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Landscape structure is a key factor for biodiversity conservation. We assessed the potential role of landscaperelated habitat structure on Hazel Grouse (Tetrastesbonasia) by recording the occurrence of the species. Hazel Grouse is a widely recognized endangered species, because of the habitat loss as a direct consequence of land use change that is jeopardizing the species survival. The aim of this study is to provide a method based on habitat suitability modelling to explore and analyse hazel grouse- landscape structure and its relation to key habitat features. We evaluated landscape factors and critical threshold for monitoring the species, in order to assess the predictive power of models based on field surveys, ancillary information and high resolution infrared aerial photographs. We tested Maximum Entropy (MaxEnt) algorithm, in order to predict the distribution of Hazel Grouse species, in the complex mountain landscape within the study site. This presence-only modelling method has showed numerous advantages over many other approaches. It is based on a function that links the fitness of individuals to their environment. Our results were highly consistent with field knowledge, showing that habitat modelling generated using spatial statistics and GIS can effectively help in the characterization of habitat requirements and the localization of the species suitable habitat. We found also that key drivers of Hazel Grouse distribution are not only environmental factors, in particular linked to elevation in mountain areas, but also landscape heterogeneity is a key feature. Hence, conservation of this species will require land management practices that maintain heterogeneous landscape; in particular small forest openings are essential for the species. The approach developed seem to be a promising operational tool for local and regional managers interested in species monitoring and management within the long term.

Landscape Structure Indicators as a Key Feature in Habitat Selection: an Operational Approach to Conservation Planning