Skip to main content
Dryad logo

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

Citation

de Vries, Jan Peter Reinier; Koma, Zsófia; WallisDeVries, Michiel F.; Kissling, W. Daniel (2021), Data and code for: Identifying fine-scale habitat preferences of threatened butterflies using airborne laser scanning, Dryad, Dataset, https://doi.org/10.5061/dryad.g79cnp5pf

Abstract

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.

Methods

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 m2, 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.

Usage Notes

See additional details in ReadMe files.

Funding

Netherlands eScience Center, Award: ASDI.2016.014