CSIRO PUBLISHING
Wildlife Research, 2011, 38, 419–425
www.publish.csiro.au/journals/wr
Is the relationship between predator and prey abundances
related to climate for lynx and snowshoe hares?
Jim Hone A,D, Charles J. Krebs A,B and Mark O’Donoghue C
A
Institute for Applied Ecology, University of Canberra, Canberra, ACT 2601, Australia.
Department of Zoology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
C
Yukon Fish and Wildlife Branch, PO Box 310 Mayo, Yukon YOB 1MO, Canada.
D
Corresponding author. Email: [email protected]
B
Abstract
Context. Predator dynamics may be related to prey abundance and influenced by environmental effects, such as climate.
Predator–prey interactions may be represented by mechanistic models that comprise a deterministic skeleton with stochastic
climatic forcing.
Aims. The aim of this study was to evaluate the effects of climate on predator–prey dynamics. The lynx and snowshoe hare
predator–prey system in the Kluane region of the Yukon, Canada, is used as a case study. The specific hypothesis is that
climate influences the relationship between lynx and hare abundance.
Methods. We evaluate 10 linear relationships between predator and prey abundance and effects of climate. We use data on
lynx and snowshoe hare abundance over 21 years in the Yukon as the predator–prey system, and three alternative broad-scale
climate indices: the winter North Atlantic Oscillation (NAO), the Pacific North American (PNA) index and the North Pacific
index (NPI).
Key results. There was more support, as assessed by Akaike weights (wi = 0.600), evidence ratio (=4.73) and R2 (=0.77) for
a model of predator (lynx) and prior prey (hare) abundance with an effect of prior climate (winter NAO) when combined in a
multiplicative, rather than in an additive, manner. The results infer that climate changes the amplitude of the lynx cycle with
lower predator (lynx) abundance with positive values of winter NAO for a given hare density.
Conclusions. The study provides evidence that predator–prey dynamics are related to climate in an interactive manner.
The ecological mechanism for the interactive effect is not clear, and alternative hypotheses are proposed for future evaluation.
Implications. The study implies that changes in climate may alter predator–prey relationships.
Additional keywords: climate change, Lynx canadensis, North Atlantic Oscillation, population dynamics, predator–prey
models.
Introduction
A plethora of predator–prey models exist (May 1981; Bonsall
and Hassell 2007) and it is not obvious which model has most
empirical support for studying predator density. Predator
dynamics may be related to prey density and environmental
effects, such as climate, as well as competitors and pathogens.
Long-term monitoring can be used to assess trends in wildlife
including predator and prey abundance and can also be used to
evaluate ecological theory (Williams et al. 2002:681; Nichols and
Williams 2006).
Climate influences wildlife dynamics (Andrewartha and Birch
1954; Stenseth et al. 2002; Hallett et al. 2004). A broad-scale
measure of climate is the winter North Atlantic Oscillation (NAO;
see below for description) (Hurrell 1995; Ottersen et al. 2001).
Climate, measured as the NAO, has a strong influence on moose
(Alces alces) annual population growth rate in the presence of
canine parvovirus (CPV) in wolves (Canis lupus), an important
CSIRO 2011
predator of moose (Wilmers et al. 2006). In the absence of
CPV the effect of climate is much weaker. Hence, climate may
act directly on dynamics or may interact indirectly with wildlife
abundance, either through predators or prey. Post et al. (1999)
reported that an increase in winter snow correlated with wolves
killing more moose, reducing moose abundance and leading to
increased growth of balsam fir (Abies balsamea).
The NAO is a broad-scale climate index related to air pressure
differences between Iceland and Portugal that has continentalwide effects (Hurrell 1995; Hallett et al. 2004). The NAO is
significantly related to weather across North America including
in the southern Yukon (Stenseth et al. 2004; fig. 1b). The winter
NAO is considered more related to surface temperatures in
Canada than the Pacific North American (PNA) index, another
broad-scale climate index, and is likely the source of a common
structure of lynx dynamics across Canada (Stenseth et al. 1999).
The PNA index reflects air pressure differences across North
10.1071/WR11009
1035-3712/11/050419
420
Wildlife Research
J. Hone et al.
70
(a)
60
50
40
30
20
10
0
0
climate influences the relationship between lynx and prior hare
abundance. We evaluated this by assessing support for models of
lynx abundance with, and without, effects of climate included.
We showed that there is most support for models of the
relationship between lynx and hare abundances that include an
interactive effect of prior winter climate and prior hares.
Materials and methods
Models
There are several ways effects of prey, predators and climate
can be represented mathematically in ecological models. These
are evaluated here as alternative hypotheses in the sense of
Chamberlin (1965). The models evaluated represent
combinations of linear effects of prey and climate on lynx
density. The ecological models formulated here are
mechanistic (Sibly and Hone 2002) models that comprise a
deterministic skeleton with stochastic climatic forcing, in the
sense of Coulson et al. (2004), and are non-demographic, and
non-spatial models. Intraguild predation (Polis et al. 1989), for
example wolves and wolverines (Gulo gulo) killing lynx
(O’Donoghue 1997), is not included in the models to restrict
the size of the analysis. The number of models was small as
recommended (Anderson 2008). Ecological models of annual
lynx population growth rate (r) were examined previously
(Hone et al. 2007), so are not evaluated here.
Model 1 assumes a linear relationship (Fig. 1a) between lynx
abundance (Lt) and hare abundance in the previous year (Ht–1) as
reported by Brand et al. (1976) and O’Donoghue et al. (1997).
The model is:
Lt ¼ a þ bHt1
0.5
1.0
1.5
2.0
2.5
3.0
70 (c)
60
50
40
30
20
10
0
0
Low W
High W
0.5
1.0
Low W
High W
0.5
1.0
1.5
Prey density
1.5
2.0
2.5
3.0
2.5
3.0
Prey density
Predator density
Predator density
Prey density
70
(b)
60
50
40
30
20
10
0
0
ð1Þ
The intercept (a) can be greater than zero if lynx have
alternative food, so even if hares become extinct (H = 0), the
lynx could survive by eating the alternative food. Model 1 is
Predator density
Predator density
America (Trenberth and Hurrell 1994). Another broad-scale
climate index in North America is the North Pacific index
(NPI) (Trenberth and Hurrell 1994). The NPI is a measure of
sea level air pressure in the North Pacific Ocean between
November and March (Trenberth and Hurrell 1994). The
temperature across large areas of North America is correlated
with the NPI (Trenberth and Hurrell 1994; fig. 10).
Lynx (Lynx canadensis) populations cycle over 8 to 10 years
across northern Canada and have done so apparently for over
200 years (Elton and Nicholson 1942). The main prey of lynx is
the snowshoe hare (Lepus americanus) (O’Donoghue et al. 1997,
1998; Krebs et al. 2001). The lynx–hare system is one of the
classic predator–prey interactions described in many ecology
textbooks. Lynx abundance is positively related to the abundance
of snowshoe hares (Brand et al. 1976; Slough and Mowat 1996;
O’Donoghue et al. 1997). Models of the lynx–hare system (for
example, Trostel et al. 1987; Akcakaya 1992; Royama 1992;
Stenseth et al. 1997; Tyson et al. 2010) focus on predator–prey
relationships in various ways, but do not examine effects of
climate on the relationship between lynx and hare abundance.
Climate may be important in lynx dynamics. Lynx trapping
records in parts of Canada are positively correlated with minimum
temperatures in the winter two years previous (Moran 1953) and
hare survival over winter is negatively related to snow (Watt
1973; fig. 5.9) in Minnesota. In the Yukon, predation is the main
cause of mortality of juvenile and adult hares (O’Donoghue 1994;
Krebs et al. 2001). Climate, by changing snow conditions, may
influence the lynx functional response. For example, hard-packed
snow is reported to be associated with a higher kill rate of hares by
lynx than soft snow (Stenseth et al. 2004). The higher kill rate
could generate higher lynx abundance as a numerical response.
The aim of this study was to evaluate the effects of climate
on predator–prey dynamics. The lynx and snowshoe hare
predator–prey system in the Kluane region of the Yukon was
used as a case study. The specific hypothesis was that prior winter
2.0
2.5
3.0
70
(d )
60
50
40
30
20
10
0
0
Low W
High W
0.5
1.0
1.5
2.0
Prey density
Fig. 1. (a) The hypothesised relationship between predator (lynx) density index (Lt) and prior prey (snowshoe hare) density
(Ht–1) as described in model 1, and derived relationships between predator density index and prey density with (b) an interactive
effect of climate (Wt–1) as described in model 2, (c) an additive effect of climate as described in model 3 and (d) interactive and
additive effects of climate as described in model 4. Numerical values are hypothetical.
Lynx, hares and climate
Wildlife Research
consistent with the results of O’Donoghue et al. (1997) if b > 0.
A threshold hare density of 0.5 hares per ha for lynx population
persistence has been suggested (Ruggiero et al. 2000). The
existence of such a threshold would generate an intercept (a)
below 0 on the y-axis (lynx abundance), which would correspond
to an intercept to the right of the origin on the x-axis
(hare abundance). In model 1, for example, lynx in the winter
of 1988–89 (Lt) were related to hares in autumn (August or
September) of 1987 (Ht–1). Hence, for example, high hare
abundance in year t–1 (1987) could result in high lynx
survival the next winter (1987–88) and high lynx reproductive
success the next spring (1988), leading to high lynx abundance in
the winter of year t (1988–89). The timing reflects that used in
past analyses (O’Donoghue et al. 1997). Preliminary analysis
evaluated whether there was evidence of a curved relationship
between lynx and hares. The exponent (c) of a power relationship,
Lt = a + bHt–1c, had a 95% CI that included 1.0, implying support
for a linear (c = 1) relationship between lynx and hare density.
Hence, only linear models are evaluated here.
The relationship between lynx and prior hares in Equation 1
could be influenced by climate in several ways. The effect may be
on the slope (b), the intercept (a) or both. Such effects could occur
on hare or lynx survival from year t–1 to year t maybe relating to
predator efficiency and snow conditions, and effects may also
occur through lynx fecundity relating also to predator foraging
efficiency.
If the slope (b) of the relationship between lynx and hares in
model 1 is assumed to be linearly related to climate (Wt–1), such as
b = m + dW, then after substitution, rearrangement and given that
m = b when Wt–1 = 0, it can be shown that:
Lt ¼ a þ bHt1 þ dWt1 Ht1
ð2Þ
which shows an interaction of hares and climate (Fig. 1b). This is
model 2, which is analogous to the hypothesis of Stenseth et al.
(2004), which included an interaction term for effects of snow
hardness and hare density in the lynx functional response. Model
2 reduces to model 1 if the effect of climate is zero (Wt–1 = 0), or
d = 0 when there is no effect of climate. In this model, for example,
lynx in the winter of 1988–89 (Lt) were related to hares in autumn
(August) of 1987 (Ht–1) and climate in the winter of 1987–88
(Wt–1).
If the intercept (a) of the relationship between lynx and hares in
model 1 is assumed to be linearly related to climate (W), such as
421
a = h + f Wt–1, then after substitution, rearrangement and given
that h = a when Wt–1 = 0, it can be shown that:
Lt ¼ a þ bHt1 þ f Wt1
ð3Þ
which is an additive model of effects of hares and climate (Fig. 1c)
and is model 3. Model 3 is hence different from the interaction of
climate and hares in the results of Stenseth et al. (2004). Model 3
reduces to model 1 if Wt–1 = 0, as can occur with a climate index
such as the NAO, or f = 0 when there is no effect of climate.
If the intercept (a) of the relationship between lynx and hares
in model 1 is assumed to be linearly related to climate (W), such
as a = h + f Wt–1, and if the slope (b) of the relationship between
lynx and hares in model 1 is assumed to be linearly related to
climate (Wt–1), such as b = m + dWt–1, then after substitution and
rearrangement it can be shown that:
Lt ¼ a þ bHt1 þ f Wt1 þ dWt1 Ht1
ð4Þ
which is model 4 (Fig. 1d). The model has an additive effect of
climate and an interaction of climate and hares.
The effect of climate in models 2, 3 and 4 can be evaluated
using one or more measures of climate. In this study, three broadscale measures of climate were evaluated, the winter North
Atlantic Oscillation (NAO = A), the Pacific North American
(PNA = P) and the North Pacific index (NPI = N). These
alternative climate measures were investigated to determine
whether predator (lynx) dynamics in the Yukon in western
Canada were more related to climate in the North Pacific
region or to the dominant north Atlantic influence as
represented by the winter NAO. Hence, there are three models
(models 2, 3 and 4) for each of the three climate measures (A, P
and N), and one model with no climate component (model 1), for
a total of 10 models (Table 1).
Data
The study site was the boreal white spruce (Picea glauca) forests
near Kluane Lake in the Yukon, north-western Canada (Krebs
et al. 2001). The lynx density index was estimated by snow track
counts between October and March each year along 25 km of the
old Alaska Highway, and hare density on two trapping grids by
the jackknife estimator in mark–recapture analysis (Krebs 1999)
in August or September each year (Hone et al. 2007). Lynx
snow track counts were estimated using the ratio (Jolly) estimate
Table 1. The residual sums of squares (RSS), parameters (K), Akaike information criterion corrected for sample size (AICc), Akaike weights (vi),
coefficients of determination (R2) of models of predator (lynx, Lt) and prey (hares, Ht–1) abundance and climate (Wt–1)
Climate was measured as: the winter NAO, A; the Pacific North American, P; and North Pacific Index, N; the model with the highest Akaike weight is shown in bold
Model
Equation
1
2A
3A
4A
2P
3P
4P
2N
3N
4N
Lt = a + bHt–1
Lt = a + bHt–1 + dAt–1 Ht–1
Lt = a + bHt–1 + fAt–1
Lt = a + bHt–1 + fAt–1 + dAt–1 Ht–1
Lt = a + bHt–1 + d Pt–1 Ht–1
Lt = a + bHt–1 + f Pt–1
Lt = a + bHt–1 + f Pt–1 + dPt–1 Ht–1
Lt = a + bHt–1 + d Nt–1 Ht–1
Lt = a + bHt–1 + f Nt–1
Lt = a + bHt–1 + f Nt–1 + dNt–1 Ht–1
RSS
K
AICc
wi
R2
4005.99
2464.80
3045.86
2459.98
3380.61
3869.52
3155.78
2858.00
3580.93
2717.46
3
4
4
5
4
4
5
4
4
5
117.683
110.572
115.017
114.031
117.207
120.044
119.262
113.680
118.416
116.122
0.017
0.600
0.065
0.107
0.022
0.005
0.008
0.127
0.012
0.038
0.62
0.77
0.71
0.77
0.68
0.63
0.70
0.73
0.66
0.74
Wildlife Research
J. Hone et al.
120
verified 5 October 2011) were used as one broad-scale measure of
climate (Hallett et al. 2004). The NPI was also accessed from the
Jim Hurrell website. The PNA index was averaged here for the
winter months December to March. Pacific North American index
data were accessed from the USA National Weather Service
website (http://www.cpc.noaa.gov, verified 5 October 2011).
Relative support for all models was evaluated using the Akaike
information criterion (AIC) corrected for sample size (AICc),
Akaike weights (wi) assessed weight of evidence and evidence
ratios and coefficients of determination (R2) were also estimated
(Anderson 2008) using SAS (Freund and Little 1986). The
regression analyses assume normally distributed errors and
inspection of residuals supported the assumption.
Results
Lynx (Fig. 2a) and hare (Fig. 2b) densities cycled over years.
Climate varied over years, as measured by winter NAO (Fig. 2c),
the PNA index (Fig. 2d) and the NPI (Fig. 2e). There was
no simple relationship between lynx abundance and winter
(a)
Snowshoe hares /ha
100
80
60
40
20
0
19
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
(b)
1.0 (d )
0.8
0.6
0.4
0.2
0
1986 1988
–0.2
–0.4
–0.6
–0.8
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
2
19
94
19
96
19
98
20
00
20
02
20
04
20
06
20
08
0
19
9
8
19
9
19
8
6
14 (e)
12
10
8
6
4
2
0
19
8
Winter NAO
North Pacific index
6
(c)
5
4
3
2
1
0
1986 1988
–1
–2
–3
–4
–5
3.5
3.25
3.0
2.75
2.5
2.25
2.0
1.75
1.5
1.25
1.0
0.75
0.5
0.25
0
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
20
06
20
08
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02
20
04
20
06
20
08
Lynx tracks (Jolly)/ track night/
100 km (± 95% CI)
(Krebs 1999) for unequal lengths of transect sample segments.
Data were collected annually between 1987–88 and 2008–09
inclusive. Track counts are highly correlated with actual lynx
density (r = 0.82, d.f. = 7, P < 0.01) (Hone et al. 2007); the latter
estimated by intensive snow tracking and radio-tracking studies
(O’Donoghue 1997). To evaluate whether the track counts were
an artefact of weather conditions rather than directly related to
actual lynx density, a partial correlation analysis was used, with
the effects of actual lynx density removed. A significant effect
would suggest track counts were influenced by climate and a nonsignificant result would imply no effect, or no detectable effect of
climate on track counts. Partial correlation analysis showed that
lynx track counts over nine years were not significantly correlated
with the winter NAO (r = –0.32, d.f. = 9–3 = 6, P = 0.43), the PNA
index (r = 0.33, d.f. = 6, P = 0.45) or the NPI (r = –0.52, d.f. = 6,
P = 0.18) after adjusting for effects of actual lynx density. Hence,
there was no evidence that the lynx density index (tracks in the
snow) was an artefact of weather conditions. Winter (December
to March inclusive) station-based NAO data, accessed from
the Jim Hurrell website (http://www.cgd.ucar.edu/cas/jhurrell,
Pacific North American index
422
Fig. 2. Trends in mean abundance of (a) lynx and (b) snowshoe hares in the Kluane region of the Yukon, (c) in the winter North
Atlantic Oscillation, (d) Pacific North American index and (e) North Pacific index. Error bars are 95% CIs. Years for lynx refer to the
start of winter (1988 = 1988–89) and for hares refer to the autumn census of the year shown.
Lynx, hares and climate
Wildlife Research
NAO (R2 = 0.03, n = 21), the PNA (R2 = 0.02, n = 21) or NPI
(R2 = 0.004, n = 21).
The hypothesis (model 2A) with most support (w2 = 0.60,
R2 = 0.77) included a positive effect of prey (hare) density, and an
interaction of climate (measured as the winter NAO) and prey
density (Table 1). The interaction term was negative when the
NAO value was positive corresponding to lower lynx density,
and positive when the NAO value was negative, corresponding
to higher lynx density. The evidence ratio of the best (2A) to
the second best (2N) model was 4.73. The best fitting model
reconstructed the main features of the lynx cycles (Fig. 3) though
the first two peaks in lynx abundance lagged one year. The
equation for the best model for the years 1988–89 to 2008–09
was:
Lt ¼ 0:50 þ 30:51 Ht1 3:33 Wt1 Ht1
where Lt is mean lynx tracks per track night per 100 km of transect
at time t; Ht–1 is hare density per hectare at time t–1; and Wt–1 is the
winter NAO index at time t–1 (Table 2).
The 95% CI of the estimated intercept (a) in all models (i.e. in
10 of 10 analyses) included 0.0, implying no evidence of a
threshold hare density for lynx to occur. The parameter
estimates of all models estimated over the full dataset are
shown in Table 2.
The effect of winter NAO on the positive relationship between
lynx and previous hare abundance, as estimated by model 2A, is
shown in Fig. 4. The results infer that positive values of the winter
NAO correspond to lower lynx abundance, and negative winter
NAO values correspond to higher lynx abundance for a given hare
density (Fig. 4). This result is preliminary, as most winter NAO
values were positive (Fig. 2c) during the period of study.
The models (2A, 4A, 2P, 4P, 2N and 4N) with an interaction of
climate and hares account for nearly all the evidence as assessed
by Akaike weights (Swi = 0.9007; Table 1). Model 1 assuming
effects of hares only had very little relative support (w1 = 0.0172;
Table 1). Models using the PNA index and the NPI had little
relative support (Swi = 0.2112).
423
Discussion
Some predator–prey relationships are known to be related to
climate, such as reported for wolves (Post et al. 1999; Wilmers
et al. 2006), and the analysis here provides evidence for such
effects for lynx and snowshoe hares. The results support the
positive relationship between lynx and prior hare abundance
reported previously (Brand et al. 1976; O’Donoghue et al.
1997), but the new result here provides evidence that the
relationship is related in an interactive manner to climate,
especially the winter NAO. The present study extends the
Table 2. Details of model parameter (a, b, f and d) estimates (SE) for
predator–prey (lynx–hare) models for the years 1988–89 to 2008–09
inclusive (n = 21)
na, not applicable. The model (2A) with the highest Akaike weight is shown in
bold. Parameter a is the intercept, b is the coefficient of the effect of hares
(Ht–1), f is the coefficient of the effect of climate (Wt–1) and d is the coefficient
of the effect of the interaction of climate and hares (Wt–1 Ht–1)
Model
1
2A
3A
4A
2P
3P
4P
2N
3N
4N
a
b
f
d
3.258
(5.057)
0.502
(4.158)
5.224
(4.605)
0.036
(4.979)
1.469
(4.873)
0.468
(6.192)
6.138
(6.440)
1.376
(4.444)
25.753
(16.155)
–21.729
(25.041)
21.748
(3.894)
30.510
(4.083)
24.237
(3.642)
30.906
(4.725)
22.062
(3.679)
22.814
(4.154)
19.966
(4.124)
50.893
(11.354)
23.603
(3.990)
63.274
(17.439)
na
na
na
–3.333
(0.994)
na
–3.374
(1.417)
0.419
(2.296)
na
6.052
(7.596)
–13.399
(12.175)
na
–2.793
(1.911)
2.755
(2.938)
–3.601
(1.790)
8.680
(4.757)
na
15.994
(8.156)
–2.979
(1.108)
na
–4.431
(1.907)
110
Lynx tracks (Jolly)/ track night/
100 km in year t
Lynx tracks (Jolly)/ track night/
100 km (± 95% CI)
100
90
Observed
80
70
60
Reconstructed
50
40
30
20
10
100
NAO = –1
80
NAO = 1
60
NAO = 3
40
20
0
0
0.25 0.5 0.75 1.0 1.25 1.5 1.75 2.0 2.25 2.5 2.75 3.0
Hares/ha in year t–1
20
08
20
06
20
04
20
02
20
00
98
19
19
96
19
94
19
92
19
90
19
88
0
19
86
120
Fig. 3. Observed (solid circles and solid line) lynx abundance index (snow
tracks, 95% CI) and reconstructed (open triangles and dashed line) lynx
abundance index using the best fitting model (model 2A). Winter 1988–89 is
shown as 1988 and so on.
Fig. 4. The fitted relationships, estimated by model 2A, between lynx
abundance in year t and hare density in year t–1 for three values of the
winter North Atlantic Oscillation (NAO). As the winter NAO value increases
from –1 to 3 the predicted lynx abundance is lower for a given hare density.
The observed data are shown as solid circles for values of the NAO greater than
1.0, and as open circles for values of the NAO less than 1.0.
424
Wildlife Research
previous results of effects of temperature (Moran 1953; Watt
1973) and the models of lynx–hare relationships (Trostel et al.
1987; Akcakaya 1992; Royama 1992; Stenseth et al. 1997;
Tyson et al. 2010) by providing evidence of a hare–climate
interaction and its relationship with lynx abundance. Models
with a hare–climate interaction have nearly all the support in
the AICc analysis.
We suggest that climate may alter the effects of hare
abundance on lynx abundance, though we do not know the
mechanism. The results here infer that positive values of NAO
broadly correspond to lower lynx abundance and negative
values of NAO broadly correspond to higher lynx abundance,
for a given hare density. A higher kill rate of hares by lynx with
hard-packed snow, as reported by the analysis of Stenseth et al.
(2004), would be expected to correspond to higher lynx
abundance, in contrast to our findings, unless a higher kill rate
in year t–1 results in fewer hares in year t–1 and hence fewer lynx
in year t. Analysis in the present study of lynx sinking depth
(the inverse of snow hardness) and winter NAO at Kluane
(C. J. Krebs and M. O’Donoghue, unpubl. data) shows a nonsignificant correlation (r = –0.37, d.f. = 6, P = 0.37), though
inferences are limited by the small sample size. If climate
in year t–1 modifies the effects of hares in year t–1 on lynx
in year t, then possible mechanisms are through effects on lynx or
hare survival (in year t–1 to year t), lynx fecundity (in year t), or
both. Additional research data are required to differentiate
between these hypotheses.
The relationship between the local weather and the broad-scale
winter NAO weather index was unclear. During the period of
study, the NAO was weakly correlated with mean minimum
temperature (C) (r = –0.35, d.f. = 13, P = 0.20), mean maximum
temperature (r = –0.38, d.f. = 13, P = 0.17), snow depth (cm)
(r = 0.38, d.f. = 13, P = 0.16) and extreme maximum
temperature (r = 0.32, d.f. = 13, P = 0.25), weather data for
Whitehorse, ~150 km east of Kluane Lake, during December
to March inclusive. The weather data were accessed from the
Environment Canada website (http://www.climate.weather
office.ec.gc.ca, verified 5 October 2011). The low correlations
of the winter NAO with local weather conditions reflect the
broad-scale nature of the NAO index and have been reported
elsewhere (Stenseth et al. 2002, 2003; Stenseth and Mysterud
2005). The analyses provide more support for an association of
weather as measured by the winter NAO than the two North
Pacific climate indices (PNA and NPI).
Lynx dynamics may alter if climate change causes more
negative or more positive values of NAO. From our analysis, a
prolonged sequence of positive NAO values is predicted to
correspond to lower lynx abundance (Fig. 4) and dampen the
lynx cycle oscillations. Recently, NAO values have been mainly
positive (Fig. 2c, and Hurrell 1995) but if that changes to a
negative phase as occurred in the 1960s it may lead to higher lynx
density (Fig. 4) than shown in the present analysis. If climate
change causes a change in the frequency of extreme weather,
then winters with very high or very low NAO values would
be expected to generate more pronounced changes in lynx
abundance, i.e. the amplitude of the lynx cycles would
increase, although this would depend on the actual year-toyear sequence of high and low NAO values. Climate-induced
reductions in lynx abundance may generate increases in hare
J. Hone et al.
abundance, given experimental evidence of top-down effects of
lynx on hares (Krebs et al. 2001). These are hypotheses that could
be evaluated by future monitoring, in the sense of Nichols and
Williams (2006). With more pronounced troughs in abundance
there is a higher probability of lynx abundance going to such low
values that local extinction may occur. However, local extinction
seems unlikely, as solution of the relationships (Figs 3, 4) shows
that across all hare densities mean lynx abundance is positive.
Also, lynx are quite mobile (Krebs et al. 2001) so lynx
populations may be re-established by immigration. The lynx is
classified as a threatened species in the USA (Ruggiero et al.
2000) and climate change may influence that conservation status.
Lynx population persistence has been suggested to require
long-term hare density of at least 0.5 (Ruggiero et al. 2000) or 1.5
(Murray et al. 2008) hares per ha. The existence of such a
threshold would have generated an intercept to the right of the
origin on the x-axis (hare abundance) in Fig. 4. All estimated
intercepts were not different from 0 (the origin). The results here
show that lynx populations can exist for short periods in the
Kluane region even when hare density is low, such as below 0.5
hares per ha (Figs 2b, 4). Alternative prey of lynx include red
squirrels (Tamiasciurus hudsonicus) (O’Donoghue et al. 1998)
and some individual lynx can specialise on red squirrels when
hares are very scarce.
This study provides evidence that predator–prey dynamics
may be related to a broad-scale climate index in an interactive
manner. The study demonstrates how monitoring data can be
used to evaluate ecological theory. We encourage the use of
monitoring data to evaluate such ecological theory, including
other predator–prey systems.
Acknowledgements
We thank Elizabeth Hofer, Peter Upton, Alice Kenney and the lynx snow
tracking team at Kluane Lake. Funds were provided by the Natural Sciences
and Engineering Research Council of Canada and the Australian Academy of
Science. Animal handling procedures were approved by the Animal Care
Committee of the University of British Columbia. We thank A. R. E. Sinclair,
S. Boutin, M. Boyce, D. R. Anderson, D. Pedersen and J. Nichols for
discussions and comments. The University of Canberra is thanked for support.
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Manuscript received 17 January 2011, accepted 9 August 2011
http://www.publish.csiro.au/journals/wr