ORIGINAL ARTICLE

Effects of habitat quality and hiking trails on the occurrenceof Black Grouse (Tetrao tetrix L.) at the northern fringe of alpinedistribution in Austria

Markus Immitzer • Ursula Nopp-Mayr •

Margit Zohmann

Received: 8 November 2012 / Revised: 1 August 2013 / Accepted: 12 August 2013 / Published online: 1 September 2013

� Dt. Ornithologen-Gesellschaft e.V. 2013

Abstract The Black Grouse (Tetrao tetrix L.), listed in

Annex 1 of the European Bird Directive, inhabits the

vulnerable alpine treeline ecotone. Reacting sensitively to

modifications of the environment, it can be regarded as an

indicator species. To assess summer habitat use, we mea-

sured habitat parameters and recorded faeces (intestinal

droppings) of Black Grouse on a mountain chain of the

Northern Limestone Alps at the northern fringe of its dis-

tribution. In the area, the existing hiking trails are fre-

quently used by hikers in summer. We modelled summer

habitat use and included effects of hiking trails testing three

different buffer radii around hiking trails (i.e., 50, 100 and

150 m); the buffer radius of 50 m significantly contributed

to the final model of habitat use. Altitude, cover of grasses/

herbs, canopy cover of woody plants \5 m, canopy cover

of woody plants C5 m, grazing intensity, and the

interaction term cover of grasses/herbs 9 canopy cover of

woody plants C5 m combined with presence–absence of

hiking trails best predicted the occurrence of Black Grouse.

Our calculations yielded lower probabilities of Black

Grouse occurrence areas adjacent to hiking trails (odds of

presence reduced by 93 %). Terrestrial mapping of indirect

signs and the statistical model were appropriate to depict

significant differences of probabilities of occurrence within

and outside the buffer zone around hiking trails. Consid-

ering the habitat variables in the model, enhancing small-

scale habitat heterogeneity seems to be a recommendable

habitat management strategy; by creating a fine mosaic of

higher woody plants, dwarf shrubs and open, grassy habitat

patches, a rich supply of food and cover can be provided

within short distances.

Keywords Black Grouse � Habitat use � Summer

tourism � Logistic regression � Intestinal droppings

Zusammenfassung

Auswirkungen von Habitatqualitat und Wanderwegen

auf das Vorkommen des Birkhuhns (Tetrao tetrix L.) am

nordlichen alpinen Verbreitungsrand in Osterreich

Das Birkhuhn (Tetrao tetrix L.) ist im Anhang 1-Art der

Europaischen Vogelrichtlinie gelistet und steht somit unter

besonderem Schutz. Aufgrund hoher Lebensraumanspru-

che und seiner empfindlichen Reaktion auf Habitatveran-

derungen kann das Birkhuhn als Indikatorart fur das in

Mitteleuropa besiedelte Okoton der alpinen Baumgrenze

betrachtet werden. Wir kartierten Habitatparameter und

suchten nach Kot (Losungswalzen) des Birkhuhns auf einer

Bergkette der nordlichen Kalkalpen am nordlichen Verbrei-

tungsrand der alpinen Vorkommen. Mit diesen Daten

Communicated by F. Bairlein.

M. Immitzer and U. Nopp-Mayr contributed equally to the

manuscript.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s10336-013-0999-3) contains supplementarymaterial, which is available to authorized users.

M. Immitzer

Department of Landscape, Spatial and Infrastructure Sciences,

Institute of Surveying, Remote Sensing and Land Information,

University of Natural Resources and Life Sciences, Vienna,

Peter Jordan Straße 82, 1190 Vienna, Austria

M. Immitzer � U. Nopp-Mayr (&) � M. Zohmann

Department of Integrative Biology and Biodiversity Research,

Institute of Wildlife Biology and Game Management, University

of Natural Resources and Life Sciences, Vienna, Gregor Mendel

Straße 33, 1180 Vienna, Austria

e-mail: ursula.nopp-mayr@boku.ac.at

123

J Ornithol (2014) 155:173–181

DOI 10.1007/s10336-013-0999-3

modellierten wir mittels Logistischer Regression die

sommerliche Habitatnutzung und bezogen dabei Auswir-

kungen von Wanderwegen, welche im Untersuchungsge-

biet im Sommer haufig genutzt werden, ein. Dazu wurden

drei verschiedene Pufferradien um die Wanderwege (50,

100 und 150 m) in der Modellierung berucksichtigt, wobei

nur der 50 m-Radius signifikant zum endgultigen Habitat-

nutzungsmodell beitrug. Die Variablen Seehohe, Dec-

kungsgrad von Grasern/Krautern, Schlussgrad von Ge-

holzpflanzen \5 m, Schlussgrad von Geholzpflanzen

C5 m, Beweidungsintensitat sowie die Interaktionsterme

Deckungsgrad von Grasern/Krautern x Schlussgrad von

Geholzpflanzen C5 m ergaben in Kombination mit dem

Vorhandensein/Nichtvorhandensein von Wanderwegen das

beste Vorhersagemodell fur Birkhuhnvorkommen im Un-

tersuchungsgebiet. Die Berechnungen ergaben geringere

Vorkommenswahrscheinlichkeiten in den neben den

Wanderwegen gelegenen Bereichen (um 93 % geringer).

Die terrestrische Kartierungen indirekter Nachweise und

das statistische Modell waren geeignet, signifikante Un-

terschiede in den Vorkommenswahrscheinlichkeiten in-

nerhalb und außerhalb der Pufferzonen um die

Wanderwege darzustellen. Aufgrund der Habitatvariablen

im Modell ist die Forderung kleinraumiger Habi-

tatheterogenitat als Managementstrategie zu empfehlen;

durch die Schaffung eines Mosaiks aus hoheren verholzten

Pflanzen, Zwergstrauchern und offenen grasbewachsenen

Habitatbereichen werden innerhalb kurzer Distanzen die

Nahrungs- und Deckungsanspruche des Birkhuhns erfullt.

Introduction

The Black Grouse (Tetrao tetrix L.), listed in Annex 1 of

the European Bird Directive, is an emblematic species of

the belt around the alpine treeline. Whereas central alpine

populations of Black Grouse seem to be stable (Storch

2007), many occurrences at the fringe of alpine distribution

show a distinct decline or the species has become extinct

within the last decades (Woss and Zeiler 2003). Loss,

fragmentation and deterioration of habitats and the occur-

rence of small populations are assumed to be the major

causes of declining numbers in tetraonid species (Storch

2007). As modelled by Schaumberger et al. (2006) and

Zurell et al. (2012), climate change with its complex

interplay of demographic processes and habitat availability

may also lead to distinct range contractions of Black

Grouse in the future. For example, Schaumberger et al.

(2006) calculated a loss of 98 % of well-suited Black

Grouse habitats due to climate change in an alpine Austrian

study area. Moreover, ongoing expansions of skiing

infrastructures, abandonment of pastures followed by tree

encroachment and increasing levels of human disturbance

pose serious threats to tetraonid species (Glanzer 1985;

Pauli et al. 2001; Kromp-Kolb et al. 2003; Zeitler 2003;

Laiolo et al. 2004; Watson and Moss 2004; Parizek 2006;

Storch 2007; Patthey et al. 2008). In recent years, winter

tourism has increased both in numbers of recreationists and

in numbers of practised activities (Macchiavelli 2009),

imposing pressure on wildlife (Menoni and Magnani 1998;

Bourdeau et al. 2002). Various studies have focused on

potential impacts of winter tourism on Capercaillie (Tetrao

urogallus), Black Grouse (Tetrao tetrix) or Rock Ptarmigan

(Lagopus muta) (Brenot et al. 1996; Zeitler 2000; Watson

and Moss 2004; Arlettaz et al. 2007; Patthey et al. 2008;

Thiel et al. 2008a; Braunisch et al. 2011). In the 1980s,

summer mountain tourism stagnated in the Alps but has

slightly increased since 2000 (Muhar et al. 2007; Mac-

chiavelli 2009). Even this slight upward trend might neg-

atively affect tetraonids as it occurs during the time of

breeding and rearing of the young, leading to modifications

in spatial use patterns or in reproductive success.

Different techniques have been used to study potential

responses of birds to human disturbance (Gill 2007). For

tetraonids, techniques range from behavioural research and

analyses of demographic responses to studies on physio-

logical effects (Baines and Richardson 2007). Changes in

heartbeat rates, body temperature or stress hormone titres in

the blood are important indicators, as many grouse species

exhibit a cryptic mode of life (Ingold et al. 1992). Some

authors apply experimental disturbance trials to quantify

species-specific responses to human disturbance (Baines

and Richardson 2007). Among the non-invasive methods,

metabolites of stress hormones are measured from the fae-

ces of target species, indicating interspecific or intraspecific

stress phenomena (Millspaugh and Washburn 2004; Thiel

et al. 2005, 2008a, b). Such physiological methods need

thorough calibration in terms of season, level of human

disturbance, habitat conditions and other confounding fac-

tors (Kotrschal et al. 1998; Millspaugh and Washburn 2004;

Romero 2004; Baltic et al. 2005). However, wildlife or

habitat managers, confronted with human disturbance

issues, might lack personal, financial and technical resour-

ces to run such sophisticated analyses. Nevertheless,

information on potential human impacts on indicator or

umbrella species might be crucial for the deduction of

management strategies or protective measures.

Presence–absence analyses using indirect signs are

standard approaches for wildlife species which cannot be

easily observed directly (Klaus et al. 1990; Storch 2002;

Royle and Nichols 2003; Gruber et al. 2008). Droppings of

tetraonids are appropriate indicators of habitat use, as they

are expelled at regular times (De Juana 1994). According

to Baltic et al. (2005), Black Grouse regularly defecate 1–3

times per hour and the number of intestinal droppings

174 J Ornithol (2014) 155:173–181

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provides information on the type of activity, such as resting

or feeding (Klaus et al. 1990). Summing up, non-invasive

methods are of major interest when dealing with wildlife

species with a high conservation status. Using such meth-

ods, we studied habitat use of Black Grouse on a mountain

chain in Austria, considering impacts of tourism on the

spatial distribution of the birds via presence–absence

analysis. We addressed two major questions: (1) which

habitat characteristics explain the spatial distribution of

Black Grouse in the study area, and (2) is there any impact

of hiking trails on the probability of occurrence of Black

Grouse?

Methods

Study area

We conducted our study at the border of the Northern

Limestone Alps (Upper Austria) at the northern fringe of

alpine Black Grouse distribution (47�480N, 13�590E).

Altitudes range from 1,250 to 1,747 m a.s.l. The climate is

humid, with a mean annual precipitation of 2,100 mm and

a mean annual temperature of 3.7 �C (Petritsch 2002;

Hasenauer et al. 2003). The underlying rock of the area is

limestone.

The vegetation of the study area comprises a mixture of

different vegetation patches, including subalpine Norway

spruce (Picea abies Karst.) forests, dwarf mountain pine

(Pinus mugo Turra s.l.) stands, dwarf shrubs, grasses and

herbs. Both cattle and sheep grazing occur in summer and

the coverage of woody dwarf shrubs is actively reduced by

cutting. Dense patches of dwarf pine with sporadic inter-

mixed conifers (mainly Norway spruce, European larch

(Larix decidua Mill.), Silver fir (Abies alba Mill.), rowan

(Sorbus aucuparia L.), and European beech (Fagus sylv-

atica L.) occur and intermingle with alpine grassland,

rocky areas and dwarf shrub communities (i.e., Vaccinium

sp. and Rhododendrum sp.). As distances to the next

nearest Black Grouse occurrences range between 7.2 and

13.3 km, the isolation of the local occurrence seems likely

(Caizergues and Ellison 2002). Synchronous counts of

cocks at lekking time yielded spring densities of at least

11–15 individuals in the study area in both 2009 and 2010,

corresponding to 3.9–5.4 cocks per km2. Assuming a bal-

anced sex ratio, the total number of individuals lay around

9 birds per km2.

Sampling design

We recorded habitat variables and intestinal droppings of

Black Grouse between July and September 2009 within an

area of 280 ha, which covered the entire potential Black

Grouse habitat. At each grid point of a 100 9 100 m grid,

we recorded terrain features like altitude, slope, aspect and

relief within a 25-m radius, and we mapped cover and

height of dwarf shrubs, grasses, herbs, ferns or mosses

(Appendix 1, supplementary material; Schweiger et al.

2012). Within the entire 280-ha grid, 210 grid points were

effectively sampled, the remaining grid points being inac-

cessible. We also recorded the occurrence of single forest

trees, canopy cover, cover of rocky patches, grazing

intensity and presence of anthills. For the variable ‘‘grazing

intensity’’, an ordinal scaling was used distinguishing four

categories (no, low, medium, high). We assigned trees to

two different height classes (B5 or [5 m) to differentiate

between their stunted and full-growth appearance. We

searched for droppings within the 25-m radius for 20 min

per sample plot, classifying both summer and winter

droppings. Analogous to the mapping design of indirect

signs of Capercaillie (Storch 1999), each plot was sampled

once. To distinguish between sample plots, which were

merely randomly crossed by Black Grouse (e.g. when

moving from a feeding patch to a resting patch) from

actively selected habitat patches (e.g. for feeding, resting,

hiding), sample plots were defined as ‘‘presence’’ plots, if

we found at least three faecal pellets of Black Grouse in

one place (Schweiger et al. 2012). Sample plots with one or

two single droppings were discarded from further analyses

(n = 3). As far as possible, winter and summer droppings

were distinguished by shape and content (Zettel 1974;

Klaus et al. 1990; Rupf et al. 2011). When modelling

summer habitat use, winter droppings were excluded from

further analysis. According to information of the land

owner (a forest enterprise), the existing hiking trails are

frequently used by hikers in summer. Due to given terrain

features and vegetation structures, hikers usually did not

leave the hiking trails, which had a total length of 1.8 km

per km2.

Habitat modelling

To analyse habitat use of Black Grouse and impacts of

hiking trails on habitat use, we hypothesised that (1) spatial

use pattern of Black Grouse is a function of habitat quality,

and (2) the presence of hiking trails may distinctly modify

it. To model the spatial dimension of potential human

disturbance, we defined buffer zones around the hiking

trails. Alert and flush distances of Black Grouse depend on

the frequency of disturbance, the availability of cover, the

season, the reproductive status and habituation. Values of

flush distances range from 50 to 200 m in summer and

from a few meters up to 20 m in winter (Klaus et al. 1990;

Houard and Mure 1997, cited in Menoni and Magnani

1998; Woss 1997). Baines and Richardson (2007) did not

observe differences between sexes, but flushing distances

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differed between disturbance treatments and between sea-

sons; flushing distances of incubating and chick-rearing

hens are supposed to be lower, but disturbance treatments

during this period were not carried out for ethical reasons.

We tested three different values of buffer zones (50, 100

and 150 m) in the model. We termed sample plots as

‘‘disturbed’’ (in the sense of a potential source of distur-

bance) if the sampling area per grid point overlapped with

the buffer zone.

We applied binary logistic regression (LR) for model-

ling habitat quality for Black Grouse. We used presence–

absence data as response variable, and habitat character-

istics and buffer zones around hiking trails as explanatory

variables. We calculated pairwise correlation matrices of

the explanatory variables (except for buffer zones) with

Spearman’s rank correlation tests to reduce multicollin-

earities. In cases of high correlations between two vari-

ables (|rs| [ 0.7), we excluded the variable of assumed

lower biological relevance (in terms of hiding cover) from

further modelling (Fielding and Bell 1997; Menard 2001;

Brotons et al. 2004; Schroder 2008; Schweiger et al.

2012). Additionally, we tested high-level multicollinearity

of the explanatory variables, searching for linear combi-

nations of explanatory variables explaining a further

explanatory variable. At R2 values above 0.7, the referring

variable was excluded from further modelling (Backhaus

et al. 2008). Variables with a low observed range of

values were converted to dummy variables (i.e. grazing

occurring/not occurring). We calculated univariate LR

models with all remaining explanatory variables. In cases

of nonlinear, unimodal response, the squared term of the

variable was considered. We also tested all two-way

interaction terms of assumed biological meaning. In cases

of a statistically significant interaction of two variables,

the main effects of both variables were also included in

the model (Schroder 2000; Bollmann et al. 2005; Graf

et al. 2009; Schweiger et al. 2012). We preselected

explanatory variables using AIC (stepAIC, both direc-

tions) and removed further variables and interaction terms

in a stepwise procedure according to their level of sig-

nificance in the Wald statistic, respectively (Quinn and

Keough 2002). We evaluated overall goodness of fit by

Nagelkerkes RN2 (Backhaus et al. 2008). Model discrimi-

nation was tested using the AUC value (Hosmer and

Lemeshow 2000). We applied Hosmer–Lemeshow good-

ness-of-fit statistics (Hosmer and Lemeshow 2000; Shah

and Barnwell 2003) and evaluated the model output by

classification matrices (Hosmer and Lemeshow 2000). We

compared classification results of the model with random

classification probabilities (Backhaus et al. 2008). Statis-

tical analyses were performed with R 2.11.1 (R Devel-

opment Core Team 2010), while for spatial analyses we

used ArcGis 10.0.

Results

On 45 of 210 (21 %) sample plots, we found at least three

summer droppings of Black Grouse. In Appendix 1 (sup-

plementary material), all potential explanatory variables of

the LR model as well as the final predictors are listed. We

discarded the number of solitary trees and the cover of

dwarf shrubs accounting for multicollinearities (|rs| [ 0.7)

and the height of lichens/mosses due to a low variation of

values (Appendix 1, supplementary material). Based on the

AIC, we further reduced the dataset to 14 variables and 18

possible interactions. The AIC values could be decreased

from 270 for the model with all variables to 70 for the

model based on the reduced dataset. We then omitted all

non-significant variables, squared terms and interaction

terms according to the Wald statistic to obtain the final

habitat model for Black Grouse. This model contained six

variables (altitude, cover of grasses/herbs, canopy cover of

woody plants \5 m, canopy cover of woody plants C5 m,

grazing intensity, presence of hiking trails within 50 m

buffer radius) and one interaction term (cover of grasses/

herbs 9 canopy cover of woody plants C5 m; Table 1).

Nagelkerke’s RN2 of 0.48 indicated a good model cali-

bration and the p value of the Hosmer–Lemeshow good-

ness of fit statistic certified a very good fit (X2 = 3.4641,

df = 8, p = 0.902). The AUC value of 0.88 indicated an

excellent discrimination of the model between plots with

Black Grouse presence and absence. Table 2 shows the

classification matrix of the LR model with a cut value of

0.5. Regarding sensitivity (i.e. correct classification of

presence plots), specificity (i.e. correct classification of

absence plots), and overall classification rate (0.84), the

classification results of the model were better than a purely

random classification (0.79).

The probability of Black Grouse presence in summer

significantly increased with altitude [exp(0.013)], ranging

from 1,253 to 1,739 m a.s.l. in the potential Black Grouse

habitat (Fig. 1). For every increase of 10 m in altitude, the

odds of presence increased by 13.7 % on average (95 % CI

6.9–21.9 %, bootstrapped estimates = BE). For presence

plots, the mean altitude was 1,492 m a.s.l. (95 % CI

1,469–1,513 m a.s.l., BE) and for absence plots 1,454 m

a.s.l. (95 % CI 1,439–1,470 m a.s.l., BE). On average,

grasses or herbs covered 49.3 % of the surface of sampling

radii (95 % CI 47.0–51.8 %, BE) with mean values of

45.6 % (95 % CI 40.8–50.0 %, BE) for presence plots and

50.2 % (95 % CI 47.6–53.0 %, BE) for absence plots. The

cover of woody plants \5 m in height positively affected

Black Grouse presence (Table 1) with a mean value of

25.3 % (95 % CI 19.0–31.8 %, BE) on presence plots and

17.6 % (95 % CI 14.8–21.0 %, BE) on absence plots. An

increase of 5 % in cover of woody plants\5 m yielded on

average an increase of 22 % [exp(5 9 0.040)] of odds of

176 J Ornithol (2014) 155:173–181

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Black Grouse presence (95 % CI 7–41 %). The canopy

cover of higher woody plants (C5 m) ranged from 0 to

95 % on the sample plots with an overall mean cover of

18.6 % (95 % CI 15.8–21.5 %, BE). Hiking trails within a

50-m buffer radius around the sample plot occurred on 45

of 210 plots (i.e. 21 %). The odds of Black Grouse pres-

ence decreased on average by 92.6 % on plots, where

hiking trails were present (95 % CI decrease 64.8–99.1 %;

see also Fig. 1). Medium/high grazing intensity increased

average odds of Black Grouse presence by a factor of 8.0

compared to no/low grazing, ranging from 2.9 times up to

24.5 times within a 95 % CI (Table 1; see also Fig. 2).

Discussion

The logistic regression (LR) was an appropriate approach

to reflect habitat use of Black Grouse in summer. The

model performed well in terms of presence-absence data of

Black Grouse and referring habitat characteristics. Alti-

tude, cover of grasses/herbs, canopy cover of woody plants

\5 m, canopy cover of woody plants C5 m, grazing

intensity, and the interaction term cover of grasses/

herbs 9 canopy cover of woody plants C5 m together with

the 50-m buffer zone around hiking trails best predicted the

occurrence of Black Grouse in the LR. The final model

incorporated habitat variables, which frequently account

for habitat selection of the species (Klaus et al. 1990;

Patthey et al. 2008, 2011; Schweiger et al. 2012): Both the

cover of ground vegetation and woody plants with a low

height are well-known habitat parameters, which allow

Black Grouse to meet their feeding, hiding, resting and

rearing demands. As in other lower mountain ranges at the

fringe of distribution, the local Black Grouse occurrence in

the study area was confined to a narrow altitudinal belt

between the timberline and the mountain ridge in our study

area. Contrary to other findings (Patthey et al. 2011;

Schweiger et al. 2012), the patchiness of vegetation or the

occurrence of single intermixed coniferous trees were not

included in the final LR model. Causally interpreting this

result, several aspects seem to be important, i.e. feeding,

hiding and thermal cover demands of Black Grouse in

summer. Open grassland and grassy shrubland provide high

biomass of arthropods, being a key food source for grouse

chicks (Signorell et al. 2010). The latter authors interpret

the habitat selection of Black Grouse hens as concealment

from predators, where chosen habitat patches provide more

hiding cover but less arthropod biomass. For adult grouse,

shrubs constitute important habitat requisites, offering both

food and cover. Accordingly, cover of woody plants B5 m

was a significant explanatory variable in our LR model. As

shown by Rotelli (2004), reducing coverage of Rhodo-

dendron sp. in the course of habitat management may

improve habitat quality due to enhanced accessibility for

Black Grouse, increased insolation on the ground and

small-scale heterogeneity.

In our model, increasing cover of grasses/herbs led to

increasing probabilities of Black Grouse occurrence, where

a certain cover of woody plants C5 m was given, providing

both open habitat patches rich in arthropod supply and

cover in summer. This conforms with field observations,

where Black Grouse preferred small groups of trees,

Table 1 Coefficients of parameters (B), standard error (SE), Wald statistics (z value), level of significance (p), odds [Exp(B)] and 95 %

confidence interval of odds [95 % CI Exp(B)] of the final habitat model for Black Grouse (Tetrao tetrix)

Variable B SE z value p Exp(B) 95 % CI Exp(B)

Intercept -20.287 5.404 -3.754 \0.001

Altitude 0.013 0.003 3.841 \0.001 1.013 (1.007; 1.020)

Cover of herbs, grasses -0.069 0.024 -2.838 0.005 0.934 (0.887; 0.976)

Cover of woody plants \5 m height 0.040 0.014 2.806 0.005 1.040 (1.013; 1.071)

Cover of woody plants C5 m height -0.120 0.046 -2.617 0.009 0.887 (0.805; 0.962)

Hiking trail: 50-m buffer radius (1) -2.606 0.899 -2.899 0.004 0.074 (0.009; 0.352)

Grazing intensity: medium/high (1) 2.074 0.538 3.853 \0.001 7.955 (2.928; 24.528)

Cover of herbs, grasses 9 cover of

woody plants C5 m height

0.004 0.001 3.772 \0.001 1.004 (1.002; 1.007)

Table 2 Classification matrix of the final LR model, including

specificity (spec) and sensitivity (sens)

Observed Total

Absence Presence

Predicted

Absence 154 22 176

Presence 11 23 34

Total 165 45 210

Correct classification 0.93 (speca) 0.51 (sensb) 0.84 (overall)

a Specificity (spec) correct classification rate of absence plotsb Sensitivity (sens) correct classification rate of presence plots

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regularly occurring at the natural tree line, rather than

solitary trees (Grunschachner-Berger, personal communi-

cation). The cover of woody plants \5 m represents both

food supply and hiding cover. In the study area, every 5 %

increase in cover of woody plants \5 m led to an increase

in odds of Black Grouse presence of more than 20 %. Even

in the presence of potential human disturbance (‘‘50-m

buffer radius around hiking trails’’ = 1), high probabilities

of Black Grouse occurrence (about 70 %) were calculated

in case that the cover of woody plants \5 m reached high

values at higher altitudes.

Altogether, enhancing small-scale habitat heterogeneity

by creating a fine mosaic of higher woody plants, dwarf

shrubs and open, grassy habitat patches, thus providing

Fig. 1 Predicted probability of Black Grouse (Tetrao tetrix) presence

as a function of canopy cover of woody plants\5 m (%) and altitude

(m a.s.l.), setting the variable ‘‘50-m buffer radius around hiking

trails’’ to ‘‘absent’’ (a) or to ‘‘present’’ (b). Other input variables were

set to the mean value (cover of grasses/herbs = 49.3 %, canopy cover

of woody plants C5 m = 18.6 %) and grazing intensity to zero

Fig. 2 Predicted probability of Black Grouse presence as a function

of altitude (m a.s.l.) and canopy cover of woody plants \5 m (%),

setting the variable ‘‘grazing intensity’’ to ‘‘not grazed’’ (a) or to

‘‘grazed’’ (b). Other input variables were set to the mean value (cover

of grasses/herbs = 49.3 %, canopy cover of woody plants

C5 m = 18.6 %) and the 50 m buffer radius around hiking trails to

zero

178 J Ornithol (2014) 155:173–181

123

both food supply and cover within short distances, seems to

be a recommendable habitat management strategy. Main-

taining this habitat patchiness in secondary Black Grouse

habitats, which were created by clearings of forest areas

and by alpine pasturing, might also be important. In our

study, medium to high grazing intensity positively affected

probabilities of Black Grouse occurrence. On average, odds

of presence were about 8 times higher if grazing occurred

on the sample plot.

Accounting for potential responses of Black Grouse to

human disturbance, the model fit could be distinctly

improved by including the buffer zone around hiking trails.

Thus, only the buffer radius of 50 m yielded significant

results, whereas higher buffer radii were omitted. This

corresponded to other studies, characterizing Black Grouse

as a species which may react markedly to human distur-

bance (e.g. Braunisch et al. 2011) either expressed by

reduced cock densities in skiing resorts in Northern Italy

(Rotelli 2002) or by elevated stress hormone metabolites in

droppings in Switzerland (Arlettaz et al. 2007). Patthey

et al. (2008) found a clear negative effect of outdoor winter

sports on the numbers of displaying cocks in the Swiss

Alps. Patthey et al. (2011) also observed hens avoiding

roads, forest tracks and walking paths during the vegetation

period. Our model clearly showed a reduced probability of

occurrence in generally suitable, but potentially disturbed,

zones. The areal impact of linear sources of potential dis-

turbance might be underestimated compared to infrastruc-

tures with a larger spatial extension (e.g. ski pistes,

mountain stations of lifts, wind energy plants). In our case,

the potentially disturbed 50 m zone both sides of hiking

trails caused a reduced probability of occurrence of Black

Grouse on 21 % of the entire area. At the border of dis-

tribution, where Black Grouse regularly inhabit only a

narrow belt between the tree line and alpine grasslands, and

where the species is likely to be limited by habitat avail-

ability, protection of remaining undisturbed areas might

become crucial. However, low mountain occurrences of

Black Grouse often benefit from alpine tourism, as scenic

alpine meadows are cultivated and the tree line is artifi-

cially lowered to create attractive scenery for tourism.

Consequently, considering both beneficial and detrimental

effects of alpine tourism in spatial planning means a bal-

ancing act in terms of habitat management for Black

Grouse.

However, some critical points have to be considered

when dealing with presence–absence models and referring

to habitat features. The interpretation of detection–non-

detection data as presence–absence data might be tricky, as

detection rates might underestimate real presence. Conse-

quently, false absence data might only result from detec-

tion failures (MacKenzie 2005). Certainly, the

interpretation of presence–absence data based on terrestrial

mapping of indirect signs may be violated by search biases

(i.e. higher rates of undetected faeces in very dense and

difficult of access vegetation strata). Considering the decay

of Black Grouse faeces in the moist summer climate,

absence of signs on a sample plot might not be equivalent

to avoidance by a species (Storch 2002; Gruber et al.

2008). However, a searching time of 20 min per sample

plot (within a 25-m radius) seems to be an approved basis

for inferring the spatial distribution of Black Grouse.

Droppings of tetraonids give a fairly good estimate of the

time budget, as they are expelled at regular time intervals

(De Juana 1994). As we classified sample plots as ‘‘pres-

ence’’ plots if we found at least 3 droppings, we assume

that the birds stayed at least 1 h on the respective sample

plot. Consequently, the time Black Grouse spent on our

‘‘presence plots’’ should be representative for resting,

feeding and sleeping loci, and should thus be a valid

indicator of habitat use. In general, the information content

of presence–absence data may distinctly vary depending on

species abundance. In case of low abundances, suitable

habitat patches may not be inhabited, whereas habitat

patches of lower quality might be intensely colonized if

high population densities occur (O’Connor 1986; Van

Horne 2002). A criticism of many habitat models is that

they only account for environmental characteristics,

implying that carrying capacities mainly develop due to the

included habitat features (e.g. cover of vegetation; Sch-

lossberg and King 2008). As stated by Gill (2007), species

abundances and habitat selection are frequently impaired

by predation, interspecific competition or human impact

(e.g. disturbance). However, these impact factors often

show a locally specific influence on species abundances,

thereby complicating the deduction of generally applicable

habitat evaluation approaches.

Conclusion

We conclude that habituation of the local Black Grouse

occurrence to predictable sources of disturbance might be

lower than previously assumed. Contrary to other wildlife

species, which may cope with regular, predictable human

disturbance (e.g. chamois), the sensitivity of Black Grouse

has been demonstrated by several authors and various

methods (Menoni and Magnani 1998; Rotelli 2002; Patthey

et al. 2008, 2011; Braunisch et al. 2011). Wildlife man-

agers, searching for arguments and concepts in habitat and

species conservation, may readily carry out field mapping

of intestinal droppings and deduce presence–absence pat-

terns. Combining these data with output from habitat

models allows for a spatially explicit planning of outdoor

activities. Thereby, avoidance of suitable habitat patches

by indicator/umbrella species can be depicted and

J Ornithol (2014) 155:173–181 179

123

demonstrated to various groups of recreationists. Habitat

management for Black Grouse should also aim at an

enhancement of cover supply, at least within distances of

50 m from potential sources of disturbance. Landscape

planning should account for wildlife sensitivity to human

disturbance and for the ongoing decrease of wildlife hab-

itats in the treeline ecotone. Any further amplification of

outdoor sports or recreational activities has to be evaluated

as a serious potential impairment of Black Grouse habitats

(Braunisch et al. 2011), which are already declining due to

abandonment of alpine pastures and the effects of climate

change (Theurillat and Guisan 2001; Travis 2003; Laiolo

et al. 2004).

Acknowledgments We are grateful to the Schaumburg Lippische

forest enterprise and Hartmut Beham for the permission to conduct

the study and the financial support. Harald Brenner, Iris Kempter and

Vera Liebl assisted in the field. We thank Veronika Grunschachner-

Berger for sharing her field experience and her valuable comments on

the manuscript. We also thank the anonymous reviewers for useful

comments on the manuscript.

References

Arlettaz R, Patthey P, Baltic M et al (2007) Spreading free-riding

snow sports represent a novel serious threat for wildlife. Proc R

Soc Lond B 274:1219–1224. doi:10.1098/rspb.2006.0434

Backhaus K, Erichson B, Plinke W, Weiber R (2008) Multivariate

Analysemethoden—Eine anwendungsorientierte Einfuhrung,

12th edn. Springer, Berlin

Baines D, Richardson M (2007) An experimental assessment of the

potential effects of human disturbance on black grouse Tetrao

tetrix in the North Pennines, England. Ibis 149:56–64. doi:10.

1111/j.1474-919X.2007.00638.x

Baltic M, Jenni-Eiermann S, Arlettaz R, Palme R (2005) A

noninvasive technique to evaluate human-generated stress in

the black grouse. Ann NY Acad Sci 1046:81–95. doi:10.1196/

annals.1343.008

Bollmann K, Weibel P, Graf RF (2005) An analysis of central Alpine

capercaillie spring habitat at the forest stand scale. For Ecol

Manag 215:307–318. doi:10.1016/j.foreco.2005.05.019

Bourdeau P, Corneloup J, Mao P (2002) Adventure sports and tourism

in the French mountains: dynamics of change and challenges for

sustainable development. Curr Issues Tour 5:22–32. doi:10.

1080/13683500208667905

Braunisch V, Patthey P, Arlettaz R (2011) Spatially explicit modeling

of conflict zones between wildlife and snow sports: prioritizing

areas for winter refuges. Ecol Appl 21:955–967. doi:10.1890/09-

2167.1

Brenot JF, Catusse M, Menoni E (1996) Effets de la station de ski de

fond du Plateau de Beille (Ariege) sur une importante population

de grand tetras Tetrao urogallus. Alauda 64:249–260

Brotons L, Thuiller W, Araujo MB, Hirzel AH (2004) Presence-

absence versus presence-only modelling methods for predicting

bird habitat suitability. Ecography 27:437–448. doi:10.1111/j.

0906-7590.2004.03764.x

Caizergues A, Ellison L (2002) Natal dipersal and its consequences in

black grouse Tetrao tetrix. Ibis 144:478–487

De Juana E (1994) Family tetraonidae (grouse). In: Del Hoyo J,

Elliott A, Sargatal J (eds) Handbook of the birds of the world,

vol 2. Lynx, Barcelona

Fielding AH, Bell JF (1997) A review of methods for the assessment

of prediction errors in conservation presence/absence models.

Environ Conserv 24:38–49. doi:10.1017/S0376892997000088

Gill JA (2007) Approaches to measuring the effects of human

disturbance on birds. Ibis 149:9–14. doi:10.1111/j.1474-919X.

2007.00642.x

Glanzer U (1985) Effects of land use changes on bird life, example:

tetrao tetrix and Lagopus lagopus. Trans Congr Intern Union

Game Biol 17:501–507

Graf RF, Mathys L, Bollmann K (2009) Habitat assessment for forest

dwelling species using LiDAR remote sensing: capercaillie in

the Alps. For Ecol Manag 257:160–167. doi:10.1016/j.foreco.

2008.08.021

Gruber B, Reineking B, Calabrese JM et al (2008) A new method for

estimating visitation rates of cryptic animals via repeated

surveys of indirect signs. J Appl Ecol 45:728–735. doi:10.

1111/j.1365-2664.2007.01406.x

Hasenauer H, Merganicova K, Petritsch R et al (2003) Validating

daily climate interpolations over complex terrain in Austria.

Agric For Meteorol 119:87–107. doi:10.1016/S0168-1923(03)

00114-X

Hosmer DW, Lemeshow S (2000) Applied logistic regression, 2nd

edn. Wiley-Interscience, New York

Houard T, Mure M (1997) Les tetras-lyres des vallons de Salese et

Mollieres, parc national du Mercantour, domaine vital et

influence du tourisme. Rev Ecol Terre Vie Suppl 4:165–197

Ingold P, Huber B, Mainini B, Marbacher H, Neuhaus P, Rawyler A,

Roth M, Schnidrig R, Zeller R (1992) Freizeitaktivitaten–ein

gravierendes Problem fur Tiere. Ornithol Beob 89:205–216

Klaus S, Bergmann H–H, Marti C et al (1990) Die Birkhuhner: Tetrao

tetrix und T. mlokosiewiczi. Ziemsen, Wittenberg Lutherstadt

Kotrschal K, Hirschenhauser K, Mostl E (1998) The relationship

between social stress and dominance is seasonal in greylag

geese. Anim Behav 55:171–176. doi:06/anbe.1997.0597

Laiolo P, Dondero F, Ciliento E, Rolando A (2004) Consequences of

pastoral abandonment for the structure and diversity of the alpine

avifauna. J Appl Ecol 41:294–304. doi:10.1111/j.0021-8901.

2004.00893.x

Macchiavelli A (2009) Alpine tourism: development contradictions

and conditions for innovation. J Alp Res (online), 97-1|2009.

doi:10.4000/rga.843. http://rga.revues.org/843. Accessed 8

November 2012

MacKenzie DI (2005) What are the issues with presence-absence data

for wildlife managers? J Wildl Manag 69:849–860. doi:10.2193/

0022-541X(2005)069[0849:WATIWP]2.0.CO;2

Menard S (2001) Applied logistic regression analysis, 2nd edn. Sage,

Thousand Oaks

Menoni E, Magnani Y (1998) Human disturbance of grouse in France.

Grouse News 15:4–8

Millspaugh JJ, Washburn BE (2004) Use of fecal glucocorticoid

metabolite measures in conservation biology research: consid-

erations for application and interpretation. Gen Comp Endocri-

nol 138:189–199. doi:16/j.ygcen.2004.07.002

Muhar A, Schauppenlehner T, Brandenburg C, Arnberger A (2007)

Alpine summer tourism: the mountaineers’ perspective and

consequences for tourism strategies in Austria. For Snow Landsc

Res 81:7–17

O’Connor RJ (1986) Dynamical aspects of avian habitat use. In:

Verner J, Morrison ML, Ralph CJ (eds) Wildlife 2000. Modeling

habitat relationships of terrestrial vertebrates. University of

Wisconsin Press, Madison, pp 235–240

Parizek T (2006) Almen und Almwirtschaft im Uberblick. In:

Bundesministerium fur Land- und Forstwirtschaft, Umwelt und

Wasserwirtschaft: PAP ALP Austria, Großarl

Patthey P, Wirthner S, Signorell N, Arlettaz R (2008) Impact of

outdoor winter sports on the abundance of a key indicator

180 J Ornithol (2014) 155:173–181

123

species of alpine ecosystems. J Appl Ecol 45:1704–1711. doi:10.

1111/j.1365-2664.2008.01547.x

Patthey P, Signorell N, Rotelli L, Arlettaz R (2011) Vegetation

structural and compositional heterogeneity as a key feature in

Alpine black grouse microhabitat selection: conservation man-

agement implications. Eur J Wildl Res 58:59–70. doi:10.1007/

s10344-011-0540-z

Pauli H, Gottfried G, Grabherr G (2001) High summits of the Alps in

a changing climate. The oldest observation series on high

mountain plant diversity in Europe. In: Walther GR, Burga CA,

Edwards PJ (eds) Fingerprints of climate change—adapted

behaviour and shifting species ranges. Kluwer, New York,

pp 139–149

Petritsch R (2002) Anwendung und Validierung des Klimainterpola-

tionsmodels DAYMET in Osterreich. Diploma thesis, University

of Natural Resources and Life Sciences Vienna, Vienna

Quinn GP, Keough MJ (2002) Experimental design and data analysis

for biologists. University Press, Cambridge

Kromp-Kolb H, Gerersdorfer T, Aspock H Baier P, Schopf A, Gepp J,

Graf W, Moog O, Kromp B, Kyek M, Pintar M, Formayer H,

Parz-Gollner R, Rabitsch, W, Scheifinger H, Schmutz S,

Jungwirth M, Vogl W, Winkler H (2003) Auswirkungen von

Klimaanderungen auf die Tierwelt—derzeitiger Wissensstand,

fokusiert auf den Alpenraum und Osterreich. im Auftrag des

BMLFUW, Endbericht

R Development Core Team (2010) R: a language and environment for

statistical computing. R Foundation for Statistical Computing,

Vienna

Romero LM (2004) Physiological stress in ecology: lessons from

biomedical research. Trends Ecol Evol 19:249–255. doi:16/j.

tree.2004.03.008

Rotelli L (2002) The black grouse project in the Veglia-Devero

natural park: research and implications for management in the

Western Italian Alps. Grouse News 24:14–17

Rotelli L (2004) Modificazione degli habitat riproduttivi del fagiano

di monte (Tetrao tetrix) e declino dele sue popolazioni. In:

AAVV (ed) Atti del convegno ‘‘Miglioramenti ambientali a fini

faunistici: esperienze dell’arco alpino a confronto’’, S. Michele

all’Adige,Trento, 5 giugno 2003, Sherwood 96. Trento, pp 57–62

Royle JA, Nichols JD (2003) Estimating abundance from repeated

presence-absence data or point counts. Ecology 84:777–790

Rupf R, Wyttenbach M, Kochli D, Hediger M, Lauber S, Ochsner P,

Graf R (2011) Assessing the spatio-temporal pattern of winter

sports activities to minimize disturbance in capercaillie habitats.

Ecol Mont 3:23–32. doi:10.1553/eco.mont-3-2s23

Schaumberger J, Schardt M, Guggenberger T, Gallaun H, Schaum-

berger A, Deutz A, Gressmann G, Gasteiner J (2006) GIS-

gestutzte Ermittlung der Veranderung des Lebensraumes alpiner

Wildtierarten bei Anstieg der Waldgrenze aufgrund Kli-

maveranderung. Endbericht StartClim 2005, Universitat fur

Bodenkultur, Wien

Schlossberg S, King DI (2008) Modeling animal habitats based on

cover types: a critical review. Environ Manag 43:609–618.

doi:10.1007/s00267-008-9159-6

Schroder B (2000) Zwischen Naturschutz und Theoretischer Okolo-

gie: Modelle zur Habitateignung und raumlichen Populations-

dynamik fur Heuschrecken im Niedermoor. Dissertation,

Technical University Braunschweig

Schroder B (2008) Species in dynamic landscapes—patterns, pro-

cesses, and functions. Habilitation treatise, University of

Potsdam,

Schweiger AK, Nopp-Mayr U, Zohmann M (2012) Small-scale

habitat use of black grouse (Tetrao tetrix L.) and rock ptarmigan

(Lagopus muta helvetica Thienemann) in the Austrian Alps. Eur

J Wildl Res 58:35–45. doi:10.1007/s10344-011-0537-7

Shah BV, Barnwell BG (2003) Hosmer-Lemeshow goodness of fit

test for survey data. In: Annual meeting of the AmericanStatistical Association, joint statistical meeting-section on survey

research methods, San Francisco, CA. pp 3778–3781

Signorell N, Wirthner S, Patthey P et al (2010) Concealment from

predators drives foraging habitat selection in brood-rearing

Alpine black grouse Tetrao tetrix hens: habitat management

implications. Wild Biol 16:249–257. doi:10.2981/09-028

Storch I (1999) Auerhuhnschutz im Bergwald: Methoden und

Konzepte. Wildbiologische, Munchen

Storch I (2002) On spatial resolution in habitat models: can small-

scale forest structure explain capercaillie numbers? Conserv

Ecol 6:1–25

Storch I (2007) Conservation status of grouse worldwide: an update.

Wildl Biol 13:5–12. doi:10.2981/0909-6396(2007)13[5:

CSOGWA]2.0.CO;2

Theurillat JP, Guisan A (2001) Potential impact of climate change on

vegetation in the European Alps: a review. Climatic Change

50:77–109

Thiel D, Jenni-Eiermann S, Palme R (2005) Measuring corticosterone

metabolites in droppings of capercaillies (Tetrao urogallus). Ann

NY Acad Sci 1046:96–108. doi:10.1196/annals.1343.009

Thiel D, Jenni-Eiermann S, Braunisch V et al (2008a) Ski tourism

affects habitat use and evokes a physiological stress response in

capercaillie Tetrao urogallus: a new methodological approach.

J Appl Ecol 45:845–853. doi:10.1111/j.1365-2664.2008.01465.x

Thiel D, Jenni-Eiermann S, Jenni L (2008b) Der Einfluss von

Freizeitaktivitaten auf das Fluchtverhalten, die Raumnutzung

und die Stressphysiologie des Auerhuhns Tetrao urogallus.

Ornithol Beob 105:85–96

Travis JMJ (2003) Climate change and habitat destruction: a deadly

anthropogenic cocktail. Proc R Soc Lond B 270:467–473.

doi:10.1098/rspb.2002.2246

Van Horne B (2002) Approaches to habitat modelling: the tensions

between pattern and process and between specificity and

generality. In: Scott JM, Heglund PJ, Morrison ML et al (eds)

Predicting species occurrences: issues of accuracy and scale, 1st

edn. Island Press, Washington, pp 63–72

Watson A, Moss R (2004) Impacts of ski-development on ptarmigan

(Lagopus mutus) at Cairn Gorm, Scotland. Biol Conserv

116:267–275. doi:16/S0006-3207(03)00197-6

Woss M (1997) Landschaftsplanerische Analyse der Gelandesport-

problematik unter Berucksichtigung sozialer und wildokologi-

scher Aspekte des Tourenschilaufs, Diploma thesis, University

of Natural Resources and Life Sciences Vienna, Vienna

Woss M, Zeiler H (2003) Building projects in black grouse habitats—

assessment guidelines. Sylvia (suppl) 39:87–96

Zeitler A (2000) Human disturbance, behaviour and spatial distribu-

tion of black grouse in skiing areas in the Bavarian Alps. Cahiers

Ethol 20:381–400

Zeitler A (2003) Maintaining black grouse wintering habitats by

alpine pasture management plans. Sylvia (suppl) 39:97–102

Zettel J (1974) Nahrungsokologische Untersuchungen am Birkhuhn

Tetrao tetrix in den Schweizer Alpen. Ornithol Beob 71:186–246

Zurell D, Grimm V, Rossmanith E, Zbinden N, Zimmermann NE,

Schroder B (2012) Uncertainty in predictions of range dynamics:

black grouse climbing the Swiss Alps. Ecography 35:590–603.

doi:10.1111/j.1600-0587.2011.07200.x

J Ornithol (2014) 155:173–181 181

123