genetic diversity in caribou linked to past and future climate change

1

Genetic diversity in caribou linked to past and future

climate change

Supplementary Information

Section Overview:

Section S1- Supplementary Methods (p. 2)

A) Genetic analyses (p. 2)

B) Species distribution modelling and range shift through time (p. 6)

C) Factors influencing genetic diversity (p. 9)

Section S2- Supplementary Text (p. 11)

A) Genetic results (p. 11)

B) SDMs and range shift through time (p. 14)

C) Factors influencing genetic diversity (p. 15)

Nuclear genetic diversity (p. 15)

Mitochondrial genetic diversity (p. 15)

D) Caveats (p. 16)

i) Genetic markers (p. 16)

ii) Incongruence between empirical and modelled spatial genetic structures (p. 16)

Section S3- Supplementary Figures 1 to 9 (p. 18)

Section S4- Supplementary Tables 1 to 14 (p. 26)

References for Section S1–S4

Genetic diversity in caribou linked to past and future climate change

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE2074

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 1

© 2013 Macmillan Publishers Limited. All rights reserved.

http://www.nature.com/doifinder/10.1038/nclimate2074

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SECTION S1- METHODS

A) Genetic analyses

Sample collection and DNA extraction. Overall, fifty-nine sampling areas were defined according to

known geographic herd ranges (Figure 1; Supplementary Table 1). A total of 1297 samples were

collected, which came from a broad diversity of ecosystems spanning a latitude range of ~ 30° in the

northern hemisphere, ranging from southern Quebec, Canada (lat. 48°), to Svalbard, Norway (lat.

78°). Tissue samples were mostly collected over the last decade and consisted of blood clots, ear

punches and hair handfuls obtained during field studies or muscle acquired from hunted individuals.

Samples were stored in 95% ethanol or frozen (-20°C) until genetic analyses. DNA was extracted from

dried blood and muscle according to the salt extraction protocol by 1. We extracted DNA from hair and

fresh blood samples using DNeasyTM Tissue and Blood Kits (Qiagen, Inc., Valencia, CA, USA),

respectively. We followed the manufacturer’s protocol, with minor modifications for hair samples. Up to

30 guard hair roots were cut off and placed into 1.5 ml tubes containing 180 µl of ATL buffer, 20 µl of

Proteinase K (20 mg/ml) and 30 µl of DTT (100 mg/ml). Samples were incubated overnight at 37°C

and the remaining steps followed the manufacturer’s protocol.

Microsatellite genotyping. Initial DNA was used as a template in all polymerase chain reactions

(PCRs). Optimized microsatellite markers were used in quintuplex and sextuplex PCRs, totalling 16

microsatellite markers: Nvhrt16 and Nvhrt30 2, Rt1, Rt5, Rt6, Rt7, Rt9s, Rt24 and Rt27 3, BL42,

BM4513 and BM6506 4, BMS745 and BMS1788 5, FCB193 6 and OheQ 7 (Supplementary Table 2).

Individuals were genotyped in 10-µL multiplex reactions containing 3 µl of DNA (5-50 ng/µl) and

1×Multiplex PCR MasterMix (Qiagen, Valencia, CA, USA). One primer pair was fluorescently labelled

(fluorescent tags: 6-FAM, PET, NED or VIC) and primer concentrations ranged from 0.08 to 0.4 µM

(Supplementary Table 2). The PCR profile consisted of an initial denaturing of 15 min at 95 °C,

followed by 35 cycles at 94 °C for 45 s, 54 °C for 90 s, 72 °C for 1 min, and a final extension at 72 °C

for 30 min. Multiplexes 1 and 2 could be pooled after PCR. All PCR products were ran on an ABI 3130xl

Genetic Analyser 16 capillary system (Applied Biosystems, Forster City, CA, USA) and sized with

internal lane standard (500 LIZ; Applied Biosystems) using the program GENEMAPPER 4.0 (Applied

Biosystems). To check for genotyping consistency, 10% of samples were amplified and genotyped


3

twice. No departure from Hardy–Weinberg and linkage equilibrium was detected at the herd level after

using a Bonferroni correction in FSTAT 2.9.4 8.

Microsatellite genetic structure. We investigated the genetic structure of populations calculating

overall and pair-wise FST values and testing their significance using 10000 permutations in FSTAT. We

expected an increase of genetic differentiation among herds at the margin of the caribou range. The

relationship between genetic distance and latitude was tested by grouping herds according to their

latitudinal position in 5° latitude strips. We estimated the mean pairwise genetic distance of each

population with all the other populations within each latitudinal strip, excluding between genetic

lineage (Euro-Beringia vs North America) and between continent (Eurasia vs America) population pairs.

Genetic distance was estimated as mean pairwise FST/(1- FST) corrected by the geographic distance to

take into account the effect of isolation by distance (see below).

Genetic structure was also analyzed without considering information about the geographic

origin of individuals using a Bayesian Markov chain Monte Carlo clustering analysis as implemented in

the software STRUCTURE 2.3.3 9. We assumed an admixed model with correlated allele frequencies 10.

Twenty independent runs from K = 1 to K = 20 were performed using 1,250,000 iterations with the

first 250,000 removed as a burn in. We used the ∆K method of Evanno et al. 11 to select the number of

populations best fitting the data set. We ran 30 additional runs for the inferred optimal K values and

the 10 runs having the highest likelihood were averaged using CLUMPP 1.1.1 12. We used the Greedy

algorithm with random input order and 10,000 permutations to align the runs and the G’ pairwise

matrix of genetic similarity. Population structure was also analyzed using a mean-centered principal

component analysis (PCA) with R VERSION 3.0.0 13 using ‘adegenet’ 1.3.0 package 14, an approach that

does not rely on any population genetic assumptions underlying STRUCTURE analyses 9. Although the

assumptions and methodology of the two methods differ, a recent study has shown that while

admixture-based models are more suitable for discrete and partially admixed populations (e.g.,

secondary contact after historical allopatry), PCA is more useful with continuous patterns of

differentiation (e.g., isolation by distance) 15.

Isolation by distance versus genetic clustering. A heterogeneous geographic dispersion of

samples can have a substantial influence on inferred patterns of genetic clustering, especially at a


4

worldwide scale, and can reveal genetic clusters that are biologically meaningless 16. Population

clustering may, indeed, be an artefact resulting from uneven sampling along a genetic cline 17.

Therefore, we used Mantel 18 and partial Mantel tests 19 to assess whether herds from the same

genetic cluster (obtained with STRUCTURE analyses) had a degree of genetic similarity greater than the

one predicted by geographic distance alone. To do this, we created a genetic cluster distance matrix

where population pairs were scored as 0 if they belong to the same genetic cluster and 1 if they belong

to a different cluster, and then computed partial Mantel tests to compare this matrix with a pairwise

genetic distance matrix while controlling for geographic distance. We also used partial Mantel tests to

analyze the effect of geographic distance on pairwise genetic distance after controlling for genetic

clustering. Pairwise genetic distances were based on Nei’s Da distance. For each pair of populations,

we calculated geographic distance in kilometres based on great circle distances using the package

‘geosphere’ 1.2-27 implemented in R VERSION 3.0.0 13 , and according to the 'Vincenty (ellipsoid)'

method. To make our between-continent distance estimates more reflective of past caribou migration

patterns, we also calculated pairwise geographic distances considering the Bering Strait (66°0′0″N,

169°0′0″W) as an obligatory waypoint. The distance between two points is then the sum of the great

circle distances between the points and the waypoint in the path connecting them. Mantel tests (one-

sided) were conducted with R version 3.0.0 using the package ‘ecodist’ 1.1.4 20, and significance was

assessed with 10,000 permutations.

Nuclear demographic history

A Bayesian approach designed to investigate past population decline or expansion was applied to test

whether genetic data were consistent with the climatic reconstructions. We used a coalescent-based

approach implemented in MSVAR version 1.3 21,22. This method uses a MCMC approach to estimate the

posterior distribution of (i) the current effective population size (N0), (ii) the ancestral population size

at the time of demographic change to (N1) and (iii) the time since that change (T). The change in

population size was assumed to be exponential, and mutations were assumed to follow a step-wise-

mutational model. Realistic but wide uniform priors distribution were chosen, so that available

knowledge was used without favoring certain values of parameters. The generation time was fixed to 4

years 23.


5

We conducted the analysis on both genetic lineages. Owing to a restriction of the model, which

assumes that there are no more than 2000 coalescent and mutational events in the genealogy, we

used the algorithm sinf.exe provided in Beaumont’s package in order to produce subsample files of 200

caribou from each lineage dataset. We excluded microsatellite loci displaying irregular mutational

steps, keeping 11 out of 16 loci (i.e., BL42, BM4513, BM6506, NVHRT30, OheQ, Rt1, Rt24, Rt27, Rt5,

Rt6 and Rt7). For reach data set, six independent chains of 109 update steps were carried out with

2.104 lines of output. The first 25% of steps of the chains were discarded as burn-in, the rest being

considered to constitute a sample of the stationary distribution. All output files from MSVAR were

analyzed using the “coda” package 24 in R version 3.0.0 13. For each data set, convergence was

assessed by computing the multivariate extension of Gelman and Rubin’ s diagnostic 25,26 on the six

independent Markov chains. Gelman and Rubin’ s diagnostic is based on the computation of the ratio of

the pooled-chains variance over the within-chain variance, which should be close to 1 if the chains

converge to the target distribution. Posterior densities from individual runs were also examined

visually, to check for overall consistency in shape. All runs that reach convergence were combined for

density estimation, conducted with the “coda” package, for the estimation of 95% highest probability

density (HPDs) limits.

Mitochondrial DNA sequencing. We sequenced 1,147bp of the mitochondrial cytochrome b (cyt b)

gene for a subset of the available samples (n = 167) across the species range using primer pairs

detailed in Supplementary Table 3. The xytochrome b was amplified via simple or nested PCR in a 25

µl solution containing 2 µl of template DNA (5-50 ng/µl) or 1 µl of the first PCR round, 2 µl of dNTPs

(0.2 mM each), 5 µl of 5× buffer, 0.25 µl of each primer (10µM), 1.5 µl of MgCl2 (25mM), 13.8 µl of

distilled water, and 0.2 µl of Taq DNA polymerase (1 U; Promega, Madison, WI, USA). The PCR profile

started with 5 min of denaturation at 95°C, followed by 35 cycles at 95°C for 45 s, 50°C for 40 s, 72°C

for 2.5 min, and ended with a final extension step at 72º C for 10 min. PCR products were checked by

electrophoresis on 1% agarose gels. Amplicons were directly sequenced in both directions using a Big

Dye Terminator Kit (Applied Biosystems, Foster City, CA) on an ABI 3730 Genetic Analyzer (Applied

Biosystems, Forster City, CA, USA). Furthermore, we retrieved from GenBank 73 cyt b haplotypes

representing 178 additional samples (Supplementary Table 4, 27,28).


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Phylogenetic analysis. Sequences were edited with MEGA 5 29, aligned using CLUSTALX 2.0 30 and

visually inspected with SEAVIEW 4.3.3 31. The models of DNA substitution were selected using

JMODELTEST 0.1.1 32, based on the Akaike Information Criterion (AIC). The HKY + G + I substitution

model best fitted the cyt b dataset. That model was enforced to estimate phylogenetic relationships

among haplotypes with MRBAYES 3.1.2 33. MRBAYES was run twice with four simultaneous chains for 107

generations, and trees were sampled every 1000 generations. Convergence of the chains was assessed

with TRACER 1.5.0 34. A burn-in period of 106 was discarded before calculating the majority-rule

consensus tree. The unrooted tree was visualized with FIGTREE 1.3.1 35.

Divergence time. We inferred timing to the most recent common ancestor of caribou lineages using a

Bayesian relaxed clock uncorrelated lognormal method in BEAST 1.7.1 36. We assumed a HKY + G + I

model of nucleotide substitution, based on JMODELTEST (see above), and a relaxed uncorrelated

lognormal molecular clock model, which assumes independent mutation rates on different branches 37.

To calibrate the model, we used a secondary calibration point based on the divergence time estimated

to the most recent common ancestor of the Odocoileini tribe (caribou + American deer genera)

(5.8±0.2 MYA; 95% highest posterior densities (HPDs): 4.1 – 6.2) 38. Consequently, members of the

Capreolinae subfamily were also added to our caribou dataset (Supplementary Table 5). Monophyly of

the caribou ingroup was enforced, and the MCMC chains were run with 200 millions iterations with

trees sampled every 10,000 iterations. The first 20% of the iterations were discarded as burn-in. Log-

files were analyzed in TRACER, effective sample sizes (ESS) were used to evaluate MCMC convergence

within chains and a maximum clade credibility tree of median heights was constructed using

TREEANNOTATOR from BEAST package with a posterior probability cutoff of 0.5.

B) Species distribution modelling and range shift through time

Species distribution modelling (SDM). Species distribution models, or SDMs, have been developed

over the last three decades to improve knowledge and forecast species distributions. While primarily

developed to estimate the current distribution of species for which we have incomplete sampling, SDMs

have also been heavily utilized over the last decade to forecast species distributions in a warmer

future. They have also been used for reconstructing the distribution of species in the distant past and


7

may complement information from fossil distribution when fossil records are not sufficient to infer past

species distribution 40 (see also Supplementary Figure 1).

We used SDMs to predict the geographic distribution of climatically suitable areas for caribou and

investigate whether current and past bioclimatic conditions might be responsible for observed patterns

of genetic diversity and structure. We first modelled the current climate-based distribution of caribou

using information on current climate (averaged from 1950 to 2000) obtained from the Climatic

Research Unit 41. We used the following variables: total annual precipitation, summer precipitation,

winter precipitation, annual mean temperature, summer mean temperature, and winter mean

temperature, because these were also available for the past (see below). Species distribution data

were obtained from the IUCN range map (available from http://www.iucnredlist.org/) from which

10,000 occurrence points were randomly sampled across the species range. We based our modelling

on a range map instead of occurrences in order to avoid underestimation due to incomplete or

heterogeneous sampling points. Pseudo-absences were generated by selecting 10,000 random points

across the Holarctic region. We modelled the distribution of the species using the “Biomod” R package

42. Ensemble forecasting approaches have been shown to significantly improve the accuracy of species

distribution models43. Therefore, we used and combined the results of five different statistical

techniques to model the distribution of the species: (1) generalized linear model (GLM), (2)

generalized additive model (GAM), (3) generalized boosting model (GBM), (4) multivariate adaptive

regression splines (MARS) and (5) Random Forest (RF). Those modelling techniques are among the

best performing 42,44,45 and have proven useful to hindcast and forecast species distribution 40. To

evaluate the predictive performance of the species distribution model, we used a random subset of

70% of the data to train every model, and used the remaining 30% to test it. Models were evaluated

using a relative-operating-characteristic (ROC) curve and the area-under-the-curve (AUC) 46. We

repeated the split 50 times and recalculated the average AUC of the repeated split-samples, which

gave a more robust estimate of the predictive performance of the models.

Range shift through time. To obtain the predicted distribution of caribou since the LGM (c. 21 000

years BP), we projected contemporary species–climate relationships from SDMs to the past at different

time frames (every 1000 years). Simulations of past climate were obtained from a global ocean-

atmosphere climate model based on the Hadley Centre climate model (HadCM3) 47 and for further


8

details see 48. We produced past temperature and precipitation maps with a 50 km spatial resolution

over the world. The SDM of caribou was hindcasted using past climate layers to produce climatic

suitability maps at each 1000-year time step. We validated the hindcasted model projections using

available fossil records compiled from 49,50 (see Supplementary Figure 1). Because historical and fossil

datasets consist of presence-only data, we used the Boyce index to evaluate the predictive power of

the hindcasted model. Contrary to common evaluation measures for presence-absence data, like AUC,

which require presence and absence, the Boyce index only requires presence data and measures how

much model predictions differ from random distribution of the observed presences across the

prediction gradients 51,52. It is thus the most appropriate metric to validate a model in the case of fossil

presences. Positive values indicate a model for which present predictions are consistent with the

distribution of presences in the evaluation dataset, values close to zero mean that the model is not

different from a random model, negative values indicate counter predictions, i.e., predicting poor

quality areas where presences are more frequent. We extracted the probabilities from hindcasted

model at each fossil location for its corresponding time period and measured the predictive power

using the Boyce index as in 52.

To simulate the spatial dynamic of the species after the glaciations, we first transformed the

probability maps obtained from the SDM projections into presences and absences using the ROC plot

method 53 and considering regions known to have been covered by ice 54,55,56 as unsuitable during each

time period. We used the user-defined scenario approach as in 40 and implemented in the “Migclim” R

package 57. We first defined potential refugia occupied by the two main genetic lineages at the LGM

(21kya), as areas distinguished by discontinuous suitability for the species, i.e., suitable areas located

south and northwest of the Laurentide Ice Sheet in North America (Panel “21 ka” in Supplementary

Figure 2 and Supplementary Figure 5; see “Results”). This corresponds to the user-defined scenario

proposed in 40. Then, for each following time-step (every 1000 years) up to the present time, we let

any suitable pixel from a given timeframe t be colonized by the genetic group from the closest suitable

pixel from timeframe t-1, a procedure that we refer to as diffusion.

Finally, we compared the match of the current genetic structure predicted by the simulated

scenarios with the empirical population genetic data following 40. For this, we assigned each genetically

analyzed population to a clade by considering the highest assignment probability obtained when

applying 1) Bayesian genetic clustering approaches (with STRUCTURE) or 2) mtDNA haplotypes


9

frequency. A population was assumed to be properly recovered by the model if it was assigned to the

same genetic cluster both at the end of the simulation process and with the direct molecular approach.

Stability of climatic suitability. Because stability, or its opposite, climatic velocity, can greatly

influence species spatial genetic structure and diversity 58, we generated a map of stability of caribou

climatic suitability through time since the last glaciations (ca. 21 ka). We obtained the continuous

climatic suitability maps from the SDMs by summing the values of the pixels across all time frames. A

stable climatic suitable area is thus defined as having conserved a high climatic suitability for caribou

during the longest time period. We used the same procedure to compute a stability map for the next

70 years by stacking the climatic suitability maps obtained at each 10-year interval until 2080.

Simulations of future climate were based on the same HadCM3 climate change model and the IPCC4

A1B future emission scenario from the Intergovernmental Panel on Climate Change 59, available at 10-

year intervals from 2020 to 2080. We computed the climatic stability for the past and the future by

stacking suitability maps for the past since the LGM and for the next 70 years into the future. Finally,

we also measured the loss of surfaces (in number of pixels) of the two lineages in 2080 as done in 40.

All computations were performed at the Vital-IT (www.vital-it.ch) Centre for high-performance

computing of the Swiss Institute of Bioinformatics.

C) Factors influencing genetic diversity.

At the worldwide scale, if climate was a major factor driving lineage population sizes, we would expect

expansion and contraction of a lineages’ geographical range to mirror population increases and

declines, respectively. Range size dynamic were then compared with demographic histories inferred

from mtDNA. Range size dynamics was based on the surface of available climatic suitable area (in

kilometres) at each time frame from the LGM to present (Supplementary Figure 5). The lineage

demographic histories were inferred from the coalescent-based estimation of changes in effective

population size through time (Bayesian skyline plot), which allows detection of changes in global

genetic diversity, as explained above.

At the population scale, we analyzed which variables best explained the current distribution of genetic

diversity within caribou herds at both nuclear (microsatellite markers) and mitochondrial (cyt b gene)

levels using Generalized Linear Models GLMs; 60. We quantified nuclear genetic diversity for each


10

studied herd using autosomal microsatellites to calculate allelic richness (AR) and expected

heterozygosity (HE) (Supplementary Table 1). AR was estimated using the rarefaction method

implemented in HP-RARE 1.0 61 and HE was calculated using GENALEX 6.2 62. Mitochondrial diversity was

quantified as haplotype diversity (h) and nucleotide diversity (π) using DNASP 3.5 63. We only

considered herds for which ≥ 5 mtDNA samples were available, to avoid sample size biases –an

approach also used for caribou by 64. We considered four explanatory covariates in the GLMs: 1)

latitude; 2) climatic stability (see below); 3) log-transformed census population size, reviewed from

the literature; and 4) genetic cluster, estimated as the probability of population membership (q) for

each herd according to STRUCTURE analyses for K = 2 (see results section). Models included quadratic

effects for latitude to test for the potential higher levels of genetic diversity in populations located at

mid-latitudes in comparison with those located in the most southern- and northernmost margins of the

species distribution range 65. We excluded from these analyses the introduced population from Iceland

and the domestic herds from Eurasia (Supplementary Table 1), as the patterns of genetic diversity of

these populations are likely to be highly influenced by domestication and human management. The

precision of genetic diversity estimates may differ among populations due to differences in sample

sizes (Supplementary Table 1). To take this into account, we used a weighted, least-squares method,

where weight equals the sample size for each studied population. We used an Information Theoretic

approach 66 to choose which variables best predict genetic diversity. We tested 17 a priori candidate

models and the null model. We ranked candidate models using the Akaike’s Information Criterion for

small sample sizes (AICc) as implemented in the R package “AICcmodavg” 67, and calculated ΔAICc

and AICc weights. Models with ΔAICc≤2 were considered equivalent 66. We used a standard model-

averaging method to calculate parameter estimates of equivalent models 66. As GLM does not provide

R2 values, we computed the explained variance as: R2= 1 – (A/B) where A = sum [(values OBSERVED –

values PREDICTED)2] and B = sum [(values OBSERVED – mean(values OBSERVED))2]. Prior to the information

theoretic analysis, we ensured that there was no significant collinearity among explanatory variables,

by calculating variance inflation factors (VIF) using the package “car” implemented in R. Collinearity

diagnostic tests for all models revealed no multicollinearity problems: the highest VIF was 4.5 and

multicollinearity begins to affect parameter estimates when the VIF values are >10 (for a discussion

see 68).


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Section S2- Supplementary text

A) Genetic results

Microsatellite genetic structure. The statistic ΔK described by 11 clearly showed a major split

between two genetic clusters; the highest ΔK was obtained for K = 2 (Supplementary Figure 2). Using

only the ‘Log probability of data’ as described in 9, it was not obvious which number of clusters best fits

our data. At larger Ks, Ln P(D) continued to slightly increase (Supplementary Figure 2). The ad hoc

statistic ΔK is known to detect the uppermost level of population structure, when sublevels of genetic

structure exist 11. At regional scale, studies have previously shown that several hierarchical levels of

genetic structure exist within caribou e.g., 23,64,69,70. However, given the clear peak obtained with ΔK

for K=2, we put more emphasizes here on the uppermost hierarchical levels of genetic structure within

the species and discussed its implications at a circumpolar phylogeographic scale.

The most widespread genetic cluster includes herds from Eurasia and North-western America

(Figure 1). This cluster, hereafter named the Euro-Beringia clade, notably includes herds from

Svalbard, Iceland, Greenland and Peary caribou samples from Bathurst Island (Nunavut) (Figure 1a).

The second cluster, hereafter named the North American clade, groups herds distributed from the

island of Newfoundland to the interior plains of Canada. Most herds are assigned to one of these two

clusters at high probability of population membership (Qpop > 0.90; Figure 1a). Further inspection of

STRUCTURE results for K = 3 revealed a third distinctive genetic cluster in Fennoscandia (Supplementary

Figure 3). Similarly, the first two PCA axes revealed a cut-off separating Euro-Beringia and North

America clades on the first axis (Figure 1b). Consistent with the results obtained with STRUCTURE, a

distinctive Fennoscandian cluster emerged from the Euro-Beringia clade on the second axis, as well as

the two geographically isolated populations in Greenland and Svalbard. The secondary contact zones

between the clusters are located in central and western Canada for the Euro-Beringia/North America

clades and in Eastern Finland for the Fennoscandia/Euro-Beringia clades, where admixture at both

individual and herd levels was observed (Figure 1a and Supplementary Figure 3).

The degree of genetic differentiation among herds ranged from FST = 0.00 to FST = 0.69 with

an overall FST = 0.126 (P < 0.001; 95% CI: 0.117-0.137). Herds from Greenland and Svalbard

displayed the highest levels of genetic differentiation both between them (FST = 0.69, P < 0.001) and

in comparison with other herds (average FST-Greenland = 0.44±0.07; average FST-Svalbard = 0.41±0.67). A

higher genetic differentiation among herds was observed at the margin of the species distribution


12

range, i.e., at lower and higher latitudes. We observed a quadratic relationship between mean pairwise

FST and latitude (linear term: F1, 55 = 9.94, P < 0.01; quadratic term: F1,55 = 32.39, P < 0.001; Figure

4d). The relationship was still significant when herds from Svalbard and Greenland were removed from

the analyses (linear term: F1,53 = 31.51, P < 0.001; quadratic term: F1,53 = 38.49, P < 0.001).

Isolation by distance versus genetic clustering. Pairwise genetic distance Da increased with

geographic distance (Mantel r: 0.57, P <0.001; Supplementary Table 6). Both genetic clustering and

waypoint distance were correlated with pairwise genetic distance (Da) after controlling for each other

in partial Mantel tests, indicating that both variables were associated with genetic differentiation

among herds and explained additional variance (Supplementary Table 6). These patterns were

stronger (i.e., higher Mantel r) when the analyses were carried out excluding the isolated herds from

Greenland and Svalbard (Supplementary Table 6).

Nuclear demographic history. In testing for MCMC chain convergence, we observed Brooks, Gelman

and Rubins corrected scale reduction factors well below the threshold value of 1.1 for all parameters

and hence assumed reasonable convergence had been achieved. The last 75% updates of each chain

were thereafter combined into a single chain with 30,000 data points for each population, and

parameter values were estimated from six combined consensus chains. Posterior distributions from

individual chains were also inspected graphically and found to be very similar and unimodally smooth,

despite differing starting values. Results from the consensus chains from both Euro-Beringia and North

America supported a scenario of population size expansion (Supplementary Figure 4 and

Supplementary Table 7). Estimates of current population size (mean N0: North America = 82,985;

Euro-Beringian = 2,443,431) and ancestral population size (mean N0: North America = 3,243; Euro-

Beringian = 2,798) differed between the lineages (Supplementary Figure 4). The time since the onset

of decline (T) overlapped considerably between populations (Supplementary Figure 4), although the

mean estimated value of T for the two populations differed from ca. 10 ka in North America to ca. 2.2

ka in Euro-Beringian (Supplementary Table 7). It is, however, worth noting that MSVAR is highly

computational (several days were typically necessary for one replicate/run). To keep manageable

computing times, we performed the analyses on restricted data sets generated by random resampling

(n = 200 in each lineage, and from several demes). Population structure, genetic diversity and the


13

sampling scheme play major roles in detecting and quantifying population size changes 71. The

statistical methods that we used to detect population size changes ignore population subdivision71,

expected across the circumpolar range of caribou. The worldwide structured populations, in

conjunction with our sampling scheme (subsamples of 200 genotypes in several demes), may generate

incorrect genetic signatures and patterns of demographic decline or expansion.

Mitochondrial phylogenetic patterns. Bayesian phylogenetic reconstruction, including sequences

retrieved from GenBank (n = 345, 122 haplotypes differing by ≥1 nucleotide substitution;

Supplementary Table 8), confirmed a clear split between two mtDNA clades (Figure 1d), corresponding

to populations from Euro-Beringia and North America (Figure 1c). Some herds located in the secondary

contact area (e.g., along a straight-line from the Hudson Bay to the Rocky Mountains foothills

westward, Figure 1c) that were admixed at nuclear microsatellite loci displayed mtDNA haplotypes that

belong to the two different clades (Figure 1a). Support for phylogeographic structure within clades was

limited, as they were essentially polytomies or without consistent geographic structure (Figure 1b).

Microsatellite data also suggested regional diversification (e.g., Fennoscandia), but this was not

supported by mtDNA data (albeit see, 72). In contrast to previous results 72, mtDNA data did not

support a Fennoscandia clade, because haplotypes found in Scandinavia were also present in Siberia

and North America and were all included within the Euro-Beringia clade. Principal component analysis

of microsatellite data and further inspection of STRUCTURE results for higher K values revealed what past

climatic reconstructions had missed, i.e., a third distinctive genetic cluster in Fennoscandia and two

highly differentiated populations in Greenland and Svalbard (Figure 1b), unrevealed by past climatic

reconstructions. These were probably shaped in situ through the putative processes of vicariance after

the last glacial maximum, either through early domestication (e.g., Fennoscandia) or current

geographic isolation (West Greenland and Svalbard).

Divergence time. The root calibration of 5.8 ± 0.2 Myr yielded a mean (±SD) mutation rate estimate

of 2.23 x 10-2 ± 5.01x10-5 substitution site-1 Myr-1 with 95% HPDs of 1.85x10-2 – 2.62x10-2. The timing

of the split between the Euro-Beringia and North America lineages was estimated at 0.300 Myr (95%

highest posterior densities (HPDs): 0.184 – 0.430), i.e., during the middle Pleistocene, a time of

widespread continental glaciations in North America, which largely predates the onset of the last glacial


14

maximum (LGM: 21 ± 2 ka). This figure is consistent with divergence time estimated for genetic

lineages of other ungulates in North America, e.g., 73,74.

B) SDMs and range shift through time

The predicted distribution of caribou at 1 ka is consistent with the observed current distribution (Figure

2 and Supplementary Figure 5). The predictive power of the models was good sensu Swets 75 (AUC:

GLM=0.849, GAM=0.851, GBM=0.847, MARS=0.854, RF=0.859). The Boyce index of hindcasted

model validation with fossil records was positive and indicated a good performance to predict past

distribution range (BINDEX=0.58). Two main hindcasted centers of distribution at the oldest projected

timeframe were identified (Figure 2 and Supplementary Figure 5): Beringia-Eurasia, which was

probably partially fragmented around the Bering Strait, and North America. During the period from 21

ka to present, caribou experienced important range contractions and expansions following climatic

variations. During the LGM, extensive climatic suitable areas were identified in Euro-Beringia, whereas

suitable areas were reduced south of the ice-sheet covering North America. This implies more severe

population contractions in North America than in the Euro-Beringia region. As the climate began to

warm up, the Euro-Beringia and North America clusters continuously expanded their ranges until a

contact zone arose in Central Canada. The observed secondary contact between the two main

hindcasted centers of distribution predicted by simulations of species historical migration occurred ~8

ka and is now located along a suture line that almost perfectly matches the contact zone predicted by

genetic data (microsatellite: 85% and cyt b: 87% of match; Figure 3a). The comparison of simulation-

and genetic-based assignments showed, however, a notable incongruence in Greenland that was

assigned to the North America clade according to the simulation-based assignment, whereas samples

were genetically assigned to the Euro-Beringian clade. It is possible that Greenland was colonized

through a long-distance dispersal event (not considered in our simulations) and/or that strong

geographic barriers (e.g., large inlets) prevented dispersal from the south. Other wrong assignments

occurred at the contact zone between the main genetic lineages, that is located slightly further south

compared to simulation predictions.


15

C) Factors influencing genetic diversity

Nuclear genetic diversity. According to the best-selected model (ωi=0.46, R2=0.45; Supplementary

Tables 9 to 12), nuclear genetic diversity (HE and AR) was positively associated with climatic suitable

area over the last 21 ka (Figure 4b and Supplementary Figure 8b) and it increased and then decreased

with latitude (Figure 4a and Supplementary Figure 8a). When the two highly isolated populations from

Greenland and Svalbard were excluded from the analyses, we also found a similar pattern with latitude

and climatic stability (Supplementary Tables 9 to 12). Genetic diversity was significantly higher in

Euro-Beringia (HE ± sd = 0.75 ± 0.13) in comparison to North America populations (HE ± sd = 0.69 ±

0.04), although these differences were only retained in the model obtained excluding Greenland

(HE=0.27) and Svalbard (HE=0.29). Similarly, census population size had a positive effect on HE only

when these two herds were excluded from the analyses (ωi=0.91, R2=0.80). Models performed with AR

showed similar patterns with latitude and climatic stability as those reported for HE (Supplementary

Table 11 and 12 and Supplementary Figure 8).

Mitochondrial genetic diversity. We observed overall lower haplotype (h ± sd) and nucleotide (π ±

sd) diversity in North America (h ± sd: 0.774 ± 0.031, π ± sd: 1.84x10-03 ± 0.2x10-03) in comparison

to Euro-Beringia (h ± sd: 0.981 ± 0.003, π ± sd: 6.30x10-03 ± 0.16x10-03; permutation tests: all P-

values < 0.01; Supplementary Table 8 and Supplementary Figure 8). At the herd level, climatic

stability was best explained by the current haplotype (h; ωi=0.52, R2=0.57; Figure 4c) and nucleotide

(π; ωi=0.36, R2=0.54, Supplementary Figure 8c) diversity within caribou herds (Supplementary Table

13 and 14). Models performed without Svalbard showed a similar pattern (data not shown).


16

D) Caveats

i) Genetic markers

Although our data indicate the usefulness of combining phylogeographic data sets based on

microsatellite loci, cytochrome b mitochondrial DNA sequence data and SDM, the drawn conclusions

rely on several assumptions. We used supposedly neutral genetic variation as a surrogate for

intraspecific genetic diversity, but this may not have occurred (but see 76,77). We specifically chose

microsatellite loci and the cyt b mitochondrial DNA because they are among the most common

markers for large-scale assessments of genetic population structure and phylogeography studies. The

control region has also been regularly used in population genetic or phylogeography studies of caribou

and reindeer for example see 64,72,78, but because our preliminary analyses conducted with control

region indicated that this fragment does not allow inferring accurate relationships among haplotypes at

worldwide scale, we used the cytochrome b following other studies27,28. With decreases in sequencing

costs, comparative homologous multi-locus data sets will become more readily available. An important

issue for SDM-based predictions is the difficulty of implementing inter-specific interaction, dispersal

and migration scenarios into projections of past and future distribution.

ii) Incongruence between empirical and modelled spatial genetic structures

Although the simulation of the caribou range shift through time recovered most of the

populations’ genetic structure, some populations were incorrectly assigned. Primarily, these mostly

concerned the secondary contact zone between clades located in central and western Canada, where

admixture at both individual and herd levels was observed (Figure 1). The simulation predicted that

the North America clade would occupy an area slightly more northward than observed, and

erroneously to be at the origin of the Greenland population. These inconsistencies may stem from

uncertainty related to ice sheet or climatic reconstructions. During the deglaciation of the Northern

Hemisphere, which started around 20,000 years ago and ended around 8,000 years ago 79, giant lakes

formed at the edges of ice sheets in North America and Eurasia 80. Some of these lakes were dammed

by the disappearing ice sheets and on some occasions these dams failed, producing gigantic floods.

The largest of these lakes was Glacial Lake Agassiz ice dam, dammed by the waning Laurentide Ice

Sheet 80. Such features, if not taken into account in SDMs, may lead to an overestimation of the past

caribou distribution and to incongruence between the empirical and modelled spatial genetic

structures. In addition, hindcasting current realized niche of species suffer from limitations such as


17

changes in biotic interactions (or human pressure 49) and potential evolution of the species niche

envelope 81. However, the change in species distribution highlighted in this study was mostly driven by

glaciations history, for which past reconstruction is less subject to uncertainty. Thus, such

uncertainties are not likely affecting the strong patterns observed in our study and should not affect

our overall conclusions.


18

SECTION S3- SUPPLEMENTARY FIGURES 1-9

Supplementary Figure 1 | Worldwide distribution of 14C dated caribou fossils, aged between 21 ka

and 1 ka. Data retrieved from 49,50.


19

Supplementary Figure 2 | Mean posterior probability (ln P(D) ± SD (black dots, left y-axis) and ∆K

(grey dots, right y-axis) given different numbers of genetic clusters (K ∈ [1-20]), each run was

performed 20 times for an admixture model with uncorrelated gene frequencies in STRUCTURE.


20

Supplementary Figure 3 | STRUCTURE clustering analysis of caribou based on 16 microsatellite loci

typed for 1,297 individuals. The number of genetic clusters (K) best fitting the data was inferred using

STRUCTURE without considering any prior information about the geographic origin of individuals. Data

were averaged with CLUMPP over the 10/50 highest likelihood runs (H’ index, a measure of similarity

between runs was > 0.99). See discussion in the Supplemental Material for an explanation of how K

values were chosen for presentation. Each individual is represented by a thin vertical line that is

partitioned into K coloured segments that indicate the individual’s probability of belonging to the

clusters with that colour. Individuals are sorted according to cluster membership and grouped into

main regions and herds (see Supplementary Table 1 for further information on herds).


21

Supplementary Figure 4 | Combined consensus chains from MSVAR. Posterior density distributions of

present (N0, black) and ancestral (N1, grey) population size on logarithmic scale for North America (A)

and Euro-Beringia (C). Supplementary Figures S4B and S4D show the time since the onset of

expansion (T) in years on a logarithmic scale for North America and Euro-Beringia, respectively.


22

Supplementary Figure 5 | Hindcasted distribution of caribou genetic lineages, as defined by the

spatial distribution simulation from 21 ka to present. Colours correspond to the localization and range

shift of the two genetic lineages overtime: North American clade in blue and Euro-Beringian clade in

red. The species range distribution model showed a high correspondence with the observed genetic

lineages (microsatellite: 85% of match; mtDNA: 87% of match with the 1 ka panel). Grey regions

represent unsuitable areas, i.e., areas falling below the ROC threshold (see Methods). Light blue

regions correspond to areas covered by ice.


23

Supplementary Figure 6 | Future predicted distribution of caribou for the 2020-2080 period under

the A1B IPCC4 climate change scenario from the global ocean-atmosphere climate model based on the

Hadley Centre climate model (HadCM3). Sienna and yellow regions represent future suitable and

unsuitable areas for caribou, respectively.


24

Supplementary Figure 7 | Relationships between allelic richness (Ar) and (a) latitude and (b)

climatic stability for the microsatellite data set. Ar is expressed as statistical residuals. (c) Relationship

between mtDNA nucleotide diversity π and climatic stability (for n≥5 sequenced samples per herd). Dot

colours correspond to Bayesian membership of each population to the North American

clade, considering two genetic clusters (K=2; blue for North American clade and red for Euro-Beringian

clade, respectively) obtained with STRUCTURE (see Fig. 1). Dot size is proportional to the (log10) census

size of the analyzed populations. Regression lines (solid lines) and 95% confidence intervals (dashed

lines) of the predicted models are represented. (d) Difference in HE and Ar between the two lineages.

*** Differences were only significant when populations from Greenland (HE:0.271 and Ar:1.7) and

Svalbard (HE:0.291 and Ar:1.75) were removed from the analyses (see Supplementary Tables 9 and

11).


25

Supplementary Figure 8 | Worldwide distribution of caribou genetic diversity estimated with

microsatellites and mtDNA data. For mtDNA, only herds with n≥5 sequenced samples per locality were

used.


27

SECTION S4 - SUPPLEMENTARY TABLES 1-14

Supplementary Table 1 | Geographical location and estimates of genetic variability for caribou herds. For microsatellite loci, statistics

include observed number of individuals genotyped per herd (Nnuc), observed heterozygosity (HO), expected heterozygosity (HE), allelic

richness (Na), and allelic richness estimated by rarefaction (Ar) and for mtDNA, number of individuals sequenced per herd (Nmt; including

additional samples retrieved from GenBank, see Supplementary Table 4), number of observed haplotypes (Nh), haplotype diversity (h) and

nucleotide diversity (π).

Num Herd Province/State Country Abrr. lat long Nnuc HO HE Na Ar Nmt Nh π h

1 Kangerlussuaq-Sisimiut Greenland Greenland KaSi 67.08 -50.9 29 0.281 0.271 2.4375 1.7 4 2 0.0010 0.50 2 Svalbard Svalbard Norway Sval 78.11 15.41 20 0.25 0.291 2.3125 1.75 10* 2 0.0004 0.36 3 Hardangervidda Norway Norway Harda 60.1 7.03 14 0.784 0.771 7.3125 3.87 9 3 0.0048 0.42 4 Snohetta Norway Norway Snoh 62.3 9.2 24 0.718 0.768 7.5 3.72 8 2 0.0048 0.54 5 Finnmark ¥ Norway Norway FinK 70 25.1 24 0.636 0.704 7 3.37 3 3 0.0065 1 6 Varanger ¥ Norway Norway Vara 70.37 30 12 0.734 0.707 5.9375 3.37 - - - - 7 Iceland ∆¥ Iceland Iceland Icel 65.09 -15.07 27 0.472 0.552 4.1875 2.54 4 1 0.0000 0 8 Finland Finland Finland Finl 64.4 29.3 23 0.775 0.766 8.3125 3.81 4 2 0.0030 0.50 9 Wrangel ∆¥ Chukotka Russia Wran 71.25 -179.67 6 0.708 0.654 4.125 3.24 2 2 0.0010 1 10 Nenetsky ¥ Yamalia Russia Nenet 68.32 53.16 7 0.759 0.697 5.25 3.59 2 2 0.0010 1 11 Yamal ¥ Yamalia Russia Yaml 69.96 70.09 3 0.771 0.663 3.625 3.63 2 2 0.0050 1 12 Taimyr Taimyr Russia Tayr 71.55 90.08 61 0.832 0.851 13.1875 4.36 2 2 0.0072 1 13 Lena Yakutia Russia Lena 72.45 127.38 16 0.84 0.831 9.625 4.33 2 2 0.0090 1 14 Olenek Yakutia Russia Yaku 73.09 120.14 20 0.782 0.759 7 3.74 4 4 0.0075 1 15 Western Arctic Alaska USA WesA 67.52 -158.3 25 0.806 0.845 11.5625 4.4 18* 13 0.0067 0.93

16 Northern Alaska Peninsula Alaska USA NoAP 57.56 -156.95 20 0.78 0.788 8.6875 3.96 1 1 0.0000 0

17 Teshekpuk Alaska USA Tesh 69.21 -154.79 20 0.843 0.85 11 4.46 14* 10 0.0073 0.93 18 Denali Alaska USA Dena 63.33 -150.5 6 0.709 0.636 4.9375 NA 4 4 0.0063 1 19 Central Arctic Alaska USA CenA 70.02 -148.95 22 0.815 0.838 11 4.36 32* 25 0.0066 0.98 20 White Mountains Alaska USA WhiM 65.53 -147.59 20 0.803 0.823 9.3125 4.22 2 2 0.0070 1 21 Porcupine Yukon Canada Porc 67.67 -141.04 29 0.843 0.847 12.125 4.4 33* 22 0.0069 0.96


28

22 Tay River Yukon Canada TayR 62.15 -132.35 7 0.799 0.79 6.6875 4.25 6 5 0.0070 0.93 23 South Nahanni Yukon Canada SNah 62.18 -128.59 22 0.802 0.821 10 4.2 2 2 0.0110 1 24 Bluenose East North West Territories Canada BluE 66.13 -117.85 31 0.844 0.857 12.0625 4.45 11* 4 0.0036 0.67 25 Bathurst North West Territories Canada Bath 64.44 -112.42 28 0.838 0.854 11.875 4.44 2 2 0.0020 1 26 Dolphin-Union Nunavut Canada DoUn 69.55 -109.36 14 0.853 0.795 7.5625 4.02 12* 7 0.0073 0.88 27 Beverly Nunavut Canada Beve 61.96 -104.58 18 0.836 0.818 10.6875 4.28 2 2 0.0090 1 28 Ahiak/Beverly Nunavut Canada AhBv 64.55 -104.3 32 0.837 0.849 11.875 4.39 2 2 0.0080 1 29 Bathurst Island Nunavut Canada Peary 75.77 -99.78 20 0.731 0.701 5.4375 3.26 7 2 0.0051 0.57 30 Qamanirjuaq Nunavut Canada Qama 60.52 -97.94 22 0.816 0.834 10.5625 4.32 4 4 0.0027 1 31 Besa Prophet British Columbia Canada BePr 57.47 -123.37 21 0.8 0.794 7.9375 3.97 3 3 0.0073 1 32 Narraway British Columbia Canada Narr 54.39 -120.3 20 0.78 0.771 7.8125 3.83 4 2 0.0007 0.67 33 A La Peche British Columbia Canada ALPe 53.54 -118.79 20 0.802 0.773 7.1875 3.8 5 3 0.0092 0.8 34 Columbia-North British Columbia Canada ColN 51.66 -118.63 24 0.754 0.754 7.5 3.65 1 1 0.0000 0 35 Chinchaga Alberta Canada Chin 57.51 -119.01 20 0.797 0.776 7.5 3.81 5 2 0.0078 0.60 36 Caribou Mountain Alberta Canada CarM 59.19 -115.59 20 0.769 0.761 8.25 3.83 2 2 0.0050 1 37 RedEarth Alberta Canada RedE 57.1 -114.7 20 0.715 0.738 6.75 3.54 4 1 0.0000 0 38 Cold Lake Alberta Canada CoLa 54.46 -110.18 20 0.745 0.778 7.875 3.89 9 1 0.0000 0 39 Naosap Lake Manitoba Canada NaoL 54.86 -101.4 23 0.762 0.733 6.9375 3.52 3 2 0.0013 0.67 40 The Bog Manitoba Canada TBog 53.35 -101.18 8 0.648 0.66 5.1875 3.41 1 1 0.0000 0 41 Harding Lake Manitoba Canada HarL 56.11 -98.22 20 0.643 0.674 6.5625 3.27 2 1 0.0000 0 42 Charron Lake Manitoba Canada CharL 53 -95.78 19 0.707 0.735 7.9375 3.63 2 2 0.0130 1 43 La Sarre Quebec Canada LaSa 48.77 -79.17 23 0.691 0.694 6.5625 3.33 2 2 0.0050 1 44 Val d'Or Quebec Canada VaOr 47.74 -78.21 35 0.587 0.631 6.125 2.94 8 4 0.0024 0.79 45 Temiscami Quebec Canada Temi 50.57 -75.48 23 0.722 0.731 7.75 3.55 1 1 0.0000 0 46 Rivière-aux-Feuilles Quebec Canada Leaf 56.89 -73.95 25 0.678 0.72 8.4375 3.57 4 1 0.0000 0 47 Port-Neuf Quebec Canada PoNe 49.13 -70.41 35 0.657 0.696 5.875 3.21 - - - - 48 Pipmuacan Quebec Canada Pipm 49.66 -70.27 29 0.691 0.713 7.1875 3.39 2 2 0.0020 1 49 Manicouagan Quebec Canada Manic 50.96 -68.53 34 0.632 0.726 7.75 3.51 4 1 0.0000 0 50 Gaspesie Quebec Canada Gasp 48.93 -66.28 29 0.557 0.596 4.375 2.73 4 1 0.0000 0 51 Rivière-George Quebec Canada Geor 55.72 -63.99 25 0.693 0.746 8.625 3.69 9 3 0.0057 0.64 52 La Romaine Quebec Canada Roma 50.98 -63.36 31 0.649 0.689 6.4375 3.25 2 1 0.0000 0 53 Bowater Quebec Canada Bowa 50.45 -71.75 33 0.635 0.696 7.125 3.32 2 2 0.0050 1 54 Lake Joseph Labrador Canada LJos 52.45 -64.65 37 0.725 0.741 8.0625 3.56 23* 8 0.0046 0.80 55 Torngat Labrador Canada Torn 58.24 -63.22 23 0.729 0.751 8.875 3.7 2 2 0.0010 1 56 RedWine Labrador Canada Rwin 53.21 -61.63 20 0.697 0.701 6.5625 3.44 2 2 0.0050 1 57 Mealy Mountain Labrador Canada Mealy 53.67 -57.68 14 0.69 0.692 6.0625 3.44 8 4 0.0033 0.75


29

58 Gaff Topsails Newfoundland Canada GaTop 49.15 -56.65 13 0.619 0.642 5.25 3.12 4 3 0.0018 0.70 59 Pot Hill Newfoundland Canada PHill 48.59 -55.72 11 0.614 0.631 4.375 3.01 - - - -

na: not estimated for this herd because the 6 individuals sampled failed to amplify at locus BL42. ¥: indicates domestic populations. ∆: indicates introduced populations. *: Sample size was increased with samples retrieved from GenBank (see Supplementary Table 4).


30

Supplementary Table 2 | Primer description, fluorescent dye and concentration for the microsatellite markers used in this study. Asterisks

indicate primers that have been modified from their originally published sequences and kindly provided by David Paetkau, Wildlife Genetics

International, Nelson, BC, Canada. Number of alleles (Na) and observed (HO) and expected (HE) heterozygosity are indicated for each marker.

Locus Dye Multiplex F R Primer (µM)

Na HO HE Ref. BL42 PET 1 GCATTTTTGTGTTAATTTCATGC CAAGGTCAAGTCCAAATGCC 0.3 25 0.748 0.769 4 BMS745 * PET 1 TGCAAGCTGTGAGGAGGAG AGGGACTTGTTACCCGTGG 0.1 11 0.655 0.665 5 FCB193 NED 1 TTCATCTCAGACTGGGATTCAGAAAGGC GCTTGGAAATAACCCTCCTGCATCCC 0.15 16 0.746 0.765 6 NVHRT16 PET 1 ATTCTAAGCCCAAATAATCTT TCTAAGGGGTCTGTGTCTT 0.4 16 0.590 0.665 2 OheQ * VIC 1 AGACCTGATTACAATGTGTCAGTGAAGGTCTTC GATGGACCCATCCAGGCAACCATCTAG 0.2 19 0.780 0.797 7 BM6506 FAM 2 GCACGTGGTAAAGAGATGGC AGCAACTTGAGCATGGCAC 0.2 20 0.752 0.743 4 BMS1788 * VIC 2 ATTCATATCTACGTCCAGATTCAGATTTCTTG GGAGAGGAATCTTGCAAAGG 0.1 32 0.790 0.816 5 NVHRT30 VIC 2 GTGGAGCATTGTGTATGTGT GCCCCCACTGTGTTTT 0.15 16 0.694 0.758 2 Rt24 NED 2 TGTATCCATCTGGAAGATTTCAG CAGTTTAACCAGTCCTCTGTG 0.2 21 0.689 0.722 3 Rt6 FAM 2 TTCCTCTTACTCATTCTTGG CGGATTTTGAGACTGTTAC 0.4 18 0.745 0.757 3 BM4513 PET 3 TCAGCAATTCAGTACATCACCC GCGCAAGTTTCCTCATGC 0.3 41 0.752 0.828 4 Rt1 FAM 3 TGCCTTCTTTCATCCAACAA CATCTTCCCATCCTCTTTAC 0.15 18 0.813 0.798 3 Rt27 FAM 3 CCAAAGACCCAACAGATG TTGTAACACAGCAAAAGCATT 0.1 22 0.592 0.718 3 Rt5 NED 3 CAGCATAATTCTGACAAGTG AATTCCATGAACAGAGGAG 0.1 21 0.703 0.776 3 Rt7 VIC 3 CCTGTTCTACTCTTCTTCTC ACTTTTCACGGGCACTGGTT 0.15 16 0.715 0.727 3 Rt9s VIC 3 TGAAGTTTAATTTCCACTCT CAGTCACTTTCATCCCACAT 0.3 19 0.780 0.760 3


31

Supplementary Table 3 | Primer description for the mtDNA marker (cyt b gene) used in this study. All primers have been modified from

their originally published sequences.

L H Ref.

cyt b - Rangifer LGL765 5'-GAAAAACCACCGTTGTCATTCAACT-3’ LGL766 5'-GTTTAATTAGAACTTCAGCTTTGGG-3' 82

Nested cyt b - Rangifer L14153 5′-TCAATGACCAACATCCGAAA-3′ H15399 5′-GGGTGTTGATAGTGGGGCTA-3 83


32

Supplementary Table 4 | Specimen information of sequences retrieved from GenBank and used for mtDNA analyses. Geographic origin of

samples and GenBank Accession No. are provided. Haplotype frequencies are indicated in parentheses see 27,28 for details.

Locality (Abbr.) Subspecies Genbank accession numbers Western Arctic, Alaska, USA (WesA) R.t.caribou AY726683 (5), AY726684 (1), AY726686 (2), AY726687 (1),

AY726689 (1), AY726700 (1), AY726702 (1), AY726704 (1), AY726719 (1), AY726725 (1), AY726726 (1), AY726727 (1), AY726730 (1).

Central Arctic, Alaska, USA (CenA) R.t.grantii AY726680 (1), AY726682 (1), AY726683 (3), AY726684 (1), AY726685 (4), AY726686 (1), AY726687 (1), AY726688 (1), AY726690 (1), AY726692 (1), AY726697 (1), AY726698 (1), AY726701 (1), AY726703 (1), AY726711 (1), AY726712 (1), AY726714 (1), AY726715 (1), AY726717 (1), AY726718 (1), AY726721 (1), AY726723 (1), AY726724 (1), AY726728 (1), AY726730 (1).

Teshekpuk, Alaska, USA (Tesh) R.t.grantii AY726683 (1), AY726684 (3), AY726685 (1), AY726686 (1), AY726694 (1), AY726698 (1), AY726708 (1), AY726716 (3).

Porcupine, Yukon, Canada (Porc) R.t.grantii AY726682 (5), AY726683 (3), AY726684 (2), AY726685 (1), AY726686 (1), AY726687 (3), AY726688 (2), AY726690 (1), AY726691 (1), AY726692 (1), AY726693 (1), AY726695 (1), AY726696 (2), AY726697 (1), AY726699 (1), AY726707 (1), AY726709 (1), AY726710 (1), AY726713 (1), AY726722 (1).

Bluenose, North West Territories, Canada (Blue) R.t.grantii AY726679 (6), AY726680 (3). Dolphin-Union, North West Territories, Canada (DoUn)

R.t.groenlandicus AY726679 (3), AY726680 (1), AY726691 (2), AY726705 (1), AY726706 (1), AY726720 (1), AY726729 (1).

Kimmirut, Nunavut, Canada (Kimm) R.t.groenlandicus AY726679 (5), AY726680 (4), AY726689 (2). Alberta, Canada R.t.caribou AY726676 (3). Val d'Or, Quebec, Canada (VaOr R.t.caribou AY726677 (3), AY726678 (3). Rivière-George, Quebec, Canada (GeoR) R.t.caribou AY726674 (4), AY726675 (1), AY726681 (2). Newfoundland, Canada R.t.caribou AY726672 (3), AY726673 (1). Norway (Harda) R.t.tarandus DQ673123 (6) Magadan, Siberia, Russia (Maga) R.t.tarandus AY726730 (9), DQ673122 (5), DQ673126 (2), DQ673127

(3), DQ673132 (1), DQ673133 (1), DQ673135 (1) Sweden (Snoh) R.t.tarandus DQ673125 (3) Svalbard (Sval) R.t.platyrhynchus DQ673124 (5)


33

Supplementary Table 5 | Capreolinae specimen information of sequences retrieved from GenBank

and used in the Time to Most Recent Common Ancestor analyses. Geographic origin of the samples

and GenBank Accession No. are indicated.

Tribe Genus species Common name Geographic range GenBank Acc. No. Ref.

Alceini Alces alces Eurasian elk North Eurasia AJ000026 84

Capreolini Capreolus capreolus Roe deer Palaearctic AJ000024 84

Hydropotes inermis Chinese water deer China and Korea AJ000028 84

Odocoileini Mazama americana Red brocket Northern South America AJ000027 84

Odocoileus hemionus Mule deer North America AF091630 84

Odocoileus virginianus White-tailed deer Central and North America DQ379370 38

Blastocerus dichotomus Marsh deer South America DQ379306 38

Mazama gouazoubira Gray brocket South America DQ379308 38

Pudu puda Pudu South America DQ379309 38

Hippocamelus antisensis Huemul South America DQ379307 38


34

Supplementary Table 6 | Results from simple and partial Mantel tests examining the effects of

distance and genetic clustering on population differentiation. Genetic distances are based on Nei’s Da

distance and geographic distance (km) on the waypoint distance (see Methods). For each Mantel test,

a period separates the main matrices on the left from the covariate matrix on the right (e.g., “Nei's Da

~ Clustering.Distance“ test the correlation between “Nei’s Da and “Clustering“ controlling for

“Distance”). All tests (one-sided) were conducted with R version 3.0.0 13 using the package ‘ecodist’

version 1.1.4 20, and significance was assessed with 10,000 permutations. We used a sequential

Bonferroni technique to correct for multiple testing 85 considering an overall significance level of 0.05.

Mantel r P-value llim.2.5% ulim.97.5%

Worldwide scale

Nei's Da ~ Distance 0.57 0.001 0.54 0.60

Nei's Da ~ Clustering 1.Distance 0.31 0.001 0.27 0.37

Nei's Da ~ Distance.Clustering 1 0.50 0.001 0.48 0.53



Without Greenland and Svalbard

Nei's Da ~ Distance 0.63 0.001 0.60 0.65




Nei's Da ~ Distance.Clustering 2 0.48 0.001 0.45 0.51 1 – Two clusters were considered: Euro-Beringia and North America.

2 – Three clusters were considered: Euro-Beringia, North America, and Fennoscandia


35

Supplementary Table 7 | Means and 95% Highest Probability Density (HPD) intervals of present

population size (log N0), past population size (log N1) and time in years since onset of increase (log T)

estimated using MSVAR.

mean 95% HPD intervals

North America log N0 4.92 4.19-5.59

log N1 3.51 2.89-4.22

log T 4.03 3.39-4.76

Euro-Beringia log N0 6.39 4-76-5.45

log N1 3.45 1.28-8.03

log T 3.35 1.45-5.31


36

Supplementary Table 8 | Genetic diversity within Euro-Beringia and North America clades for cyt b

mtDNA (1,145bp).

Euro-Beringia North America Worlwide

Number of sequences 260 85 345

Number of haplotypes, nh 105 17 122

Number of singleton S 117 18 128

Haplotype diversity, h ± sd 0.981 ± 0.003 0.774 ± 0.031 0.976 ± 0.004

Nucleotide diversity, π ×103 ± sd 6.30 ± 0.16 1.84 ± 0.20 7.39 ± 0.14 Average number of nucleotide differences, k 7.21 2.11 8.464


37

Supplementary Table 9 | Generalized Linear Models (GLM) explaining expected heterozygosity (HE) in caribou herds. For each model,

differences in AICc are compared to the lowest-scoring model (ΔAICc). Number of parameters (K) and AICc weight (ωi) are given. Bold type

highlights the best and/or equivalent models (∆AIC≤2) for each dataset. See methods for details about the four explanatory covariates

considered in the GLMs: Latitude, climatic stability (Stability), census population size (Size) and genetic clustering (Clustering).

Whole data set Without Greenland and Svalbard No. Models K AICc ∆AICc ωi K AICc ∆AICc ωi 1 Latitude 3 -69.16 23.71 0 3 -161.2 29.18 0 2 Stability 3 -79.02 13.85 0 3 -139.57 50.81 0 3 Clustering 3 -72.26 20.61 0 3 -164.43 25.95 0 4 Size 3 -71.02 21.85 0 3 -148.94 41.45 0 5 Latitude + Stability 4 -78.1 14.77 0 4 -159.38 31.01 0 6 Latitude + Clustering 4 -70.53 22.34 0 4 -169.85 20.54 0 7 Latitude + Size 4 -68.68 24.19 0 4 -164.21 26.17 0 8 Stability + Clustering 4 -77.57 15.3 0 4 -172.32 18.07 0 9 Stability + Size 4 -76.69 16.18 0 4 -152.38 38.01 0 10 Latitude + Latitude2 4 -78.57 14.3 0 4 -177.29 13.09 0

11 Latitude + Stability + Clustering 5 -83.92 8.95 0.01 5 -170.86 19.53 0

12 Latitude + Stability + Size 5 -76.35 16.52 0 5 -161.9 28.48 0

13 Latitude + Stability + Clustering + Size 6 -83.23 9.64 0 6 -181.12 9.26 0.01

14 Latitude + Latitude2 + Stability 5 -92.87 0 0.46 5 -181.01 9.37 0.01

15 Latitude + Latitude2 + Stability + Clustering 6 -92.11 0.76 0.31 6 -185.16 5.22 0.07

16 Latitude + Latitude2 + Stability + Size 6 -90.35 2.52 0.13 6 -181.19 9.2 0.01

17 Latitude + Latitude2 + Stability + Clustering + Size 7 -89.51 3.36 0.09 7 -190.38 0 0.91 18 Null 2 -70.04 22.83 0 2 -120.42 69.96 0


38

Supplementary Table 10 | Parameter estimates ( β) with their standard error (SE) and 95% confidence intervals (CI) for the best and

equivalent models (∆AICc ≤2) for expected heterozygosity (HE) in caribou herds. Parameter estimates that are not significantly different from

0 are in bold. See methods for details about the four explanatory covariates considered in the GLMs: Latitude, climatic stability (Stability),

census population size (Size) and genetic clustering (Clustering).

Whole data set a Without Greenland and Svalbard β SE 2.50% 97.50% β SE 2.50% 97.50% Intercept -2.11x10+00 8.82x10-01 -3.84x10+00 -3.82x10-01 -5.37x10-01 3.91x10-01 -1.52x10+00 5.78x10-01 Latitude 9.60x10-02 2.81x10-02 4x10x10-02 1.51x10-01 4.19x10-02 1.26x10-02 1.32x10-02 7.08x10-02 Latitude2 -8.24x10-04 2.23x10-04 -1.26x10-03 -3.88x10-04 -3.46x10-04 1.00x10-04 -5.74x10-04 -1.12x10-04 Stability 1.74x10-05 4.02x10-06 9.48x10-06 2.52x10-05 4.61x10-06 1.73x10-06 9.73x10-07 8.18x10-06 Clustering -6.56x10-02 5x10x10-02 -1.66x10-01 3.45x10-02 -6.66x10-02 1.94x10-02 -1.09x10-01 -2.52x10-02 Size 1.39x10-02 5.05x10-03 3.71x10-03 2.52x10-02

a Model-averaged parameter estimates ( β) with their unconditional standard error (SE) and 95% confidence intervals.


39

Supplementary Table 11 | Generalized Linear Models (GLM) explaining allelic richness (AR) in caribou herds. For each model, differences in

AICc are compared to the lowest-scoring model (ΔAICc). Bold type highlights the best and/or equivalent models (∆AIC≤2) for each dataset.

Number of parameters (K) and AICc weight (ωi) are given. See methods for details about the four explanatory covariates considered in the

GLMs: Latitude, climatic stability (Stability), census population size (Size) and genetic clustering (Clustering).

Whole data set Without Greenland and Svalbard No. Models K AICc ∆AICc ωi K AICc ∆AICc ωi 1 Latitude 3 99.75 29.93 0.00 3 30.45 37.36 0.00 2 Stability 3 89.36 19.54 0.00 3 54.07 60.98 0.00 3 Clustering 3 94.36 24.54 0.00 3 28.33 35.24 0.00 4 Size 3 97.02 27.21 0.00 3 42.55 49.46 0.00 5 Latitude + Stability 4 91.70 21.89 0.00 4 32.52 39.43 0.00 6 Latitude + Clustering 4 96.67 26.86 0.00 4 21.64 28.55 0.00

7 Latitude + Size 4 98.89 29.07 0.00 4 26.27 33.18 0.00 8 Stability + Clustering 4 86.93 17.11 0.00 4 20.66 27.57 0.00 9 Stability + Size 4 90.35 20.53 0.00 4 39.35 46.26 0.00

10 Latitude + Latitude2 4 84.79 14.98 0.00 4 8.16 15.07 0.00

11 Latitude + Stability + Clustering 5 83.37 13.56 0.00 5 21.45 28.36 0.00

12 Latitude + Stability + Size 5 92.36 22.55 0.00 5 28.71 35.62 0.00

13 Latitude + Stability + Clustering + Size 6 81.73 11.92 0.00 6 8.57 15.48 0.00

14 Latitude + Latitude2 + Stability 5 69.81 0.00 0.38 5 4.15 11.06 0.00

15 Latitude + Latitude2 + Stability + Clustering 6 69.90 0.08 0.37 6 0.64 7.55 0.02

16 Latitude + Latitude2 + Stability + Size 6 72.31 2.50 0.11 6 2.64 9.55 0.01

17 Latitude + Latitude2 + Stability + Clustering + Size 7 71.93 2.12 0.13 7 -6.91 0.00 0.96 18 Null 2 103.82 34.01 0.00 2 73.48 80.39 0.00


40

Supplementary Table 12 | Parameter estimates ( β) with their standard error (SE), 95% confidence intervals (CI) and for the best and

equivalent models (∆AIC≤2) that predicted allelic richness (Ar) in caribou herds. Parameter estimates that are not significantly different from

0 are in bold. See methods for details about the four explanatory covariates considered in the GLMs: Latitude, climatic stability (Stability),

census population size (Size) and genetic clustering (Clustering).

Whole data seta Without Greenland and Svalbard β SE 2.50% 97.50% β SE 2.50% 97.50% Intercept -1.37x10+01 4.47x10+00 -2.25x10+01 -4.94x10+00 -6.43 X 10+00 2.45 X 10+00 -1.12 X 10+01 -1.51 X 10+00 Latitude 5.74x-01 1.41 X 10-01 2.98 X 10-01 8.51 X 10-01 3.29 X 10-01 7.89 X 10-02 1.69 X 10-01 4.82 X 10-01 Latitude2 -4.80x10-03 1.11x10-03 -6.97x10-03 -2.62x10-03 -2.69x10-03 6.29x10-04 -3.91x10-03 -1.41x10-03 Stability 8.65x10-05 1.99x10-05 4.75x10-05 1.26x10-04 2.94x10-05 1.08x10-05 7.74x10-06 5.11x10-05 Clustering -3.65x10-01 2.47x10-01 -8.49x10-01 1.20x10-01 -4.13x10-01 1.18x10-01 -6.51x10-01 -1.71x10-01 Size 9.75x10-02 3.08x10-02 3.58x10-02 1.61x10-01

a Model-averaged parameter estimates ( β) with their unconditional standard error (SE) and 95% confidence intervals.


41

Supplementary Table 13 | Generalized Linear Models (GLM) explaining mtDNA genetic diversity in caribou herds (for n≥5 sequenced

samples per herd). For each model, differences in AICc are compared to the lowest-scoring model (ΔAICc). Bold type highlights the best

and/or equivalent models (∆AIC≤2) for each dataset. Number of parameters (K) and AICc weight (ωi) are given. See methods for details

about the four explanatory covariates considered in the GLMs: Latitude, climatic stability (Stability), census population size (Size) and genetic

clustering (Clustering).

Nucleotide diversity π Haplotype diversity h No. Models K AICc ∆AICc ωi K AICc ∆AICc ωi 1 Latitude 3 85.44 6.03 0.02 3 10.35 12.48 0 2 Stability 3 79.41 0 0.36 3 -2.13 0 0.58 3 Clustering 3 83.98 4.57 0.04 3 11.56 13.68 0 4 Size 3 81.75 2.34 0.11 3 5.44 7.57 0.01 5 Latitude + Stability 4 82.87 3.47 0.06 4 1.33 3.46 0.1 6 Latitude + Clustering 4 87.4 7.99 0.01 4 13.7 15.83 0 7 Latitude + Size 4 85.16 5.75 0.02 4 8.92 11.05 0 8 Stability + Clustering 4 82.3 2.89 0.08 4 0.77 2.89 0.14 9 Stability + Size 4 82.69 3.28 0.07 4 1.34 3.47 0.1 10 Latitude + Latitude2 4 81.91 2.5 0.1 4 11.89 14.02 0

11 Latitude + Stability + Clustering 5 85.46 6.06 0.02 5 4.6 6.73 0.02

12 Latitude + Stability + Size 5 86.68 7.27 0.01 5 5.41 7.54 0.01

13 Latitude + Stability + Clustering + Size 6 90.1 10.69 0 6 9.48 11.61 0

14 Latitude + Latitude2 + Stability 5 83.51 4.1 0.05 5 5.43 7.56 0.01

15 Latitude + Latitude2 + Stability + Clustering 6 88.37 8.97 0 6 9.35 11.47 0

16 Latitude + Latitude2 + Stability + Size 6 88.03 8.62 0 6 10.24 12.36 0

17 Latitude + Latitude2 + Stability + Clustering + Size 7 94.07 14.66 0 7 15.38 17.51 0 18 Null 2 83.41 4 0.05 2 9.13 11.25 0


42

Supplementary Table 14 | Parameter estimates ( β) with their standard error (SE) and 95% confidence intervals (CI) for the best models

(∆AIC≤2) that predicted Nucleotide diversity π and Haplotype diversity h in caribou herds. Parameter estimates that are not significantly

different from 0 are italicized. See methods for details about the four explanatory covariates considered in the GLMs: Latitude and climatic

stability (Stability).

Nucleotide diversity π Haplotype diversity h β SE 2.50% 97.50% β SE 2.50% 97.50% Intercept 2.90x10+00 1.07x10+00 7.95x10-01 5.01x10+00 3.81x10-01 9.77x10-02 1.89x10-01 5.72x10-01 Stability 2.84x10-04 1.03x10-04 8.26x10-05 4.86x10-04 4.15x10-05 9.35x10-06 2.31x10-05 5.98x10-05


44

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genetic diversity in caribou linked to past and future climate change

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