New concepts, models, and assessments of climate-wise connectivity

Abstract

Empirical studies and habitat suitability modeling project significant shifts in species distributions in response to climate change. Because habitat fragmentation can impede species range shifts, wildlife corridors may have increasing importance in enhancing climate resilience for species persistence. While habitat connectivity has been studied for over four decades, the design of connectivity specifically to facilitate species movement in response to climate change is a relatively new challenge. We conducted a systematic review of 116 relevant papers from 1996–2017. Research focused on assessing the utility of habitat connectivity for climate change adaptation by species (N = 29) and modeling and mapping climate-wise connectivity for planning purposes (N = 55). Others addressed fundamental questions of connectivity related to climate adaptation (N = 31). Based on empirical data and computer simulations examining species range shifts in response to climate change at leading edges of current distributions; it is clear that large protected areas connected through linkages, and stepping stones embedded in a permeable matrix promote population persistence and facilitate range expansion. We identified 13 approaches to modeling climate-wise connectivity based on either focal species or landscape structure. When prioritizing areas for connectivity conservation, approaches include focusing on connecting areas of low climate velocity, refugia, climate analogs, or linking current to future suitable habitats. Riparian corridors should be considered in connectivity plans because of their importance as natural movement corridors, climate gradients, and refugia. Guidance is provided on selecting the best methods for connectivity design depending on the objectives, available data, and landscape context. Future research needs to evaluate the functionality of climate-wise connectivity models for facilitating range shifts and compare connectivity outcomes across modeling approaches.