Habitat selection in the post-breeding period by Hazel Grouse Tetrastes bonasia in the Bohemian Forest
Tobias Ludwig & Siegfried Klaus
Journal of Ornithology ISSN 2193-7192 J Ornithol DOI 10.1007/s10336-016-1365-z
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Author's personal copy J Ornithol DOI 10.1007/s10336-016-1365-z
ORIGINAL ARTICLE
Habitat selection in the post-breeding period by Hazel Grouse Tetrastes bonasia in the Bohemian Forest Tobias Ludwig1
•
Siegfried Klaus2
Received: 8 February 2016 / Revised: 3 June 2016 / Accepted: 10 June 2016 ! Dt. Ornithologen-Gesellschaft e.V. 2016
Abstract The Hazel Grouse (Tetrastes bonasia) is a cryptic forest-obligate bird species with special habitat requirements, including structured mixed forests at the local scale and good connectivity at the landscape level. These conditions are rarely met in Central European forests, with the exception of those in mountainous areas, such as the Bohemian Forest, Czech Republic. To explain habitat use in the Bohemian Forest by the Hazel Grouse during the post-breeding period more precisely, we used a small dataset as part of a long-term study to model the probability of occurrence for Hazel Grouse. We found that site occupancy by Hazel Grouse in the Bohemian Forest was high in dense spruce forests characterized by short sighting distances (20–40 m). It increased sharply with small proportions of deciduous trees (5–10 %) in a coniferdominated forest and remained high at intermediate ground vegetation height (20–50 cm). Other elements that were positively associated with site occupancy of Hazel Grouse were the presence of anthills and fallen logs, and higher crown closure. Site occupancy decreased with a higher proportion of grass cover but was positively influenced by higher proportions of herbs and bilberry. Outcomes of this study may be used to inform forest and conservation
Communicated by T. Gottschalk. & Tobias Ludwig
[email protected] Siegfried Klaus
[email protected] 1
Wildlife Ecology and Management, University of Freiburg, Tennenbacher Str. 4, 79106 Freiburg, Germany
2
Max-Planck-Institute for Biogeochemistry, Hans-Kno¨ll-Str. 10, 07745 Jena, Germany
practitioners for improvements of Hazel Grouse habitat in Central European mountain areas but also to assess forest sites for their suitability as Hazel Grouse habitats. Keywords Forestry ! Hazel Grouse ! Habitat model ! Logistic regression ! GLM ! Sˇumava Zusammenfassung Habitatwahl des Haselhuhns Tetrastes bonasia nach der Brutperiode im Bo¨hmerwald Das Haselhuhn als heimliche, kryptisch gefa¨rbte Waldvogelart mit speziellen Habitatanspru¨chen beno¨tigt reich strukturierte Mischwa¨lder auf lokaler Ebene und gute Konnektivita¨t auf Landschaftsebene. In Mitteleuropa sind diese Voraussetzungen selten zu finden, z. B. in Mittelgebirgen wie dem Bo¨hmerwald in der Tschechischen Republik. Fu¨r eine genauere Beschreibung des Haselhuhn-Habitats nach der Brutperiode verwendeten wir den Teildatensatz einer Langzeitstudie und modellierten die Haselhuhn-Vorkommenswahrscheinlichkeit. Wir fanden eine hohe Besiedelungswahrscheinlichkeit in dichten Fichtenwa¨ldern mit eng begrenzter Durchsichtigkeit (Sichtweiten 20-40 m). Die Antreff-Wahrscheinlichkeit fu¨r Haselhu¨hner nahm stark zu, wenn den Nadelholzbesta¨nden Laubholzarten (5-10%) beigemischt waren. Sie blieb hoch, bei guter Auspra¨gung der Bodenvegetation (mittlere Ho¨hen 20-50 cm). Das Vorkommen von Ameisenhu¨geln und liegendem Totholz sowie hoher Kronenschlussgrad waren weitere Elemente, mit denen die Besiedelung durch das Haselhuhn positiv verknu¨pft war. Die Besiedelungswahrscheinlichkeit wurde ebenfalls positiv durch ho¨here Anteile von Kra¨utern und Heidelbeere beeinflusst. Sie nahm ab, mit ho¨heren Deckungsgraden von Gra¨sern. Die Ergebnisse dieser Studie
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ko¨nnen der Verbesserung von Haselhuhn-Habitaten in mitteleuropa¨ischen Gebirgsregionen dienen, durch Information von Wald- und Naturschutzpraktikern. Sie ko¨nnen zudem genutzt werden, um Waldstandorte hinsichtlich ihrer Eignung fu¨r das Haselhuhn zu bewerten.
Introduction The distribution and abundance of forest-dwelling species, such as small- to medium-sized forest birds, are driven by factors operating at levels from local vegetation structure, over forest stand mosaic patterns, to the landscape scale (Bissonette 1997; Drapeau et al. 2000; Storch 2003; Zhang et al. 2013). Information about local forest structure aspects is crucial for forest managers, because forest operations can have immediate effects on forest-dwelling species (Houle et al. 2010; Lycke et al. 2011; Trudeau et al. 2011). The Hazel Grouse (Tetrastes bonasia) is a cryptically colored medium-size forest bird, adapted to dense forest succession, but also to multi-layered and old growth forests with gaps and rejuvenation spots (Swenson 1991a; Swenson and Angelstam 1993; Bergmann et al. 1996). Hazel grouse can be regarded an indicator species for natural-like forests in Europe resulting from natural disturbances. The structural richness of Hazel Grouse habitats includes high ˚ berg et al. diversity in tree and ground layer composition (A 2003; Pakkala et al. 2014). Beside natural forest, this grouse can live in managed forests, if special conditions are met. In a primeval forest landscape, at small to larger scale, mosaics of different succession stages may result from forest disturbances, such as fire, wind throw, snow damage, or bark-beetle infection (Scherzinger 1976; Swenson 1991a; Bergmann et al. 1996; ˚ berg et al. 2003; Klaus 2009). Generally, the close A vicinity of dense coniferous cover and deciduous food trees of the genera Betula, Salix, Alnus, Populus, Sorbus, and/or Corylus, which provide buds and catkins as the main food in winter seems to be crucial for this small forest-dwelling grouse (Bergmann et al. 1982, 1996; Pynno¨nen 1954; Scherzinger 1976). Conifers of the genera Picea, Abies, Pinus, and to a lesser extent Larix, and deciduous riparian forest in northeastern Siberia (Swenson et al. 1995) and the Carpathians (Kajtoch et al. 2015) provide cover, lowering the Hazel Grouse’s predation risk. Our earlier studies in the Bohemian Forest, southwestern Czech Republic, have shown that the probability of occupancy of a given habitat site by Hazel Grouse increases with tree species richness, being highest along creeks with Alder Alnus glutinosa and at lower slopes with Birch Betula pendula and Hazel Corylus avellana. In mixed stands of Norway Spruce Picea abies and Beech Fagus silvatica,
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or spruce and Rowan Sorbus aucuparia at higher altitudes, we found that the probability of occupancy declined, indicating that occupancy follows a rank order of preference of the winter food trees (Klaus 1995, 1996; Klaus and Sewitz 2000). In contrast to the intense studies of Hazel Grouse ecology in Sweden (Swenson 1991a, b; Swenson et al. 1995; Swenson and Angelstam 1993), there are only few recent studies on Hazel Grouse habitat choice in Central Europe. Based on studies in the Bavarian Forest National Park, which is adjacent to the Bohemian Forest, Mu¨ller et al. (2009) built a predictive habitat-suitability model for Hazel Grouse, showing that habitat heterogeneity, stand structure, presence of rowan and Willow Salix caprea, presence of root plates of fallen trees, and borderlines are predictive variables. In the northern Carpathians (Southern Poland), the most important factors are the presence of Bilberry Vaccinium myrtillus, clearings, and pioneer trees (Kajtoch et al. 2012). In the Swiss Alps, Hazel Grouse was found to prefer spruce stands with high portions of tall rowans, forest edges, and dense shrub layer in winter and a diverse mosaic of canopy closure and stand structure at the larger scale (Scha¨ublin and Bollmann 2011). Rich horizontal and vertical structured forest stands, edge density, and a well-developed shrub and herb layer were reported to be essential habitat variables in the Swiss Jura mountains (Mathys et al. 2006). The main aim of this study was to test empirically the response of Hazel Grouse to local-scale forest structure in autumn in a long-studied area of the Bohemian Forest. We expected dense forest structures to influence positively the presence of Hazel Grouse in the study area. We used generalized linear models (GLMs) to test this assumption and to quantify the influence of forest and vegetation structure and composition on Hazel Grouse occupancy.
Methods Study area Our 100-km2 study area included sites located between 600 m (Rejstejn) and 1253 m a.s.l., (top Antigl/Sokol; Fig. 1). The Bohemian Forest is an extensive, 120 km-long mountain range along the border between the Czech Republic, Germany, and Austria. It is one of the largest and oldest mountain ranges in Central Europe. Secondary Norway spruce forests are the dominating tree cover, but remnants of virgin spruce forests are still present at altitudes above 1200 m a.s.l. and on steep rocky slopes. Other forest types, such as mixtures of beech, spruce, fir Abies alba, with a few individuals of maple Acer pseudo-platanus, have remained only locally at altitudes between 600 and 1100 m a.s.l. (e.g., the Boubin Reserve; Sˇip 2006).
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Fig. 1 Location of the study area (hatched rectangle) in the central part of the Bohemian Forest, including the small town of Kasˇperske Hory at its northern edge. Dark-gray areas National parks of Sˇumava (north of state border) and Bavarian Forest (south of state border),
light-gray areas landscape reserve Bohemian Forest (north of state border) and nature park Bavarian Forest (south of state border). White box on inset map (upper right) depicts the position of the national parks along the German–Czech Republic border
After World War II, a large-scale mosaic of meadows inside closed forests and different forest succession stages emerged on the abandoned farmland (former German population) up to 1150 m a.s.l.. Numerous streams and bogs with Pinus mugo and Betula pubescens are typical for the area. Outside the Alps, the Bohemian Forest is one of the largest areas occupied by Hazel Grouse in Central Europe (Klaus 1995, 1996, 2007; Klaus et al. 2003). Before 1950, the density of this species in the spruce-dominated landscape was low, but the numbers subsequently increased, apparently due to the natural succession of pioneer trees on abandoned fields and meadows. In addition, the Spruce Picea abies was planted, mostly in small plots, which formed mosaics with a high density of forest edges throughout the whole region. The increase in the number and size of young mixed forests was accompanied by a pronounced increase in Hazel Grouse numbers (Kucera 1975; Klaus 1991, 1995). Consequently, Sˇumava a special case in Central Europe because it is the only area where the Hazel Grouse has increased in recent times, and its population is relatively stable up to the present (2014; Klaus 2007).
The National Park Sˇumava (Bohemian Forest— 68,520 ha) was founded in 1991, and at that time about 50 % of our study area became part of this reserve. Contrary to IUCN (International Union for Conservation of Nature and Natural Resources) rules, timber harvesting was stopped in only \20 % (1st zone) of the national park area, whereas 80 % of the area outside the 1st zone was subject to forest harvesting at varying intensities over the years. Test for Hazel Grouse presence Hazel grouse were recorded during the period 1972–2015, when a population was studied in the central part of the Bohemian Forest (district Klatovy, Czech Republic) and locations with Hazel Grouse were mapped. Here we use the term ‘‘Hazel Grouse site’’ or ‘‘site’’ instead of ‘‘territory’’ because most of the indications of Hazel Grouse presence were found by indirect evidence and not by territorial activities of the birds. Hazel Grouse presence was controlled along fixed routes (in total 80 km; Klaus 1995) using indirect indications (dust-bathing places, droppings, feathers, and tracks) and by testing the reactions of males to whistling, following the methods described by
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Wiesner et al. (1977) and Swenson (1991b). All of the sites judged to be positive for Hazel Grouse presence (positive sites) were of adequate habitat quality, as indicated by the presence of Hazel Grouse over several years and by habitat descriptions in the literature (Pynno¨nen 1954; Bergmann et al. 1982; Swenson 1991a; Bergmann et al. 1996). In October 2011 and 2012, we examined 103 known Hazel Grouse sites along fixed transects for presence of the species and recorded vegetation parameters (habitat variables) at the sites. In addition, we arbitrarily placed 84 control sites at a minimum distance of 500 m from known positive sites. Control sites were roughly positioned equidistant between two adjacent Hazel Grouse sites. Since Hazel Grouse sites are fragmented with 90 % of the study area being even-aged forest, the probability of placing control sites in unoccupied but suitable Hazel Grouse sites was very low. Habitat variables To explain how habitat features influence Hazel Grouse site occupancy, we recorded habitat variables at the local scale of forest plots. A forest plot was defined as the area within a circle of 20 m radius, which is about the area that can be efficiently viewed. We also chose this approach due to its successful application in previous studies, such as on Capercaillie (Storch 2002), and to ensure comparability with future, as yet unpublished studies. Mapped local scale factor variables included presence of fallen logs (pole stage and bigger) as well as anthills and forest stage, the latter defined in categories that are commonly used by forest managers (Table 1). We estimated crown cover as the percentage of sky covered, and the proportion of deciduous trees and grass, herb, and bilberry cover as the percentage of ground covered (Table 2). We measured ground vegetation height with a ruler and determined the density of cover as the sighting distance from the position of the observer to the nearest dense tree vegetation cover, Table 1 Details of categorical predictor variables used to explain the probability of Hazel Grouse (Tetrastes bonasia) occurrence, together with the number of observations per factor level
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estimated in steps of about 20 m. We also recorded presence of forest gaps, percentage tree rejuvenation cover, presence of creeks, open meadows, and dust bathing sites. Modeling procedure We used GLMs to explore the probability of occurrence of Hazel Grouse along gradients of vegetation and forest composition as the presence of a link-function in these models allows for various response term and error distributions (McCullagh and Nelder 1989). We deployed a logit-link function (logistic regression) to our binary response variable. To reduce multicollinearity between predictor variables (Graham 2003), we considered Spearmans Rho (q) of \0.7 as an acceptable correlation measure. Only proportions of main tree species and deciduous tree species were negatively correlated above this level. We retained the latter predictor for further analyses because coniferous trees (spruce) dominate the study area and deciduous trees were expected to be the limiting factor. Before model calibration, we checked for nonlinearity of predictor effects on Hazel Grouse occurrence by using linear and squared terms in univariate models for each predictor. We then built a first full model and removed variables that contributed to variance inflation. This procedure resulted in exclusion of the presence of forest gaps, tree rejuvenation cover, presence of creeks and open meadows, and availability of dust bathing sites. We then applied an information–theoretic (IT) approach (Burnham and Anderson 2002), based on Akaike’s information criterion (AIC), to the ten remaining variables (Tables 1, 2). The AIC–IT approach compares different models that are based on a priori-formulated hypotheses about the focal species (Burnham et al. 2010). The procedure constructs models with different variable combinations, which are then ranked according to their difference from the AIC of the best model. Models within a narrow range of AIC values are assumed to reflect (the unknown) reality best
Variable code
Variable
Factor levels
Fallen
Fallen logs
Absent 1
N 23 137
C2
27
Hills
Anthills
Absent
95
Succ
Forest stage (main stand)
Present 1: Young
92 35
2: Thicket
60
3: Pole-stage
25
4: Mature
30
5: Old
23
6: Age structured, multi-layered
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Author's personal copy J Ornithol Table 2 Details of continuous predictor variables used to explain the probability of Hazel Grouse occurrence, together with basic statistics of the investigated variables Variable code
Variable and measuring unit
Mean
SD
Minimum
Maximum
Crown
Crown cover (%)
71.28
15.63
0
100
Decid
Proportion of deciduous tree species (%)
13.26
20.25
0
100
Veg
Ground vegetation height (in cm) including rejuvenation
22.78
13.38
0
80
Gras
Grass cover (%)
17.49
17.21
0
100
Herb
Herb cover (%)
6.04
10.74
0
50
Bilb
Bilberry cover (%)
11.97
14.63
0
80
Sight
Sighting distance (in m)
39.22
33.03
5
120
SD standard deviation
(Burnham and Anderson 2002). We first included all variables and removed sighting distance in a second analysis stage. For all resulting models we calculated AIC with a correction for small sample size (AICc) and the Akaike weights, with the aim to rank the models that provided the best balance between variance and bias, according to their change in AIC (DAICc). We averaged all models with DAICc of \2 and calculated the model-averaged coefficients, as well as relative importance for each variable. We validated our models with fivefold cross-validation. The dataset was divided into five bins and the best models (with and without sighting distance) run with four-fifths of the data. For evaluation, we used the remaining fifth of the data to report the threshold-independent area under the receiver operating curve (ROC) and its standard deviation (SD). The area under the ROC curve (AUC) was used to test whether the discrimination ability of the models was better than random classification. The critical AUC is at 0.5, when random selections from the positive group have the same odds of a higher score as random selections from the negative group. Values of[0.7 and[0.8 are considered to represent good and excellent discrimination ability of the model, respectively (Hosmer and Lemeshow 2000). We also used threshold-dependent measures, namely, correct classification rate and Kappa statistics. The latter measures the actual agreement minus the agreement expected by chance. We report Cohen’s kappa (j) at the optimized threshold, i.e., at the probability cut-off level that maximizes the coefficient of prediction agreement. Agreement is moderate at j values from 0.4 to 0.55, good at j values from 0.55 to 0.7, very good from 0.7 to 0.85, and excellent from 0.85 to 0.99 (Monserud and Leemans 1992). In addition, we plotted calibration curves to visualize how well model predictions matched Hazel Grouse occupancy observed in the field. For all statistical analysis, we used the open source statistical software RStudio version 0.99.467 (RStudio Team 2015), with packages ‘dismo version 1.0-12
(Hijmans et al. 2015), ‘PresenceAbsence’ version 1.1.9 (Freeman 2012), and ‘MuMIn’ version 1.15.1 (Barton 2014).
Results Predictors of Hazel Grouse occupancy Of the 187 sites that were entered the analyses, 73 had a recorded Hazel Grouse presence. The number of Hazel Grouse records per transect ranged between 0 and 12 (mean 5.3, SD 4.3). Selection for best models (DAICc \2) that explain Hazel Grouse occurrence yielded five models when sighting distance was included (Table 3) and nine models after sighting distance was excluded (Table 4). The resulting relative influence of variables and parameters in the final averaged models are listed in the Appendix (Tables 5, 6). Sighting distance had a relative influence of 1 and was thus present in all of the best-ranking models, one of which was a univariate model. This predictor alone explained 38 % of the deviance. Of the nine remaining variables, four contributed to the best models with DAICc of \2. These models had a maximum of three variables (Table 3) and explained about 40 % of the deviance. Predictors decreased in importance in the following order: number of fallen logs, number of anthills, and proportion of deciduous tree species (Table 5 in Appendix). After excluding sighting distance, the explained deviance was between 20 and 26 %. The best-ranking models consisted of three to five variables from a total set of seven variables that remained after model dredging and checking for models with DAICc \2 (Table 4). The sequence of variable importance differed from those with sighting distance included and also encompassed ground vegetation height, crown cover, grass cover, and forest stage (Table 6 in Appendix). The proportion of deciduous
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Author's personal copy J Ornithol Table 3 Ranking of the logistic regression models to identify variables that explain probability of Hazel Grouse occurrence
Ranking
Candidate modelsa
AICc
DAICc
w
D2 (%)
AUC
SD
CCR
j
1
Fallen ? sight
157.87
0.00
0.33
40.2
0.87
0.024
79.6
0.60
2
Sight
158.33
0.46
0.26
38.3
0.85
0.036
80.2
0.61
3
Fallen ? hills ? sight
159.55
1.68
0.14
40.3
0.87
0.020
80.2
0.60
4
Hills ? sight
159.59
1.72
0.14
39.4
0.85
0.035
80.2
0.61
5
Decid ? sight
159.80
1.92
0.13
39.4
0.84
0.030
79.1
0.58
See Table 2 for explanation of variables AICc akaike´s information criterion corrected for small sample size; DAICc change in AICc; w akaike weight of the best ranked models (wjDAICc \2); D2 explained deviance; AUC area under curve, SD standard deviation of AUC after fivefold cross-validation; CCR correct classification rate; j Cohen’s kappa a
Proportion of deciduous trees (decid) entered with a squared term
Table 4 Ranking of the logistic regression models with exclusion of sighting distance Ranking
Candidate modelsa
AICc
DAICc
w
D2 (%)
AUC
SD
CCR
j
1
Hills ? crown ? decid ? veg
211.45
0.00
0.18
22.2
0.78
0.084
73.8
0.44
2
Fallen ? hills ? decid ? veg
211.66
0.21
0.16
22.1
0.77
0.075
72.7
0.42
3
Hills ? decid ? veg
212.35
0.90
0.11
20.1
0.77
0.090
71.1
0.41
4
Gras ? hills ? decid ? veg
212.45
1.00
0.11
20.9
0.76
0.098
73.3
0.45
5
Fallen ? gras ? hills ? decid ? veg
212.53
1.08
0.10
22.6
0.76
0.080
72.7
0.44
6
Hills ? crown ? decid ? veg ? succ
212.61
1.17
0.10
26.2
0.76
0.048
74.3
0.48
7 8
Crown ? decid ? veg ? succ Gras ? hills ? crown ? decid ? veg
212.94 213.06
1.49 1.61
0.08 0.08
25.2 22.4
0.76 0.77
0.035 0.096
73.8 72.7
0.49 0.42
9
Fallen ? hills ? crown ? decid ? veg
213.19
1.74
0.07
23.3
0.78
0.084
72.7
0.47
See Table 2 for explanation of variables and footnote to Table 3 for abbreviations a
Ground vegetation height, proportion of deciduous trees, and crown cover were entered into models with squared terms
trees and ground vegetation height had the largest relative variable importance and all models included both of these variables. The next-ranking variables had relative influences of B0.92, with the following descending order: number of anthills, crown cover, number of fallen logs, grass cover, and forest stage (Table 6 in Appendix). Herb and bilberry cover did not enter into any of the models at DAICc \2 but were present in models within the range DAICc \4 (not shown). Hazel grouse occupancy was related to sighting distance following a sigmoid curve that showed a pronounced threshold (Fig. 2). The turning point of the sigmoid curve occurred at a sighting distance of 20 m, when the probability of Hazel Grouse occurrence was near 0.6. The probability of occurrence dropped to 0.2 at a 40-m sighting distance and approached zero at C60 m. The directions of the other variable influences on predicted Hazel Grouse site occupancy were as follows: Hazel grouse occupancy showed a unimodal response to the proportion of deciduous trees (Fig. 2). Within the range of 5–30 % deciduous trees, the probability of Hazel
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Grouse occurrence increased steeply, reaching its maximum at about 30–40 %. At [60 % deciduous trees, the suitability of habitat for Hazel Grouse declined (Fig. 2). The presence of anthills was a positive predictor of Hazel Grouse presence. Ground vegetation height was a positive predictor of Hazel Grouse presence, with the highest probability of presence found at a height of about 40 cm (Fig. 2). Our results suggest that the probability of occurrence decreased at a ground vegetation height of [60 cm. A unimodal relationship curve for crown cover revealed a maximum probability of Hazel Grouse occurrence at between 60 and 80 % closure. Increased probability of Hazel Grouse occupancy was associated with the presence of fallen logs. The presence of one fallen log was better than no fallen log, but Hazel Grouse occurrence probability reached higher values when there were two or more fallen logs present (Fig. 2). The probability of Hazel Grouse occurrence diminished with increasing grass cover and decreased to \0.4 at grass cover of C20 %.
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Fig. 2 Partial dependence of the probability of Hazel Grouse (Tetrastes bonasia) occurrence on predictor variables that describe forest vegetation and structure at the scale of small sites. Solid line Mean, dashed lines 5 % and 95 % percentiles. Graphs for continuous variables were plotted by varying the variable under consideration over the range of values observed in the field and keeping all other
Herb cover had a relatively weak, but positive, relative influence on Hazel Grouse occurrence. Maximum values of 0.6 were achieved at 40 % herb cover. Bilberry cover had a relatively weak, but positive, relative influence on Hazel Grouse occurrence, with the highest values of occurrence probability, 0.6, between 40 and 60 % bilberry cover. In terms of forest stage, the probability of Hazel Grouse occurrence was highest in young plantations (0–20 years) of spruce and thickets (20–40 years). It was lower in medium-aged to older stands ([40–100 years). The probability of Hazel Grouse occurrence was about 30 % in multi-layered forest stands (‘‘Plenterwald’’) (Fig. 4 in the Appendix).
variables in the model at their median values. Boxplots show responses to factor variables based on the original data. Dark horizontal lines in boxes Medians, Upper and lower sides of box show lower and upper 25 % of predictions, whiskers span from highest to lowest occurrence probabilities, excluding outliers (open circles)
Model validation The models explaining Hazel Grouse site occupancy discriminated well between occupied sites and control sites (Tables 3, 4). With sighting distance as a predictor, crossvalidation of the best model yielded a mean AUC value of 0.87 ± 0.024 (SD). On average, these models attained a correct classification rate (CCR) of 79–80 % and Cohen’s j of 0.58–0.61 at a threshold of 0.5 (Table 3). Without sighting distance, the respective values for the best model were AUC = 0.78 ± 0.084, a CCR of between 71 and 74 %, and Cohen’s j values of between 0.41 and 0.49 (Table 4). Predicted probabilities of occurrence were allocated along the diagonals of calibration plots (Fig. 3), which show the fit between predictions and observed
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occupancy in classes (bins) of similar prediction values. Our models thus displayed good calibration, as well as reasonable refinement, as demonstrated by the fully covered range of possible prediction values between zero and one (Fig. 3).
Discussion Model performance The models presented here paper identify habitat variables that influence the occurrence of Hazel Grouse in the central Bohemian Forest during the autumn. They may also be relevant in determining habitat suitability for Hazel Grouse in other seasons as well because the Hazel Grouse is a territorial species and will thus use similar habitat features throughout a large part of the year (Swenson 1991a). This hypothesis is supported by additional controls of known Hazel Grouse sites during spring (2003) and late summer (2007) in the study area. Nevertheless, the models presented require further testing with independent data, especially before application to other areas and other seasons. Our models of Hazel Grouse site occupancy attained a good fit and a high predictive power. The AUC values obtained from cross-validation were well above the critical value of 0.7, which is a reasonable result for a dataset of this size. The ability to discriminate between occupied and unoccupied sites was better in models that included sighting distance as a covariate. These models had high AUC values (Table 2) and thus reached excellent discrimination abilities, according to Hosmer and Lemeshow (2000), and good agreement, according to the Cohen’s kappa scale (Monserud and Leemans 1992). However, model calibration and refinement were slightly better when sighting distance was excluded. Specifically, data records were more evenly distributed along prediction classes in models without sighting distance, and observed proportions matched predicted values better (Fig. 3). Hence, models without sighting distance (Table 4) may be used to predict Hazel Grouse occupancy along a gradient of habitat suitability values. On the other hand, sighting distance may be a good variable to make effective dichotomous forecasts between potential Hazel Grouse sites and unsuitable sites, given that large-scale forest composition and configuration permit the presence of Hazel Grouse. Following the removal of sighting distance from the model, the proportion of deciduous trees became the most important variable. In addition, the variables vegetation height and crown closure entered the best models. This result indicates that sighting distance includes information
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Fig. 3 Calibration plots for the best model with (a) and without sighting distance (b) as a covariate (compare Tables 3, 4). These plots illustrate observed occurrences as proportions of sites surveyed over predictions made by the models. Vertical lines 95 % confidence bands under the assumption of a binomial distribution, numbers above each vertical line number of sites surveyed in each prediction class
from several other variables and thus summarizes forest and vegetation structure information. Determinants of Hazel Grouse occupancy Several morphological and behavioral features of the Hazel Grouse (Scherzinger 1976; Potapov 1985; Bergmann et al. 1996; Klaus 1996) and Chinese Grouse Tetrastes sewerzowi (Klaus et al. 1998; Sun et al. 2003; Klaus 2009) indicate adaptation of this genus to the youngest and densest parts of forest succession. Both are the smallest grouse species, subject to predation by very effective grouse predators (Goshawk Accipiter gentilis, Sparrow Hawk A. nisus, Red Fox Vulpes vulpes, and Mustelidae spp.). Effective concealment in dense vegetation is one way to circumvent predation, a feature which is illustrated by our result of a positive response of Hazel Grouse to ground vegetation height. The probability of Hazel Grouse occurrence in this study was also positively linked to crown cover up to a maximum between 60 and 80 % cover. This finding supports the preference of Hazel Grouse for dense cover observed in other studies (Swenson 1991a; Swenson and Angelstam 1993; Scha¨ublin and Bollmann 2011). The spruce forests in Hazel Grouse habitats of the Bohemian Forest were characterized by a mosaic of dense forests and gaps, which contributed to a mean value of 71 % crown closure in the study area (Table 2). Conifers, mainly spruce, are most important in providing cover to Hazel Grouse throughout the huge Palaearctic area, including the study area (Fig. 1; for summary, see Bergmann et al. 1996). As spruce was the most abundant tree species in the study area (85 % of plots visited), cover was not a limiting factor. In contrast, the supply of buds and catkins of deciduous trees, the dominant winter food of Hazel Grouse, is often
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limited in managed forests. Alder (Swenson and Boag 1993; Swenson and Angelstam 1993), birch, willow, hazel, mountain ash, and beech (Klaus 1995) render a sprucedominated forest suitable for Hazel Grouse occupancy. As demonstrated in Fig. 2, occupancy of Hazel Grouse sites sharply increased even with small proportions of deciduous trees (1–10 %), which was also reported by Swenson and Angelstam (1993). Deciduous-dominated forests are likely to be too open in winter, and such forests are avoided by Hazel Grouse due to the relatively higher predation risk (Swenson 1993). According to many studies throughout the distribution area, mixed forests of deciduous (food) and coniferous trees (cover) are general characteristics of Hazel Grouse habitat (see Bergmann et al. 1996 for review). We also found a positive effect of anthills (Formica spp.) on Hazel Grouse occurrence (Fig. 2). Ants prefer sunny edges and openings in a dense forest. In the relatively cold boreal spruce-dominated mountain forest, ants are an important insect food for chicks, similar to other tetraonid species, like Capercaillie Tetrao urogallus (Klaus et al. 1989), Black Grouse T. tetrix (Klaus et al. 1990), and Siberian Grouse Falcipennis falcipennis (Hafner and Andreev 1998). However, our data were not collected during the chick-rearing period. A possibly alternative or complementary hypothesis might be that ants and Hazel Grouse share some similar requirements for adequate amounts of woody debris in the forest. In our study, we found just such an association between Hazel Grouse occupancy and the presence of fallen logs (Fig. 2), with the highest values observed at frequencies of C2 fallen logs/ 1200 m2 (16/ha). Dead wood on the ground can affect Hazel Grouse occupancy in a number of ways. It is an indicator of structural richness of the forest habitat in combination with the presence of insects as a food supply on and inside rotten stems. In addition, fallen logs are structures that support trajectories and outlook points in dense ground vegetation—and not only for Hazel Grouse (Klaus 1996). Similar to Capercaillie (Storch 1993), we found that the presence of bilberry was beneficial to the Hazel Grouse, with the highest occurrence probabilities at 20–50 % bilberry cover (Fig. 2). Like other grouse species, Hazel Grouse profit from the presence of bilberry throughout the year if snow cover is not too deep, using the shrub as ground cover (nest and broods) and its berries, leaves, buds, and shoots as food (Bergmann et al. 1996). In the absence of bilberry, forbs play a major role in ground feeding. Herb cover had a less pronounced, but also positive influence, on Hazel Grouse occurrence. The higher abundance of Hazel Grouse in forests growing on richer soils (abandoned agriculture land) is assumed to result from a higher biodiversity of forbs. This was found in parts
of our study area (Klaus 1995), in parts of Fennoscandia (Nieminen et al. 1995), and in the Carpathian mountains (M. Mikolasˇ, personal communication). Alder creek systems are another example of preferred Hazel Grouse habitats with high diversity of herbs and forbs (Klaus 1991; Swenson 1993; Swenson et al. 1994; Kajtoch et al. 2015). Hazel grouse responded negatively to high grass cover in this study. The acidic soils of the study area normally favor shrubs of the Ericaceae family. Dense grass cover (\40 %) competes with Vaccinium spp. and/or forbs and normally diminishes the habitat quality for Hazel Grouse by favoring small rodents and medium-sized predators (Marcstrom et al. 1988), resulting in higher predation losses. Finally, Hazel Grouse response to forest stage (Fig. 4 in the Appendix) in this investigation confirmed the abovementioned findings that denser forests provide favorable habitat structures for Hazel Grouse. In age-class forests in ˚ berg et al. the study area (Klaus 1996) and in Sweden (A 2003), the highest Hazel Grouse densities were found in 20- to 40-year-old stands and also in multilayered oldgrowth forests (Scherzinger 1979). In the West Carpathian mountains of Poland, Kajtoch et al. (2015) found bird species diversity (including Hazel Grouse) to be highly and positively correlated with tree species diversity, higher number of large trees, and dead wood.
Conclusions and conservation implications In the study reported here we investigated local factors that explain the probability of Hazel Grouse occurrence at the scale of small forest sites. Territory size and the configuration (proximity) of necessary elements were not within the scope of this paper. However, the density of favorable sites will be a factor determining territory size and the number of Hazel Grouse in an area. Therefore, large-scale forest management that locally attempts to create the vegetation structures presented in this study can support Hazel Grouse and populations of other species. Our study supports the notion that Hazel Grouse in mountain forests of Central Europe can be regarded as an indicator for heterogeneous and diverse forests. In southern Finland, Pakkala et al. (2014) reported that the Redbreasted Flycatcher Ficedula parva, Pygmy Owl Glaucidium passerinum, and Three-toed Woodpecker Picoides tridactylus are the best multi-scale indicators for the biodiversity of forest ecosystems. We also found these species in the Bohemian Forest, in addition to locally situated Ural Owl Strix uralensis and Capercaillie, in Hazel Grouse habitat patches. The multi-layered forests in our study area have structural similarities to alpine spruce forests in the
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Swiss Alps, as shown by Zellweger et al. (2014) who used light detection and ranging (LiDAR) technology to characterize forests. These authors reported that canopy height heterogeneity explained a large proportion of Hazel Grouse occurrence in their study area. Canopy height heterogeneity summarizes different parameters in an alpine forest landscape, such as gaps with Vaccinium and mountain ash, rejuvenation spots, and old and dead trees—all of which are typical parameters favoring Hazel Grouse. Whereas vertical LiDAR remote sensing measures are useful for predicting species occurrence over large areas, horizontal estimates, such as sighting distance and forest vegetation composition, can be more easily communicated to forest managers and thus support site-specific conservation management for Hazel Grouse. The creation of Hazel Grouse habitat features, including tree species richness, structural diversity (gaps, canopy height heterogeneity, fallen logs), and mosaic structure in the ground layer and at larger forest stand scales, is not difficult to accomplish with modern forestry techniques. The main results of our studies have been forwarded earlier to the local Forest Administration of Kasˇperske Hory (CZ) and to the administrative agencies of Sˇumava National Park (Klaus 2014). Acknowledgments Thanks are due to the administrative agencies of Sˇumava national park for continuous interest and support. We are grateful to W. Wiltschko and DO-G for a Grant to S.K. (since 2011), to Ralf Siano for helpful comments, and to Jon Swenson for correcting the manuscript and for fruitful cooperation during all our work on Hazel Grouse and Chinese Grouse Tetrastes sewerzowi. Shin-Jae Rhim and one anonymous reviewer provided valuable comments that improved an earlier draft of this manuscript.
Appendix See Tables 5, 6 and Fig. 4. Table 5 Parameters of the averaged model Variable
RI
Averaged b
Sighting distance
1.00
-0.088 sight
Fallen logs
0.47
One: 1.14; two and more: 1.99
Anthills
0.28
Present: 0.32
Proportion of deciduous tree species
0.13
1.19 decid–4.15 decid2
Relative variable importance (RI) and model averaged regression coefficients (averaged b) highlight the variables that best explain Hazel Grouse probability of occurrence
Table 6 Parameters of the averaged model without sighting distance Variable
RI
Averaged b
Proportion of deciduous tree species
1.00
6.44 decid–9.18 decid2
Ground vegetation height
1.00
5.95 veg–5.34 veg2
Anthills
0.92
Present: 0.91
Crown cover
0.52
3.56 crown–7.19 crown2
Fallen logs
0.34
One: 1.12; two and more: 1.76
Grass cover
0.29
-0.014
Forest stage
0.18
Thicket: -0.77, pole-stage: -1.26, timber: -2.01, old forest: -1.81, plenter forest: -1.74
RI and model averaged regression coefficients (averaged b) highlight the variables that best explain Hazel Grouse probability of occurrence
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Author's personal copy J Ornithol Fig. 4 Dependence of Hazel Grouse occurrence probability on forest stage of the main stand. Dark horizontal lines in boxes Medians, Upper and lower sides of box show lower and upper 25 % of predictions, whiskers span from highest to lowest occurrence probabilities, excluding outliers (open circles)
References ˚ berg J, Swenson JE, Angelstam P (2003) The habitat requirements A of Hazel Grouse (Bonasa bonasia) in managed boreal forest and applicability of forest stand descriptions as a tool to identify suitable patches. For Ecol Manage 175:437–444. doi:10.1016/ S0378-1127(02)00144-5 Barton K (2014) MuMln: Multi-model inference. R package version 1.10.5. http://cran.r-project.org/package=MuMln Bergmann H, Klaus S, Mu¨ller F, Wiesner J (1982) Das Haselhuhn Bonasa bonasia, 2nd edn. A. Ziemsen Verlag, Wittenberg Lutherstadt Bergmann HH, Klaus S, Mu¨ller F et al (1996) Die Haselhu¨hner. Westarp Wissenschaften, Magdeburg Bissonette JA (ed) (1997) Wildlife and landscape ecology: effects of pattern and scale. Springer, Berlin Burnham KP, Anderson DR (2002) Model selection and multi-model inference: a practical information–theoretical approach. Springer, Berlin Burnham KP, Anderson DR, Huyvaert KP (2010) AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav Ecol Sociobiol 65:23–35. doi:10.1007/s00265-010-1029-6 Drapeau P, Leduc A, Giroux JF et al (2000) Landscape-scale disturbances and changes in bird communities of boreal mixedwood forests. Ecol Monogr 70:423–444. doi:10.1890/00129615(2000)070[0423:LSDACI]2.0.CO;2 Freeman EA and Moisen G (2008) PresenceAbsence: An R Package for Presence-Absence Model Analysis. J Stat Softw 23(11):1–31 Graham M (2003) Confronting multicollinearity in ecological multiple regression. Ecology 84:2809–2815 Hafner F, Andreev AV (1998) Das Sichelhuhn—Wundervogel der Amurtaiga. Naturwissenschaftlicher Verein fu¨r Ka¨rnten, Klagenfurt Hijmans RJ, Phillips S, Leathwick J, Elith J (2015) dismo: Species Distribution Modeling. R package version 1.0-12. http://CRAN. R-project.org/package=dismo Hosmer D, Lemeshow S (2000) Applied logistic regression, 2nd edn. Wiley, New York Houle M, Fortin D, Dussault C et al (2010) Cumulative effects of forestry on habitat use by gray wolf (Canis lupus) in the boreal forest. Landsc Ecol 25:419–433. doi:10.1007/s10980-009-94202
Kajtoch L, Zmihorski M, Bonczar Z (2012) Hazel Grouse occurrence in fragmented forests : habitat quantity and configuration is more important than quality. Eur J Wildl Res 131:1783–1795. doi:10. 1007/s10342-012-0632-7 Kajtoch Ł, Wilk T, Bobrek R, Matysek M (2015) The importance of forests along submontane stream valleys for bird conservation: The Carpathian example. Bird Conserv Int 1–16. doi:10.1017/ S0959270915000179 Klaus S (1991) Effects of forestry on grouse populations: case studies from the Thuringian and Bohemian forests, Central Europe. Ornis Scand 22:218–223 Klaus S (1995) Hazel Grouse in the Bohemian Forest—results of a 20-year study. In: Jenkins D (ed) Proc International Symposium on Grouse 6. World Pheasant Association, Reading, UK. and Istituto Nazionale per la Fauna Selvatica, Ozzano dell‘Emilia, Italy, pp 27–33 Klaus S (1996) Hazel grouse in the Bohemian Forest: results of a 24-year-long study. Silva Gabretta 1:209–220 Klaus S (2007) A 33-year study of Hazel Grouse Bonasa bonasia in the Bohemian Forest, Sˇumava, Czech Republic: effects of weather on density in autumn. Wildl Biol 13:105–108. doi:10.2981/0909-6396(2007)13[105:AYSOHG]2.0.CO;2 Klaus S (2009) Forest grouse and wilderness—survival without management impacts. Europe’s Wild Heart. Conference Report. Srni, Bavarian Forest National Park, Sˇumava National Park, Vimperk and Grafenau, Czech Republic, 2009, pp 35–37 Klaus S (2014) Situation of the Hazel Grouse Tetrastes bonasia in the National Park Sˇumava and in the Sˇumava Landscape Reserve— activities of the Galliforme Specialist Group of IUCN. Grouse News Newsl Grouse Spec Gr 48:7–8 Klaus S, Sewitz A (2000) Ecology and conservation of Hazel grouse Bonasa bonasia in the Bohemian Forest (Sumava, Czech Republic). Tetrˇevovitı´—Tetraonidae na prˇelomu tisı´ciletı´. In: Stubbe M (ed) Proc Int Conf Tetraonids—Tetraonids at the break of the millenium. Deutscher Landwirtschaftsverlag Berlin, Ceske Budejovice, pp 138–1446 Klaus S, Andreev A, Bergmann H et al (1989) Die Auerhu¨hner, Die Neue B. Westarp Wissenschaften, Magdeburg Klaus S, Bergmann H-H, Marti C et al (1990) Die Birkhu¨hner. Ziemsen Verlag, Lutherstadt Wittenberg Klaus S, Scherzinger W, Sun YH (1998) Territorial- und werbeverhalten des chinahaselhuhns (Bonasia sewerzowi). J Ornithol 139:185–186
123
Author's personal copy J Ornithol Klaus S, Martens J, Andreev AV, Sun YH (2003) Bonasa bonasia (Linnaeus, 1758) Haselhuhn. In: Martens J, Eck S, Sun YH (eds) Atlas der Verbreitung palaearktischer Vo¨gel. Lieferung 20. Erwin Stresemann-Gesellschaft fu¨r pala¨arktische Avifaunistik, Berlin, pp 1–15 Kucera L (1975) Verbreitung und Populationsdichte von Auerhuhn (Tetrao urogallus), Birkhuhn (Lyrurus tetrix) und Haselhuhn (Tetrastes bonasia) im westlichen Teil von Sumava (CSSR). Ornithol Mitt 27:160–169 Lycke A, Imbeau L, Drapeau P (2011) Effects of commercial thinning on site occupancy and habitat use by spruce grouse in boreal Quebec. Can J For Res 41:501–508. doi:10.1139/X10-226 Marcstrom V, Kenward R, Engren E (1988) The impact of predation on boreal tetraonids during vole cycles: an experimental study. J Anim Ecol 57:859–872 Mathys L, Zimmermann NE, Zbinden N, Suter W (2006) Identifying habitat suitability for Hazel Grouse Bonasa bonasia at the landscape scale. Wildl Biol 12:357–366. doi:10.2981/09096396(2006)12[357:IHSFHG]2.0.CO;2 McCullagh P, Nelder JA (1989) Generalized linear models, 2nd edn. Chapman and Hall, London Monserud RA, Leemans R (1992) Comparing global vegetation maps with the kappa statistic. Ecol Modell 62:275–293. doi:10.1016/ 0304-3800(92)90003-W Mu¨ller D, Schro¨der B, Mu¨ller J (2009) Modelling habitat selection of the cryptic Hazel Grouse Bonasa bonasia in a montane forest. J Ornithol 150:717–732. doi:10.1007/s10336-009-0390-6 Nieminen M, Tornberg R, Sulkava S, Marjakangas A (1995) Habitat selection by the Hazel Grouse. Suom Riista 41:35–41 Pakkala T, Linde´n A, Tiainen J et al (2014) Indicators of forest biodiversity: which bird species predict high breeding bird assemblage diversity in boreal forests at multiple spatial scales? Ann Zool Fennici 51:457–476. doi:10.5735/086.051.0501 Potapov R (1985) Die Familie der Rauhfußhu¨hner. In: Potapov R (ed) Fauna SSSR, vol 3. T.2. - Leningrad (in Russian) Pynno¨nen A (1954) Beitrage zur Kenntnis der Lebensweise des Haselhuhns, Tetrastes bonasia (L.). Riistatiet Julk Pap Game Res 12:1–90 RStudio Team (2015) RStudio: integrated development for R. RStudio Inc., Boston Scha¨ublin S, Bollmann K (2011) Winter habitat selection and conservation of Hazel Grouse (Bonasa bonasia) in mountain forests. J Ornithol 152:179–192. doi:10.1007/s10336-010-0563-3 Scherzinger W (1976) Rauhfuß-Hu¨hner. Schriftenr Nationalpark Bayer Wald 2, pp 6–71 Scherzinger W (1979) Zum Feindverhalten des Haselhuhnes (Bonasa bonasia). Vogelwelt 100:205–217 Sˇ´ıp M (2006) Urwald Boubı´n (Kubany) – Das Nationalreservat und seine Geschichte. Edition Sagitta, Oldenburg. ISBN 978-39810824-0-1
123
Storch I (1993) Habitat selection by capercaillie in summer and autumn: is bilberry important? Oecologia 95:257–265. doi:10. 1007/BF00323498 Storch I (2002) On spatial resolution in habitat models: can smallscale forest structure explain Capercaillie numbers? Conserv Ecol 6:art6 Storch I (2003) Linking a multiscale habitat concept to species conservation. In: Bissonette JA, Storch I (eds) Landscape ecology and resource management: linking theory with practice. Island Press, Washington, pp 303–320 Sun YH, Swenson JE, Fang Y et al (2003) Population ecology of the Chinese grouse, Bonasa sewerzowi, in a fragmented landscape. Biol Conserv 110:177–184. doi:10.1016/S0006-3207(02)00187-8 Swenson J (1991a) Social organization of Hazel grouse and ecological factors influencing it. University of Alberta, Edmonton Swenson JE (1991b) Evaluation of a density index for territorial male Hazel Grouse Bonasa bonasia in spring and autumn. Ornis Fenn 68:57–65 Swenson JE (1993) The importance of alder to Hazel Grouse in Fennoscandian boreal forest: evidence from four levels of scale. Ecography (Cop) 16:37–46. doi:10.1111/j.1600-0587.1993. tb00057.x Swenson JE, Angelstam P (1993) Habitat separation by sympatric forest grouse in Fennoscandia in relation to boreal forest succession. Can J Zool 71:1303–1310. doi:10.1139/z93-180 Swenson JE, Boag DA (1993) Are Hazel Grouse Bonasa bonasia monogamous. Ibis (Lond 1859) 135:463–467 Swenson J, Saari L, Bonczar Z (1994) Effects of weather on Hazel Grouse reproduction: an allometric perspective. J Avian Biol 25:8–14 Swenson JE, Andreev AV, Drovetskii SV (1995) Factors shaping winter social organization in Hazel Grouse Bonasa bonasia: a comparative study in the eastern and western Palearctic. J Avian Biol 26:4–12 Trudeau C, Imbeau L, Drapeau P, Mazerolle MJ (2011) Site occupancy and cavity use by the northern flying squirrel in the boreal forest. J Wildl Manage 75:1646–1656. doi:10.1002/jwmg. 224 Wiesner J, Bergmann HH, Klaus S, Mu¨ller F (1977) Siedlungsdichte und Habitatstruktur des Haselhuhns (Bonasa bonasia) im Waldgebiet von Bialowieza (Polen). J Ornithol 118:1–20. doi:10.1007/BF01647354 Zellweger F, Morsdorf F, Purves RS et al (2014) Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment. Biodivers Conserv 23:289–307. doi:10.1007/s10531-013-0600-7 Zhang J, Kissling WD, He F (2013) Local forest structure, climate and human disturbance determine regional distribution of boreal bird species richness in Alberta, Canada. J Biogeogr 40:1131–1142. doi:10.1111/jbi.12063
