An efficient method to exploit LiDAR data in animal ecology

1. 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. 2. 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 of 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. 3. First, we reduce the dimensionality o...