic structure, while controlling for the influence of the
additional variables (e.g., for neutral genetic structure we controlled
for spatial and environmental effects). These partial RDAs control for
the influence of either environment, space or neutral genetic structure
by first removing their effects from the functional genetic dataset, and
then performing an RDA on the residual matrix (according to the
procedure outlined in Oksanen 2012). Significance of each model was
assessed by permutation tests, where the \emph{NR1D1} matrix was
randomly permuted 999 times, and the strength of the relationship
between the observed or permuted matrices and the independent variables
were compared (\selectlanguage{greek}α \selectlanguage{english}= 0.05). It has been shown that even if environment and
spatial patterns perfectly explain genetic variation, the total inertia
(variance) explained can be substantially less than 1 (\selectlanguage{ngerman}Økland 1999).
Because of this, we followed the recommendations of Økland (1999), and
partitioned the variance based on the total \emph{explainable} variance
in our full model. To do so we used the full model to estimate the total
explainable variation of our combined datasets, and the conditional
models (partial RDAs) to partition the total explainable variation into
4 components: (1) variation attributed purely to environment, (2)
variation attributed purely to space, (3) variation attributed purely to
neutral genetic structure and (4) joint (collinear) variation attributed
to environment, space and neutral genetic structure.

As we had a large dataset over a large geographic area which might have
obscured environmental gradients of interest, we also reduced our
dataset into two halves (``eastern lynx'' including individuals sampled
within or east of Manitoba (N= 1,466), and ``western lynx'' including
individuals sampled within or west of Alberta/ Yukon (N= 423); Fig. 1A),
and repeated the analysis on each subset independently to reduce
unexplained environmental variation in the data when analyzed from a
continental scale. We were specifically interested in the eastern lynx
dataset, where preliminary analyses showed indications of selection in
peripheral and insular populations of lynx (Koen et al., 2015, Prentice
et al. 2017b). Further, Row et al. (2014), reported a correlation
between neutral genetic variability and a winter climate gradient in
lynx across the Pacific-North American (PNO) and North Atlantic
Oscillation (NAO) climatic systems in eastern Canada, and suggested
restricted dispersal in lynx between Manitoba and Quebec supporting the
potential for selection in this region.

\textbf{RESULTS}

\emph{Inter-specific comparison of the NR1D1 cTNR}

We have previously reported a non-overlapping allelic range between
Canada lynx and bobcat individuals (excluding introgressed individuals)
and signatures of positive selection in Canada lynx using
F\textsubscript{ST} outlier-based analysis (Prentice et al. 2017a).
Here, across a much larger-scale screening of lynx and bobcat
individuals, we identified the same 7 alleles at the NR1D1 locus with
the same non-overlapping patternAll identified bobcat samples had
genotypes comprising the smaller 3 \emph{NR1D1} alleles and all
identified Canada lynx samples had genotypes comprising the larger
4\emph{NR1D1} alleles with the exception of 10 putatively introgressed
individuals (described below). Gradients in allele frequencies were
observed in both lynx and bobcat (Fig., 2). In lynx, allele frequencies
were largely similar across the range we surveyed, with the exception of
the most southern population sampled (New Brunswick lynx and Quebec lynx
sampled south of the St. Lawrence River), which showed a substantial
increase in the frequency of the smallest allele observed in lynx (Fig.,
2B). Insular populations of lynx (Newfoundland and Cape Breton Island)
were fixed for the most common lynx allele (Fig., 2B). Of the 3 alleles
identified in bobcats, the intermediate allele was found most
prevalently across the range we surveyed whereas the smallest and
largest allele were observed most prevalently (although not exclusively)
in western and eastern bobcat, respectively (Fig., 2B). In comparison,
neutral microsatellite markers were found to have largely overlapping
allelic ranges across the two sister species; on average, 42.7\% of
alleles per locus overlapped, across all 14 presumably neutral loci. We
note however, that the range of overlap was variable across loci (from
0-83.3\%), with 2/14 loci showing no overlap in alleles between lynx and
bobcat.

We further identified 10 putative Canada lynx samples with mixed
genotypes (i.e., 1 ``bobcat'' and 1 ``lynx'' allele; Fig 1B). These
samples were all visually identified as Canada lynx, and contained
neutral microsatellite profiles and control region mtDNA sequences
consistent with Canada lynx. In addition, we evaluated samples from
introgressed animals previously identify by Koen et al., (2014). Of the
5 samples they identified as introgressed bobcats, 4 had genotype
profiles consistent with bobcats at the \emph{NR1D1} locus (i.e., they
had 2 ``bobcat'' alleles), and 1 individual failed to amplify. Of the 2
samples identified by Koen et al., (2014) as introgressed lynx, 1 had a
genotype profile consistent with lynx at the \emph{NR1D1} locus (2
``lynx'' alleles), and the other had a mixed profile (1 ``lynx'' and 1
``bobcat'' allele).

\emph{Genetic structure of neutral microsatellites and the NR1D1 cTNR}

Our Jost's D plot comparing neutral microsatellites and the
\emph{NR1D1}locus showed that the \emph{NR1D1} locus consistently fell
outside of the expected range of neutrality estimated with our 14
neutral microsatellites, suggesting that this locus may be under
selection in lynx across their range (Fig., 3). Further, estimates of
Jost's D at the\emph{NR1D1} locus for all pairwise comparisons between
Cape Breton Island, Newfoundland, and the mainland population of lynx
were lower than neutral expectations, suggesting that they are much more
similar than expected based on gene flow. Alternatively, all comparisons
including lynx sampled south of the St. Lawrence River were higher than
neutral expectations, suggesting that they are much more different than
expected based on gene flow alone (Fig., 3).

\emph{Environmental associations of the NR1D1 cTNR}

We retained the first four principal components for our ecological
association RDA analyses, which explained \textasciitilde{}86\% of the
variation in our environmental variable dataset (Table 3). According to
the eigenvectors, the first principal component was most highly
influenced by precipitation variables (precipitation of the wettest
quarter, precipitation of the driest quarter and average snow cover;
Table 4). Our second component was largely influenced by habitat
(proportion of suitable and unsuitable lynx habitat; Table 4). The third
component was influenced by both temperature (mean temperature of the
warmest quarter) and average snow depth (Table 4). Our fourth component
was explained by a combination of precipitation (precipitation of the
wettest and driest quarters, and average snow depth) and temperature
(mean temperature of the warmest quarter; Table 4).

The partial RDA models describing environmental and spatial effects
on\emph{NR1D1} genetic variation (controlling for neutral genetic
structure) were only significant in the eastern Canada lynx data subset
(p=0.001 and 0.008 for space and environment, respectively; Table 5). In
this data subset, the total explainable variation was 0.2128, of which
the effects of purely neutral genetic structure accounted for the
largest proportion of variance (63.20\% of total explainable variation,
p=0.001), followed by the joint (collinear) effects of neutral
structure, environmental and spatial data (28.81\% of total explainable
variation, p=0.001), with purely environmental and spatial data
contributing much smaller, but still significant proportions of
variation (4.93\% and 3.05\% total explainable variation in space and
environment, respectively; Table 5). In the full dataset, only the full
model and partial RDA model describing the effects of neutral genetic
structure (controlling for environmental \& spatial data) were
significant (p=0.001; Table 5). Here, the total proportion of
explainable variation was lower (0.1592), and largely comprised of the
effects of purely neutral structure (71.73\%), and the joint effects of
neutral structure and environment/space (26.01\%), with
environmental/spatial effects contributing small, insignificant amounts
to explained variation (0.19\% and 2.07\% for space and environment,
respectively; Table 5). No models in the western data subset were
significant suggesting that neither neutral genetic structure nor
environmental or spatial variation explain genetic variation at
the\emph{NR1D1} locus in this dataset (Table 5).

\textbf{DISCUSSION}

Multiple analytical approaches suggest the influence of selection at
the\emph{NR1D1} locus in Canada lynx, a widely dispersing,
broadly-distributed mammal. The non-overlapping allelic ranges observed
here between Canada lynx and bobcat at this cTNR might be the result of
selection favouring different allele length variants in sister species
with largely allopatric distributions. The high mutability of cTNR loci
support the possibility that convergent alleles have manifested between
these closely-related species; however, our failure to characterize
large numbers of lynx alleles in bobcats and vice versa given our large
sample sizes concentrated around the area of range overlap suggests that
selection pressures may have prevented these alleles from persisting in
either species and reaching high frequencies. In contrast, we observed
high degrees of overlap in most of our neutral microsatellite markers,
however, we note that 2/14 of these presumably neutral loci showed no
overlap in alleles between lynx and bobcat. Thus, we cannot exclude the
possibility that patterns of non-overlapping allelic ranges in both
functional and neutral loci could be the result of drift occurring in
largely allopatric species. Alternatively, some of our presumably
neutral loci may be in linkage with loci under selection. Ultimately,
the use of additional analytical assessments of the \emph{NR1D1} cTNR
are critical to substantiate the role of selection on this locus.

We observed gradients in allele frequencies at the \emph{NR1D1} locus
within each species, suggesting that variations in environmental and/or
spatial patterns could be influencing selection intra-specifically. For
example, our most southern sampled lynx population (lynx sampled south
of the St. Lawrence River in New Brunswick and Quebec), showed a
substantial increase in the frequency of the smallest allele observed in
lynx. This is concordant with a latitudinal gradient of allele
frequencies in lynx, where the smaller lynx-specific alleles (those
closest to the range of alleles observed in southern-adapted bobcats)
are found more frequently in more southern distributed lynx.
Characterization of lynx at their southern range periphery (i.e.
throughout the contiguous United States) would help further elucidate
this pattern.

All 10 of the samples we identified with both lynx and bobcat alleles
(0.004\% of sampled individuals) were within or in close proximity to
the area of range overlap between lynx and bobcat (note that the
individual sampled in Alberta lacked associated spatial information and
was thus plotted as the centroid of the province). As these samples were
all otherwise characterized as Canada lynx, our finding could result
from 1 of 2 mechanisms (or a combination of both): (1) bobcat alleles
are being introduced into southern lynx populations via multiple
generations of introgression, and/or (2) convergent evolution
of\emph{NR1D1} alleles in lynx, such that the deletion of repeats has
resulted in the introduction of ``bobcat'' alleles into lynx populations
via mutation without introgression. The maintenance of these convergent
``bobcat'' alleles in lynx may then have been maintained via selection
in individuals at the southern range periphery. While it may be
predicted that bobcat alleles should be selectively disadvantageous to
lynx throughout their range, they may be retained in peripheral
populations if they provide a competitive advantage or adaptive benefit
to environmental conditions at the southern range edge.

We found that almost all of the known introgressed lynx and bobcats
identified by Koen et al., (2014) had genotype profiles at
the\emph{NR1D1} cTNR locus consistent with their previously identified
parental species. While some remained close to the area of overlap, all
of these samples were trapped outside of the currently recognized range
overlap of lynx and bobcat, supporting the hypothesis that selection
favours the retention of pure genotypes comprised of alleles belonging
to the parental species. The only introgressed sample containing a mixed
profile was that of a lynx trapped in Saskatchewan, however, location
data for this sample was imprecise, limiting our interpretation.

While the observation of a split lynx-bobcat profile at the
\emph{NR1D1}locus in 1 known introgressed individual provides evidence
for introgression, the occurrences of split profiles in 10 individuals
otherwise characterized as lynx also supports the convergence
hypothesis. These alternative explanations could be more explicitly
tested by genotyping historical specimens in areas where lynx and bobcat
ranges did not overlap, or characterizing species-specific sequence
variants in the flanking sequence of the \emph{NR1D1} locus.
Additionally, characterization of bobcats throughout their southern
distribution may allow for the differentiation of these two competing
hypotheses. For example, if lynx alleles are found to persist in
southern distributed bobcats, introgression would be less probable.

The results of introgression of lynx and bobcat alleles could have
significant implications for conservation. The overall success of
hybridization and introgression induced by range expansions is often
driven by interactions between biotic and abiotic factors on multiple
spatial and temporal scales (Muhlfeld et al., 2014), the outcomes of
which can carry both positive and negative effects. In a positive light,
hybridization and introgression can increase the adaptive potential of
closely-related species through periods of climate change (e.g., Becker
et al., 2013). Alternatively, hybridization may have irreversible
evolutionary consequences by reducing the fitness of parental species
through the erosion of pure genomes and disruption of co-adapted gene
complexes that have evolved together over thousands of generations. The
extent to which either of these alternatives apply to the conservation
of Canada lynx and bobcat remains unknown, but only low levels of
hybridization and introgression have been detected between these species
to date (Koen et al., 2014). The expansion of bobcats northward, and
subsequent hybridization and introgression with lynx, however, may allow
for the persistence and continued range expansion of bobcats by
introducing more ``northern adapted'' lynx alleles into the bobcat gene
pool at the \emph{NR1D1} locus and other loci of adaptive importance.

Within Canada lynx, several indicators of selection on the
\emph{NR1D1}cTNR persisted. Using our Jost's D outlier approach, all
pairwise comparisons showed estimates of Jost's D at the \emph{NR1D1}
locus that were outside of neutral expectations of gene flow.
Specifically, estimates of Jost's D for pairwise comparisons between
Cape Breton Island, Newfoundland and mainland Canada were more similar
than expected based on neutral expectations of gene flow (Fig., 3),
suggesting convergence across these populations. Alternatively, all
pairwise comparisons involving lynx sampled south of the St. Lawrence
River were higher than neutral expectations, suggesting that this
population of lynx is more genetically different than would be expected
by gene flow alone (Fig., 3), and supporting divergent selection of this
population despite ongoing gene flow.

Although we did not detect any environmental associations in our full
dataset, this may have been caused by noise generated from our large
geographic scale that may mask subtler environmental associations, which
can vary with spatial scale (e.g., Kozakiewicz et al., 2019). We also
note that there was considerable variation in the size and amalgamation
of sampling units used here (i.e., traplines, drainages etc.), leading
to inconsistent sampling error across our study area. Thus, in some
areas we may not have been able to detect true environmental
associations due to a lack of resolution.

The significant influence of space and environment in our analysis when
the effects of neutral genetic variation were removed suggests that
selection may be influencing the pattern of genetic variation at
the\emph{NR1D1} locus beyond background population structure and gene
flow. This pattern was only evident in the eastern half of our study
area, and appeared to be especially influenced by \emph{NR1D1} allele
frequencies in peripheral areas of the east. We note, however, that
although significant, spatial and environmental variables accounted for
the smallest proportion of total explainable variation
(\textasciitilde{}8\% combined), and neutral genetic structure
(accounting for 63.2\% explainable variation) is still the most
important factor contributing to the variation in \emph{NR1D1} alleles
in eastern lynx. This suggests that the biological relevance of the
variables we have chosen for our environmental datasets may be limited
in respect to the \emph{NR1D1}locus.

Overall, a large proportion of variation in the \emph{NR1D1} locus
remained unexplained using our neutral structure, environmental and
spatial datasets, ranging from 74.5\% in the western data subset to
84.1\% in the full dataset. While most of this is likely to be
attributed to the methodology we used (i.e., polynomial distortions and
a lack-of-fit of our data to the response model; Økland 1999) it is also
possible that environmental variables unaccounted for in our analyses
might influence selection for \emph{NR1D1} alleles in lynx. For example,
long-lived mammals occupying higher latitudes are particularly
vulnerable to climate change, as they often rely on photoperiod
variability to cue proactive responses to changing seasons rather than
responding reactively (Bradshaw \& Holazpfel, 2008; Bronson, 2009).
Photoperiod can strongly influence circadian activity in Canada lynx
(Kolbe \& Squires, 2007) and the closely related Iberian lynx
(\emph{Lynx pardinus} , Beltrán \& Delibes, 1994). As clock genes are
largely regulated by external cues including photoperiod, the evolution
of clock gene cTNRs would be necessary to allow for photoperiodic
response mechanisms of lynx where photoperiod and temperature regimes
become uncoupled under a changing climate. Other spatially structured
drivers of selection could include the NAO and PNO climatic systems,
which are associated with the spatial genetic structure of Canada lynx
at neutral markers (Row et al., 2014), and may also influence selection
in unknown ways. Further, Agee (2000) reported significant clines in
habitat quality and availability for lynx across their range. Given the
significance of space in our eastern lynx dataset, some of these
unmeasured, spatially correlated variables may be influencing genetic
variation at the \emph{NR1D1} locus.

One of the difficulties in detecting signatures of selection in cTNR
loci is that of the multiple emerging software platforms for identifying
outlier loci (i.e., F\textsubscript{ST} or environmental association
approaches), most have been developed for large SNP datasets where tens-
to hundreds-of-thousands of loci are analyzed simultaneously. Thus, such
approaches are likely to lack power for analyses on datasets of a
smaller size, or of single loci. Here, we investigate the potential for
selection on the \emph{NR1D1} locus using comparisons of genetic
differentiation between neutral and functional genetic datasets, and an
ordination analysis (RDA). One of the major benefits of ordination
analyses is that they do not require assumptions such as HWE or LD to be
met, which are generally violated in loci under the influence of
selection. Although our analysis doesn't directly rely on assumptions of
LD, however, we cannot exclude the possibility of selection acting on a
locus in linkage with the \emph{NR1D1} gene, rather than directly on our
cTNR marker itself. An additional benefit of our conditional RDA
analysis was our ability to control for background neutral population
structure when assessing environmental associations between one or few
outlier loci.

Unlike analyses that identify selection using differentiation-based
approaches (i.e., F\textsubscript{ST} outliers), a drawback of
environmental association analyses arises because evolution does not
necessarily follow environmental change perfectly (Meril\selectlanguage{russian}ӓ \& Hendry,
2014). Thus, true environmental associations may not be identified, even
if they are responsible for driving the observed changes in functional
genetic diversity. Alternatively, our analysis may have excluded
important environmental variables that are associated with the
distribution of alleles at the \emph{NR1D1} cTNR locus, or been too
coarse to detect a relationship. Thus, the use of finer resolution data
and/or the investigation of additional environmental variables shown to
influence lynx population structure and habitat selection may help
explain greater variation in genetic structure at the \emph{NR1D1} and
other cTNR loci.

\textbf{CONCLUSIONS}

As global climate change becomes an increasing threat to biodiversity,
our understanding of the adaptive potential of species and the
integration of this information into management strategies will be
critical in preventing species loss. Thus, determining the rates and
mechanisms of species responding to environmental change is an important
goal for the conservation of species at risk. Our use of a candidate
gene approach allowed for the \emph{a} \emph{priori} selection of a gene
with characterized functions, which we hypothesized to be under
selection in our species of interest, Canada lynx. We detected multiple
signatures of selection in both an intra-specific comparison of
closely-related species (Canada lynx and bobcat), and inter-specifically
in Canada lynx at the \emph{NR1D1} clock gene cTNR. The possibility for
hybrid-mediated gene flow via introgression between lynx and bobcats can
erode the pure genomes of the parental species, and can carry positive
and/ or negative consequences by, for example, facilitating the
northward range expansion of bobcats. The small yet significant
influence of both environment and space on this clock gene locus within
the eastern distribution of lynx suggests that environmental factors are
influencing the distribution of genetic variation at this locus, some of
which may be tightly correlated with space (i.e., latitude). More
broadly, signatures of selection on the \emph{NR1D1} locus within lynx
suggests that cTNR loci may play a role in generating functional
diversity within mammals that can be influenced by selection.
Ultimately, the high mutation rates of cTNR loci may allow for
contemporary rates of evolution in wild populations. While selection on
cTNR loci has already been suggested in wild populations of birds
(Johnsen et al., 2007; Liedvogel et al., 2009) and fish (e.g., O'Malley
et al., 2010), to our knowledge this is the first report that provides
evidence for selection on a cTNR locus in a highly vagile wild mammal.
Although cTNRs are largely under-represented in the wildlife literature,
they may be important targets of study for those aiming to understand
the rapid adaptive potential of species under environmental change.

\textbf{ACKNOWLEDGEMENTS}

The authors would like to acknowledge the North American Fur Auctions
(NAFA) and the Canadian Museum of Nature (CMN) for co-operation in
facilitating sample collection. We thank all lab and field personnel for
assistance, and JR Row, EL Koen and CM Watt for the contribution of
neutral genetic datasets.

This work was funded by the Natural Sciences and Engineering Research
Council of Canada (NSERC) (grant numbers STPGP 391719-10) and Discovery
Grants to Dr. J. Bowman and Dr. P. Wilson as well as Canada Research
Chairs Program funding to Dr. P. Wilson, and the Ontario Ministry of
Natural Resources and Forestry.

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\textbf{DATA ACCESSIBILITY}

Neutral microsatellite data are available from the Dryad Digital
Repository: .

Data for the \emph{NR1D1} locus will be made available on a public
repository pending revision/ acceptance of this article.

\textbf{AUTHORS CONTRIBUTIONS}

MBP, JB \& PJW conceived the ideas and designed the methodology.

JB, KK, DLM \& PJW facilitated sample collection.

MBP performed the laboratory work, analyzed the data and led the writing
of the manuscript.

All authors contributed critically to the drafts and gave final approval
for publication.

\textbf{Table 1.} Sample size and location of sampling of Canada lynx
(\emph{Lynx canadensis} ), bobcat (\emph{Lynx rufus} ) and introgressed
lynx-bobcat individuals across North America. Sample sizes are provided
for datasets of 14 neutral microsatellite markers, the
\emph{NR1D1}locus, and the combined data. Samples missing
\textgreater{}2 neutral loci were omitted.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\textbf{Species}\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\textbf{Province/State}\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\textbf{Sample Size (Neutral markers)}\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\textbf{Sample Size} \textbf{(\emph{NR1D1})}\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\textbf{Total Sample Size} \textbf{(Combined data)}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Canada lynx\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Alaska\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
86\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
87\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
89\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Yukon\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
24\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
28\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
British Columbia\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
190\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
193\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
196\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Alberta\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
104\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
109\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
110\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Manitoba\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
132\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
153\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
154\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Ontario\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
654\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
738\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
747\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Quebec\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
460\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
459\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
467\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Labrador\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
19\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
21\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
21\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
New Brunswick\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
14\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
15\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
15\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Cape Breton Island\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
39\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
42\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
46\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Newfoundland\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
26\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
25\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
26\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Total\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1,748\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1,869\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1,899\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Bobcat\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Oregon\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Washington\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
12\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
British Columbia\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
63\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
66\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
67\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Idaho\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
59\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
63\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
67\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Montana\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
66\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
68\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
69\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Utah\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Wyoming\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Manitoba\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
12\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Texas\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Oklahoma\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Minnesota\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
78\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
96\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
97\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Ontario\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
20\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Wisconsin\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Michigan\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
50\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
51\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
53\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Tennessee\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Nova Scotia\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
90\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
92\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
93\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
West Virginia\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
South Carolina\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Virginia\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
6\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
New York\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
36\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
35\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
36\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Vermont\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
9\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Quebec\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
20\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Maine\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
42\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
43\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
45\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
New Brunswick\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
102\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
106\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
117\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Total\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
675\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
726\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
750\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Introgressed lynx\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Saskatchewan\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Manitoba\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Total\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Introgressed bobcats\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Manitoba\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Texas\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
New York\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Total\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
5\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} Description of environmental variables used in RDA
analyses of 1,899 Canada lynx (\emph{Lynx canadensis} ) sampled
throughout Canada.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Variable} & \textbf{Description} & \textbf{Spatial Resolution} &
\textbf{Temporal Resolution} & \textbf{Reference}\tabularnewline
\midrule
\endhead
Human influence & Influence index created by combining scores across 9
global data layers covering human population pressure (population
density), human land use and infrastructure (built-up areas, nighttime
lights, land use/land cover), and human access (roads, coastlines,
navigable rivers). & 1km & 1995-2004 & Wildlife Conservation Society \&
Center for International Earth Science Information Network
(2005)\tabularnewline
Snow cover & National Centers for Environmental Prediction North
American Regional Reanalysis is a high-resolution reanalysis of the
North American region including assimilated precipitation. & 5km &
Annual averages taken during winter (October-March) between 1979-2003 &
Mesinger et al., (2006)\tabularnewline
Snow depth & & & &\tabularnewline
Mean temperature of the warmest quarter & Part of 19 bioclimatic
variables estimated monthly and averaged annually for North America. &
10km & Annual averages estimated between 1981-2010 & McKenny et al.,
(2011)\tabularnewline
Mean temperature of the coldest quarter & & & &\tabularnewline
Precipitation of the wettest quarter & & & &\tabularnewline
Precipitation of the driest quarter & & & &\tabularnewline
Land cover & Global composites and land cover maps allocate land cover
into 22 categories, using input observations from the 300m MERIS sensor
on board the ENVISAT satellite mission. This data was reclassified into
a binary matrix to estimate the proportion of suitable (1) and
unsuitable (0) lynx habitat. & 300m & January-December 2009 & European
Space Agency \& Universit\selectlanguage{ngerman}é Catholique de Louvain (2010)\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{ngerman}

\textbf{Table 3} . Results of a principal components analysis (PCA) on 9
environmental variables characterizing the environment of Canada lynx
across their range in North America.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Principal Component} & \textbf{Proportion of Variance Explained
(\%)} & \textbf{Cumulative Proportion of Variance Explained (\%)} &
\textbf{Eigenvalue}\tabularnewline
\midrule
\endhead
1 & 41.57 & 41.57 & 3.7412\tabularnewline
2 & 20.79 & 62.36 & 1.8710\tabularnewline
3 & 14.92 & 77.28 & 1.3428\tabularnewline
4 & 8.67 & 85.95 & 0.7802\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 4} . Eigenvectors of a principal components analysis (PCA)
on 9 environmental variables characterizing the environment of Canada
lynx across North America. The most influential variables on each
component are represented in bold text.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Environmental variable} & \textbf{PC1} & \textbf{PC2} &
\textbf{PC3} & \textbf{PC4}\tabularnewline
\midrule
\endhead
Mean snow cover & \textbf{-0.3655} & 0.3392 & -0.3302 &
-0.1584\tabularnewline
Mean snow depth & -0.2931 & 0.3209 & \textbf{-0.4326} &
\textbf{-0.3993}\tabularnewline
Mean temperature of the warmest quarter & 0.1154 & -0.3632 &
\textbf{-0.5576} & \textbf{0.4793}\tabularnewline
Mean temperature of the coldest quarter & 0.3431 & -0.1637 & 0.3117 &
-0.3505\tabularnewline
Precipitation of the wettest quarter & \textbf{0.3939} & -0.0795 &
-0.3289 & \textbf{-0.3953}\tabularnewline
Precipitation of the driest quarter & \textbf{0.4407} & -0.0423 &
-0.2107 & \textbf{-0.3870}\tabularnewline
Proportion of unsuitable lynx habitat & 0.3072 & \textbf{0.5531} &
0.0832 & 0.1990\tabularnewline
Proportion of suitable lynx habitat & -0.3031 & \textbf{-0.5491} &
-0.0907 & -0.2131\tabularnewline
Average human influence & 0.3374 & 0.0883 & -0.3579 &
0.2682\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 5} . Results of RDA analyses testing for associations
between neutral genetic structure, environmental variables, space, and
functional genetic variation at the \emph{NR1D1} locus in 3 subsets of
data for Canada lynx: the full dataset, the ``eastern'' data subset, and
the ``western'' data subset. The significance of the full and
conditioned models are given by P-values (significant values shown in
bold), and the total explainable inertia (variance) is given for each
model. The proportion of inertia (variance) represents the proportion of
total explainable variance in the full model that can be attributed to
the effects of (1) purely spatial variables, (2) purely environmental
variables, (3) purely neutral genetic structure and (4) the joint
(collinear) effects of the spatial, environmental and neutral genetic
structure datasets.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllll@{}}
\toprule
\begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{Model}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{Full dataset}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{Full dataset}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{Full dataset}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{``Eastern'' subset of data}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{``Eastern'' subset of data}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{``Eastern'' subset of data}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{``Western'' subset of data}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{``Western'' subset of data}\strut
\end{minipage} & \begin{minipage}[b]{0.07\columnwidth}\raggedright\strut
\textbf{``Western'' subset of data}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{P-value}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{Inertia (variance)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{Proportion of inertia (variance)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{P-value}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{Inertia (variance)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{Proportion of inertia (variance)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{P-value}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{Inertia (variance)}\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\textbf{Proportion of inertia (variance)}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Full model NR1D1 \textasciitilde{} Environment + Space + Neutrals\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{0.001}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.1592\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{0.001}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.2128\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.140\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.2548\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Conditioned model (pure spatial effects controlling for environment \&
neutral genetic structure) NR1D1 \textasciitilde{} Space +
Condition(Environment + Neutrals)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.808\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.0003\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.0019\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{0.001}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.0105\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.0493\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.533\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.0024\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.0094\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Conditioned model (pure environmental effects controlling for spatial \&
neutral genetic structure) NR1D1 \textasciitilde{} Environment +
Condition(Space + Neutrals)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.062\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.0033\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.0207\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{0.008}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.0065\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.0305\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.551\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.0052\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.0204\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Conditioned model (pure neutral genetic structure effects controlling
for environment \& space) NR1D1 \textasciitilde{} Neutrals +
Condition(Environment + Space)\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{0.001}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.1142\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.7173\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{0.001}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.1345\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.6320\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.154\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.2426\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.9521\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Joint environmental, spatial \& neutral genetic structure effects\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0414\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.2601\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0613\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.2881\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0046\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
0.0181\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1} . (A) Samples of Canada lynx (\emph{Lynx canadensis} ,
black and grey circles), bobcat (\emph{Lynx rufus} , red circles),
amplified at 14 neutral microsatellites and the \emph{NR1D1} cTNR locus.
Coordinates for lynx samples represent the centroid of sampling units
(defined in methods). Samples of Canada lynx subdivided into ``eastern''
(black circles) and ``western'' (grey circles) lynx groups for RDA
analyses. Coordinates of bobcat samples represent centroids of counties
(USA), traplines (Canada), or province/state when finer resolution
spatial information was not available. (B) Map of introgressed Canada
lynx (\emph{Lynx canadensis} ) and bobcat (\emph{Lynx rufus} )
identified in Koen et al. (2014), and putative introgressed lynx
identified via mixed genotypes at the \emph{NR1D1} locus (current
study). Samples from Saskatchewan, Alberta and Texas are plotted as the
centroid of province/state. All other samples are plotted as centroids
of traplines (Canada) or counties (USA) where the individual was
trapped. Ranges of lynx and bobcat are represented by the solid grey and
hatched grey areas, respectively. Online version in colour.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} (A) Canada lynx (\emph{Lynx Canadensis} , black
points) and bobcat (\emph{Lynx rufus,} red points) genotypes at the
\emph{NR1D1} locus plotted by latitude. (B) Allele frequency
distribution of the\emph{NR1D1} cTNR locus in Canada lynx and bobcat.
The inset map shows a greater resolution of eastern Canada (outlined by
the square on the full map). Canada lynx samples are characterized by
the largest 4 alleles (shown in colour), and bobcat by the smallest 3
alleles (shown in black, white and grey). Size of the pie charts
correspond to sample sizes. Bobcat sites with \textless{}10 samples were
removed from this figure. Ranges of lynx and bobcat are represented by
the solid grey and hatched grey areas, respectively. Online version in
colour.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} Pairwise point estimates of Jost's D for 14 neutral
microsatellites (black points), with bars representing standard error of
the mean, and point estimates of Jost's D for the \emph{NR1D1} locus
(white points) of 1,738 and 1,859 Canada lynx (\emph{Lynx canadensis} )
at neutral markers and the \emph{NR1D1} locus, respectively. Site names
are abbreviated and represent lynx from the four genetic clusters
identified by STRUCTURE analyses: Cape Breton Island (CBI), Newfoundland
(NFLD), lynx sampled from south of the St. Lawrence River in New
Brunswick and Quebec (QBS\_NB), and all remaining lynx sampled from
continental Canada (MAINLAND).

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

