Identifying fine‐scale habitat preferences of threatened butterflies using airborne laser scanning

Jan Peter Reinier De Vries,W Daniel Kissling,Zsófia Koma,Michiel F Wallisdevries,Reid Tingley

doi:10.1111/ddi.13272

Abstract

AbstractAimLight 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.LocationThe Netherlands.MethodsWe 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.ResultsAll 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 conclusionsVertical 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.

Highlights

Butterflies and other invertebrates have declined severely in recent decades, especially in parts of Europe where structured monitoring schemes have revealed long-term population declines (Hallmann et al, 2017; van Swaay et al, 2006)
In addition to the Random Forest (RF) results, we present in the appendix the receiver operating characteristics (ROC) curves for all species distribution models (SDMs) algorithms (Figure S3–S5) and the response curves averaged across the three SDM algorithms (Figure S6–S8)
Using high-quality butterfly presence–absence data derived from a national monitoring scheme, we show that landscape-level habitat structures are especially important for both grassland and woodland species and that medium-to-high vegetation structures are crucial for woodland species

Summary

Introduction

Butterflies and other invertebrates have declined severely in recent decades, especially in parts of Europe where structured monitoring schemes have revealed long-term population declines (Hallmann et al, 2017; van Swaay et al, 2006). A reduction of landscape conversion and an increase in conservation efforts have slowed down butterfly declines since 1990 (Carvalheiro et al, 2013; van Strien et al, 2016), a large part of the Dutch butterfly species remain highly vulnerable and are still declining (van Swaay, 2019; van Strien et al, 2019). This shows the urgent need of sustaining and increasing efforts to preserve butterflies and their habitats

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