Assessing biodiversity by remote sensing in mountainous terrain: the potential of LiDAR to predict forest beetle assemblages.
Abstract
Effective biodiversity management can only be implemented if data are available on assemblage-environment relationships. The level of detail needs to be relevant to the scale of planning and decision making. A number of remote-sensing methods are available, but there are few studies that link information collected at both landscape and local scales. This is particularly true for arthropods even though these organisms are ecologically very important. We assessed the predictive power of habitat variables measured by airborne laser scanning (light detection and ranging; LiDAR) to model the activity, richness and composition of assemblages of forest-dwelling beetles. We compared the results with data acquired using conventional field methods. We sampled beetles with pitfall traps and flight-interception traps at 171 sampling stations along an elevation gradient in a montane forest. We found a high predictive power of LiDAR-derived variables, which captured most of the predictive power of variables measured in ground surveys. In particular, mean body size and species composition of assemblages showed considerable predictability using LiDAR-derived variables. The differences in the predictability of species richness and diversity of assemblages between trap types can be explained by sample size. We expect predictabilities with R2 of up to 0.6 for samples with 250 individuals on average. The statistical response of beetle data and the ecological interpretability of results showed that airborne laser scanning can be used for cost-effective mapping (LiDAR:field survey:beetles 15:100:260 Euro ha-1) of biodiversity even in remote mountain areas and in structurally complex habitats, such as forests. Synthesis and applications. The strong relationship between characteristics of beetle assemblages to variables derived by laser scanning provides an opportunity to link data from local ground surveys of hyperdiverse taxa to data collected remotely at the landscape scale. This will enable conservation managers to evaluate habitats, define hotspots or map activity, richness and composition of assemblages at scales relevant for planning and management. In addition to the large area that can be sampled remotely, the grain of the data allows a single tree to be identified, which opens up the possibility of planning management actions at local scales.