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Master Thesis

Silvicultural Measures for the Conservation of the Capercailliein the Special Forest Reserve of Amden - A first assessment ofeffectivenessa first assessment of effectiveness

Author(s): Bircher, Nicolas

Publication Date: 2011

Permanent Link: https://doi.org/10.3929/ethz-a-006525916

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Silvicultural Measures for the Conservation of the

Capercaillie in the Special Forest Reserve of Amden

A first assessment of effectiveness

Master Thesis

Nicolas Bircher

Submitted in January 2011

Department of Environmental Sciences, ETH Zurich

Conducted at the Swiss Federal Research Institute WSL

Advisor: Prof. Dr. Harald Bugmann, Forest Ecology, Institute of Terrestrial

Ecosystems, ETH Zurich

Co-Advisor: Dr. Kurt Bollmann, Conservation Biology, Swiss Federal Research

Institute WSL

Cover picture: Capercaillie cock Tetrao urogallus

(source: http://www.surfbirds.com/blog/bbc/9836/Updates.html; date accessed: January 14th 2011)

Silvicultural Measures for the Conservation of the

Capercaillie in the Special Forest Reserve of Amden

A first assessment of effectiveness

Master student:

Nicolas Bircher

Luegislandstrasse 23

CH-8051 Zürich

nicolas.bircher@gmx.ch

Advisor:

Prof. Dr. Harald Bugmann

Institut für Terrestrische Ökosysteme

ETH Zürich

Universitätstrasse 16

CH-8092 Zürich

harald.bugmann@env.ethz.ch

Co-Advisor:

Dr. Kurt Bollmann

Biodiversität und Naturschutzbiologie

Eidgenössische Forschungsanstalt WSL

Zürcherstrasse 111

CH-8903 Birmensdorf

kurt.bollmann@wsl.ch

Abstract

The Capercaillie Tetrao urogallus is a habitat specialist, which is resident in the boreal

and mountainous coniferous forests of Europe. Habitat loss and fragmentation in

combination with a high susceptibility to human disturbances have put Capercaillie

populations under considerable pressure all over Europe. In Switzerland, the species’

numbers significantly declined as well between 1968/71 and 2001, and are split into small

subpopulations. In the context of the Swiss National Action Plan for the Capercaillie

launched by the Federal Office for the Environment, the municipality of Amden has

established a special forest reserve for the conservation of the Capercaillie in 2006. Since

then, silvicultural measures were conducted specifically to increase the proportion of

suitable habitat for this endangered grouse species. This study is a first assessment of

how these measures affected the small-scaled habitat quality of the treated forest

patches and whether they are accepted as habitat by the Capercaillie in summer. 2010, a

species survey was conducted on 33 treated forest patches. This survey provided

evidence, by finding of feathers, about the use (presence) or the no-use (absence) of the

treated forest patches by the Capercaillie. Moreover, an assessment of vegetation

composition and structure was conducted on the same forest patches. Values of factors,

which were induced by Capercaillie ecology, were compared between presence and

absence patches. The important predictors of Capercaillie presence were determined by

logistic regression. Generally, presence patches were higher situated than absence

patches and had a higher proportion of coniferous trees, bilberry Vaccinium myrtillus and

suitable habitat in the vicinity of the patch. In addition, canopy cover on patches with use

by the Capercaillie was lower. Suitable habitat in the vicinity to the forest patch, canopy

cover and bilberry cover were the decisive factors for Capercaillie occurrence on the

treated forest patches. Bilberry cover was the most powerful determinant. This study

concludes that the conducted silvicultural measures have had the desired effect.

However, a more selective approach for the future selection of forest patches is favored

so that measures are primarily conducted where they promise to be most effective. The

application of the habitat suitability classification after Schroth, so far the only selection

criterion, should be supplemented by further criteria. Not only the low suitability of the

forest patch itself but also the suitability of its surroundings should be taken into

consideration for patch selection. The priority should also be higher on the upper

montane fir-spruce forests than on the montane fir-beech forests since bilberry, as major

determinant, is distinctively stronger associated with forest sites of the upper montane

vegetation zone. Silvicultural measures promise to have a faster and more sustainable

effect in this zone than in the fir-beech forests where the effect of thinning is neutralized

after a short time by the ingrowth of beech trees.

Keywords: Special forest reserve, Capercaillie, Tetrao urogallus, species conservation,

habitat selection, logistic regression, forestry measures, Switzerland

Zusammenfassung

Das Auerhuhn Tetrao urogallus ist ein Habitatsspezialist, der in den borealen und

alpinen Nadelwäldern Europas beheimatet ist. Lebensraumverlust und -fragmentierung

sowie eine hohe Empfindlichkeit gegenüber menschlichen Störungen haben den

Auerhuhnbeständen europaweit stark zugesetzt. In der Schweiz haben die

Auerhuhnpopulationen zwischen 1968/71 und 2001 ebenfalls stark abgenommen und

sind in kleine Bestände aufgeteilt. Im Rahmen des vom Bundesamt für Umwelt (BAFU)

lancierten Aktionsplans für das Auerhuhn wurde 2006 in der Gemeinde Amden ein

Sonderwaldreservat für dessen Erhaltung und Förderung ausgewiesen. Seither führte

man gezielt forstliche Eingriffe durch, um den Anteil an geeignetem Lebensraum zu

erhöhen. Diese Studie untersuchte zum ersten Mal, wie sich diese Eingriffe auf die

kleinräumige Habitatsqualität der behandelten Waldbestände auswirken und ob diese

vom Auerhuhn als Lebensraum im Sommer angenommen werden. 2010 wurde in 33

behandelten Waldbeständen eine Spurentaxation durchgeführt, die anhand von

Federfunden Hinweise auf deren Nutzung (Präsenz) bzw. Nicht-Nutzung (Absenz) durch

das Auerhuhn lieferte. Zudem wurde in denselben Flächen die

Vegetationszusammensetzung und –struktur standardisiert erfasst. Werte von Faktoren,

die für die Ökologie des Auerhuhns relevant sind, wurden anschliessend auf Präsenz und

Absenzflächen verglichen. Mittels logistischer Regression wurden die entscheidenden

Einflussgrössen für die Anwesenheit des Auerhuhns ermittelt. Präsenzflächen waren in

der Regel höher gelegen als Absenzflächen und verfügten über einen höheren Anteil an

Nadelbäumen, Heidelbeersträucher Vaccinium myrtillus und gut geeignetem Lebensraum

in der Umgebung. Zudem wiesen die durch das Auerhuhn genutzten Flächen einen

deutlich tieferen Baumdeckungsgrad auf. Gut geeigneter Lebensraum in der Umgebung,

der Baumdeckungsgrad sowie der Deckungsgrad der Heidelbeersträucher stellten sich

als die ausschlaggebenden Faktoren für die Anwesenheit des Auerhuhns in den

behandelten Waldbeständen heraus. Der Deckungsgrad der Heidelbeersträucher war

dabei die stärkste Einflussgrösse. Diese Studie kommt zum Schluss, dass die

durchgeführten forstlichen Massnahmen die beabsichtigte Wirkung erzielten. Einen

selektiveren Ansatz bei der Auswahl der Eingriffsflächen wäre aber zu favorisieren, damit

Massnahmen vorwiegend dort durchgeführt werden, wo sie am effektivsten sind. So

sollte die Anwendung der Lebensraum-Klassifizierung nach Schroth, bisher das einzige

Auswahlkriterium, noch durch weitere Kriterien ergänzt werden. Nicht nur die geringe

Eignung der Forstfläche selber sondern auch die Qualität der angrenzenden Waldflächen

sollte bei der Planung berücksichtigt werden. Es wird weiter empfohlen, im Waldreservat

Amden den Fokus mehr auf die hochmontanen Tannen-Fichtenwälder als auf die

montanen Tannen-Buchenwälder zu legen, denn die Heidelbeere als entscheidende

Komponente ist deutlich stärker mit Waldstandorten der hochmontanen Höhenstufe

assoziiert. Forstliche Eingriffe versprechen da also eine schnellere Wirkung, welche

zudem noch nachhaltiger ist als in Tannen-Buchenwälder, wo die Wirkung von

Auflichtungen schon nach kurzer Zeit durch das Einwachsen der Buche neutralisiert wird.

Keywords: Sonderwaldreservat, Auerhuhn, Tetrao urogallus, Artenschutz, Habitatswahl,

logistische Regression, forstliche Massnahmen, Schweiz

Contents

1. Introduction ........................................................................................................... 1

2. Methods ................................................................................................................. 6

2.1 Study area ......................................................................................................... 6

2.2 Sampling design................................................................................................ 8

2.2.1 Criteria for selecting study patches ............................................................. 8

2.2.2 Study patch selection ............................................................................... 10

2.3 Data collection ................................................................................................ 10

2.3.1 Search of indirect evidence ...................................................................... 11

2.3.2 Search of direct evidence ......................................................................... 12

2.3.3 Habitat data .............................................................................................. 12

2.4 Data analysis .................................................................................................. 18

3. Results ................................................................................................................. 20

3.1 Characteristics of forest patches with and without use by Capercaillie ........... 20

3.2 Univariate analysis .......................................................................................... 22

3.3 Multivariate analysis ........................................................................................ 23

4. Discussion .......................................................................................................... 25

5. Implications for the management of the special forest reserve of Amden .... 32

Acknowledgements ................................................................................................ 33

References .............................................................................................................. 34

Appendix ................................................................................................................. 39

1

1. Introduction

For decades, the Capercaillie populations across Europe have been decreasing

(Mollet et al., 2003; Storch, 2007). Although the reasons for this long ongoing

process are diverse (Bollmann, 2006; Storch, 2007), some factors are especially

pivotal for the tremendous decline of the Capercaillie: the loss of habitat area and

quality within its main range, the boreal forests of Scandinavia and Russia as well as

mountainous coniferous forests in Central Europe (Klaus et al., 1989), are primarily

attributed to changes in forest management in the 20th century (Storch, 2007). In

Scandinavia, old-growth forests were prescribed burned and thinned, wet forest sites

drained on a large scale and in this way transformed into production forests with

uniform age-classes and altered, even exotic tree species composition (Östlund et

al., 1997). Germany faced a similar transformation by planting monotonous needle

forests after clear-cuts. Nowadays, forest stands lack a diverse structure and

composition, with just a few contiguous forests still in existence (Küchli, 1997). In

contrast, Swiss forests experienced a decrease in use particularly during the second

half of the 20th century (Bürgi, 1997; 1999), as wood was cut less and less.

Therefore, as these forests matured, wood stock consistently increased (Brassel and

Brändli, 1999; Brändli, 2010), forest stands became dark and dense and, their

structure became more uniform since a late seral stage has not been reached yet. In

addition to habitat alteration, the Capercaillie faces declining genetic diversity as the

exchange between subpopulations is impeded by increased landscape fragmentation

caused by impassable barriers, including settlements, highways or areas with intense

agriculture (Storch, 2007; Mollet et al., 2008). Moreover, remaining populations

experience increased human disturbance both in summer and in winter due to

outdoor activities like hiking, biking or snowshoeing, which bring people to originally

remote and undisturbed areas (Thiel et al., 2008).

These developments are problematic for the Capercaillie as a habitat specialist with

high demands to its living space (see Appendix A). This grouse species inhabits

boreal coniferous forest across the northwestern and central Palearctic region

(Eiberle, 1974; Rolstad and Wegge, 1987). It requires old-growth, well structured

forests with an intermediate canopy cover (40-70%) (Storch, 1993; Graf et al., 2007)

and a good ground vegetation (Picozzi et al., 1992; Bollmann et al., 2008a), including

bilberry Vaccinium myrtillus (Storch, 1993; Schroth, 1994) as an important food

source in summer (Klaus et al., 1989). Especially during the breeding season, the

ground nesting Capercaillie depends on sufficient cover that is within close range of

its food source. Only a rich herbaceous vegetation layer (Baines et al., 2004), with a

high level of structural elements, such as basal-branched trees, root plates or clumps

of young spruces, along with edge habitat (Bollmann et al., 2008a), i.e. changes

between different habitat/vegetation types, guarantee sufficient protection and

nutrition on a very fine scale. Once detected by a predator, Capercaillies fly across

gaps in forest stands to escape. Therefore, a good abundance of forest aisles

2

(Bollmann et al., 2008a) is necessary for the largest grouse species (Rolstad et al.,

1988; Klaus et al., 1989) to maneuver.

The discussion about annual home range use is quite controversial. While in some

areas the summer and winter habitat can be distinctively separated or just slightly

overlapping (Storch, 1995,1999), they coincide nearly completely in other regions

(Imhof, 2007). However, in general it is agreed that Capercaillies neither migrate nor

do they show any strong tendency to move great distances (Klaus et al., 1989;

Storch, 1995; Segelbacher and Storch, 2002). Therefore, their ecological

requirements for the winter season have to be covered on the same small-scale area,

which also serves as a summer habitat or at least within its vicinity.

From spring to fall, Capercaillies feed on leaves, shoots, buds, seeds, berries and

fruits. During winter time, they shift their diet completely to coniferous needles (Klaus

et al., 1989), preferably those of Scots pine Pinus sylvestris or silver fir Abies alba

(Picozzi et al., 1992; Mollet and Marti, 2001). Due to this low-energy food they reduce

their level and range of activity to a minimum (Gjerde and Wegge, 1987). This

requires habitat features that allow roosting, feeding and resting all in one. Basal-

branched trees, preferably silver fir or Norway spruce Picea abies, and trees with

strong horizontal branches in the upper third are preferred (Lanz and Bollmann,

2008). Both during the summer season while nesting on the ground and during the

winter time while they keep activity low (Gjerde and Wegge, 1987), Capercaillies are

very susceptible to any disturbance caused by predators and, most notably, humans

(Storch, 1999).

Narrow habitat preferences and the low tolerance of human activities within their

home range have put many Capercaillie populations in Europe under considerable

pressure. In Switzerland, Capercaillie populations have declined particularly

significantly since the end of the seventies. During surveys conducted from 1968 to

1971, at least 1100 cocks could be recorded (Glutz von Blotzheim et al., 1973; Mollet

et al., 2003). In similar surveys conducted in 2001, only 450 to 500 cocks and the

corresponding numbers of hens were detected (Bollmann, 2006; Mollet et al., 2008).

Today, it is assumed that the Swiss Capercaillie population is divided in at least five

regional populations (see Appendix B) (Mollet et al., 2008). However, there is little to

no exchange between these populations (Mollet et al., 2003). These regional

populations are split into smaller local populations (Bollmann and Graf, 2008) and

they are partially isolated from each other due to urban and agricultural areas, high

mountain ranges and dense forests, which are unsuitable habitats (Mollet et al.,

2008).

Efforts to protect and conserve the Capercaillie in Switzerland began in the seventies

(Mollet et al., 2008). In 1977, the bird was put on the red list for endangered animal

species for the first time (Bruderer and Thönen, 1977). Currently, it is still classified

as “endangered“ (Keller et al., 2010). During the last 10 years several Capercaillie

research projects have contributed to an increased knowledge of its conservation

3

(Bollmann et al., 2008b). However, until recently a nationwide coordination of needed

conservation measures was lacking. In 2008 the Swiss Action Plan for the

Capercaillie, as part of the Swiss conservation program of priority bird species, was

published. It defines the national strategy for the protection and conservation of the

Capercaillie in Switzerland and provides information about the goals, measures,

organization and the financial framework (Mollet et al., 2008; Stadler et al., 2008).

The main goal is to halt the Capercaillie’s decline and the recovery of existing

populations. Over the longer term, until 2035, the populations should achieve the size

and range present between 1968 to 1971(Mollet et al., 2008). For this purpose, the

Swiss Action Plan groups promising areas into two categories of priority for

conservation interventions:

Areas of first priority need to have both sufficient habitat potential based on the

model of Graf et al. (2004), with substantial current Capercaillie populations

(Bollmann et al., in press). These regions are seen as source populations, centers for

the resettlement of adjacent potential areas (Mollet et al., 2008).

Areas of second priority do not hold a current Capercaillie population, but they show

good habitat potential in close proximity and within the dispersal range of existing

populations (Mollet et al., 2008).These areas can function as stepping stones,

allowing existing subpopulations to connect (Graf et al., 2004; Stadler et al., 2008).

Nevertheless, for an efficient conservation strategy, the regional circumstances have

to be taken into consideration. Therefore, the Swiss Action Plan provides specified

guidelines and information for every Capercaillie region (Mollet et al., 2008).

A promising instrument for the Capercaillie conservation in first priority areas is the

establishment of special forest reserves (Bollmann et al., 2008b). In natural forest

reserves, the focus lies on natural dynamics, excluding management interventions

(Bollmann, 2006). However, in special forest reserves conservation measures are

stipulated in an article and purposefully conducted to aid recovery. Therefore, it is

possible to attain ecological conditions, which under natural circumstances would

take much longer to occur.

Some special forest reserves specifically for Capercaillie conservation have already

been established in the cantons of Schwyz, Glarus, St.Gallen (Mollet et al., 2008),

Vaud, Bern, Neuchâtel, Obwalden, Zug and Grison (K. Bollmann, personal

comment). Since the Capercaillie has been shown to be a remarkable umbrella

species (Suter et al., 2002; Bollmann et al., 2004), the implementation of a special

forest reserve promises a high effectiveness for general conservation purposes as

well. An umbrella species is considered to be a representative of a particular

community whose conservation is expected to also have positive effects on other

species (Roberge and Angelstam, 2004; Bollmann, 2006). In this case other

endangered bird species of subalpine forests (e.g. Three-toed Woodpecker, Pygmy

Owl, Woodcock) may also profit from Capercaillie conservation (Suter et al., 2002;

Pakkala et al., 2003).

4

In 2006, the municipality of Amden in the Canton St.Gallen established an agreement

with the canton to create a special forest reserve (Ehrbar et al., 2011). The main goal

is the conservation, and if necessary, the improvement of forest quality as a habitat

for the Capercaillie. Beside the Capercaillie populations in the Canton of Schwyz, the

forests of the Canton St.Gallen are home to the next largest and most important

subpopulations in the north-eastern Prealps (Region 4a, see Appendix B) (Mollet et

al., 2008). Within the Canton of St.Gallen, the forests of Amden belong to the largest

and most important Capercaillie areas (Bollmann, 2006) with the largest regional

population (Kormann, 2009). Since the area is both currently settled by the

Capercaillie and shows a potentially suitable habitat, (Graf et al., 2004) it is identified

as a first priority area (Mollet et al., 2008). Moreover, Jacob (2006) and Kormann

(2009) showed that the area is in a genetic exchange with populations from the

Toggenburg region, and it not only acts as a source population, but also as a

stepping stone biotope (Bollmann, 2006; Ehrbar et al., 2011).

Several planning tools exist to assess priority forest stands for habitat improvement.

First, in Amden, a new forest stand map was published. Furthermore, the entire

forest area was mapped and classified concerning Capercaillie habitat suitability

(Fürer, 2001; Ehrbar et al., 2011). For that, the method of Schroth (1994) was used,

which assigns a value from 1 (optimally suitable) to 5 (unsuitable) to each forest

stand. The method takes into consideration factors including summer food, ground

cover and stand structure as crucial criteria. According to this map, one third of the

forest reserve of Amden was classified as unsuitable Capercaillie habitat, with

another third as less suitable. The final third of the area consisted of forest stands

classified as suitable to optimally suitable (Ehrbar et al., 2011).

Necessary measures were derived by means of two methodologies. The first

identified specific descriptions of forest stand structure and composition (target state);

the second used carefully chosen indicator plots (“Weiserflächen”) to determine the

actual state of the different forest areas. These indicator plots are representative of

the entire forest reserve. Subsequently, after comparing actual and target states,

necessary silvicultural measures were defined. Further information and details can be

found in Ehrbar et al. (2011). Forest stands or patches which, according to the habitat

suitability map, were classified as less suitable or unsuitable (Schroth category 4 and

5), were treated as a priority. According to (Ehrbar et al., 2011) from 2006 to the end

of 2009, 52 interventions (e.g. planting, logging, tending) were conducted.

One fundamental component of efficient and long-term work in special forest

reserves is the assessment of the impact and success of conservation measures

taken with regard to the main goal. In the case of Amden, it serves the period

examination of the measure’s effects concerning conservation and recovery of the

Capercaillie populations (Stadler et al., 2008). Such project-related, small-scaled

control of habitat quality is required by the Swiss Action Plan and realized in defined

time intervals (normally a couple of years) (Mollet et al., 2008). In the case of the

5

Amden special forest reserve, a first encompassing assessment is planned for 2016,

i.e. 10 years after the start of the project (Ehrbar et al., 2011). Beside this control,

which includes the entire forest reserve, the treated forest patches are separately

examined for habitat use by Capercaillie in shorter time intervals. Therefore, every

two to three years on average after a silvicultural intervention, there should be a

control whether these treated forest patches are accepted as habitat or not (Ehrbar et

al., 2011).

This master thesis examines, for the first time since the start of the project in 2006,

33 treated forest patches for use by the Capercaillie. The survey is based on a

search for molt feathers in summer within a set of study patches as a measure of

Capercaillie presence or absence. Additionally, as a second component, an

assessment of forest stand parameters in the same set of study patches was

conducted. It was the goal of this study to answer the following questions: 1) In which

treated forest patches do Capercaillies occur and in which do they not? 2) How do

composition and characteristics of forest stands with evidence of the Capercaillie

differ from stands without evidence? 3) Based on these insights, what first feedback

can be given to the responsible persons of the forest reserve Amden? Are the

planned and conducted measures actually target-aimed? 4) Can any

recommendations be made about possible adjustments of the measures respectively

about adjustments of the prioritization of the forest stands?

6

2. Methods

2.1 Study area

The study area was located in the special forest reserve of Amden in the canton of

St. Gallen, biogeographically associated to the Capercaillie region 4a (Mollet et al.,

2003) (see Appendix B). The reserve’s perimeter covered an area of 1772 hectares,

55 percent (975 hectares) of it being forest (Ehrbar et al., 2011). The reserve spans

the large woodlands of the municipality of Amden from the lake Walen towards the

Obertoggenburg on both sides of the watershed of the rivers Thur and Linth (see

Figure 1). The stocked area is dominated by fir-spruce (36%) and fir-beech (47%)

forest stands.

Figure 1: Location of the special forest reserve of Amden with the forest patches (blue) that

were treated between 2006 and 2009. Patches that were investigated in this study are orange

framed.

The region’s climate is oceanic. The average annual precipitation of 1930 mm is

characteristic due to the location at the northern edge of the Alps (Ehrbar et al.,

7

2011). The remarkable amount of rain in combination with impermeable soils is the

reason for the occurrence of many mires and wetlands, which intersperse the forest

area and cause a diversely structured landscape (Brülisauer, 2006) (see Figure 2). In

total, 70 hectares of bogs and 220 hectares of fens exist within the reserve perimeter

(Ehrbar et al., 2011). The rest of the area includes forest, alpine pastures and tundra

grassland. The reserve stretches from its lowest point at 1040 meters a.s.l. to an

altitude of 2101 meters a.s.l. (peak of the Leistchamm). The upper tree-line is at

about 1800 meters a.s.l. (Ehrbar et al., 2011). This altitudinal range is

phytosociologically corresponding to the “montane,” “upper montane” and “subalpine”

zone according to the classification of altitudinal vegetation zones in Frehner et al.

(2005) and Ott et al. (1997). Overall 253 different forest stand units including

transitions and tessellated patches were mapped in the reserve (Ehrbar et al., 2011),

indicating a high variety of site conditions.

a)

b)

Figure 2: Characteristic for Amden is a

diversely structured landscape with a mosaic

of forest, wetland and alpine vegetation.

a) / b) fen/meadow at Vorder Höhi, c) bog at

Hinter Höhi (Photos by Nicolas Bircher).

c)

8

2.2 Sampling design

2.2.1 Criteria for selecting study patches

The patches that were examined in this thesis (“study patches”) were selected from

all forest patches that silvicultural measures were conducted on (“treated forest

patches”) between 2006 and 2009 (see Figure 1). Habitat suitability map, forest

stand data and management plan were provided as GIS layers by Rolf Ehrbar, head

of the regional forestry commission office Waldregion 4 See. The data show the

treatment state and process of the reserve management at the end of 2009.

Measures conducted after this point in time were not considered in this study.

To be approved as a study patch, a treated forest patch had to fulfill the following

criteria:

Patch had to pass spatial stratification

Logging had to be the conducted silvicultural measure

Patch had to contain raster cells for the Capercaillie search

Patch had to contain sampling plots for the assessment of forest stand

variables

Patch size had to be ≥900m2

These five criteria are explained in detail:

Spatial stratification

Treated forest patches were distributed across the entire special forest reserve of

Amden and occurred on both sides of the watershed. For this study, only treated

forest patches on one side of the watershed in the catchment area of the Thur

(towards the Toggenburg region) were included. By doing so, the investigations were

focused on the core distribution of Capercaillies in the reserve for the past ten years

and neglected sporadic occurrence on the other side of the watershed (K.Bollmann,

pers. comm.).

Silvicultural measure

Forest patches were treated very differently (planting, thicket resp. pole wood

tending, logging, salvage logging). In this thesis, only forest patches where logging

was the conducted measure were considered in order to avoid any bias caused by

different measures and to draw conclusions for one concrete treatment.

Raster cells for Capercaillie search

For the Capercaillie search, a raster of 50m x 50m cells was applied. Initially, this

raster was placed over the entire forest reserve. Only cells that covered a treated

9

forest patch to at least 50 percent were included in the study (Figure 3). They were

applied to guarantee a systematic and proportionally appropriate survey of the study

patch. The Capercaillie search was conducted within these raster cells. Parts of

raster cells which were located outside of a forest patch, were not controlled of

course.

Sampling plots for assessment of forest stand variables

The variable assessment had to be done independently from the search for

Capercaillie since its purpose was not to characterize the habitat right where

evidence of Capercaillie was found, but to describe the treated forest patch in

general. Bollmann et al. (2005) recommend a minimum area of 500m2 to adequately

measure forest structure in subalpine spruce forests. For this study, quadratic study

plots of 30m x 30m (900m2) were chosen. A point layer with 30m x 30m resolution

(30 meters distance between points) was randomly put over the forest reserve.

These points were planned to function as plot centers (see yellow dots in Figure 3).

Only points situated within treated forest patches were relevant. Since the sampling

plots of 30 to 30 meters had to be situated completely within the forest patch, the set

of points was reduced again to those with a distance of at least 21.2 meters (half the

diagonal of a 30m x 30m square) to the patch edge.

Figure 3: Treated forest patch (orange area) with 50m x 50m raster cells for the Capercaillie

search (purple hatched) and points (yellow) as centres of sampling plots for the assessment of

forest stand variables.

Due to limited time for the field work, the assessment of forest stand variables could

not be conducted completely proportional to the forest patch size.

Eventually, a compromise was found by implementing one sampling plot per 15’000

square meters, i.e. area size = 900 - 15’000m2 1 sampling plot; area size =

15’001m2 - 30’000m2 2 sampling plots etc. In this way, the amount of sampling

plots was kept to a manageable number while guaranteeing a certain degree of

proportional representation as well.

10

Forest patch size

Forest patches with an area < 900m2 (30m x 30m) were neglected from the start

since they were not large enough for at least one sampling plot.

2.2.2 Study patch selection

After selecting 31 treated forest patches which fulfilled the criteria mentioned above,

one patch was excluded later during the field season because just a light treatment

had been conducted recently and signs of it were hardly visible. Therefore, a

comparison with other forest patches with much stronger treatments did not seem

appropriate.

Out of the 30 remaining patches, three were split into two separate parts for the

statistical analysis. Due to a considerable altitude gradient among other things, these

particular patches showed a clear heterogeneity in the sense of tree species

composition, ground vegetation cover and forest structure across the treated area.

Quite problematic was the fact that evidence of Capercaillie was found in one part of

each patch, which, in its habitat character, considerably differed from the rest of the

forest stand. By splitting, the forest patches were divided into a patch with

Capercaillie presence resp. into one with absence and the data from the forest stand

assessment could be applied appropriately and more accurately to the concerning

parts

Eventually, the sampling size amounted to 33 study patches (Figure 1) with a total of

226 raster cells for the Capercaillie search and 55 sampling plots for the assessment

of forest stand variables. All study patches together covered an area of 57.6 hectares

within the forest reserve. The altitude of the study patches ranged from 1150 meters

a.s.l. to 1517 meters a.s.l.. Patch size was rather heterogeneous and varied from

2’248 square meters to 50’802 square meters while the average patch area was

17’467 square meters.

2.3 Data collection

Data were collected between August 3rd and October 8th 2010. Late summer is a

convenient time to search for feathers, since Capercaillie conduct its molt during July

and August (Klaus et al., 1989). According to Mollet and Bollmann (2009) the

probability of finding feathers in August in the forest reserve is much higher than in

July. Therefore, the survey within the predefined raster cells took place in August. In

September and in the beginning of October, the forest variable assessment of the

study patches was conducted.

11

2.3.1 Search of indirect evidence

A transect survey method was applied to standardize the search effort for each raster

cell. By following a curved transect line stretching over the whole raster cell, it was

guaranteed that a maximum area was covered (for details see Schäublin and

Bollmann (2011)). Structural elements and sites that are preferentially used by the

Capercaillie were controlled with special attention. These elements included hill tops

and ridges, places behind trees, tree stumps, clumps of young spruce trees, basally-

branched (coniferous) trees and piles of branches (see Figure 4).

a)

b)

Figure 4: Elements like places behind trees (a)

ridges with bilberry (b) or tree stumps (c) are

preferably visited by the Capercaillie (Photos

by Nicolas Bircher).

c)

A pocket PC (HP iPAQ hx2000) with a Geographic Information System (ArcPad

version 7.0.1) and an attached GPS was used to orientate in the field. ArcGIS layers

with the raster cells and the patch boundaries were displayed on this handheld

device, sites with feathers could be located and directly entered into it.

If a raster cell showed direct evidence, such as sighting, or indirect evidence, such as

finding feathers, the corresponding study patch was defined as a “presence patch”.

If no evidence could be found in any raster cell, the classification was an “absence

patch”.

12

A first allocation of found feathers to the bird species already happened in the field.

One feature which is very characteristic for grouse species (Tetraonidae) and helpful

for their identification is the so called aftershaft (Figure 5). This downy feather grows

from the underside of the base of the main feather shaft (Erritzoe et al., 2007).

Figure 5: Body feathers with aftershaft, modified from Mollet and Marti (2001).

In addition to the aftershaft, Capercaillie can also be identified by the mere size and

coloration of the feather as well as the habitat of its discovery (see Appendix F). Size

and coloration even allow the gender of the Capercaillie to be determined (Klaus et

al., 1989). All feathers were examined again after the field season by the author and

K. Bollmann in the laboratory. Four of the feathers showed features of the grouse

family, but could not definitely be allocated to the Capercaillie (K. Bollmann, pers.

comm.; R.Winkler, written comm.). Therefore, they were not considered as evidence

of presence.

2.3.2 Search of direct evidence

No basic search for direct evidence was conducted since Capercaillies are very

secret and shy birds (Maumary et al., 2007). Therefore, the probability of detection is

very low. Nevertheless, during the field work some encounters with Capercaillies

happened by chance and were considered to be evidence as well.

2.3.3 Habitat data

In total, 44 habitat parameters were recorded to characterize the study patches

(Table 1). 34 of these parameters were measured or derived from field data for each

of the 55 sampling plots. If a study patch contained more than one sampling plot, a

mean value was derived.

Temperature, precipitation and solar radiation data were provided by Niklaus

Zimmermann from the Swiss Federal Research Institute WSL. They were available

as ArcGIS data with a raster cell size of 25m x 25m. Slope was derived from a digital

elevation model (dhm25) from swisstopo (2010).

aftershaft

13

Six parameters – age of the study patch, study patch size, suitable surroundings,

unsuitable surroundings, fir-spruce forest and fir-beech forest – were not derived from

the field data, but from the forest stand data and the management plan: the age of

the forest patch is understood as the length of time since the measures were

conducted (i.e. 2010 – year of treatment). The variables “suitable” and “unsuitable”

surroundings were extracted from GIS data. They were calculated in ArcMap (version

9.3.1) using the habitat suitability map by delineating a 50m buffer around each patch

and assessing the proportion in terms of the area within this buffer for both classes

“suitable” and “unsuitable” (see Figure 6). These two classes were derived from the

five suitability categories of Schroth (1994): Suitable surroundings included the

Schroth categories “optimally suitable” (1) and “suitable” (2), unsuitable surroundings

included the Schroth categories “still suitable” (3), “less suitable” (4) and “unsuitable”

(5). A third category had to be implemented for patches within the buffer that were

not classified with the Schroth method. Reasons why they were not assigned with a

Schroth category were:

Outside of the forest area

Outside of the forest reserve

Within another treated forest patch so that due to the conducted treatment the

suitability classification was not accurate anymore (habitat qualification was

conducted in 2000 (Fürer, 2001; Bollmann, 2006)

Figure 6: Design for the determination of the variables suitable and unsuitable surroundings.

Ehrbar et al. (2011) grouped upper classes of vegetation site communities from the

directives for sustainability in protection forests (NaiS project) (Frehner et al., 2005)

into three types of treatment: montane fir-beech forests, upper montane fir-spruce

forests and others. If these types react differently to silvicultural measures this may

have consequences for their treatment and the selection of new forest patches.

Therefore, there is a high interest regarding the use of fir-beech and fir-spruce forests

by Capercaillie in the Amden forest reserve (Ehrbar et al., 2011). Thus, the

proportions of these two types of treatment (variables “Fir-beech forest” and “Fir-

spruce forest”) were measured by means of GIS data for every study patch.

50m

14

In the field, plot centers were located with a GPS. If a plot center was situated at an

inaccessible location (e.g. creek, steep slope) or its surrounding area did not

correspond with the character of the actual treated forest patch (e.g. swampy hollow

without logging) then it was moved 10 to 15 meters to an appropriate place. At the

center, the altitude was measured with the GPS. Then, the plot was marked out

parallel to the slope.

A full callipering was conducted for the entire plot. In a callipering, all trees in a

defined area with a diameter equal or larger than a certain threshold are identified

and their diameters measured. For this study, a dbh (diameter at breast height i.e.

1.3m above ground) of 12 centimeters was chosen (Keller, 2005). The tree species

present were Norway spruce Picea abies, silver fir Abies alba, beech Fagus

sylvatica, rowan Sorbus aucuparia, sycamore maple Acer pseudoplatanus and Scots

elm Ulmus glabra. From this callipering, several variables could be derived: the total

stem number per plot, respectively the stem numbers of each species (tree density),

the total basal-area per plot as well as the basal area of each tree species per forest

patch (dominance). Furthermore, the importance value (relative tree density +

relative dominance) was calculated for each tree species according to Kuers

(2005).This last variable integrates the phytosociologically important parameters tree

density and tree dominance into one measure. It is an indicator for the relative

importance and dominance of a tree species in a particular forest stand (Barker et al.,

2002).

To analyze the growth structure of the forest stands two variables were defined: one

variable called “succession,” which was expressed as the ratio of the number of trees

on a study patch with a dbh above the median and the number of trees with a dbh

below the median. The median of dbh was calculated over all surveyed study

patches and amounted to 35 centimeters. This variable served as an indicator

whether the forest patch was rather characterized by thinner trees (dbh<35cm) or

thicker i.e. “older” trees (dbh>35cm). The second variable was named “very large

trees,” which was expressed as the number of trees on a study patch above a certain

dbh threshold. As threshold the 90% percentile (59 centimeters) of the dbh

distribution over all surveyed forest patches was chosen.

15

Table 1: Name, abbreviation and definition of variables used to quantify forest patches.

Group Variable Abbreviation Unit Definition

Site

parameters

Age AGE number Number of years since forest measures were conducted (2010 - year of treatment)

Size SIZE (m2) Size of the study patch

Slope SLOPE degree (°) Slope in degrees

Temperature TEMP (1/100°C) Monthly mean of average temperature (1961-1990) in degrees C (Zimmermann and Kienast, 1999; Zimmermann and Roberts, 2001)

Precipitation PRECIP (1/10mm) Monthly mean precipitation sum (1961-1990) (Zimmermann and Kienast, 1999; Zimmermann and Roberts, 2001)

Solar radiation SOLRAD kJ/day Monthly global potential shortwave radiation kJ/day = monthly potential diffuse shortwave radiation (Kumar et al., 1997) + monthly potential direct shortwave radiation (Kumar et al., 1997)

Altitude ALT (m) Altitude in meters above sea level (a.s.l.)

Habitat structure

Fir-spruce forest

FIRSPR (%) Proportion of the treatment type upper montane fir-spruce forests on the study patch

Fir-beech forest

FIRBEE (%) Proportion of the treatment type montane fir-beech forests on the study patch

Total stem number

N_tot number Total number of alive tree stems within sampling plot

Stem number spruce

N_SPR

number

Number of alive stems of particular species within sampling plot

Stem number fir

N_FIR number

Stem number beech

N_BEE number

Stem number rowan

N_ROW number

Stem number sycamore maple

N_SMA number

Stem number Scots elm

N_ELM number

Total basal area

G_tot (m2) Sum of the cross-sectional areas at a height

of 1.3 m (DBH) of all trees of a sampling plot

16

Basal area spruce

G_SPR (m2)

Sum of the cross-sectional areas at a height of 1.3 m (DBH) of a particular tree species

Basal area fir G_FIR (m2)

Basal area beech

G_BEE (m2)

Basal area rowan

G_ROW (m2)

Basal area syc. maple

G_SMA (m2)

Basal area Scots elm

G_ELM (m2)

Importance value spruce

IV_SPR value (density of particular tree species/ sum of all densities)*100 + (total basal area of each individual species/ sum of the basal areas of all species)*100

Importance value fir

IV_FIR value

Importance value beech

IV_BEE value

Importance value rowan

IV_ROW value

Importance value syc. maple

IV_SMA value

Importance value Scots elm

IV_ELM value

Suitable surroundings

SUIT (%) Proportion of suitable habitat (Schroth categories 1 and 2) within a 50 meter buffer area around the study patch

Unsuitable surroundings

UNSUIT (%) Proportion of unsuitable habitat (Schroth categories 3, 4 and 5) within a 50 meter buffer area around the study patch

Coniferous trees

CONIFTR (%) Proportion of coniferous trees (number of coniferous trees / total tree number) per sampling plot

Deciduous trees

DECIDTR (%) Proportion of deciduous trees (number of deciduous trees / total tree number) per sampling plot

Very large trees

LARGETR number Number of trees with dbh above 59 centimeters (90% percentile) in a sampling plot

Succession SUCC value Ratio of the number of trees with dbh above 35 centimeters (median) to the number of trees with dbh below 35cm

Canopy cover estimated

CANCO_EST (%) Estimated ground area covered by tree layers

Canopy cover measured

CANCO_MES (%) Proportion of 25 points within a sampling plot covered by the crown of trees

17

Shrub/ ground cover

Shrub cover SHRCO (%) Area covered by shrubs estimated in categories for a sampling plot: 1= <1%, 2= 1-9%, 3= 20-20%, 4= 21-35%, 5= 36-50%, 6= 51-75%, 7= 76-100%

Ground vegetation cover

GRVECO (%) Area covered by ground vegetation estimated in categories for a sampling plot: 1= <1%, 2= 1-9%, 3= 20-20%, 4= 21-35%, 5= 36-50%, 6= 51-75%, 7= 76-100%

Cover of Vaccinium species

VACCO (%) Area covered by Vaccinium species estimated in categories for a sampling plot: 1= <1%, 2= 1-9%, 3= 20-20%, 4= 21-35%, 5= 36-50%, 6= 51-75%, 7= 76-100%

Bilberry cover BILBCO (%) Area covered by bilberry Vaccinium myrtillus estimated in categories for a sampling plot: 1= <1%, 2= 1-9%, 3= 20-20%, 4= 21-35%, 5= 36-50%, 6= 51-75%, 7= 76-100%

Edge lines Clumps/Basal-branched trees

CLUBBTR (m) Total boundary length of clumps of young spruce trees and basal-branched coniferous trees in meters

Root plates ROOTPL (m) Total length of root plate diameters

Comfort Ant hills ANTHILL number Number of ant hills within a sampling plot

Canopy cover was estimated for each sampling plot. However, the variable was

slightly modified compared to the method of the Swiss National Forest Inventory (LFI)

(Keller, 2005) by considering every tree layer (upper, middle and lower layer) and not

only the upper layer. Since the Capercaillie depends on good ground vegetation,

every tree layer influencing the amount of light reaching the forest floor has to be

considered. Besides estimating canopy cover, this variable was also measured using

a vertical densitometer at 25 points, which were systematically distributed along five

transects within the sampling plot (see Appendix C). At each point, it was assessed

whether the spot vertically above was covered or not covered by the canopy. The

proportion of the covered spots resulted in the canopy cover. This method was

implemented for having a standardized procedure besides estimating and increasing

the confidence into data accuracy. However, this concern showed to be unfounded,

since estimates and measures of canopy cover in the plots were highly correlated

(Pearson correlation = 0.875).

A group of variables was recorded on five 2m x 2m subplots (see Appendix C).

These variables included the shrub cover, ground vegetation cover, cover of

Vaccinium species and bilberry cover in particular. Modified classes of the National

Forest Inventory (LFI) (Keller, 2005) were used for these variables. After Keller

(2005), the shrub layer is defined to be all woody plants with a height between 0.5

and 3 meters. For this study, the variable was modified including all woody plants

with a height between 0.5 and 5 meters. The shrub layer is seen as an element of

hindrance for the Capercaillie since it impedes the detection of predators and

maneuvering during flight (Bollmann et al., 2008a). This variable, however, turned out

to be barely analyzable for two reasons: first, well-developed shrub vegetation hardly

18

existed on the study patches after the recent logging and, therefore, only some data

could be collected. Second, the modification of the variable definition proved to be

unfortunate because confusion arose between counting a 4 or 5 meter tall plant as a

shrub or a tree. For these reasons, the shrub cover, although recorded as accurately

as possible, was not used in the further analyses.

Ground vegetation cover was defined following Keller (2005). Berries from the

Vaccinium species, particularly from bilberry, are an important food source for the

Capercaillie in summer and fall (Klaus et al., 1989; Storch, 1993). During the field

survey, bilberry proved to be the only Vaccinium plant occurring in a significant

amount. The only other Vaccinium plant found on the plots was lingonberry

Vaccinium vitis-idaea in some sparse single shoots. Therefore, the variable “cover of

Vaccinium species” was discarded and only bilberry cover used for further analysis.

As important hideout elements for the Capercaillie, basally-branched trees and

clumps of young coniferous trees were combined to one variable. Since silvicultural

measures have been conducted just a short while ago, there has not been enough

time yet for the development of regeneration and trees with distinctive basal

branches in the formerly dark and dense forest stands. Consequently, these

elements were hardly found on the study patches. Root plates were really scarce in

the treated patches as well. Therefore, these variables could not be considered for

the statistical analysis due to small numbers. The same applies to ant hills, which

were hardly found.

2.4 Data analysis

Capercaillie presence and absence was used as the response variable, while habitat

data served as the explanatory variables. All statistical analyses were conducted with

the statistic software SPSS 17. First, a descriptive statistic approach was chosen to

analyze the current situation on the study patches. Second, the habitat variables with

the highest explanatory potential for Capercaillie presence and absence were

identified using logistic regression. These variables were then used to build a

multivariate model to predict Capercaillie occurrence.

For presence and absence patches, means and standard errors were calculated

separately for each independent variable. The non-parametric hypothesis Mann–

Whitney U test was used to analyze whether these values were significantly different.

Since all independent variables were continuous, no other test had to be applied. The

level of significance was p<0.05.

To avoid over fitting and limited interpretability (Guisan and Zimmermann, 2000), the

original set of 44 independent variables had to be reduced for model-building in

several steps:

19

First, bivariate correlations were analyzed to check for multicollinearity between the

explanatory variables, a factor which according to Hosmer and Lemeshow (2000) can

restrict the power of logistic regression models. Fielding and Haworth (1995) used a

threshold of 0.7 to determine high correlation. According to their recommendation,

variable pairs with high correlation (Pearson correlation >0.7) were further scrutinized

and the variable with less explanatory potential considering Capercaillie occurrence

was excluded from further analysis.

The second step included the calculations of univariate models for all explanatory

variables. These logistic regression models served to exclude all variables whose

explanatory potential did not significantly (p>0.05) explain the variance in the data

(Hosmer and Lemeshow, 2000). For regression analysis, Fowler et al. (1990)

recommend to transform proportion data. Proportions may cause problems since

both distribution tails are truncated on a scale between zero and one. According to

Gotelli and Ellison (2004) an arcsin-transformation was used to increase the

accuracy of the model.

In a last step, the significant univariate variables were joined in any possible

combination. To attain the final model, the various variable combinations were ranked

according to the Akaike Information Criterion (AIC) ) (Akaike, 1974; Mazerolle, 2006).

The AIC considers the number of parameters and the overall fit of the actual model

(log-likelihood). The higher the log-likelihood and the fewer parameters involved, the

better is the model according to AIC (Mazerolle, 2006). To compare different models,

two measures derived from the AIC value can be used. First, the delta AIC (AIC)

corresponds to the difference among a particular model with the “best” model.

Generally, a AIC < 2 can be considered as support for the model, while values from

3 upwards indicate the model to be rather unlikely (Mazerolle, 2006). Second, Akaike

weight () is a probability indicator for a particular model to be “the best among the

whole set of candidate models” (Mazerolle, 2006, p.173).

For the multivariate analysis only habitat structure parameters (see Table 1) were

considered. These variables primarily influence the habitat suitability for the

Capercaillie on a small-scale, like a forest patch. They can directly be influenced by

forest management and measures. Site parameters were analyzed in the univariate

analysis, but they were not further used in the multivariate approach.

20

3. Results

3.1 Characteristics of forest patches with and without use by

Capercaillie

In 12 out of 33 studied forest patches feathers from the Capercaillie were detected

(Figure 7). In three patches only once evidence was found, in the other nine patches,

several sites with feathers were discovered. Feathers were found at 45 sites in total,

about one quarter of them stem from hens and all the rest were from cocks.

Figure 7: Forest patches with and without evidence of Capercaillie.

The time period since the treatment was not related to the occurrence of Capercaillie

on the forest patches. Both just recently treated forest patches and patches with a

treatment three or four years ago were used and not used by the Capercaillie.

Among the site parameters, four variables showed significant differences between

study patches with Capercaillie presence and absence, respectively (Table 2). Study

patches where evidence of Capercaillie was found were on average higher situated

(1408±23 (S.E.) m a.s.l. vs. 1278±14 m a.s.l.) than study patches without evidence.

Presence patches showed a higher monthly mean precipitation sum with

198.44±1.64 mm compared to absence patches (189.73±1.72mm). Corresponding to

the altitude, temperature was obviously lower on forest patches which were used by

the Capercaillie (9.4°C) than on absence patches (10.0°C). Presence patches were

surrounded by a significantly higher proportion of suitable Capercaillie habitat (18%)

than absence patches (3.3%). The proportion of the treatment type fir-spruce forests

(FIRSPR) was significantly higher on forest patches with Capercaillie occurrence

(59.26±10.47 %) than on patches without occurrence (13.17±4.60 %). In contrast, the

21

proportion of fir-beech forest (FIRBEE) was significantly higher in absence patches

(69.62±5.99 %) than in presence patches (33.10±10.20 %).

Table 2: Variables of examined forest patches with a significant difference between patches

used (presence) and non-used (absence) by Capercaillie. All data refer to a quadratic plot size

of 900 m2.

Capercaillie presence Capercaillie absence Mann-Whitney U Test

Variable (Abbr.) Mean (n=12) ±Std. Error Mean (n=21) ±Std. Error Z-value p

ALT 1407.92±23.24 (m a.s.l.) 1277.77±14.45 (m a.s.l.) −3.799 <0.001

TEMP 936.38±12.29 (1/100°C) 1005.90±8.09 (1/100°C) −3.687 <0.001

PRECIP 1984.46±16.43 (1/10mm) 1897.39±17.22 (1/10mm) −3.144 0.002

SUIT 17.92±4.51 (%) 3.33±1.90 (%) −3.078 0.002

FIRBEE 33.10±10.20 (%) 69.62±5.99 (%) −2.662 0.008

FIRSPR 59.26±10.47 (%) 13.17±4.60 (%) −3.432 0.001

N_BEE 4.46±1.88 6.96±0.97 −1.973 0.049

N_SMA 0.04±0.04 1.63±0.54 −2.846 0.004

CONIFTR 82.92±6.27 (%) 68.48±4.01 (%) −2.137 0.033

DECIDTR 17.08±6.27 (%) 31.52±4.01 (%) −2.137 0.033

G_SMA 0 (m2) 0.06±0.03 (m

2) −2.649 0.008

IV_SMA 0 0.07±0.02 −2.737 0.006

CANCO_MES 47.83±2.40 (%) 62.83±3.19 (%) −2.831 0.005

BILBCO 20.59±3.96 (%) 4.22±0.98 (%) −3.41 0.001

The current forest structure and composition of presence and absence patches is

clearly different (see Table 2). In general, the proportion of coniferous trees was

significantly higher in presence patches (~83%) than in absence patches (~68%).

Absence patches showed higher stem numbers of beech (Mann-Whitney U-Test, Z=

−1.973, p= 0.049) and sycamore maple (Mann-Whitney U-Test, Z= −2.846, p= 0.004)

than presence patches. Horizontal patch structure differed among absence and

presence patches with regard to canopy cover. Mean canopy cover in presence

patches amounted to 48% and 63% in absence patches, respectively. A conspicuous

difference occurred between presence and absence patches in ground vegetation

composition. Forest patches with use by the Capercaillie had a bilberry cover of 21%

on average, patches without Capercaillie use had only 4% (see Table 2).

The mean of total stem number was not significantly different between presence and

absence patches. However, the minimum stem number in absence patches (11

22

trees/900 m2) was lower than the minimum in presence patches (15). Presence

patches also showed a higher maximum stem number (58) than absence patches

(45).

Figure 8: Box plots with median, minimum and maximum of the number

of very large trees in patches with and without use by the Capercaillie.

The mean number of very large trees per patch did not significantly differ between

forest patches with and without use by Capercaillies. By tendency, there were more

trees with a dbh >59 centimeters in presence patches (median: 2.8 vs. 1.3) (Figure

8).The maximum number of very large trees was six in both categories. Opposite to

absence patches, presence patches always contained at least one very large tree.

3.2 Univariate analysis

Six parameters showed significant differences among presence and absence

patches in the univariate logistic regression models (Table 3). Four of these

variables, which are related to the small-scaled habitat use of the Capercaillie, were

subject to further (multivariate) analysis. All variables yielded surprisingly high

explanatory power, with temperature and bilberry cover at the top (R2>0.5). Bilberry

cover also had the strongest influence on Capercaillie occurrence (=9.63) closely

followed by canopy cover (=−9.3).

23

Table 3: Significant variables, their regression coefficients, p-values and R2 from the univariate

analysis.

Variable p Nagelkerke R2

TEMP −0.041 0.003 0.555

PRECIP 0.016 0.008 0.346

SUIT 4.707 0.005 0.349

CONIFTR 3.307 0.030 0.216

CANCO_MES −9.299 0.011 0.342

BILBCO 9.629 0.003 0.522

3.3 Multivariate analysis

The four habitat parameters being significant in the univariate analysis were used in

any possible combination to build multivariate models (Table 4). The first four models

(Nr.1-4) only contain one variable and have already been part of the univariate

analysis (see chapter 3.2). Although they can be regarded as rather simple, they

were, however, included into the model selection by AIC. As recommended by

Mazerolle (2006) AIC and were used to select the best models. The models no. 3,

8, 10 and 14 (marked grey in Table 4) showed both a AIC < 2 and the highest

values. Therefore, they were considered to be the best models and the subject of

further analyses.

Table 4: 15 models, built with all possible combinations of the 4 significant variables from the

univariate analysis. Their AICc value, AIC and are displayed. The four best models are

highlighted in grey.

No. Model AICc ∆AIC ω

1 CONIFTR 39.717 10.810 0.0011

2 CANCO 35.768 6.867 0.0080

3 BILBCO 29.557 0.656 0.1794

4 SUIT 35.687 6.786 0.0084

5 CONIFTR+CANCO 35.705 6.804 0.0083

6 CONIFTR+BILBCO 31.589 2.688 0.0650

7 CONIFTR+SUIT 35.805 6.904 0.0079

8 CANCO+BILBCO 28.901 0 0.2491

9 CANCO+SUIT 34.719 5.818 0.0136

10 BILBCO+SUIT 29.549 0.648 0.1802

11 CONIFTR+CANCO+BILBCO 31.297 2.396 0.0752

12 CONIFTR+CANCO+SUIT 35.764 6.863 0.0081

13 CONIFTR+BILBCO+SUIT 31.888 2.987 0.0559

14 CANCO+BILBCO+SUIT 30.547 1.646 0.1094

15 CONIFTR+CANCO+BILBCO+SUIT 33.106 4.205 0.0304

24

Table 5 describes the best models obtained by the selection according to AIC. The

explanatory power of all four models is relatively high (0.522≤R2≤0.611) as are the

correct prediction rates (84.8 ‒ 87.9%). Out of the four initial parameters, the variable

“coniferous trees” does not occur any more in any of the four models. On the other

side, “bilberry cover” (BILBCO) is represented in each of them. Moreover, its

coefficient is always the largest one (7.499≤≤9.629) showing a strong positive

relation to Capercaillie occurrence.

Table 5: Results of the logistic regression models 3, 8, 10 and 14 with coefficients, standard

errors and P values. Moreover, the Nagelkerke R2 and the correct prediction rates of the four

models are reported.

Model Nr. R

2 Correct prediction Coefficients S.E. P

3 0.522 84.8 Intercept −3.413 1.085 0.002

BILBCO 9.629 3.19 0.003

8 0.593 87.9 Intercept 2.404 3.644 0.509

CANCO −6.453 4.134 0.119

BILBCO 8.128 3.289 0.013

10 0.578 84.8 Intercept −3.589 1.163 0.002

BILBCO 8.207 3.306 0.013

SUIT 3.045 2.048 0.137

14 0.611 87.9 Intercept 0.905 3.969 0.820

CANCOV −4.871 4.326 0.260

BILBCO 7.499 3.367 0.026

SUIT 1.989 2.283 0.384

The influence of the suitable surroundings is positive for the occurrence of

Capercaillies as well. In contrast, canopy cover shows a negative relation to

Capercaillie presence. However, only the variable bilberry cover contributes

significantly (p<0.05) to the models. The other two variables do not reach the

significance level in any of the four models (Table 5).

25

4. Discussion

This study shows that Capercaillie accepts treated forest patches already a few years

after forest measures have been conducted. On about one third of the 33 examined

forest patches evidence for use by the Capercaillie was found. The time period since

the intervention did not have an essential influence on Capercaillie presence.

Evidence was found both on patches with treatments dating from 2006 and on such

with interventions in 2009. Rudmann (2001) had similar experiences in the

Toggenburg region where treated forest patches were colonized already two years

after a treatment. Bollmann et al. (2008a) conclude that Capercaillie reacts to

changes in small-scaled forest structure. However, the way the preferred habitat

structure is achieved, whether naturally or by forest management, does not really

matter. Nevertheless, the applied measures in the forest reserve of Amden were

successful in the way that forest patches which, according to the method of Schroth

(1994) were classified as “unsuitable” or “less suitable” in 2006, were now accepted

as habitat.

Here the question arises whether the habitat structure on the treated forest patches

has changed so heavily in such a short period that it got suitable for the Capercaillie.

Or is it possible that the needs of this grouse species are not taken correctly into

consideration by the categories respectively the criteria of the habitat suitability map

after Schroth and, therefore, the map’s classification is not appropriate? This study

represents just a snapshot. Therefore, it cannot determine how much the habitat

structure on the examined forest patches has changed since the treatment was

conducted. A quantification of the change on the treated forest patches over time

requires several subsequent controls to build up a time series respectively at least a

second control for a proper comparison. In 2016 at the latest, within a comprehensive

assessment, a new suitability map shall reveal whether the treated patches have

improved as a habitat for the Capercaillie (Ehrbar et al., 2011).

The habitat suitability assessment with the Schroth method (Schroth, 1994) mainly

focuses on the needs of Capercaillie in the summer season: vital bilberry layer, good

cover, structure richness, edge lines and enough space to fly. According to Ehrbar et

al. (2011), the habitat suitability map was the only criterion for the selection of the

forest patches to be treated at the beginning of the project. Based only upon this

map, the interventions were conducted across the entire forest reserve. The species

survey (search for feathers) conducted within this study in August 2010 however

shows that the distribution of presence and absence patches does not follow the

same pattern of the interventions’ distribution. Patches with evidence of Capercaillie

were situated in distinctly higher locations (average of 1410 meters a.s.l.) than

patches without use (av. 1280 meters a.s.l.). This result is confirmed by the variables

temperature and precipitation. Both variables were highly correlated with altitude in

this study and, according to the regression analysis, were significantly interrelated

with the presence respectively absence of Capercaillie on the forest patches. The

26

variable “suitable surroundings” yielded results that should influence the planning of

coming interventions. On average, presence patches were surrounded by 18% of

suitable Capercaillie habitat, absence patches only by 3.3%. This variable was also

an essential predictor of Capercaillie occurrence in the regression analysis. These

results are interesting in two ways. On the one hand, they are an indication that the

mapping according to Schroth identifies the suitable Capercaillie habitat correctly.

Moreover, the knowledge about the positive relation between the proportion of

suitable surroundings and the occurrence probability of the Capercaillie on the forest

patches extends the application potential of the habitat suitability map. For the future

planning, not only the habitat suitability of the concerned forest patch but also the

suitability of its surroundings should be taken into consideration.

In presence patches, canopy cover amounted to 48% on average. This result agrees

with canopy cover values from other studies and opinions from experts (Klaus et al.,

1989; Storch, 1994; 2007; von Hessberg and Beierkuhnlein, 2000; Graf et al., 2007).

Bollmann et al. (2008a) defined a canopy cover between 30 and 70% (optimally 40-

60%) as one requirement for suitable Capercaillie habitat. Canopy cover on absence

patches was significantly higher (on average 70%), it lay at the upper end of the

recommended range. According to this thesis, logging as a treatment measure

seems to have the desired effect. However, an average value of 70% is distinctly

above the optimal range between 40 and 60%. Furthermore, some forest patches

showed canopy cover values up to 80% and so, clearly missed the optimum of the

cover range. For the Prealps, Graf et al. (2007) identified canopy cover as a

significant predictor for the occurrence of Capercaillie. They also interpret canopy

cover and its variation as an indicator for the proportion of inner edge lines. Based on

the regression analysis, this study also identified canopy cover as a crucial variable

for the occurrence of Capercaillies. According to the results, the probability of

occurrence considerably increases with decreasing canopy cover. The relationship

between canopy cover and edge lines was not part of the analysis in this thesis. The

assumption of Graf et al. (2007) appears to be plausible though. Due to increased

incidence of light a low canopy cover positively influences the proportion of edge

lines within the forest stand.

Total stem number was not a significant predictor for Capercaillie habitat use. This is

somewhat surprising as Imhof (2007) argues that there is a relationship between

canopy cover, stem number and maneuverability in flight within the patch: The larger

the stem number, the higher the canopy cover, the worse the maneuverability in flight

for the Capercaillie. Supporting Imhof’s statement, this study found a positive relation

between canopy cover and stem number as well (Pearson correlation = 0.555).

However, total stem number could not be confirmed as an important predictor.

According to observations during the field season, one explanation could be that

logging was not conducted evenly across the entire selected forest patch.

Sometimes, it happened clumped so that aisles or corridors were cut out.

Capercaillies use them for starting and landing and as an escape route as well (Lanz

and Bollmann, 2008). Therefore, as long as forest aisles were available, even forest

27

patches with a relatively high tree density could be flown and used by the

Capercaillie. The range of stem numbers on presence patches from 15 to 58 trees is

another indication that not the mere number but the spatial stem distribution is

decisive.

Bilberry cover turned out to be the major determinant for the occurrence of

Capercaillie on the treated forest patches. The four best models of the multivariate

regression analysis retained bilberry cover as a significant and the most powerful

predictor. The importance of bilberry for the Capercaillie as food during the

vegetation period is well-known (Klaus et al., 1989). A well-developed bilberry layer

provides this grouse species with both an energy-rich food source and cover on the

ground (Storch, 1993). This vegetation type is often also rich in invertebrates, which

are the primary diet of the chicks (Atlegrim and Sjöberg, 1995; Wegge et al., 2005;

Wegge and Kastdalen, 2008). In their spring habitat model for Capercaillie in the

Central Alps, Bollmann et al. (2005) could not retain bilberry cover as an explanatory

variable. They concluded that the occurrence of Capercaillie in spring is not bound to

a minimal degree of bilberry cover. Rolstad et al. (1988) observed that the use of

forest stands in a stadium of late succession by Capercaillie increased gradually

during the summer, probably related to the increasing importance of the associated

well-growing bilberry as a food source. In addition to the season, regional factors

seem to play a role as well. Research from Bollmann et al. (2005; 2008a) in several

study areas in the Swiss Alps about the small-scale habitat use of the Capercaillie did

not show bilberry as a significant explanatory variable although Capercaillie occurred

there as well. A reason for that may be that in the Central Alps, the proportion of

subalpine forests is higher than in the Prealps. Subalpine forests generally show a

lower productivity resulting in a lower stem density (Brassel and Brändli, 1999). It is

assumed that this open forest structure allows the development of a distinct ground

vegetation whose dwarf shrubs and herbaceous plants (e.g. Melampyrum sp.

(Friedrich, 2006)) might compensate the rare bilberry occurrence (Bollmann et al.,

2005; 2008a). Bilberry rather occurs in higher abundance in the more productive

upper montane coniferous forests like those widely distributed in the Prealps

(Bollmann et al., 2005). This study can confirm this statement by having identified

bilberry as a predictor for the occurrence of the Capercaillie in the Amden forest

reserve during the breeding season. Baines et al. (2004) showed a positive

relationship between bilberry cover and Capercaillie breeding success. Bollmann et

al. (2008a) propose an extensive bilberry cover as an essential habitat element for

the species’ management. Regarding bilberry cover in the forest reserve Amden, the

differences between the examined forest patches were striking. Presence patches

showed a mean value of 20.6%, which is quite satisfactory and lies in the range of a

“well suitable” habitat according to the method of Schroth (1994). The increased

incidence of light, which is attributable to the conducted logging, seems to favor

bilberry growth on these forest patches. On the other side, the average value of

bilberry cover on absence patches was substantially lower (4.2%). Apparently, on

these sites the conducted measures have not had the desired effect (yet).

28

Bilberry grows preferentially on acidic and nutrient-poor soils (Ott et al., 1997; Grey-

Wilson, 2006). These conditions can be found in spruce and fir-spruce forests of the

subalpine and upper montane vegetation zones so that bilberry is mainly associated

with them (Ott et al., 1997; Rudmann, 2001). Bilberry is not so abundant in the lower

situated montane fir-beech forests. Often, extensive beech regeneration inhibits the

development of a herbaceous layer and the development of bilberry. In terms of area,

especially the upper montane fir-spruce forests as well as the montane fir-beech

forests are important for the Amden forest reserve. This study shows that forest

patches with a high proportion of coniferous trees were preferably used by the

Capercaillie. Presence patches contained a significantly higher percentage of

conifers than absence patches. The proportion of coniferous trees was not retained

by the final model of the multivariate analysis. However, the regression analysis of

the single variable showed a significant positive relationship between the proportion

of conifers and the occurrence probability of Capercaillie. Evidence suggests that, for

the conservation of Capercaillie in the Amden forest reserve, the focus for habitat

measures should be on the upper montane fir-spruce forests. According to the

experiences of Rudmann (2001), forest sites of the upper montane zone can reach

good habitat suitability after silvicultural measures have been conducted, while

montane fir-beech forests rather lack this ability. Since a well-developed bilberry

cover is the main criterion to distinguish Schroth classes (Schroth, 1994), Rudmann’s

perception makes sense.

Fir-beech forests not only have shortcomings concerning habitat suitability, it also

appears that silvicultural measures do no attain the same strong and temporally

persistent effect as in fir-spruce forests. Both beech and sycamore maple were

significantly more numerous on absence patches. Observations during the field work

indicate that beech and maple trees in the middle and the upper layer close gaps in

the canopy, which were caused by the logging, already after a few years. This

perception is corroborated by Ott et al. (1997). Therefore, an increased incidence of

light on the forest ground is provided for just a short time. Probably, it is too short for

an extensive herbaceous layer and a sufficient amount of bilberry to develop.

However, if an intervention of greater dimension is conducted to get larger gaps,

beech regeneration arises extensively and inhibits the development of lush ground

vegetation as well. Moreover, dense beech regeneration in the shrub layer can

heavily restrict mobility and maneuverability of the Capercaillie (Bollmann et al.,

2008a). Experiences from the Toggenburg point out that, without regular tending,

forest sites with fast regeneration of beech retransformed again to unsuitable habitat

in a short time (Rudmann, 2001).

Discussion of methodology

This master thesis presents the first assessment of the impact of forest measures to

improve habitat suitability in the special forest reserve of Amden. Of course, the

selection and treatment of the patches has been conducted from a forest

29

management perspective. The challenge for this study was to develop a design,

which applies to the variety of treated patches in the sense of size, location and

forest structure. In this work, the study patches were not freely selectable according

to scientific criteria, but corresponded in form and composition to the management

plan of the forestry of Amden. Solely, the number of patches to be examined was

self-defined. For reason of time limitation, a complete assessment of all treated forest

patches was not realizable. The part of the forest reserve within the catchment area

of the river Linth was neglected in this study. As already mentioned, regular habitat

use in summer by the Capercaillie has not been recorded there for 10 years.

Therefore, this stratification was considered to be appropriate. Out of the remaining

patch set, all patches where logging has been conducted were deliberately chosen,

since this is the primary thinning measure. The area of the 33 studied forest patches

represents with 57.6 hectares more than a third of the total area that was treated until

2009. Therefore, the obtained findings and the derived recommendations should be

relevant for the entire special forest reserve of Amden.

For the assessment of forest stand variables, a plot size of 30 m x 30 m proved to be

very suitable. On the one hand, the plot was sufficiently large to describe the

character as well as the structure of a forest stand appropriately (Bollmann et al.,

2005). On the other hand, the survey was still feasible with an acceptable

expenditure of time. Furthermore, this plot size allowed integrating smaller forest

patches into the study as well. The set of forest stand variables that were recorded

and analyzed consist on one side of parameters induced by Capercaillie ecology

(e.g. proportion of conifers, ground vegetation cover). On the other side, parameters

were included which, to my knowledge, have been hardly surveyed for Capercaillie

research but were relevant more often in studies linked to forest ecology (e.g. basal

area or importance value). Previous studies related to Capercaillie research have

already conducted assessments of forest stand variables (e.g. Picozzi et al., 1992;

Bollmann et al., 2005). However, this work, with its standardized and systematically

conducted survey together with the broad range of recorded parameters, strongly

based on the practical orientated forestry, may still extend the perspective of

Capercaillie experts and conservationists. For instance, a full callipering to survey a

forest stand is an established methodology among foresters but bridging the gap to

Capercaillie ecology is a recent approach.

The description and characterization of the treated forest patches on the basis of

sampling plots was quite a challenge. Ideally, the number of sampling plots per forest

patch would have been proportional to patch size. But the required number of

sampling plots would have been too large for this master thesis. Therefore, the forest

patches had to be grouped into size categories and attributed with a limited number

of sampling plots. Although, some forest patches may not have been recorded in all

their heterogeneity, this approach should still guarantee an appropriate

representation.

30

The search for feathers proved to be a suitable methodology to find evidence of

Capercaillie in summer. In winter, the search for droppings is an effective way, when

the dark dropping is easily detectable on the frozen, white snow layer (Klaus et al.,

1989; Imhof, 2007). However, in the summer season, droppings are hard to detect

within the ground vegetation, while feathers lie normally on the vegetation. The

systematic search within predefined raster squares was time consuming, but on the

other side, a rigorous cover in terms of surface area was ensured. Raster squares of

50m x 50m were large enough to allow the use of a GPS with an inaccuracy of a few

meters for orientation. On the other side, the raster square size still allowed to keep

an overview about all structural elements within it and to search them properly.

The classification of the treated forest patches into presence and absence patches

carried the risk that, by mistake, a forest patch was declared an absence patch

although it was used as a habitat by the Capercaillie. During the field work, the

experience was made that feathers were mainly found around the structural elements

that were already mentioned. These structural elements were checked on every

forest patch with the same thoroughness. It can be assumed that feathers would

have been found, if they had been there. Therefore, the risk of misclassification

seems to be very low.

The number of examined treated forest patches represents the entire treated area of

the reserve very well. However, statistically a sampling size of 33 is relatively small.

This had consequences for the variable analysis. According to Harrell et al. (cited in:

Guisan and Zimmermann, 2000) not more than n/10 predictors should be retained in

the final model whereas n is the number of samplings. The reduction of the

explanatory variables was therefore consequently conducted. Moreover, the site

parameters were not integrated into the multivariate analysis although they certainly

have an explanatory potential and are important for principal management decisions

in the special forest reserve of Amden. For the variable reduction, instead of an

univariate approach, variables could have been selected from functional groups e.g.

groups of need like food, cover, roosting. The univariate approach was chosen

because it is conducted unbiased by any previous knowledge or expectations and

mitigates the risk of losing an important parameter in this stage of the analysis.

The final models point out the crucial factors for Capercaillie occurrence on the

treated forest patches. However, an interpretation has to be done carefully because a

model validation was not possible due to the lack of similar data from other study

areas or forest reserves.

Outlook

A comprehensive assessment of the forest reserve is planned for 2016 including

several different surveys and controls: an effect analysis on the indicator plots, a

control of the habitat suitability, an analysis of Capercaillie distribution in the forest

reserve and population estimates (Ehrbar et al., 2011). Besides broad investigations

31

like in 2016, subsequent studies are necessary for a continuous control of treated

forest stands and their use by the Capercaillie. A quantification of structure changes

on the forest patches would be possible with a series of controls existing. The

controls provide the forest management with valuable feedbacks about efficiency and

effect of their conducted measures. Changes or adaptations to the management plan

could be done swiftly and flexibly.

Concern is growing about a lack of Capercaillie leks in the special forest reserve of

Amden. At least for now, there is no knowledge about leks which are actively used

(R.Ehrbar, pers. comm.). Research is necessary to localize and protect possibly still

existing leks.

The trade-off of forest roads for the Capercaillie should be part of closer investigation.

Forest roads play a controversial role since their use by the foresters for habitat

improvement is on one side beneficial for the Capercaillie. On the other side, these

roads are also used for recreational activities e.g. hiking, biking or snowshoeing.

Capercaillies are susceptible to human disturbances both in summer and winter

(Storch, 1999; Thiel et al., 2008). It would be interesting to assess how much these

human activities influence the occurrence of Capercaillie in the forest reserve of

Amden and whether there is a threshold of minimal distance between their habitat

and infrastructural facilities such as forest roads.

32

5. Implications for the management of the special forest

reserve of Amden

If conservation of the Capercaillie is expected to be successful, its preferred habitat

has to be conserved and extended in the vicinity of occupied habitat patches (Storch,

1994; Bollmann et al., 2008c; Mollet et al., 2008). This study showed that

Capercaillies favor restored habitat patches in coniferous forests with an intermediate

canopy cover and a rich layer of bilberry. Logging is a promising measure, since

forest stands are thinned and canopy cover therefore decreased. This causes the

amount of light reaching the ground to increase, stimulating the development of rich

ground vegetation. Forestry practitioners of Amden successfully treated forest

patches to improve the habitat quality. This thesis showed that a third of the

examined forest patches were used by the Capercaillie after the treatment within the

first years. The results further show that the habitat suitability map is a useful and

valid tool to set priorities and to select forest patches to be treated. However, the

results suggest that it should not be the only selection factor. So far treated forest

patches were distributed across the entire forest reserve. This thesis recommends a

more selective approach, since upper montane fir-spruce forests turned out to be

more promising for Capercaillie conservation than the montane fir-beech forests.

Forest managers should focus on these coniferous forests since they are often

associated with bilberry. The effect of logging lasts longer than in montane fir-beech

forests where measures need to be conducted repeatedly and are, therefore, more

expensive. Moreover, new treatments should be done in vicinity to forest patches that

are already suitable habitat for Capercaillie. Consequently, conservation measures

are better to be focused on a particular altitudinal belt of the special forest reserve

Amden. The advantage is that existing contiguous areas of good habitat quality are

extended and, at the same time, new forest patches are turned into suitable

Capercaillie habitat with increased efficiency.

33

Acknowledgements

I would like to thank Kurt Bollmann for his competent and motivating support

throughout my entire master thesis. I was lucky to have had a supervisor with so

much dedication and willingness to give advice whenever a question came up. My

advisor Harald Bugmann showed large interest in the thesis’ procedure and gave me

valuable and constructive feedbacks. Rolf Ehrbar was my contact to the special

forest reserve of Amden and provided me with the forest stand data and the

management plan. My fellow students Lukas Glanzmann and Tobias Tschopp did not

hesitate to assist me during the physically demanding assessment of the forest stand

variables. Raffael Winkler from the natural history museum of Basel gave feedback

about some feathers that were hard to identify. Adrian Lanz at WSL gave me useful

tips for the statistical analysis. I specially thank Jim Comiskey from the U.S. National

Park Service’s Mid-Atlantic Network and Ellen Ferrante from the U.S. National

Science Foundation for proofreading my work.

34

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Appendix

Appendix A: Small-scaled habitat requirements of the Capercaillie

Appendix B: Distribution of the Capercaillie in Switzerland in 2001

Appendix C: Form for cover measurement / Sampling plot structure

Appendix D: Main form for the assessment of forest stand variables

Appendix E: Form for callipering

Appendix F: Capercaillie feathers

Appendix G: Some impressions of examined forest patches

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Appendix A: Small-scaled habitat requirements of the Capercaillie

(Mollet and Marti, 2001)

Appendix B: Distribution of the Capercaillie in Switzerland in 2001

(Mollet et al., 2008) (The distribution areas are black shaded and

insuperable zones grey marked.)

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Appendix C: Form for cover measurement / Sampling plot structure

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Appendix D: Main form for the assessment of forest stand variables

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Appendix E: Form for callipering

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Appendix F: Capercaillie feathers (Klaus et al., 1989)

Feathers of the Capercaillie cock

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Feathers of the Capercaillie hen

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Appendix G: Some impressions of examined forest patches (Photos by Nicolas

Bircher)

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