Where to Restore Ecological Connectivity? Detecting Barriers and Quantifying Restoration Benefits

Abstract

Landscape connectivity is crucial for many ecological processes, including dispersal, gene flow, demographic rescue, and movement in response to climate change. As a result, governmental and non-governmental organizations are focusing efforts to map and conserve areas that facilitate movement to maintain population connectivity and promote climate adaptation. In contrast, little focus has been placed on identifying barriers—landscape features which impede movement between ecologically important areas—where restoration could most improve connectivity. Yet knowing where barriers most strongly reduce connectivity can complement traditional analyses aimed at mapping best movement routes. We introduce a novel method to detect important barriers and provide example applications. Our method uses GIS neighborhood analyses in conjunction with effective distance analyses to detect barriers that, if removed, would significantly improve connectivity. Applicable in least-cost, circuit-theoretic, and simulation modeling frameworks, the method detects both complete (impermeable) barriers and those that impede but do not completely block movement. Barrier mapping complements corridor mapping by broadening the range of connectivity conservation alternatives available to practitioners. The method can help practitioners move beyond maintaining currently important areas to restoring and enhancing connectivity through active barrier removal. It can inform decisions on trade-offs between restoration and protection; for example, purchasing an intact corridor may be substantially more costly than restoring a barrier that blocks an alternative corridor. And it extends the concept of centrality to barriers, highlighting areas that most diminish connectivity across broad networks. Identifying which modeled barriers have the greatest impact can also help prioritize error checking of land cover data and collection of field data to improve connectivity maps. Barrier detection provides a different way to view the landscape, broadening thinking about connectivity and fragmentation while increasing conservation options.

Advances in restoration ecology: rising to the challenges of the coming decades

“…Landscape connectivity is increasingly seen as a key conservation and restoration goal, particularly as a strategy to allow biotic movement in response to changing environments (Roever et al 2013, Tambosi et al 2014, Okin et al 2015. Deciding what and where to restore is a key challenge for future landscapescale restoration efforts (McRae et al 2012, Torrubia et al 2014. Decisions may be aided by technological advances around, for instance, the application of climate velocity models in conjunction with more traditional ecological models to ascertain areas of potential future habitat suitability as targets for restoration efforts (Hamann et al 2015).…”

Amphibian decline, pond loss and reduced population connectivity under agricultural intensification over a 38 year period

“…In metapopulations, the long-term viability of species is dependent on dispersal-which is the successful breeding of an individual in a place other than where it was born. Practical nature conservation has often been focussed on enhancing key breeding sites, but there is increasing awareness of the need for protection and restoration of habitat corridors and other components in the environment that promote dispersal (Sutcliffe et al 2003;McRae et al 2012). An animal group in which metapopulation structure may be especially pronounced is the Holarctic amphibians, including frogs, toads, newts and salamanders.…”

Emerging Opportunities for Landscape Ecological Modelling

“…Notable advances have been made across landscape ecology and wider land systems science, and methods and findings continue to improve in sophistication and insight [12][13][14]. One of the greatest contributions of landscape ecological modelling has been informing spatial planning for conservation, where it has offered an important complementary approach to classical metapopulation theory by explicitly incorporating the contribution of the matrix (the environment between the habitat patches) [15][16][17][18][19]. However, this computational approach is not free of challenges.…”