Major histocompatibility complex diversity is positively ...Major histocompatibility complex...

Major histocompatibility complex diversity is positivelyassociated with stream water temperatures in proximatepopulations of sockeye salmon

W. A. LARSON1, P. J . L IS I2 , J . E . SEEB, L . W. SEEB & D. E. SCHINDLER

School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, USA

Keywords:

genetic diversity;

local adaptation;

major histocompatibility complex;

pathogens;

sockeye salmon;

temperature;

temperature variation.

Abstract

Local adaptation to heterogeneous environments generates population

diversity within species, significantly increasing ecosystem stability and flows

of ecosystem services. However, few studies have isolated the specific mech-

anisms that create and maintain this diversity. Here, we examined the rela-

tionship between water temperature in streams used for spawning and

genetic diversity at a gene involved in immune function [the major histo-

compatibility complex (MHC)] in 14 populations of sockeye salmon (Oncor-

hynchus nerka) sampled across the Wood River basin in south-western

Alaska. The largest influence on MHC diversity was lake basin, but we also

found a significant positive correlation between average water temperature

and MHC diversity. This positive relationship between temperature and

MHC diversity appears to have been produced by natural selection at

very local scales rather than neutral processes, as no correlation was

observed between temperature and genetic diversity at 90 neutral mark-

ers. Additionally, no significant relationship was observed between tem-

perature variability and MHC diversity. Although lake basin was the

largest driver of differences in MHC diversity, our results also demonstrate

that fine-scale differences in water temperature may generate variable

selection regimes in populations that spawn in habitats separated by as

little as 1 km. Additionally, our results indicated that some populations

may be reaching a maximum level of MHC diversity. We postulate that

salmon from populations in warm streams may delay spawning until late

summer to avoid thermal stress as well as the elevated levels of pathogen

prevalence and virulence associated with warm temperatures earlier in

the summer.

Introduction

Genotypic and phenotypic diversity within species can

generate portfolio effects that maintain ecosystem

functions and flows of ecosystem services, even in

the presence of significant environmental fluctuation

(Hilborn et al., 2003; Figge, 2004; Schindler et al.,

2015). Landscape heterogeneity is thought to play a

major role in promoting this diversity (e.g. Quinn

et al., 2001; Eckert et al., 2010); however, elucidating

the environmental factors that generate and maintain

certain genotypes and phenotypes through natural

selection has been difficult in nonmodel organisms.

This knowledge gap prevents resource managers from

effectively prioritizing habitats and populations for

conservation (Luck et al., 2003). Further, with antici-

pated climate change, there is a need to understand how

populations may respond to new environmental condi-

tions generated by changing thermal and precipitation

Correspondence: Wesley A. Larson, School of Aquatic and Fishery

Sciences, University of Washington, 1122 NE Boat Street, Box 355020,

Seattle, WA 98195-5020, USA.

Tel.: +1 760 613 7282; fax: +1 715 346 3624;

e-mail: [email protected] address: College of Natural Resources, University of Wisconsin-

Stevens Point, 800 Reserve Street, Stevens Point, WI 54481, USA2Present address: Center for Limnology, University of Wisconsin, 680 N.

Park Street, Madison WI 53705, USA

ª 2016 EUROPEAN SOC I E TY FOR EVOLUT IONARY B IOLOGY . J . E VOL . B I O L .

1JOURNAL OF EVOLUT IONARY B IO LOGY ª 20 1 6 EUROPEAN SOC I E TY FOR EVOLUT IONARY B IO LOGY

doi: 10.1111/jeb.12926

regimes. Here, we combine genetic and environmental

data to investigate the influence of water temperature on

adaptive genetic diversity at an immune response gene,

the major histocompatibility complex (MHC), in sockeye

salmon (Oncorhynchus nerka) sampled across a single river

basin in south-western Alaska (USA).

Major histocompatibility complex genes encode

molecules that are involved in the recognition of

pathogens, representing an important component of

immune defence in vertebrates (Potts & Wakeland,

1990; Matsumura et al., 1992). The MHC is divided

into two major classes, class I and class II. MHC class I

molecules bind to antigens derived from intracellular

pathogens whereas MHC class II molecules are found

on specialized antigen-presenting cells and bind to

antigens from extracellular pathogens. Different class I

and class II variants often recognize different patho-

gens, leading to high levels of polymorphism and

strong signals of selection across populations and spe-

cies (reviewed in Piertney & Oliver, 2006; Bernatchez

& Landry, 2003). Many studies have hypothesized that

these patterns can be attributed to spatial variation in

pathogen communities or pathogen virulence (e.g.

Miller et al., 2001; Ekblom et al., 2007; Evans et al.,

2010; Gomez-Uchida et al., 2011). However, few have

linked MHC diversity to environmental features, such

as temperature, that may influence pathogen commu-

nities or organism vulnerability to them (but see

Dionne et al., 2007).

Temperature and temperature variation can signifi-

cantly influence pathogen communities and the ability

of pathogens to infect their hosts. For example, higher

temperatures can increase the virulence, diversity and

prevalence of pathogens (Mitchell et al., 2005; Luque &

Poulin, 2008; Karvonen et al., 2013). Additionally, high

temperature variation can facilitate pathogen transmis-

sion at lower mean temperatures (Paaijmans et al.,

2010) and influence the developmental rate of patho-

gens (Vangansbeke et al., 2015).

The relationship between temperature and the

effects of pathogens is especially evident in salmon,

which often complete an arduous migration from the

marine environment to their natal freshwater habitats

to spawn. Elevated temperatures during this migration

and on the spawning grounds can increase pathogen

prevalence and virulence and eventually lead to pres-

pawn mortality (reviewed in Miller et al., 2014). It is

also important to note that salmon populations dis-

play differences in thermal tolerance over both large

and small spatial scales (Eliason et al., 2011, 2013;

Anttila et al., 2014). Although most of the research

on this topic has focused on metabolic responses to

thermal stress, it is likely that thermotolerant popula-

tions have also evolved mechanisms for coping with

increased levels of pathogen prevalence and virulence

associated with high temperatures. Physiological stress

on salmon at warm temperatures may also make

them more vulnerable to pathogen infection. For

example, salmon from warmer streams may delay

spawning to avoid thermal stress and the increased

levels of pathogen prevalence and virulence associated

with warmer temperatures that occur during summer.

Indeed, for sockeye salmon populations spawning in

south-west Alaska, there is a positive correlation

between summer stream water temperatures and the

seasonal timing of spawning (Lisi et al., 2013). Differ-

ent patterns of MHC diversity may have also evolved

in salmon populations that experience thermal stress

compared to those that are not subjected to elevated

temperatures and, therefore, likely reflect evolution-

ary responses to thermal stress in a population’s evo-

lutionary history.

In theory, MHC diversity in salmon populations that

experience high temperatures should increase in

response to elevated levels of pathogen prevalence and

virulence. Dionne et al. (2007) tested this hypothesis

and found that water temperature, measured as accu-

mulated degree days, was positively correlated with

MHC diversity across 34 populations of Atlantic salmon

(Salmo salar). Sampling occurred over a 12° latitudinal

gradient, establishing that MHC diversity is associated

with temperature across large spatial scales. However, it

is unclear whether variation in MHC diversity is associ-

ated with fine-scale differences in temperature com-

monly found among tributaries within river basins (Lisi

et al., 2013).

At the basin scale, topographic features and differ-

ences in water sources can produce substantial varia-

tion in water temperature that can span thermal

differences found in rivers over broad latitudinal gradi-

ents (Poole & Berman, 2001; Beechie et al., 2013; Lisi

et al., 2015). The influence of temperature on MHC

diversity has been suggested at the basin scale

(McGlauflin et al., 2011; Larson et al., 2014), but has

not been thoroughly investigated. Here, we investigated

the influence of temperature and temperature variation

on MHC diversity in sockeye salmon (Oncorhynchus

nerka) from 14 streams in the Wood River basin in

south-western Alaska. Previous studies have demon-

strated that differences in MHC diversity are associated

with life history type (beach, river, stream) in popula-

tions of sockeye salmon from the Wood River basin

(McGlauflin et al., 2011; Larson et al., 2014). Our

results extend these findings and suggest that, although

lake basin has a large effect of MHC diversity, fine-scale

differences in thermal regimes can result in differences

in MHC diversity among stream populations that are

separated by as little as 1 km. We also investigated the

relationship between temperature patterns throughout

the summer and spawn timing and found that changes

in spawn timing may represent an additional mecha-

nism to cope with the increased levels of pathogen

prevalence and virulence associated with high water

temperatures.

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2 W. A. LARSON ET AL.

Materials and methods

Study site

The Wood River basin (59°200N, 158°400W) drains a

series of five large interconnected lakes, fed by numer-

ous small first- to fourth-order streams. The drainage is

essentially free of anthropogenic development and

annually provides productive spawning habitat for

more than 1 million sockeye salmon that escape the

commercial fishery and return to their natal spawning

areas with high fidelity (stray rate < 1%, Hendry et al.,

2004). A significant portion of these salmon spawn in

small streams with ecologically significant differences in

spawn timing over the summer months (Lisi et al.,

2013; Schindler et al., 2013). Streams that support sal-

mon vary in a number of geomorphic characteristics

that affect temperature including width, depth, water-

shed slope, drainage area, elevation, contribution of

snow to stream flow and the presence of lakes in the

headwaters (Lisi et al., 2013, 2015; Lisi & Schindler,

2014). In general, streams draining flatter watersheds

are often fed by lakes and are generally warmer than

streams draining steeper, higher elevation watersheds

dominated by snowmelt. Temperatures can differ by as

much as 16 °C among proximate streams, creating a

mosaic of thermal variation across the system (Arm-

strong et al., 2010; Ruff et al., 2011). The amount of

daily temperature variation also differs among streams,

likely due to differences in shading, flow, buffering by

small upstream lakes and water residence time (Isaak &

Hubert, 2001; Caissie, 2006; Lisi et al., 2013).

Genetic and temperature data

We obtained genetic data for sockeye salmon from 14

streams in the Wood River basin as described in Larson

et al. (2014) (Table 1, Fig. 1). These data consisted of

genotypes from a 180-base pair fragment of the MHC

class II b1 exon (~ 46 individuals/stream, see Table S1

for exact sample sizes) and 90 putatively neutral single

nucleotide polymorphisms (SNPs) (~ 95 individuals/

stream, see Table S1 for exact sample sizes). The region

of the MHC that we sequenced contained 16 polymor-

phic sites, and all sites coded for nonsynonymous

changes in amino acids, suggesting that the MHC is

under strong selection in this system (Larson et al.,

2014). The 90 neutral SNPs were described by Elfstrom

et al. (2006) and Storer et al. (2012) and were found to

be putatively neutral according to a test for selection

described in Larson et al. (2014). See Larson et al.

(2014) for methods used to extract DNA and obtain

genotypes.

Genetic diversity was quantified with three measures:

observed heterozygosity (HO), allelic richness (AR) and

amino acid diversity (aa div, MHC only). Estimates of

HO and AR were available from Larson et al. (2014),

and aa div was calculated as the average number of

amino acid substitutions per site within each popula-

tion in MEGA6 (Tamura et al., 2013). Patterns of popu-

lation structure in the neutral data set were visualized

with a neighbour-joining tree based on Nei’s DA dis-

tance with 1000 bootstrap replicates. The tree was con-

structed in POPTREE2 (Takezaki et al., 2010). We also

obtained estimates of pairwise-FST based on the neutral

Table 1 Collection data and summary statistics for 14 populations in this study. Population numbers correspond to Fig. 1.

Pop # Population Lake Group

Loci

Temp (°C) PC1 PC2

MHC Neutral

HO HE AR aa div HO He AR

1 Uno Creek Beverley Upper Basin 0.79 0.84 9.89 0.083 0.26 0.26 1.85 5.93 �2.61 1.31

2 Moose Creek Beverley Upper Basin 0.91 0.86 15.66 0.079 0.25 0.25 1.85 12.14 3.30 0.01

3 Kema Creek Nerka Upper Basin 0.88 0.87 15.57 0.089 0.25 0.26 1.86 11.05 1.87 �2.57

4 Joe Creek Nerka Upper Basin 0.84 0.79 14.77 0.075 0.27 0.27 1.88 6.64 �1.83 1.83

5 Pick Creek Nerka Upper Basin 0.75 0.80 16.59 0.078 0.25 0.26 1.88 6.25 �2.34 �2.74

6 Lynx Cold Tributary Nerka Upper Basin 0.79 0.79 11.76 0.075 0.26 0.26 1.86 5.77 �2.63 1.13

7 Lynx Creek Nerka Upper Basin 0.89 0.88 14.79 0.085 0.26 0.26 1.87 11.67 2.99 1.00

8 Teal Creek Nerka Upper Basin 0.77 0.80 11.90 0.077 0.26 0.26 1.87 9.19 0.53 1.12

9 Stovall Creek Nerka Upper Basin 0.89 0.87 12.77 0.088 0.26 0.26 1.87 12.72 3.86 �1.34

10 Happy Creek Aleknagik Lower Basin 0.53 0.50 8.86 0.049 0.27 0.26 1.89 7.38 �1.43 0.46

11 Hansen Creek Aleknagik Lower Basin 0.35 0.33 4.95 0.034 0.27 0.26 1.87 7.53 �1.41 �1.47

12 Eagle Creek Aleknagik Lower Basin 0.57 0.56 8.95 0.053 0.26 0.26 1.86 7.62 �1.36 �1.60

13 Bear Creek Aleknagik Lower Basin 0.47 0.45 8.85 0.039 0.26 0.26 1.86 6.83 �2.11 0.57

14 Whitefish Creek Aleknagik Lower Basin 0.66 0.57 9.67 0.046 0.27 0.26 1.87 11.62 3.18 2.31

Summary statistics are reported for the MHC and the set of 90 neutral SNPs. HO is observed heterozygosity, HE is expected heterozygosity,

AR is allelic richness, aa div is amino acid diversity, Temp is the average temperature from July 1 through August 31, PC1 is the PCA score

from axis one in Fig. 3 and corresponds to temperature, and PC2 is the score from axis two and corresponds to temperature variation. See

Table S1 for more information on genetic and environmental data.

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Association between MHC and temperature in salmon 3

SNPs from Larson et al. (2014) to conduct partial Man-

tel tests. Tests for departures from linkage and Hardy–Weinberg equilibrium in the MHC and neutral data sets

were conducted by Larson et al. (2014); no significant

deviations were found.

Temperature data for each stream were obtained dur-

ing the primary period of stream residence for adult sal-

mon in the Wood River basin (July 1–August 31) as

described in Lisi et al. (2013). We did not include temper-

ature data from the rest of the year because data coverage

was poor and the primary period of adult salmon resi-

dence corresponds to the summer months which are the

most thermally stressful for salmon (Eliason et al., 2011).

Temperature was recorded at hourly intervals with I-

button� temperature recorders (� 0.50 °C; Maxim Inte-

grated Products, Sunnyvale, CA, USA) and hobo level

loggers (� 0.15 °C; Onset Computer Corp., Bourne, MA,

USA) placed within active spawning grounds in each

stream in the channel thalweg (raw temperature data

available from http://depts.washington.edu/aksalmon/

swakstreamtemp/). Between 2 and 5 years of tempera-

ture, data were recorded for each stream, and we used

these data to calculate ten metrics that reflect tempera-

ture or temperature variation (Table 2). Years did not

necessarily overlap across all sites (see Table S2 for tem-

perature metrics by year).

We conducted an analysis of variance to investigate

the amount of variation in thermal metrics that was

partitioned among streams compared to among years.

The variation among streams was much higher than

among years for the majority of the metrics prompt-

ing us to average the metrics across years (Table S3).

Approximately 10 days of temperature data were

missing from Kema Creek in 2007 and Uno Creek in

2013. Temperature metrics calculated from years with

missing data did not differ appreciably from those

without missing data prompting us to retain the

Kema Creek 2007 and Uno Creek 2013 samples

(Table S2).

Relationship between water temperature andgenetic diversity

Before exploring the relationship between temperature

and genetic diversity at the MHC, we investigated pat-

terns of neutral genetic differentiation to ensure that

our results would not be confounded by population

structure. Populations were broadly separated into the

two groups according to the phylogenetic tree based on

neutral SNPs (Fig. 2): (i) streams from lakes Nerka and

Beverley (upper basin) and (ii) streams from Lake

Aleknagik (lower basin). The relationship of Whitefish

Creek (population 14) was not well resolved, so we

grouped it with the lower basin based on geographic

proximity. Differences in MHC HO and AR between

groups were assessed with 1000 permutations in FSTAT

(version 293, Goudet, 1995), and differences in MHC

aa div, temperature, and temperature variation were

assessed with permutations tests conducted in R (ver-

sion 3.1, 10 000 permutations, R Development Core

Team, 2013). Highly significant differences in MHC HO,

AR, and aa div existed between these groups, but no

significant differences in neutral HO, neutral AR, tem-

perature, or temperature variation were found (see

Results). We z-scored standardized estimates of MHC

diversity by group in all subsequent analyses (unless

otherwise specified) to facilitate comparisons across

population groups with differing levels of diversity.

Principal component analysis (PCA) was used to

summarize the dominant trends in the genetic data set.

The PCA based on genetic data was conducted with all

five measures of genetic diversity (Table 2); measures

of MHC diversity were z-standardized as mentioned

above. The PCA was conducted on a correlation matrix.

We also conducted a PCA on the MHC variables alone

using a variance–covariance matrix.

Principal component analysis on the temperature

data set was conducted using a correlation matrix. Vari-

ables quantifying temperature were strongly associated

with PC1, and variables quantifying temperature varia-

tion were associated with PC2 (Fig. 3, see Results). To

Fig. 1 Map of study system. Upper basin populations are denoted

with circles, and lower basin populations are denoted with

triangles. Points are coloured by average summer temperature

(July 1–August 31). The numbers adjacent to each population

correspond to those found in Table 1.




http://depts.washington.edu/aksalmon/swakstreamtemp/

http://depts.washington.edu/aksalmon/swakstreamtemp/

simplify further analyses, we used PCA scores for PC1

as a proxy for temperature within each stream, and

scores for PC2 as a proxy for temperature variation (un-

less otherwise specified). We did not adopt a similar

PCA approach for the genetic data because the number

of variables was much smaller and the different mea-

sures of diversity were not as strongly correlated.

We conducted simple and multiple linear regressions to

test the hypothesis that genetic diversity was associated

with tributary water temperatures in streams of the Wood

River basin. Before conducting regressions, we tested for

normality in response variables with a Shapiro–Wilk test,

and all response variables were approximately normally

distributed (P > 0.05). We then generated least squares

linear regressions between temperature (summarized as

PCA scores) and genetic variables to test the hypothesis

that genetic diversity was associated with tributary water

temperatures in streams of the Wood River basin. The sta-

tistical significance of each regression was evaluated with

an F-test implemented in R (R Development Core Team,

2013, a = 0.05). If a regression was significant, then we

investigated whether a linear model represented the best

fit to the data by evaluating the fit of additional nonlinear

functions with the Akaike’s information criterion (AIC,

Anderson & Burnham, 2002). Models with DAIC values

< 2 were considered to have substantial support (Burn-

ham & Anderson, 2004). We also estimated the relative

importance of different predictor variables in significant

regressions. Specifically, we conducted multiple linear

regressions between genetic variables (standardized by

basin) and raw values of variables associated with either

temperature or temperature variation then assessed the

relative importance of each variable with the lmg method

implemented in R package relaimpo (Groemping, 2006).

We also constructed multiple linear regressions with

genetic diversity as the response variable and temperature

and lake basin as the two predictor variables to investigate

the relative influence of temperature and lake basin on

genetic diversity. The relative importance of each predictor

variable was estimated with the lmgmethod implemented

Table 2 Explanation of genetic and environmental variables used in this study.

Variable Classification Explanation

Predictor variables

Avg temp Temperature Average temperature across the spawning season (July–August)

Max temp Temperature Maximum observed temperature

Min temp Temperature Minimum observed temperature

Avg top 10 Temperature Average temperature of the 10 warmest days

Num days over 10 Temperature Count of the numbers of days with average temperature over 10 °C

Daily CV Temperature variation CV for each day averaged across days. Measures variation within days.

Daily SD Temperature variation SD for each day averaged across days. Measures variation within days.

CV across days Temperature variation CV of mean daily temperatures. Measure of temperature variation across days.

CV all temps Temperature variation CV of all temperature readings. Measures temperature variation within and across days.

Response variables

MHC HO MHC diversity The proportion of individuals with two MHC alleles in each population

MHC AR MHC diversity Standardized measure of the number of MHC alleles in each population

MHC aa div MHC diversity Quantifies the amount of functional diversity within and among individuals in each population

Neutral HO Neutral diversity The proportion of individuals with two alleles averaged across 90 neutral SNPs in each population

Neutral AR Neutral diversity Standardized measure of the number of alleles averaged across 90 neutral SNPs for each population

Temp range Temperature variation Highest temperature observed – lowest temperature observed

HO is observed heterozygosity, AR is allelic richness, aa div is amino acid diversity. Neutral measures of genetic diversity were obtained from

90 putatively neutral SNPs.

DA

Lower Basin

Upper Basin

9

6

3

2

8

7

1

5

4

14

13

12

11

10

64

74

50

62

53

20

6

17

10

33

60

0.0005

Fig. 2 Neighbour-joining tree based on DA distance for 90 neutral

SNPs. Upper basin populations are denoted with circles, and lower

basin populations are denoted with triangles. Population numbers

are found next to each symbol and correspond to those in Table 1.

Bootstrap values from 1000 replicates are located next to each

node.




in the R package relaimpo, and measures of genetic diver-

sity were not standardized by basin for this analysis.

Finally, we used partial Mantel tests to evaluate the

relationship between temperature variables and genetic

diversity while controlling for neutral population struc-

ture (pairwise-FST calculated from neutral SNPs). The

significance of each test was assessed with 10 000

randomizations.

Relationship between spawn timing and streamtemperature

We plotted temperatures throughout July and August

for each stream and sampling year to investigate warming

and cooling trends during the period of stream residence

for adult salmon in the Wood River basin. Locally

weighted scatterplot smoothing (LOESS) regressions were

used to visualize trends in the data. We also overlaid the

average date of salmon entry for each stream from 2009

to 2011 (Table S1, Lisi et al., 2013; Schindler et al., 2013),

the average length of salmon residence (Schindler et al.,

2010) and the peak period of the spawning season

(Schindler et al., 2010) on the plots. We did not investi-

gate the relationship between average temperature and

spawn timing because these two variables have already

been shown to be highly correlated in the Wood River

basin (Lisi et al., 2013; Schindler et al., 2013).

Results

Genetic data

Extensive variation in MHC diversity existed across the

14 streams in our study (Table 1, Table S1). MHC HO

ranged from 0.35 to 0.91, MHC AR ranged from 4.95 to

16.59, and MHC aa div ranged from 0.034 to 0.089.

This contrasted with patterns observed for the neutral

SNPs, where HO and AR only varied by 0.02 across all

populations (Table 1, Table S1).

Population structure at the neutral SNPs was sepa-

rated into two groups: streams from lakes Nerka and

Beverley (upper basin) and streams from Lake Alek-

nagik (lower basin) (overall FST = 0.009, Fig. 2,

Table S4). However, bootstrap support for this separa-

tion was only 60%. This result coupled with the small

neutral FST indicates that neutral structure in this sys-

tem is relatively shallow (c.f., Larson et al., 2014). It is

also important to note that populations from lakes Bev-

erly (populations 1–2) and Nerka (populations 3–9)were interspersed in the phylogenetic tree, suggesting

that population structure in the upper basin is not par-

titioned by lake. Additionally, the relationship of

Whitefish Creek (population 14) was not well resolved;

we included this population with the lower basin group

based on geography. MHC HO, AR and aa div were sig-

nificantly higher for upper basin streams (P < 0.01),

whereas no significant differences between the upper

and lower basins were found for neutral HO and AR

(P > 0.05). We z-scored standardized measures of MHC

diversity in subsequent analyses based on these results

unless otherwise specified (see Methods).

The PCA including all five measures of genetic diver-

sity demonstrated large loadings for MHC variables on

PC1 (average loading = 0.53) and large loadings for neu-

tral variables on PC2 (average = 0.64), suggesting that

MHC diversity across streams is not correlated with neu-

tral diversity (PC1 = 42% of variance explained,

PC2 = 28%, Fig. S2). A PCA constructed using only

MHC data displayed a large loading for HO on PC1 (load-

ing = 0.64, PC1 66% of variance), indicating that MHC

HO explained the largest amount of variance in MHC

diversity (Fig. S3). MHC AR and aa div also contributed to

PC1 (average loadings = 0.54) but had much larger load-

ings on PC2 (average loading = 0.70, PC2 24% of vari-

ance explained). Both PCAs constructed using genetic

data indicated that warm streams contain higher levels of

MHC diversity than cold streams (Figs S2 and S3).

Stream water temperature

A PCA constructed with temperature variables indicated

that the largest source of variance was associated with

temperature (PC1 = 63%), whereas within-stream vari-

ability in temperature explained less variance and was

associated with PC2 (27% of the variance explained;

−3 −2 −1 0 1 2 3 4

−4−2

02

4

PC1 (63% of variance)

PC

2 (2

7% o

f var

ianc

e)

TemperatureTemperature variation

Cold Warm

High daily variation

High variation across days

1

6

4

13 10

11

12

5 3

9

2

7

14

8

Fig. 3 Principal component analysis (PCA) of temperature

variables across the 14 streams in this study. Population numbers

are adjacent to each point and correspond to those from Table 1.

Circles correspond to upper basin populations, and triangles

correspond to lower basin populations. Variable loadings are

plotted with arrows. A PCA with the temperature variable loadings

labelled is found in Fig. S1. See Table 2 for more information on

temperature variables.




Fig. 3, Fig. S1; see Table S1 for raw data). All five vari-

ables associated with average, max and min tempera-

ture had large loadings (average loading = 0.39) on

PC1 and small loadings (average = 0.06) on PC2,

whereas the five variables associated with temperature

variation (coefficient of variation, standard deviation,

range) had large loadings on PC2 (average = 0.43) but

not on PC1 (average = 0.18) (Table S1).

Streams were generally characterized as warm or

cold according to the PCA (Fig. 3), with only one

stream (Teal Creek, population 8), appearing as inter-

mediate. Warm and cold streams were found in both

the lower and upper basins, and at least one warm

and one cold stream was present in each lake. Most

streams displayed intermediate levels of temperature

variation within and across days. Exceptions included

Whitefish Creek (population 14), which displayed high

variation across days, and Kema and Pick Creeks (pop-

ulations 3, 5), which displayed high daily variation

(Fig. 3).

Relationship between water temperature andgenetic diversity

Simple regression analysis demonstrated a significant

positive relationship between temperature (PC1) and

standardized MHC HO (P < 0.001, adjusted R2 = 0.58,

Table 3, Fig. 4). A nearly significant positive relation-

ship was also found between temperature and MHC aa

div (P = 0.08, adjusted R2 = 0.17, Table 3, Fig. S4). In

comparison, regressions between temperature and neu-

tral diversity showed no significant relationships

(P > 0.5, adjusted R2 = 0, Table 3, Fig. 4, Fig. S4).

Additionally, no significant associations between tem-

perature variation and genetic diversity were found

(P ≥ 0.15, Table 3, Fig. S4).

A linear model provided the best fit for the relation-

ship between water temperature and MHC HO accord-

ing to AIC model selection (AIC weight = 0.74,

DAIC = 0, Table S5). Neither exponential, logarithmic

nor second-order polynomial functions were supported

by AIC (AIC weight < 0.15, DAIC > 3). Each of the five

variables associated with water temperature displayed

similar relative importance values in a multiple linear

regression with MHC HO as the response variable and

temperature as the predictor variables (Table S6).

Multiple regressions with genetic diversity as the

response variable and temperature and lake basin as

predictor variables indicated that lake basin had a larger

influence on MHC diversity than temperature

(Table 4). The lake basin variable contributed signifi-

cantly (P < 0.05) to all regressions with MHC diversity

as a response variable, whereas a temperature variable

was only significant in one regression (MHC HO vs.

temperature (PC1) and lake basin). No significant rela-

tionship existed in regressions with neutral genetic

diversity as a response variable.

A positive association between temperature and MHC

HO was found with a partial Mantel test controlling for

genetic structure (P < 0.002, Mantel R = 0.54, Table 3).

Statistical support for all other associations was low

(P ≥ 0.16, Table 3). It is important to note that partial

Mantel tests have been criticized for inflated type I

error (e.g. Raufaste & Rousset, 2001); therefore, the

results from these tests should be interpreted with cau-

tion. However, Legendre et al. (2015) argue that partial

Mantel tests are still appropriate when analyses incor-

porate dissimilarity matrices such at the matrix of pair-

wise-FST values that we utilized to summarize neutral

structure.

Relationship between spawn timing and streamtemperature

Stream temperatures were relatively consistent

throughout the summer months for both warm and

cold streams in most years (Fig. S5). However, annual

variation in snowpack did produce differences in ther-

mal sensitivity to warmer summer weather over our

study period (Lisi et al., 2015; Fig. 5, Fig. S5). In partic-

ular, a deep winter snow pack and several weeks of

warm sunny weather in July 2013 produced unusually

large differences in water temperature among streams

over the spawning season. For instance, Stovall Creek

reached 25 °C and Whitefish Creek reached 19 °C on

July 27. In comparison, streams, cooled by snowmelt

such as Lynx Cold Tributary (Lisi et al., 2015), had

much cooler maximum temperatures (~ 11 °C) over

the same period (Fig. 5).

Consistent with their spawning dates in previous

years (within 2–5 days, Schindler et al., 2013), salmon

populations from Bear Creek and Lynx Cold Tributary

(cool streams) entered their spawning grounds around

the 15th of July and experienced relatively cool condi-

tions (~ 5–8 °C) for the duration of their spawning sea-

son (Fig. 5b, d). Salmon entered Stovall and Whitefish

Creeks (warm streams) to spawn several weeks later,

and by doing so, experienced thermal conditions that

were on average two to four degrees cooler compared

to those that they would have experienced had they

entered at the same time as other early spawning

stream populations (Fig. 5a, c).

Discussion

We found that lake basin had the largest influence on

MHC diversity across 14 streams used by sockeye sal-

mon for spawning in the Wood River basin, but we also

demonstrated that MHC HO was positively associated

with water temperature across these streams. The rela-

tionship between temperature and MHC HO was likely

driven by selection rather than neutral processes

because no significant relationship between tempera-

ture and HO at 90 neutral SNPs was found.




Additionally, no significant relationships between tem-

perature and any other measures of genetic diversity

were found, and temperature variation was not signifi-

cantly associated with any metric of MHC or neutral

diversity included in this study.

We speculate that the significant relationship we

observed between temperature and MHC HO was dri-

ven by increased pathogen-mediated selection due to

increases in pathogen virulence, prevalence, and diver-

sity in warm environments (Mitchell et al., 2005; Luque

& Poulin, 2008; Karvonen et al., 2013). However, we

did not observe the same significant relationship

between temperature and MHC AR. Two major

hypotheses have been proposed to explain pathogen-

mediated selection on the MHC in wild populations: (i)

negative frequency-dependent selection and (ii)

Table 3 Results from linear regressions and partial Mantel tests comparing relationships between genetic and temperature variables.

Predictor variable Response variable Lm R2 Lm slope Lm int Lm P Mantel R Mantel P

Temperature (PC1) MHC HO 0.58 0.30 0.00 0.00 0.54 0.00

Temperature variation (PC2) MHC HO 0.00 0.08 0.00 0.63 0.01 0.36

Temperature (PC1) MHC AR 0.02 0.12 0.00 0.27 �0.02 0.50

Temperature variation (PC2) MHC AR 0.00 �0.11 0.00 0.53 �0.01 0.46

Temperature (PC1) MHC aa div 0.17 0.19 0.00 0.08 0.09 0.16

Temperature variation (PC2) MHC aa div 0.02 �0.18 0.00 0.28 0.09 0.17

Temperature (PC1) Neutral HO 0.00 0.00 0.26 0.54 0.00 0.41

Temperature variation (PC2) Neutral HO 0.09 0.00 0.26 0.15 0.06 0.27

Temperature (PC1) Neutral AR 0.00 0.00 1.87 0.70 �0.06 0.35

Temperature variation (PC2) Neutral AR 0.00 0.00 1.87 0.98 �0.13 0.16

Measures of MHC diversity were z-standardized by group (lower basin vs. upper basin). The partial Mantel tests reported here compared

the relationship between temperature variables and genetic diversity while controlling for neutral structure as measured by pairwise-FST at

90 neutral SNPs (Table S4). Lm R2 is the correlation coefficient of the linear regression that has been adjusted for the number of predictors

in the model, Lm slope is the slope of the regression, Lm int is the intercept of the regression, Lm P is the P-value of the regression, Mantel

R is the correlation statistic for the Mantel test, and Mantel P is the P-value for the Mantel test. Significant relationships are in bold. See

Table 2 for more information on the variables used in this analysis and Fig. 4, Fig. S4 for visualizations of each regression.

−4 −2 0 2 4

−2−1

01

2

Upper basinLower basin

Sta

ndar

dize

d H

O

(a) MHC

Temperature (PC1)−4 −2 0 2 4

0.22

0.23

0.24

0.25

0.26

0.27

0.28

HO

(b) Neutral

Temperature (°C)68101214

Fig. 4 Relationship between water temperature (PC1, Fig. 3), and HO for (a) the major histocompatibility complex (MHC) and (b) 90

neutral SNPs. Points are coloured by average summer temperature (July 1–August 31). The relationship between temperature and HO was

significant for the MHC (P < 0.001) but not for the neutral SNPs (P = 0.54). Regression statistics are found in Table 3, and regressions not

shown here are found in Fig. S4.




overdominance (Takahata & Nei, 1990; Slade & McCal-

lum, 1992). The negative frequency-dependent selec-

tion hypothesis postulates that rare alleles confer a

selective advantage when individuals are exposed to

new pathogen strains or species, leading to the presence

of many rare alleles (high AR) in populations experienc-

ing high pathogen loads, whereas the overdominance

hypothesis suggests that heterozygosity will increase

when pathogen-mediated selection is strong because

heterozygous individuals are resistant to a wider array

of pathogens. The significant relationship that we

observed between temperature and MHC HO suggests

that the strength of overdominant selection at the MHC

in our study system may increase with temperature.

Evidence for a similar increase in negative frequency-

dependent selection (increase in AR) with temperature

was generally absent. However, it is important to note

that distinguishing between negative frequency-depen-

dent selection and overdominance at the MHC in wild

populations has proven to be extremely difficult (Ejs-

mond et al., 2010; Spurgin & Richardson, 2010). There-

fore, we cannot definitively separate the effects of

temperature on overdominant and negative frequency-

dependent selection in our study system.

We did not observe a significant relationship between

within-stream variability in temperature and MHC

diversity. These results suggest that the strength of

pathogen-mediated selection is more influenced by

average thermal conditions rather than temperature

variability in our study system. The few studies investi-

gating the relative influence of temperature and tem-

perature variation on pathogen prevalence and

virulence demonstrate a complex interplay between

these two variables (e.g. Paaijmans et al., 2010; Van-

gansbeke et al., 2015). For example, Paaijmans et al.

(2010) found that high temperature variation at low

temperatures can increase malaria transmission in

humans, whereas high temperature variation at high

temperatures decreases transmission. Temperature vari-

ation likely influences pathogen communities in our

study system, but the complexities of these interactions

may have prevented us from demonstrating a clear

relationship between temperature variation and MHC

diversity.

Major histocompatibility complex diversity differed

significantly between population groups, with popula-

tions from the upper basin (lakes Beverly and Nerka),

displaying higher diversity than lower basin populations

(Lake Aleknagik). Larson et al. (2014) also observed dif-

ferences in MHC diversity among lakes in the Wood

River basin in addition to differences among spawning

ecotypes (beach, river, stream). Sockeye salmon in the

Wood River basin spend a large portion of their life

cycle (up to 3 years) rearing in nursery lakes and likely

spend the majority of this time in lakes that are proxi-

mate to their streams of origin (Quinn, 2005). These

juvenile salmon are exposed to different environments

depending on the lake that they inhabit, possibly

resulting in the differences in MHC diversity that we

observed due to differences in pathogen exposure (c.f.,

McClelland et al., 2013; Miller et al., 2001). For exam-

ple, it is possible that pathogen communities in Lakes

Nerka and Beverley are more diverse than those in

Lake Aleknagik, resulting in higher levels of MHC

Table 4 Results of multiple linear regressions investigating the relationship between unstandardized genetic diversity (response variable)

and two predictor variables (temperature and basin).

Response

variable

Temperature

variable

Temp

est

Temp

err

Temp

T

Temp

P

Basin

est

Basin

err Basin T Basin P All R2 All P

Temp

rel imp

Basin

rel imp

MHC HO Temperature (PC1) 0.023 0.007 3.368 0.006 0.296 0.035 8.520 0.000 0.882 0.000 0.190 0.810

MHC HO Temperature

Variation (PC2)

0.011 0.014 0.804 0.438 0.320 0.047 6.766 0.000 0.773 0.000 0.011 0.989

MHC AR Temperature (PC1) 0.267 0.238 1.123 0.286 5.228 1.199 4.361 0.001 0.618 0.002 0.115 0.885

MHC AR Temperature

Variation (PC2)

�0.284 0.366 �0.777 0.453 5.465 1.209 4.519 0.001 0.596 0.003 0.033 0.967

MHC aa div Temperature (PC1) 0.001 0.001 1.663 0.125 0.036 0.003 10.770 0.000 0.908 0.000 0.066 0.934

MHC aa div Temperature

Variation (PC2)

�0.001 0.001 �0.888 0.394 0.037 0.004 10.437 0.000 0.893 0.000 0.010 0.990

Neutral HO Temperature (PC1) 0.000 0.001 �0.390 0.704 �0.004 0.003 �1.286 0.225 0.006 0.386 0.139 0.861

Neutral HO Temperature

Variation (PC2)

0.001 0.001 1.568 0.145 �0.004 0.003 �1.485 0.166 0.176 0.137 0.527 0.473

Neutral AR Temperature (PC1) 0.000 0.001 �0.260 0.800 �0.004 0.007 �0.595 0.564 �0.130 0.783 0.213 0.787

Neutral AR Temperature

Variation (PC2)

0.000 0.002 �0.038 0.971 �0.004 0.007 �0.657 0.525 �0.137 0.809 0.002 0.998

Abbreviations for the genetic and temperature variables used for each regression are found in Table 2 and basin designations (lower vs.

upper) are found in Table 1. Temperature is abbreviated temp, estimate is abbreviated est, error is abbreviated err, and T is the t-statistic

from the multiple regression. Significant P values (P < 0.05) are in bold. Rel imp is a metric of the relative importance of each variable in

the regression and was calculated using the lmg method implemented in the R package relaimpo. Larger values indicate higher importance.




diversity in sockeye salmon that spawn in tributaries

connected to these lakes. It is also possible that differ-

ences in pathogen virulence among these lakes may be

responsible for the MHC diversity that we observed. No

previous studies have investigated differences in patho-

gen communities among lakes in the Wood River basin,

but future research in this area could illuminate poten-

tial drivers of MHC variation.

Comparison of our results with a previous study in

Atlantic salmon (Dionne et al., 2007) provides further

support for our conclusions and also highlights poten-

tially important differences between salmon species.

Dionne et al. (2007) sampled 34 populations of Atlantic

salmon over a large latitudinal gradient and observed a

positive relationship between temperature and MHC

diversity (measured as AR and aa div). A positive linear

relationship also existed between temperature and

MHC HO (P < 0.01, not originally reported in Dionne

et al., 2007). The results from our study are generally

similar to those of Dionne et al. (2007). However, the

05

1015

2025

Tem

pera

ture

(°C

)

July 1 July 15 Aug 1 Aug 15 Aug 31

05

1015

2025


05

1015

2025

Tem

pera

ture

(°C

)


05

1015

2025


(a) Stovall (b) Lynx Cold Tributary

(c) Whitefish (d) Bear

Fig. 5 Plots of July and August temperatures for four streams sampled in 2013. The plot illustrates differences in thermal regimes for two

warm streams [(a) Stovall and (c) Whitefish] and two cold streams [(b) Lynx Cold Tributary and (d) Bear]. Grey points represent

individual temperature measurements, and a line of best fit was constructed using a locally weighted scatterplot smoothing (LOESS)

regression. The red vertical line indicates the average date that salmon enter each stream (Table S1), the light red shading indicates the

average period of adult salmon residence within each stream (~ 3 weeks), and the darker red shading indicates the peak period of the

spawning season (~ 2 weeks). Plots for all streams and years sampled are available in Fig. S5.




ranges of HO, AR and aa div in our study were over two

times larger, despite the fact that Dionne et al. (2007)

sampled nearly the entire latitudinal range of Atlantic

salmon in North America. Additionally, Dionne et al.

(2007) found that variation in MHC diversity occurred

over broad spatial scales, contrasting the fine-scale dif-

ferentiation observed in our study. The large differences

in patterns of MHC diversity between sockeye and

Atlantic salmon parallel the thermal tolerance charac-

teristics of these species. Sockeye salmon show large

differences in thermal tolerance over extremely small

spatial scales (Eliason et al., 2011, 2013), whereas

Atlantic salmon show little variation in temperature tol-

erance over large latitudinal gradients (Anttila et al.,

2014). This similarity between surveys of MHC varia-

tion and thermal tolerance suggests that Atlantic sal-

mon may have adopted a more generalist approach to

cope with high temperatures and the associated high

levels of pathogens, whereas sockeye salmon appear to

be locally adapted to the thermal regimes and pathogen

communities present in the environments that they

experience.

Values of MHC HO in warm streams from lakes Bev-

erly and Nerka were close to a maximum value of one,

suggesting that continued selection favouring heterozy-

gous individuals over individuals with specific MHC

alleles is unlikely to result in fitness increases. Dionne

et al. (2007) also observed this apparent maximum pla-

teau of selection. Specifically, Dionne et al. (2007)

observed that MHC diversity increased rapidly at rela-

tively low temperatures but increased more slowly as

temperatures increased. The idea of a maximum pla-

teau of adaptation was tested empirically by Silander

et al. (2007), who found that the fitness of populations

adapting to their environment does not increase indefi-

nitely due to the finite nature of natural populations

and the effects of mutation. These results along with

those of our study suggest that natural populations can

reach a plateau of adaptive potential that may prevent

them from adapting to environmental changes such as

those expected from climate change. In this case, other

evolutionary mechanisms, such as behavioural avoid-

ance of high temperatures through changes in spawn

timing, may help maintain population persistence

(Reed et al., 2011).

Salmonids commonly show population-specific dif-

ferences in migration and spawn timing associated

with spawning ground temperature (Webb & McLay,

1996; Beechie et al., 2006; Kovach et al., 2015). This

variation is typically attributed to differences in the

rate of larval development between warm and cold

streams (Quinn, 2005). However, populations from

warmer streams that delay spawning by a few weeks

are also less likely to encounter lethal temperatures

as streams become progressively cooler in the late

summer and early fall (Miller et al., 2014; Lisi et al.,

2015). For example, we show that salmon spawning

in two warm streams (Stovall and Whitefish) avoided

potentially lethal temperatures in July 2013 because

they consistently do not enter these streams until

early August and likely remained in cool water of

the lake during periods of warm weather prior to

spawning. Temperatures in Stovall Creek in particular

approached or exceeded the five day lethal tempera-

ture for sockeye salmon (22 °C, Servizi & Jensen,

1977) for more than five days in July 2013, indicat-

ing that salmon entering the stream during this time

would have likely experienced high levels of pres-

pawn mortality (Crossin et al., 2008). Further, Lisi

et al. (2015) showed that water temperatures in Sto-

vall and Whitefish creeks are highly sensitive to

changes in air temperatures, such as those experi-

enced during heat waves, and may have experienced

substantially warmer temperatures in the past than

documented here. Taken together, these results sug-

gest that delayed spawn timing in warm streams

could complement MHC diversity by representing a

flexible, but heritable life-history strategy for avoiding

both thermal stress in midsummer and the increased

prevalence and virulence of pathogens associated with

warm temperatures.

Our study provides some of the first evidence that

fine-scale differences in temperature can influence

MHC diversity. We hypothesize that the variation in

MHC diversity that we observed is due to variable

selection pressures caused by differences in pathogen

communities or pathogen virulence among habitats.

Although a strong relationship between temperature

and pathogen diversity, prevalence and virulence has

been previously demonstrated (e.g. Mitchell et al.,

2005; Luque & Poulin, 2008; Karvonen et al., 2013),

we cannot definitively conclude that all of the patterns

that we observed are due to pathogen-mediated selec-

tion because we did not directly sample pathogens –only the association between temperature and MHC

diversity. Future studies should attempt to quantify

pathogen communities and pathogen virulence across

our study system to confirm the relationships that we

discussed. Additionally, the relationship between tem-

perature and MHC diversity should be investigated in

other systems to confirm that the patterns we observed

are not simply a result of the unique nature of the

Wood River basin.

In conclusion, we found that MHC diversity was

highly influenced by lake basin but also discovered a

significant positive relationship between water tempera-

ture and MHC diversity that was not likely produced

through neutral processes. This result suggests that dif-

ferences in temperature among proximate streams may

influence pathogen-mediated selection and promote

population diversity on the scale of only a few kilome-

tres. Additionally, we postulated that variation in

spawn timing among populations may represent an

adaptive behavioural mechanism for avoiding summer




thermal stress and the increased pathogen prevalence

and virulence associated with high temperatures. Our

findings are especially relevant given the anticipated

impacts of climate change. Many studies have

attempted to address whether organisms will be able to

adapt to a changing climate (reviewed in Hoffmann &

Sgro, 2011). Although these studies often assume that

responses to climate change will be similar across rela-

tively large spatial scales (1000s of km), our findings

suggest that adaptation to climate change may also

occur on much smaller scales. This fine-scale diversity

can help to maintain ecosystem stability and ecological

processes (Schindler et al., 2015), even in the face of

environmental fluctuation.

Acknowledgments

We thank the Alaska Department of Fish and Game,

especially Tyler Dann, for providing data for this pro-

ject. Additionally, we thank Chris Boatright, Jackie Car-

ter, and multiple field technicians and graduate

students from the Alaska Salmon Program for assisting

with sample collection and providing additional data.

We also thank Carita Pascal for her excellent laboratory

assistance, Curry Cunningham, Rachel Hovel and Jan-

ice Kerns for statistical advice, Kristen Gruenthal for

her editorial comments, and the staff of the Wood-

Tikchik State Park for enabling our research. Funding

was provided by grants from the US National Science

Foundation, Gordon and Betty Moore Foundation,

Western Alaska Landscape Conservation Cooperative,

and the Harriet Bullitt Professorship. WAL and PJL

were supported by US National Science Foundation

Graduate Research Fellowships (grant # DGE-0718124).

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Supporting information

Additional Supporting Information may be found

online in the supporting information tab for this article:

Figure S1 Principal component analysis (PCA) of tem-

perature variables with the loadings for each variable

labelled. Population numbers are adjacent to each point

and correspond to those from Table 1. Circles corre-

spond to upper basin populations and triangles corre-

spond to lower basin populations. Variable loadings are

plotted with arrows. See Table 2 for more information

on the variables used in this analysis.

Figure S2 Principal component analysis (PCA) of

genetic diversity at the MHC and neutral markers

across the 14 streams in this study. Population num-

bers are adjacent to each point and correspond to

those from Table 1. Circles correspond to upper basin

populations and triangles correspond to lower basin

populations. Variable loadings are plotted with arrows

and points are coloured by average summer tempera-

ture (July 1–August 31). See Table 2 for more infor-

mation on the indices of genetic diversity used in this

analysis.

Figure S3 Principal component analysis (PCA) of MHC

diversity across the 14 streams in this study. Population

numbers are adjacent to each point and correspond to

those from Table 1. Circles correspond to upper basin

populations and triangles correspond to lower basin

populations. Variable loadings are plotted with arrows

and points are coloured by average summer tempera-

ture (July 1–August 31). See Table 2 for more informa-

tion on the indices of MHC diversity used in this

analysis.

Figure S4 Relationships between temperature (PC1,

Fig. 3), and temperature variation (PC2, Fig. 3) for

three MHC diversity indices and two neutral diversity

indices. MHC diversity indices are z-standardized and

points are coloured by average summer temperature

(July 1–August 31). Regression statistics for each com-

parison are found in Table 3.

Figure S5 Plots of July and August temperatures for

each stream and sampling year. Grey points represent

individual temperature measurements, and a line of

best fit was constructed using a locally weighted scat-

terplot smoothing (LOESS) regression. The red vertical

line indicates the average date that salmon enter each

stream (Table S1), the light red shading indicates the

average period of adult salmon residence within each

stream (~ 3 weeks), and the darker red shading indi-

cates the peak period of the spawning season

(~ 2 weeks).

Table S1 Detailed genetic and environmental data for

each sample collection. Collection names and numbers

correspond to those in Table 1 and Fig. 1. N is the

number of individuals sampled. Measures of genetic

diversity were z-standardized. The Temperature (PC1)

and temperature variation (PC2) predictor variables

represent PCA scores derived from the PCA in Fig. 3

and the response variables are described in Table 2.

Table S2 Sampling dates (in Julian days) and Tempera-

ture data for each stream and year included in the

study. Temperature and temperature variation variables

correspond to those described in Table 2. Temperature

variables are in °C. Data in bold are averages across

years including and not including years with missing

data (see methods).

Table S3 Results from an analysis of variance investi-

gating the amount of variation in thermal variables that

was partitioned among streams compared to among

years. Abbreviations for thermal variables are found in

Table 2. Significant P values (P < 0.05) are in bold.

Table S4 Pairwise-FST values for each population calcu-

lated with 90 neutral SNPs. Population numbers corre-

spond to those in Table 1 and Fig. 1.

Table S5 Model selection analysis for the relationship

between standardized MHC HO and temperature (PC1,

Fig. 3). The best model as chosen by AIC is in bold.

Table S6 Results of a multiple linear regression investi-

gating the relationship between standardized MHC

heterozygosity and five temperature variables. Abbrevi-

ations for temperature variables are found in Table 2.

Relative importance is a metric of the relative impor-

tance of each variable in the regression and was calcu-

lated using the lmg method implemented in the R

package relaimpo. Larger values indicate higher impor-

tance. The adjusted R2 for this regression was 0.60 and

the P-value was 0.02.

Received 8 December 2015; revised 16 June 2016; accepted 23 June

2016




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