This issue’s cover image shows a red deer stag (Cervus elaphus) in the Bavarian Forest National Park situated in south-eastern Germany. The park comprises three major forest types differentiated from high to low altitude: sub-alpine spruce forests with Norway spruce followed by mixed mountain forests with Norway spruce, White fir, and European beech until it reaches spruce forests in the valley bottoms with Norway spruce, Mountain ash and birches. Starting from the 1990s, extensive infestation by bark beetles dramatically changed the forest structures and created a varying canopy ranging from open forests dominated by dead wood to dense stands. This high forest heterogeneity makes the park particularly suitable for the calibration of new analytical approaches deployed to disentangle fine-scale habitat selection in herbivores.

Ciuti et al. illustrate a powerful approach to reduce the dimensionality of LiDAR data describing 3D vegetation structure, generating predictors able to retain most of LiDAR-point-cloud variability and boost the performances of ecological models. They combine fine-scale satellite telemetry data collected in roe deer and red deer and exemplify the new approach by documenting deer selection for understory vegetation at unprecedented fine scale. LiDAR exceeds any other environmental covariates in predicting deer habitat selection, showing that it can boost several applications such as species distribution and habitat suitability models.

Photo credit: © Rainer Simonis