An efficient method to exploit LiDAR data in animal ecology. Issue 4 (27th November 2017) - Ciuti, Simone; Tripke, Henriette; Antkowiak, Peter; Gonzalez, Ramiro Silveyra; Dormann, Carsten F.; Heurich, Marco
Abstract: Light detection and ranging (LiDAR) technology provides ecologists with high‐resolution data on three‐dimensional vegetation structure. Large LiDAR datasets challenge predictive ecologists, who commonly simplify point clouds into structural attributes (namely LiDAR‐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 LiDAR 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 LiDAR point cloud, then we explain the results using post‐modelling LiDAR‐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 LiDAR point cloud by principal component analysis (PCA) to fewer predictors. Second, we show that LiDAR‐PCs are capable to outperforming commonly used environmental predictors in ecological modelling, including LiDAR‐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 LiDAR‐PCs, 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 LiDAR‐based metrics as model predictors, but rather generate principal components able to capture most of the LiDAR point cloud variability. Our study demonstrates that LiDAR‐PCs can boost ecological models. We envision a potential use of LiDAR‐PCs 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.