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ORIGINAL INVESTIGATION

Predicting animal performance through climatic and plant phenology

variables: The case of an omnivore hibernating species in Finland

Claudia Melisa,�, Ivar Herfindala,c, Kaarina Kauhalab, Reidar Andersenc,Kjell-Arild Høgdad

aCentre for Conservation Biology, Department of Biology, Norwegian University of Science and Technology, Realfagbygget,

N-7491 Trondheim, NorwaybFinnish Game and Fisheries Research Institute, Itainen Pitkakatu 3 A, FI-20520 Turku, FinlandcMuseum of Natural History and Archaeology, NTNU, N-7491 Trondheim, NorwaydNorut Information Technology AS, N-9291, Tromsø, Norway

Received 15 August 2008; accepted 17 December 2008

Abstract

Annual variation in the environment is expected to influence individual performance, e.g. measured as bodycondition, such as body mass or fat deposition, through its direct or indirect effects on food abundance andavailability. Such environmental variation is traditionally measured by climatic observation, but recently, measures ofenvironmental phenology obtained from satellite images have been successfully used. We examined the performance ofclimatic and plant phenology variables in explaining body condition of an invasive omnivore species: the raccoon dogNyctereutes procyonoides. We collected data on fat deposition of juveniles in southern Finland from the end of June tothe beginning of November. A four-parametric logistic model was fitted separately for each province to the data bynon-linear regression procedure and the residuals were compared to the expected average as measure of individualperformance. These values were then analysed with respect to the environmental variables. Climatic variablesdescribing spring conditions performed better than plant phenology variables in explaining the variation in fatdeposition. Harsh spring conditions negatively affected the amount of fat deposed during the growing season. Plantphenology variables, effective in explaining life history traits in herbivores, might not reflect variation in foodabundance and quality for omnivore species. We propose that in Europe raccoon dogs will benefit from climatewarming, because of a longer growing season, but increased spring precipitation in the form of snow at higher latitudesmight compensate for the effect of greater primary productivity and outline the border of their expansion towardsharsher environments.r 2009 Deutsche Gesellschaft fur Saugetierkunde. Published by Elsevier GmbH. All rights reserved.

Keywords: Fat reserves; Nonindigenous species; Normalized difference vegetation index (NDVI); Nyctereutes procyonoides;

Raccoon dog

Introduction

Invasion by nonindigenous species has been recog-nized as one of the main threats to global biodiversity,second only to habitat loss and fragmentation (Walker

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1616-5047/$ - see front matter r 2009 Deutsche Gesellschaft fur Saugetierkunde. Published by Elsevier GmbH. All rights reserved.

doi:10.1016/j.mambio.2008.12.001 Mamm. biol. 75 (2010) 151–159

�Corresponding author. Tel.: +4793859351.

E-mail addresses: claudia.melis@bio.ntnu.no,

cozyredwolf@hotmail.com (C. Melis).

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and Steffen 1997). The impact of alien species is a majorconcern throughout the world and their managementand control is likely to become a main challenge forconservation biologists in the next decades (Allendorfand Lundquist 2003). For this reason it is important toidentify good predictors describing the environmentalfactors, which affect the expansion of these speciesthrough recruitment and dispersion rates.

The raccoon dog Nyctereutes procyonoides is anopportunistic generalist omnivore, and the only canidwhich in boreal environments adopts hibernation (albeithibernation characterized by only shallow reductions inbody temperature and metabolism similar to bears andother winter inactive carnivores) (e.g. Kauhala 1993;Mustonen et al. 2007). Originally raccoon dogs weredistributed only in eastern Asia, but after introductionto the wild by Russians in European parts of the formerSoviet Union during the first half of the 20th century,they became widespread both in central and in northernEurope (Helle and Kauhala 1991). One of the mainreasons behind their success is the high rate ofrecruitment, which is probably due to a combinationof ecological adaptations such as hibernation (in harshclimate), monogamy and omnivory (Helle and Kauhala1995; Kauhala 1996).

Many mammals adopt hibernation as winteringstrategy, and this adaptation has generally beenconsidered a response to high seasonality in environ-mental productivity (Swan 1974; Boyce 1979). Hiberna-tion is defined as a prolonged state of regulatedhypothermia, during which the animals decrease theirmetabolism, gradually using up their body fat reserves(e.g. Geiser 1988). For mammals in boreal and arcticregions, seasonal energetic bottlenecks typically occurduring winter, when thermoregulatory requirementsincrease and food resources are scarce (e.g. Swan 1974;Boyce 1979; Humphries et al. 2004). Survival ofhibernating mammals throughout the winter dependsprimarily on their energy reserves at the onset of wintersleep, the rate at which the energy stored is depletedduring winter, and the length of the winter (Humphrieset al. 2002). Accordingly, both climatic and energeticconstraints operate as limiting mechanisms to thegeographical distribution of hibernating mammals(Humphries et al. 2004). However, temporal variationin the environment can also influence populationdynamics, e.g. through survival and recruitment. Parti-cularly, factors influencing body condition (fat reserves)at the onset of the hibernating period, and the durationof this period are expected to be crucial (Humphrieset al. 2002).

The reproductive output of the raccoon dog isinfluenced by spatial variation in climate and foodavailability. For example, the abundance of food inspring (voles) and late summer (berries) influenced thebody condition of young (measured in terms of fat

reserves) and therefore their sexual maturation (Kauhala1993). On the other hand, harsh spring conditions,indicated by deep snow at the end of March, delayed thetime of ovulation of females, thus shortening the periodof growth and fat deposition of the young (Helle andKauhala 1991, 1995).

Meteorological observations, such as temperature,snow depth and snow cover duration, have been widelyused as a proxy for harsh climatic conditions andproductivity of the environment in ecological studies ofendotherms (e.g. Jedrzejewski et al. 2002; Anderson andJetz 2005; Melis et al. 2006). Recently, animal perfor-mance has successfully been linked with remote sensedindices of vegetation like the normalized differencevegetation index (NDVI) and fraction of photosyntheticabsorbed radiation (FPAR), obtained from satelliteimages (reviewed in Pettorelli et al. 2005). These indicescorrelate closely with vegetation biomass and netprimary production (e.g. Myneni et al. 1995, 2002),and are expected to provide a more direct measure of theenvironmental variation, which the individuals experi-ence. For example, with time series of NDVI it ispossible to describe the annual variation in photosyn-thetic activity, i.e. plant phenology. Such variables havesuccessfully been used to explain variation in life historytraits in herbivores (Garel et al. 2006; Herfindal et al.2006a, b; Pettorelli et al. 2006), and to relate variation inhome range size in carnivores to habitat productivity(Herfindal et al. 2005; Nilsen et al. 2005). If correlationbetween plant phenology and body condition wasobserved in carnivores, e.g. because vegetation produc-tivity affects them indirectly through prey abundance,this correlation should be even stronger in a trueomnivore, like the raccoon dog, whose diet consists ofvegetable matter for a considerable proportion (Kauhalaet al. 1993, 1998). However, to our knowledge, remotesensed data on environmental productivity has neverbeen related to body condition in omnivores. Moreover,northern temperate and arctic mammals need toaccumulate fat reserves during a brief period of highfood abundance, whether they hibernate or not. There-fore, they are expected to depend even more on theproductivity of habitat during the favourable season.This can influence the population growth rate throughsurvival and recruitment. In raccoon dogs more than40% of the annual reproductive output is contributed byyearling females (Helle and Kauhala 1995). Accord-ingly, factors that affect body condition of the youngestage class have the potential to greatly influencerecruitment the subsequent year, since recruitment isrelated also to the body condition of the mother (Helleand Kauhala 1995).

In this paper we investigated how environmentalvariables describing environmental conditions duringspring and summer perform in explaining body condi-tion in raccoon dogs. Particularly, we compared the

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performance of climate observations against plantphenology obtained from annual curves of NDVI. Weexpect that annual variation in both climate and plantphenology will influence the pattern of fat deposition injuvenile raccoon dogs, through their direct or indirectinfluence on food abundance and availability, and willbe strong predictors of body condition in summer.

Methods

Data on fat deposition of juvenile raccoon dogs werecollected in Finland from 1987 to 1997 and in 1999 inthe province of Hame (751 carcasses, Fig. 1) and from1987 to 1990 in the province of Kymi (144 carcasses,Fig. 1). The fat reserves were measured by weighing tothe nearest 0.1 g the fat tissue located around thekidneys and dorsally in the abdominal cavity of animalsculled between the 29 June and the 1 November (seeKauhala 1993 for more details). Since there are nosexual differences in body size and in the amount of fatreserves in juvenile raccoon dogs (Kauhala 1993), wepooled data for males and females.

In order to measure the individual deviation in fatdeposit from what was expected, we extracted residuals

from a non-linear regression separately for eachprovince. This was done because the individual observa-tions were sampled throughout the summer andautumn, and any deviation from the expected relation-ship between date and fat deposit would indicate thatthe individuals have better or poorer condition thanexpected. Several parametric models could predict theshape of a growth curve (France et al. 1996). The fatdeposition is expected to increase slowly in the startbecause of physical constraints in juveniles to store fat.In the end of the growth period, the fat deposition isexpected to flatten out, because there is a limit on howmuch fat it is possible to store. Accordingly, we chose tofit a curve of the logistic family (France et al. 1996). Weused non-linear least squares (nls) regressions with self-start functions to estimate the parameters that explainthe shape of the curve. Thus, we fitted the fatcomposition index (Y) as a function of time (t) to astandard logistic model (SL) with three parameters:

Y ¼ a.ð1þ eðb�tÞ=gÞ (1)

with a indicating the right side asymptote of the fatvalue, b the date at the inflection point, and g a numericscale parameter on the input axis. In addition, we fitteda four-parametric logistic model (FPL):

Y ¼ ðdþ ða� dÞÞ.ð1þ eðb�tÞ=gÞ (2)

with notation as above and d representing the left sideasymptote of fat value. We compared these modelsregarding the ability to fit the data by their residualdeviance and AIC values. For details regarding nls andthe self-start functions, see Pinheiro and Bates (2002)and Venables and Ripley (2002). In order to reduceheteroscedasticity, we ln-transformed the fat values(Y in Eqs. (1) and (2)) before fitting the nls. The nlswas fitted separately for Hame and Kymi.

We used both climatic variables and satellite-derivedindices of plant phenology to explain the variation in fatdeposition. Climatic variables were obtained from theFinnish Meteorological Institute (Table 1). These werechosen based on to their biological meaning accordingto literature and included duration of snow cover higherthan 30 cm, mean snow depth in March, meantemperature in March, and mean temperature and totalprecipitation during early summer (May–June). How-ever, snow cover duration was highly correlated withsnow depth (Table 2) and was therefore excluded fromthe analyses (e.g. Graham 2003).

We used variables describing the plant phenologyextracted from the annual NDVI curves based on theGlobal Inventory Monitoring and Modelling System(GIMMS) data set (Karlsen et al. 2006). The GIMMSdata set consists of the maximum values of NDVI for15-day periods with a spatial resolution of approxi-mately 8� 8 km2, covering the world and available from

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Fig. 1. The two provinces in Finland from where data on fat

deposition of juvenile raccoon dogs were collected. The dotted

line represents the northern limit of distribution of raccoon

dogs (after Helle and Kauhala 1995).

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1982 until present (Karlsen et al. 2006). The NDVI isbased on the relationship between reflected red andnear-infrared radiation from the ground. It is found tobe closely related to the photosynthetic activity, plantbiomass and net primary productivity (Myneni et al.1995). The GIMMS data set allows the calculation ofannual NDVI curves and the extraction of variables thatdescribe the annual plant phenology (Reed et al. 1994;Pettorelli et al. 2005). For each pixel in the GIMMSdata we calculated the following plant phenologyvariables (Table 1): onset of spring, peak value,integrated NDVI (annual net primary productivity)and derived spring NDVI. For a more detaileddescription of these variables see Reed et al. (1994),Garel et al. (2006) and Herfindal et al. (2006a). The fourplant phenology variables were averaged annuallywithin each of the two provinces (Fig. 1), excludingpixels representing large areas of open water.

We used multiple linear regressions to find environ-mental variables that explained the variation in theresiduals extracted from the curve of fat deposition.

Because some of the explanatory variables describe theenvironmental conditions later in the season than theobservation date for some of the individuals, weexcluded observations earlier than 1 August. This givesa total of 719 and 143 observations for Hame and Kymi,respectively. To account for the interdependence that iscaused by multiple observations within year andprovince, these two variables were added as randomfactors in a linear mixed model approach (Pinheiro andBates 2002). A model selection procedure based onAkaike Information Criterion corrected for smallsample sizes (AICc, Burnham and Anderson 2002) wasfirst run separately for the two sets of explanatoryvariables (climatic and plant phenology) to find outwhich variables of each set better explained variation inexpected fat deposition. The variables that wereincluded in the ‘best models’ of each set were thenmerged in the same global model, and model averagingprocedure was performed to rank the variables in orderof importance and obtain averaged estimates (Burnhamand Anderson 2002). The analyses were done using R

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Table 1. Climate and plant phenology variables used in the linear mixed effects models explaining variation in fat deposition in

juvenile raccoon dogs during summer.

Climatic variables

Duration of snow cover (snw_dur) Number of 15-day periods when the snow depth 430 cm

March temperature (March_T) Mean temperature in March (1C)

March snow depth (March_snw) Mean snow depth in March (cm)

Early summer temperature (es_T) Mean monthly temperature from 1 May–30 June (1C)

Early summer rainfall (es_rain) Mean monthly rainfall from 1 May–30 June (mm)

Plant phenology variables

Onset of spring (os) Day when normalized difference vegetation index (NDVI) value represents the birch leaf burst

Peak value (pv) NDVI value at peak time, maximum productivity of the year

Annual productivity (int) Time-integrated NDVI values for one growing season

Derived spring NDVI (dn) The rate of increase in NDVI value at onset of spring

Table 2. Pearson’s product moment correlation between explanatory variables.

March_T Duration es_T es_rain os pv int dn

March_snw �0.492� 0.860��� 0.222ns �0.174ns 0.244ns �0.123ns �0.315ns 0.404ns

March_T – �0.383ns 0.474ns �0.216ns �0.426ns 0.288ns 0.271ns 0.169ns

Duration – – 0.083ns �0.083ns 0.153ns 0.003ns �0.059ns 0.391ns

es_T – – – �0.412ns 0.235ns 0.187ns �0.336ns 0.738��

es_rain – – – – 0.043ns 0.163ns 0.300ns �0.339ns

os – – – – – 0.379ns �0.468ns 0.512�

pv – – – – – – 0.315ns 0.246ns

int – – – – – – – �0.320ns

Abbreviations as in Table 1.

ns: not significant.

df ¼ 14.�Po0.05.��Po0.01.���Po0.001.

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2.7.1 Software (R Development Core Team 2008),where the linear mixed models were run with theadd-on package lme4 (Bates 2007). The mixed modelswere fitted with maximum likelihood for the calculationof the AICc values, while the parameter estimates andtheir standard error were from models fitted withrestricted maximum likelihood (Pinheiro and Bates2002).

Results

The index of fat deposition ranged from 0.2 to 299.6,and showed a clear increase with date in both provinces(Fig. 2). The two different types of logistic curves(standard logistic model (SL, Eq. (1)) and four-parametric logistic model (FPL, Eq. (2))) did not differsubstantially in describing the accumulation of fatthrough the season (Fig. 2). In the data from Hame,the residual standard error was lower for FPL (0.729)compared to the SL (0.763), while there were only smalldifferences in Kymi (FPL: 0.766, SL: 0.763). Anevaluation of the two different models by AIC suggestedthat FPL was best in Hame (AICFPL ¼ 1662.250,AICSL ¼ 1672.010), while in Kymi, SL was rankedhighest (AICFPL ¼ 337.660, AICSL ¼ 335.910). Thedifference in AIC between the two models in Kymirepresents approximately the penalty by adding oneparameter, indicating that the fit of the two is somewhatsimilar. Thus, we chose to use FPL to describe thechange in fat through the season for both provinces, andextracted the residuals for each observation from thepredicted relationship as a measure of individualvariation in fat index accounted for date.

The model selection performed on climatic variablesshowed that March temperature, March snow depthand early summer temperature were included in the setof ‘best models’ (Table 3a). In fact, the distance in AICc

(DAICc) from the first ranked model was o2 both forthe second and for the third ranked models. Similarly,the evidence ratio did not help us in identifying one bestmodel (Burnham and Anderson 2002). We thereforechose to retain these three variables for further analyses.Regarding the model selection based on the plantphenology variables (Table 3b), the set of ‘best models’included all the initial variables: onset of spring, peakvalue, annual productivity and derived spring NDVI(Table 3). To compare the performance of the bestclimatic and plant phenology variables we wanted to runa model selection procedure with the best variables ofeach set. However, we could not have a global modelincluding both derived spring NDVI and early summertemperature, due to their high intercorrelation (Table 2)(Graham 2003). Both showed to be important and wereincluded in the set of ‘best model’s when a modelselection was run separately on the global modelsincluding one of the two variables (Table 3c and d).However, the AICc of the model including derivedspring NDVI (1840.210) was lower than that of themodel including early summer temperature (1841.760),indicating that the first set of variables had a betterexplanatory power. We therefore chose to providemodel averaging estimates for this model (Table 4).The model averaging on the global model obtainedincluding the best variables of both sets (March snowdepth, March temperature, onset of spring, totalproductivity, peak value and derived spring NDVI,Table 4) showed that March temperature and Marchsnow depth were the most important variables explain-ing fat deposition of raccoon dog and influenced itconsistently in one direction. High fat index waspositively associated with high temperatures and lowsnow depths in March (Table 4). Amongst plantphenology variables, although lower ranked, onset ofspring, peak value and derived spring NDVI were allinfluencing consistently in one direction the variation in

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Fig. 2. Change in fat index for juvenile raccoon dog from Hame and Kymi (Finland) through the summer season. The solid line

shows the fitted model based on a four-parametric logistic model while the dashed line shows the fitted model based on a standard

logistic model. Fat index values were ln-transformed prior to the model fitting.

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fat deposition: the fat deposition was negativelyassociated with a late onset of spring and low peakand derived spring NDVI values. The effect of annualnet primary productivity on fat deposition had aconsiderable degree of uncertainty (Table 4).

Discussion

The annual variation in fat deposition, as measuredby the deviance from the predicted logistic relationshipbetween fat accumulation and date, was more closely

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Table 3. Sets of linear mixed models explaining the residuals of fat deposition in juvenile raccoon dogs in the provinces of Hame

and Kymi (Finland).

Model K AICc DAICc oi

(a) Climatic variables – Global model: Fat deposition�March_snw+March_T+es_T+es_rain

March_snw+March_T 5 1841.760 0.000 0.276

March_T 4 1842.262 0.502 0.215

March_T+es_T 5 1842.971 1.211 0.151

(b) Plant phenology variables – Global model: Fat deposition�os+pv+int+dn

os+pv 5 1854.034 0.000 0.163

os 4 1854.253 0.219 0.146

os+pv+int 6 1854.525 0.492 0.128

pv 4 1854.995 0.962 0.101

dn 4 1855.837 1.803 0.066

Intercept only 3 1855.916 1.883 0.064

int 4 1855.958 1.924 0.062

(c) Global model with dn: Fat deposition�March_snw+March_T+os+pv+int+dn

dn+os+pv+March_snw 7 1840.210 0.000 0.209

March_T+March_snw 5 1841.760 1.550 0.096

dn+os+pv+int+March_snw 8 1842.040 1.830 0.084

(d) Global model with es_T: Fat deposition�March_snw+March_T+os+pv+int+es_T

March_T+March_snw 5 1841.760 0.000 0.146

March_T 4 1842.262 0.502 0.114

es_T+March_T 5 1842.971 1.211 0.080

int+March_T+March_snw 6 1843.587 1.827 0.059

int+March_T 5 1843.626 1.866 0.058

Province and year were set as random factors. The models were ranked by the corrected Akaike Information Criterion (AICc) (K ¼ number of

parameters, including intercept, random factor and error term; DAICc ¼ difference in AICc between a given and the best model; oi ¼ Akaike’s

weights, i.e. normalized likelihoods of the models). Only models with DAICco2 are shown. Abbreviations as in Table 1. Global model including: (a)

only climatic variables, (b) only plant phenology variables, (c) the best variables of both sets without early summer temperature, (d) the best variables

of both sets without integrated spring NDVI.

Table 4. Averaged estimates of the linear mixed model with the residuals of fat deposition in juvenile raccoon dogs in the provinces

of Hame and Kymi (Finland) as dependent variable.

Estimate SE 95% CI lower 95% CI upper Relative importance

Intercept 0.567 1.084 -1.557 2.692 1.000

March_snw �0.008 0.003 �0.015 �0.002 0.734

March_T 0.073 0.023 0.028 0.118 0.664

os �0.147 0.050 �0.246 �0.049 0.571

dn 3.060 1.232 0.645 5.474 0.550

pv 2.918 1.119 0.724 5.112 0.508

int �0.021 0.042 �0.102 0.061 0.289

The relative variable importance was obtained by summing Akaike’s weights of the models that contained each variable. Abbreviations as in Table 1.

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related to climatic variables, such as temperature andsnow depth in spring, than to variables that describeplant phenology as derived from satellite images. Eventhough the fat accumulation was related to the latter setof variables, after accounting for climatic variablesrelated to spring condition the fit of the models did notincrease considerably by adding variables of plantphenology (Table 3). Accordingly, the fat accumulationin juvenile raccoon dogs seems to be strongly influencedby snow and temperature in early spring (Table 4).

Our result confirms on a temporal scale the findings ofHelle and Kauhala (1995), who suggested that snowdepth affects the time of oestrus in females and thereforethe day of birth of juveniles, which again affects the startof fat deposition. Furthermore, there are some con-straints in the amount of fat that a hibernating animalcan store, which is about 35–50% of total body mass(Millar and Hickling 1990). At some point, themetabolic cost of maintaining fat reserves, or the riskassociated with carrying them, will exceed the benefits(Pond 1981). Therefore, at some stage of the growthperiod, the juvenile raccoon dog will have to prioritizefat deposition with respect to growth and the body massreached at that date will constrain the quantity of fatthat they can store. So if the juveniles are smaller atbirth because of harsh spring conditions, we mightexpect them to store less fat.

Measures of plant phenology derived from satelliteimages have performed well in explaining variation inlife history traits in herbivore species (Garel et al. 2006;Herfindal et al. 2006a, b; Pettorelli et al. 2006), butmight not reflect variation in food abundance andquality for omnivore species. This is a rather contra-dictory result, because climate has often been used inanimal ecology studies as a proxy of the productivity ofvegetation (reviewed in Stenseth et al. 2002). Thus, whenit has been shown previously on a spatial scale that thereis a clear relationship between climate and raccoonperformance (Kauhala 1993; Helle and Kauhala 1991,1995), we expected this to be even more emphasized byusing variables that are more closely related to thetiming and amount of environmental productivity.However, several factors may weaken the response ofraccoon dog to the satellite-derived environmentalvariables. First, plant phenology parameters may notbe good measures of food availability for raccoon dog.Food resources exploited by raccoon dog, such as voles,carrion (Kauhala et al. 1998; Selva et al. 2003) andcompost heaps, might be available in spring before thegrowing season of the vegetation starts, as long asraccoon dogs can move efficiently around. Secondly,there are some limitations in the use of satellite data inthis context, related to the presence of a high percentageof agriculture areas, lakes, coniferous forest and otherland use classes that can dampen the phenologicalsignature of the natural vegetation (K.-A. Høgda,

personal observation) that raccoon dogs experience inthe study area. This might have contributed in hidingthe relationship between fat deposition in juvenileraccoon dogs and plant phenology.

The relationship between climatic variables and thepattern of fat deposition in juvenile raccoon dogs (whichin turn affects the reproductive output; Helle andKauhala 1995) allows us to make some predictions onthe future distribution of this species in the light of theproposed climate change. Most of the observed climaticchanges have been in the direction of reducing climaticconstraints to plant growth (Fraedrich et al. 2001).However, in the climate change scenarios there is amarked contrast between winter and summer change ofprecipitation patterns, where seasonality is likely toincrease. In the Canadian arctic, Humphries et al. (2004)predicted that seasonally inactive mammal speciesshould experience a substantial increase in abundanceand distribution in response to climate change. How-ever, species inactive in winter might experienceproblems at higher altitudes, because of lack ofsynchronisation between plant phenology and climaticconditions, i.e. they emerge earlier in the season, becausethey use air temperature as a cue, but the snow is stilltoo deep for finding food (Inouye et al. 2000). Ourresults suggest that in Europe raccoon dogs are likely tobenefit from climate warming, but more abundantspring precipitation in the form of snow at higherlatitudes could compensate for the positive effect ofincreased primary productivity and might become theultimate constraint to their expansion towards harsherenvironments. When predicting further range expansionof invasive species, one needs to detect the environ-mental variables with highest explanatory power andbiological relevance. Although appealing both from theresults of previous studies on herbivores and for theirhigh spatial–temporal resolution, variables describingplant phenology derived from satellite images seem toperform worse than more common climatic observa-tions for a species that has a different life historystrategy. This should be taken in account when planningmanagement actions, since control and eradication ofalien species is generally very difficult and expensive (e.g.Pimentel et al. 2000) and identification of potentialdispersal corridors is a key issue to effectively preventboth primary and secondary invasions with the lowestmanagement cost (Byers et al. 2002).

Acknowledgements

We would like to thank C.J. Tucker at GoddardSpace Flight Center (USA) for providing the GIMMSdata set and an anonymous referee who greatlycontributed in improving an early version of themanuscript. This project was funded by the Norwegian

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Directorate for Nature Management. We are alsograteful to all the Finnish hunters, who collected thecarcass material and all the technical assistants whoexamined the carcasses. Finnish Academy of Sciencesfinanced the raccoon dog project.

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