Using LiDAR‐derived vegetation metrics for high‐resolution, species distribution models for conservation planning. Issue 3 (26th March 2013)

• Record Type: Journal Article
• Authors: Farrell, S. L.; Collier, B. A.; Skow, K. L.; Long, A. M.; Campomizzi, A. J.; Morrison, M. L.; Hays, K. B.; Wilkins, R. N.
• Is Part Of: Ecosphere. Volume 4:Issue 3(2013)
• Page Start: 1
• Page End: 18
• Publication Date: 2013-03-26
• Subjects: endangered species -- habitat occupancy -- resource selection -- species distribution models; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1890/ES12-000352.1
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract : Advances in remotely sensed data for characterizing habitat have enabled development of spatially explicit predictive species distribution models (SDM) that can be essential tools for management. SDMs commonly use coarse‐grain metrics, such as forest patch size or patch connectivity, over broad spatial extents. However, species distributions are likely driven in part by local, fine‐grained habitat conditions. Conservation and management are often planned and applied locally, where coarse predictions may be uninformative or not sufficiently precise. We investigated the integration of high‐resolution LiDAR (Light Detection and Ranging) with avian point sampling data to develop a detection‐corrected occupancy model to quantify habitat‐occurrence relationships for two species with different habitats: the endangered golden‐cheeked warbler ( Setophaga chrysoparia ) and black‐capped vireo ( Vireo atricapilla ) on a military installation in central Texas. We compared occupancy models that used only the more conventional, coarse remotely sensed metrics to models that also incorporated high‐resolution LiDAR‐derived metrics for vegetation height and canopy cover, to assess their use for predicting distributions. Models including LiDAR‐derived vegetation height and canopy cover metrics were competitive for both species, and models without LiDAR‐derived vegetation height had substantially lower model weights and explanatory strength. Area under curve estimates for the highest ranked models were high for warblers (0.864) and moderate for vireos (0.746). Using the best supported models for each species, we predicted the occurrence distribution for both species. The resulting predictions provide a decision support tool that enables assessment of the status, impacts, and mitigation of impacts to endangered species habitat on the installation due to land management and military training activities that is more standardized and accurate than current assessment approaches based on visual gestalt of habitat and expert opinion. Additionally, although previous species distribution models have been created for our focal species, most fail to match the grain and extent of most management actions or include local, fine‐grained metrics that influence distributions. In contrast, we demonstrate that use of LiDAR with species occurrence data can provide precision and resolution at a scale that is relevant ecologically and to the operational scale of most conservation and management actions.