Journal of Animal
Ecology 2006
75, 1110–1118
Population characteristics predict responses in moose body
mass to temporal variation in the environment
Blackwell Publishing Ltd
IVAR HERFINDAL, BERNT-ERIK SÆTHER, ERLING JOHAN SOLBERG*,
REIDAR ANDERSEN and KJELL ARILD HØGDA†
Department of Biology, Norwegian University of Science and Technology, N-7491 Trondheim, Norway; *Norwegian
Institute for Nature Research, Tungasletta 2, N-7485 Trondheim, Norway; and †NORUT IT AS, N-9291, Tromsø,
Norway
Summary
1. A general problem in population ecology is to predict under which conditions
stochastic variation in the environment has the stronger effect on ecological processes.
By analysing temporal variation in a fitness-related trait, body mass, in 21 Norwegian
moose Alces alces (L.) populations, we examined whether the influence of temporal
variation in different environmental variables were related to different parameters that
were assumed to reflect important characteristics of the fundamental niche space of the
moose.
2. Body mass during autumn was positively related to early access to fresh vegetation
in spring, and to variables reflecting slow phenological development (low June temperature, a long spring with a slow plant progression during spring). In contrast, variables
related to food quantity and winter conditions had only a minor influence on temporal
variation in body mass.
3. The magnitude of the effects of environmental variation on body mass was larger in
populations with small mean body mass or living at higher densities than in populations
with large-sized individuals or living at lower densities.
4. These results indicate that the strongest influence of environmental stochasticity on
moose body mass occurs towards the borders of the fundamental niche space, and
suggests that populations living under good environmental conditions are partly buffered
against fluctuations in environmental conditions.
Key-words: Alces alces, environmental variation, fundamental niche, life-history variation, normalized difference vegetation index.
Journal of Animal Ecology (2006) 75, 1110–1118
doi: 10.1111/j.1365-2656.2006.1138.x
Introduction
The importance of body mass as a life-history trait in
mammals is well documented (Clutton-Brock & Harvey
1983; Sæther 1997; Lindström 1999). In ungulates,
variation in body mass is closely related to environmental conditions (Sæther 1997; Post & Stenseth 1999).
For instance, in temperate and arctic ungulate populations
experiencing high variation in climate and food availability between seasons, large body mass is associated
with small snow depths during winters, probable due to
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society
Correspondence: Ivar Herfindal, Department of Biology,
Norwegian University of Science and Technology, N-7491
Trondheim, Norway. Tel.: +47 73596253. Fax: +47 73596100.
E-mail: [email protected]
increased access to food (Cederlund, Sand & Pehrson
1991; Loison, Langvatn & Solberg 1999; Solberg et al.
2004), or high temperatures, which decrease the energetic
expenditure during winter (Clutton-Brock & Albon
1983; Sæther & Gravem 1988). Summer conditions
affect body mass through variation in food quality and
quantity. In temperate regions, a long photosynthetic
active season with high temperatures increases food
quantity (Ericsson, Ball & Danell 2002), whereas cold
and wet summers are found to increase food quality
(Sæther 1985; Bø & Hjeljord 1991; Solberg & Sæther
1994; Gaillard et al. 1996; Langvatn et al. 1996; Sæther
et al. 1996). In addition to the direct effect of the environment, body mass can be affected by density effects
due to increased food competition (Skogland 1983;
Gaillard et al. 1996; Forchhammer et al. 2001; Weladji
1111
Temporal variation
in moose body mass
& Holand 2003) and long-term habitat deterioration
due to over-grazing (Skogland 1985; Côte et al. 2004).
High densities may also increase the impact of environmental variation (Coulson et al. 2001; Weladji & Holand
2003). In populations experiencing seasonal variation
in food availability or quality, density dependence in
body mass has often been related to population density
during the season with poor food availability (Sæther
1997), typically the winter season in temperate and
arctic populations (Skogland 1983). However, this effect
will depend on the distribution and quality of winter
and summer grazing ranges (Skogland 1983), e.g. summer density may be important if body condition after
winter depends on the ability to store fat during the
previous summer (Skogland 1983; Stewart et al. 2005).
The fundamental niche is a combination of environmental factors that allow a population of a species to
persist (Hutchinson 1957), i.e. to have a population
growth rate λ larger than 1. This simple definition
ignores the effects of density dependence [see Holt,
Knight & Barfield (2004) for a more general approach],
but whatever the mechanisms for variation in body
size, we believe that body size is a fitness-related trait that
can be used as an index of the localization of the population in the multidimensional niche space. Accordingly, we suggest that populations with small relative
body sizes are located more towards the border of the
realized niche than populations with larger body sizes
(Holt & Gaines 1992; Holt & Gomulkiewicz 1997).
Here our aim is to examine whether there is a relationship between temporal variation in a fitness-related
trait (body mass) within populations and differences in
mean body mass among populations. We consider the
mean body mass to reflect the position of the population
within the multidimensional niche space (Hutchinson
1957) and hence the suitability of the prevailing environmental conditions for the moose. We examine two
hypotheses for such a relationship. One hypothesis
suggests a positive relationship between temporal variation in body mass and the suitability of the habitat
because few constraints affect the positive effects of
environmental factors. Alternatively, if individuals
are not able to buffer the environmental effects under
harsh conditions, we expect larger temporal variation
in populations with smaller individuals. These hypotheses were evaluated by examining temporal variation in
body mass of moose Alces alces (L.) calves and yearlings
across Norway.
Methods
  
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Animal
Ecology, 75,
1110–1118
Data were collected from 21 Norwegian municipalities
covering most of the geographical range of moose distribution in Norway (Garel et al. 2006). For details
regarding the study area and municipalities included in
the analyses, see Garel et al. (2006). Data from hunter
killed moose was collected from 1982 to 2002. For each
moose, we had information on carcass mass (body mass
from here), sex, age and date of kill. Only calves and
yearlings killed in September and October were
included in the analyses to avoid large variation in body
mass during the autumn. We only included municipalities
in which data were available for more than 10 individuals
for 10 or more years in each sex- and age-group. Body
growth from calf to yearling was estimated as Gt = Wy,t −
Wc,t−1, where Wy,t and Wc,t is yearling and calf body
mass, respectively, in year t. Owing to small sample
sizes for yearling females, temporal variation in body
growth was only analysed in 14 time series of males.
 
We used satellite-derived variables based on the normalized difference vegetation index (NDVI) from the
GIMMS data set (Zhou et al. 2001) to describe environmental phenology. The GIMMS data are 15 days
maximum composite of NDVI values with a spatial
resolution of 8 × 8 km2, and covering the period 1982
till present (Zhou et al. 2001). The NDVI is an index of
the relationship between reflected red and near-infrared
radiation from the ground, and is found to represent
the greenness of the vegetation, or the photosynthetic
activity (Myneni et al. 1995). We calculated the following parameters based on the annual curve of NDVI
values: onset of spring, length of growing season, derived
spring NDVI, peak value, peak time, length of spring,
integrated NDVI (Table 1). For more details about these
variables and the GIMMS data set and processing, see
Reed et al. (1994), Zhou et al. (2001) and Pettorelli
et al. (2005a). All parameters were calculated annually
for each pixel in the GIMMS data set, and mean values
were calculated for municipalities, using pixels with
centre inside municipality and below the tree limit.
Summer temperature, a variable that not necessarily
is reflected by the plant phenology curve, may also
influence plant quality (Deinum 1984; Langvatn et al.
1996). Thus, we also included mean temperatures during May and June in our analyses. We assigned values
from closest weather station representing similar climate
type (e.g. coastal, continental) in those municipalities
in which weather data were not available. As a measure
of winter severity, we used mean winter temperature
and the North Atlantic Oscillation winter index (NAO;
Hurrell 1995). The winter temperature was calculated
as the mean of monthly temperature means for December, January and February. The NAO for the study
period was retrieved from http://www.cgd.ucar.edu/
cas/jhurrell/indices.data.html#naostatdjfm (read 10
May 2005). A high positive NAO index is generally
associated with relatively warm winters and high precipitation in the northern Atlantic coastal Europe,
whereas low values of the index tend to result in cold
winters and low precipitation (Hurrell 1995).
For each municipality, we used the number of hunter
killed moose per square kilometre (below tree line) as
an index of population density. However, because the
1112
I. Herfindal et al.
Table 1. Description of the explanatory variables used to analyse the effects of environmental variation on moose body mass (βi)
and proportion of variance in moose body mass explained by the environmental variable i ( p in eqn 1). Variables abbreviations
are given in parentheses
Explanatory parameter
Description
Relative moose density (RPD)
Moose killed during hunt/municipality area size below tree limit, divided by the annual
above-ground vegetational biomass
Relative moose density previous year
1-year lag in relative
moose density (RPD1)
May temperature (MT)
June temperature (JT)
Winter temperature (WT)
Onset of spring (OS)
Length of growing season (LGS)
Peak time (PT)
Peak value (PV)
Length of spring (LS)
Derived spring NDVI (DSN)
Integrated NDVI (IN)
NAO (NAO)
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Animal
Ecology, 75,
1110–1118
Mean temperature for May
Mean temperature for June
Mean temperature for December, January and February. Relates to winter harshness
Week number in the year when the NDVI value increase above the value that represent
birch leaf burst. Indicates when green vegetation becomes available as forage
Number of weeks between onset of spring and onset of autumn (measured as week number
in the year where NDVI value drops below the same threshold value used to calculate onset
of spring). Indicates how long green vegetation is available as forage
Week number in summer where NDVI value reaches its highest value
The NDVI value at peak time. Relates to the biomass productivity when it is on top during
growing season
Number of weeks between onset of spring and peak time. Indicates how long fresh
vegetation with high nutritious value is available
The NDVI value at onset of spring – NDVI value the previous 15 days composite image.
Indicates how fast the plant develop during early spring
Sum of the NDVI values through the plant growing season. Relates to the production of
foliage during growing season
Winter (December–March) North Atlantic Oscillation index. Correlates with
temperature and precipitation during winter.
number of individuals per unit area does not necessarily
reflect the number of individuals per unit food, we
divided the number of moose shot per square kilometre
of forest with the above-ground biomass estimated for
the municipality [for further details regarding this
index of population density, see Garel et al. (2006) and
Solberg et al. (2006)]. This relative population density
(RPD) represents the moose density relative to the
biomass of vegetation in an area. Because there may
be delayed effects of population density in ungulate
population dynamics (e.g. Solberg et al. 1999), we also
included relative population density the previous year
in our analyses (RPD1).
As a measure of the suitability of the habitat, we used
the mean body mass of calves and yearlings in the population (PMBM). The PMBM was calculated by averaging the body mass for each age and sex group within
a municipality, standardized these for all municipalities,
and use the mean of these standardized values for each
age and sex group within a municipality. Moreover,
because the effect of environmental condition can be
modified by population density (Portier et al. 1998;
Weladji & Holand 2003), we used the mean relative
population density (MRPD) within municipality during the study period, standardized among populations,
as an index of intraspecific food competition. Populations with on average heavy or light individuals are
referred to as large- or small-size populations, whereas
populations with high or low relative density are referred
to as high- or low-density populations, respectively,
 
Environmental conditions and body mass were calculated as mean values within a municipality, grouped
on year, age and sex. These values were standardized
within municipality, age and sex.
To reduce the influence of autocorrelation in the time
series, we analysed the first order differentiates (Chatfield
1989) of body mass and body growth. Accordingly, in
the subsequent analyses body mass and body growth
refers to the first order differentiates and not body mass
and growth per se. We calculated the first order differentiates as ∆Wk,t = Wk,t − Wk,t−1 where W is body mass
in year t, and k is one of the four categories male calf,
female calf, male yearling or female yearling. Similarly,
we also analysed the first order differentiate of annual
variation in body growth from calf to yearling ∆Gt, and
for all the environmental variables (Table 1). Let βj,i
denote the effects of environmental variable i (see
Table 1) on ∆Wt or ∆Gt in population j. Following
Sæther, Sutherland & Engen (2004) and assuming that
the environmental covariates can be introduced as random effects, the fraction of the environmental variance
2
in body mass σ e explained by covariate i in population
j is
2

p j ,t = var(β j ,i u j ,i ) var ∑ β j ,i u j ,i  + σ j  ,

 i

eqn 1
where βj,i is the effect size of environmental variable i in
population j, uj,i represents the covariate i (Table 1) in
1113
Temporal variation
in moose body mass
2
population j and σ j is the residual component of the
environmental variance in population j that is not
explained by any of the environmental covariates i. We
calculated βj,i and pj,i for each age and sex separately. We
then analysed the variation in βi and pi in relation to the
population characteristics (mean body mass and mean
relative density of the populations) with linear models.
Age and sex as well as the interactions between age and
sex, age and population characteristics, and sex and
population characteristics were included as independent variables, except in the analysis of body growth from
calf to yearling ∆Gt , for which only male data were
available. The selection of the best models explaining
the variation in βi and pi was based on AICc values and
AICc weights w (Burnham & Anderson 2002).
Results
The mean body mass among populations was 66·93 kg
± 6·08 SD, 63·42 kg ± 5·90 SD, 139·69 kg ± 10·77 SD
and 130·18 kg ± 9·48 SD for male calves, female calves,
male yearlings and female yearlings, respectively.
However, there were large differences in the range of
body mass between the populations (male calf: 59·06–
75·49 kg, female calf: 55·93 –71·08 kg, male yearling:
127·59 –154·60 kg, female yearling: 122·77–141·55 kg).
     
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Animal
Ecology, 75,
1110–1118
Overall, higher body masses were found for all age- and
sex-classes after summers with an early start of vegetation
growth (βOS = −0·164, t = −3·91, d.f. = 57, P < 0·001),
after a spring that started with a slow progression in
plant development [derived spring NDVI; yearlings
only (βDSN = −0·295, t = −5·02, d.f. = 26, P < 0·001)],
after a spring that lasted long before the peak of the
primary production (βLS = 0·108, t = 2·38, d.f. = 57,
P = 0·020), after long growing seasons (βLGS = 0·119,
t = 2·40, d.f. = 57, P = 0·020), and after summers with
low temperatures in May (βMT = −0·149, t = −3·36,
d.f. = 57, P = 0·001) and June (βJT = −0·220, t = −6·05,
d.f. = 57, P < 0·001). Thus, higher body masses were
found in years with a slow phenological development of
plants. In addition, higher body masses were associated
with cold winters (βWT = −0·081, t = −2·02, d.f. = 57,
P = 0·048). A two-way  with age and sex as fixed
factors revealed no significant (P > 0·10) sex differences
in the effects of any environmental variable on body
mass. However, yearling body mass was associated
with a slow plant progression in early spring (derived
spring NDVI), whereas calf body mass did not show
any significant relationship with this variable (,
F1,56 = 8·43, P = 0·005). None of the other sets of βi
showed age-specific effects (P > 0·10).
June temperature explained the highest proportion
of the temporal variation in body mass ( pJT = 0·180
± 0·204 SD), followed by the length of spring ( pLS =
0·158 ± 0·175 SD), derived spring NDVI ( pDSN = 0·156 ±
0·186 SD), and May temperature (pMT = 0·145 ± 0·141
SD), whereas NAO and relative population density explained a smaller proportion of the variation in body mass
(pNAO = 0·106 ± 0·124 SD and pRPD = 0·093 ± 0·135 SD).
High body growth from calf to yearling was associated
with years with low June temperature (−0·241, t = −3·08,
d.f. = 13, P = 0·009), and June temperature also explained
the highest proportion of variance in growth from calf
to yearling ( pJT = 0·170 ± 0·187 SD), followed by winter
temperature and NAO (pWT = 0·156 ± 0·177 SD and
pNAO = 0·106 ± 0·128 SD).
     
  
The effect of peak time (time in summer when photosynthetic activity reaches its highest level) and length of
spring on the variation in body mass decreased with
increasing mean body mass in the population (Table 2,
Fig. 1a). In small-sized populations the effect was
positive, whereas in large-sized populations there was
barely any effect (Fig. 1a). Similarly, in small-sized
populations there were negative effects of high winter
temperatures and NAO, but as the mean body mass
in a population increased, these effects more or less
disappeared (Table 2, Fig. 1a). These patterns were
similar for all age- and sex-classes. The other sets of βi
did not covary in any degree with mean body mass between
populations (Table 2, Fig. 1a). The negative effect of
June temperature on growth from calf to yearling was
stronger in small-sized populations than in large-sized,
populations (estimate of slope = −0·141 ± 0·075 SD).
The effects of length of growing season and integrated
NDVI (variables related to biomass quantity) on the
variation in body mass were higher in high-density
populations than in low-density populations (Table 2,
Fig. 1b), but regarding the length of growing season,
only present for yearlings (Table 2, Fig. 1b). No relationship was found between the effects of environmental
variation on growth from calf to yearling and mean
relative population density.
The overall pattern in proportion of variance in body
mass explained by the environmental variables (p) was
a decrease with increasing mean body mass in the population, and an increase with increasing mean relative
population density. Thus, the environmental variables
explained more of the variation in body mass for smallsized populations and populations with high relative
densities, as expected when looking at how the βvalues varied with these two population characteristics
(Figs 1a,b). Moreover, the p was higher for yearlings
than for calves and the decrease in p with increasing
mean body mass and decreasing relative density in
populations was more evident for yearlings than for
calves.
Discussion
In temperate ungulates, quantity, accessibility and
quality of forage are important for body growth and
Table 2. The best models for the relationship between the regression coefficient βi (the effect of environmental covariate i on mean body mass ∆Wt) and
1114
(a)Herfindal
the population
I.
et al.mean body mass (PMBM) or (b) mean relative population density (MRPD). The effects of age and sex are included in the models. Only
models with ∆AICc less than two compared with the best model (∆AICc = 0) is presented. X indicates variables included in the model. An interaction
between two explanatory variables is denoted with *, and w is the AICc-weights. For abbreviations of dependent variables i, see Table 1
(a) Population mean body mass
βi
Age
Age*
sex
(b) Mean relative population density
Age*
PMBM
w
0·00
0·94
0·00
0·08
1·95
1·98
0·303
0·190
0·246
0·237
0·093
0·091
0·00
0·48
0·72
0·79
1·61
0·00
1·53
0·172
0·135
0·120
0·116
0·077
0·377
0·175
0·00
0·52
1·77
0·00
0·14
1·40
0·00
0·12
1·74
1·76
0·263
0·203
0·108
0·261
0·244
0·130
0·230
0·216
0·096
0·095
X
0·00
1·95
0·00
0·36
1·61
0·346
0·131
0·248
0·208
0·111
X
0·00
0·90
0·00
0·78
1·46
0·00
0·51
1·88
0·00
1·77
0·310
0·198
0·238
0·161
0·114
0·261
0·202
0·108
0·261
0·141
PMBM
βOS
X
βLGS
X
X
X
X
βPV
X
X
X
X
X
βLOS
βIN
X
X
X
X
X
βPT
βDSN
Sex*
PMBM
∆AICc
Sex
X
X
X
X
X
X
X
X
X
X
X
X
X
Age
Sex
X
X
X
X
X
X
X
X
βMT
X
X
X
X
X
βNAO
X
X
X
βRPD
X
X
βRPD1
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Animal
Ecology, 75,
1110–1118
Age*
MRPD
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
βJT
Age*
sex
X
X
X
βWT
MRPD
X
X
development (White 1983; Andersen & Sæther 1992).
During summer, access to food is seldom restricted, but
quantity and quality may vary considerably (Sæther &
Heim 1993). Our results suggest that during spring and
summer, variation in forage quality is more important
than quantity for temporal variation in moose autumn
body mass. Variables related to biomass production
(particularly integrated NDVI and peak value) showed
little effect on the body mass. In contrast, variables
related to forage quality, particularly during spring and
early summer (derived spring NDVI, May and June
temperature) had large effect on the body mass and
X
X
X
X
Sex*
MRPD
∆AICc
w
0·00
1·57
0·00
0·03
0·11
0·43
0·60
0·00
0·72
0·97
0·302
0·138
0·148
0·146
0·140
0·120
0·110
0·231
0·161
0·142
0·00
0·08
1·48
1·55
0·00
0·89
1·67
0
1·57
0·222
0·213
0·106
0·102
0·263
0·168
0·114
0·382
0·174
0·00
0·29
0·69
0·81
1·05
0·00
1·87
0·00
0·36
0·86
1·01
1·61
0·00
0·90
0·00
0·75
0·164
0·142
0·117
0·110
0·097
0·372
0·146
0·193
0·162
0·126
0·117
0·087
0·304
0·194
0·306
0·210
growth from calf to yearling. Furthermore, large body
masses were found in years that gave early access to
fresh vegetation after the winter (Fig. 1). This indicates
that forage quality, rather than quantity, is most important for moose body mass. This has previously been
suggested to occure both during summer (Sæther 1985;
Sæther et al. 1996; Hjeljord & Histøl 1999; Solberg
et al. 1999) and winter (Andersen & Sæther 1992;
Sæther et al. 1996). Such a relationship between variation in a life-history trait, e.g. the body mass, and quality
of food seems to be a general pattern in temperate and
arctic ungulates (e.g. for red deer Cervus elaphus L.,
1115
Temporal variation
in moose body mass
Fig. 1. The regression coefficient βi of environmental covariates on body mass in relation to (a) population mean body mass
(PMBM) or (b) mean relative population density (MRPD). Solid and dashed lines indicate the relationship for yearlings and
calves, respectively. PMBM and MRPD are standardized among populations. Sex-specific differences in the effects are not
indicated as only May temperature showed sex-specific differences. Asterisk after the variable name (see Table 1) indicates a
significant effect of PMBM (a) or MRPD (b) on the distribution of βi (Table 2).
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Animal
Ecology, 75,
1110–1118
Langvatn & Albon 1986; Albon & Langvatn 1992;
Langvatn et al. 1996; Mysterud et al. 2001a; Pettorelli
et al. 2005b; for reindeer and caribou Rangifer tarandus
spp., Reimers, Klein & Sørumgård 1983; White 1983;
Post & Klein 1999; for sheep Ovis aries L., Mysterud
et al. 2001b; Steinheim et al. 2004; for roe deer Capreolus
capreolus L., Gaillard et al. 1996). This supports the
theory of Klein (1970) that foraging conditions in
spring and early summer is most important for variation in demographic traits and population dynamics of
northern ungulates. Because this occurs during the
period of the year with highest body growth rate, even
small variation in the forage quality can have large
impact on body mass and development through a
multiplier effect (White 1983; Cook et al. 2004). The
duration of access to high-quality forage will also affect
the period for rapid development of body tissue and fat
reserves (Hjeljord & Histøl 1999; Ericsson et al. 2002),
hence the positive effect of the length of the growing
season in all populations (Fig. 1).
Environmental conditions, such as temperature and
precipitation, affect the growth rate and chemical composition of plants (Deinum 1984; Chapin et al. 1995).
The effect can differ between growth forms (Chapin
et al. 1995), but in general, cold and wet weather during
the growing period improves quality of forage, e.g. by
increasing the nitrogen/carbon (N/C) ratio (Deinum
1984; Bø & Hjeljord 1991; Lenart et al. 2002). In contrast,
a warm and sunny growth period increases the fibre
content, decreases the N/C ratio, decreases digestibility,
and thus decrease the forage value (Lenart et al. 2002).
One of the most important ecological mechanisms
1116
I. Herfindal et al.
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Animal
Ecology, 75,
1110–1118
affecting short-time fluctuations in body mass of temperate and arctic ungulates therefore seems to be the
climatic influences on the plants quality as forage, and
the length of the period this forage is available (Hjeljord
& Histøl 1999; Ericsson et al. 2002). This can create
cohort effects in body mass that can have lasting impact
on the reproductive performance of the cohort (Sæther
& Haagenrud 1985; Solberg et al. 1999, 2004), and in
turn on the population dynamics (Solberg et al. 1999,
2004; Beckerman et al. 2002; Gaillard et al. 2003).
In this study, the effect of environmental variation
increased with decreasing mean body mass and
increasing relative density of the population. This can
have several explanations. First, the variance in the
independent variable affects the regression coefficient
and hence the proportion of variance explained by the
independent variable (Sokal & Rohlf 1995, pp. 464–
465). If the variance in the environmental variables
(Table 1) is correlated with regional variation in mean
body mass, this covariation can explain the relationship between βi or pi, and mean body mass (Table 2,
Fig. 1a). In fact, such a confounding correlation with
mean body mass was present for some of the environmental variables, although mainly due to the effect of
four populations. However, after removing these four
populations, similar relationships were found between
βi or pi and mean body mass and density of populations
as in Table 2 and Fig. 1. Thus, the results and conclusions were not affected by the confounding relationship
between the variance in the environmental variables
and mean body mass.
Secondly, differences in body mass among Norwegian moose population may be influenced by gradients
in environmental factors influencing forage abundance
or quality (Sæther et al. 1996; Herfindal et al. unpublished data), e.g. if small-sized populations are more
likely to be localized further away from the optimum in
the fundamental niche space than large-sized populations. The position in the niche will also be affected
by density-dependent effects. However, these effects
are rarely included in traditional niche definitions
(Hutchinson 1957; Holt & Gaines 1992; but see Holt
et al. 2004). Furthermore, because intraspecific competition will be less intense at low population densities
relative to the available biomass (Côte et al. 2004;
Stewart et al. 2005), individuals may be better buffered
against temporal variation in climate. Such buffers,
being larger body mass or better access to critical
resources, can prevent individuals from utilizing important body tissue during periods of food shortage, and
make them better able to retain a stable body condition
(Reimers 1984; Sæther & Gravem 1988; Cederlund
et al. 1991). Accordingly, individuals that lose more of
their body tissue during winter may be more dependent
on high-quality forage during summer for compensating
their losses than larger individuals. A similar pattern
was suggested for wild reindeer, for which recruitment
rates were less affected by climatic harshness in populations living under favourable environmental conditions
than in populations in poorer environments (Skogland
1985).
Thirdly, body mass-dependent responses to temporal environmental variation may also be related to
the large differences in body growth rates recorded
among Norwegian moose populations (Garel et al.
2006). Accordingly, large-sized moose grew faster than
smaller-sized moose (Garel et al. 2006), and thus may
have reached a higher proportion of the adult body
mass as yearlings. Because the period of early growth
and development is the most sensitive for environmental
variation in mammals (Lindström 1999), smaller-sized
moose may need longer periods of body growth, which
could result in higher sensitivity to fluctuations in environmental conditions. However, although larger-sized
moose grow faster than moose of smaller size, they also
grew for a longer period of time (Garel et al. 2006).
Thus, the difference in proportion of adult body mass
gained as yearling did not differ among populations. In
addition, if there was a smaller effect of environmental
variation on individuals close to adult body mass, the
environmental variables should explain a higher proportion of the variation in body mass for calves than
yearlings because yearlings are closer to adult body
mass. This was not the case in the present study because
the proportion of variance in body mass explained by
the environmental variables was higher for yearlings
than for calves, and in particular for smaller-sized
moose or for moose living at high densities (see ‘Environmental effects in relation to population characteristics’
in Results). This indicates that there is a buffer of
resources available for larger-sized moose during the
first winter when forage shortage often leads to loss of
body tissue and reduction in body condition (Sæther &
Gravem 1988).
To summarize, our results suggest that the relative
influence of environmental fluctuations on body mass
is less in populations with large individuals or in
populations with low densities than in populations
with smaller individuals or higher densities because the
individuals are more likely to be buffered against environmental stochasticity when the resource availability
is large. This concurs with previous studies showing
stronger effects of density-independent processes in
populations already weakened by density dependence
or other factors (Skogland 1985; Sæther 1997; Hallet
et al. 2004). Furthermore, although the ecological
mechanisms influencing life-history traits (e.g. body
mass) may vary considerably over relatively short
geographical distances (Mysterud et al. 2001b), this
suggests that we still can be able to predict, based on
knowledge of basic population characteristics, under
which conditions the effects of environmental stochasticity are expected to be most prominent.
Acknowledgements
We are grateful to comments from J. Fryxell and A.
Loison on previous versions of the manuscript. We also
1117
Temporal variation
in moose body mass
thank Compton J. Tucker at Goddard Space Flight
Center, USA, for providing us the GIMMS data set,
and the thousands Norwegian moose hunters, and
local moose managers for providing the moose data.
This project was funded by the Directorate for nature
management and the Research Council of Norway
(programs NORKLIMA and Changing Landscapes).
References
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Animal
Ecology, 75,
1110–1118
Albon, S.D. & Langvatn, R. (1992) Plant phenology and the
benefits of migration in a temperate ungulate. Oikos, 65,
502–513.
Andersen, R. & Sæther, B.-E. (1992) Functional response
during winter of a herbivore, the moose, in relation to age
and size. Ecology, 73, 542–550.
Beckerman, A., Benton, T.G., Ranta, E., Kaitala, V. &
Lundberg, P. (2002) Population dynamic consequences of
delayed life-history effects. Trends in Ecology and Evolution,
17, 263–269.
Bø, S. & Hjeljord, O. (1991) Do continental moose ranges
improve during cloudy summers? Canadian Journal of
Zoology, 69, 1875–1879.
Burnham, K.P. & Anderson, D.R. (2002) Model Selection and
Multimodel Inference. A Practical Information-Theoretic
Approach, 2nd edn. Springer, New York.
Cederlund, G.N., Sand, H.K.G. & Pehrson, Å. (1991) Body
mass dynamics of moose calves in relation to winter severity. Journal of Wildlife Management, 55, 675–681.
Chapin, F.S. III, Shaver, G.R., Giblin, A.E., Nadelhoffer, K.J.
& Laundre, J.A. (1995) Responses of arctic tundra to experimental and observed changes in climate. Ecology, 76, 694–
711.
Chatfield, C. (1989) The Analysis of Time Series. An Introduction, 4th edn. Chapman & Hall, London.
Clutton-Brock, T.H. & Albon, S.D. (1983) Climatic variation
and body weight of red deer. Journal of Wildlife Management,
47, 1197–1201.
Clutton-Brock, T.H. & Harvey, P.H. (1983) The functional
significance of variation in body size among mammals.
Advances in the Study of Mammalian Behavior, 7 (eds J.F.
Eisenberg & D.G. Kleiman), pp. 632–663. American Society of Mammalogists, Shippensburg.
Cook, J.G., Johnson, B.K., Cook, R.C., Riggs, R.A., Delcurto,
T., Bryant, L.D. & Irwin, L.L. (2004) Effects of summerautumn nutrition and parturition date on reproduction and
survival of elk. Wildlife Monographs, 155, 1–61.
Côte, S.D., Rooney, T.P., Tremblay, J.-P., Dussault, C. &
Waller, D.M. (2004) Ecological impacts of deer overabundance. Annual Review of Ecology and Systematics, 35, 113–
147.
Coulson, T., Catchpole, E.A., Albon, S.D., Morgan, B.J.T.,
Pemberton, J.M., Clutton-Brock, T.H., Crawley, M.J. &
Grenfell, B.T. (2001) Age, sex, density, winter weather, and
population crashes in Soay sheep. Science, 292, 1528–1531.
Deinum, B. (1984) Chemical composition and nutritive value
of herbage in relation to climate. The Impact of Climate on
Grass Production and Quality (eds H. Riley & A.O. Skjelvåg),
pp. 338–350. The Norwegian State Agricultural Research
Stations, Ås.
Ericsson, G., Ball, J.P. & Danell, K. (2002) Body mass of
moose calves along an altitudinal gradient. Journal of Wildlife Management, 66, 91–97.
Forchhammer, M.C., Clutton-Brock, T.H., Lindström, J. &
Albon, S.D. (2001) Climate and population density induce
long-term cohort variation in a northern ungulate. Journal
of Animal Ecology, 70, 721–729.
Gaillard, J.-M., Delorme, D., Boutin, J.-M., Van Laere, G. &
Boisaubert, B. (1996) Body mass of roe deer fawns during
winter in 2 contrasting populations. Journal of Wildlife
Management, 60, 29–36.
Gaillard, J.-M., Loison, A., Toïgo, C., Delorme, D. & Van
Laere, G. (2003) Cohort effects and deer population
dynamics. Ecoscience, 10, 412– 420.
Garel, M., Solberg, E.J.S., Sæther, B.-E., Herfindal, I. &
Høgda, K.-A. (2006) The length of growing season and
adult sex-ratio affect sexual size dimorphism in moose.
Ecology, 87, 745–758.
Hallett, T.B., Coulson, T., Pilkington, J.G., Clutton-Brock, T.H.,
Pemberton, J.M. & Grenfell, B.T. (2004) Why large-scale
climate indices seem to predict ecological processes better
than local weather. Nature, 430, 71–75.
Hjeljord, O. & Histøl, T. (1999) Range–body mass interactions
of a northern ungulate – a test of hypothesis. Oecologia,
119, 326–339.
Holt, R.D. & Gaines, M.S. (1992) Analysis of adaptation in
heterogeneous landscapes: implications for the evolution of
fundamental niches. Evolutionary Ecology, 6, 433–447.
Holt, R.D. & Gomulkiewicz, R. (1997) The evolution of
species’ niches: a population dynamic perspective. Case
Studies in Mathematical Modelling: Ecology, Physiology,
and Cell Biology (eds H.G. Othmer, F.R. Adler, M.A. Lewis
& J.C. Dalton), pp. 25 –50. Prentice Hall, New York.
Holt, R.D., Knight, T.M. & Barfield, M. (2004) Allee effects,
immigration, and the evolution of species’ niches. American
Naturalist, 163, 253–262.
Hurrell, J.W. (1995) Decadal trends in the North Atlantic
Oscillation: regional temperatures and precipitation. Science,
269, 676–679.
Hutchinson, G.E. (1957) Concluding remarks. Population
studies: Animal ecology and demography. Cold Spring
Harbor Symposia on Quantitative Biology, 22, 415–427.
Klein, D.R. (1970) Tundra ranges north of the boreal forest.
Journal of Range Management, 23, 8–14.
Langvatn, R. & Albon, S.D. (1986) Geographic clines in body
weight of Norwegian red deer: a novel explanation of Bergmann’s rule. Holarctic Ecology, 9, 285–293.
Langvatn, R., Albon, S.D., Burkey, T. & Clutton-Brock, T.H.
(1996) Climate, plant phenology and variation in age of first
reproduction in a temperate herbivore. Journal of Animal
Ecology, 65, 653–670.
Lenart, E.A., Bowyer, R.T., Ver Hoef, J. & Ruess, R.W. (2002)
Climate change and caribou: effects of summer weather on
forage. Canadian Journal of Zoology, 80, 664–678.
Lindström, J. (1999) Early development and fitness in birds
and mammals. Trends in Ecology and Evolution, 14, 343–
348.
Loison, A., Langvatn, R. & Solberg, E.J. (1999) Body mass
and winter mortality in red deer calves: disentangling sex
and climate effects. Ecography, 22, 20–30.
Myneni, R.B., Hall, F.G., Sellers, P.J. & Marshak, A.L. (1995)
The interpretation of spectral vegetation indexes. IEEE
Transactions on Geoscience and Remote Sensing, 33, 481–
486.
Mysterud, A., Langvatn, R., Yoccoz, N.G. & Stenseth, N.C.
(2001a) Plant phenology, migration and geographical variation
in body weight of a large herbivore: the effect of a variable
topography. Journal of Animal Ecology, 70, 915–923.
Mysterud, A., Stenseth, N.C., Yoccoz, N.G., Langvatn, R. &
Steinheim, G. (2001b) Nonlinear effects of large-scale climatic
variability on wild and domestic herbivores. Nature, 410,
1096–1099.
Pettorelli, N., Vik, J.O., Mysterud, A., Gaillard, J.-M.,
Tucker, C.J. & Stenseth, N.C. (2005a) Using the satellitederived NDVI to assess ecological responses to environmental change. Trends in Ecology and Evolution, 20,
503–510.
Pettorelli, N., Mysterud, A., Yoccoz, N.G., Langvatn, R. &
Stenseth, N.C. (2005b) Importance of climatological downscaling and plant phenology for red deer in heterogeneous
1118
I. Herfindal et al.
© 2006 The Authors.
Journal compilation
© 2006 British
Ecological Society,
Journal of Animal
Ecology, 75,
1110–1118
landscapes. Proceedings of the Royal Society of London.
Series B: Biology Sciences, 272, 2357–2364.
Portier, C., Festa-Bianchet, M., Gaillard, J.-M., Jorgenson, J.T.
& Yoccoz, N.G. (1998) Effects of density and weather on
survival of bighorn sheep lambs (Ovis canadensis). Journal
of Zoology, 245, 271–278.
Post, E. & Klein, D.R. (1999) Caribou calf production and
seasonal range quality during a population decline. Journal
of Wildlife Management, 63, 335–345.
Post, E. & Stenseth, N.C. (1999) Climatic variability, plant
phenology, and northern ungulates. Ecology, 80, 1322–
1339.
Reed, B.C., Brown, J.F., Darrel, V., Loveland, T.R., Merchant,
J.W. & Ohlen, D.O. (1994) Measuring phenological variability from satellite imagery. Journal of Vegetation Science,
5, 703 –714.
Reimers, E. (1984) Body composition and population regulation of Svalbard reindeer. Rangifer, 4, 16 –21.
Reimers, E., Klein, D.R. & Sørumgård, R. (1983) Calving
time, growth rate, and body size of Norwegian reindeer on
different ranges. Arctic and Alpine Research, 15, 107–118.
Sæther, B.-E. (1985) Annual variation in carcass weight of
Norwegian moose in relation to climate along a latitudinal
gradient. Journal of Wildlife Management, 49, 977–983.
Sæther, B.-E. (1997) Environmental stochasticity and population dynamics of large herbivores: a search for mechanisms.
Trends in Ecology and Evolution, 12, 143–149.
Sæther, B.-E. & Gravem, A.J. (1988) Annual variation in
winter body condition of Norwegian moose calves. Journal
of Wildlife Management, 52, 333–336.
Sæther, B.-E. & Haagenrud, H. (1985) Geographical variation
in body weight and sexual size-dimorphism of Norwegian
moose (Alces alces). Journal of Zoology, 206, 83–96.
Sæther, B.-E. & Heim, M. (1993) Ecological correlates of
individual variation in age at maturity in female moose
(Alces alces): the effects of environmental variability.
Journal of Animal Ecology, 62, 482–489.
Sæther, B.-E., Andersen, R., Hjeljord, O. & Heim, M. (1996)
Ecological correlates of regional variation in life history of
the moose Alces alces. Ecology, 77, 1493–1500.
Sæther, B.-E., Sutherland, W.J. & Engen, S. (2004) Climate
influences on avian population dynamics. Advances in
Ecological Research, 35, 185–209.
Skogland, T. (1983) The effects of density dependent resource
limitation on size of wild reindeer. Oecologia, 60, 156–168.
Skogland, T. (1985) The effects of density-dependent resource
limitations on the demography of wild reindeer. Journal of
Animal Ecology, 54, 359–374.
Sokal, R.R. & Rohlf, F.J. (1995) Biometry, 3rd edn. W.H.
Freeman, New York.
Solberg, E.J. & Sæther, B.-E. (1994) Male traits as life-history
variables: annual variation in body mass and antler size in
moose (Alces alces). Journal of Mammalogy, 75, 1069–1079.
Solberg, E.J., Sæther, B.-E., Strand, O. & Loison, A. (1999)
Dynamics of a harvested moose population in a variable
environment. Journal of Animal Ecology, 68, 186–204.
Solberg, E.J., Loison, A., Gaillard, J.-M. & Heim, M. (2004)
Lasting effects of conditions at birth on moose body mass.
Ecography, 27, 677–687.
Solberg, E.J., Rolandsen, C.M., Heim, M., Grøtan, V., Garel,
M., Sæther, B.-E., Nilsen, E.B., Austrheim, G. & Herfindal,
I. (2006) Elgen i Norge sett med jegerøyne – En analyse av
jaktmaterialet fra overvåkingsprogrammet for elg og det
samlede sett elg-materialet for perioden 1966–2004. NINA
Rapport, 125, 1–197.
Steinheim, G., Weladji, R.B., Skogan, T., Ådnøy, T., Skjelvåg,
A.O. & Holand, Ø. (2004) Climatic variability and effects
on ungulate body weight: the case of domestic sheep.
Annales Zoologici Fennici, 41, 525–538.
Stewart, K.M., Bowyer, R.T., Dick, B.L., Johnson, B.K. &
Kie, J.G. (2005) Density-dependent effects on physical
condition and reproduction in North American elk: an
experimental test. Oecologia, 143, 85–93.
Weladji, R.B. & Holand, Ø. (2003) Global climate change and
reindeer: effects of winter weather on the autumn weight
and growth of calves. Oecologia, 136, 317–323.
White, R.G. (1983) Foraging patterns and their multiplier
effects on productivity of northern ungulates. Oikos, 40,
377–384.
Zhou, L.M., Tucker, C.J., Kaufmann, R.K., Slayback, D.,
Shabanov, N.V. & Myneni, R.B. (2001) Variations in northern
vegetation activity inferred from satellite data of vegetation
index during 1981–99. Journal of Geophysical ResearchAtmospheres, 106, 20069–20083.
Received 18 November 2005; accepted 17 May 2006