期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2020
卷号：117
期号：42
页码：26254-26262
DOI：10.1073/pnas.2001823117
出版社：The National Academy of Sciences of the United States of America
摘要：Tropical forest ecosystems are facing unprecedented levels of degradation, severely compromising habitat suitability for wildlife. Despite the fundamental role biodiversity plays in forest regeneration, identifying and prioritizing degraded forests for restoration or conservation, based on their wildlife value, remains a significant challenge. Efforts to characterize habitat selection are also weakened by simple classifications of human-modified tropical forests as intact vs. degraded, which ignore the influence that three-dimensional (3D) forest structure may have on species distributions. Here, we develop a framework to identify conservation and restoration opportunities across logged forests in Borneo. We couple high-resolution airborne light detection and ranging (LiDAR) and camera trap data to characterize the response of a tropical mammal community to changes in 3D forest structure across a degradation gradient. Mammals were most responsive to covariates that accounted explicitly for the vertical and horizontal characteristics of the forest and actively selected structurally complex environments comprising tall canopies, increased plant area index throughout the vertical column, and the availability of a greater diversity of niches. We show that mammals are sensitive to structural simplification through disturbance, emphasizing the importance of maintaining and enhancing structurally intact forests. By calculating occurrence thresholds of species in response to forest structural change, we identify areas of degraded forest that would provide maximum benefit for multiple high-conservation value species if restored. The study demonstrates the advantages of using LiDAR to map forest structure, rather than relying on overly simplistic classifications of human-modified tropical forests, for prioritizing regions for restoration.
