DOI: 10.1111/j.1366-9516.2006.00284.x © 2006 The Authors

502

Journal compilation © 2006 Blackwell Publishing Ltd www.blackwellpublishing.com/ddi

Diversity and Distributions, (Diversity Distrib.)

(2006)

12

, 502–510

BIODIVERSITYRESEARCH

ABSTRACT

We examined the influence of ‘seasonal fine-tuning’ of climatic variables on the per-formance of bioclimatic envelope models of migrating birds. Using climate data andnational bird atlas data from a 10

×

10 km uniform grid system in Finland, we testedwhether the replacement of one ‘baseline’ set of variables including summer (June–August) temperature and precipitation variables with climate variables tailored(‘fine-tuned’) for each species individually improved the bird-climate models. Thefine-tuning was conducted on the basis of time of arrival and early breeding of thespecies. Two generalized additive models (GAMs) were constructed for each of the63 bird species studied, employing (1) the baseline climate variables and (2) the fine-tuned climate variables. Model performance was measured as explanatory power(deviance change) and predictive power (area under the curve; AUC) statisticsderived from cross-validation. Fine-tuned climate variables provided, in many cases,statistically significantly improved model performance compared to using the samebaseline set of variables for all the species. Model improvements mainly concernedbird species arriving and starting their breeding in May–June. We conclude that theuse of the fine-tuned climate variables tailored for each species individually on thebasis of their arrival and critical breeding periods can provide important benefits forbioclimatic modelling.

Keywords

Bird atlas, boreal regions, distribution, model accuracy, species-climate model.

INTRODUCTION

Recent studies suggest that the ongoing climate change has

already caused changes in bird species phenology, migration and

distribution patterns (Thomas & Lennon, 1999; Both & Visser,

2001; Ahola

et al

., 2004; Brommer, 2004; Crick, 2004). Simula-

tions of the impacts of climate change on species distributions

are often based on bioclimatic envelope models (e.g. Berry

et al

.,

2002; Thuiller, 2003). These models relate present species

distributions to selected aspects of present climate and fit the

derived models into different climate change scenarios to predict

the potential changes of species’ geographical distributions

(Huntley, 1995; Pearson & Dawson, 2003; Thuiller, 2003). The

approach has been applied to birds by, e.g. Berry

et al

. (2001),

Peterson

et al

. (2002), Peterson (2003) and Araújo

et al

. (2005a,b).

A crucial factor in developing species distribution models,

including bioclimatic envelope models, is the use of the most

appropriate predictor variables possible. As argued by Austin

(2002), modellers should, wherever possible, use direct and more

proximal (e.g. rainfall) rather than indirect or distal predictors

(e.g. altitude, latitude). However, the demands for careful

selection of variables are especially high in bioclimatic models.

This is because these models and their projections can have an

important role in conservation planning and political debates.

Thus, there are high demands on their accuracy and plausibility

(Araújo

et al

., 2005a). Recent research has highlighted the

fact that bioclimatic models can be vulnerable to a number of

uncertainties (Pearson & Dawson, 2003; Hampe, 2004; Araújo

et al

., 2005b; Luoto

et al

., 2005), e.g. critical issues in model

building and the selection of climatic variables for a given study

(Kadmon

et al

., 2003; Thuiller

et al

., 2004; Thuiller, 2004; Araújo

et al

., 2005a; Beaumont

et al

., 2005). In the case of migratory

birds, one potential source of uncertainty is the delineation of

the climatic variables used in modelling bird species responses.

Hitherto, very little attention has been paid to the selection of

as accurate climatic variables as possible in the migratory

Finnish Environment Institute, Research

Department, Research Programme for

Biodiversity, Helsinki, Finland

*Correspondence: R. K. Heikkinen, Finnish Environment Institute, Research Department, Research Programme for Biodiversity, P.O. Box 140, FIN-00251 Helsinki, Finland. Tel.: + 358 9 40300249; fax: + 358 9 40300290; E-mail: risto.heikkinen@ymparisto.fi

Blackwell Publishing Ltd

Does seasonal fine-tuning of climatic variables improve the performance of bioclimatic envelope models for migratory birds?

R. K. Heikkinen*, M. Luoto and R. Virkkala

Fine-tuning of bird-climate models

© 2006 The Authors

Diversity and Distributions

,

12

, 502–510, Journal compilation © 2006 Blackwell Publishing Ltd

503

birds–climate impacts models. Such attention can be particularly

important at high latitudes (e.g. North Europe), from where

several bird species migrate south for the winter (Newton &

Dale, 1996). The breeding ranges of these species may be largely

determined by the climate conditions prevailing at the time of

their arrival, courtship and breeding (Virkkala, 1991; Huntley,

1995; Lennon

et al

., 2000).

Climate variables used in bird–climate models have varied

considerably. Some studies have employed only mean annual

precipitation and temperature values (e.g. Peterson, 2003;

Seoane

et al

., 2003). By contrast, Araújo

et al

., (2005a) used, for

example, temperature of the coldest and warmest month and

July–September precipitation in addition to annual values.

Some other multi-species studies have focused more on the

relationships between birds and climate conditions of spring or

early summer, i.e. precipitation and temperature of April–June

(Forsman & Mönkkönen, 2003; Lemoine & Böhning-Gaese,

2003; Virkkala

et al

., 2005) or May–July (Lennon

et al

., 2000;

Berry

et al

., 2001). However, it has rarely been examined whether

applying climate variables tailored (‘seasonally fine-tuned’)

separately for each of the bird species individually instead of

using one common set of climate variables for all the studied

species would increase model performance (cf. Huntley, 1995).

This paucity of bird–climate modelling studies considering the

fine-tuning of variables is surprising because autecological

studies have indicated that the climate conditions during the

period of courtship and the early weeks of breeding can

markedly influence the occupancy patterns and breeding success

of birds (Redpath

et al

., 2002; Rodríguez & Bustamante, 2003;

Jovani & Tella, 2004). Consequently, many multi-species model-

ling studies that have included a large number of migratory bird

species (e.g. Brotons

et al

., 2004; Araújo

et al

., 2005b) may have

been exposed to a possible mismatch of climate variables and

critical periods in breeding, and to lowered model accuracy for

these species.

In this study, we used generalized additive models (GAM) and

distribution data of 63 migratory bird species in Finland at the

resolution of 10

×

10 km to examine whether the seasonal

fine-tuning of climate variables based on the arrival and early

breeding periods of the studied species improves the performance

of bird–climate models. Specifically, we addressed the following

questions: (1) Does the replacement of baseline summer (June–

August) climate variables with variables tailored for each species

individually significantly improve the accuracy of the models?

(2) Are the possible model improvements related to the time of

arrival and breeding of the studied bird species?

METHODS

Study area

Finland covers an area of

c

. 338,000 km

2

in northern Europe

between latitudes 59

°

30

′

and 70

°

N. The climate shows char-

acteristics of both an oceanic and a continental climate, the

continentality growing inland and eastwards (Tuhkanen, 1984).

The majority of the country has a boreal climate, with a decrease

in rainfall and temperature from the south-western hemiboreal

zone (mean annual temperature

c

. 5

°

C and mean annual precipi-

tation 600–700 mm) to the subarctic region in northernmost

Finland (

−

2

°

C and 400 mm). Biogeographically, Finland is

located mainly in the boreal coniferous vegetation zone, and the

landscape is largely dominated by forests and mires.

Bird data

The studied 63 land bird species bred and/or foraged in the

main terrestrial habitats: 19 species occurred primarily in forests,

17 species in agricultural and bushy habitats, 13 species in

mires, 9 species in marshes and coastal wetlands and 5 species

in mountain heaths (see Appendix S1 in Supplementary

material). Nomenclature of the species follows Dickinson (2003).

The species were assigned to three groups according to their

average arrival–early breeding period: those arriving and having

their early breeding period in (1) March–April (2) April–May, or

(3) May–June (see Appendix S1). This classification was based

on a literature survey of the key publications of the migratory

periods of bird species in Finland (e.g. Hildén

et al

., 1979;

Pöyhönen, 1995).

The information on the distribution of species and the level of

survey activity was extracted from the second bird atlas survey in

Finland, which was carried out in 1986–1989 and included 3800

squares of 10

×

10 km (Väisänen

et al

., 1998). Recorders and

organizers of the survey graded the survey activity in each square

according to six categories: 0 = no observations, 1 = occasional

observations, 2 = fair survey, 3 = satisfactory survey of the

square, 4 = well surveyed and 5 = thoroughly surveyed square

(Väisänen

et al

., 1998). We used only squares with survey

activities of 2–5 in our analysis. Consequently, the data used in the

analyses consisted of 2861 squares. Väisänen

et al

. (1998) listed

the breeding status of bird species recorded in each of the grid

squares in four classes: 0 = not found, 1 = breeding possible,

2 = breeding probable and 3 = confirmed breeding. For the

analysis of this study, we combined classes 1, 2 and 3 as a species-

present variable.

Climate data

Climate data produced by the Finnish Meteorological Institute,

using the same 10

×

10 km grid system, were employed as pre-

dictors of the bird distribution data (Venäläinen & Heikinheimo,

2002). The climate data included mean values for the period

1985–1989 for all climatic variables. Because we focused on

analysing the impacts of fine-tuning on the performance of

bird–climate models, we used a limited number of variables.

The baseline set of predictors simulated the commonly used

approach of using one set of explanatory variables for all species.

It included three variables: mean temperature of the coldest

month (MTCO), mean summer temperature (defined as June–

August; TEMPSUM), and mean summer precipitation (June–

August; PRESUM). For the fine-tuning analysis we calculated the

mean values for three pairs of months: mean temperature of

March–April (TEMMA), April–May (TEMAM) and May–June

R. K. Heikkinen

et al.

© 2006 The Authors

504

Diversity and Distributions

,

12

, 502–510, Journal compilation © 2006 Blackwell Publishing Ltd

(TEMMJ), and mean precipitation of March–April (PREMA),

April–May (PREAM) and May–June (PREMJ). MTCO was

included to indicate the general harshness of the environmental

conditions in each grid square.

Model calibration

Generalized additive models were developed using

(Generalized Regression Analysis and Spatial Prediction)

(Lehmann

et al

., 2003) in S-Plus (version 6.1 for Windows,

Insightful Corp.). All the GAMs were built using a binomial

distribution of error and a logistic link function, and a stepwise

selection procedure to select important explanatory variables

and the level of complexity of the response shapes of the various

species to each variable. A starting model including all continuous

predictors smoothed with 4 degrees of freedom was fitted first.

Next, the stepwise

procedure proceeded in a loop that

aimed to eliminate one variable at a time from the full model. At

each step, the least significant variable was dropped from the

model or converted to a linear form, after which the loop

proceeded with the remaining variables (see Lehmann

et al

.,

2003). The final models for different species may thus include

different numbers of predictors with either 1 or 4 degrees of

freedom for the spline smoother. The variable dropping or

conversion to linear form was tested in the first set of GAMs

using Akaike’s Information Criterion (AIC) (Akaike, 1974).

The modelling procedure included building two GAMs for

each species. In the first set of GAMs (‘baseline GAMs’), the three

baseline climate variables (MTCO, TEMPSUM, PRESUM) were

used as the three explanatory variables. In the second set of

GAMs (‘fine-tuned GAMs’), mean summer temperature was

replaced by one of the fine-tuned temperature variables

(TEMMA, TEMAM, TEMMJ) and mean summer precipitation

by one of the precipitation variables (PREMA, PREAM, PREMJ),

based on the information on the average arrival-early breeding

periods of the studied species (Appendix S1). In all the models,

the three climate predictors included were subjected to the

stepwise variable selection process in order to develop

parsimonious models, as described in the previous paragraph.

Model evaluation

The models were evaluated in two ways. First, the explained

deviance in each model (i.e. the ratio of explained deviance vs.

the total deviance) was investigated. This was regarded as the

‘explanatory’ power of the model. Second, we examined the

cross-validation statistics in each model (the ‘predictive’ power of

the model). Cross-validation was made with four spatially

random subsets of the entire data set. Each subset was dropped

from the model, the model was recalculated and predictions

were made for the omitted data points (see Lehmann

et al

.,

2003). Predictive power was then measured using the area under

the curve (AUC) of a receiver operating characteristic (ROC)

plot (Fielding & Bell, 1997).

Model validation using cross-validation based on spatially

random sets of grid squares may be vulnerable to the effects of

spatial autocorrelation in the predictor and response variables

(e.g. Legendre, 1993; Koenig, 1999). Cross-validation based on

random regions (Peterson, 2001) or random blocks of, e.g.

100

×

100 km (cf. Augustin

et al

., 2001) may circumvent

autocorrelation problems better but they may cause other kind

of difficulties for validation. For example, use of large random

regions may lead to loss of information because not all species

necessarily occur in acceptable numbers in both calibration and

evaluation data sets (see Peterson, 2001). Random blocks of,

e.g. 100

×

100 in in size would work slightly better in this respect.

However, as several of the studied species have very narrow dis-

tributions in Finland, the model validation using random blocks

would also result in losing species information. Validation using

both random regions and random blocks may be vulnerable to

the risks of comparing different sampling strategies or field

survey activities instead of evaluating a model (Lehmann

et al

.,

2003).

There is a plethora of techniques available for examining

spatial autocorrelation structure and its impacts (e.g. Legendre,

1993; Lichstein

et al

., 2002; Diniz-Filho

et al

., 2003). However,

we do not examine spatial autocorrelation in our data with such

techniques here, mainly because (1) in some biogeographical

study settings similar to our work, autocorrelation in species data

have been shown to be largely accounted for by the similarly auto-

correlated climate predictors (Diniz-Filho

et al

., 2003), and (2),

our climate variables are rather closely correlated (see Table 1)

and thus we presume that potential autocorrelation would have

a broadly equal impact on the validation of the baseline and on

fine-tuned GAMs, and would not alter our comparative results.

However, instead of applying more sophisticated techniques

to examine autocorrelation issues, we use a simple ad hoc, but

prudent, course of action to take its potential impacts into

account in the modelling. One of the core impacts of spatial

autocorrelation is that type I errors may be inflated, i.e. coeffi-

cients declared to be significantly different from zero when in

fact they are not (Legendre, 1993). A simple means to take this

into account is to reduce the probability levels in model inference

tests (Koenig, 1999; Guisan & Thuiller, 2005). Consequently, we

re-ran all our GAMs using the fourfold cross-validation process

Table 1 Correlations (Spearman’s rho) between the two ‘baseline’ summer (June–August) climate variables and three ‘fine-tuned’ climate variables (March–April; April–May; May–June)

Summer temperature Summer precipitation

Temperature

March–April 0.899*** —

April–May 0.939*** —

May–June 0.947*** —

Precipitation

March–April — 0.603***

April–May — 0.661***

May–June — 0.521***

***P < 0.001.

Fine-tuning of bird-climate models

© 2006 The Authors

Diversity and Distributions

,

12

, 502–510, Journal compilation © 2006 Blackwell Publishing Ltd

505

but accompanied with

F

-tests using a stringent probability limit

(0.001) for the selection of the predictors. The results of the

AIC-based and

F

-test–based GAMs were then compared.

To examine the differences in the explanatory and predictive

power of the models, we compared the explained deviance

and AUC values from the baseline GAMs vs. fine-tuned GAMs

for each species. Because deviance and AUC data were not sig-

nificantly different from normal distribution (Kolmogorov-

Smirnov tests,

P

> 0.05), we used paired

t

-test for measuring the

significance between the baseline and fine-tuned models.

Because we expected a priori that fine-tuned climatic variables

would improve the model fitting, we used one-tailed

t

-tests in all

analyses. Paired one-tailed

t

-tests were conducted first for all the

63 studied species simultaneously, and then for the early and mid

(March–April and April–May) arriving bird species (‘early and

mid arrivers’;

n

= 29) vs. late spring (May–June) arriving species

(‘late arrivers’;

n

= 34) separately. The early and mid arrivers

were lumped in this analysis because of the low number of

species (

n

= 6) included in the first group.

As an example, we produced projected future distributions for

selected study species based on both the derived baseline and the

fine-tuned GAMs and using the climate data obtained from the

HadCM3 general circulation model (GCM) under the business-as-

might-be-usual (BAMBU) scenario A2, compiled by the EC FP6

Integrated Project ALARM (http://www.alarmproject.net). This

was carried out to examine whether the observed differences

in the predicted current distributions from the baseline and

fine-tuned models increase when the models are employed to

produce predictions of future distributions.

RESULTS

The summer temperature and the three fine-tuned temperature

variables were highly correlated with each other, whereas the

correlations between precipitation variables were moderate

(Table 1). Concurring with our a priori expectations, the explan-

atory power of fine-tuned models (model selection using AIC)

was on average significantly higher (mean percent of deviance

explained = 35.1%) than that of baseline models (mean = 34.5%)

(paired one-tailed

t

-test; d.f. = 62;

t

=

−

2.135;

P

= 0.018). In

addition, the predictive power (AUC) of the fine-tuned GAMs

(mean = 0.887) was slightly but statistically significantly higher

than that of the baseline GAMs (mean = 0.861) (paired one-

tailed

t

-test;

t

=

−

1.948;

P

= 0.028).

Separate analysis of the explanatory and predictive powers of

early mid and late arrivers revealed that the differences between

the performance of baseline and fine-tuned AIC-based GAMs

was caused by the models for May–June arrivers. Both the

deviance and AUC values were statistically significantly higher in

late arrivers models using fine-tuned climate variables than in

the baseline GAMs (Table 2). Moreover, in the models for

late arrivers there was a clear general trend towards fine-tuned

models providing higher performance (deviance change, 24

vs. 10 species; AUC, 22 vs. 8 species).

The results of the GAMs based on model selection using the

F

-

test and a probability level of 0.001 (Appendix S2 in Supplementary

material) were very similar to those of AIC-based GAMs. Thus,

correcting the model inferences for the potential autocorrelation

effects simply by reducing the probability levels did not alter our

results, and in the remaining part of the paper we will focus on

AIC-based modelling results.

The set of species for which the fine-tuning resulted in the

highest increase in model performance included birds from dif-

ferent taxonomical and habitat type groups, including turtle

dove

Streptopelia turtur

, arctic warbler

Phylloscopus borealis

,

hawfinch

Coccotraustes coccotraustes

, ring ouzel

Turdus torquata

,

grey heron

Ardea cinerea

, moorhen

Gallinula chloropus

and

black-tailed godwit

Limosa limosa

. The highest (24%) increase in

the predictive power was in the case of the black-tailed godwit.

As an example, the predicted distributions are presented for the

grey heron and the arctic warbler (Fig. 1). The overall pattern

of predicted probability of occurrence for the two species did

not vary greatly between the baseline and fine-tuned GAMs.

However, there were clear regional spatial differences in the

model accuracy. The fine-tuned models predicted better, e.g. the

northernmost occurrences of the grey heron and certain agglom-

erations of occurrences of both species along the eastern border

of the country.

Figure 2 shows the predicted future distributions for four

species based on fitting the derived species-specific baseline and

fine-tuned GAMs in the climate data obtained from the

HadCM3 GCM under the BAMBU scenario A2. The projected

distributions are, in the case of the arctic warbler, the same (both

models predict that a suitable climate for the species will not

exist in Finland in the future). In the case of the red-breasted

flycatcher

Ficedula parva

the differences are relatively small.

Table 2 Mean (± standard error) values of explained deviance (‘explanatory power’) and AUC (‘predictive power’) of the distribution models for (a) land bird species arriving in March–April and April–May (n = 29) and (b) arriving in May–June (n = 34). The models employ three ‘baseline’ climate variables (mean temperature of the coldest month, mean June–August temperature, mean June–August precipitation) or three ‘fine-tuned’ climate variables (mean temperature of the coldest month, mean temperature and mean precipitation of March–April/April–May/May–June, the latter two selected on the basis of species-specific arrival-early breeding period). Statistical tests by paired one-tailed t-test; AUC derived from fourfold cross-validation test; ranks: negative/positive/tied (negative rank = fine-tuned model < baseline model; positive rank = fine-tuned model > baseline model)

Species

group

Baseline

climate

variables

Fine-tuned

climate

variables t P Ranks

(a) Early mid arrivers

Deviance 0.319 ± 0.028 0.326 ± 0.026 −1.117 0.137 12/17/0

AUC 0.844 ± 0.014 0.852 ± 0.011 −1.258 0.109 13/14/2

(b) Late arrivers

Deviance 0.367 ± 0.026 0.373 ± 0.026 −2.422 0.011 10/24/0

AUC 0.876 ± 0.010 0.880 ± 0.010 −3.171 0.002 8/22/4

R. K. Heikkinen

et al.

© 2006 The Authors

506

Diversity and Distributions

,

12

, 502–510, Journal compilation © 2006 Blackwell Publishing Ltd

Figure 1 Recorded and projected distributions of two species: (a) recorded occurrence of the grey heron in grid squares of 10 × 10 km in 1986–1989, and probability of occurrence of the species based on (b) the ‘baseline’ bioclimatic envelope model, and on (c) the ‘fine-tuned’ bioclimatic model; (d) recorded occurrence of the arctic warbler in 1986–1989, and probability of occurrence of the species based on (e) the ‘baseline’ bioclimatic envelope model, and on (f) the ‘fine-tuned’ bioclimatic model. Probability is shown on a three-level scale. D2 = percentage of explained deviance; AUC = the area under the curve of a receiver operating characteristic (ROC) plot.

Fine-tuning of bird-climate models

© 2006 The Authors

Diversity and Distributions

,

12

, 502–510, Journal compilation © 2006 Blackwell Publishing Ltd

507

However, the projections for the grey heron and the turtle dove

show that the slight differences in the predicted current distribu-

tions may be more important when the models are fitted to the

climate scenarios. The difference in the AUC between the baseline

and fine-tuned GAMs was greatest in the case of the grey heron

(Appendix S1), which also showed the greatest difference in the

projected future distribution between the two models.

DISCUSSION

A number of bioclimatic envelope modelling studies have

employed multi-species simulations based on one set of climate

variables for all the studied species (e.g. Thuiller, 2003; Brotons

et al., 2004; Araújo et al., 2005a, b), although the variables

included have varied between the studies. For sedentary organ-

isms, especially plants, this approach may work well. Variables

such as mean annual temperature and precipitation, minimum

temperature of the coldest month, growing degree days and

moisture availability can have strong links with the physiology

and growth of plants (Huntley et al., 1995). The distributions

and densities of resident bird species may also be adequately

modelled using a combination of winter and summer climate

variables or mean annual variables (Huntley, 1995; Forsman &

Mönkkönen, 2003; Seoane et al., 2003).

However, more mobile species such as migratory birds pose

challenges for developing accurate bioclimatic models. As Huntley

(1995) argued, it is probable that one set of climate variables will

not be applicable to all birds in the manner that one set of key

variables (e.g. growing degree days, mean temperature of the

coldest month and moisture availability) can be applied to a wide

range of terrestrial plant species. Our results suggested that the

explanatory and predictive power of the species–climate models

especially for birds arriving and breeding in May–June in Finland

can be enhanced by replacing the summer climate variables with

fine-tuned climate variables. Although the absolute increases

in the amount of explained deviance and cross-validation AUC

values were generally small, they were rather systematic and

statistically significant. We consider these differences important

for three reasons. First, as the fine-tuned climate variables always

provided better or equally good model performance than

the baseline summer variables, we see no reason not to use the

ecologically more reasonable fine-tuned variables in modelling

Figure 2 Projected future distributions of four bird species in Finland based on models calibrated with climate and species data from the 1980s and fitted to the climate variable data obtained from the HadCM3 GCM under the BAMBU scenario A2 for the year 2050: the occurrence of (a) the grey heron (Ardea cinerea) based on the ‘baseline’ GAM and (b) the ‘fine-tuned’ GAM; (c) the turtle dove (Streptopelia turtur) based on the baseline GAM and (d) the fine-tuned GAM; (e) the arctic warbler (Phylloscopus borealis) based on the baseline GAM and (f) the fine-tuned GAM; and (g) the red-breasted flycatcher (Ficedula parva) based on the baseline GAM and (h) the fine-tuned GAM. Black dots represent the 10-km grid squares modelled as suitable for the species (projected presence) and unmarked were areas modelled as not suitable (projected absence). To determine the probability thresholds at which the predicted values for species occupancy are optimally classified as absence or presence values, we used prevalence of the species as the probability level as suggested by Liu et al. (2005).

R. K. Heikkinen et al.

© 2006 The Authors508 Diversity and Distributions, 12, 502–510, Journal compilation © 2006 Blackwell Publishing Ltd

the species studied here. In other words, the use of fine-tuned

predictors should inherently improve the mechanistic basis of

the bioclimatic envelope models for migratory birds. Second,

particularly in the results for the late arrivers, there was a clear

trend of the fine-tuned GAMs outcompeting the baseline GAMs.

Third, recent studies have suggested that even slight differences

between current predictions from different bioclimatic models

can be exacerbated when the models are used for simulating

future distributions (Thuiller, 2003; Araújo et al., 2005b). However,

it should be noted that in the work of Thuiller (2003) and Araújo

et al. (2005b), the differences between the simulated future

distributions were caused by the differences between modelling

methods. One of the lessons learned from the results of our study

is that a priori selection of the climate predictors can also result

in intensified differences in the future projections of species

distributions and represents another important source of

uncertainty in bioclimatic models (cf. Heikkinen et al. in press).

The reason why the increases in the model performances

between baseline and fine-tuned models were not greater is

probably the result of the considerably high correlations between

the mean spring and summer temperatures for the period of

1985–1989 in Finland (Table 1; see also Järvinen, 1989). Summer

climate variables can thus act as statistically reasonable, but

ecologically imprecise, surrogate predictors of migratory bird

species distributions. Moreover, it is probable that the impact of

climatically deviating years (e.g. exceptionally warm or cold

springs) on the occupancy and density patterns of migratory

birds in boreal regions (see Järvinen, 1989; Virkkala, 1991; Ahola

et al., 2004) may be ‘lost’ in the mean climate values of longer-

term data, even though their influence in the species distributions

may be visible in the atlas maps.

Our results have some general implications for the bioclimatic

envelope modelling of birds. First, if one combination of climate

variables is used in modelling studies including migratory bird

species, it is intuitively more appropriate to use the mean

temperature or precipitation of periods such as May–July (e.g.

Lennon et al., 2000; Lemoine & Böhning-Gaese, 2003) than

variables extending to August–September (cf. Brotons et al.,

2004). The potential dangers of the mismatch in migratory bird–

climate models in boreal regions are most apparent in the case of

waders, such as whimbrel Numenius phaeopus and greenshank

Tringa nebularia studied here, which finish their breeding and the

adult birds start their ‘autumn’ migration already in June–July. In

the modelling of such species, late summer climate conditions

have a smaller role than spring and early summer climate.

However, this does not necessarily mean that the climate of all

other months than the migratory or residence periods is

completely irrelevant for the distribution and occupancy rates of

migratory birds. For example, lack of winter rains or severe cold

periods in winter may result in limited vegetation growth or food

(e.g. insects) availability and thus lower the suitability of habitats

for birds in the following summer (cf. Rodríguez & Bustamante,

2003). However, the relative explanatory power of migratory-

residence period climate vs. the climate conditions of the rest of

the year has rarely been examined in the same multiple regression

settings. The few results available, including those of Lennon

et al. (2000), have suggested that the single most powerful

explanatory variable for individual species distributions is May–

July temperature.

Second, we argue that distribution modelling of migratory

birds would benefit from using climate variables tailored for each

species individually. Recent autecological studies on migratory

birds have shown that particularly the climate conditions during

the arrival, courtship and early breeding period affect the

occupancy patterns and breeding success of birds (Redpath et al.,

2002; Rodríguez & Bustamante, 2003; Jovani & Tella, 2004). In a

detailed study of the lesser kestrel Falco naumanni (Fleischer,

1758), Rodríguez & Bustamante (2003) showed that the

occupancy rates among the colonies were best explained by the

temperature and rainfall during the courtship period in April–

May, the rainfall in spring being also a key determinant of the

nest success rate and the mean number of chicks per nest. Incor-

porating species-specific knowledge of the critical climate factors

from autecological studies into the multi-species bioclimatic

modelling exercises would decrease the uncertainty of the

models stemming from the possibly loosely delimited climate

variables, and yield information on the response of the species to

climate in different parts of its geographical range (cf. Redpath

et al., 2002).

In our results, the application of fine-tuned variables

improved significantly the overall model performance of the

studied species and especially the models of May–June arrivers.

This may be related to the phenomenon discussed by Kalela

(1952) who argued that migratory birds that overwinter in the

tropics and arrive in the breeding regions in the late spring can

respond to increased spring temperatures by a prolongation of

migration and a rapid occupation of new areas. Such species

include insectivorous birds such as grasshopper warbler

Locustella naevia and reed warbler Acrocephalus dumetorum,

which showed a rapid spread of distribution in the warm periods

in the 1930s in Finland. Moreover, Väisänen et al. (1998) argued

that the expansion of, e.g. the river warbler Locustella fluviatilis in

the 1980s and 1990s can be explained by prolonged migration as

a result of the warming of late spring. However, Kalela linked the

prolongation of migration-improved spring climate relation-

ships also to certain short-distance migrants that arrive earlier in

spring to northern Europe, such as lapwing Vanellus vanellus and

moorhen G. chloropus. Similarly, in our results, the group of

species with the greatest improvements in model performance

did not form a uniform group consisting of long-distance tropical

migrants.

We conclude that the use of the fine-tuned climate variables

tailored for each species individually on the basis of their arrival

and critical breeding periods can provide important benefits. In

other words, researchers can improve the accuracy and plausibility

of the migratory birds–climate models by applying fine-tuned

climate variables instead of the general approach used in multi-

species modelling studies (i.e. applying one set of predictors for

all species). Taking the species differences in arrival and breeding

periods into account is particularly essential at high latitudes, e.g.

North Europe, where the proportion of the species migrating for

the winter can be 50% or more (Newton & Dale, 1996). Overall,

Fine-tuning of bird-climate models

© 2006 The AuthorsDiversity and Distributions, 12, 502–510, Journal compilation © 2006 Blackwell Publishing Ltd 509

it is evident that bioclimatic modelling of migratory birds poses

greater challenges for investigators than developing useful

models for more sedentary organisms.

ACKNOWLEDGEMENTS

Niko Leikola and Stefan Fronzek helped in aggregating the

climate and bird data. The comments by the four referees helped

greatly in improving our paper. This research was funded by the

EC FP6 Integrated Project ALARM (GOCE-CT-2003-506675).

REFERENCES

Ahola, M., Laaksonen, T., Sippola, K., Eeva, T., Rainio, K. &

Lehikoinen, E. (2004) Variation in climate warming along the

migration route uncouples arrival and breeding dates. Global

Change Biology, 10, 1610–1617.

Akaike, H. (1974) A new look at statistical model identification.

IEEE Transactions on Automatic Control, AU-19, 716–722.

Araújo, M.B., Pearson, R.G., Thuiller, W. & Erhard, M. (2005a)

Validation of species-climate impact models under climate

change. Global Change Biology, 11, 1504–1513.

Araújo, M.B., Whittaker, R.J., Ladle, R.J. & Erhard, M. (2005b)

Reducing uncertainty in projections of extinction risk from

climate change. Global Ecology and Biogeography, 14, 529–538.

Augustin, N.H., Cummings, R.P. & French, D.D. (2001) Exploring

spatial vegetation dynamics using logistic regression and a

multinomial logit model. Journal of Applied Ecology, 38, 991–

1006.

Austin, M.P. (2002) Spatial prediction of species distribution: an

interface between ecological theory and statistical modelling.

Ecological Modelling, 157, 101–118.

Beaumont, L.J., Hughes, L. & Poulsen, M. (2005) Predicting

species distributions: use of climatic parameters in BIOCLIM

and its impact on predictions of species’ current and future

distributions. Ecological Modelling, 186, 250–269.

Berry, P.M., Vanhinsbergh, D., Viles, H.A., Harrison, P.A.,

Pearson, R.G., Fuller, R.J., Butt, N. & Miller, F. (2001) Impacts

on terrestrial environments. Climate change and nature conser-

vation in Britain and Ireland: modelling natural resource

responses to climate change (the MONARCH project) (ed. by

P.A. Harrison, P.M. Berry and T.E. Dawson), pp. 43–149.

UKCIP, Oxford.

Berry, P.M., Dawson, T.P., Harrison, P.A. & Pearson, R.G. (2002)

Modelling potential impacts of climate change on the bio-

climatic envelope of species in Britain and Ireland. Global Ecology

and Biogeography, 11, 453–462.

Both, C. & Visser, M.E. (2001) Adjustment to climate change is

constrained by arrival date in a long-distance migrant bird.

Nature, 411, 296–298.

Brommer, J.E. (2004) The range margins of northern birds shift

polewards. Annales Zoologici Fennici, 41, 391–397.

Brotons, L., Thuiller, W., Araújo, M.B. & Hirzel, A.H. (2004)

Presence–absence versus presence-only habitat suitability

models: the role of species ecology and prevalence. Ecography,

27, 165–172.

Crick, H.Q.P. (2004) The impact of climate change on birds. Ibis,

146 (Suppl. 1), 48–56.

Dickinson, E.C. (ed.) (2003) The Howard and Moore complete

checklist of the birds of the world, 3rd edn. Christopher Helm,

London.

Diniz-Filho, J.A.F., Bini, L.M. & Hawkins, B.A. (2003) Spatial

autocorrelation and red herrings in geographical ecology.

Global Ecology and Biogeography, 12, 53–64.

Fielding, A. & Bell, J. (1997) A review of methods for the assess-

ment of prediction errors in conservation presence/absence

models. Environmental Conservation, 24, 38–49.

Forsman, J.T. & Mönkkönen, M. (2003) The role of climate in

limiting European resident bird populations. Journal of

Biogeography, 30, 55–70.

Guisan, A. & Thuiller, W. (2005) Predicting species distributions:

offering more than simple habitat models. Ecology Letters, 8,

993–1009.

Hampe, A. (2004) Bioclimate envelope models: what they detect

and what they hide. Global Ecology and Biogeography, 13, 469–

476.

Heikkinen, R.K., Luoto, M., Araújo, M.B., Virkkala, R., Thuiller,

W. & Sykes, M.T. (in press) Methods and uncertainties in

bioclimatic envelope modelling under climate change. Progress

in Physical Geography.

Hildén, O., Tiainen, J. & Valjakka, R. (1979) Muuttolinnut.

Kirjayhtymä, Helsinki. (in Finnish)

Huntley, B. (1995) Plant species’ response to climate change:

implications for the conservation of European birds. Ibis, 137(Suppl. 1), 127–138.

Huntley, B., Berry, P.M., Cramer, W. & McDonald, A.P. (1995)

Modelling present and potential future ranges of some

European higher plants using climate response surfaces. Journal

of Biogeography, 22, 967–1001.

Järvinen, A. (1989) Geographical variation in temperature

variability and predictability and their implications for the

breeding strategy of the pied flycatcher Ficedula hypoleuca.

Oikos, 54, 331–336.

Jovani, R. & Tella, J.L. (2004) Age-related environmental sensitivity

and weather-mediated nestling mortality in white storks Ciconia

ciconia. Ecography, 27, 611–618.

Kadmon, R., Farber, O. & Danin, A. (2003) A systematic analysis

of factors affecting the performance of climatic envelope

models. Ecological Applications, 13, 853–867.

Kalela, O. (1952) Changes in the geographic distribution of

Finnish birds and mammals in relation to recent changes in

climate. Fennia, 75, 38–51.

Koenig, W.D. (1999) Spatial autocorrelation of ecological

phenomena. Trends in Ecology and Evolution, 14, 22–25.

Legendre, P. (1993) Spatial autocorrelation: trouble or new

paradigm? Ecology, 74, 1659–1673.

Lehmann, A., Overton, J.M. & Leathwick, J.R. (2003) :

generalized regression analysis and spatial prediction. Ecological

Modelling, 160, 165–183.

Lemoine, N. & Böhning-Gaese, K. (2003) Potential impact of

global climate change on species richness of long-distance

migrants. Conservation Biology, 17, 577–586.

R. K. Heikkinen et al.

© 2006 The Authors510 Diversity and Distributions, 12, 502–510, Journal compilation © 2006 Blackwell Publishing Ltd

Lennon, J.J., Greenwood, J.J.D. & Turner, J.R.G. (2000) Bird

diversity and environmental gradients in Britain: a test of the

species-energy hypothesis. Journal of Animal Ecology, 69, 581–

598.

Lichstein, J., Simons, T., Shriner, S. & Franzreb, K. (2002) Spatial

autocorrelation and autoregressive models in ecology. Ecological

Monographs, 72, 445–463.

Liu, C., Berry, P.M., Dawson, T.P. & Pearson, R.G. (2005)

Selecting thresholds of occurrence in the prediction of species

distributions. Ecography, 28, 385–393.

Luoto, M., Pöyry, J., Heikkinen, R.K. & Saarinen, K. (2005)

Uncertainty of bioclimate envelope models based on geo-

graphical distribution of species. Global Ecology and Biogeography,

14, 575–584.

Newton, I. & Dale, L. (1996) Relationship between migration

and latitude among the west European birds. Journal of Animal

Ecology, 65, 137–146.

Pearson, R.G. & Dawson, T.P. (2003) Predicting the impacts of

climate change on the distribution of species: are bioclimate

envelope models useful? Global Ecology and Biogeography, 12,

361–371.

Peterson, A.T. (2001) Predicting species’ geographic distributions

based on ecological niche modeling. Condor, 103, 599–605.

Peterson, A.T. (2003) Projected climate change effects on Rocky

Mountain and Great Plain birds: generalities on biodiversity

consequences. Global Change Biology, 9, 647–655.

Peterson, A.T., Ortega-Huerta, M.A., Bartley, J., Sánchez-Cordero,

V., Soberón, J., Buddemeier, R.H. & Stockwell, D.R.B. (2002)

Future projections for Mexican faunas under global climate

change scenarios. Nature, 416, 626–629.

Pöyhönen, M. (1995) Muuttolintujen matkassa. Kustannuso-

sakeyhtiö Otava, Helsinki. (in Finnish)

Redpath, S.M., Arroyo, B.E., Leckie, F., Bouwman, K. & Thirgood,

S.J. (2002) Temperature and hen harrier productivity: from

local mechanisms to geographical patterns. Ecography, 25,

533–540.

Rodríguez, C. & Bustamante, J. (2003) The effect of weather on

lesser kestrel breeding success: can climate change explain

historical population declines? Journal of Animal Ecology, 72,

793–810.

Seoane, J., Viñuela, J., Díaz-Delgado, R. & Bustamante, J. (2003)

The effects of land use and climate on red kite distribution in

the Iberian peninsula. Biological Conservation, 111, 401–414.

Thomas, C.D. & Lennon, J.J. (1999) Birds extend their ranges

northwards. Nature, 399, 213.

Thuiller, W. (2003) — optimizing predictions of species

distributions and projecting potential future shifts under

global change. Global Change Biology, 9, 1353–1362.

Thuiller, W. (2004) Patterns and uncertainties of species’ range

shifts under climate change. Global Change Biology, 10, 2020–

2027.

Thuiller, W., Brotons, L., Araújo, M.B. & Lavorel, S. (2004)

Effects of restricting environmental range of data to project

current and future distributions. Ecography, 27, 165–172.

Tuhkanen, S. (1984) A circumboreal system of climatic-

phytogeographical regions. Acta Botanica Fennica, 127, 1–50.

Väisänen, R.A., Lammi, E. & Koskimies, P. (1998) Distribution,

numbers and population changes of Finnish breeding birds.

Otavan Kirjapaino, Keuruu. (In Finnish with English

summary)

Venäläinen, A. & Heikinheimo, M. (2002) Meteorological data

for agricultural applications. Physics and Chemistry of the

Earth, 27, 1045–1050.

Virkkala, R. (1991) Population trends of forest birds in a Finnish

Lapland landscape of large habitat blocks: consequences of

stochastic environmental variation or regional habitat alteration?

Biological Conservation, 56, 223–240.

Virkkala, R., Luoto, M., Heikkinen, R.K. & Leikola, N. (2005)

Distribution patterns of boreal marshland birds: modelling the

relationships to land cover and climate. Journal of Biogeography,

32, 1957–1970.

SUPPLEMENTARY MATERIAL

The following material is available online at

www.blackwell-synergy.com/loi/ddi

Appendix S1. List of 63 Finnish bird species used in the migratory

birds-climate modelling (GAMs), based on two different sets of

climate variables: (1) one common ‘baseline’ set of variables

(mean temperature of the coldest month, mean June–August

temperature, mean June–August precipitation), and (2) three

‘fine-tuned’ climate variables (mean temperature of the coldest

month, mean temperature and mean precipitation of March–

April/April–May/May–June.

Appendix S2. Mean values of explained deviance and the area

under the curve (AUC) of the F-test-based distribution models

for (1) land bird species arriving in March–April and April–May

(n = 29) and (2) arriving in May–June (n = 34).