An analysis of central Alpine capercaillie spring habitat at the ...

www.elsevier.com/locate/foreco

Forest Ecology and Management 215 (2005) 307–318

An analysis of central Alpine capercaillie spring habitat

at the forest stand scale

Kurt Bollmann a,*, Patrick Weibel a,b, Roland F. Graf a

a Swiss Federal Institute for Forest, Snow and Landscape Research, Zurcherstrasse 111, CH-8903 Birmensdorf, Switzerlandb Department of Environmental Sciences, Swiss Federal Institute of Technology Zurich ETH, CH-8092 Zurich, Switzerland

Received 23 September 2004; received in revised form 27 April 2005; accepted 10 May 2005

Abstract

We investigated the small-scale habitat selection of capercaillie (Tetrao urogallus) with respect to forest stand composition

and topographic characteristics in managed alpine forests of the central Alps in eastern Switzerland. In 12 study areas, we

compared 184 study plots in forests stands used (presence) or unused (absence) by the species in spring. A logistic regression

model with four predictor variables correctly classified 68.5% of the study plots for capercaillie presence or absence. The model

was validated with a 5-fold data-splitting procedure. Four forest stand variables significantly contributed to the model: canopy

cover, field-layer cover, the number of basal-branched solitary trees and stand structure. We could not confirm a direct response

of capercaillie to bilberry cover in the study areas although bilberry was more abundant in presence plots. Our model is a

practical tool for forest managers in the central Alps to assess the abundance and distribution of suitable habitat patches, to

monitor trends in habitat suitability dynamics, and to increase the vegetation-related carrying capacity for capercaillie by

improving vegetation structure. Suitable capercaillie habitat is characterised by an intermediate canopy cover (30–60%), a well-

developed field-layer and the presence of several basal-branched solitary trees. The model is partly based on forest stand

variables used in national and regional inventories. However, the inventories would be improved for conservation purposes if

information on basal-branched solitary trees as well as field-layer type and composition, preferably the abundance of Ericaceae,

Vaccinium and Rubus species, were also collected.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Central Alps; Forest stand description; Habitat model; Logistic regression; Presence–absence; Tetrao urogallus; Model validation

1. Introduction

Habitat suitability models (HSM) have increas-

ingly gained attention by the scientific and conserva-

* Corresponding author. Tel.: +41 1 739 2411;

fax: +41 1 739 2215.

E-mail address: [email protected] (K. Bollmann).

0378-1127/$ – see front matter # 2005 Elsevier B.V. All rights reserved

doi:10.1016/j.foreco.2005.05.019

tion communities during the last years (Guisan and

Zimmermann, 2000; Scott et al., 2002). The models

emphasise relationships between an organism and its

habitat if the environmental variables have direct

ecological relationships with the organism (Vaughan

and Ormerod, 2003). Thus, habitat models are a

formalised description of the distribution of a species

in relation to environmental conditions (Morrison

.

K. Bollmann et al. / Forest Ecology and Management 215 (2005) 307–318308

et al., 1998; Scott et al., 2002), and represent valuable

tools for conservation planning if the models are

carefully validated (e.g., Mladenoff et al., 1999; Woolf

et al., 2002). Therefore, conservationists often use

habitat models to assess the amount and distribution of

suitable habitat for an endangered species. Holloway

et al. (2003) recommended to utilize such information

for the development of species recovery programmes.

Unfortunately, a HSM for one specific region may

have only limited potential for generalisation among

regions (Storch, 2002; Graf et al., submitted for

publication). Thus, when transferring results of a

habitat model from one region to another the

specificity of these results has to be considered. We

expect such specificity to be notably distinct in alpine

ecosystems where the heterogeneous topography

causes significant regional differences in climate,

precipitation and patchiness (e.g., Franz, 1979;

Grabherr et al., 2003).

The capercaillie is a large forest-dwelling galliform

bird threatened throughout central Europe (Storch,

2000). The species has narrow habitat preferences

(e.g., Sjoberg, 1996; Storch, 2001), is seen as an

indicator species for naturally open coniferous forests

and has been documented as an umbrella for high bird

species diversity (Suter et al., 2002). Distribution

patterns and sizes of regional capercaillie populations

are influenced by ecological factors that exert their

effect on various spatial scales: from the local stand

(0.5–100 ha) up to the region (10–100 km2) (Andren,

1994; Storch, 1997, 2002, 2003; Kurki et al., 2000).

According to Storch (1997) a minimum of three scales

are necessary to describe the habitat of the species: the

extent of forest and its juxtaposition with open land at

the large scale (furthermore called landscape scale),

the stand mosaic at the intermediate (forest) scale and

the vegetation structure at the small (stand) scale.

The capercaillie is a red-listed species in Switzerland

(Storch, 2000; Keller et al., 2001) in urgent need of a

National Species Action Plan (Bollmann et al., 2002).

The remaining populations are found in the large

contiguous forests of the upper montane and subalpine

zones of the Jura mountains, the northern Pre-Alps, the

eastern Alps and the southern valleys of the Grisons

(Glutz von Blotzheim, 1973; Mollet et al., 2003). So far,

the habitat model of Schroth (1990, 1994) has been used

to assess suitable habitat for capercaillie at the stand

level in Switzerland (BUWAL, 2001). This procedure

was successful to predict core areas of capercaillie

presence in the Jura mountains and the Pre-Alps.

However, the applicability of the model has not been

tested in the forests of the dry inner alpine valleys of the

Alps containing the stronghold (35–45%) of the

national capercaillie population. Due to its central

alpine characteristics the Grisons are delineated as a

separate region in the Swiss Capercaillie Action Plan

(Mollet et al., in preparation). The central Alps differ

from the Pre-Alps by having a more continental climate

with lower annual precipitation rate, cold winters and

the upper timber line at the altitude of 2300 m (Pre-

Alps: 1800 m).

Vegetation structure and habitat components at the

stand scale have been studied in several regions (sensu

Klaus et al., 1989; Storch, 2001; Sachot et al., 2003).

Forests occupied by capercaillie typically have a

structure characterised by intermediate canopy cover,

high proportions of old and open stands and extensive

field-layer (Eiberle, 1976; Rolstad, 1988; Wegge et al.,

1992; Schroth, 1994; Storch, 1995; Picozzi et al., 1996;

Catt et al., 1998). Furthermore, capercaillie are well-

known to prefer habitats rich in ericaceous shrubs, in

particular bilberry (Vaccinium myrtillus) (e.g., Storch,

1993a,b; Schroth, 1994; Lieser, 1996; Saniga, 1998;

Selas, 2000). Because forestry practices can be

detrimental to capercaillie habitat (Klaus, 1991; Wegge

et al., 1992; Sjoberg, 1996; Kurki et al., 2000) and no

habitat model is available for the Grisons, we aimed at

evaluating the capercaillie’s habitat preferences at the

stand scale for a region less affected by recent

population decline than the Pre-Alps (Storch, 2000;

Segelbacher and Storch, 2002; Mollet et al., 2003). The

central Alps could be the stronghold of future

capercaillie conservation in central Europe. Accord-

ingly, the objective of this study was to identify a small

sample of predictor variables available in national and

regional forestry inventories that differentiate best for

preferred capercaillie spring habitat in this region. We

addressed two major questions: (1) by which key factors

do forest stands in the central Alps used (presence) and

unused (absence) by capercaillie differ? and (2) in what

respect do habitat preferences at the stand scale in the

central Alps differ from the well-known habitats in

coniferous forests of the Pre-Alps and lower mountain

ranges of central Europe? In this paper we present

results from 12 study areas representative of forests in

the central Grisons.

K. Bollmann et al. / Forest Ecology and Management 215 (2005) 307–318 309

2. Methods

2.1. Study region and study design

The study was conducted in the Swiss Alps in the

canton of Grisons. The study region comprised

approximately 235 km2 of the central and northern

Grisons (Fig. 1) and is part of the national capercaillie

region 4 b (Mollet et al., 2003). Mountain ranges rise

up to 2000–2500 m above sea level and are separated

by two main valleys of 15–35 km length at 660–

1630 m altitude. The valleys show a typical altitudinal

zonation with settlements, farmland, forests, alpine

pastures and mountain ridges. Capercaillie was

patchily distributed and restricted to coniferous and

mixed-coniferous forests in the study region.

We used the cantonal capercaillie inventory (Swiss

Ornithological Institute and Canton of Grisons, unpub-

lished map) as a means of stratifying and classifying the

study region for a systematic plot sampling (Ratti and

Garton, 1994). The cantonal inventory is based on the

Fig. 1. Distribution of the 12 study areas in the study region of the central

Chur; D, Davos; T, Tiefencastel. DHM25 # 2005 swisstopo (BA056996

national surveys of 1971 (Glutz von Blotzheim, 1973)

and 1985 (Marti, 1986) and has continuously been

supplemented by observations of professional wildlife

wardens. We systematically searched all formerly and

recently occupied forests for indirect and direct

evidences of capercaillie presence from April 4 to

May 31,2002.Hereby, weconcentratedonkey elements

of capercaillie winter and spring habitat: feeding and

roosting trees, rest sites, low-branched trees, lek areas,

internal forest edges. This approach was because

capercaillie has a strong preference for few structural

and nutritional forest components during winter

(Klaus et al., 1989; Storch, 2001). Capercaillie records

included sightings and indirect evidence of capercaillie

presence, mainly faeces, feathers and footprints.

Surveying has only conducted during weather and

snow conditions that allowed detecting indirect signs of

capercaillie presence.

We estimated a total of 64–74 males in the study

region. Twelve study areas were selected along an

altitudinal gradient within the valley of Landwasser,

Grisons in Switzerland (small map). Squares indicate localities: Ch,

).


Table 1

List of the 12 study areas in the central Alpine study region of the canton of Grisons, Switzerland

No. Nearest village/local

name

Altitudinal range

(m a.s.l.)

Area (ha) No. of study plots

(no. of class ‘‘presence’’, %)

Vegetation

zone

1 Davos/Buelenwald 1708–2012 167 14 (3, 21.4) Subalpine

2 Clavadel/Wurzenwald 1590–1968 158 15 (8, 53.3) Subalpine

3 Monstein/Rotschwald 1650–1997 154 14 (5, 35.7) Subalpine

4 Monstein/Sagenwald 1603–2025 103 13 (6, 46.2) Subalpine

5 Filisur/Lochwald 1430–1977 152 14 (6, 42.9) Subalpine

6 Filisur/Gruenwald 1367–1720 199 15 (7, 46.7) Subalpine

7 Wiesen/Bannwald 1576–1928 171 18 (6, 33.3) Subalpine

8 Alvaneu/Got Davains 1563–1879 255 16 (7, 43.8) Subalpine

9 Alvaneu/Got Dafora 1414–1913 169 14 (6, 42.9) Subalpine

10 Lenz/Bova Gronda 1444–1616 179 12 (8, 66.7) Subalpine

11 Mon/Got Grond 1453–1856 514 27 (14, 48.1) Subalpine

12 Rhazuns/Feuns 982–1469 117 12 (3, 25.0) Montane

Total All sites 982–2025 2339 184 (79, 41.3) Montane–subalpine

For each study area the local name, altitudinal range, area, total number of study plots and number and percentage of the class ‘‘presence’’ as well

as the altitudinal vegetation zone (after Brassel and Braendli, 1999) are given.

Albula and Domleschg (Table 1). Eleven study areas

were situated in the subalpine zone between 1360 and

2030 m in the valleys of Landwasser and Albula.

Norway spruce (Picea abies) was the dominant tree

species. At sun-exposed and dry locations Scots pine

(Pinus silvestris) was widespread. One study area was

located between 980 and 1470 m in the montane zone.

There, Norway spruce and Common Beech (Fagus

silvatica) were the dominant tree species. At all study

areas, the forest belt extended between the timber line

and the upper edge of farmland surrounding the

settlements of the valleys. The study region is part of a

tourist region, but most recreational activities were

concentrated along few forest roads and hiking trails.

2.2. Classification of study areas and study plots

We classified each study area as either capercaillie

‘‘presence’’ or ‘‘absence’’ according to the distribution

of signs of the species. A presence perimeter

encompassed all locations of direct (sightings) and

indirect (droppings, tracks, feathers) evidences of the

species detected during the surveys buffered with a

distance of 250 m. Presence perimeters accounted for

41.3% of all study areas. The remaining parts of a study

area were classified as ‘‘absence’’. Between 12 and 27

quadratic study plots of 25 m � 25 m (625 m2) were

placed in each study area depending on its size and

contour. Thus, larger study areas held more plots in

general than smaller ones (see Table 1). We chose size

and shape of study plots so as to consider that (a) forest

structure variables need a minimum area of 200–

500 m2 to be adequately measured (Braun-Blanquet,

1964; Ph. Duc, pers. comm.), (b) Burki (1981)

recommended at least 400–600 m2 to characterise

stand structure of subalpine spruce forests and (c) data

of cover variables can more easily be estimated in

square than in circular plots because squares can be

divided in equal-sized cells (Higgins et al., 1994). The

location of each plot was determined by a random

process in GIS with a minimal distance of 100 m

between two neighbouring plots. With this approach

we minimized the problem of spatial autocorrelation

and gave regard to small-scale variation of alpine forest

stand characteristics (Larsson, 2001).

2.3. Habitat description

We use the term habitat as the resources and

conditions present in an area that produce occupancy –

including survival and reproduction – by the species in

question (Hall et al., 1997). Habitat characteristics

were measured between June 5 and 25, 2003. We

chose 26 variables to describe each single plot: four

topographic parameters, 21 variables for structural and

vegetation aspects, and one for the study area

(Table 2). To facilitate the transformation of scientific

results to practical applications, we chose as many


Table 2

Twenty-six variables used to characterise 184 study plots of 625 m2 at 12 study areas within the central Alpine distribution range of capercaillie

in the canton of Grisons, Switzerland

Variable Shortcut Data type Definition

Study area STUARE Categorical 1 of 12 exclusive capercaillie survey areas

Elevation ELEVAT Metric Meter, above see level

Aspect ASPECT Ordinal In 8 categories

Steepness of slope SLOPE Metric In degrees

Relative position in the field POSIT Categorical See Table 3

Field-layer cover FIECOV Metric Percentage of forest floor covered by ground vegetation

Berry-shrub cover BERCOV Metric Percentage of forest floor covered by Rubus and Vaccinium spp.

Bilberry cover BILCOV Metric Percentage of forest floor covered by Vaccinium myrtillus

Height of bilberry layer BILHGT Metric Mean height of Vaccinium myrtillus

Spruce in the tree layer SPRUCE Metric Percentage of Picea abies in the tree layer

Fir in the tree layer FIR Metric Percentage of Abies alba in the tree layer

Mountain pine in the tree layer MOPINE Metric Percentage of Pinus mugo in the tree layer

Larch in the tree layer LARCH Metric Percentage of Larix decidua in the tree layer

Scots pine in the tree layer SCPINE Metric Percentage of Pinus sylvestris in the tree layer

Beech in the tree layer BEECH Metric Percentage of Fagus sylvatica in the tree layer

Crown closure CRWCOV Ordinal See Table 3

Canopy cover CANCOV Metric Percentage of forest floor covered by trees

Cover of bushes BUSCOV Metric Percentage of forest floor covered by bushes

Stand structure/vertical layers STRUCT Ordinal One-layered, multi-layered

Stem density STEMDE Metric Number of live trees per sample plot

Number of tree clusters (‘‘Rotten’’) ROTTEN Metric Number of tree clusters per sample plot

Number of fallen dead trees DEADTR Metric Number of laying dead trees per sample plot

Number of root-plates ROOTPL Metric Number of root-plates per sample plot

Number of tree stumps STUMPS Metric Number of stumps per sample plot

Number of basal-branched solitary trees SOLITAR Metric Number of basal-branched solitary trees per sample plot

Number of ant nests ANTNES Metric Number of ant nests per sample plot

variables from the Swiss National and Cantonal Forest

Inventories as possible (Brassel and Braendli, 1999).

We further added variables that are not part of the

inventories but are known from literature and own

field experience to be important components of the

species’ habitat (i.e., bilberry cover, root-plates, basal-

branched solitary trees).

Each study plot was located in the field by the

combined use of a map (1:25,000), compass and

altimeter. The centre and extent of the plot were

marked with a measuring tape. We walked along a

transect line parallel to the slope through the plot

centre and measured or estimated the variables for

each quarter of the sample plot. Bilberry height was

taken as the average of five measurements. In cases

where plot description was not possible by a single

transect due to topographic constraints, the procedure

was repeated along the orthogonal line through the

plot centre. We estimated several structural and the

cover of the various vegetation variables with support

of the technical methodology of the Swiss National

Forest Inventory (Brassel and Lischke, 2001) and by

the method of Braun-Blanquet (1964), respectively.

Stem density, number of ‘‘Rotten’’ (group of

aggregated trees of various heights with a common

basal crown belt), number of laying dead trees,

number of stumps, number of root-plates and number

of basal-branched solitary trees were counted within

the plot area.

3. Statistics

3.1. Analysis and reduction of predictor variables

We used 26 variables for the description of the

study plots at the stand scale. Following Fielding and

Haworth (1995) and Menard (2001) we excluded one

of any two variables exceeding a pairwise Pearson

correlation of 0.7 to limit co-linearity in the predictor


Table 3

Transformation of the ordinal variables ‘‘relative position in the

field’’ and ‘‘crown closure’’

Variable Ordinal Metric

Relative position in the field Ridge & hilltop 4

Plateau & upper hillside 3

Middle hillside 2

Base of hill 1

Crown closure Crowded 95%

Normal 85%

Loose 70%

Open 50%

Sparse 30%

Grouped/crowded 40%

Grouped/normal 40%

Complete 60%

For the variable ‘‘relative position in the field’’ a metric scaling was

used that represents flight options of capercaillie (4: good, 3: fair, 2:

moderate, 1: poor). The eight categories of the Swiss National Forest

Inventory for crown closure (Brassel and Lischke, 2001) were

transformed in metric values representing average figures for these

categories.

variables. The variables known from literature to be

potential predictors of capercaillie habitat were

included in the analysis: bilberry cover, canopy cover,

stand structure, number of tree clusters. Excluded

variables were: berry-shrub cover, height of bilberry

layer, crown closure, stem density, cover of bushes.

Since the study areas were not chosen randomly with

regard to aspect and elevation we did not consider

these two variables for model building. Relative

position in the field and crown closure were assessed

as ordinal variables in the field. They were trans-

formed to continuous variables for analyses (Table 3).

Finally, 19 variables and study area as categorial

variable were used for model building.

3.2. Model analyses

A logistic regression model of the GLM family

(Aitkin et al., 1989; Menard, 2001) was calibrated by

comparing data of absence and presence plots.

Following Hosmer and Lemeshow (2000) we applied

a first order polynomial as linear predictor, a binomial

error distribution, and a log-likelihood function. The

squared variables were included for model selection if

the response variable showed a unimodal relationship

with predictor variables. We used the procedure

‘‘backward stepwise’’ to optimise the model.

According to the recommendations of Hosmer and

Lemeshow (2000), Schroder (2000) and Menard

(2001) we set the level of significance for a variable

to be excluded at 0.10. Thus, the probability to

overlook an existing relationship among model

variables was reduced. Final models retained uncor-

related variables that were significant at this level. We

calculated prediction accuracies by a confusion matrix

(Fielding and Bell, 1997). All statistical analyses were

performed with SPSS 11.0.

3.3. Model evaluation

To assess the accuracy and predictive power of our

model we used a split-sample approach (Van Houwe-

lingen and Le Cessie, 1990). Following Huberty (1994)

and Manel et al. (1999) we calculated a model derived

from a calibration set of 80% of the study plots which

was applied to the remaining plots (validation set). The

plots were selected randomly, and the process was

repeated five times. Mean accuracy measures were

derived from the five independent tests. The quality of

the models was assessed by the percentage of plots

correctly assigned (CCR), the false positive and the

false negative rate, as well as Cohen’s kappa coefficient

(proportion of specific agreement, see Guisan and

Zimmermann (2000), Manel et al. (2001)). Agreement

is perfect when Kappa = 1, and not better than expected

by chance when Kappa = 0. We further calculated the

area under the curve (AUC) value as measure of overall

accuracy independent of any threshold.

4. Results

4.1. The habitat suitability model

Because we found a unimodal relationship between

canopy cover and the probability of capercaillie

occurrence, we added the square of canopy cover as an

additional variable to the initial set of 20 predictor

variables. The final habitat model retained four of

them (Table 4). All were biotic predictor variables and

significantly contributed to the model equation. The

model did not select bilberry cover.

In total 68.5% of all study plots were correctly

classified, 64.4% in presence plots and 71.2% in

absence plots (Table 5). The other accuracy measures


Table 4

Results matrix of the logistic regression model after 15 steps with

the procedure ‘‘backward stepwise (likelihood ratio)’’

Multi-variate analysis

Variable B S.E. Wald p-Value

CANCOV 0.108 0.040 7.093 0.008

CANCOV_Sq �0.001 0.001 7.144 0.008

FIECOV 0.017 0.009 3.428 0.064

SOLITAR 0.518 0.191 7.383 0.007

STRUCT 0.885 0.475 3.471 0.062

CONSTANT �4.493 1.323 11.533 0.001

The listed variables significantly contributed to the model equation.

Fig. 2. Cumulative percentages of 22 classes of canopy cover for

absence and presence plots.

indicated a moderate fit (Kappa = 0.41, AUC = 0.78).

The mean accuracy values for the cross-validation

were generally higher, but still mean a reasonable fit.

The rate of false positive classifications shows that the

model simulated in average 4 of 10 cases as presence

even though they were classified as absence by us. The

respective figure for the false negative classifications

was much smaller.

4.2. The habitat parameters

The model revealed that the probability of caper-

caillie occurrence was positively influenced by an

increasing extent of field-layer and number of basal-

branched solitary trees. Onaverage,presence plotshada

field-layer cover of 77.38 � 2.16% (mean � S.E.). The

respective figures for absence plots were 54.50 �3.30%. The highest probability of capercaillie presence

has to be expected in forest stands with a field-layer

coverabove70%.Seventy-sevenpercentof thepresence

plots met this threshold. The number of basal-branched

solitary trees ranged from 0 to 4 in the study plots

Table 5

Characteristics and accuracy measures of the final habitat suitability

model (HSM) predicting presence or absence of capercaillie on the

study plots (n = 184) in the central Grisons

Method HSM Validation

Prevalence 0.430 0.432

AUC 0.778 0.827

CCR, optimal 0.696 0.761

Kappa, optimal 0.409 0.516

False positive rate 0.419 0.225

False negative rate 0.152 0.256

Mean validation measures were derived from a split-sample proce-

dure (4:1) repeated five times.

and was higher in plots with capercaillie presence than

with absence (Mann–Whitney U-test, Z = �4.780,

p < 0.001, d.f. = 1). There was at least one such tree

in 60.5% of presence plots but only in 36.2% of absence

plots. The capercaillie preferred forest stands with

intermediate canopy cover. Presence plots (31.58 �1.74%) separated best from absence plots (46.00 �2.66%) within the range from 25 to 65% (Fig. 2). The

vertical structure was multi-layered in 17 of 79 (21.5%)

presence plots and in 12 of 93 absence plots (11.4%;

Chi-square = 10.563, p < 0.032, d.f. = 1).

5. Discussion

Several habitat suitability models for capercaillie

have been developed in central Europe (Schroth, 1990,

1994; Storch, 2002; Sachot et al., 2003) to guide and

support capercaillie management. These models con-

cern peripheral (edge) regions of the species’ distribu-

tion. Unfortunately the models have not been evaluated

for central Alpine forest conditions nor have separate

models been developed. This bias of habitat analyses

and models towards edge regions of capercaillie

distribution is delicate from the conservation perspec-

tive because of three reasons. First, the species’ general

population decline is accompanied by a contraction of

the distribution area from the periphery to the core

(Nievergelt and Hess, 1984; Storch, 2000; Segelbacher

and Storch, 2002) – a common phenomenon for

declining species (Ceballos and Ehrlich, 2002). Second,


the central Alps encompass a large part of the total

Alpine capercaillie population (Storch, 2000; Mollet

et al., 2003). Third, habitat models are most reliable at

the region and scale where they have been developed

(Verbyla and Litvaitis, 1989; Wiens, 1989; Block et al.,

1994; Hall et al., 1997; Storch, 2002; Graf et al.,

submitted for publication).

5.1. Spring habitat selection at the forest stand

scale in the central Alps

Our logistic model of capercaillie habitat prefer-

ences was built on stand scale variables of forest

structure, food availability and topography. We did not

analyse spatial issues such as the amount of habitat

and the relative location of suitable patches (juxta-

position). The final model contained four vegetation-

specific (biotic) variables. Canopy cover is a key factor

in capercaillie habitats (e.g., De Franceschi and

Bottazzo, 1991; Gjerde, 1991; Picozzi et al., 1992;

Storch, 1993a,b). Figures of optimal canopy cover

were found to be in a range of 40% (Gjerde, 1991) to

80% (De Franceschi and Bottazzo, 1991). The latter is

too high and not suitable for capercaillie in the Alps

according to our experience. In agreement with

Leclercq (1987), Gjerde (1991) and Storch

(1993a,b) we found a preference of capercaillie for

a low to intermediate canopy cover (25–65%). This

preference guarantees that several requirements of

habitat quality are fulfilled in the forests of central

Grisons: enough light and warmth for the development

of a sufficient field-layer and associated insect

communities, a stem density that does not restrict

flight movements by capercaillie within the forest, and

space for the development of internal forest edges.

The relationship between field-layer and capercaillie

presence shows that the probability of the species’

occurrence is highest in forest stands with a well-

developed field-layer cover above 60%. The fact that

we demonstrated this relationship for winter–spring

habitat let us hypothesise that the winter home range of

capercaillie is more or less nested within the species’

summer habitat in the central Alps. Field-layer cover

has often been documented as important structural

component and trophic resource for capercaillie during

the growing season. The proximity to a well-developed

field-layer is important for capercaillie (i.e., Glutz von

Blotzheim, 1973; Storch, 1993c; Suchant, 2002)

because a high amount of Ericaceae dwarf-shrubs

offers good feeding options (Storch, 1993a). Simulta-

neously it enhances breeding success (Baines et al.,

2004) by providing shelter against predators and for

nesting sites (Rolstad, 1988; Storch, 1994).

The significance of bilberry for capercaillie

occurrence has been emphasised by many authors

(Glutz von Blotzheim, 1973; Rolstad, 1988; Klaus,

1991; Picozzi et al., 1992; Storch, 1993a; Schroth,

1994; Lieser, 1996; Saniga, 1998; Selas, 2000; von

Hessberg and Beierkuhnlein, 2000). In our model

bilberry cover was not retained. Although we found

higher abundance of bilberry in presence areas

(Z = �2.655, p < 0.01, d.f. = 1), we assume that the

variable was only represented in the model as a facet

of field-layer. Capercaillie spring distribution does not

seem to be restricted to forest stands with a minimum

amount of bilberry. But the relationship of capercaillie

presence and bilberry abundance can change with

season. Usually, winter home ranges are smaller and

separated from summer home ranges (Rolstad et al.,

1988; Rolstad and Wegge, 1989; Storch, 1995) and the

association of capercaillie with bilberry grows

stronger with the growing season progressing (Storch,

1993a). Thus, some plots of real summer presence

may have been declared as absence plots during our

spring surveys as the false positive rate of 0.4 of the

HSM probably indicates. Nevertheless, during sub-

sequent summer field work for habitat description, we

found signs of capercaillie presence in only two out of

105 plots previously classified as ‘‘absence’’.

Species composition of the tree layer did not

significantly influence the probability of capercaillie

presence. This may be surprising at first sight because

capercaillie exclusively feeds on needles of coniferous

trees during winter (e.g., Klaus et al., 1989; Summers

et al., 2004) and prefers either pines, silver fir or

spruce, depending on which tree species are present

(i.e., Gjerde, 1991; Spidsø and Korsmo, 1994; Lieser,

1996; von Hessberg and Beierkuhnlein, 2000). Since

our study area mainly lies in the subalpine zone, the

availability and distribution of coniferous tree species

might not be a limiting factor for capercaillie presence

in the study area.

Scherzinger (1974) mentioned basal-branched

solitary trees as important structural components of

a suitable habitat, and Gjerde (1984, 1991) reported

frequent roosting of cocks at the base of spruce trees


during winter. We found a similar pattern: resting sites

were shifted from the crowns of the trees to the ground

during the snow melt period in late winter when little

alternative ground cover was available. At that time

of the year, basal-branched solitary trees – mostly

Norway spruce – offer good possibilities to hide, rest

and roost below the protective roof of the ground-

touching branches.

Only 21.5% of our study plots had a multi-layered

structure. Several authors mention that capercaillie

habitats are preferably multi-layered (Leclercq, 1987;

Suchant, 1992; Suter et al., 2002) whereas Storch

(1993a,b) could not find a preference of the

capercaillie for multi-layered forest stands. The effect

of this characteristic on habitat suitability seems to

depend on growing condition and former harvest type.

Accordingly, we do not postulate multi-layered stand

structure as indispensable condition for the species

presence in Alpine forests. There, naturally open

forests may consist of old-growth one-layered stands

offering adequate habitat quality.

In summary, canopy cover, field-layer cover,

availability of basal-branched solitary trees and stand

structure are the essential factors for defining

capercaillie winter and spring habitat suitability at

the stand scale in the central Alps. Forest stands with

an intermediate canopy cover of 30–60%, a field-layer

above 60%, and several basal-branched solitary trees

may optimally combine the essential habitat compo-

nents at the local scale. Further, multi-layered stands

seem to influence the overall habitat quality positively

but are not mandatory for the species’ presence.

5.2. Limitations of the model

In addition to the factors included in our model,

habitat suitability for capercaillie is also influenced by

the frequency and distribution of human disturbance

(Meile, 1982; Klaus et al., 1989; Klaus and Augst,

1995) and the location of a habitat within a large-scale

habitat pattern (Andren, 1994; Kurki et al., 2000;

Storch, 2003). These two topics were not part of this

study. Thus, the conclusions of this paper are only

valid for capercaillie winter–spring habitats at the

forest stand-scale with a ‘‘compatible’’ level of human

disturbance.

Other than in coniferous forests of the Pre-Alps and

many smaller mountain ranges of central Europe, the

central Alps allow only stands with lower stem density

and generally open canopy structure because of the

harsh environmental conditions of the upper subalpine

zone (Brassel and Braendli, 1999). Normally, these

forests have a well-developed field-layer that, depend-

ing on exposition and soil type, is often dominated by

dwarf-shrubs, and are thus potentially suitable for

capercaillie over large areas. Further, stand structures

with tree clusters (‘‘Rotten’’) are more frequent in this

zone than in lower areas of the coniferous forest belt

(U. Buhler, pers. com.). Capercaillie seems to cope

well with these types of forest stands in the central

Alps although bilberry cover is generally sparse to

moderate. Coniferous forests of the Pre-Alps at lower

altitude are more productive and grow faster on

average. Accordingly, stands of good habitat quality

with a concentration of structural and nutritional

resources can naturally only be found at nutrient-poor

sites with slow-growing trees (ridges, topographic

edges, mires) resembling boreal forest structures.

Thus, diversely structured and open stands are rarer

and patchily distributed. Further, they are often

accompanied with bilberry in the field-layer. This

could explain the more pronounced relationship of

capercaillie occurrence and bilberry availability in the

Pre-Alps than in regions with an intrinsically lower

forest productivity (subalpine forests in higher

elevated areas, i.e., >1700 m) and thus contiguous

stands with moderate canopy cover.

There is general consensus that model evaluation

from an independent data set provides one of the few

robust procedures for validation (Fielding and Bell,

1997; Manel et al., 1999; Guisan and Zimmermann,

2000). Since we lack an independent data set, we

approximated such an evaluation method by a 5-fold

partitioning of the original data set of the study area.

The accuracy measures of the original habitat model

and the evaluation set provided a moderate agreement.

Our figures correspond well with the ones of Storch’s

small-scale habitat suitability model (Storch, 2002,

2003). She concluded that the predictive power of

habitat models could be enhanced when landscape

characteristics would be included. By combining our

habitat model with the large-scale model from Graf

et al. (submitted for publication) the Kappa-value

increased from 0.41 to 0.52. Assuming that we

accounted for the most important predictor variables

at the stand and landscape scales, we conclude that the


model agreement can be enhanced by a multi-scale

approach. The improvement was only moderate

because also other intrinsic and extrinsic factors,

i.e., human disturbance, predation or seasonality,

influence the distribution of a species (Morrison et al.,

1998). Furthermore, we cannot expect large differ-

ences in the Kappa-values among the stand- and

landscape-scale models because (a) both presence and

absence plots of most study areas lay within the

predicted area of the landscape-scale habitat model of

capercaillie in the Alps and (b) our study design was

not assessed to rigidly test for landscape scale effects.

5.3. Implications for conservation

We were able to show a relationship between the

occurrence of various habitat factors within a forest

stand and the presence of capercaillie. Our model is a

practical tool for forest managers of the central Alps

engaged in capercaillie conservation. The model has

the advantage that habitat becomes operational, i.e.,

practical, measurable and understandable (sensu Noss,

1990; Peters, 1991), because it is partly based on forest

stand variables used in regional and national

inventories. However, the inventories would be

improved for conservation purposes if the variables

‘‘basal-branched solitary tree’’ and ‘‘field-layer type’’

and ‘‘composition’’ were added. The latter preferably

includes Ericaceae, Vaccinium and Rubus species. A

random population dynamic process excluded, the

model allows the forest managers (1) to identify

vegetation-related components of high probability of

capercaillie presence, (2) to assess the abundance and

distribution of habitat patches of various suitability,

(3) to monitor habitat suitability dynamics over space

and time and (4) to create suitable habitat for the

species and to improve the vegetation-related carrying

capacity of an area. Until today, we have not tested the

model in other alpine areas of capercaillie distribution,

but we would expect it to work sufficiently well in

naturally coniferous forests in the inner valleys of

the Alps.

Acknowledgements

We thank the cantonal authorities for forest and

wildlife of the canton of Grisons for their authorisation

to use the capercaillie inventory and to conduct this

study. Ch. Hofmann provided statistical support. We are

grateful to W. Suter, U. Buhler, and two anonymous

reviewers for their valuable comments on the manu-

script. The study was partly financed by the Zurcher

Tierschutz and the Swiss National Science Foundation.

References

Aitkin, M., Anderson, D., Francis, B., Hinde, J., 1989. Statistical

Modelling in GLIM. Oxford Ststistical Science Series. Claren-

don Press, Oxford.

Andren, H., 1994. Effects of habitat fragmentation on birds and

mammals in landscapes with different proportions of suitable

habitat: a review. Oikos 71, 355–366.

Baines, D., Moss, R., Dugan, D., 2004. Capercaillie breeding

success in relation to forest habitat and predator abundance.

J. Appl. Ecol. 41, 59–71.

Block, W.M., Morrison, M.L., Verner, J., Manley, P.N., 1994.

Assessing wildlife-habitat-relationships models: a case study

with California oak woodlands. Wildl. Soc. Bull. 22, 549–561.

Bollmann, K., Keller, V., Zbinden, N., Muller, W., 2002. Prioritare

Vogelarten fur Artenforderungsprogramme in der Schweiz.

Ornithol. Beob. 99, 301–320.

Brassel, P., Braendli, U.-B., 1999. Schweizerisches Landesforstin-

ventar. Ergebnisse der Zweitaufnahme 1993–1995. Haupt Ver-

lag, Bern.

Brassel, P., Lischke, H. (Eds.), 2001. Swiss National Forest

Inventory: Methods and Models of the Second Assessment.

Swiss Federal Research Institute WSL, Birmensdorf.

Braun-Blanquet, J., 1964. Pflanzensoziologie: Grundzuge der Vege-

tationskunde, Springer, Wien.

Burki, A., 1981. Bestandsstrukturen im Gebirgsfichtenwald. Beiheft

zu den Zeitschriften des Schweizerischen Fortsvereins 69, 181.

BUWAL, 2001. Auerhuhn und Waldbewirtschaftung. VU-7021-D,

Bundesamt fur Umwelt, Wald und Landschaft, Bern.

Catt, D.C., Baines, D., Picozzi, N., Moss, R., Summers, R.W., 1998.

Abundance and distribution of capercaillie Tetrao urogallus in

Scotland 1992–1994. Biol. Conserv. 85, 257–267.

Ceballos, G., Ehrlich, P.R., 2002. Mammal population losses and the

extinction crisis. Science 296, 904–907.

De Franceschi, P.F., Bottazzo, M., 1991. Capercaillie Tetrao uro-

gallus and forest management in the Tarvisio Forest (Eastern

Alps Italy) in 1982–1988. Ornis Scand. 22, 192–196.

Eiberle, K., 1976. Zur Analyse eines Auerwildbiotopes im Schwei-

zerischen Mittelland. Forstwiss. Cent. bl. 95, 108–124.

Fielding, A.H., Haworth, P.F., 1995. Testing the generality of bird-

habitat models. Conserv. Biol. 9, 1466–1481.

Fielding, A.H., Bell, J.F., 1997. A review of methods for the

assessment of prediction errors in conservation presence/

absence models. Environ. Conserv. 24, 38–49.

Franz, H., 1979. Okologie der Hochgebirge, Ulmer, Stuttgart.

Gjerde, I., 1984. A telemery study of activity patterns in capercaillie,

Tetrao urogallus, during the winter season. M.Sc. Thesis. Uni-

versity of Bergen, Bergen, Norway.


Gjerde, I., 1991. Cues in winter habitat selection by Capercaillie I.

Habitat characteristics. Ornis Scand. 22, 197–204.

Glutz von Blotzheim, U.N., 1973. Handbuch der Vogel Mitteleu-

ropas, Band 5: Galliformes und Gruiformes. Akademische

Verlagsgesellschaft, Wiesbaden.

Grabherr, G., Nagy, L., Thompson, D.B.A., 2003. An outline of

Europe’s Alpine areas. In: Nagy, L., Grabherr, G., Korner, C.,

Thompson, D.B.A. (Eds.), Alpine Biodiversity in Europe.

Springer, Berlin Heidelberg, pp. 3–12.

Graf, R.F., Bollmann, K., Suter, W., Bugmann, H. On the generality

of habitat distribution models: a case study of capercaillie in

three Swiss regions, submitted for publication.

Guisan, A., Zimmermann, N.E., 2000. Predictive habitat distribution

models in ecology. Ecol. Model. 135, 147–186.

Hall, L.S., Krausmann, P.R., Morrison, M.L., 1997. The habitat

concept and a plea for standard terminology. Wildl. Soc. Bull.

25, 173–182.

Higgins, K.F., Oldemeyer, J.L., Jenkins, K.J., Clambey, G.K.,

Harlow, R.F., 1994. Vegetation sampling and measurement.

In: Bookhout, T.A. (Ed.), Research and Management Techniques

for Wildlife and Habitats. The Wildlife Society, Bethesda, MD,

USA, pp. 567–591.

Holloway, G.J., Griffiths, G.H., Richardson, P., 2003. Conservation

strategy maps: a tool to facilitate biodiversity action planning

illustrated using the heath fritillary butterfly. J. Appl. Ecol. 40,

413–421.

Hosmer, D.W., Lemeshow, S., 2000. Applied Logistic Regression.

In: Wiley Series in Probability and Mathematical Statistics,

second ed. John Wiley & Sons Inc., New York.

Huberty, C.J., 1994. Applied Discriminant Analysis. Wiley series in

probability and mathematical statistics. John Wiley & Sons,

New York.

Keller, V., Zbinden, N., Schmid, H., Volet, B., 2001. Liste der

gefahrdeten Brutvogelarten der Schweiz. BUWAL-Reihe Vollzug

Umwelt, Bundesamt fur Umwelt, Wald und Landschaft, Bern und

Schweizerische Vogelwarte, Sempach, Bern und Sempach.

Klaus, S., 1991. Effects of forestry on grouse populations: case

studies from the Thuringian and Bohemian forests, Central

Europe. Ornis Scand. 22, 218–223.

Klaus, S., Augst, U., 1995. Warum starb das Auerhuhn (Tetrao

urogallus) im Elbsandsteingebirge aus? Naturschutzreport 10,

109–123.

Klaus, S., Andreev, A.V., Bergmann, H.-H., Muller, F., Porkert, J.,

Wiesner, J., 1989. Die Auerhuhner, second ed. Die Neue Brehm-

Bucherei. A. Ziemsen Verlag, Wittenberg Lutherstadt.

Kurki, S., Nikula, A., Helle, P., Linden, H., 2000. Landscape

fragmentation and forest composition effects on grouse breeding

success in boreal forests. Ecology 81, 1985–1997.

Larsson, T.-B., 2001. Biodiversity evaluation tools for European

forests: biodiversity in European forests. Ecol. Bull. 50, 19–26.

Leclercq, B., 1987. Influence de quelques pratiques sylvicoles sur la

qualite des biotopes a Grand Tetras (Tetrao urogallus) dans le

massif du Jura. Acta ¨cologica/¨col. Gener. 8, 237–246.

Lieser, M., 1996. Zur Nahrungswahl des Auerhuhns Tetrao urogal-

lus im Schwarzwald. Ornithol. Beob. 93, 47–58.

Manel, S., Dias, J.M., Ormerod, S.J., 1999. Comparing discriminant

analysis, neural networks and logistic regression for predicting

species distributions: a case study with a Himalayan river bird.

Ecol. Model. 120, 337–347.

Manel, S., Williams, H.C., Ormerod, S.J., 2001. Evaluating pre-

sence-absence models in ecology: the need to account for

prevalence. J. Appl. Ecol. 38, 921–931.

Marti, Ch., 1986. Verbreitung und Bestand des Auerhuhns Tetrao

urogallus in der Schweiz. Ornithol. Beob. 83, 67–70.

Meile, P., 1982. Skiing facilities in alpine habitat of black grouse and

capercaillie. In: Lovel, T.W.I. (Ed.), International Symposium

on Grouse, World Pheasant Association, Reading UK, Dalhousie

Castle, Edinburgh, pp. 87–92.

Menard, S., 2001. Applied Logistic Regression Analysis, Quanti-

tative applications in the social sciences, second ed. Sage

Publications, London.

Mladenoff, D.J., Sickley, T.A., Wydeven, A.P., 1999. Predicting gray

wolf landscape recolonization: logistic regression models vs.

new field data. Ecol. Appl. 9, 37–44.

Mollet, P., Badilatti, B., Bollmann, K., Graf, R.F., Hess, R., Jenny,

H., Mulhauser, B., Perrenoud, A., Rudmann, F., Sachot, S.,

Studer, J., 2003. Verbreitung und Bestand des Auerhuhns Tetrao

urogallus in der Schweiz 2001 und ihre Veranderungen im 19.

und 20. Jahrhundert. Ornithol. Beob. 100, 67–86.

Morrison, M.L., Marcot, B.G., Mannan, R.W., 1998. Wildlife-

Habitat Relationships: Concepts and Applications, second ed.

University of Wisconsin Press, Madison.

Nievergelt, B., Hess, R., 1984. In: Brugger, E.A. (Ed.), Verander-

ungen im Bereich der Fauna. Umbruch im Berggebiet, Haupt,

Bern, pp. 337–351.

Noss, R.F., 1990. Indicators for monitoring biodiversity: a hierarch-

ical approach. Conserv. Biol. 4, 355–364.

Peters, R.H., 1991. A Critique for Ecology. Cambridge University

Press, Cambridge, UK.

Picozzi, N., Catt, D.C., Moss, R., 1992. Evaluation of Capercaillie

habitat. J. Appl. Ecol. 29, 751–762.

Picozzi, N., Moss, R., Catt, D.C., 1996. Capercaillie habitat, diet and

management in a Sitka spruce plantation in central Scotland.

Forestry 69, 373–388.

Ratti, J.T., Garton, E.O., 1994. Research and experimental design.

In: Bookhout, T.A. (Ed.), Research and Management Tech-

niques for Wildlife and Habitats. The Wildlife Society,

Bethesda, pp. 1–23.

Rolstad, J., 1988. Autumn habitat of capercaillie in southeastern

Norway. J. Wildl. Manage. 52, 747–753.

Rolstad, J., Wegge, P., 1989. Capercaillie populations and modern

forestry—a case for landscape ecological studies. Finn. Game

Res. 46, 43–52.

Rolstad, J., Wegge, P., Larsen, B.B., 1988. Spacing and habitat use of

capercaillie during summer. Can. J. Zool. 66, 670–679.

Sachot, S., Perrin, N., Neet, C., 2003. Winter habitat selection by

two sympatric forest grouse in western Switzerland: implica-

tions for conservation. Biol. Conserv. 112, 373–382.

Saniga, M., 1998. Diet of the capercaillie (Tetrao urogallus) in a

Central-European mixed spruce-beech-fir and mountain spruce

forest. Folia Zool. 47, 115–124.

Scherzinger, W., 1974. Interpretation einer Bestandesaufnahme an

Auerhuhnern im Nationalpark Bayerischer Wald. Allg. Forst Z.

29, 828–829.


Schroder, B., 2000. Zwischen Naturschutz und Theoretischer Oko-

logie: Modelle zur Habitateignung und raumlichen Populations-

dynamik fur Heuschrecken im Niedermoor. Ph.D. Thesis.

Technische Universitat Braunschweig, Braunschweig.

Schroth, K.-E., 1990. Kartierung von Auerhuhnhabitaten im

Nordschwarzwald. In: Auerwild, A. (Ed.), Auerwild in

Baden-Wurttemberg: Rettung oder Untergang? Landesfortsver-

waltung Baden-Wurttemberg, Stuttgart, pp. 90–107.

Schroth, K.-E., 1994. Zum Lebensraum des Auerhuhns (Tetrao

urogallus L.) im Nordschwarzwald. Mitteilungen der forstli-

chen Versuchs- und Forschungsanstalt Baden-Wurttemberg,

133 pp.

Scott, J.M., Heglund, P.J., Morrison, M., Haufler, J.B., Wall, W.A.

(Eds.), 2002. Predicting Species Occurrences: Issues of Accu-

racy and Scale. Island Press, Washington, DC.

Segelbacher, G., Storch, I., 2002. Capercaillie in the Alps: evidence

of metapopulation structure and population decline. Mol. Ecol.

11, 1669–1677.

Selas, V., 2000. Population dynamics of capercaillie Tetrao urogal-

lus in relation to bilberry Vaccinium myrtillus production in

southern Norway. Wildl. Biol. 6, 1–11.

Sjoberg, K., 1996. Modern forestry and the capercaillie. In:

DeGraaf, M., Miller, R.I. (Eds.), Conservation of Faunal Diver-

sity in Forested Landscapes. Chapman & Hall, London, pp. 111–

135.

Spidsø, T.K., Korsmo, H., 1994. Selection of feeding trees by

capercaillie Tetrao urogallus in winter. Scand. J. For. Res. 9,

180–184.

Storch, I., 1993a. Habitat selection by capercaillie in summer and

autumn–is bilberry important? Oecologia 95, 257–265.

Storch, I., 1993b. Patterns and strategies of winter habitat selection

in alpine capercaillie. Ecography 16, 351–359.

Storch, I., 1993c. Forest Structure, Landscape Mosaic, and Caper-

caillie Conservation: A Central European perspective, XXI

IUGB Congress, Halifax, Nova Scotia, Canada, 120–125.

Storch, I., 1994. Habitat and survival of capercaillie Tetrao urogallus

nests and broods in the Bavarian Alps. Biol. Conserv. 70, 237–

243.

Storch, I., 1995. Annual home ranges and spacing patterns of

capercaillie in central Europe. J. Wildl. Manage. 59, 392–400.

Storch, I., 1997. The importance of scale in habitat conservation for

an endangered species: the capercaillie in Central Europe. In:

Bissonette, J.A. (Ed.), Wildlife and Landscape Ecology: Effects

of Pattern and Scale. Springer-Verlag, New York, pp. 310–330.

Storch, I. (Ed.), 2000. Grouse: Status Survey and Conservation

Action Plan 2000–2004. WPA/BirdLife/SSC Grouse Specialist

Group. IUCN, Gland, Switzerland and Cambridge, UK and the

World Pheasant Association, Reading, UK.

Storch, I., 2001. Capercaillie. BWP Update. J. Birds Western

Palearctic 3, 1–24.

Storch, I., 2002. On spatial resolution in habitat models: can small-

scale forest structure explain Capercaillie numbers? Conserv.

Ecol. 6(1): 6 [online] URL: http://www.consecol.org/vol6/iss1/

art6.

Storch, I., 2003. Linking a multiscale habitat concept to species

conservation. In: Bissonette, J.A., Storch, I. (Eds.), Landscape

Ecology and Resource Management: Linking Theory with

Practice. Island Press, Washington, DC, pp. 302–320.

Suchant, R., 1992. Habitat-Struktur-Kartierung fur Auerwild und

Haselwild im mittleren Schwarzwald. Allg. Forst Z. 47, 32–34.

Suchant, R., 2002. Die Entwicklung eines mehrdimensionalen

Habitatmodells fur Auerhuhnareale (Tetrao urogallus L.) als

Grundlage fur die Integration von Diversitat in die Waldbau-

praxis. Ph.D. Thesis. Universitat Freiburg, Freiburg.

Summers, R.W., Proctor, R., Thorton, M., Avey, G., 2004. Habitat

selection and diet of the Capercaillie Tetrao urogallus in Aber-

nethy Forest, Strathspey Scotland. Bird Study 51, 58–68.

Suter, W., Graf, R.F., Hess, R., 2002. Capercaillie (Tetrao urogallus)

and avian biodiversity: testing the umbrella-species concept.

Conserv. Biol. 16, 778–788.

Van Houwelingen, J.C., Le Cessie, S., 1990. Predictive value of

statistical models. Stat. Med. 9, 1303–1325.

Vaughan, I.P., Ormerod, S.J., 2003. Improving the quality of dis-

tribution models for conservation by addressing shortcomings in

the field collection of training data. Conserv. Biol. 17, 1601–

1611.

Verbyla, D.L., Litvaitis, J.A., 1989. Resampling methods for eva-

luation of classification accuracy of wildlife habitat models.

Environ. Manag. 13, 783–787.

von Hessberg, A., Beierkuhnlein, C., 2000. Vegetationsstrukturen in

den Habitaten des Auerhuhns Tetrao urogallus im Fichtelge-

birge. Ornithol. Anz. 39, 159–174.

Wegge, P., Rolstad, J., Gjerde, I., 1992. Effects of boreal forest

fragmentation on capercaillie grouse: empirical evidence and

management implications. In: McCullough, D.R., Barrett, R.H.

(Eds.), Wildlife 2001: Populations. Elsevier Applied Science,

Essex, pp. 738–749.

Wiens, J.A., 1989. Spatial scaling in ecology. Funct. Ecol. 3, 385–

397.

Woolf, A., Nielson, C.K., Weber, T., Gibbs-Kieninger, T.J., 2002.

Statewide modeling of bobcat, Lynx rufus, habitat in Illinois

USA. Biol. Conserv. 104, 191–198.

http://www.consecol.org/vol6/iss1/art6

http://www.consecol.org/vol6/iss1/art6

An analysis of central Alpine capercaillie spring habitat at the ...

Documents

Transcript of An analysis of central Alpine capercaillie spring habitat at the ...