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Article

Altitudinal Shift of Tetrao urogallus in an Alpine Natura 2000 Site: Implications for Habitat Restoration

by
Tommaso Sitzia
1,*,
Thomas Campagnaro
1,
Matteo Dainese
2,
Francesco Dallabrida
1,
Silvano Mario Mattedi
3 and
Alessia Portaccio
1
1
Università degli Studi di Padova, Department of Land, Environment, Agriculture and Forestry, Viale dell’Università 16, 35020 Legnaro, Padua, Italy
2
EURAC Research Institute for Alpine Environment, Drususallee 1, 39100 Bozen, Italy
3
Località Cernidor 12, 38123 Trento, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(6), 1164; https://doi.org/10.3390/app9061164
Submission received: 10 August 2018 / Revised: 1 March 2019 / Accepted: 12 March 2019 / Published: 19 March 2019
(This article belongs to the Section Environmental Sciences)
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Abstract

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Observed shifts in the altitudinal distribution of capercaillie confirms the relevance of habitat restoration actions.

Abstract

Capercaillie (Tetrao urogallus L.), a territorial galliform species, is known to prefer mature conifer stands with canopy gaps and a vigorous understory of ericaceous species. Capercaillie is a useful umbrella species that has recently shown declining population trends and distribution changes in its southern geographic range. We aim to identify and assess the possible changes in summer capercaillie habitat selection between 2001 and 2011 in the Scanuppia Natura 2000 site (south-eastern Alps). The area is dominated by spruce (Picea abies (L.) Karsten) forests, followed by mixed forests, scrub, and open habitats. In both years, summer presence–absence of capercaillie was verified through the detection of droppings over 10 m radius circular plots located along contour lines (1500–1800 m). A set of environmental and habitat features was also surveyed. While overall population numbers remain unchanged over the surveyed period, results have shown an altitudinal shift in capercaillie distribution. Habitat variables had a stronger effect on the presence of capercaillie in 2001 than in 2011. Land cover and climate change are likely among the drivers of the shift in altitudinal distribution. This confirms the relevance of habitat restoration actions and to monitor changes in factors explaining capercaillie habitat selection.

1. Introduction

In mountain regions, global changes linked to climate and human impacts are serious threats to animal species. This issue is particularly relevant for those species with limited dispersal ability during part of or all their life stages [1]. Besides impacts on habitat quality due to climate change, land use and cover changes are usually considered to have important impacts on biodiversity [2]. Upper shifts of the Alpine timberline were reported to be more due to land cover changes than climate change, with a probable interaction between the two [3]. Indeed, sensitive species in mountain environments are more likely to be threatened by land cover changes, which are usually associated with direct human impact. At high altitudes, since physical space is already a limitation [4], the encroachment by shrubs and trees might result in the complete loss of habitat suitable for specialized residents of relatively open forest stands [5], including species of conservation concern [6].
One important example is the capercaillie (Tetrao urogallus L.), a species of conservation interest according to the Birds Directive (2009/147/EC) of the European Union. This species is found in boreal and temperate forests of Europe (including Russia) and its geographical range stretches from the Alps, Pyrenees, and Balkans to northern Norway. Capercaillie is thought to be an umbrella species for other mountain birds [7], making any change in its habitat distribution a good predictor of changes affecting sympatric bird species. The average home range size of capercaillie in the Alps is around 550 ha [8]. Despite this relatively large home range, the species shows narrow habitat preferences [9,10]. Habitat suitability models have been developed at the stand and the landscape scale. Using presence–absence data, Graf et al. [11] found that the best single large scale to build habitat suitability models corresponds to a small annual home range, about 250 ha. Ideally, data collected at large scale should be combined with those collected at the stand scale to efficiently develop management guidelines for capercaillie conservation [12].
At the stand scale, capercaillie prefers mature, structurally diverse forests characterized by canopy gaps and rich undergrowth with the presence of Vaccinium species [13]; branches of trees are used as shelters and roosts [12] and conifer needles form most of the capercaillie winter diet [14]. At the stand mosaic scale, ecotonal zones are important for nesting and brood rearing [13], as well as the proportion of forests, particularly coniferous [11]. At the landscape scale, habitat patches are mostly occupied when their size is 800–1100 ha (minimum: ca. 50 ha) and their distance from the nearest occupied patch is less than 10 km [15]. The uppermost slopes at the tree line are outside capercaillie altitudinal range because they offer little habitat, such as bilberry and trees [16]. Therefore, land cover and climate change together with human pressures, such as tourism, have all been highlighted as impacting on this species [17,18,19,20].
In the present study, we repeated twice over a decade the investigation of the relationships of capercaillie with habitat structure, at a single stand scale over a 400 ha study area. Our study case is located in a mountainous area on the southern edge of the Alps, which has undergone open land loss and forest expansion in recent decades [21]. At this southern edge of distribution, capercaillie population numbers do not show tendency to cycle [22] and have recently been declining [23]. Two surveys of capercaillie presence–absence, one in 2001 and the other in 2011, were compared to detect any change in the stand-level species-habitat relationships. The results will permit on the one hand to assess the consistency of capercaillie habitat suitability models across time, on the other hand to provide guidelines for managing and conserving habitats for this and other sympatric species living in the studied area.

2. Materials and Methods

2.1. Study Area

This study was conducted in the IT3120018 “Scanuppia” Natura 2000 site, an area (529 ha) protected under the Directive 92/43/CEE [24] in Trento Province, south-eastern Alps (45°57′ N 11°09′ E) (Figure 1). The forest is dominated by Norway spruce (Picea abies (L.) Karsten) with some areas covered with silver fir (Abies alba Miller), European larch (Larix decidua Miller), and European beech (Fagus sylvatica L.). Mixed stands as well as small pastures are also found in the area.
The forest management plan [25] highlights a decreasing pressure on natural resources. Half of the forests in the protected area are grazed, although numbers of grazing animals decreased from more than 130 cows and 900 sheep in 1839, to 75 cows in 2007. At the beginning of the 20th century, timber yield was higher than 700 m3/y and firewood production more than 20,000 kg/y [25]. Since the 1950s timber harvests have been reduced to zero and the site has been integrally protected since 1990. The reduction in the intensity of logging and grazing has led to rewild the area with the gradual spread of shrubs and vertical stratification of the canopy structure. The existence of this trend is supported both by aerial photos observation (Figure 2) and by the expansion of trees and shrubs on former open lands which has been quantified (0.1% forest cover annual growth rate) in the region by comparing remote sensed data from different time periods [5,26].
The altitude of the Natura 2000 site ranges between 1450 m and 2130 m a.s.l. (highest summit located at its north-eastern vertex) and has western facing slopes. To understand recent climate changes, data series from 1989 to 2010 available from three nearby (12 km NW of the study area) weather stations on Mt. Bondone can be used as they are at a similar altitude (1490–1552 m a.s.l.) and latitude to our study site. Mean annual rainfall and temperature of this period are 1407 mm and 5.7 °C, respectively. Using a linear time trend model, the averaged surface air temperature has increased by 1.3 °C in April (mean = 3.6 °C, std = 1.2), 2.5 °C in May (mean = 8.8 °C, std = 1.3), and 2.7 °C in June (mean = 12 °C, std = 1.5). Similar trends have been observed in maximum temperatures. These changes may affect capercaillie hatching time and number of brood rearing females, and hence breeding success [18].
Following a request to the Autonomous Province of Trento, which performs them annually, we obtained historical data derived from local censuses of capercaillie presence. The annual estimated number of individuals is available for a period between 1991 and 2011 and, for the latest years, capercaillie population ranged between 20 and 30 individuals with an estimated annual breeding success that varied from 0.7 to 3.5 chicks per female (mean = 2.5). In general, for this period of analysis, no marked decreasing or increasing population trend was observed.

2.2. Data Collection

Data on capercaillie presence–absence and environmental variables were collected in 2001 and 2011. A systematic sampling design formed by a set of 86 plots distributed at regular distances of 250 m was set along contour lines between 1500 and 1800 m a.s.l. (i.e., altitude interval of 50 m). Surveys were conducted in 10-m radius circular plots (314 m2) (Figure 1b). In 2011 a random subset of 45 plots was fully re-surveyed with the same protocol applied in 2001. We also collected presence–absence data and identified the habitat type for those plots of 2001 not fully re-surveyed in 2011.
Presence–absence of capercaillie was recorded in the plots through the detection of droppings with a 15-min search. In both years, the survey was done during the post-reproductive season in summer (July–August). Droppings are a valid indicator of this species presence [27] and summer droppings can be distinguished from winter ones as they are partially coated with uric acid [28], with consequent parts of lighter color, and being less curved and solid. This approach addresses typical detectability issues for direct observation of elusive species that, for capercaillie, are usually dependent on sex and habitat type [29,30].
We assessed habitat type (canopy dominance) and structure (stand development stage) (Table 1). Vegetation was divided into four categories: tree (height ≥ 5 m), shrub (woody plants with height < 5 m), dwarf shrubs (i.e., Vaccinium spp., Polygala chamaebuxus, Erica carnea, Orthilia secunda, Paederota bonarota and Thymus praecox) and herbaceous layer. Mean height of each layer was also measured. We also recorded several habitat variables that could have been important for capercaillie: aspect, slope, altitude, and percentage cover of litter and of tree, shrub, dwarf shrub, and herbaceous layers. Deadwood was recorded as either fine woody debris, i.e., estimated percentage cover of pieces with diameter < 10 cm, or coarse woody debris, i.e., length of pieces with diameter ≥ 10 cm. Topographic position was assigned according to four classes: high slope, middle slope, valley, and ridge. Finally, signs of recent silvo-pastoral activities were recorded, such as: stumps of recently felled trees, plant species indicating grazing pressure, proximity to mountain huts, evidence of livestock trampling, and livestock feces.

2.3. Data Analysis

To understand the relationship between capercaillie and the studied environment we related response and predictor variables through boosted regression tree (BRT) modelling as it enables complex species-habitat relationships to be detected and explained [31]. This statistical framework has been successfully adopted for shedding light on the importance of single environmental features on the occurrence of bird species [32,33]. It combines regression trees and boosting to improve predictive power. Regression trees relate a response to predictors by recursive binary splits, producing a decision tree. Boosting is a machine-learning iterative procedure to obtain better predictive performance through multiple learning algorithms. This method enabled a set of variables different in type (both categorical and continuous) to be incorporated and non-linear relationships to be detected between the response variable and predictors [34].
The literature has highlighted the importance of both the canopy composition and the stand structure on capercaillie habitat selection; two factors that can be shaped through human interventions such as silvicultural operations [19,20,35]. We therefore first analyzed the relative importance of habitat type and structure (Table 1) [36]. The second analysis was conducted with the quantitative environmental and habitat variables (Table 2). When independent variables resulted correlated (correlation < |0.6|), only one was included in the analysis [37].
In both analyses, the response variable was the presence-absence (1-0) of capercaillie during summer 2001 and 2011. Following Elith et al. [34] indications, tree complexity, learning rate, and bag-fraction parameters were defined for the final models comparing predictive performance and fitting with 1000 trees, through the Bernoulli distribution of errors. The building procedure and cross-validation of BRT helped to control for overfitting [38]. Indeed, this method has been successfully applied with small sample sizes [33]). Model performance was evaluated with a 10-fold cross-validation, a commonly adopted approach that enables either a portion or all data at a certain stage to be used to test and fit the model. Furthermore, when not informative, variables were dropped as model simplification is more useful with small sample sizes.
The relative contribution of each predictor was estimated in the model and based on the number of times that the predictor was selected in each recursive binary split node, weighted by the squared improvement to the model as a result of each split, and averaged over all trees. The contribution of each variable is scaled so that the sum adds up to 100, with higher numbers indicating stronger influence on the response.
The overall statistical model was performed following specific guidelines [34] and using the ‘gbm’ package 2.1.5 [39] for R statistical software [40].
We report the results of the BRT model in two ways. First, we present a histogram showing the relative influence of variables produced from the ‘summary.gmb’ function of the R software package ‘gbm’. Then, only for the variables with relative importance ≥ 10%, we plot the fitted functions in the BRT model using partial dependence functions that show the effect of a variable or group of variables on capercaillie summer occurrence after accounting for the average effects of all other variables in the model. BRT can be used with a variety of response types. If the response variable is binary, then the package ‘gbm’ of R adopts the Bernoulli logistic regression. The fitted values are the logarithms of the odds p/(1 − p) where p is the probability of capercaillie occurrence for a given value of the predictor.

3. Results

The type of habitat played a stronger role than its structure in both years (Figure 3). Its relative importance increased in 2011 compared to 2001. The BRT model for 2001 had an explained deviance of 20%, a cross-validation correlation of 0.39 and standard error of 0.12. Instead, the model for 2011 had an explained deviance of 26%, a cross-validation correlation of 0.46 and standard error of 0.09.
Moreover, in both years, spruce forests (paA, paF, paM, paX) showed a positive influence on capercaillie presence, while fir (aa) and beech (fs) forests were avoided. No important changes occurred between 2001 and 2011 (Figure 3). Habitat structure types (i.e., development stages) did not show a clear pattern considering both years. In 2001 there was an indicative positive relationship of the presence of capercaillie with mature (fma) and over-mature (fri) stands, while pole dense canopies (pe) had a negative impact.
The BRT models with quantitative environmental and habitat variables had an explained deviance of 29%, a cross-validation correlation of 0.36 and standard error of 0.14 in 2001, while in 2011 they were 26%, 0.42 and 0.17, respectively.
A remarkable outcome is the much higher influence of altitude (Alt) in 2011 than in 2001 (Figure 4). In 2001, in addition to altitude, the following variables had a relatively important influence on the capercaillie summer presence: cover of spruce in the tree layer (CSp) and the mean height of spruce trees (HSp), cover of berries (CVv, CVm) and dwarf shrubs (CSu). Instead in 2011, apart from altitude (Alt), only slope (Slo) and cover of spruce (CSp) in the tree layer had a relative influence higher than 3%.
Overall, only a limited number of explanatory variables had a relative influence ≥ 10% (Figure 5). Among these, for both years, altitude was a fundamental environmental factor. More specifically, in 2001, capercaillie was most commonly found at altitudes from 1570 to 1670 m a.s.l. with a decreasing trend at altitudes higher than 1670–1700 m a.s.l. On the contrary, the species had clearly moved to higher altitudes in 2011, ranging from 1620 to 1750 m a.s.l., with this factor being extremely important compared to other explanatory variables for the species (i.e., 65.3% of relative influence). Furthermore, there was no negative trend in presence of the species for higher altitudes.
Factors related to spruce were important for the presence of capercaillie. In 2001, capercaillie preferred forests where this tree species proportion exceeded 50% and had a mean height over 17–18 m. In 2011, it is relatively evident that the species avoid slopes lower than 20%, without having preference for steep slopes.

4. Discussion

We observed an altitudinal shift of capercaillie based on the comparison of presence–absence data between 2001 and 2011. Even if altitude gained the highest relative influence in 2011, habitat features, particularly those associated with spruce canopy cover, played an important role in both years.

4.1. Species-Habitat Relationships

In our study area, capercaillie presence seemed to be influenced by a series of habitat factors, which have also previously been reported in the literature. For instance, the absence of wild berries seemed to have a deterrent effect on capercaillie presence. Availability of this food source is recognized as an essential feature for the bird’s diet [41] as it is thought to determine brood survival [13]. In general, abundance of dwarf shrubs—possibly with more than 20% bilberry cover [42]—is a characteristic that defines a good capercaillie habitat [7,12,14,41]. Spruce had a positive influence on the bird species presence. For instance, the lower branches of spruce can provide roosts and shelters [12]. Furthermore, the positive relationship between capercaillie presence and spruce mean height is consistent with its preference for mature stands [14].
The importance of altitude together with a preference for higher altitudes in 2011 than in 2001 indicated a shift in capercaillie presence. This was also observed for capercaillie in neighboring regions [43].
In 2011, slope appeared as an explanatory variable for capercaillie presence with a negative influence of slopes lower than 20% and no preference for steep slopes. Accordingly, Graf et al. [11] recorded capercaillie more often on gentle slopes, mountain ridges, or upper slopes, rather than on very steep topography, toe slopes, or valley bottoms. In both years, the cover of the herbaceous layer was positively related to capercaillie presence, and this might be due to the fact that grass cover can favor V. myrtillus [44]. However, the herbaceous layer height had a negative influence, as this may have negatively influenced berries and the type and amount of invertebrates, in turn impacting on food availability for the chicks [45]. Lastly, grazing and silvicultural activities did not have an influence in our study because their intensity is currently moderate [46].

4.2. Management Implications

Even though changes in the relationships between capercaillie and its habitat may not be seen as negative per se, we agree with other authors [15,23] that the maintenance of suitable habitats for local populations is strongly recommended. Data for 2001 highlighted the necessity of maintaining both mature and over-mature stands. Therefore, actions should integrate conservation measures for multiple benefits [47], thus address both forests and open habitats such as shrublands. In the first case, recommended actions include low-impact forestry practices that emulate natural disturbances and ensure the presence of not too dense stands, where there are mature development stages [46]. In the second case, several studies and applications, conducted in the southern Alps [48,49,50,51,52], agreed in suggesting different actions, depending on the successional stage.
Alpine scrub, mainly consisting of dwarf mountain pine (Pinus mugo Turra), is the dominant land cover type above the timberline (Figure 6). It is encroaching abandoned pastures. It is a habitat not suitable for capercaillie, because of the lack of berries and its dense structure which hinders easy movement in and through the canopy [53]. This means that the habitat available to capercaillie inside this Natura 2000 site is under pressure. The observed altitudinal shifts of capercaillie close to unsuitable mountain pine scrublands confirms the relevance of habitat restoration actions. For example, in pastures not completely encroached by mountain pine, it is appropriate to promptly intervene, making openings according to an irregular design and subsequently mowing them, so that a mosaic of bushes and open areas is kept for as long as possible. These actions may be in conflict with the limitations imposed by environmental regulations, such as the Habitats Directive [47,54]. For example, any land use change that involves trimming of dwarf mountain pine may contrast with the need to protect the priority European Union habitat 4070 * “Bushes with Pinus mugo and Rhododendron hirsutum (Mugo-Rhododendretum hirsuti)”.

4.3. Conclusions and Further Research

Spatial requirements of capercaillie are large, a lively population of capercaillie might require around 250 km2 [55]. Moreover, number and size of suitable habitat patches and the distance between them determine capercaillie occupancy and dispersal [15,17]. The observed shift in capercaillie altitudinal distribution could therefore be restricted to our study area. In fact, a possible drawback of our study is the limited area investigated and the relatively small number of surveyed plots. Nevertheless, models of this species built with regional-scale data may not be appropriate for understanding habitat preferences and the effects of their changes [56], and proposing restoration actions at the stand scale. Periodic stand-level monitoring would thus improve the understanding of regional trends, because we observed that the variables with the highest predictive powers changed across time.
In addition to what was observed in our study, other factors may have played a role. For example, changes in ungulate populations might also influence habitat use by capercaillie [57]. Human disturbance might be another contributing factor. Tourism and forest management [19,46] at lower altitudes could have emphasized the effects of land cover changes pushing capercaillie upwards in the post-breeding season. Lastly, being a key factor in other European contexts, increased predation at lower altitudes may have been driven by habitat cover changes [18,58,59] and capercaillie mobility might have been an adaptation to avoid predation [13].
This study focused on data recorded in two years during one season. However, while land use changes likely played a major role, it must be emphasized that additional studies over longer time-spans are needed to detect consistent trends and exclude short-term phenological effects related to the summer growing season. Additional research should focus on linking the observed altitudinal shift to other likely drivers.

Author Contributions

Conceptualization, S.M.M. and T.S.; Methodology, S.M.M. and T.S.; Formal Analysis, M.D. and T.S.; Investigation, F.D., S.M.M. and T.S.; Writing-Original Draft Preparation, A.P., T.C., T.S.; Writing-Review & Editing, all authors; Visualization, A.P., T.C., M.D., T.S.

Funding

The 2001 survey was funded by the Autonomous Province of Trento within a service contract with S.M.M., M.D. was supported by a postdoctoral research fellowship “Grant” (Dept. TESAF Decree no. 36/Protocol no. 1006/2011), T.C. was supported by a postdoctoral research fellowship (Dept. TESAF Decree no. 35/Protocol no. 2010/2017), A.P. was supported by a PhD grant from the Fondazione Cariparo.

Acknowledgments

The authors thank the Autonomous Province of Trento for supporting this study and three anonymous referees for the comments received on previous versions of this manuscript.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, nor in the decision to publish the results.

References

  1. Hulme, P.E. Adapting to climate change: Is there scope for ecological management in the face of a global threat? J. Appl. Ecol. 2005, 42, 784–794. [Google Scholar] [CrossRef]
  2. Lehsten, V.; Sykes, M.T.; Scott, A.V.; Tzanopoulos, J.; Kallimanis, A.; Mazaris, A.; Verburg, P.H.; Schulp, C.J.E.; Potts, S.G.; Vogiatzakis, I. Disentangling the effects of land-use change, climate and CO2 on projected future European habitat types. Glob. Ecol. Biogeogr. 2015, 24, 653–663. [Google Scholar] [CrossRef]
  3. Gehrig-Fasel, J.; Guisan, A.; Zimmermann, N.E. Tree line shifts in the Swiss Alps: Climate change or land abandonment? J. Veg. Sci. 2007, 18, 571–582. [Google Scholar] [CrossRef]
  4. Körner, C. The use of ‘altitude’ in ecological research. Trends Ecol. Evol. 2007, 22, 569–574. [Google Scholar] [CrossRef] [PubMed]
  5. Sitzia, T.; Semenzato, P.; Trentanovi, G. Natural reforestation is changing spatial patterns of rural mountain and hill landscapes: A global overview. For. Ecol. Manag. 2010, 259, 1354–1362. [Google Scholar] [CrossRef]
  6. Campagnaro, T.; Frate, L.; Carranza, M.L.; Sitzia, T. Multi-scale analysis of alpine landscapes with different intensities of abandonment reveals similar spatial pattern changes: Implications for habitat conservation. Ecol. Indic. 2017, 74, 147–159. [Google Scholar] [CrossRef]
  7. Suter, W.; Graf, R.F.; Hess, R. Capercaillie (Tetrao urogallus) and avian biodiversity: Testing the umbrella-species concept. Conserv. Biol. 2002, 16, 778–788. [Google Scholar] [CrossRef]
  8. Storch, I. Annual home ranges and spacing patterns of capercaillie in Central Europe. J. Wildl. Manag. 1995, 59, 392–400. [Google Scholar] [CrossRef]
  9. Bollmann, K.; Jenni, L.; Perrin, N.; Suter, W. Naturschutzforschung am Auerhuhn in der Schweiz: Eine Übersicht. Der Ornithologische Beobachter 2008, 105, 5–16. [Google Scholar]
  10. Rolstad, J.; Wegge, P. Capercaillie Tetrao urogallus populations and modern forestry—A case for landscape ecological studies. Finn. Game Resour. 1989, 46, 43–52. [Google Scholar]
  11. Graf, R.F.; Bollmann, K.; Suter, W.; Bugmann, H. The importance of spatial scale in habitat models: Capercaillie in the Swiss Alps. Landsc. Ecol. 2005, 20, 703–717. [Google Scholar] [CrossRef]
  12. Bollmann, K.; Weibel, P.; Graf, R.F. An analysis of central Alpine capercaillie spring habitat at the forest stand scale. For. Ecol. Manag. 2005, 215, 307–318. [Google Scholar] [CrossRef]
  13. Storch, I. Habitat and survival of capercaillie Tetrao urogallus nests and broods in the Bavarian Alps. Biol. Conserv. 1994, 70, 237–243. [Google Scholar] [CrossRef]
  14. Summers, R.W.; Proctor, R.; Thorton, M.; Avey, G. Habitat selection and diet of the Capercaillie Tetrao urogallus in Abernethy Forest, Strathspey, Scotland. Bird Study 2004, 51, 58–68. [Google Scholar] [CrossRef]
  15. Bollmann, K.; Graf, R.F.; Suter, W. Quantitative predictions for patch occupancy of capercaillie in fragmented habitats. Ecography 2011, 34, 276–286. [Google Scholar] [CrossRef]
  16. Storch, I. Habitat selection by capercaillie in summer and autumn: Is bilberry important? Oecologia 1993, 95, 257–265. [Google Scholar] [CrossRef] [PubMed]
  17. Angelstam, P. Habitat thresholds and effects of forest landscape change on the distribution and abundance of black grouse and capercaillie. Ecol. Bull. 2004, 51, 173–187. [Google Scholar]
  18. Baines, D.; Aebischer, N.J.; Macleod, A. Increased mammalian predators and climate change predict declines in breeding success and density of Capercaillie Tetrao urogallus, an old stand specialist, in fragmented Scottish forests. Biodivers. Conserv. 2016, 25, 2171–2186. [Google Scholar] [CrossRef]
  19. Mikoláš, M.; Tejkal, M.; Kuemmerle, T.; Griffiths, P.; Svoboda, M.; Hlásny, T.; Leitão, P.J.; Morrissey, R.C. Forest management impacts on capercaillie (Tetrao urogallus) habitat distribution and connectivity in the Carpathians. Landsc. Ecol. 2017, 32, 163–179. [Google Scholar] [CrossRef]
  20. Wegge, P.; Rolstad, J. Clearcutting forestry and Eurasian boreal forest grouse: Long-term monitoring of sympatric capercaillie Tetrao urogallus and black grouse T. tetrix reveals unexpected effects on their population performances. For. Ecol. Manag. 2011, 261, 1520–1529. [Google Scholar] [CrossRef]
  21. Tudoroiu, M.; Eccel, E.; Gioli, B.; Gianelle, D.; Schume, H.; Genesio, L.; Miglietta, F. Negative elevation-dependent warming trend in the Eastern Alps. Environ. Res. Lett. 2016, 11, 044021. [Google Scholar] [CrossRef]
  22. Cattadori, I.; Hudson, P. Are grouse populations unstable at the southern end of their range? Wildl. Biol. 2000, 6, 213–218. [Google Scholar] [CrossRef]
  23. Segelbacher, G.; Storch, I. Capercaillie in the Alps: Genetic evidence of metapopulation structure and population decline. Mol. Ecol. 2002, 11, 1669–1677. [Google Scholar] [CrossRef] [PubMed]
  24. Campagnaro, T.; Sitzia, T.; Bridgewater, P.; Evans, D.; Ellis, E.C. Half earth or whole earth: What can natura 2000 teach us? BioScience 2019, 69, 117–124. [Google Scholar] [CrossRef]
  25. P.A.T. Piano di assestamento dei beni silvo-pastorali—Riserva Naturale Guidata della Scanuppia, validità 2006–2015; Ufficio Pianificazione e Selvicoltura, Provincia Autonoma di Trento: Trento, Italy, 2007. [Google Scholar]
  26. Sitzia, T.; Trentanovi, G. Maggengo meadow patches enclosed by forests in the Italian Alps: Evidence of landscape legacy on plant diversity. Biodivers. Conserv. 2011, 20, 945–961. [Google Scholar] [CrossRef]
  27. Poggenburg, C.; Nopp-Mayr, U.; Coppes, J.; Sachser, F. Shit happens … and persists: Decay dynamics of capercaillie (Tetrao urogallus L.) droppings under natural and artificial conditions. Eur. J. Wildl. Res. 2018, 64, 29. [Google Scholar] [CrossRef]
  28. Zwickel, F.; Bendell, J. Blue Grouse: Their Biology and Natural History; National Research Council of Canada: Ottawa, ON, Canada, 2004.
  29. Wilkinson, N.I.; Langston, R.H.W.; Gregory, R.D.; Gibbons, D.W.; Marquiss, M. Capercaillie Tetrao urogallus abundance and habitat use in Scotland, in winter 1998–99. Bird Study 2002, 49, 177–185. [Google Scholar] [CrossRef]
  30. Thiel, D.; Ménoni, E.; Brenot, J.-F.; Jenni, L. Effects of recreation and hunting on flushing distance of capercaillie. J. Wildl. Manag. 2007, 71, 1784–1792. [Google Scholar] [CrossRef]
  31. Compton, T.J.; Bowden, D.A.; Roland, P.C.; Hewitt, J.E.; Ellis, N. Biophysical patterns in benthic assemblage composition across contrasting continental margins off New Zealand. J. Biogeogr. 2013, 40, 75–89. [Google Scholar] [CrossRef]
  32. Fern, R.R.; Morrison, M.L. Mapping critical areas for migratory songbirds using a fusion of remote sensing and distributional modeling techniques. Ecol. Inform. 2017, 42, 55–60. [Google Scholar] [CrossRef]
  33. Bae, S.; Reineking, B.; Ewald, M.; Mueller, J. Comparison of airborne lidar, aerial photography, and field surveys to model the habitat suitability of a cryptic forest species—The hazel grouse. Int. J. Remote Sens. 2014, 35, 6469–6489. [Google Scholar] [CrossRef]
  34. Elith, J.; Leathwick, J.R.; Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 2008, 77, 802–813. [Google Scholar] [CrossRef] [PubMed]
  35. Miettinen, J.; Helle, P.; Nikula, A.; Niemelä, P. Large-scale landscape composition and capercaillie (Tetrao urogallus) density in Finland. Annales Zoologici Fennici 2008, 45, 161–173. [Google Scholar] [CrossRef]
  36. Trentanovi, G.; Campagnaro, T.; Rizzi, A.; Sitzia, T. Synergies of planning for forests and planning for Natura 2000: Evidences and prospects from northern Italy. J. Nat. Conserv. 2018, 43, 239–249. [Google Scholar] [CrossRef]
  37. Dormann, C.F.; Elith, J.; Bacher, S.; Buchmann, C.; Carl, G.; Carré, G.; Marquéz, J.R.G.; Gruber, B.; Lafourcade, B.; Leitão, P.J.; et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 2013, 36, 27–46. [Google Scholar] [CrossRef]
  38. Elith, J.; Graham, C.H.; Anderson, R.P.; Dudík, M.; Ferrier, S.; Guisan, A.; Hijmans, R.J.; Huettmann, F.; Leathwick, J.R.; Lehmann, A.; et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 2006, 29, 129–151. [Google Scholar] [CrossRef]
  39. Ridgeway, G. Gbm: Generalized Boosted Regression Models. R Package Version 2.1.1. 2015. Available online: http://CRAN.R-project.org/package=gbm (accessed on 21 July 2015).
  40. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Wien, Austria, 2013. [Google Scholar]
  41. Spidsø, T.K.; Stuen, O.H. Food selection by capercaillie chicks in southern Norway. Can. J. Zool. 1988, 66, 279–283. [Google Scholar] [CrossRef]
  42. Broome, A.; Connolly, T.; Quine, C.P. An evaluation of thinning to improve habitat for capercaillie (Tetrao urogallus). For. Ecol. Manag. 2014, 314, 94–103. [Google Scholar] [CrossRef]
  43. Borgo, A. Gallo cedrone Tetrao urogallus. In Carta Delle Vocazioni Faunistiche del Veneto; Bon, F., Mezzavilla, F., Scarton, F., Eds.; Regione del Veneto e Associazione Faunisti Veneti: Venezia Mestre, Italy, 2013; pp. 373–384. [Google Scholar]
  44. Montané, F.; Guixé, D.; Camprodon, J. Canopy cover and understory composition determine abundance of Vaccinium myrtillus L., a key plant for capercaillie (Tetrao urogallus), in subalpine forests in the Pyrenees. Plant Ecol. Divers. 2016, 9, 187–198. [Google Scholar] [CrossRef]
  45. Baines, D.; Wilson, I.A.; Beeley, G. Timing of breeding in Black Grouse Tetrao tetrix and Capercaillie Tetrao urogallus and distribution of insect food for the chicks. Ibis 1996, 138, 181–187. [Google Scholar] [CrossRef]
  46. Mikoláš, M.; Svitok, M.; Tejkal, M.; Leitão, P.J.; Morrissey, R.C.; Svoboda, M.; Seedre, M.; Fontaine, J.B. Evaluating forest management intensity on an umbrella species: Capercaillie persistence in central Europe. For. Ecol. Manag. 2015, 354, 26–34. [Google Scholar] [CrossRef]
  47. Campagnaro, T.; Trentanovi, G.; Sitzia, T. Identifying habitat type conservation priorities under the Habitats Directive: Application to two Italian biogeographical regions. Sustainability 2018, 10, 1189. [Google Scholar] [CrossRef]
  48. De Franceschi, P.F. Pianificazione e gestione delle risorse faunistiche; Ministero dell’Ambiente, Azienda Regionale delle Foreste del Veneto: Padova, Italy, 1993. [Google Scholar]
  49. De Franceschi, P.F. Strategie di gestione dei Tetraonidi sulle Alpi Italiane: Il Fagiano di monte (Tetrao tetrix). Supplemento alle Ricerche di Biologia della Selvaggina 1995, 22, 725–738. [Google Scholar]
  50. Odasso, M.; Mayr, S.; De Franceschi, P.F.; Zorzi, S.; Mattedi, S. Miglioramenti ambientali a fini faunistici: Localizzazione delle zone, priorità e modalità gestionali per interventi a favore di Lepre comune, Fagiano di monte, Coturnice e Re di quaglie; Provincia, Assessorato all’ Agricoltura e alla Montagna, Servizio Faunistico: Trento, Italy, 2002. [Google Scholar]
  51. Masutti, L.; Battisti, A. La gestione forestale per la conservazione degli habitat della Rete Natura 2000; Regione del Veneto, Accademia Italiana di Scienze Forestali: Venezia, Italy, 2007. [Google Scholar]
  52. Brugnoli, A.; Giovannini, G. Arbusteti della fascia subalpina ed interventi per la fauna in Trentino. L’Italia Forestale e Montana 2009, 64, 15–24. [Google Scholar] [CrossRef]
  53. Saniga, M. Ecology of the capercaillie (Tetrao urogallus) and forest management in relation to its protection in the West Carpathians. J. For. Sci. 2003, 49, 229–239. [Google Scholar] [CrossRef]
  54. Sitzia, T.; Campagnaro, T.; Grigolato, S. Ecological risk and accessibility analysis to assess the impact of roads under Habitats Directive. J. Environ. Plan. Manag. 2016, 59, 2251–2271. [Google Scholar] [CrossRef]
  55. Grimm, V.; Storch, I. Minimum viable population size of capercaillie Tetrao urogallus: Results from a stochastic model. Wildl. Biol. 2000, 6, 219–225. [Google Scholar] [CrossRef]
  56. Graf, R.F.; Bollmann, K.; Sachot, S.; Suter, W.; Bugmann, H. On the generality of habitat distribution models: A case study of capercaillie in three Swiss regions. Ecography 2006, 29, 319–328. [Google Scholar] [CrossRef]
  57. Chirichella, R.; Mustoni, A.; Apollonio, M. Alpine landscape and canopy cover from 1973 to 2011: Are roe and red deer population dynamics linked? Wildl. Res. 2017, 44, 504–513. [Google Scholar] [CrossRef]
  58. Oja, R.; Pass, E.; Soe, E.; Ligi, K.; Anijalg, P.; Laurimaa, L.; Saarma, U.; Lõhmus, A.; Valdmann, H. Increased nest predation near protected capercaillie leks: A caveat against small reserves. Eur. J. Wildl. Res. 2018, 64, 6. [Google Scholar] [CrossRef]
  59. Jim-Lino, K.; Joy, C.; Simone, C.; Rudi, S.; Ilse, S. Range loss of a threatened grouse species is related to the relative abundance of a mesopredator. Ecosphere 2017, 8, e01934. [Google Scholar]
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Figure 1. (a) The black star indicates the study area in northern Italy. (b) Distribution of the capercaillie sampling plots (circle = summer presence–absence surveyed in 2001 and 2011, empty circle = habitat surveyed in 2001, filled circles = habitat surveyed in 2001 and 2011) in the Scanuppia Natura 2000 site (black dashed line) is reported (background: 2008 aerial photo).
Figure 1. (a) The black star indicates the study area in northern Italy. (b) Distribution of the capercaillie sampling plots (circle = summer presence–absence surveyed in 2001 and 2011, empty circle = habitat surveyed in 2001, filled circles = habitat surveyed in 2001 and 2011) in the Scanuppia Natura 2000 site (black dashed line) is reported (background: 2008 aerial photo).
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Figure 2. Historical aerial photos comparison which show the reduction of open lands from (a) 1954; to (b) 1973; and (c) 2006, in the Scanuppia Natura 2000 site.
Figure 2. Historical aerial photos comparison which show the reduction of open lands from (a) 1954; to (b) 1973; and (c) 2006, in the Scanuppia Natura 2000 site.
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Figure 3. Relative influence of habitat canopy (VEG) and structure (STR) and relationship of each habitat category with capercaillie summer occurrence in 2001 and 2011 in a generalized boosted regression model obtained by integrating out all the other categories. A logistic regression was used, and y-axes report the fitted values on log-odds scale. The red line corresponds to a probability of 0.5 (odds (p) = 1, log odds (p) = 0). Explanatory variables, with their codes, are reported in Table 1.
Figure 3. Relative influence of habitat canopy (VEG) and structure (STR) and relationship of each habitat category with capercaillie summer occurrence in 2001 and 2011 in a generalized boosted regression model obtained by integrating out all the other categories. A logistic regression was used, and y-axes report the fitted values on log-odds scale. The red line corresponds to a probability of 0.5 (odds (p) = 1, log odds (p) = 0). Explanatory variables, with their codes, are reported in Table 1.
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Figure 4. Relative influence of quantitative variables in a BRT model for capercaillie summer occurrence in 2001 and 2011. Signs indicate whether the relationship was negative (−) or positive (+) monotonic or non-monotonic (+ −). Explanatory variables, with their codes, are reported in Table 2.
Figure 4. Relative influence of quantitative variables in a BRT model for capercaillie summer occurrence in 2001 and 2011. Signs indicate whether the relationship was negative (−) or positive (+) monotonic or non-monotonic (+ −). Explanatory variables, with their codes, are reported in Table 2.
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Figure 5. Relationship of the most influential variables (relative influence ≥ 10%) with capercaillie summer occurrence in 2001 and 2011, obtained by integrating out all other variables in a generalized boosted regression model. A logistic regression was used, and y-axes report the fitted values on log-odds scale (explanatory variables, with their codes, are reported in Table 2).
Figure 5. Relationship of the most influential variables (relative influence ≥ 10%) with capercaillie summer occurrence in 2001 and 2011, obtained by integrating out all other variables in a generalized boosted regression model. A logistic regression was used, and y-axes report the fitted values on log-odds scale (explanatory variables, with their codes, are reported in Table 2).
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Figure 6. Distribution of land cover types in the Scanuppia Natura 2000 site. The altitudinal distribution of capercaillie in 2011, currently centered at 1620 to 1750 m a.s.l., is closer than in 2001 to the dwarf mountain pine scrub, which is unsuitable to capercaillie (source: Provincia Autonoma di Trento, Servizio Sistemazione Montana, 2001).
Figure 6. Distribution of land cover types in the Scanuppia Natura 2000 site. The altitudinal distribution of capercaillie in 2011, currently centered at 1620 to 1750 m a.s.l., is closer than in 2001 to the dwarf mountain pine scrub, which is unsuitable to capercaillie (source: Provincia Autonoma di Trento, Servizio Sistemazione Montana, 2001).
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Table
Table 1. Habitat type and structure categories.
Table 1. Habitat type and structure categories.
Variable DescriptionClasses and Abbreviations
Habitat type (canopy composition) (VEG)(1) silver fir (aa); (2) beech (fs); (3) larch (ld); (4) spruce and fir (paA); (5) spruce and beech (paF); (6) spruce on moist soil (paM); (7) spruce on dry soil (paX); (8) pasture (pas); (9) mountain pine (pm); (10) tree plantation (rim)
Habitat structure (development stage) (STR)(1) two-layered stand (bp); (2) adult stand (fad); (3) late pole stand (fgi); (4) irregular stand (fir); (5) mature stand (fma); (6) over-mature stand (fri); (7) multi-layered stand (mt); (8) young pole stand (pe); (9) wooded pasture (pra); (10) thicket stand (sp); (11) gap (vu)
Table
Table 2. Variables used to explain changes in capercaillie presence–absence.
Table 2. Variables used to explain changes in capercaillie presence–absence.
Variable DescriptionAbbreviationUnit
AltitudeAltm a.s.l.
AspectAspcardinal orientations
Topographic positionTophigh slope, middle slope, valley, and ridge
SlopeSlo%
Cover of beechCBe%
Cover of common juniperCJu
Cover of mountain pineCDp
Cover of silver firCFi
Cover of herbaceous layerCHe
Cover of larchCLa
Cover of litterCLi
Cover of Rhododendron ferrugineumCRf
Cover of spruceCSp
Cover of dwarf shrubsCSu
Cover of Vaccinium myrtillusCVm
Cover of Vaccinium vitis-ideaCVv
Cover of fine deadwoodCFD
Height of treesHTrm
Height of shrubsHSh
Height of herbaceous layerHHe
Height of firHFi
Height of larchHLa
Height of spruceHSp
Height of dwarf shrubsHSu
Presence of broadleaf speciesPBrPresence–absence
Evidence of silvicultural interventionsSIn
Signs of grazingSGr

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Sitzia, T.; Campagnaro, T.; Dainese, M.; Dallabrida, F.; Mattedi, S.M.; Portaccio, A. Altitudinal Shift of Tetrao urogallus in an Alpine Natura 2000 Site: Implications for Habitat Restoration. Appl. Sci. 2019, 9, 1164. https://doi.org/10.3390/app9061164

AMA Style

Sitzia T, Campagnaro T, Dainese M, Dallabrida F, Mattedi SM, Portaccio A. Altitudinal Shift of Tetrao urogallus in an Alpine Natura 2000 Site: Implications for Habitat Restoration. Applied Sciences. 2019; 9(6):1164. https://doi.org/10.3390/app9061164

Chicago/Turabian Style

Sitzia, Tommaso, Thomas Campagnaro, Matteo Dainese, Francesco Dallabrida, Silvano Mario Mattedi, and Alessia Portaccio. 2019. "Altitudinal Shift of Tetrao urogallus in an Alpine Natura 2000 Site: Implications for Habitat Restoration" Applied Sciences 9, no. 6: 1164. https://doi.org/10.3390/app9061164

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