Data and code for: Identifying fine-scale habitat preferences of threatened butterflies using airborne laser scanning

Aim: Light Detection And Ranging (LiDAR) is a promising remote sensing technique for ecological applications because it can quantify vegetation structure at high resolution over broad spatial extents. Using country-wide airborne laser scanning (ALS) data, we test to what extent fine-scale LiDAR metrics capturing low vegetation, medium-to-high vegetation and landscape-scale habitat structures can explain the habitat preferences of threatened butterflies at a national extent.

Location: The Netherlands.

Methods: We applied a machine learning (random forest) algorithm to build species distribution models (SDMs) for grassland and woodland butterflies in wet and dry habitats using various LiDAR metrics and butterfly presence-absence data collected by a national butterfly monitoring scheme. The LiDAR metrics captured vertical vegetation complexity (e.g. height and vegetation density of different strata) and horizontal heterogeneity (e.g. vegetation roughness, microtopography, vegetation ...