Prioritizing forest patches to enhance habitat restoration and connectivity for the endangered saproxylic beetle Rosalia alpina (Coleoptera, Cerambycidae): a modelling approach.

Abstract

The cerambycid beetle Rosalia alpinais associated with temperate, broadleaved (mainly beech) forests containing dead or decaying wood. This species is protected under the Habitats Directive of the European Union. Given its narrow ecological niche and limited dispersal abilities, habitat fragmentation is a conservation concern for populations of R. alpina. In order to maximise the effectiveness of habitat restoration, a scientifically sound procedure for patch selection is needed. In Gipuzkoa (N Spain), we used Light Detection and Ranging (LiDAR) images to search for 20x20-m cells matching the parameterisation of a predictive and local habitat model for R. alpinaat the tree level. The cells selected under quantitative criteria were clustered to identify potential habitat patches. Conefor Sensinode software was used to estimate the importance of each of those patches in terms of the connectivity of the population, based on dispersal distances of R. alpinaand the probabilities of dispersing events among patches. We identified 380 potential habitat patches, mostly in the south-eastern sector of the study area, which were classified into “core areas” and “connecting areas”. The performance of the model was tested in the field (61% of correct assignations), although the actual occurrence of R. alpinawithin the habitat patches should be assessed in the future. This model represents a step forward in guiding the cost-effective implementation of conservation activities, through a strict preservation of the current core habitat patches and an increase in the size of connecting patches.Therefore, we show that connectivity models combining remote sensing data and local habitat selection can be an aid in conservation planning and restoration actions, probably outperforming less efficient strategies, such as random or expert selection.