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\begin{document}

\title{Coat colour mismatch improves survival: energetic advantages exceed lost
camouflage}



\author[1]{Joanie Kennah}%
\author[2]{Michael Peers}%
\author[1]{Eric Vander Wal}%
\author[2]{Yasmine Majchrzak}%
\author[3]{Allyson Menzies}%
\author[3]{Emily Studd}%
\author[4]{Rudy Boonstra}%
\author[3]{Murray Humphries}%
\author[5]{Thomas Jung}%
\author[6]{Alice Kenney}%
\author[7]{Charles Krebs}%
\author[8]{Stan Boutin}%
\affil[1]{Memorial University of Newfoundland}%
\affil[2]{University of Alberta Faculty of Science}%
\affil[3]{McGill University}%
\affil[4]{University of Toronto Scarborough}%
\affil[5]{Government of Yukon}%
\affil[6]{The University of British Columbia Department of Zoology}%
\affil[7]{University of British Columbia}%
\affil[8]{University of Alberta}%


\vspace{-1em}



  
  \date{May 17, 2024}


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\selectlanguage{english}
\begin{abstract}
Climate warming is causing asynchronies between animal phenology and
environments. Mismatched traits, like coat colour change mismatched with
snow, typically decrease survival. However, coat change does not serve a
singular adaptive benefit of camouflage, and alternate coat change
functions may confer advantages that supersede mismatch costs. We found
that mismatch reduced autumn mortality risk of snowshoe hares in the
Yukon by 86.5 \%. We suggest that the increased coat insulation and
lower metabolic rates of winter acclimatized hares confer energetic
advantages to white mismatched hares that reduce their risk of dying. We
found that white mismatched hares forage 17-77 minutes less per day than
matched brown hares between 0 and -10 \selectlanguage{ngerman}ºC, thus lowering their predation
risk and increasing survival. We found no effect of mismatch on spring
mortality risk, where mismatch occurred at warmer temperatures,
suggesting a potential temperature limit where the costs of
conspicuousness outweigh energetic benefits.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{Introduction}

Phenological mismatch is one of the most documented pathways by which
climate change negatively impacts species (Radchuk et al. 2019; Visser
\& Gienapp 2019). Earlier onset of spring and delayed onset of winter
have the potential to cause incongruous timing of seasonal phenotypes
(M\selectlanguage{ngerman}øller et al. 2008; Lehikoinen 2011; Kudo \& Ida 2013). Mismatch occurs
in the timing of numerous seasonal traits such as calving date with
plant growth onset, and laying date with peak of key food sources, and
has resulted in reduced reproductive success and recruitment (Post \&
Forchhammer 2008; Reed et al. 2013). However, the costs associated with
phenological mismatches vary within species across populations (Heard et
al. 2012; Doi et al. 2017). Species are often adapted to broad ranges of
ecological conditions, particularly those with large geographic
distributions (Valladares et al. 2014). Local adaptations and variable
selection pressures across environmental gradients alter the magnitude
of phenological mismatch across populations (Phillimore et al. 2010;
Gordo \& Doi 2012; Porkert et al. 2014). Such spatial variability in
phenology across ecological conditions may also involve differences in
the mechanistic pathways governing the demographic costs and benefits
associated with phenological mismatch across species ranges.

An example of phenological mismatch that occurs in species across
multiple taxa is coat and plumage colour change mismatched with snow
onset and melt (Zimova et al. 2016; Pedersen et al. 2017; Atmeh et al.
2018; Melin et al. 2020). At least 21 bird and mammal species in the
Northern Hemisphere change colour biannually and improved camouflage is
considered the primary function of this change (Mills \emph{et al.}2018;
Zimova \emph{et al.} 2018) As snow cover duration is forecasted to
decrease across the Northern Hemisphere (Danco et al. 2016), coat and
plumage colour mismatch is likely to increase. Mismatch may reduce
survival due to decreased camouflage (Atmeh et al., 2018; Zimova et al.,
2016; Melin et al., 2020). However, aside from colour change,
high-latitude species benefit from other winter acclimatization
strategies meant to increase cold tolerance and endure periods of food
shortage, including increasing insulation, decreasing lower critical
temperature, altering activity patterns, and, ultimately reducing daily
energy requirements (Humphries \emph{et al.} 2005; Fuglesteg \emph{et
al.} 2006; Sheriff \emph{et al.} 2009b). Accordingly, coat colour
transitions coincide with multi-trait change that differentiates long
photoperiod, i.e., summer, from short photoperiod, i.e., winter,
phenotypes (Lovegrove 2005; Boraty\selectlanguage{polish}ń\selectlanguage{english}ski \emph{et al.} 2016). As such, the
thermal and energetic benefits provided by a more insulative, white coat
and associated metabolic and thermoregulatory adaptations may outweigh
the negative costs of colour mismatch in colder conditions.

The snowshoe hare (\emph{Lepus americanus} ) is a keystone species
distributed across the boreal forests of North America (Krebs \emph{et
al.} 1995) that undergoes seasonal coat colour change to match the
presence of snow (Ferreira et al. 2017). The initiation of coat colour
change in snowshoe hares is likely affected by photoperiod (Nagorsen
1983) and in the absence of evolutionary change, is predicted to become
increasingly mismatched with anticipated reductions in snow cover
duration (Brown \& Mote 2009; Mills et al. 2013). Coat colour mismatch
may impact snowshoe hare demography, as recent studies have reported
high mortality rates in mismatched snowshoe hares at multiple locations
in the southern extent of their range, presumably due to increased
conspicuousness to predators (Zimova et al., 2014; Wilson et al., 2018).
However, the thermal benefits of winter acclimatization in hares,
including reduced metabolic rate (Sheriff \emph{et al.} 2009a), may also
affect susceptibility to predation and ultimately survival.

White winter-acclimatized snowshoe hares benefit from lower energetic
demands compared to brown summer-acclimatized hares. Indeed, while
temperatures below 0 \selectlanguage{ngerman}°C increase energetic requirements for summer
hares, white winter hares remain in their thermoneutral zone until
temperatures below -10 °C (Sheriff \emph{et al.} 2009a). As such, lower
energetic demands reduce foraging requirements for winter-acclimatized
hares (Balluffi-Fry \emph{et al.} , In Review). Balancing the trade-off
between obtaining sufficient food to meet energetic requirements and
avoiding predators is a central assumption of prey behaviour theory
(McNamara \& Houston 1987; Lima \& Dill 1990). Therefore, white
mismatched hares may benefit from lower energetic requirements, reduced
foraging time, and thus reduced predator exposure. These benefits could
compensate for the adverse effects of conspicuousness, particularly when
seasonal temperatures remain low and the energetic demands for brown
summer acclimatized hares are elevated (Balluffi-Fry \emph{et al.} , In
Review). Geographic variation in winter adaptations and acclimatization
exists across the broad geographic range of the snowshoe hare (Sheriff
et al. 2009b; Gigliotti et al. 2017). As such, the effects of coat
colour mismatch may vary across populations according to the relative
importance of the reduced camouflage cost relative to energy
conservation benefits in different ecological contexts.

Here, we test the hypothesis that reduced foraging requirements with
winter acclimatization reduces the costs of coat colour mismatch in
snowshoe hares. To examine this, we monitored the survival, coat colour,
and foraging time of individuals over the autumn and spring in southwest
Yukon, Canada. First, we predict that mismatched white hares will spend
less time foraging than matched brown individuals, particularly below
the thermoneutral zone of summer brown hares (i.e. 0 °C; Sheriff\emph{et
al.} 2009a). If this foraging difference and thus reduced time spent
vulnerable to predation outweighs the costs of conspicuousness, we
further predict no difference in survival between matched and mismatched
individuals. However, if camouflage loss is the primary driver of
predation risk during coat colour change, regardless of foraging
differences, we expect that mismatched hares are more likely to be
predated than camouflaged individuals, echoing results from previous
studies in the southern extent of their range (Zimova et al. 2016;
Wilson et al. 2018). We found that white mismatched snowshoe hares
experiencing cold temperatures in snowless environments benefitted from
reduced foraging time and thus increased survival relative to brown
matched hares.

\textbf{Material and Methods}

Study area

We studied snowshoe hares for three autumns (September
1\textsuperscript{st} to December 1\textsuperscript{st} of 2015, 2016,
and 2017) and four springs (March 1\textsuperscript{st} to May
31\textsuperscript{st} of 2015, 2016, 2017, and 2018) in southwestern
Yukon, Canada (Lat: 60.9 N, Long: -138.0 W). Snowshoe hares have been
monitored for over 40 years in this region (Krebs et al., 2018). Our
study area consists predominantly of white spruce (\emph{Picea glauca}
), trembling aspen (\emph{Populus tremuloides} ) and balsam poplar
(\emph{Populus balsamifera} ). Gray willow (\emph{Salix glauca} ) and
dwarf birch (\emph{Betula glandulosa} ) dominate the understory. The
main predators of snowshoe hares in this region include Canada lynx
(\emph{Lynx canadensis} ), coyotes (\emph{Canis latrans} ), goshawks
(\emph{Accipiter gentilis} ), and great horned owls (\emph{Bubo
virginianus} ) (Peers \emph{et al.} 2020). Snowshoe hares went through
the increase, peak, and early decline phase of their population cycle
during our study period (Krebs et al. 2018).

Field methods

The study area was divided into three 35-ha snowshoe hare trapping
areas, located within \textasciitilde{} 8 km of each other
(Peers\emph{et al.} 2020). We captured snowshoe hares using Tomahawk
live-traps (Tomahawk Live Trap Co. Tomahawk, WI, USA) baited with
alfalfa and rabbit chow. Traps were set 30 minutes before sunset and
checked either three hours after sunset or at sunrise. We attached a
numbered ear tag to each hare to identify individuals on subsequent
recaptures, and we assessed coat-colour during each capture. To evaluate
coat colour, we examined hares from the front and sides and visually
estimated their percentage white coat to the nearest 5\%. We later
binned coat colour in 10\% white categories for analyses to account for
inter- and intra- observer ranking variability. We consider 10\% bins as
reasonably precise given that intra- and inter-observer intraclass
correlation coefficients (ICC) for coat colour assessment were high
(ICC\textgreater{}0.9 in all cases, See Table S1 in Supplementary
Information). To monitor survival, we fit hares weighing \textgreater{}
1100g (n=347) with very high frequency (VHF) collars that were each
equipped with a mortality sensor (Model SOM2380, Wildlife Materials
Inc., USA, or Model MI-2M, Holohil, Canada, both \textless{} 27 ± 1 g).
We performed mortality checks of VHF collared hares almost daily, i.e.,
96.3\% of checks occurred within 1 to 3 days. To monitor behaviour, we
also fit a subset (\emph{n} =102) of VHF collared hares with an
accelerometer (model Axy3, 4 g, Technosmart, Rome, Italy).
Accelerometers measure force variation on three different axes and are
increasingly being used to infer behaviour in free-ranging animals
(Mikkelsen \emph{et al.} 2019; Studd \emph{et al.} 2019). Fully equipped
collars with both VHF and accelerometers had a total weight below 2.5\%
of each individual's body mass. Handling and collaring procedures were
approved by the University of Alberta Animal Care and Use Committee
(Protocol: AUP00001973).

We measured snow depth, snow cover, and temperature throughout our study
period. We measured snow depth on \textgreater{}60\% of days at three
locations per trapping area, in relatively open forest, to the nearest
0.5 cm. Days with missing snow depth records were linearly interpolated
using the ``zoo'' function in the zoo package in R (Zeileis \emph{et
al.} 2021). We measured snow cover by visually assessing daily landscape
photographs from three camera traps installed on each trapping area. We
calculated a combined average daily snow cover value to the nearest 10\%
in our study region. We converted \% snow cover to a binary type
variable above or below 60\% snow cover (presence/absence) for the
autumn seasons, as there were very few instances when snow cover
estimates were between 0\% and 100\%. We measured temperature at least
six times a day on each trapping area using a minimum of 2 temperature
loggers (ibutton, DS1922L, Maxim Integrated, Whitewater, USA) to obtain
a single average daily temperature value for each trapping area.

Measuring coat colour mismatch

Coat colour mismatch was defined as the difference between hare percent
white (10\% bins) and the daily percent snow cover (10\% bins for both
autumn and spring). For all analyses, we treated mismatch as a binary
variable, defining mismatch as greater than 50\% difference between hare
\% white and snow cover (\%). As such, mismatched hares were white
(\textgreater{} 50 \% white) individuals in a snowless (\textless{} 50\%
snow cover) environment. Considering that brown mismatched hares in a
snowy environment were rare (1\% of trapping records), we did not
consider this type of mismatch in analyses. Although the threshold for
mismatch used in some previous studies is 60\% contrast (Mills et al.
2013; Wilson et al. 2018), mismatch at this contrast threshold was rare
in our study region, i.e., in 11\% of trapping records, so we used 50\%
as our mismatch threshold to increase our sample size. That being said,
analyses using 40\% or 60\% thresholds for mismatch revealed similar
results (Tables S5, S6, S9, S10).

Effect of coat colour mismatch on survival

To evaluate the effect of coat colour mismatch on snowshoe hare
survival, we generated Cox's proportional hazards (CPH) models (Cox \&
Oakes 1984) with the ``coxph'' function in the survival package in R
(Therneau \emph{et al.} 2021). The CPH model is a semi-parametric
approach used to analyze binary response data, in our case: alive or
dead (Sievert \& Keith 1985). We monitored 347 hares and recorded 41
deaths over four springs and 34 deaths over three autumns. We excluded
mortality checks that exceeded seven days to limit the uncertainty in
the timing of death events (Murray \& Bastille-Rousseau 2020). We
censored 15 individuals whose collars were removed before the end of the
study period and six individuals with permanently missing VHF signals.
We pooled data from different years, trapping areas, and sex, as
exploratory analysis indicated that none of those variables had a
significant effect on autumn or spring mortality risk (Table S2).
Considering that coat colour was assessed only during capture
opportunities (on average every 13.1 ± SD: 10.8 days per individual), we
assigned coat colour for each record in our survival analysis as the
nearest coat colour assessment completed in the field (average
difference of 4.95 ± SD: 3.70 days between telemetry check and
coat-colour assessment). We removed telemetry records where a coat
colour assessment within 14 days did not exist to ensure that coat
colour and derived mismatch values were an accurate representation of
each individual at the time of the telemetry check. Results from models
using survival records within 8 days of a coat-colour assessment were
qualitatively similar to those we obtained with our chosen 14-day
threshold (see Table S3).

We generated three competing CPH models for both autumn and spring. The
first model included snow cover and snow depth, based on prior evidence
of snow effects on hare survival (Meslow \& Keith 1971; Peers et al.
2020). Our second model included those same snow variables in addition
to coat colour mismatch, our variable of interest. The third model was
the null (intercept-only) model. We used Akaike Information Criterion
for our model selection (Akaike 1974) and identified our top model based
on AIC\textsubscript{c} (Burnham \& Anderson 2002) with the package
AICcmodavg (Mazerolle 2019). We assessed multicollinearity in our top
model using the variance inflation factor (VIF) and ensured no variables
had VIF's greater than 2. The proportionality assumption of CPH models,
which implies that the hazard ratio (HR; i.e., risk of death) is assumed
to be constant over time (Joshua Chen \& Liu 2006), was met for our top
spring and autumn CPH model. Our results were not affected by
informative censoring, as we found qualitatively similar results for
both spring and autumn model coefficients when we treated censored
individuals as deaths (Murray \& Bastille-Rousseau 2020) (Table S4).

Effect of coat colour mismatch on time spent foraging

To test our proposed mechanistic pathway, whereby white mismatched hares
experience reduced energetic requirements leading to reduced foraging
time (Balluffi-Fry \emph{et al.} In Review; Sheriff \emph{et al.}2009a),
we used linear mixed-effects models using the ``lmer'' function in the
package lme4 (Bates et al. 2015). Daily time spent foraging (minutes)
was derived from tri-axial accelerometer data using behavioural
classifications previously developed in this hare population (see Studd
et al., 2019 for more information on classification methods). Daily time
spent foraging was classified over 4 second intervals at a 96\% accuracy
(Studd \emph{et al.} 2019). We recorded 1505 daily foraging records from
66 hares over the three autumns and 838 daily foraging records from 44
hares over the four springs. Similar to our survival analysis, we only
kept foraging records that were within 14 days of a coat-colour
assessment (average difference of 4.48 ± 3.51 (SD) days). We reran our
top foraging time models with data restricted to daily foraging records
that were within 8 days of a coat-colour assessments instead to ensure
that our results were not affected by this 14-day threshold, and
obtained qualitatively similar results (Table S8). To eliminate the
potential of seasonal changes in foraging impacting our results (Griffin
\emph{et al.} 2005), we restricted our data to only the autumn and
spring periods when snow cover was \selectlanguage{english}[?] 50\%, i.e., mismatch was possible
given our chosen threshold and therefore both matched and mismatched
individuals occurred simultaneously.

We generated four linear mixed-effects models per season to test for
differences in daily minutes spent foraging (our response for all
models) between matched brown hares and mismatched white hares and their
responses to changes in temperature. We included a random effect for
individual ID in all models to control for non-independence of data. We
included sex as a fixed factor in all spring models only, as exploratory
data analysis indicated that sex had a significant effect on time spent
foraging for spring but not autumn (Table S7). Furthermore, we included
year as a fixed effect in each model to account for potential effects of
yearly changes in predation risk on hare foraging behaviour
(Shiratsuru\emph{et al.} 2021). Our first model included two fixed
effects, temperature and year. Our second model included temperature,
year, and coat colour mismatch, and our third model included the same
variables as the second in addition to an interaction between mismatch
and temperature. Our fourth model was a null intercept-only model. We
checked model fit using marginal and conditional R- squared calculated
using the ``r.squaredGLMM'' function in the package MuMIn (Barton 2020),
according to Nakagawa \emph{et al.} 2017. We used Akaike Information
Criterion (Akaike 1974) to rank our four competing models and identified
our top model in each season based on AIC\textsubscript{c} (Burnham \&
Anderson 2002).We completed all statistical analyses in R version 3.6.2
(2019) (R Core Team, 2019). We considered results where P \selectlanguage{english}[?]0.05 as
significant and reported all means with +- 1 standard error. The code
used for all analyses can be found online:
https://gitlab.com/joanie.kennah/chapter\_1\_kluane.

\textbf{Results}

Permanent snow cover date, i.e., 100\% snow cover without melting until
the spring, was variable across our autumn seasons, occurring almost 3
weeks later in 2015 (November 3\textsuperscript{rd}) than in 2016
(October 16\textsuperscript{th}) and 2017 (October
17\textsuperscript{th}). Completion of snowmelt date, i.e., no more snow
on ground, was similar across study years (May 6\textsuperscript{th},
2015, May 1\textsuperscript{st} 2016, May 2\textsuperscript{nd} 2017 and
May 1\textsuperscript{st} 2018). When considering both seasons and all
years together, the prevalence of coat colour mismatched hares that
contrasted with their snowless environment was low (14\% of trapping
records) in our population. Mismatch occurred more frequently in the
autumn (19\% of trapping records) than the spring (8\% of trapping
records). The autumn with the latest permanent snow cover arrival date,
i.e., 2015, had the highest prevalence of mismatch (33\% of records).
Prevalence of mismatch in the autumns of 2016 and 2017 were 10\% and
13\% of trapping records, respectively. Spring mismatch was consistent
across years around 10\% (2015-9\% of trapping records, 2016-10\%,
2018-12\%), with the exception of 2017 when only 1\% of trapped hares
were mismatched.

Effect of coat colour mismatch on mortality

The CPH model with the strongest support in both seasons included snow
depth, snow cover and mismatch (Table S11, S12 \& S13). However, the
second highest ranking CPH model for spring, i.e., the model including
only snow variables, was within 2 \selectlanguage{greek}Δ\selectlanguage{english}AICc (AICc = 0.09) from our top
spring CPH model (Table S11). Mortality risk for mismatched hares in
autumn was significantly reduced (\emph{z} = -2.43; P=0.02) relative to
matched hares (Hazard Ratio (HR)= 0.135; 95\% Confidence Intervals (CI):
0.027, 0.679; Fig. 1a). In contrast, coat colour mismatch was positively
correlated with mortality risk for hares in the spring (Fig. 1b), but
this effect was non-significant (\emph{z} = 1.60; P= 0.11). Models were
qualitatively similar regardless of our classification of mismatch,
except when considering mismatch as a minimum 40\% contrast between coat
colour and snow cover; in this case mismatch significantly increased
mortality risk in the spring (HR= 6.780; 95\% CI: 2.390, 19.240;\emph{z}
= 3.60; P\textless{}0.001). Snow depth (\emph{z} = -2.29; P= 0.02) and
snow cover (\emph{z} = 2.98; P=0.003) significantly affected mortality
risk in the top spring model, but not in the top autumn model. In
spring, the risk of dying decreased as snow depth increased (HR=0.95;
95\% CI: 0.92, 0.993; Fig S1a) and mortality risk increased as snow
cover increased (HR=1.046; 95\% CI: 1.01, 1.08; Fig S1b).

Effect of coat colour mismatch on foraging time

Across our study years, hares foraged on average 706 \selectlanguage{ngerman}± 2.29 minutes per
day in the spring and 751 ± 1.65 minutes per day in the autumn. Coat
colour mismatch was an important predictor of daily foraging time in the
autumn, but not the spring (Table S14 and S15). The top model for autumn
foraging time included coat colour mismatch, temperature, year, and the
interaction between temperature and mismatch (Table 1). As autumn
temperature decreased, mismatched hares decreased daily foraging time,
whereas matched hares increased foraging time (Fig. 2a; Table 1). For
instance, when the temperature was - 8 °C, brown-matched hares foraged
65 minutes more per day than white-mismatched hares (Fig. 2a). The top
model for spring included temperature, year, and sex (Table 1), When
coat colour mismatch was included in our spring foraging models, its
effect on daily foraging time was non-significant (\emph{t}
=-0.759,\emph{P} \textgreater{}0.05).

\textbf{Discussion}

Phenotypes and climate change can vary widely within a species'
distribution, as can phenological mismatch and its consequences on
survival. Elucidating potential unifying mechanisms is crucial to
reconcile varied responses to phenological mismatch. We evaluated the
effect of coat colour mismatch on snowshoe hare survival in a northern
population and further tested a potential mechanism that may influence
this effect. We hypothesized that the thermal and energetic benefits of
winter acclimatization in white hares, i.e., increased coat insulation
and reduced metabolic rate (Sheriff \emph{et al.} 2009a;
Gigliotti\emph{et al.} 2017), ultimately reduce their foraging
requirements (Balluffi-Fry \emph{et al.} In Review) and thus predation
risk, which may influence the costs of coat colour mismatch.
Surprisingly, we found that mismatched hares had a higher survival than
matched hares in the autumn (Fig. 1a) but that survival did not differ
between matched and mismatched hares in the spring (Fig. 1b). Although
this result contradicts previous studies that link coat colour mismatch
in snowshoe hares to reduced survival (Zimova \emph{et al.} 2016; Wilson
\emph{et al.} 2018), our proposed mechanism for why this might be the
case is supported. Mismatched white hares spent significantly less time
foraging than matched individuals in the autumn (Fig. 2a), presumably
due to the thermal and energetic benefits of winter acclimatization.
Indeed, reduced foraging time likely decreases exposure to predators and
subsequently improves survival (Fig 1a). We reconcile our findings with
those of previous studies with a unifying factor: temperature.

Matched hares foraged longer than mismatched white individuals in the
autumn, and this difference was pronounced at lower (\textless{} --3°C)
temperatures (Fig. 1a). Given the wide range of ecological contexts,
selection pressures, and local adaptations that exist across the
distribution of snowshoe hares (Gigliotti \emph{et al.} 2017), the
cost-benefit ratio of lost camouflage versus energy conservation may
vary across populations experiencing different temperatures. For
example, northern populations experiencing cold temperatures benefit
from the energetic advantages of winter coats despite mismatch during
snow-free periods, whereas southerly populations experiencing warmer
temperatures may not. Indeed, adverse survival effects associated with
mismatch in southern snowshoe hare populations in Montana
(Zimova\emph{et al.} 2016) and Wisconsin (Wilson \emph{et al.} 2018)
occur in regions that experience warmer temperatures than those in
southwestern Yukon (Fig. 2). During the period when mismatch is possible
in Montana, autumn temperatures can range from \textasciitilde{} 3°C to
17 °C and spring temperatures can range from \textasciitilde{} 4°C to 20
°C.

The seasonal differences in mismatch effects on survival and foraging
time that we found within our study population highlight temperature as
a unifying factor affecting the survival costs of coat colour mismatch.
In spring, mismatch did not influence mortality risk (Fig. 1b) and
matched and mismatched hares spent similar amounts of time foraging
(Fig. 2b). Mismatched hares in the spring occurred at temperatures (-0.5
°C to 11°C, Fig. 2b) that were approximately within the thermoneutral
zone of both summer and winter-acclimatized hares (Sheriff \emph{et
al.}2009a). In contrast, mismatched hares in the autumn experienced
temperatures between -7°C and 4°C (Fig. 2a) which fall below the lower
critical temperature for summer-acclimatized brown hares, but not
winter-acclimatized white hares (Sheriff \emph{et al.} 2009a). Animals
must increase their energetic expenditure when they are exposed to
temperatures outside of their thermoneutral zone (Kingma \emph{et
al.}2012), which may represent a likely mechanism explaining the longer
foraging time in matched brown hares in the autumn relative to
mismatched white hares (Fig. 2a). These results further support that the
thermal and energetic benefits of winter acclimatization may outweigh
the costs of coat colour mismatch at cold temperatures.

Although camouflage is thought to be the primary adaptive benefit of
coat colour polymorphism, like most traits, alternate benefits, e.g.,
thermal and physiological, exist (Caro 2005; Duarte \emph{et al.} 2017;
Zimova \emph{et al.} 2018). We found that these alternate benefits
offset the costs of camouflage loss at cold temperatures. Our proposed
hypothesis, whereby the thermal and energetic benefits of winter
acclimatization may influence coat colour mismatch effects through
reduced time spent foraging, has the potential to reconcile
intraspecific variation among other snowshoe hare populations and merits
testing in other colour changing species, i.e. arctic hares (\emph{Lepus
arcticus} ), mountain hares (\emph{Lepus timidus} ). Climate
change-induced variation in temperature and precipitation regimes are
likely to vary across species ranges (Loarie \emph{et al.} 2009). Such
variation in climate change effects will be particularly large for
species with broad distributions, i.e., circumboreal colour-changing
species. Ultimately, as temperatures in the Northern Hemisphere are
projected to warm (Danco \emph{et al.} 2016), northern snowshoe hare
populations are likely to reach the threshold (\textgreater{} --3°C) at
which the energetic benefits of white coats are lost, and survival costs
driven by coat colour mismatch could occur (Zimova \emph{et al.} 2016;
Wilson \emph{et al.} 2018). However, elucidating the mechanisms through
which phenological mismatches may be operating is essential to enable
predictions on broad-scale changes in species distributions.

\textbf{Acknowledgements}

We thank the numerous field technicians who worked on this project, as
well as members of the Wildlife Evolutionary Ecology lab at Memorial
University of Newfoundland for comments on earlier versions of this
manuscript. We thank Sean Konkolics and Alec Robitaille for assistance
and help with statistical analyses and R code. We also thank A.
MacDonald and her family for long-term access to her trapline. We thank
the Champagne and Aishihik First Nations, and Kluane First Nation, for
allowing this work within their traditional territory. This work was
supported by the Natural Sciences and Engineering Research Council of
Canada, Northern Studies Training Program, the University of Alberta
Northern Research Award programme, the Association of Canadian
Universities for Northern Studies, the Wildlife Conservation Society
Canada, the W. Garfield Weston Foundation, Government of Yukon, and
Earth Rangers.

\textbf{References}

Akaike, H. (1974). A New Look at the Statistical Model
Identification.\emph{IEEE Trans. Automat. Contr.} , 19, 716--723.

Atmeh, K., Andruszkiewicz, A. \& Zub, K. (2018). Climate change is
affecting mortality of weasels due to camouflage mismatch. \emph{Sci.
Rep.} , 8, 1--7.

Balluffi-Fry, J., Leroux, S.J., Wiersma, Y.F., Richmond, I.C., Heckford,
T.R., Rizzuto, M., \emph{et al.} (In Review at Oecologia). Integrating
plant stoichiometry and feeding experiments: state-dependent forage
choice and its implications on body mass., bioRxiv {[}Preprint: doi:
10.1101/2021.02.16.431523{]}.

Barton, K. (2020). \emph{Multi-Model Inference. R package version
1.43.17} .

Bates, D., Mächler, M., Bolker, B.M. \& Walker, S.C. (2015). Fitting
linear mixed-effects models using lme4. \emph{J. Stat. Softw.} , 67,
1--51.

Boraty\selectlanguage{polish}ń\selectlanguage{english}ski, J.S., Jefimow, M. \& Wojciechowski, M.S. (2016). Phenotypic
flexibility of energetics in acclimated Siberian hamsters has a narrower
scope in winter than in summer. \emph{J. Comp. Physiol. B Biochem. Syst.
Environ. Physiol.} , 186, 387--402.

Brown, R.D. \& Mote, P.W. (2009). The response of Northern Hemisphere
snow cover to a changing climate. \emph{J. Clim.} , 22, 2124--2145.

Burnham, K.P. \& Anderson, D.R. (2002). \emph{Model Selection and
Multimodel Inference} . Springer, New York, New York USA.

Caro, T. (2005). The adaptive significance of coloration in
mammals.\emph{Bioscience} , 55, 125--136.

Cox, D.R. \& Oakes, D. (1984). \emph{Analysis of survival data} .
Chapman and Hall, New York, New York USA.

Danco, J.F., Deangelis, A.M., Raney, B.K. \& Broccoli, A.J. (2016).
Effects of a warming climate on daily snowfall events in the Northern
Hemisphere. \emph{J. Clim.} , 29, 6295--6318.

Doi, H., Gordo, O., Mori, T. \& Kubo, M.T. (2017). A macroecological
perspective for phenological research under climate change. \emph{Ecol.
Res.} , 32, 633--641.

Duarte, R.C., Flores, A.A.V. \& Stevens, M. (2017). Camouflage through
colour change: Mechanisms, adaptive value and ecological
significance.\emph{Phil. Trans. R. Soc. B} , 372, 1--8.

Ferreira, M.S., Alves, P.C., Callahan, C.M., Marques, J.P., Mills, L.S.,
Good, J.M., \emph{et al.} (2017). The transcriptional landscape of
seasonal coat colour moult in the snowshoe hare. \emph{Mol. Ecol.} , 26,
4173--4185.

Fuglesteg, B.N., Haga, \selectlanguage{ngerman}Ø.E., Folkow, L.P., Fuglei, E. \& Blix, A.S.
(2006). Seasonal variations in basal metabolic rate, lower critical
temperature and responses to temporary starvation in the arctic fox
(Alopex lagopus) from Svalbard. \emph{Polar Biol.} , 29, 308--319.

Gigliotti, L.C., Diefenbach, D.R. \& Sheriff, M.J. (2017). Geographic
variation in winter adaptations of snowshoe hares (Lepus
americanus).\emph{Can. J. Zool.} , 95, 539--545.

Gordo, O. \& Doi, H. (2012). Drivers of population variability in
phenological responses to climate change in Japanese birds. \emph{Clim.
Res.} , 54, 95--112.

Griffin, P.C., Griffin, S.C., Waroquiers, C. \& Mills, L.S. (2005).
Mortality by moonlight: Predation risk and the snowshoe
hare.\emph{Behav. Ecol.} , 16, 938--944.

Heard, M.J., Riskin, S.H. \& Flight, P.A. (2012). Identifying potential
evolutionary consequences of climate-driven phenological
shifts.\emph{Evol. Ecol.} , 26, 465--473.

Humphries, M.M., Boutin, S., Thomas, D.W., Ryan, J.D., Selman, C.,
McAdam, A.G., \emph{et al.} (2005). Expenditure freeze: The metabolic
response of small mammals to cold environments. \emph{Ecol. Lett.} , 8,
1326--1333.

Joshua Chen, Y.H. \& Liu, G.H.F. (2006). A note on the estimate of
treatment effect from a cox regression model when the proportionality
assumption is violated. \emph{Commun. Stat. - Theory Methods} , 35,
521--526.

Kingma, B., Frijns, A. \& Lichtenbelt, W.V.M. (2012). The thermoneutral
zone: Implications for metabolic studies. \emph{Front. Biosci. - Elit.}
, 4 E, 1975--1985.

Krebs, C.J., Boonstra, R. \& Boutin, S. (2018). Using experimentation to
understand the 10-year snowshoe hare cycle in the boreal forest of North
America. \emph{J. Anim. Ecol.} , 87, 87--100.

Krebs, C.J., Boutin, S., Boonstra, R., Sinclair, A.R.E., Smith, J.N.M.,
Dale, M.R.T., \emph{et al.} (1995). Impact of food and predation on the
snowshoe hare cycle. \emph{Science (80-. ).} , 269, 1112--1115.

Kudo, G. \& Ida, T.Y. (2013). Early onset of spring increases the
phenological mismatch between plants and pollinators. \emph{Ecology} ,
94, 2311--2320.

Lehikoinen, A. (2011). Advanced autumn migration of sparrowhawk has
increased the predation risk of long-distance migrants in
Finland.\emph{PLoS One} , 6, 1--4.

Lima, S.L. \& Dill, L.M. (1990). Behavioral decisions made under the
risk of predation: a review and prospectus. \emph{Can. J. Zool.} , 68,
619--640.

Loarie, S.R., Duffy, P.B., Hamilton, H., Asner, G.P., Field, C.B. \&
Ackerly, D.D. (2009). The velocity of climate change. \emph{Nature} ,
462, 1052--1055.

Lovegrove, B.G. (2005). Seasonal thermoregulatory responses in
mammals.\emph{J. Comp. Physiol. B Biochem. Syst. Environ. Physiol.} ,
175, 231--247.

Mazerolle, M. (2019). \emph{Model Selection and Multimodel Inference
Based on (Q)AIC(c) Version 2.2-2} .

McNamara, J.M. \& Houston, A.I. (1987). Starvation and Predation as
Factors Limiting Population Size Published by\selectlanguage{english} : Ecological Society of
America Stable URL\selectlanguage{english} : http://www.jstor.org/stable/1939235 REFERENCES
Linked references are available on JSTOR for this article\selectlanguage{english} : You may need
to log in to JST. \emph{Ecology} , 68, 1515--1519.

Melin, M., Mehtatalo, L., Helle, P., Ikonen, K. \& Packalen, T. (2020).
Decline of the boreal willow grouse (Lagopus lagopus) has been
accelerated by more frequent snow-free springs. \emph{Sci. Rep.} , 10,
1--10.

Meslow, E.C. \& Keith, L.B. (1971). A correlation analysis of weather
versus snowshoe hare populatio parameters. \emph{J. Wildl. Manage.} ,
35, 1--15.

Mikkelsen, L., Johnson, M., Wisniewska, D.M., van Neer, A., Siebert, U.,
Madsen, P.T., \emph{et al.} (2019). Long-term sound and movement
recording tags to study natural behavior and reaction to ship noise of
seals. \emph{Ecol. Evol.} , 9, 2588--2601.

Mills, L.S., Bragina, E. V., Kumar, A. V., Zimova, M., Lafferty, D.J.R.,
Feltner, J., \emph{et al.} (2018). Winter color polymorphisms identify
global hot spots for evolutionary rescue from climate
change.\emph{Science (80-. ).} , 359, 1033--1036.

Mills, L.S., Zimova, M., Oyler, J., Running, S., Abatzoglou, J.T. \&
Lukacs, P.M. (2013). Camouflage mismatch in seasonal coat color due to
decreased snow duration. \emph{Proc. Natl. Acad. Sci. U. S. A.} , 110,
7360--7365.

Moller, A.P., Rubolini, D. \& Lehikoinen, E. (2008). Populations of
migratory bird species that did not show a phenological response to
climate change are declining. \emph{Proc. Natl. Acad. Sci. U. S. A.} ,
105, 16195--16200.

Murray, D.L. \& Bastille-Rousseau, G. (2020). Estimating survival and
cause-specific mortality from continuous time observations.
In:\emph{Population ecology in practice} (eds. Murray, D.L. \&
Sandercock, B.). Wiley-Blackwell, Hoboken, New Jersey, USA, p. 448.

Nagorsen, D.W. (1983). Winter pelage colour in snowshow hares ( Lepus
americanus) from the Pacific Northwest. \emph{Can. J. Zool.} , 61,
2313--2318.

Nakagawa, S., Johnson, P.C.D. \& Schielzeth, H. (2017). The coefficient
of determination R2 and intra-class correlation coefficient from
generalized linear mixed-effects models revisited and expanded. \emph{J.
R. Soc. Interface} , 14, 1--11.

Pedersen, S., Odden, M. \& Pedersen, H.C. (2017). Climate change induced
molting mismatch? Mountain hare abundance reduced by duration of snow
cover and predator abundance. \emph{Ecosphere} , 8, 1--8.

Peers, M.J.L., Majchrzak, Y.N., Menzies, A.K., Studd, E.K.,
Bastille-Rousseau, G., Boonstra, R., \emph{et al.} (2020). Climate
change increases predation risk for a keystone species of the boreal
forest. \emph{Nat. Clim. Chang.} , 10, 1149--1153.

Phillimore, A.B., Hadfield, J.D., Jones, O.R. \& Smithers, R.J. (2010).
Differences in spawning date between populations of common frog reveal
local adaptation. \emph{Proc. Natl. Acad. Sci. U. S. A.} , 107,
8292--8297.

Porkert, J., Gashkov, S., Haikola, J., Huhta, E., Kaisanlahti-Jokimaki,
M.-L., Kuranov, B., \emph{et al.} (2014). Variation and long-term trends
in the timing of breeding of different Eurasian populations of Common
Redstart Phoenicurus phoenicurus. \emph{J. Ornithol.} , 155, 1045--1057.

Post, E. \& Forchhammer, M.C. (2008). Climate change reduces
reproductive success of an Arctic herbivore through trophic
mismatch.\emph{Philos. Trans. R. Soc. B Biol. Sci.} , 363, 2369--2375.

Radchuk, V., Reed, T., Teplitsky, C., van de Pol, M., Charmantier, A.,
Hassall, C., \emph{et al.} (2019). Adaptive responses of animals to
climate change are most likely insufficient. \emph{Nat. Commun.} , 10,
1--14.

Reed, T.E., Grtoan, V., Jenouvrier, S., Staeher, B.E. \& Visser, M.E.
(2013). Population growth in a wild bird is buffered against
phenological mismatch. \emph{Science (80-. ).} , 340, 488--491.

Sheriff, M.J., Kuchel, L., Boutin, S. \& Humphries, M.M. (2009a).
Seasonal Metabolic Acclimatization in a Northern Population of
Free-Ranging Snowshoe Hares, Lepus americanus . \emph{J. Mammal.} , 90,
761--767.

Sheriff, M.J., Speakman, J.R., Kuchel, L., Boutin, S. \& Humphries, M.M.
(2009b). The cold shoulder: Free-ranging snowshoe hares maintain a low
cost of living in cold climates. \emph{Can. J. Zool.} , 87, 956--964.

Shiratsuru, S., Majchrzak, Y.N., Peers, M.J.L., Studd, E.K., Menzies,
A.K., Derbyshire, R., \emph{et al.} (2021). Food availability and
long\selectlanguage{english}-term predation risk interactively affect antipredator
response.\emph{Ecology} , e03456.

Sievert, P.R. \& Keith, L.B. (1985). Survival of Snowshoe Hares at a
Geographic Range Boundary. \emph{J. Wildl. Manage.} , 49, 854--866.

Studd, E.K., Boudreau, M.R., Majchrzak, Y.N., Menzies, A.K., Peers,
M.J.L., Seguin, J.L., \emph{et al.} (2019). Use of Acceleration and
Acoustics to Classify Behavior, Generate Time Budgets, and Evaluate
Responses to Moonlight in Free-Ranging Snowshoe Hares. \emph{Front.
Ecol. Evol.} , 7, 1--14.

Therneau, T.M., Lumley, T., Elizabeth, A. \& Cynthia, C.
(2021).\emph{Survival Analysis. R package version 3.2-10} .

Valladares, F., Matesanz, S., Guilhaumon, F., Araujo, M.B., Balaguer,
L., Benito-Garzon, M., \emph{et al.} (2014). The effects of phenotypic
plasticity and local adaptation on forecasts of species range shifts
under climate change. \emph{Ecol. Lett.} , 17, 1351--1364.

Visser, M.E. \& Gienapp, P. (2019). Evolutionary and demographic
consequences of phenological mismatches. \emph{Nat. Ecol. Evol.} , 3,
879--885.

Wilson, E.C., Shipley, A.A., Zuckerberg, B., Peery, M.Z. \& Pauli, J.N.
(2018). An experimental translocation identifies habitat features that
buffer camouflage mismatch in snowshoe hares. \emph{Conserv. Lett.} ,
12, 1--8.

Zeileis, A., Grothendieck, G., Ryan, J.A. \& Andrews, F.
(2021).\emph{Package ``Zoo''. S3 infrastructure for regular and
irregular time series (Z's ordered observations). R package version
1.8-9} .

Zimova, M., Hacklander, K., Good, J.M., Melo-Ferreira, J., Alves, P.C.
\& Mills, L.S. (2018). Function and underlying mechanisms of seasonal
colour moulting in mammals and birds: what keeps them changing in a
warming world? \emph{Biol. Rev.} , 93, 1478--1498.

Zimova, M., Mills, L.S., Lukacs, P.M. \& Mitchell, M.S. (2014). Snowshoe
hares display limited phenotypic plasticity to mismatch in seasonal
camouflage. \emph{Proc. R. Soc. B Biol. Sci.} , 281, 1--9.

Zimova, M., Mills, L.S. \& Nowak, J.J. (2016). High fitness costs of
climate change-induced camouflage mismatch. \emph{Ecol. Lett.} , 19,
299--307.

\textbf{Figure Legends}

Fig 1. The modelled effect of coat colour mismatch on snowshoe hare
mortality risk, generated from our top supported CPH model for a) autumn
and b) spring. Points represent predicted hazard ratios (HR) for matched
and mismatched hares when snow depth and snow cover are held at zero.
Error bars represent predicted standard errors, and the dashed line
represents baseline mortality risk (i.e., HR=1).

Fig 2. Modelled effect of temperature on daily foraging time (minutes)
for matched and mismatched snowshoe hares in the snow-free period of a)
autumn (marginal R\textsuperscript{2}= 0.12, conditional
R\textsuperscript{2}=0.32) and b) spring (marginal
R\textsuperscript{2}=0.13, conditional R\textsuperscript{2}=0.28) of
2016 (the year with the most data). Data points show daily foraging
records for individuals across all study years and predicted foraging
time of mismatched hares is restricted to temperatures where mismatched
hares occurred in our study. Predicted values for daily spring foraging
time are for males.

\textbf{Table Legends}

Table 1. Summary of variables included in top-ranking linear
mixed-effects daily foraging time models for snow-free autumn and spring
periods. Daily foraging time was considered in minutes. Both autumn and
spring models also include individual ID as a random effect and the
spring model includes sex as a random effect.

\textbf{Figures}

Fig. 1\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Fig. 2\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Table}

Table 1\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Response: Daily foraging minutes\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Response: Daily foraging minutes\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Response: Daily foraging minutes\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Model\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Coefficient (\(\pm\) SE)\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
t\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
P\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Top model autumn from n=1505 daily foraging records from 66 hares\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Top model autumn from n=1505 daily foraging records from 66 hares\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Top model autumn from n=1505 daily foraging records from 66 hares\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Top model autumn from n=1505 daily foraging records from 66 hares\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Intercept\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
830.909\(\pm\)9.037\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
91.942\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Temperature\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-2.306\(\pm\)0.505\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-4.566\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
mismatch\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-17.385\(\pm\)6.170\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-2.818\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.005\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Year (2016)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-10.364\(\pm\)8.380\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-1.237\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.217\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Year (2017)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-101.607\(\pm\)12.188\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-8.336\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Temperature\(\times\)mismatch\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
5.963\(\pm\)1.210\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
4.929\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Top model spring from n= 838 daily foraging records from 44 hares\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Top model spring from n= 838 daily foraging records from 44 hares\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Top model spring from n= 838 daily foraging records from 44 hares\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Top model spring from n= 838 daily foraging records from 44 hares\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Intercept\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
858.868 \(\pm\)22.800\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
37.669\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Temperature\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-1.209\(\pm\)1.295\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-0.933\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.351\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Year (2016)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-16.582\(\pm\)19.380\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-0.856\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.396\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Year (2017)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-28.914\(\pm\)19.111\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-1.513\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.137\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Year (2018)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-93.029\(\pm\)21.089\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-4.411\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Sex (F)\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-34.832\(\pm\)17.000\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
-2.049\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0.047\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Hosted file}

\verb`ELE_kennah_SA.docx`  available at \url{https://authorea.com/users/734354/articles/711419-coat-colour-mismatch-improves-survival-energetic-advantages-exceed-lost-camouflage}

\selectlanguage{english}
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\end{document}

