Environmental and evolutionary effects on horn growth of male bighorn sheep

Abstract

The development of male secondary sexual characters such as antlers or horns has substantial biological and socio-economic importance because in many species these traits affect male fitness positively through sexual selection and negatively through trophy hunting. Both environmental conditions and selective hunting can affect horn growth but their relative importance remains unexplored. We first examined how a large-scale climate index, the Pacific Decadal Oscillation (PDO), local weather and population density influenced both absolute and relative annual horn growth from birth to three years of male bighorn sheep Ovis canadensis over 42 years. We then examined the relative influence of environmental conditions and evolution mainly driven by trophy hunting on male horn length at three years of age. Horn growth was positively influenced by low population density and warm spring temperature, suggesting that ongoing climate change should lead to larger horns. Seasonal values of PDO were highly correlated. Horn growth increased with PDO in spring or summer at low density, but was weak at high density regardless of PDO. The interaction between population density and PDO in spring or summer accounted for a similar proportion of the observed annual variation in horn growth (32% or 37%) as did the additive effects of spring temperature and density (34%). When environmental conditions deteriorated, males allocated relatively more resources to summer mass gain than to horn growth, suggesting a conservative strategy favoring maintenance of condition over allocation to secondary sexual characters. Population density explained 27% of the variation in horn length, while evolutionary effects explained 9% of the variance. Thus, our study underlines the importance of both evolution and phenotypic plasticity on the development of a secondary sexual trait. This article is protected by copyright. All rights reserved.

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Environmental and evolutionary effects on horn growth of male
bighorn sheep
Mathieu Douhard, Gabriel Pigeon, Marco Festa-Bianchet, David W. Coltman, Simon Guillemette
and Fanie Pelletier
M. Douhard (mathieu.douhard@gmail.com), G. Pigeon, M. Festa-Bianchet, D. W. Coltman, S. Guillemette and F. Pelletier, Dépt de biologie et
Centre d’Études Nordiques, Univ. de Sherbrooke, Sherbrooke, QC, J1K 2R1, Canada. – D. W. Coltman, Dept of Biological Sciences,
Univ. of Alberta, Edmonton, AB, T6G 2E9, Canada.
e development of male secondary sexual characters such as antlers or horns has substantial biological and socio-economic
importance because in many species these traits affect male fitness positively through sexual selection and negatively
through trophy hunting. Both environmental conditions and selective hunting can affect horn growth but their relative
importance remains unexplored. We first examined how a large-scale climate index, the Pacific Decadal Oscillation (PDO),
local weather and population density influenced both absolute and relative annual horn growth from birth to three years
of male bighorn sheep Ovis canadensis over 42 years. We then examined the relative influence of environmental conditions
and evolution mainly driven by trophy hunting on male horn length at three years of age. Horn growth was positively
influenced by low population density and warm spring temperature, suggesting that ongoing climate change should lead
to larger horns. Seasonal values of PDO were highly correlated. Horn growth increased with PDO in spring or summer
at low density, but was weak at high density regardless of PDO. e interaction between population density and PDO
in spring or summer accounted for a similar proportion of the observed annual variation in horn growth (32% or 37%)
as did the additive effects of spring temperature and density (34%). When environmental conditions deteriorated, males
allocated relatively more resources to summer mass gain than to horn growth, suggesting a conservative strategy favoring
maintenance of condition over allocation to secondary sexual characters. Population density explained 27% of the variation
in horn length, while evolutionary effects explained 9% of the variance. us, our study underlines the importance of both
evolution and phenotypic plasticity on the development of a secondary sexual trait.
Climate and its expression through weather conditions are
important determinants of life-history traits and population
dynamics (Elton 1924, Krebs and Berteaux 2006). While
early studies examined the effects of local weather variables,
over the last two decades researchers have recognized the
usefulness of large-scale indices such as the North Atlantic
Oscillation (NAO, Hurrell 1995) to understand the
influence of climate on plants and animals (Stenseth et al.
2002, Hallett et al. 2004, Stenseth and Mysterud 2005,
Stige et al. 2006). Large-scale climate indices often explain
more of the variation in life-history traits than local weather
because they reduce the complexity of several weather
components such as temperature, precipitation and wind
into a single variable. e relationship between large-scale
climate indices and local weather varies spatially (van de
Pol et al. 2013), but our knowledge of how climate affects
wildlife populations is biased towards western Europe
and eastern North America where the effects of NAO are
strong (Stenseth et al. 2002). us, we know little about
the potential effects of Pacific climate indices on large
herbivores in western North America (Hebblewhite 2005,
Hegel et al. 2010, Loehr et al. 2010).
As classic examples of sexually selected traits, antlers
and horns of males in large polygynous herbivores offer a
good opportunity to measure the influence of weather and
climate on individual performance (Büntgen et al. 2014).
ese weapons evolved through sexual selection over access
to females via male–male competition, female mate choice
or both (Andersson 1994). us, horn size is a major
determinant of dominance rank and mating success in adult
males of some species (Coltman et al. 2002 and Martin et al.
2013 on bighorn sheep; Robinson et al. 2006 on Soay sheep
Ovis aries; Bergeron et al. 2010 on alpine ibex Capra ibex).
Often, horn or antler growth indicates greater ability to obtain
fitness-enhancing resources: male ibex growing large horn
annuli in a given year have higher survival (von Hardenberg
et al. 2004), the most fertile male red deer Cervus elaphus have
the largest antlers (Malo et al. 2005), senescent male roe deer
Capreolus capreolus with the largest antlers are the heaviest
(Vanpé et al. 2007), and natural survival of yearling male
bighorn sheep increases with early horn growth (Bonenfant
et al. 2009a). Although several studies have reported
that weather and climate can influence growth of horns
(Table 1) and antlers (Smith 1998, Mahoney et al. 2011,
© 2016 e Authors. Oikos © 2016 Nordic Society Oikos
Subject Editor: Jessica Abbott. Editor-in-Chief: Dries Bonte. Accepted 25 November 2016
Oikos 126: 1031–1041, 2017
doi: 10.1111/oik.03799

1032
Schmidt et al. 2001), we know little about the relative
importance of genetics and plasticity on development of
horns or antlers, or about how environmental conditions
influence relative allocation of energy between body reserves
and growth of these weapons.
Body mass shows substantial seasonal fluctuations in many
temperate ungulates (Festa-Bianchet et al. 1996, Loison et al.
1999, Rughetti and Festa-Bianchet 2011). Summer mass
gain determines the amount of resources available to survive
winter (Pelletier et al. 2007). During summer, males therefore
face a tradeoff in allocation of resources between weapon
growth and mass gain, particularly when environmental
conditions are unfavorable. Festa-Bianchet et al. (2004) and
Mysterud et al. (2005) showed that for a given mass, horn
growth of lighter male bighorn sheep and antler growth of
male red deer were reduced by decreasing resource availability,
suggesting a conservative strategy that favors maintenance
over allocation to secondary sexual characters. However,
body mass does not provide a direct measure of mass gain
during the period of horn or antler growth. Examination of
the relationship between development of secondary sexual
traits and mass gain during summer is challenging because it
requires repeated measures of mass for the same animal.
Many studies investigating the effects of weather and
climate on growth of horns or antlers used data from
harvested animals (Table 1, Mysterud et al. 2005, Rivrud
et al. 2013). e advantages of using harvest data often
include a very large sample size and wide geographical
coverage (Rivrud et al. 2013, Büntgen et al. 2014, Douhard
et al. 2016a). Harvested animals, however, are not a random
sample of the population, as hunters usually select for
certain morphological or behavioral traits (Ciuti et al. 2012,
Pelletier et al. 2012, Leclerc et al. 2016). Artificial selection
is particularly obvious in trophy hunting, where hunters seek
males with large horns, antlers, or tusks, potentially leading
to rapid evolutionary changes in horn growth (Coltman
et al. 2003, Pigeon et al. 2016) and shape (Garel et al. 2007).
Analyses of temporal changes in horn growth can identify
potential evolutionary responses to selective hunting. In many
cases, however, it is difficult to determine whether a decline
represents an evolutionary response to trophy hunting or a
plastic consequence of unmeasured environmental factors
(Pérez et al. 2011, Douhard et al. 2016a).
Here we investigate how annual horn growth, both
absolute and relative to summer mass gain, varies with
weather/climate conditions and population density in male
bighorn sheep aged 0–3 years. We also compare the effects
of environmental conditions to those of evolution, mainly
driven by trophy hunting, on horn length at age three years.
Our study on the intensively monitored sheep population
at Ram Mountain, Alberta, Canada, spans 42 years, from
1972 to 2013. Males with large horns were subject to intense
trophy hunting for the first 23 years of the study. Males with
rapidly growing horns experience negative selection through
trophy hunting several years before large horns improve
reproductive success (Coltman et al. 2002). Horn length
has a heritability of 0.39 and a decline in horn length and
in breeding values of this trait until the hunt was stopped
suggested an evolutionary response to trophy hunting (Pigeon
et al. 2016). We do not know, however, whether climate and
weather contributed to the decline in horn growth. Here we
provide a unique quantification of the relative contributions
of ecological and anthropogenic variables to the development
of a secondary sexual character.
Based on previous studies (Table 1), we expected that
spring temperature and precipitation would override other
weather variables in driving variation in horn growth.
Precipitation in spring should increase horn growth. We had
no clear prediction about how spring temperature may affect
horn growth, because a positive effect was reported for alpine
ibex (Giacometti et al. 2002, Büntgen et al. 2014), whereas
a negative effect occurred for alpine chamois (Chirichella
et al. 2013). Because our study area is in western Canada,
we used the Pacific Decadal Oscillation (PDO; Mantua et al.
Table 1. Effects of weather or climate variables on horn growth of wild bovid males.
Species Type of data No. of years Variables Effect References
Alpine ibex harvest 10 temperature March–May ()Giacometti et al. 2002
Alpine ibex harvest 48 temperature March–May ()Büntgen et al. 2014
Alpine ibex harvest 48 snow cover January–May (–) Büntgen et al. 2014
Alpine ibex capture 12 temperature April–June (–) Toïgo et al. 1999*
Alpine ibex skull recovery ? precipitation May–July ()von Hardenberg et al. 2004
Alpine ibex skull recovery ? temperature November–April ()von Hardenberg et al. 2004
Spanish ibex Capra pyrenaica skull recovery 5 drought index June–September (–) Fandos 1995
Dall sheep Ovis dalli dalli capture ? precipitation March–May ()Bunnell 1978
Dall sheep harvest 42 principal component based on
temperature, precipitation and
PDO in April–May
(?) Loehr et al. 2010
Alpine chamois Rupicapra
rupicapra
harvest 5 snow cover November–April (–) Chirichella et al. 2013**
Alpine chamois harvest 5 precipitation April–May ()Chirichella et al. 2013
Alpine chamois harvest 5 temperature March–May (–) Chirichella et al. 2013
Alpine chamois harvest 5 temperature in July (–) Chirichella et al. 2013
Alpine chamois harvest 5 Temperature in December–February ()Chirichella et al. 2013
Cantabrian chamois Rupicapra
pyrenaica parva
harvest 9 precipitation in the previous year ()Pérez-Barbería et al. 1996
*only the first annual increment was considered. **only the two first annual increments were used.

1033
1997) as a large-scale climate index. If PDO captures the
overall fluctuations of relevant local weather variables, it
should provide the best measure to explain annual variation
in horn growth. However, if there is no correlation between
PDO and local weather, we predicted a weak impact of
PDO on horn growth. e effects of weather/climate can be
modulated by population density (Bonenfant et al. 2009b),
because both influence food availability and quality. We
expected interactive effects of density and weather/climate
on horn growth, as reported for juvenile survival in this
population (Portier et al. 1998). Finally, we expected males
to allocate more energy to mass gain than to horn growth
under unfavorable conditions (Festa-Bianchet et al. 2004).
Material and methods
Study area, population and sample collection
Ram Mountain is about 30 km east of the Rockies in
Alberta, Canada (52°N, 115°W, elevation 1700–2200 m).
Sheep are captured in a corral trap baited with salt from
late May to late September, and marked using visual collars
and plastic ear tags. Nearly all males (93%) are first caught
as lambs or yearlings so their exact age is known. e few
males caught as adults (7%) were aged by counting the
horn annuli. Population density, measured as the number of
females two years and older in June (Jorgenson et al. 1998),
varied markedly over the study through changes in removals,
translocations and environmental conditions (Rioux-
Paquette et al. 2011).
Horn growth continues throughout life, but varies with
age and season. It occurs mainly from April to September
(Bunnell 1978). Cessation of horn growth in winter creates
an annulus, allowing measurement of each annual growth
increment. Length of each increment was measured once to
the nearest mm, usually in the year after it was formed. e
yearly resighting rate of surviving males was 96% (Jorgenson
et al. 1997) and about 90% of males were captured each
year. Horns start to grow at about 10 weeks of age and most
lambs are born in late May or early June (Feder et al. 2008).
Hence, the first increment and part of the second develop
before a male reaches one year of age. In our analyses, we
considered annual horn increments 1 to 4, which account for
approximately 67% of asymptotic horn length (Bonenfant
et al. 2009a). Measures of subsequent horn increments were
biased towards smaller males up to 1996, because males with
fast-growing horns were at risk of being shot from four years
of age (Festa-Bianchet et al. 2004). e hunting season was
from late August to the end of October. After 1996, a more
restrictive definition of minimum horn curl for harvestable
rams severely restricted the harvest, and the hunt was closed
in 2011. Similar to Bonenfant et al. (2009a), we used the
left horn measurement. A total of 832 horn annuli from 292
males were included in analyses (131, 53, 41 and 67 males
with 4, 3, 2 and 1 increments measured, respectively).
Climate and weather data
We considered local weather variables (precipitation and
average temperature) and PDO values in seasons of biological
relevance to horn growth: winter (December–March), spring
(April–May) and summer (June–September). Weather in
spring and summer reflects conditions during horn growth.
Loehr et al. (2010) found that climate in April–May had
the greatest influence on horn growth of Dall sheep, a close
relative of bighorn sheep. During winter horn growth stops,
but winter weather can influence subsequent plant phenology
(Post and Stenseth 1999) which, in turn, may affect growth
the following spring–summer (Mysterud et al. 2001).
Precipitation (total rainfall plus water equivalent of
total snowfall in mm) and average temperature (°C) were
obtained from the Environment Canada meteorological
station at Nordegg, about 20 km west of Ram Mountain.
Unfortunately, weather data were missing for some seasons
(Supplementary material Appendix 1). PDO values (< http://
jisao.washington.edu/pdo >) were available each year from
1972 to 2013. e PDO is measured as the leading principal
component of monthly sea surface temperature in the North
Pacific from 20°N poleward (Mantua et al. 1997). It is
characterized by shifts between warm and cool phases over
an interdecadal time-scale.
Statistical analyses
We first measured interdependence and the degree of
correlation among all environmental variables using Pearson’s
correlation coefficients (r). We used regression analyses to
identify any linear or quadratic temporal trends in each of
the weather/climate variables. All statistical analyses were
conducted using R ver. 3.1.2 (< www.r-project.org >). We
considered an alpha-level level of p  0.05 as statistically
significant for all analyses.
In all analyses of annual horn growth, we used linear mixed
models with male identity as a random factor to account for
repeated measurements of the same individuals. We fitted
models with the ‘lme’ function in the ‘nlme’ library using
maximum likelihood (Pinheiro and Bates 2000). We tested the
significance of male identity with likelihood ratio test (LRT).
We also estimated repeatability, the proportion of variation
in horn growth attributed to individual heterogeneity, as the
ratio of individual variance to total variance (Nakagawa and
Schielzeth 2010, see Toïgo et al. 2013 for an application).
Horn growth was log-transformed in all models to obtain
residuals with homogeneous variance.
We tested and quantified the amount of temporal
variation in annual horn growth accounted for by each of
the covariates climate, weather and population density
using analysis of deviance (ANODEV; Skalski et al. 1993,
Grosbois et al. 2008). e ANODEV compares the deviance
of the covariate model (Mcov) versus both the baseline (Mcst)
and the full time dependent (Mt, including year as a factor)
models. e Anodev statistic Fndf,ddf is:
F(Dev McstDev Mcov)/(np Mcovnp Mcst)
(Dev McovD
ndf, ddf =−−
−eev Mt)/(np Mtnp Mcov)−
where Dev and np are, respectively, the deviance and the
number of parameters of the models. is statistic follows a
Fisher–Snedecor distribution, where the number of degrees
of freedom for the numerator (ndf) is np Mcov – np Mcst and
the number of degrees of freedom for the denominator (ddf)
is np Mt – np Mcov. e Anodev statistic is computed from

1034
All environmental covariates retained from the analyses on
annual horn growth were included as fixed effects after
appropriate transformation if necessary as detailed in Results.
Similar to Pigeon et al. (2016), pedigree and year of birth were
included as random effects. We estimated the proportion of
variance in horn length accounted for by each environmental
covariate as the difference between R² of the full model and
R² of the model excluding that specific parameter using
marginal R² formulation (Nakagawa and Schielzeth 2013).
To estimate the variance in horn length accounted for by
annual changes in breeding values, we calculated the variance
of annual mean breeding values of individuals alive in each
year. Horn length variation explained by evolution and
environmental factors was relative to the total variance in
horn length. Animal models were run using MCMCglmm
(Hadfield 2010) on three independent Markov chains for 2.6
million iterations with a burn-in of 100 000 and a thinning
of 2500. e convergence of the model was assessed from
visual inspection of the three independent Markov chains.
Data deposition
Data available from the Dryad Digital Repository: < http://
dx.doi.org/10.5061/dryad.m5648 > (Douhard et al. 2016b).
Results
Correlation and temporal trend of environmental
variables
Seasonal values of PDO were highly correlated (Table 2)
and presented a quadratic temporal trend between 1972
and 2013, increasing up to the early 1990s then declining
(Fig. 1, Supplementary material Appendix 1). Population
density showed a similar trend over time, so that it was
positively correlated with both PDOspring and PDOsummer.
PDO was weakly linked to local weather: the only
statistically significant relationships were between spring
and winter PDO and spring temperature (Table 2).
Average winter temperature increased by 0.08  0.03°C
per year from 1972 to 2013, or 3.34°C overall, whereas
winter precipitation decreased by 0.26  0.10 mm year–1
for an overall decrease of 10.66 mm, producing a negative
correlation between these variables (Table 2). Average spring
temperature varied from 1.2°C in 1982 to 6.4°C in 2006
individual-level data but it evaluates the impact of a covariate
on annual variation in horn growth in the population. e
baseline model varied between analyses but it always included
a cubic effect of age (Supplementary material Appendix 2)
and population density when testing the effects of climate
and weather covariates. Supplementary material Appendix 3
contains R code for the ANODEV procedure. We tested
the first-order interactions between climate/weather and
density successively, and if not significant, the main effects
of weather and climate. e R² of the ANODEV quantifies
the temporal variation in average horn growth accounted for
by each covariate as follows:
RDev Mcst Dev Mcov
Dev Mcst Dev Mt
anodev
2=−
−
We re-ran Mcov models by standardizing all continuous
variables (subtracting mean and dividing by standard
deviation) and including year as a random effect. is
procedure provided parameter estimates for covariates,
which are directly comparable, unbiased and robust.
Standardization does not affect ANODEV tests.
We used linear mixed models with male identity and
year as random effects to assess whether the allometric
relationship between annual horn growth and summer mass
gain varied with environmental conditions. We built an
index of environmental conditions of the current year using
covariates that influenced horn growth. Summer mass gain
was the difference between mass adjusted to 15 September
and 5 June of the same year. Adjusted mass was obtained
from repeated measurements of the same individual (see
Martin and Pelletier 2011 for more details). Most males
aged 0–3 years were captured at least twice each summer
(Festa-Bianchet et al. 2004). Horn growth and summer mass
gain were log-transformed prior to analysis to account for
the allometric link (Houle et al. 2011).
A Bayesian animal model was used to quantity the
relative contribution of environmental and evolutionary
effects, including those induced by trophy hunting (Pigeon
et al. 2016). e model partitions phenotypic variation into
its components, including the additive genetic component
(Wilson et al. 2010). We first considered an animal model
for annual horn growth, but this trait showed very little
heritability (h²  0.001, Supplementary material Appendix 4).
erefore, we used horn length at thyree years of age
adjusted to 15 September, a trait with significant heritability.
Table 2. Correlation matrix among local weather, Pacific Decadal Oscillation (PDO) and bighorn sheep population density at Ram Mountain,
Canada, between 1972 and 2013: Pearson’s r below the diagonal; p-value of a t test for H0: r  0 above the diagonal. Significant correlations
are in bold. ‘Temp’ and ‘Prec’ stand for local temperature and precipitation.
Covariate PDOspring PDOwinter PDOsummer Tempspring Tempwinter Tempsummer Precspring Precwinter Precsummer Density
PDOspring < 0.001 < 0.001 0.02 0.36 0.95 0.60 0.21 0.77 0.03
PDOwinter 0.77 0.003 0.02 0.10 0.80 0.33 0.13 0.59 0.62
PDOsummer 0.67 0.44 0.23 0.86 0.55 0.74 0.77 0.93 0.002
Tempspring 0.38 0.37 0.19 0.005 0.25 0.26 0.32 0.28 0.37
Tempwinter 0.14 0.26 –0.03 0.43 0.13 0.99 0.001 0.99 0.12
Tempsummer –0.01 –0.04 –0.10 0.20 0.25 0.42 0.6 0.10 0.11
Precspring 0.08 0.16 –0.05 –0.18 –0.00 –0.14 0.97 0.54 0.96
Precwinter –0.21 –0.26 –0.05 –0.17 –0.49 –0.10 0.01 0.36 0.11
Precsummer 0.05 0.09 0.01 0.19 –0.00 –0.28 0.11 –0.18 0.68
Density 0.34 0.08 0.46 0.15 –0.24 –0.27 –0.01 0.27 0.07

1035
1980 19902000 2010
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2
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Year
PDO
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Spring
Ye ar
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Year
−12
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Year
Temperature (°C)
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Spring
Ye ar
1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010
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Summer
Ye ar
1980 19902000 2010
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Year
Precipitation (mm)
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Ye ar
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40
60
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140
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Ye ar
1980 19902000 2010
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40
60
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100
Density
Year
Number of ewes
Figure 1. Temporal trends (linear or quadratic) in local weather, Pacific Decadal Oscillation (PDO) and bighorn sheep population density
between 1972 and 2013 at Ram Mountain, Canada. Regression lines from linear models are shown when statistically significant
(Supplementary material Appendix 1).

1036
models with and without random effect  20.26, DF  1 ,
p  0.001). Repeatability was 0.17: after accounting for
age, 17% of variation in annual increments was attributed
to individual differences. When including year as a random
effect, repeatability fell to 10%, suggesting that a large
proportion of differences among individuals in annual
horn growth was caused by inter annual differences in
environment.
Considering local weather, we found that annual horn
growth increased with spring temperature (Table 3, Fig. 2).
ere was no evidence that other weather variables influenced
horn growth and we detected no interaction between
weather and population density (Table 3). Horn growth
decreased with increasing density. e additive effects of
spring temperature and density accounted for 34% of annual
variation in horn growth. Separately, spring temperature and
density accounted for 11% and 17%, respectively, of that
variation.
Considering large-scale climate, we found that PDOspring
and density had interactive effects on horn growth
(Table 3): PDOspring had a positive effect at low density,
whereas horn growth remained weak regardless of PDOspring
at high density (Fig. 3). Because PDOspring and PDOsummer
were highly correlated (r  0.67), we found a similar
interaction for PDOsummer and density although there were
few horn measurements when PDOsummer was high and
population density low (Table 3, Supplementary material
Appendix 5). Interactions between PDO in spring or
summer and density accounted respectively for 32 and 37%
of annual variation in horn growth. When interactions with
density were ignored, PDO in summer or spring accounted
for less than 1 and 4%, respectively, of annual variation
in horn growth. PDOwinter had no statistically significant
effect on horn growth independent of population density
(Table 3).
and was positively correlated with winter temperature,
but did not show a temporal trend. Average summer
temperature increased by 0.04  0.03°C per year from
1972 to 2013, an overall increase of 1.6°C. No temporal
trend in summer and spring precipitation was detected
(Fig. 1, Supplementary material Appendix 1).
Annual horn growth variation
In the mixed model of horn annual increment with
age, male identity was highly significant (LRT between
Table 3. Effects of local weather, climate and population density on annual horn growth of male bighorn sheep aged 0–3 years, 1972 to 2013.
‘Temp’ and ‘Prec’ stand for local temperature and precipitation. PDO is the Pacific Decadal Oscillation. The population density by climate/
weather interactions are noted*. Effects of covariates were tested with an analysis of deviance (ANODEV). Fndf,ddf represents the F-statistic of
ANODEV with its associated p-value. Statistically significant effects are in bold (p  0.05). We standardized variables and included year as
a random effect to obtain parameter estimates.
Parameters Fndf,ddf p-value Estimate  SE
a) Weather and density effects
Tempspring  Density 0.241,36 0.63 0.011  0.013
Tempspring 9.651,37 0.003 0.054  0.010
Precspring  Density 1.241,34 0.27 –0.024  0.011
Precspring 0.401,35 0.53 –0.013  0.010
Tempsummer  Density 2.481,32 0.13 0.037  0.012
Tempsummer 0.461,33 0.50 0.014  0.010
Precsummer  Density  0.011,30 0.98 5.43.10  10–4  1.22  10–2
Precsummer 0.741,31 0.40 –0.017  0.010
Tempwinter  Density 0.101,38 0.75 0.005  0.008
Tempwinter 1.461,39 0.23 0.021  0.009
Precwinter  Density 0.011,32 0.92 0.002  0.010
Precwinter 0.451,33 0.51 –0.014  0.009
Density 8.321,40 0.006 –0.059  0.010
b) Climate and density effects
PDOwinter  Density 0.231,38 0.63 –0.009  0.010
PDOwinter 3.401,39 0.07 0.032  0.009
PDOspring  Density 4.231,38 0.04 –0.037  0.010
PDOsummer  Density 10.001,38 0.003 –0.051  0.008
123456
−0.3
−0.2
−0.1
0.0
0.1
0.2
Average temperature in spring (°C)
Residual horn growth
Figure 2. Effect of spring temperature on annual horn growth of
male bighorn sheep aged 0–3 years. Filled circles correspond to
average residual horn growth ( SE) after controlling for the age
and density effects.

1037
based on density and spring temperature (Supplementary
material Appendix 6).
Relative influence of evolution and environmental
conditions on horn length
Horn length at three years of age has a heritable component
(h²  0.36, see Supplementary material Appendix 4 for other
Allometry between summer mass gain and
horn growth
We examined the effects of environment on the allometric
mass–horn growth relationship by using an index of
environmental conditions based on density and PDO
in summer (Fig. 4). We found an interaction between
environmental quality and summer mass gain on horn
growth (LRT  8.95, DF  1, p  0.002). When
environmental conditions were poor, males with below-
average summer mass gain grew less horn for a given mass
gain (Fig. 4). For males achieving high summer mass gain,
relative allocation to horn growth appeared independent of
environmental conditions (Fig. 4). We found similar results
when considering an index of environmental conditions
Environmental index
0.5
1.0
1.5
2.0
Log−transformed summer
mass gain
2.0
2.5
3.0
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1.0
1.5
2.0
2.5
3.0
2.0 2.5 3.0 3.5
0.0
0.5
1.0
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2.0
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3.0
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Log−transformed annual horn growth
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(A)
(B)
Figure 4. Allometric relationship between annual horn growth and
summer mass gain of male bighorn sheep aged 0–3 years as a
function of environmental conditions experienced that year.
Population density and PDOsummer were standardized to a range of
0 to 1 to build an environmental index, defined as the sum of
population density, 1 – PDOsummer and the product of 1 – PDOsummer
and density (PDOsummer had a positive effect on horn growth
whereas density had a negative effect). (A) Environmental index
fitted as a continuous variable (higher values indicated poor
environmental conditions). (B) Environmental index fitted as a
two-level factor: less than the median (good conditions; black
circles and line; slope  0.20  0.13, p  0.11), equal to or greater
than the median (poor conditions; grey circles and line;
slope  0.66  0.12, p  0.001). Circles represent individual data,
solid and dotted lines represent the predictions and associated SE of
the model.
Density
20
40
60
80
100
PDOspring
−1
0
1
2
Residual horn growth
−0.1
0.0
0.1
(A)
−1 012
−0.2
−0.1
0.0
0.1
0.2
PDOspring
Residual horn growth
(B)
Figure 3. Interactive effects of the Pacific Decadal Oscillation
(PDO) in spring and population density on annual horn growth of
male bighorn sheep aged 0–3 years after controlling for the age. (A)
Density fitted as a continuous variable. (B) Density fitted as a two-
level factor: lower than the median population size (black circles
and line), equal to or greater than the median population size (grey
circles and line). Circles correspond to average residual horn growth
( SE); solid and dotted lines represent the predictions and
associated SE of the model.

1038
which weather influences the expression of life-history
traits (Krebs and Berteaux 2006).
Considering local weather, we found that spring
temperature positively influenced annual horn growth,
accounting for 11% of its variation. However, spring
temperature experienced during the first years of life
accounted for less than 1% of the variation in horn length at
three years of age, likely because most individuals encountered
both poor and good conditions during their first three spring
seasons, with little consistent effects on cumulative horn
growth. By contrast, the effects of population density were
likely cumulative because density varied little over any three-
year period (Marcil-Ferland et al. 2013). Warmer springs
can promote earlier and more rapid snowmelt, leading to
an earlier start of vegetation growth (Myneni et al. 1997,
Pettorelli et al. 2005). e Rocky Mountains have very cold
winters (Fig. 1), so most winter precipitation falls as snow.
Alpine ungulates appear to show contrasting patterns in
how spring weather influences growth traits. In agreement
with our study, annual horn growth of male ibex increased
with spring temperature (Giacometti et al. 2002, Büntgen
et al. 2014) and antler size of male wapiti Cervus canadensis
was positively correlated with March–April temperatures
(Smith 1998). On the other hand, warm spring temperature
or related variables often have negative effects on body
growth or mass (Rughetti and Festa-Bianchet 2012 in alpine
chamois, Pettorelli et al. 2007 in mountain goats, Oreamnos
americanus, and bighorn sheep, Douhard et al. 2013 in roe
deer). On Ram Mountain, rapid plant maturation associated
with warmer springs appeared to reduce lamb body growth
(Pettorelli et al. 2007). ese apparently contrasting results
may arise from two factors: the tradeoff between plant
productivity and length of access to high-quality forage and
likely differences in the timing of horn and mass growth.
ere is an allocation tradeoff between quantity of plant tissue
and nutritional quality (Seydack et al. 2012). Although rapid
warming during green-up can increase plant productivity, it
can also speed up maturation, shortening the period when
forage is highly digestible (Albon and Langvatn 1992). Warm
springs may also reduce spatial heterogeneity in the timing of
vegetation green-up if they generate rapid and synchronous
snowmelt over the landscape, decreasing the opportunity to
access high-quality forage during a long period (Mysterud
et al. 2001, Pettorelli et al. 2007). Horns may benefit from
warm temperatures in spring because they start growing at
a high rate (Hemming 1969) and may be less affected than
body growth by the subsequent rapid maturation of plant
tissue. Recent studies have highlighted the potential pitfalls
of extrapolating life-history responses to climate change
at different locations or across species (Martínez-Jauregui
et al. 2009, Tafani et al. 2013). We suggest that contrasting
patterns can also emerge when considering multiple growth
traits within a population.
Although we found no temporal trend in average spring
temperature, mean temperatures for all seasons are expected
to increase in Canada (Bush et al. 2014). Horn growth of
male bighorn sheep may thus increase under ongoing climate
change, as reported for alpine ibex (Büntgen et al. 2014).
Climate change may also influence the relative allocation of
resources to body and horn growth. Bighorn sheep can lose
over 20% of autumn mass during winter (Festa-Bianchet
variance components). e environmental variables entered
in the full animal model were spring temperature and the
interactive effects of density and PDOsummer experienced
between one and three years of age. Both annual values of
PDO and density were averaged during this period because
of their high temporal autocorrelation, whereas spring
temperatures from age one to three were included as separate
fixed effects because they varied substantially from one
year to the next (Fig. 1). We found that variability in horn
length was mostly driven by population density (% variance
explained  26.5, CI  [2.6; 45.2]) and evolutionary
changes (% variance explained  8.8, CI  [3.9; 22.2]).
Spring temperatures accounted for only 0.9% (CI  [–23.1;
27.1]) of variation in horn length. Compared with the effect
of density alone, a model that also included PDOsummer and
the interaction between PDOsummer and density explained
only an additional 3% of the variation in horn length (%
variance explained  29.4, CI  [14.0; 50.2]).
Discussion
We found that local weather effects on annual horn
growth of male bighorn sheep are density-independent,
whereas climate effects are mediated by an interaction
with population density. erefore, some effects of climate
change may be missed by studies that ignore changes in
population density (Bonenfant et al. 2009b). For example,
a positive effect of spring/summer PDO on horn growth
was apparent only at low density. High population density
likely reduces the availability of forage through increased
competition, slowing horn growth independently of PDO.
An influence of PDO in April–May was also reported for
horn growth of Dall sheep in the Yukon but the direction
of the effect was not specified (Loehr et al. 2010). Large-
scale climate indices often predict variation in phenotypic
traits and demographic parameters better than local
weather variables (Post and Stenseth 1999, Hallett et al.
2004, Stenseth and Mysterud 2005, but see Knape and
de Valpine 2011). In our study, the combined effects of
density and PDO in spring or summer explained 32 and
37% of annual variation in horn growth, compared to 34%
for the additive effects of density and spring temperature.
Population density was the most important driver of annual
variation in horn growth. Because the variance explained
(r²) in most ecological studies is 2.51–5.42% (Møller and
Jennions 2002), our ANODEV models may appear to
explain a high level of variance. However, many climate
studies (Lourdais et al. 2004, Jensen et al. 2006) reported r²
measured at the individual level rather than at the level of
year or cohort, where the proportion of variance explained
is inevitably higher. Large-scale climate indices can be
useful for highly mobile species such as caribou Rangifer
tarandus, where one single meteorological station cannot
capture all relevant weather conditions (Stenseth and
Mysterud 2005, Nielsen et al. 2012). Our study revealed
the usefulness of PDO also for a more sedentary species.
However, it is difficult to disentangle the effects of PDO in
spring and summer on horn growth because they are highly
correlated. Local variables, recorded at a smaller temporal
scale, can help identify the critical time window during

1039
Acknowledgements – Animal-handling procedures were approved by
the Animal Care Committee of the Univ. of Sherbrooke (protocols
MFB2009-01 and FP2012-01), affiliated to the Canadian Council
on Animal Care. We thank A. Hubbs, C. Feder, J. Hogg and J.
Jorgenson for their support of the Ram Mountain research program,
and all assistants and students who worked on Ram Mountain over
decades. We thank A. Mysterud, S. Albon and M. Morrissey for
constructive comments on the manuscript.
Funding – is work was funded by a Natural Sciences and
Engineering Research Council (NSERC) EnviroNorth post-
doctoral fellowship to MD. MFB and FP are funded by NSERC of
Canada Discovery Grants and by research grants from the Alberta
Conservation Association. FP holds the Canada Research Chair in
Evolutionary Demography and Conservation.
References
Albon, S. D. and Langvatn, R. 1992. Plant phenology and the
benefits of migration in a temperate ungulate. – Oikos 65:
502–513.
Allendorf, F. W. and Hard, J. J. 2009. Human-induced evolution
caused by unnatural selection through harvest of wild animals.
– Proc. Natl Acad. Sci. USA 106: 9987–9994.
Andersson, M. B. 1994. Sexual selection. – Princeton Univ. Press.
Bergeron, P. et al. 2010. Secondary sexual characters signal fighting
ability and determine social rank in alpine ibex (Capra ibex).
– Behav. Ecol. Sociobiol. 64: 1299–1307.
Bonenfant, C. et al. 2009a. Age-dependent relationship between
horn growth and survival in wild sheep. – J. Anim. Ecol. 78:
161–171.
Bonenfant, C. et al. 2009b. Empirical evidence of density-
dependence in populations of large herbivores. – Adv. Ecol.
Res. 41: 313–357.
Boutin, S. and Lane, J. E. 2014. Climate change and mammals:
evolutionary versus plastic responses. – Evol. Appl. 7: 29–41.
Bunnell, F. 1978. Horn growth and population quality in dall
sheep. – J. Wildl. Manage. 42: 764–775.
Büntgen, U. et al. 2014. European springtime temperature
synchronises ibex horn growth across the eastern Swiss Alps.
– Ecol. Lett. 17: 303–313.
Bush, E. J. et al. 2014. An overview of Canada’s changing climate.
– In: Warren, F. J. and Lemmen, D. S. (eds), Canada in a
changing climate: sector perspectives on impacts and
adaptation. Government of Canada, pp. 23–64.
Chevin, L.-M. 2015. Evolution of adult size depends on genetic
variance in growth trajectories: a comment on analyses of
evolutionary dynamics using integral projection models.
– Meth. Ecol. Evol. 6: 981–986.
Chirichella, R. et al. 2013. e role of geological substrate for horn
growth in ungulates: a case study on alpine chamois. – Ecol.
Evol. 27: 145–163.
Ciuti, S. et al. 2012. Human selection of elk behavioural traits in
a landscape of fear. – Proc. R. Soc. B. 279: 4407–4416.
Clutton-Brock, T. H. and Sheldon, B. C. 2010. Individuals and
populations: the role of long-term, individual-based studies
of animals in ecology and evolutionary biology. – Trends Ecol.
Evol. 25: 562–573.
Coltman, D. W. et al. 2002. Age-dependent sexual selection in
bighorn rams. – Proc. R. Soc. B. 269: 165–172.
Coltman, D. W. et al. 2003. Undesirable evolutionary consequences
of trophy hunting. – Nature 426: 655–658.
Douhard, M. et al. 2013. Variation in adult body mass of roe deer:
early environmental conditions influence early and late body
growth of females. – Ecology 94: 1805–1814.
Douhard, M et al. 2016a. Changes in horn size of Stone’s sheep
over four decades correlate with trophy hunting pressure.
– Ecol. Appl. 26: 309–321.
et al. 1996) so that summer is critical to replenish reserves.
e relationship between summer mass gain and horn
growth was weak but varied according to environmental
conditions. Males gaining little mass in summer reduced
energy allocation to horn growth when environmental
conditions deteriorated, possibly to increase chances of
survival. Males achieving high summer mass gain grew as
much horn under poor as under favorable environmental
conditions, likely because increase allocation to mass does
not influence their future survival. ese results suggest
that the conservative life-history tactics typical of adult
female large herbivores are also selected for in males (see
also Toïgo et al. 2013).
Factors affecting horn growth are not limited to ecological
conditions. Human harvest is a strong selective pressure on
morphological traits (Allendorf and Hard 2009). Horn size of
harvested bighorn males in Alberta has declined over the last
four decades (Festa-Bianchet et al. 2014), but large-horned
rams appear to persist in protected areas where selective
hunting does not occur (Pelletier et al. 2014). Evolutionary
responses to trophy hunting are apparent in male bighorn
sheep at Ram Mountain (Coltman et al. 2003, Pigeon et al.
2016). Measuring the relative influences of evolution and
plasticity underlying phenotypic variation is challenging
(Boutin and Lane 2014). Using an integral projection model
(IPM), Ozgul et al. (2010) argued that temporal changes in
body mass of yellow-bellied marmots Marmota flaviventris
resulted from ecological (plastic) rather than evolutionary
responses to environmental change. However, the use of
IPMs as parameterized to date is problematic, because these
models cannot detect evolutionary changes (Chevin 2015,
Janeiro et al. 2016). Our results suggest that population
density was the major contributor to temporal variation of
horn length, explaining about three times as much variance
as evolutionary effects. While there was strong support for
climate or weather effects in the analyses of annual horn
growth, climate or weather covariates explained very little
of the variation in horn length. We likely underestimated
the evolutionary effects due to trophy hunting because
the variance in breeding values encapsulates two opposing
evolutionary forces: artificial selection favoring small horns
and sexual selection favoring large horns. In addition, after
the near-cessation of hunting in 1996, breeding values of
horn length showed a weak tendency towards an increase
(Pigeon et al. 2016). It is also important to note that our
study population underwent a threefold change in density.
A weaker effect of density should be expected in populations
that have maintained more stable numbers, as is the case in
much of the province of Alberta where selective hunting
likely plays an absolutely similar, but proportionally much
greater, effect on changes in horn size (Festa-Bianchet et al.
2014). Our research highlights the importance of considering
the ecological, evolutionary and conservation implications
of selective harvests (Festa-Bianchet 2003, Garel et al. 2007,
Douhard et al. 2016a). Humans are changing the world at
an unprecedented rate, modifying phenotypic and genetic
traits in wild populations through, for instance, climate
change and selective harvests. Only continued long-term
individual-based studies will enable the detection of these
patterns, because they can quantify both environmental and
evolutionary effects (Clutton-Brock and Sheldon 2010).

1040
Krebs, C. J. and Berteaux, D. 2006. Problems and pitfalls in
relating climate variability to population dynamics. – Clim.
Res. 32: 143–149.
Leclerc, M. et al. 2016. Can hunting data be used to estimate
unbiased population parameters? A case study on brown bears.
– Biol. Lett. 12: 20160197.
Loehr, J. et al. 2010. e role of phenotypic plasticity in responses
of hunted thinhorn sheep ram horn growth to changing
climate conditions. – J. Evol. Biol. 23: 783–790.
Loison, A. et al. 1999. Body mass and winter mortality in red deer
calves: disentangling sex and climate effects. – Ecography 22:
20–30.
Lourdais, O. et al. 2004. Climate affects embryonic development
in a viviparous snake, Vipera aspis. – Oikos 104: 551–560.
Marcil-Ferland, D. et al. 2013. Despite catch-up, prolonged growth
has detrimental fitness consequences in a long-lived vertebrate.
– Am. Nat. 182: 775–785.
Malo, A. F. et al. 2005. Antlers honestly advertise sperm production
and quality. – Proc. R. Soc. B. 272: 149–157.
Mahoney, S. P. et al. 2011. Morphological change in Newfoundland
caribou: effects of abundance and climate. – Rangifer 31:
21–34.
Mantua, N. J. et al. 1997. A Pacific interdecadal climate oscillation
with impacts on salmon production. – Bull. Am. Meteorol.
Soc. 78: 1069–1079.
Martin, J. G. A. and Pelletier, F. 2011. Measuring growth patterns
in the field: effects of sampling regime and methods on
standardized estimates. – Can. J. Zool. 89: 529–537.
Martin, A. M. et al. 2013. Male mating competitiveness and
age-dependent relationship between testosterone and social
rank in bighorn sheep. – Behav. Ecol. Sociobiol. 67:
919–928.
Martínez-Jauregui, M. et al. 2009. Are local weather, NDVI and
NAO consistent determinants of red deer weight across three
contrasting European countries? – Global Change Biol. 15:
1727–1738.
Møller, A. and Jennions, M. D. 2002. How much variance can be
explained by ecologists and evolutionary biologists? – Oecologia
132: 492–500.
Myneni, R. B. et al. 1997. Increased plant growth in the northern
high latitudes from 1981 to 1991. – Nature 386: 698–702.
Mysterud, A. et al. 2001. Nonlinear effects of large-scale climatic
variability on wild and domestic herbivores. – Nature 410:
1096–1099.
Mysterud, A. et al. 2005. Climate-dependent allocation of
resources to secondary sexual traits in red deer. – Oikos 111:
245–252.
Nakagawa, S. and Schielzeth, H. 2010. Repeatability for Gaussian
and non-Gaussian data: a practical guide for biologists. – Biol.
Rev. 85: 935–956.
Nakagawa, S. and Schielzeth, H. 2013. A general and simple
method for obtaining R² from generalized linear mixed-effects
model. – Meth. Ecol. Evol. 4: 133–142.
Nielsen, A. et al. 2012. Are responses of herbivores to environmental
variability spatially consistent in alpine ecosystems? – Global
Change Biol. 18: 3050–3062.
Ozgul, A. et al. 2010. Coupled dynamics of body mass and
population growth in response to environmental change.
– Nature 466: 482–485.
Pelletier, F. et al. 2007. Selection on heritable seasonal plasticity of
body mass. – Evolution 61: 1969–1979.
Pelletier, F. et al. 2012. Data from selective harvests under-
estimate temporal trends in quantitative traits. – Biol. Lett.
8: 878–881.
Pelletier, F. et al. 2014. Harvest refuges do not buffer wild sheep
from selective hunting. – Ecol. Evol. 4: 3375–3382.
Pérez, J. M. et al. 2011. Reduced horn size in two wild trophy-
hunted species of Caprinae. – Wildl. Biol. 17: 102–112.
Douhard, M. et al. 2016b. Data from: Environmental and
evolutionary effects on horn growth of male bighorn sheep.
– Dryad Digital Repository, < http://dx.doi.org/10.5061/
dryad.m5648 >.
Elton, C. S. 1924. Periodic fluctuations in the numbers of animals:
their causes and effects. – J. Exp. Biol. 2: 119–163.
Fandos, P. 1995. Factors affecting horn growth in male Spanish
ibex (Capra pyrenaica). – Mammalia 59: 229–235.
Feder, C. et al. 2008. Never too late? Consequences of late birthdate
for mass and survival of bighorn lambs. – Oecologia 156:
773–781.
Festa-Bianchet, M. 2003. Exploitative wildlife management as a
selective pressure for life-history evolution of large mammals.
– In: Festa-Bianchet, M. and Apollonio, M. (eds), Animal
behavior and wildlife conservation. Island Press, pp.
191–207.
Festa-Bianchet, M. et al. 1996. e development of sexual
dimorphism: seasonal and lifetime mass changes in bighorn
sheep. – Can. J. Zool. 74: 330–342.
Festa-Bianchet, M. et al. 2004. Relative allocation to horn and
body growth in bighorn rams varies with resource availability.
– Behav. Ecol.15: 305–312.
Festa-Bianchet, M. et al. 2014. Decrease in horn size and increase
in age of trophy sheep in Alberta over 37 years. – J. Wildl.
Manage. 78: 133–141.
Garel, M. et al. 2007. Selective harvesting and habitat loss produce
long-term life-history changes in a mouflon population.
– Ecol. Appl. 17: 1607–1618.
Giacometti, M. et al. 2002. Ambient temperature in spring
affects horn growth in male alpine ibexes. – J. Mammal. 83:
245–251.
Grosbois, V. et al. 2008. Assessing the impact of climate
variation on survival in vertebrate populations. – Biol. Rev.
83: 357–399.
Hadfield, J. D. 2010. MCMC methods for multi-response
generalized linear mixed models: the MCMCglmm R Package.
– J. Stat. Softw. 33: 1–22.
Hallett, T. B. et al. 2004. Why large-scale climate indices seem to
predict ecological processes better than local weather. – Nature
430: 71–75.
Hebblewhite, M. 2005. Predation by wolves interacts with the
North Pacific Oscillation (NPO) on a western North American
elk population. – J. Anim. Ecol. 74: 226–233.
Hegel, T. M. et al. 2010. Seasonal effects of Pacific-based climate
on recruitment in a predator-limited large herbivore.
– J. Anim. Ecol. 79: 471–482.
Hemming, J. E. 1969. Cemental deposition, tooth succession, and
horn development as criteria of age in Dall sheep. – J. Wildl.
Manage. 33: 552–558.
Houle, D. et al. 2011. Measurement and meaning in biology.
– Q. Rev. Biol. 86: 3–34.
Hurrell, J. W. 1995. Decadal trends in the North Atlantic
Oscillation: regional temperatures and precipitation. – Science
269: 676–679.
Janeiro, M. J. et al. 2016. Towards robust evolutionary inference
with integral projection models. – J. Evol. Biol. doi: 10.1111/
jeb.13000.
Jensen, H. et al. 2006. Environmental influence and cohort effects
in a sexual ornament in the house sparrow, Passer domesticus.
– Oikos 114: 212–224.
Jorgenson, J. T. et al. 1997. Effects of age, sex, disease and density
on survival of bighorn sheep. – Ecology 78: 1019–1032.
Jorgenson, J. T. et al. 1998. Effects of population density on
horn development in bighorn rams. – J. Wildl. Manage. 62:
1011–1020.
Knape, J. and de Valpine, P. 2011. Effects of weather and climate
on the dynamics of animal population time series. – Proc. R.
Soc. B. 278: 985–992.

1041
linking plant metabolic performance to climate and the effects
on forage availability for large herbivores. – Koedoe 54:
1–12.
Skalski, J. R. et al. 1993. Testing the significance of individual- and
cohort-level covariates in animal survival studies. – In:
Lebreton, J.-D. and North, P. M. (eds), Marked individuals in
the study of bird populations. Birkhäuser, pp. 9–28.
Smith, B. L. 1998. Antler size and winter mortality of elk: effects
of environment, birth year, and parasites. – J. Mammal. 79:
1038–1044.
Stenseth, N. C. and Mysterud, A. 2005. Weather packages:
finding the right scale and composition of climate in ecology.
– J. Anim. Ecol. 74: 1195–1198.
Stenseth, N. C. et al. 2002. Ecological effects of climate fluctuations.
– Science 297: 1292–1296.
Stige, L. C. et al. 2006. e effect of climate variation on agro-
pastoral production in Africa. – Proc. Natl Acad. Sci. USA
103: 3049–3053.
Tafani, M. et al. 2013. Decreasing litter size of marmots over
time: a life history response to climate change? – Ecology 94:
580–586.
Toïgo, C. et al. 1999. Cohort affects growth of males but not
females in alpine ibex (Capra ibex ibex). – J. Mammal. 80:
1021–1027.
Toïgo, C. et al. 2013. Alpine ibex males grow large horns at
no survival cost for most of their lifetime. – Oecologia 173:
1261–1269.
van de Pol, M. et al. 2013. Problems with using large-scale oceanic
climate indices to compare climatic sensitivities across
populations and species. – Ecography 36: 249–255.
Vanpé, C. et al. 2007. Antler size provides an honest signal
of male phenotypic quality in roe deer. – Am. Nat. 169:
481–493.
von Hardenberg, A. et al. 2004. Horn growth but not asymmetry
heralds the onset of senescence in male alpine ibex (Capra
ibex). – J. Zool. 263: 425–432.
Wilson, A. J. et al. 2010. An ecologist’s guide to the animal model.
– J. Anim. Ecol. 79: 13–26.
Pérez-Barbería, F. J. et al. 1996. Horn growth pattern in Cantabrian
chamois Rupicapra pyrenaica parva: influence of sex, location
and phaenology. – Acta eriol. 41: 83–92.
Pettorelli, N. et al. 2005. e relative role of winter and spring
conditions: linking climate and landscape-scale plant phenology
to alpine reindeer body mass. – Biol. Lett. 1: 24–26.
Pettorelli, N. et al. 2007. Early onset of vegetation growth vs. rapid
green-up: impacts on juvenile mountain ungulates. – Ecology
88: 381–390.
Pigeon, G. et al. 2016. Intense selective hunting leads to artificial
evolution in horn size. – Evol. Appl. 9: 521–530.
Pinheiro, J. C. and Bates, D. M. 2000. Mixed-effects models in
S and S-PLUS. Statistics and computing. – Springer.
Portier, C. et al. 1998. Effects of density and weather on survival
of bighorn sheep lambs (Ovis canadensis). – J. Zool. 245:
271–278.
Post, R. and Stenseth, N. C. 1999. Climatic variability, plant phenology,
and northern ungulates. – Ecology 80: 1322–1339.
Rioux-Paquette, E. et al. 2011. Sex-differential effects of inbreeding
on overwinter survival, birth date and mass of bighorn lambs.
– J. Evol. Biol. 24: 121–131.
Rivrud, I. M et al. 2013. Hunter selection and long-term trend
(1881–2008) of red deer trophy sizes in Hungary. – J. Appl.
Ecol. 50: 168–180.
Robinson, M. R. et al. 2006. Live fast, die young: tradeoffs between
fitness components and sexually antagonistic selection on
weaponry in Soay sheep. – Evolution 60: 2168–2181.
Rughetti, M. and Festa-Bianchet, M. 2011. Seasonal changes in
sexual dimorphism in northern chamois (Rupicapra rupicapra).
– J. Zool. 284: 257–264.
Rughetti, M. and Festa-Bianchet, M. 2012. Effects of
spring–summer temperatures on body mass of chamois.
– J. Mammal. 93: 1301–1307.
Schmidt, K. T. et al. 2001. Antler length of yearling red deer is
determined by population density, weather and early life-
history. – Oecologia 127: 191–197.
Seydack, A. H. et al. 2012. Climate and vegetation in a semi-arid
savanna: development of a climate–vegetation response model
Supplementary material (Appendix oik-03799 at < www.
oikosjournal.org/appendix/oik-03799 >). Appendix 1–6.