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Environmental and evolutionary effects on horn growth of male bighorn sheep


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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 (, 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
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
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.
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:// >) 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 Pearsons
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 (< >). 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
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:// > (Douhard et al. 2016b).
Correlation and temporal trend of environmental
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
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
1980 19902000 2010
1980 1990 2000 2010
Ye ar
1980 1990 2000 2010
Temperature (°C)
Ye ar
1980 1990 2000 2010 1980 1990 2000 2010 1980 1990 2000 2010
Ye ar
1980 19902000 2010
Precipitation (mm)
Ye ar
1980 1990 2000 2010
Ye ar
1980 19902000 2010
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).
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
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
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
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.
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
Log−transformed summer
mass gain
Log−transformed horn growth
2.0 2.5 3.0 3.5
Log−transformed summer mass gain
Log−transformed annual horn growth
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.
Residual horn growth
−1 012
Residual horn growth
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.
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]).
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
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
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Supplementary material (Appendix oik-03799 at < www. >). Appendix 1–6.
... Evidence of harvest-induced changes in life-history traits of terrestrial species is limited (Gamelon et al., 2011;Kvalnes et al., 2016;Van de Walle et al., 2021;Zedrosser et al., 2011). Some ungulates selectively harvested based on the size or shape of their weapons (horns, antlers, or tusks) showed phenotypic changes consistent with hunting-induced evolution (aoudad, Ammotragus lervia and Iberian ibex, Capra pyrenaica, Pérez et al., 2011; sable antelope, Hippotragus niger, Crosmary et al., 2013;African elephant, Loxodonta africana, Campbell-Staton et al., 2021;Chiyo et al., 2015;bighorn sheep, Ovis canadensis, Pigeon et al., 2016; stone sheep, Ovis dalli stonei, Douhard et al., 2017; and European mouflon, Ovis gmelini, Garel et al., 2007). Other studies of hunted ungulates, however, documented no clear evidence of harvest-induced evolutionary changes in weaponry, suggesting that an evolutionary response to selective hunting is only expected under very restrictive conditions (Büntgen et al., 2018;Festa-Bianchet & Mysterud, 2018;Rughetti & Festa-Bianchet, 2010). ...
... We parameterized life-history and demographic processes in our model based on empirical estimates of the genetic and phenotypic components of horn length from long-term studies of two pedigreed bighorn populations in Alberta (Coltman et al., 2005;Miller et al., 2018;Pigeon et al., 2016;Poissant et al., 2008), along with the role of horn length in male reproductive success (Hogg & Forbes, 1997;Pelletier & Festa-Bianchet, 2006). Although we did not assess their relative effects, we used a probabilistic approach to indirectly take into account other environmental drivers such as weather, habitat quality or population density that also affect horn growth (Douhard et al., 2017;Festa-Bianchet et al., 2004). ...
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In terrestrial and marine ecosystems, migrants from protected areas may buffer the risk of harvest‐induced evolutionary changes in exploited populations that face strong selective harvest pressures. Understanding the mechanisms favoring genetic rescue through migration could help ensure evolutionarily sustainable harvest outside protected areas and conserve genetic diversity inside those areas. We developed a stochastic individual‐based metapopulation model to evaluate the potential for migration from protected areas to mitigate the evolutionary consequences of selective harvest. We parameterized the model with detailed data from individual monitoring of two populations of bighorn sheep subjected to trophy hunting. We tracked horn length through time in a large protected and a trophy‐hunted populations connected through male breeding migrations. We quantified and compared declines in horn length and rescue potential under various combinations of migration rate, hunting rate in hunted areas and temporal overlap in timing of harvest and migrations, which affects the migrants' survival and chances to breed within exploited areas. Our simulations suggest that the effects of size‐selective harvest on male horn length in hunted populations can be dampened or avoided if harvest pressure is low, migration rate is substantial, and migrants leaving protected areas have a low risk of being shot. Intense size‐selective harvest impacts the phenotypic and genetic diversity in horn length, and population structure through changes in proportions of large‐horned males, sex ratio and age structure. When hunting pressure is high and overlaps with male migrations, effects of selective removal also emerge in the protected population, so that instead of a genetic rescue of hunted populations, our model predicts undesirable effects inside protected areas. Our results stress the importance of a landscape approach to management, to promote genetic rescue from protected areas and limit ecological and evolutionary impacts of harvest on both harvested and protected populations.
... This conclusion would be consistent with some studies in the terrestrial realm. Phenotypic changes in horn length of a Canadian population of bighorn sheep (Ovis canadensis), a species hypothesized to be subject to huntinginduced evolution, are driven primarily by density (26.5% of the variation) and secondarily by genetic change (8.8%); 62% of the phenotypic variation is unexplained (19). The threefold difference in the contribution of density to phenotypic shifts in horn length, relative to that of evolution, may well be considerably greater in fish populations, given the higher sensitivity of ectotherms to environmental change. ...
... (A) When populations are at the beginning of a declining trajectory, the proportional influence of fisheries-induced evolution (FIE) on phenotypic changes in traits, such as age at maturity, may be small relative to density and other factors (such as sexual selection, temperature, climate, predation). (B) The influence of FIE on phenotypic change might increase as population size becomes increasingly small (19). (C) The importance of FIE to recovery appears to be secondary compared to the influence of reduced fishing mortality, magnitude of the initial population depletion, natural mortality, and other factors, such as those described in A (10). ...
... Nutritional studies of weapon development have been conducted primarily on taxa and weapons that are amenable to laboratory studies, such as the hindlegs of coreids (Gillespie et al., 2014;Joseph et al., 2016;Miller et al., 2016;Sasson et al., 2016) and the horns of scarab beetles (Emlen, 1994;Johns et al., 2014;Zinna et al., 2018). Outside the laboratory, field studies link the effects of poor nutrition and other environmental stressors to support the condition dependence and environmental plasticity of animal weapons (Azorit et al., 2002;Weladji et al., 2005;Torres-Porras et al., 2009;Landete-Castillejos et al., 2010;Foley et al., 2012;Douhard et al., 2017;Peterson et al., 2019). ...
Animal weapons are diverse and often exaggerated structures associated with sexual selection and male–male combat. Despite their diversity, our knowledge of these structures comes primarily from studies that focus on the size of the weapons and neglect other components, such as their shape or performance. Furthermore, weapons can exhibit species-level plasticity based on nutrition and other ecological variables, but few studies have documented seasonal variation in weapon plasticity. Here, we investigate how weapon size, shape and performance differ across adult male reproductive and non-reproductive Allegheny crayfish, Faxonius obscurus. Given that F. obscurus and other cambarid crayfishes exhibit cyclical moulting between reproductive and non-reproductive states, they provide an ideal system in which to investigate such questions. We found that claws of reproductive and non-reproductive male F. obscurus differ in all three weapon components we investigated; claws from reproductive males were longer, stronger and different in shape compared with claws from non-reproductive males. Wielding longer and stronger claws is likely to confer greater competitive success during the mating season, which demonstrates an unexplored pattern of seasonal weapon plasticity. We discuss these results considering our knowledge of the seasonality of weapon phenotypes and propose several avenues of future research using cambarid crayfish as a model system.
... Temperature from 1973 to 2019 was obtained from the Environment Canada meteorological station at Nordegg (52°30' N, 116°03 W, elevation: 1320 m) about 20 km West of Ram Mountain. Temperature at Nordegg is strongly correlated to temperature at Ram Mountain (Douhard et al. 2017). We used average daily temperature from May 1 to June 5, representing late spring, just before molt onset for most individuals. ...
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Anthropogenic climate change and habitat alterations increase the importance of understanding the causes and consequences of variation in phenological traits. Although the timing of phenological events may vary in response to both direct and mediated effects, methods to measure and distinguish direct and mediated effects have seldom been used. We used a Bayesian structural equation model (SEM) to evaluate potential direct and mediated effects of intrinsic individual and environmental factors on the timing and progression of spring molt in bighorn sheep. The SEM showed that molt phenology varied across years, was earlier in prime-aged and in heavier individuals, slower in males, and later in lactating ewes, especially if they were light. These results highlight how individual variation in intrinsic traits and life-history leads to substantial variation in a phenological trait. Indirect effects in the SEM predicted a delay in sheep molt phenology at high population density mediated through negative density effects on body mass and lactation probability. Cooler temperatures in late spring were also predicted to delay molt phenology via a negative effect on body mass. Finally, lactation reduced ewe mass which was predicted to delay molt phenology. This mediated effect thus increased the total delay (sum of direct and mediated effects) in molt phenology experience by lactating ewes. Our results underline the importance of estimating direct and indirect effects when modeling phenological traits. Because indirect effects could substantially affect estimates of total plasticity, they should be critically important to accurately predict phenological mismatches and demographic consequences of environmental change.
... We used birth year information or age at harvest year to determine the year each increment was grown and classified annual disease exposure status accordingly. Increment growth predominately occurs from March to August of each year, coinciding with resource availability (Douhard et al. 2017); thus, die-off events at the end of the horn growing season are likely to have minimal effect. We assigned 2 disease covariates, referred to as increment-exposed and increment-duration. ...
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Secondary sexual traits (e.g., horns and antlers) have ecological and evolutionary importance and are of management interest for game species. Yet, how these traits respond to emerging threats like infectious disease remains underexplored. Infectious pneumonia threatens bighorn sheep (Ovis canadensis) populations across North America and we hypothesized it may also reduce horn growth in male sheep. We assess the effect of pneumonia on horn size in male bighorn sheep using 12 herd datasets from across the western United States that had horn growth and disease data. Disease resulted in 12–35% reduction in increment (yearly) length and 3–13% reduction in total horn length in exposed individuals. The disease effect was prolonged when pathogens continued to circulate in sheep populations. Further, disease likely delays the age at which horns reach ¾‐curl and prevents achievement of full‐curl. This is further evidenced with 6 of the 12 herds experiencing an increase in average age at harvest following die‐off events. Management of bighorn sheep for horn size and for population maintenance has focused on factors including nutrition, environmental conditions, and genetic diversity. We demonstrate that disease plays an important role in horn size: pneumonia disease outbreak events significantly reduced horn growth in male bighorn sheep, and continued horn stunting occurred when chronically infected individuals remained present in the population.
... Our results confirm the negative impact of population density on both horn length and body mass in bovids (reviewed by Bonenfant et al. 2009b andDouhard et al. 2017), and they also show that, except for male and female bighorn sheep, this impact is proportionally stronger on body mass than on horn length. Nevertheless, static allometric intercepts were generally lower at high than at low density, indicating that horns grown at high density were shorter for a given body mass than those grown at low density. ...
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Little is known about the effects of environmental variation on allometric relationships of condition‐dependent traits, especially in wild populations. We estimated sex‐specific static allometry between horn length and body mass in four populations of mountain ungulates that experienced periods of contrasting density over the course of the study. These species displayed contrasting sexual dimorphism in horn size; high dimorphism in Capra ibex and Ovis canadensis and low dimorphism in Rupicapra rupicapra and Oreamnos americanus. The effects of density on static allometric slopes were weak and inconsistent while allometric intercepts were generally lower at high density, especially in males from species with high sexual dimorphism in horn length. These results confirm that static allometric slopes are more canalized than allometric intercepts against environmental variation induced by changes in population density.
... Temperature and precipitation affect individual mass changes in this population, making them relevant variables to investigate resource allocation trade-offs (Douhard et al., 2018). We also used the annual mean of the Pacific Decadal Oscillation (PDO), which is negatively related to horn growth (Douhard et al., 2017). This climatic index measures shifts between decades of warm and dry winters, and decades of cooler winters with more precipitation in the Canadian Rocky Mountains (Trenberth and Hurrell, 1994;Whitfield et al., 2010). ...
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The rate of senescence may vary among individuals of a species according to individual life histories and environmental conditions. According to the principle of allocation, changes in mortality driven by environmental conditions influence how organisms allocate resources among costly functions. In several vertebrates, environmental conditions during early life impose trade-offs in allocation between early reproduction and maintenance. The effects of conditions experienced during early life on senescence, however, remain poorly documented in wild populations. We examined how several early-life environmental conditions affected reproductive and survival senescence in wild bighorn sheep. We found long-term effects of high population density at birth, precipitations during the winter before birth, and temperature during the winter following birth that decreased survival after 7 years of age. High temperature during the first summer and autumn of life and high Pacific decadal oscillation decreased reproductive success at old ages. However, harsh early-life environment did not influence the rate of senescence in either survival or reproduction. Contrary to our expectation, we found no trade-off between reproductive allocation prior to senescence and senescence. Our results do show that early-life environmental conditions are important drivers of later survival and reproductive success and contribute to intra-specific variation in late-life fitness, but not aging patterns. These conditions should therefore be considered when studying the mechanisms of senescence and the determinants of variation in both survival and reproductive senescence at older ages.
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Potential negative artificial selection on horn size is a concern for many harvested ungulates. The mountain goat (Oreamnos americanus) has distinct black horns, but targeting animals based on horn size in the field can be challenging. We analyzed over 23,000 horn records that included base circumference and total length, from which we also derived horn volume, from mountain goats harvested in Alaska, British Columbia, and the Northwest Territories from 1980 to 2016. We tested 3 potential drivers of horn size variation: geographical location, environmental conditions, and artificial selection. We found no support for a latitudinal effect with surprisingly little variation across the sampling distribution. The Pacific Decadal Oscillation had the largest effect outside age in the model, suggesting a role of climate in shaping variation. Mountain goats harvested closer to roads had larger horns, indicating that ease of access might allow hunters to be more selective, though the effect size was small. Our findings reinforce the value of accurate and complete record keeping on horn size, age, and sex of harvested animals, and highlight the importance of explicitly considering climate and accessibility when devising management strategies for the mountain goat. We explored possible climatic, geographic and anthropogenic drivers of variation in horn size of the North American mountain goat over the past 40 years. We recommend continued and transparent record keeping for all harvested mountain goats by government authorities.
A bstract In species with long gestation, females commit to reproduction several months before parturition. If cues driving conception date are uncoupled from spring conditions, parturition could be mistimed. Mismatch may increase with global change if the rate of temporal changes in autumn cues differs from the rate of change in spring conditions. Using 17 years of data on climate and vegetation phenology, we show that autumn temperature and precipitation, but not vegetation phenology, explain parturition date in bighorn sheep. Although autumn cues drive the timing of conception, they do not predict conditions at parturition in spring. We calculated the mismatch between individual parturition date and spring green‐up, assessed whether mismatch increased over time and investigated the consequences of mismatch on lamb neonatal survival, weaning mass and overwinter survival. Mismatch fluctuated over time but showed no temporal trend. Temporal changes in green‐up date did not lead to major fitness consequence of mismatch. Detailed data on individually marked animals revealed no effect of mismatch on neonatal or overwinter survival, but lamb weaning mass was negatively affected by mismatch. Capital breeders might be less sensitive to mismatch than income breeders because they are less dependent on daily food acquisition. Herbivores in seasonal environments may access sufficient forage to sustain lactation before or after the spring ‘peak’ green‐up, and partly mitigate the consequences of a mismatch. Thus, the effect of phenological mismatch on fitness may be affected by species life history, highlighting the complexity in quantifying trophic mismatches in the context of climate change.
The quantification of repeatability has enabled behavioral and evolutionary ecologists to assess the heritable potential of traits. For behavioral traits that vary across life, age-related variation should be accounted for to prevent biasing the microevolutionary estimate of interest. Moreover, to gain a mechanistic understanding of ontogenetic variation in behavior, among- and within-individual variance should be quantified across life. We leveraged a 30-year study of painted turtles (Chrysemys picta) to assess how age contributes to variation in the repeatability of nesting behaviors. We found that four components of nesting behavior were repeatable, and that accounting for age increased the repeatability estimate for maternal choice of canopy cover over nests. We detected canalization (diminished within-individual variance with age) of canopy cover choice in a reduced dataset despite no shift in repeatability. Additionally, random regression analysis revealed that females became more divergent from each other in their choice of canopy cover with age. Thus, properly modeling age-related variance should more precisely estimate heritable potential, and assessing among- and within-individual variance components in addition to repeatability will offer a more mechanistic understanding of behavioral variation across age.
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Integral projection models (IPMs) are extremely flexible tools for ecological and evolutionary inference. IPMs track the distribution of phenotype in populations through time, using functions describing phenotype-dependent development, inheritance, survival and fecundity. For evolutionary inference, two important features of any model are the ability to (i) characterize relationships among traits (including values of the same traits across ages) within individuals, and (ii) characterize similarity between individuals and their descendants. In IPM analyses, the former depends on regressions of observed trait values at each age on values at the previous age (development functions), and the latter on regressions of offspring values at birth on parent values as adults (inheritance functions). We show analytically that development functions, characterized this way, will typically underestimate covariances of trait values across ages, due to compounding of regression to the mean across projection steps. Similarly, we show that inheritance, characterized this way, is inconsistent with a modern understanding of inheritance, and underestimates the degree to which relatives are phenotypically similar. Additionally, we show that the use of a constant biometric inheritance function, particularly with a constant intercept, is incompatible with evolution. Consequently, current implementations of IPMs will predict little or no phenotypic evolution, purely as artifacts of their construction. We present alternative approaches to constructing development and inheritance functions, based on a quantitative genetic approach, and show analytically and through an empirical example on a population of bighorn sheep how they can potentially recover patterns that are critical to evolutionary inference. This article is protected by copyright. All rights reserved.
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Quantifying temporal changes in harvested populations is critical for applied and fundamental research. Unbiased data are required to detect true changes in phenotypic distribution or population size. Because of the difficulty of collecting detailed individual data from wild populations, data from hunting records are often used. Hunting records, however, may not represent a random sample of a population. We aimed to detect and quantify potential bias in hunting records. We compared data from a long-term monitoring project with hunting records of brown bears (Ursus arctos) in Sweden and investigated temporal trends (1996–2013) in the ratio of yearlings to adult females, yearling mass and adult female mass. Data from hunting records underestimated the decline in yearling and adult female mass over time, most likely owing to the legal protection of family groups from hunting, but reflected changes in the ratio of yearlings to adult females more reliably. Although hunting data can be reliable to approximate population abundance in some circumstances, hunting data can represent a biased sample of a population and should be used with caution in management and conservation decisions.
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Selective harvest may lead to rapid evolutionary change. For large herbivores, trophy hunting removes males with large horns. That artificial selection, operating in opposition to sexual selection, can lead to undesirable consequences for management and conservation. There have been no comparisons of long-term changes in trophy size under contrasting harvest pressures. We analyzed horn measurements of Stone's rams (Ovis dalli stonei) harvested over 37 years in two large regions of British Columbia, Canada, with marked differences in hunting pressure to identify when selective hunting may cause a long-term decrease in horn growth. Under strong selective harvest, horn growth early in life and the number of males harvested declined by 12% and 45%, respectively, over the study period. Horn shape also changed over time: horn length became shorter for a given base circumference, likely because horn base is not a direct target of hunter selection. In contrast, under relatively lower hunting pressure, there were no detectable temporal trends in early horn growth, number of males harvested, or horn length relative to base circumference. Trophy hunting is an important recreational activity and can generate substantial revenues for conservation. By providing a reproductive advantage to males with smaller horns and reducing the availability of desirable trophies, however, excessive harvest may have the undesirable long-term consequences of reducing both the harvest and the horn size of rams. These consequences can be avoided by limiting offtake.
Identifying factors shaping secondary sexual traits is essential in understanding how their variation may influence male fitness. Little information is available on the allocation of resources to antler growth in territorial ungulates with low sexual size dimorphism. We investigated phenotypic and environmental factors affecting both absolute and relative antler size of male roe deer in three contrasting populations in France and Sweden. In the three populations, we found marked age‐specific variation in antler size, with an increase in both absolute and relative antler size between yearling and prime‐age stages, followed by a decrease (senescence) for males older than 7 years. Antler size increased allometrically with body mass. This increase was particularly strong for senescent males, suggesting the evolution of two reproductive tactics: heavy old males invested particularly heavily in antler growth (potentially remaining competitive for territories), whereas light old males grew small antlers (potentially abandoning territory defense). Finally, environmental conditions had little effect on antler size: only population density negatively affected absolute antler size in one of the three populations. Antler size may therefore provide an honest signal of male phenotypic quality in roe deer. We discuss the implications of these results in terms of territory tenure and mating competition.
Greenland ice-core data have revealed large decadal climate variations over the North Atlantic that can be related to a major source of low-frequency variability, the North Atlantic Oscillation. Over the past decade, the Oscillation has remained in one extreme phase during the winters, contributing significantly to the recent wintertime warmth across Europe and to cold conditions in the northwest Atlantic. An evaluation of the atmospheric moisture budget reveals coherent large-scale changes since 1980 that are linked to recent dry conditions over southern Europe and the Mediterranean, whereas northern Europe and parts of Scandinavia have generally experienced wetter than normal conditions.