OOS 36-4 - Predicting forest insect assemblages from helicopter:  An application of airborne lidar

Thursday, August 6, 2009: 9:00 AM
Mesilla, Albuquerque Convention Center
Jorg Muller, Nationalpark Bayerischer Wald and Roland Brandl, Faculty of Biology, University of Marburg, Department of Animal Ecology, Marburg, Germany
Background/Question/Methods

Effective conservation strategies for biodiversity can only be implemented if data on diversity-environment relationships are available. However, such assessments and analyses for arthropod diversity are still scarce, even though these organisms are ecologically important and diverse. We assessed the predictive power of habitat variables measured by using airborne laser scanning (LiDAR) for assemblages of forest-dwelling beetles, and compared the results with data acquired using conventional field methods. We sampled forest beetle assemblages with pitfall traps and flight interception traps at sampling stations in a temperate mountain forest.

Results/Conclusions

We found a high predictive power of LiDAR-derived variables, which captured most of the predictive power of variables measured in ground surveys. Beetle assemblages of both trap types and mean body size of beetles in pitfall traps could be predicted with R² > 0.2. The prediction of the number of species and individuals was better for beetles in flight-interception traps. Most differences in predictability can be explained by sample size; we expect predictabilities with an R² of up to 0.5 for samples >250 individuals. The statistical response of our beetle data and the ecological interpretability of our results show that airborne laser scanning can rapidly assess biodiversity in ecological studies even in remote mountain areas and in structurally complex habitats, such as forests.

The strong relationship of beetle assemblages and different functional groups to variables derived by airborne laser scanning opens promising opportunities to incorporate fine-scale assessments of forest landscape variation into environmental monitoring and management, especially in remote areas.

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