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 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.