Requirements of a habitat specialist in Swiss mountain forests – an assessment of forest structure and composition using laser remote sensing and field data

Abstract

Species richness in forest ecosystems largely depends on habitat structure and composition. These attributes can be assessed in field surveys, however, such data often lacks in spatial extent. Remote sensing technologies such as light detection and ranging (LiDAR) provide alternative tools to quantify structural elements across relatively broad areas at a fine resolution. To study the habitat requirements of hazel grouse (Bonasa bonasia), an indicator species of structurally rich forest stands, we assessed the structure and composition of Swiss mountain forests over three biogeographical regions. We designed a sample based field survey of forest structure and composition and a LiDAR based assessment of vertical and horizontal forest structures using a nationwide LiDAR dataset with a mean point density of 1.4 m2. The dependent variable consisted of hazel grouse presence/absence data at a resolution of 1 km2. Species distribution models were computed for both variable sets separately and in combination, using boosted regression trees, a statistical machine learning technique. Model performance assessment based on explained deviance and AUC showed that the combined model performed best, with over 55% explained deviance in the observed data, followed by the field and LiDAR models. The field model revealed that hazel grouse favored evenly distributed, rich ground vegetation, optimally with a substantial portion of bilberry (Vaccinium myrtillus). The abundance of tall rowans (Sorbus aucuparia), basal branched trees and a high percentage of resource trees in the shrub layer were found to be further essential habitat elements. LiDAR was powerful in detecting important structural features, whereby the horizontal forest structure explained more of the deviance than the vertical forest structure. The most influential LiDAR variable was a measure of canopy height heterogeneity. Apart from indicating structurally rich forest stands, it probably also served as a proxy of compositional aspects such as the abundance of light demanding resource trees and shrubs or of a well developed ground vegetation. To support habitat management, we derived variable thresholds at a relevant spatial scale (1 km2) for forest management. Our study showed that LiDAR provides adequate means to assess structural habitat elements area-wide, thus overcoming the difficulties associated with sample based field assessments. The best model fit, however, was obtained by combining LiDAR variables with compositional variables from the field survey. Hence, we successfully bridged the gap between different ecologically relevant scales, such as habitat configuration and structure at the regional scale and the abundance of habitat elements at the local scale. The methods applied in this study can also be used to identify hotspots of forest structural richness, a matter of interest in the light of emerging attempts to conserve biodiversity in forests.