The Importance of Spatial Scale in Habitat Models: Capercaillie in the Swiss Alps

Abstract

The role of scale in ecology is widely recognized as being of vital importance for understanding ecological patterns and processes. The capercaillie (Tetrao urogallus) is a forest grouse species with large spatial requirements and highly specialized habitat preferences. Habitat models at the forest stand scale can only partly explain capercaillie occurrence, and some studies at the landscape scale have emphasized the role of large-scale effects. We hypothesized that both the ability of single variables and multivariate models to explain capercaillie occurrence would vary with the spatial scale of the analysis. To test this hypothesis, we varied the grain size of our analysis from 1 to just over 1100 hectares and built univariate and multivariate habitat suitability models for capercaillie in the Swiss Alps. The variance explained by the univariate models was found to vary among the predictors and with spatial scale. Within the multivariate models, the best single-scale model (using all predictor variables at the same scale) worked at a scale equivalent to a small annual home range. The multi-scale model, in which each predictor variable was entered at the scale at which it had performed best in the univariate model, did slightly better than the best single-scale model. Our results confirm that habitat variables should be included at different spatial scales when species-habitat relationships are investigated.

Effects of habitat and land use on breeding season density of male Asian Houbara Chlamydotis macqueenii

“…All models were run in R 3.0.2 (R Core Team 2013). Potential spatial autocorrelation in model-averaged residuals was examined by Moran's I in R (Graf et al 2005).…”

Integrating Expert Knowledge into Mapping Ecosystem Services Trade-offs for Sustainable Forest Management

“…The quantification procedure selected here resembles thus more a conservation perspective, in which humans value places with a diversity of species, especially the more charismatic animals and plants, and where retaining a full complement of wild species has a distinct value in itself (Mace et al 2012). The quantification of suitable habitats for Capercaillie was based on a Capercaillie-habitat-suitability model for Switzerland (Graf et al 2005). We used the results of univariate logistic regression models to predict the suitability of forest patches for Capercaillie.…”

A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models

“…(Vogeler et al, 2013); managing lands for multiple uses including providing wildlife habitat and timber resources; setting aside conservation areas or prioritizing restoration efforts for species of conservation interest (Graf et al, 2005;Graf et al, 2009); and for monitoring changes in the landscape, habitat, and habitat patch connectivity (Osborne et al, 2001). Continuous remote sensing data also facilitates characterizing the vertical and horizontal distribution of habitat in new ways creating opportunities to expand on our understanding of drivers of habitat selection and species distributions at multiple scales.…”

“…Predicted habitat maps provide a valuable tool for management and conservation planning (Mason et al, 2003). Maps highlighting important areas for species distributions may be useful for forecast models identifying potential impacts of climate change (Maclean et al, 2008), the prioritization of conservation resources (Graf et al, 2005), trade-off analyses for landscape planning, monitoring habitat change through time , local scale management planning (Graf et al, 2009), among many other potential applications (Figure 1). The challenge of habitat modeling/mapping is balancing generality, detail, and accuracy (Mason et al, 2003).…”