An efficient method to exploit Li DAR data in animal ecology

Light detection and ranging (Li DAR ) technology provides ecologists with high‐resolution data on three‐dimensional vegetation structure. Large Li DAR datasets challenge predictive ecologists, who commonly simplify point clouds into structural attributes (namely Li DAR ‐based metrics such as canopy height), which are used as predictors in ecological models, potentially with loss of relevant information. We illustrate an efficient alternative approach to reduce the dimensionality of Li DAR data that aims at minimal data filtering with no a priori assumptions on the ecology of the target species. We first fit the ecological model exploiting the full variability in the Li DAR point cloud, then we explain the results using post‐modelling Li DAR ‐data classification for ecological interpretation only. This is the classical logic of explorative, hypothesis generating and predictive statistics, rather than testing specific vegetation‐structural hypotheses. First, we reduce the dimensionality of the Li DAR point cloud by principal component analysis ( PCA ) to fewer predictors. Second, we show that Li DAR ‐ PC s are capable to outperforming commonly used environmental predictors in ecological modelling, including Li DAR ‐based metrics. We exemplify this by modelling red deer ( Cervus elaphus ) and roe deer ( Capreolus capreolus ) resource selection in the Bavarian Forest National Park, Germany. After fitting the ecological model, we provide an interpretation of the information included in Li DAR ‐ PC s, which allows users to draw conclusions whenever using them as predictors. We make use of the PCA rotation matrix and post‐modelling data classification, and document deer selection for understorey vegetation at unprecedented fine scale. Our approach is the first attempt in animal ecology to avoid the use of Li DAR ‐based metrics as model predictors, but rather generate principal components able to capture most of the Li DAR point cloud variability. Our study demonstrates that Li DAR ‐ PC s can boost ecological models. We envision a potential use of Li DAR ‐ PC s in several applications, particularly species distribution and habitat suitability models. We demonstrate an application of our approach by building suitability maps for both deer species, which can be used by practitioners to visualize model spatial predictions and understand the type of forest structures selected by deer.