DOI: 10.5061/dryad.g79cnp5pf
title: Data and code for: Identifying fine-scale habitat preferences of threatened butterflies using airborne laser scanning

description: 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 openness and woodland edge extent). We assessed the relative
 variable importance and interpreted response curves of each LiDAR metric
 for explaining butterfly occurrences. Results: All SDMs showed a good to
 excellent fit, with woodland butterfly SDMs performing slightly better
 than those of grassland butterflies. Grassland butterfly occurrences were
 best explained by landscape-scale habitat structures (e.g. open patches,
 microtopography) and vegetation height. Woodland butterfly occurrences
 were mainly determined by vegetation density of medium-to-high vegetation,
 open patches and woodland edge extent. The importance of metrics generally
 differed between wet and dry habitats for both grassland and woodland
 species.  Main Conclusions: Vertical variability and horizontal
 heterogeneity of vegetation structure are key determinants of butterfly
 species distributions, even in low-stature habitats such as grasslands,
 dunes and heathlands. The information content of low vegetation LiDAR
 metrics could further be improved with country-wide leaf-on ALS data or
 surveys from drones and terrestrial laser scanners at specific sites.
 LiDAR thus offers great potential for predictive habitat distribution
 modelling and other studies on ecological niches and invertebrate-habitat
 relationships.
description: Presence-absence data of four butterfly species were derived from
 the Dutch butterfly monitoring scheme, which systematically collects
 butterfly occurrence data by conducting weekly surveys along fixed
 transect routes throughout the flight season (April to
 September). LiDAR data were derived from the third
 country-wide ALS campaign (AHN3) in the Netherlands (see https://ahn.arcgisonline.nl/ahnviewer), conducted in the years 2014–2019 in leaf-off conditions (northern hemisphere winter, December–March). The data have an average point density of 6–10 points per m<sup>2</sup>, an overall point cloud accuracy of 10 cm and a vertical standard deviation of 5 cm (https://ahn.nl/kwaliteitsbeschrijving). From the LiDAR point clouds, we derived 12 LiDAR metrics that captured the vertical complexity and horizontal heterogeneity of the vegetation. A total of six LiDAR metrics reflected the vertical complexity of vegetation. Those were directly derived from the LiDAR point cloud using a 25 m radius around each centroid. In addition to the six vertical complexity metrics (25 m radius), we further extracted six metrics reflecting the horizontal heterogeneity of vegetation in either 25 m (vegetation roughness) or 100 m (landscape-scale microtopography or vegetation structure) around each section centroid.
description: See additional details in ReadMe files.