Liczner Amanda R, Pither Richard, Bennett Joseph R, Bowman Jeff, Hall Kimberly R, Fletcher Robert J, Ford Adam T, Michalak Julia L, Rayfield Bronwyn, Wittische Julian, Pither Jason
Okanagan Institute for Biodiversity, Resilience and Ecosystem Services University of British Columbia Kelowna British Columbia Canada.
National Wildlife Research Centre Environment and Climate Change Canada Ottawa Ontario Canada.
Ecol Evol. 2024 Sep 1;14(9):e70231. doi: 10.1002/ece3.70231. eCollection 2024 Sep.
Maintaining and restoring ecological connectivity will be key in helping to prevent and reverse the loss of biodiversity. Fortunately, a growing body of research conducted over the last few decades has advanced our understanding of connectivity science, which will help inform evidence-based connectivity conservation actions. Increases in data availability and computing capacity have helped to dramatically increase our ability to model functional connectivity using more sophisticated models. Keeping track of these advances can be difficult, even for connectivity scientists and practitioners. In this article, we highlight some key advances from the past decade and outline many of the remaining challenges. We describe the efforts to increase the biological realism of connectivity models by, for example, isolating movement behaviors, population parameters, directional movements, and the effects of climate change. We also discuss considerations of when to model connectivity for focal or multiple species. Finally, we reflect on how to account for uncertainty and increase the transparency and reproducibility of connectivity research and discuss situations where decisions may require forgoing sophistication for more simple approaches.
维持和恢复生态连通性对于预防和扭转生物多样性丧失至关重要。幸运的是,在过去几十年里开展的越来越多的研究增进了我们对连通性科学的理解,这将有助于为基于证据的连通性保护行动提供信息。数据可用性和计算能力的提高极大地增强了我们使用更复杂模型来模拟功能连通性的能力。即使对于连通性科学家和从业者来说,跟踪这些进展也可能很困难。在本文中,我们重点介绍过去十年的一些关键进展,并概述许多仍然存在的挑战。我们描述了通过例如分离运动行为、种群参数、定向运动和气候变化的影响来提高连通性模型生物学真实性的努力。我们还讨论了何时为重点物种或多个物种模拟连通性的考量因素。最后,我们思考如何应对不确定性并提高连通性研究的透明度和可重复性,并讨论在哪些情况下决策可能需要放弃复杂性而采用更简单的方法。