景观连通性
生物扩散
网络分析
计算机科学
生态学
人口
环境资源管理
地理
数据科学
社会学
工程类
环境科学
生物
电气工程
人口学
作者
Brett G. Dickson,Christine M. Albano,Ranjan Anantharaman,Paul Beier,Joe Fargione,Tabitha A. Graves,Miranda Gray,Kimberly R. Hall,Josh Lawler,Paul B. Leonard,Caitlin E. Littlefield,Meredith L. McClure,John Novembre,Carrie A. Schloss,Nathan H. Schumaker,Viral B. Shah,David M. Theobald
摘要
Conservation practitioners have long recognized ecological connectivity as a global priority for preserving biodiversity and ecosystem function. In the early years of conservation science, ecologists extended principles of island biogeography to assess connectivity based on source patch proximity and other metrics derived from binary maps of habitat. From 2006 to 2008, the late Brad McRae introduced circuit theory as an alternative approach to model gene flow and the dispersal or movement routes of organisms. He posited concepts and metrics from electrical circuit theory as a robust way to quantify movement across multiple possible paths in a landscape, not just a single least-cost path or corridor. Circuit theory offers many theoretical, conceptual, and practical linkages to conservation science. We reviewed 459 recent studies citing circuit theory or the open-source software Circuitscape. We focused on applications of circuit theory to the science and practice of connectivity conservation, including topics in landscape and population genetics, movement and dispersal paths of organisms, anthropogenic barriers to connectivity, fire behavior, water flow, and ecosystem services. Circuit theory is likely to have an effect on conservation science and practitioners through improved insights into landscape dynamics, animal movement, and habitat-use studies and through the development of new software tools for data analysis and visualization. The influence of circuit theory on conservation comes from the theoretical basis and elegance of the approach and the powerful collaborations and active user community that have emerged. Circuit theory provides a springboard for ecological understanding and will remain an important conservation tool for researchers and practitioners around the globe.
科研通智能强力驱动
Strongly Powered by AbleSci AI