© 2006 The Authors DOI: 10.1111/j.1466-822x.2006.00262.x

34

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

Global Ecology and Biogeography, (Global Ecol. Biogeogr.)

(2007)

16

, 34–42

RESEARCHPAPER

Blackwell Publishing Ltd

The role of land cover in bioclimatic models depends on spatial resolution

Miska Luoto*, Raimo Virkkala and Risto K. Heikkinen

ABSTRACT

Aim

We explored the importance of climate and land cover in bird species distributionmodels on multiple spatial scales. In particular, we tested whether the integration ofland cover data improves the performance of pure bioclimatic models.

Location

Finland, northern Europe.

Methods

The data of the bird atlas survey carried out in 1986–89 using a10

×

10 km uniform grid system in Finland were employed in the analyses. Landcover and climatic variables were compiled using the same grid system. The dependentand explanatory variables were resampled to 20-km, 40-km and 80-km resolutions.Generalized additive models (GAM) were constructed for each of the 88 land birdspecies studied in order to estimate the probability of occurrence as a function of(1) climate and (2) climate and land cover variables. Model accuracy was measured bya cross-validation approach using the area under the curve (AUC) of a receiver operatingcharacteristic (ROC) plot.

Results

In general, the accuracies of the 88 bird–climate models were good at allstudied resolutions. However, the inclusion of land cover increased the performanceof 79 and 78 of the 88 bioclimatic models at 10-km and 20-km resolutions, respec-tively. There was no significant improvement at the 40-km resolution. In contrastto the finer resolutions, the inclusion of land cover variables decreased the model-ling accuracy at 80km resolution.

Main conclusions

Our results suggest that the determinants of bird species distri-butions are hierarchically structured: climatic variables are large-scale determinants,followed by land cover at finer resolutions. The majority of the land bird species inFinland are rather clearly correlated with climate, and bioclimate envelope modelscan provide useful tools for identifying the relationships between these species andthe environment at resolutions ranging from 10 km to 80 km. However, the notablecontribution of land cover to the accuracy of bioclimatic models at 10–20-km reso-lutions indicates that the integration of climate and land cover information canimprove our understanding and model predictions of biogeographical patternsunder global change.

Keywords

Bioclimatic models, bird atlas, birds, climate, distribution, Finland, land cover,

modelling.

*Correspondence: Miska Luoto, Finnish Environment Institute, Research Department, Research Programme for Biodiversity, P.O. Box 140, FIN-00251 Helsinki, Finland. E-mail: miska.luoto@ymparisto.fi

Finnish Environment Institute, Research

Department, Research Programme for

Biodiversity, P.O. Box 140, FIN-00251 Helsinki,

Finland

INTRODUCTION

Assessments of the potential effects of climate change on

biodiversity and the future range shifts of species have often been

based on bioclimatic envelope models (Bakkenes

et al

., 2002;

Beaumont & Hughes, 2002; Berry

et al

., 2002; Pearson &

Dawson, 2003; Thuiller, 2003; Huntley

et al

., 2004; Thuiller

et al

.,

2005). These modelling strategies correlate current species

distributions with climate variables, and then project spatial

shifts in species climatic envelopes according to the climate

change scenarios (Pearson & Dawson, 2003; Thuiller, 2003,

2004).

Scale matters in bioclimatic models

© 2006 The Authors

Global Ecology and Biogeography

,

16

, 34–42, Journal compilation © 2006 Blackwell Publishing Ltd

35

It has recently been emphasized that many factors other

than climate can also significantly influence the geographical

distributions of species and the rate of changes in distribution

(Hampe, 2004). One of the potentially important factors is land

cover, although its effects have only sporadically been considered

in bioclimatic models (but see Dirnböck

et al

., 2003; Pearson

et al

., 2004; Thuiller

et al

., 2004a). The scarcity of studies

employing both climatic and land cover predictors in bioclimatic

modelling is surprising, because many papers have reported

statistically significant relationships between land cover variables

and the regional or nationwide distribution and richness

patterns of species (e.g. Fuller

et al

., 1997; Siriwardena

et al

.,

2000; Maes

et al

., 2003; Eyre

et al

., 2004; Heikkinen

et al

., 2004;

Fuller

et al

., 2005; Luoto

et al

., 2006).

The problems and impacts of scale have long been a central

issue in ecological (Wiens

et al

., 1987; Wiens, 1989; Levin, 1992;

Palmer & White, 1994) and biogeographical studies (Rahbek &

Graves, 2001; Blackburn & Gaston, 2002; Willis & Whittaker,

2002). The concept of scale consists of two important attributes:

the unit of sampling and the geographical space covered. The

first attribute is defined by ‘grain’ (or ‘resolution’) and ‘focus’,

grain being the size of the common analytical unit and focus

the area represented by each data point. The second

attribute is ‘extent’, describing the geographical area over which

comparisons are made (Wiens, 1989; Scheiner, 2003; Rahbek,

2005).

Considering the relative contributions of different explanatory

factors on species–environment relationships, the current

paradigm is that climate governs species distributions on broad

biogeographical scales (Currie, 1991; Huntley

et al

., 1995;

Parmesan, 1996) whereas land cover and spatial distribution of

suitable habitats affect species occupancy patterns, particularly

at finer spatial resolutions (Bailey

et al

., 2002; Pearson

et al

.,

2004). The limited results available from bioclimatic modelling

studies suggest that the predictive power of models at a coarse

50-km resolution are not greatly improved by the inclusion

of land cover variables (Thuiller

et al

., 2004a), whereas at finer

resolution such inclusion can significantly improve spatial

predictions for certain plant (Pearson

et al

., 2004) and butterfly

species (Hill

et al

., 1999). However, whether these trends revealed

from the rather heavily human-influenced European regions

also prevail in other biomes, such as boreal regions, requires

further studies. Moreover, there is a clear shortage of studies in

which the impact of integrating land cover data in bioclimatic

models is examined systematically across a range of spatial

resolutions (but see Thuiller

et al

., 2004b). Thus, conclusions

about the potential contributions of climate vs. land cover have

hitherto generally been based on analyses conducted at only one

spatial resolution.

Thuiller

et al

. (2004a) suggested that at the European coarse

resolution, variation in the land cover is mainly driven by

climate. In other words, due to the strong co-inertia between land

cover and climate, the climate variables can effectively account

for most of the explanatory power of land cover. However, very

little effort has been made to examine the changes in the level of

this coinertia over a range of scales.

In this study we used the distribution data of 88 bird species in

Finland and land cover and climate data at four different spatial

resolutions, ranging from 10

×

10 km to 80

×

80 km, and gener-

alized additive models (GAM) to examine whether land cover

variables can significantly improve the performance of species

distribution models based purely on climatic predictors (cf.

Thuiller

et al

., 2004a). We explored the accuracy of the different

models across the four spatial resolutions considered. We also

investigated how large a proportion of the variation of land cover

is accounted for by climatic variables (coinertia between land

cover and climate) at different spatial resolutions.

MATERIALS AND METHODS

Study area

Finland covers an area of

c

. 338,000 km

2

(land area 305,000 km

2

)

in northern Europe between latitudes 59

°

30

′

and 70

°

N. The

climate of Finland shows characteristics of both an oceanic and a

continental climate and is subject to Atlantic, Arctic and Siberian

influences, 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 precipitation 600–700 mm) to the subarctic

region in northernmost Finland (

−

2

°

C and 400 mm). Biogeo-

graphically Finland is located mainly in the boreal coniferous

vegetation zone. The landscape is largely dominated by forests

and mires. Arable land and built-up areas cover about 14% of the

total land area of Finland.

Bird data

We included 88 land bird species in our analysis. All studied

species had their northern or southern range boundaries in

Finland. Species were breeding and/or foraging in all the main

terrestrial habitats: 40 species occurred primarily in forests, 18

species in agricultural and bushy habitats, 14 in mires, 9 in

marshes and coastal wetlands and 7 in mountain heaths. We

consider this sample as representative of the Finnish land bird

species because it includes the majority of the families and

species traits (such as different migratory and foraging patterns)

found among almost 200 land bird species breeding in Finland.

The information on distribution of the target species and the

level of survey activity was extracted from the second bird atlas

survey in Finland, the field work for which was carried out in

1986–89 (Väisänen

et al

., 1998). Survey activity categories and

the way survey design was conducted are presented in detail in

Väisänen (1989). The bird atlas is unusual because of its high

mapping intensity and fine-scale resolution, and because it

consists of precisely georeferenced sampling locales rather than

interpolated maps with unknown survey activity.

There are about 3800 atlas squares of 10

×

10 km in Finland

using a uniform grid. 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 (i.e. singing or

M. Luoto

et al.

© 2006 The Authors

36

Global Ecology and Biogeography

,

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, 34–42, Journal compilation © 2006 Blackwell Publishing Ltd

displaying male observed once in typical nesting habitat),

2 = breeding probable (e.g. singing or displaying male with a

persistent territory observed, or female or pair present on more

than 1 day in the same place, or bird observed building nest) and

3 = confirmed breeding (see Väisänen, 1989). For the analysis of

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

variable. Bird atlas observers and organizers of the atlas 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 grid squares with survey activities of 2–5 in

our analysis. Consequently, the data used in the analyses

consisted of 2861 squares, which is 75% of all the squares (Fig. 1).

The data were resampled to 20

×

20 km (

n

= 1014), 40

×

40 km

(

n

= 261) and 80

×

80 km (

n

= 82) grid squares for further analyses.

Climate data

We used climatic data produced by the Finnish Meteorological

Institute as predictor variables for the bird distribution data,

using the same 10

×

10 km grid system (Venäläinen & Heikin-

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

1985–89 for all climatic variables. The number of candidate

climatological explanatory variables was high and they were

clearly intercorrelated; therefore particular attention was paid

to the ecological plausibility of the relationships between the

response variable and the explanatory variables. We focused

on three bioclimate variables which affect the numbers,

reproduction and survival of breeding birds: mean tempera-

ture in April–June (MTAJ), precipitation in April–June (PREAJ)

and mean temperature of the coldest month (MTCO) (see Forsman

& Mönkkönen, 2003). As in the case of species data, climate data

were resampled to 20-km, 40-km and 80-km resolutions.

Land cover data

We utilized a new European land cover and land-use classifica-

tion CORINE (Coordination of Information on the Environ-

ment) as land cover information in the analyses (European

Commission, 1994). The classification of land cover into

CORINE classes in Finland is based on automated interpretation

of Landsat ETM+ satellite images (from the years 1999–2002)

and data integration with existing digital maps (Härmä

et al

.,

2004). The national CORINE data base for Finland is in raster

format and has a spatial resolution of 25 m, which makes it of

fine resolution compared with European CORINE land cover

data with the minimum mappable unit of 25 ha. Satellite image-

derived land cover data were combined with existing digital

land-use and soil information produced by the National Land

Survey of Finland. The proportions of land cover types were

calculated as the cover for each grain size from a digital CORINE

data base using an ArcView Spatial Analyst (version 3.2, ESRI,

Redland, CA, USA).

Land cover classes were selected to match as accurately as

possible with habitat requirements of the studied 88 bird species.

For example, marshland cover, which was included as one land

cover class in the present work, was selected in a specific study as

a significant variable for explaining the distribution of all

marsh-nesting bird species (Virkkala

et al

., 2005). The CORINE

data used in this study comprise eight classes of land cover: (1)

arable land

(ARA) includes cereal and fodder fields, (2)

low-

intensity agricultural land

(LOIA) consists of cattle grazing areas

and different meadow types, and also long-term fallow fields and

small forest patches surrounded by arable land, (3)

deciduous

forest

(DECI) includes closed forests dominated by broad-leaved

deciduous tree species, e.g. birches (

Betula pendula and Betula

pubescens

), grey alder (

Alnus incana

) and aspen (

Populus

tremula

), (4) the

coniferous forest

(CONI) class covers all other

forested areas except predominantly deciduous forests, e.g. forest

types from full-grown coniferous forests to felling areas and clear

cuttings. The most predominant tree species are Norway spruce

(

Picea abies

) or Scots pine (

Pinus sylvestris

), (5)

wetland

(WETL)

covers mires and other peat bogs, (6)

marshland

(MARSH)

includes inland and coastal marshes, (7) the

alpine

(ALP) class

consists of sparsely vegetated areas and bare ground, typically

above the tree limit in mountain areas in northern Finland,

(8)

built-up areas

(BUILT) comprise areas mainly with fabricated

or human-made surface materials (e.g. buildings, roads and

green urban areas).

Climate–land cover relationships

We used two multivariate methods to examine the correlation

and co-inertia between the three climatic and eight land cover

Figure 1 Location of Finland in northern Europe and the distribution of 10 × 10 km squares with good quality bird data available in Finland.

Scale matters in bioclimatic models

© 2006 The Authors

Global Ecology and Biogeography

,

16

, 34–42, Journal compilation © 2006 Blackwell Publishing Ltd

37

predictors included in the study: principal components analysis

(PCA) and the outlying mean index (OMI). First, following

Myklestad and Birks (1993) and Heikkinen

et al

. (1998), all cli-

matic and land cover variables were subjected simultaneously

to a correlation matrix PCA in order to investigate the cor-

relation structure between these variables (see Legendre &

Legendre, 1998) separately at four spatial resolutions. Next,

following Thuiller

et al

. (2004a), we applied the OMI

method, as implemented in ADE-4 software (Thioulouse

et al

.,

1997). OMI is a co-inertia analysis, which searches for axes

that maximize the covariance between the row coordinates

of the two tables (see Doledec

et al

., 2000). The method was

used here to examine the co-variation between the climatic

and land cover variables, with the main emphasis on analysing

how much of the variation in land cover can be captured by

the three climatic variables, indicated in the results as total

inertia. OMI was applied by performing a normalized PCA on

the climate data and then linking the land cover table to the PCA

output.

Model calibration

We used generalized additive models (GAMs) in the model

calibration. GAMs were performed using GRASP (generalized

regression analysis and spatial prediction) in S-PLUS (version 6.1

for Windows, Insightful Corp.) (Lehmann

et al

., 2002; Maggini

et al

., 2006). The GAM models were built using a stepwise

selection procedure to select relevant 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 four degrees of free-

dom was fitted first. The variable dropping or conversion to

linear form was then tested using Akaike’s information criterion

(AIC) (Akaike, 1974). Because the response variables

represent binary data (presence or absence of species), a

binomial distribution of error via a logistic link function was

applied.

Model evaluation

The selected models were evaluated by two methods. The

first method was based on the explained deviance of the models,

i.e. how large a proportion the explained deviance was of the

total deviance in each model. This was regarded as the explana-

tory power of the model. The other method was cross-validation,

which was made with four subsets of the entire data set, each

subset containing an equal number of randomly selected

data points. Each subset was then dropped from the model, the

model was recalculated and predictions were made for the

omitted data points. Combination of the predictions from

the different subsets was then plotted against the observed data

(Lehmann

et al

., 2002). Model performance was measured

using the area under the curve (AUC) of a receiver operating

characteristic (ROC) plot (Fielding & Bell, 1997). This

evaluation method is often considered as the predictive power of

the model.

RESULTS

Climate–land cover relationships

The first three axes of the PCA based on all three climate and

eight land cover variables accounted for 60.3–76.5% of the total

variation in climate and land cover at the four spatial resolutions

(Table 1). The total amount of variation captured by PCA

increased gradually with increasing resolution (Pearson correlation

coefficient

r

= 0.975,

P

= 0.025,

n

= 4). The PCA results sug-

gested that some of the variables are clearly correlated with each

other, e.g. the area of arable land, built-up areas, mean tempera-

ture of the coldest month and mean temperature of April–June

However, variation in other land cover variables, particularly

deciduous and coniferous forests and marshland, was not

strongly related to the three climate variables. In the OMI results

the climate variables accounted for 34.1–39.8% of the total

variation in the land cover data (Table 1). There was a positive,

but statistically insignificant, relationship between the total

inertia and spatial resolution (

r =

0.836,

P

= 0.193,

n

= 4).

Explanatory power of the models

The prevalences (the ratio of presence squares to the total

sample) of the 88 bird species ranged from 0.004 to 0.792 (mean

0.248) at the 10-km resolution (Table 2). The amount of

deviance explained by the 88 bird–climate models ranged from

6.0% to 72.3% with a mean of 34.0% at the finest 10-km resolution,

whereas at the largest resolution the explanatory power varied

Table 1 Climate–land cover relationships at different spatial resolutions. Percentages of variation accounted for the first three axes of principal components analysis (PCA), including all three climate and eight land cover variables. Percentages of explained inertia based on outlying mean index analysis (OMI)

Resolution (km) PCA OMI

10 60.30 34.14

20 64.61 36.97

40 70.50 39.13

80 76.50 39.81

Table 2 Minimum and maximum values and mean and standard errors (SE) of species (n = 88) prevalence (the ratio of presence squares to the total sample) across scales. The range of prevalence values is 0–1, where 0.0 = absence in every square and 1.0 = presence in every grid square

Scale (km) Min. Max. Mean SE

10 0.004 0.792 0.248 0.025

20 0.009 0.876 0.322 0.026

40 0.031 0.946 0.463 0.029

80 0.073 0.976 0.566 0.027

M. Luoto

et al.

© 2006 The Authors

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Global Ecology and Biogeography

,

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, 34–42, Journal compilation © 2006 Blackwell Publishing Ltd

between 4.4% and 98.7% with a mean of 60.8% (Table 3). The

inclusion of land cover increased the explanatory power of

bioclimatic models by more than 25% at all spatial resolutions.

The increase was statistically significant (Wilcoxon signed rank

test,

P

< 0.001) at all four studied resolutions. At the 10-km,

20-km and 40-km resolutions the inclusion of land cover vari-

ables improved the explanatory power of all 88 bird species

models, whereas at the 80-km resolution the inclusion improved

only 31 models out of 88. For all the variables selected into all

88 bird species models at the four resolutions see Appendix S1 in

Supplementary Material.

Cross-validation results of the models

The model performance was assessed using AUC based on cross-

validation. In general, the accuracies of the 88 bird–climate

models were rather good at all studied resolutions (Table 3). At

the finest resolution, mean accuracies of the models varied from

low to very high (0.65–0.99), with a mean of 0.86. At the coarsest

resolution the AUC values of the bird–climate models varied

between 0.47 and 0.99, with a mean of 0.87.

The inclusion of land cover increased the accuracy of bio-

climatic models at the 10-km and 20-km resolutions (Wilcoxon

signed rank test,

P

< 0.001). There was no statistically significant

change at the 40-km resolution (

P =

0.0663), whereas at the

80-km resolution the inclusion of land cover variables decreased

the modelling accuracy with statistical significance (

P =

0.0053).

The rank test results remained the same after a Bonferroni cor-

rection for multiple tests (

n

= 8).

At the finest three resolutions of climate–land cover, 39–55 of

the 88 species had an AUC value higher than 0.90, indicating that

44–63% of our bird–climate–land cover models had an excellent

model performance (Table 4). Overall, the value of AUC was

below 0.7 in only 3.6% of all the models in all the analyses

(25/704), and could thus be regarded as poor (AUC 0.6–0.69)

or failed (AUC < 0.6) (see Swets, 1988).

As an example, the performance of the models is presented for

two bird species, the marsh harrier,

Circus aeruginosus

, and the

grey-headed woodpecker,

Picus canus

(Fig. 2), both of which had

a prevalence of 0.1 at the 10-km resolution. For the marsh harrier

both the explained deviance and the AUC value, indicating

modelling performance, increased from the pure climate to the

climate–land cover model, whereas in the grey-headed woodpecker

the inclusion of land cover did not significantly improve the

performance of the pure climate model (Fig. 2). Furthermore, the

positive correlation between the predicted probabilities of occurrence

between climate and climate–land cover models was high in the

grey-headed woodpecker but low in the marsh harrier (Fig. 3).

DISCUSSION

The spatial scale on which species distribution modelling is

undertaken is of fundamental importance for the results and

inferences of ecological and biogeographical studies (Wiens,

1989; Lawler

et al

., 2004; Pearson

et al

., 2004; Rahbek, 2005). The

choice of spatial extent and resolution can directly affect the

results of any given analysis and the comparability of conclusions

between studies (Rahbek, 2005). Rahbek & Graves (2001)

presented the effect of grain size on models of avian species

richness across South America. They demonstrated that

increases in grain size resulted in increases in model fit, and that

the relative contributions of variables changed with resolution.

In the context of bioclimatic modelling, it has been proposed

that climate impacts on the distribution of species are most

apparent on macroscales with broad spatial extents and coarse

resolutions (Currie, 1991; Huntley

et al

., 1995; Pearson

et al

.,

2004). The validity of the use of bioclimate models has been

questioned (Woodward & Beerling, 1997; Hampe, 2004),

because inherently they consider climate as the only determinant

of species distributions. In response to this criticism it has been

proposed that land cover and biotic interactions may play

Table 3 Mean (± SE) values of explained deviance and AUC (the area under the curve of a receiver operating characteristic plot) of the 88 land bird species based on pure climate variables and climate and land cover variables combined at the different scales of resolution. Statistical tests by Wilcoxon signed rank test, critical significance level P = 0.05/8 = 0.0063. Ranks: negative/positive/tied. Negative or positive rank refers to pure climate variables

Scale (km) Climate variable

Climate–land

cover variables z P Ranks

Explained deviance

10 0.340 ± 0.016 0.427 ± 0.016 8.147 < 0.0001 88/0/0

20 0.410 ± 0.016 0.516 ± 0.016 8.147 < 0.0001 88/0/0

40 0.514 ± 0.017 0.690 ± 0.017 8.147 < 0.0001 88/0/0

80 0.608 ± 0.022 0.907 ± 0.010 8.134 < 0.0001 86/2/0

AUC

10 0.859 ± 0.007 0.883 ± 0.006 6.927 < 0.0001 79/8/1

20 0.878 ± 0.007 0.898 ± 0.007 6.589 < 0.0001 78/9/1

40 0.894 ± 0.007 0.900 ± 0.007 1.837 0.0663 52/34/2

80 0.868 ± 0.011 0.854 ± 0.010 2.790 0.0053 31/57/0

Scale

(km)

Climate Climate–land cover

< 0.70 0.70–0.79 0.80–0.89 0.90–1.00 < 0.70 0.70–0.79 0.80–0.89 0.90–1.00

10 2 12 50 24 2 4 43 39

20 2 10 38 38 3 4 26 55

40 2 6 27 53 3 3 30 52

80 6 7 31 44 5 14 44 25

Table 4 Number of species–climate models and species–climate–land cover models in different accuracy classes of AUC at the four different scales of resolution

Scale matters in bioclimatic models

© 2006 The Authors Global Ecology and Biogeography, 16, 34–42, Journal compilation © 2006 Blackwell Publishing Ltd 39

a relatively minor role in governing species distributions on

national to continental scales, where climate is expected to

exert a dominant role (Pearson & Dawson, 2003). Moreover,

it has been presented that within the suitable climate space of

the species other factors influence its distribution in a hierarchical

manner, different factors being better correlates at different

scales. Thus, land cover and species interactions are assumed to

determine species occupancy patterns at finer spatial resolu-

tions to a greater extent than climate (Hill et al., 1999; Bailey

et al., 2002; Opdam & Wascher, 2004; Pearson et al., 2004).

Previous analyses of the potential effect of land cover on

species distribution models have indicated that land cover

information often significantly improves the explanatory power

of models on local and regional scales (e.g. Stefanescu et al.,

2004; Venier et al., 2004). Here, we provide evidence that the

explanatory power of land cover might also be important on

broader scales, and that the availability of suitable habitats

probably plays a role in shaping the dynamic responses of species

to changes in climate. However, many previous studies have

investigated the role of land cover only at a single spatial

resolution, in contrast to the multiresolution approach adopted

in our study.

The results of OMI analyses of environmental data suggested

that in Finland, at the resolutions of 10–80 km, the spatial

variation of land cover is moderately correlated with spatial

variation between climate variables. However, it appears that

about two-thirds of the variation in the land cover data is not

explained by climate variables. The amount of co-inertia

increased gradually with increasing resolution, which implies

that the degree of covariance of land cover and climate variables

depends on the resolution of the study. At the European 50-km

resolution, Thuiller et al. (2004a) found that land cover was

mainly driven by climate. In their study bioclimatic variables

Figure 2 Projected distributions of two species with different modelling accuracies and habitat preferences: the occurrence of marsh harrier (Circus aeruginosus): (a) climate model and (b) climate-land cover model; and the occurrence of grey-headed woodpecker (Picus canus): (c) climate model and (d) climate-land cover model. Black dots represent the sampling plots where the species was present, and shaded areas are the areas modelled as suitable for the species. 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). D2 = percentage of explained deviance and AUC = the area under the curve of a receiver operating characteristic (ROC) plot.

Figure 3 The correlation between probability values derived from the climate and climate-land cover models: (a) marsh harrier (Circus aeruginosus) and (b) grey-headed woodpecker (Picus canus). Probability values vary between 0.0 and 1.0.

M. Luoto et al.

© 2006 The Authors40 Global Ecology and Biogeography, 16, 34–42, Journal compilation © 2006 Blackwell Publishing Ltd

explained 96% of the land cover distribution. Our observation of

a lower correlation between climate and land cover over a smaller

geographical range, compared with the result of Thuiller et al.

(2004a), is an important outcome as it supports the idea that

including both types of variables may bring essential informa-

tion into bioclimate envelope modelling, particularly on the

national and regional scales (see Pearson et al., 2004).

Our modelling showed a relatively close fit between the three

climate variables and the distributions of different bird species at

all studied resolutions, with almost 90% of our bird–climate

models having a good or excellent model performance (Swets,

1988). This suggests that occupancy patterns of bird species in

Finland are rather clearly correlated with climate. Consequently,

bioclimate envelope models can provide useful tools for identify-

ing the relationships between these species and the environment

at resolutions ranging from 10 km to 80 km (Pearson & Dawson,

2003). However, the inclusion of land cover increased the

modelling accuracy of bioclimatic models at 10-km and 20-km

resolutions, whereas the model performance did not improve at

40–80-km resolutions. On the two finest scales, the inclusion of

land cover variables increased the cross-validation results in the

majority of the models. On the other hand, our results support

the conclusion of Thuiller et al. (2004a) that species distributions

at coarser resolutions, in our case 40 km and 80 km, are driven

mainly by climatic factors. On the coarser scales, there is an

underlying high heterogeneity of land cover in each grid cell,

which will effectively decrease the pure effect of land cover in the

species distribution models. In other words, grid cells of this size

inevitably harbour patches of most or all land cover types occur-

ring in the region, which lowers the environmental variation

between the grid cells. Employing finer resolutions has clear

advantages in species distribution modelling, particularly as it

enables researchers to identify regions with suitable climate but

unsuitable land cover for a given species (Pearson et al., 2004).

Our two species examples show the ecological basis in the

species responses in pure climate and in climate–land cover

models (Fig. 2). The prediction of grey-headed woodpecker

distribution was not greatly improved by including land cover

variables in the model, in contrast to the marsh harrier. The grey-

headed woodpecker is a resident species, which mainly utilizes

ants on the ground when foraging (Rolstad & Rolstad, 1995).

Therefore, the distribution of the grey-headed woodpecker is

restricted largely by snow depth: the species is clearly most

abundant in the south-western coast and archipelago (Väisänen

et al., 1998), where snow cover is irregular and snow depth is, on

average, low. Consequently, climatic variables predict the

distribution of the grey-headed woodpecker well and the predic-

tion of distribution is continuous in both climate and climate–

land cover models. The migratory marsh harrier is highly

dependent on the distribution of marshland areas, which occur

patchily in Finland (Virkkala et al., 2005). As a consequence, the

inclusion of land cover variables increased considerably

the predictive power of the pure climate model, and the

predicted distribution of the marsh harrier in the climate–land

cover model is scattered, in contrast to that in the pure climate

model.

CONCLUSIONS

The findings reported here have substantial implications for

biodiversity modelling studies, particularly in the context of

global change. Inclusion of land cover information into pure

bioclimatic models produced often strikingly different results at

different spatial resolutions, and the role of land cover in the

modelling accuracy varied across grain sizes. These results draw

attention to the importance of the resolution at which dependent

and response variables are recorded in the biogeographical

studies. Most importantly, if the detail with which sample

attributes are discriminated can affect the inferences of biogeo-

graphical and ecological studies, determination of the proper

resolution of any analysis should be incorporated carefully into

the study design (Turner, 1989). Until recently, very few model-

ling studies have addressed climate–land cover interactions,

although it is recognized that ignoring habitat distribution and

focusing only on climate response surfaces may result in less

accurate species distribution projections in climate change

studies (cf. Bakkenes et al., 2002). In our study, the inclusion of

land cover variables clearly improved the performance of the bird

distribution models at resolutions of 10 km and 20 km. This

suggests that the determinants of bird species distributions are

hierarchically structured, reinforcing the conclusions of Pearson

et al. (2004) and Pearson & Dawson (2003) that climatic vari-

ables are large-scale determinants, followed by land cover at finer

resolutions.

ACKNOWLEDGEMENTS

A study of this nature would not be possible without hundreds of

volunteers who contributed their data to the bird atlas. Stefan

Fronzek and Riitta Teiniranta helped in aggregating the climate

and land cover data for this study. M. Bailey helped with correc-

tion of the English text. Different parts of this research were

funded by the EC FP6 Integrated Project ALARM (GOCE-CT-

2003-506675).

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Editor: Tim Blackburn

SUPPLEMENTARY MATERIAL

The following material is available online at

www.blackwell-synergy.com/loi/geb

Appendix S1 Selected environmental variables of the 88 bird

species–climate–land cover models at four different resolutions

based on generalized additive modelling (GAM).

BIOSKETCHES

Miska Luoto acts as a programme director at the Thule

Institute, University of Oulu. He is a physical geographer

interested in spatial analysis and modelling in

biogeography and geomorphology. His present research

emphasis is on the integration of climate and land cover

data in biodiversity modelling.

Raimo Virkkala acts as a senior research scientist at the

Finnish Environment Institute. He has studied the effects

of habitat changes on forest biota, and the significance of

reserve networks in preserving habitats and species

diversity. His main interests include landscape level and

scale-dependent analyses of bird species distribution

patterns.

Risto K. Heikkinen is a senior research scientist at the

Finnish Environment Institute. He has experience in

multivariate and modelling analysis of species

compositional and ecological trends. His latest research

includes assessments of nature reserve network,

conservation biology of boreal environments and impacts

of climate change on biodiversity.