Predicting areas important for ecological connectivity throughout Canada

Abstract

Governments around the world have acknowledged that urgent action is needed to conserve and restore ecological connectivity to help reverse the decline of biodiversity. In this study we tested the hypothesis that functional connectivity for multiple species can be estimated across Canada using a single, upstream connectivity model. We developed a movement cost layer with cost values assigned using expert opinion to anthropogenic land cover features and natural features based on their known and assumed effects on the movement of terrestrial, non-volant fauna. We used Circuitscape to conduct an omnidirectional connectivity analysis for terrestrial landscapes, in which the potential contribution of all landscape elements to connectivity were considered and where source and destination nodes were independent of land tenure. Our resulting map of mean current density provided a seamless estimate of movement probability at a 300 m resolution across Canada. We tested predictions in our map using a variety of independently collected wildlife data. We found that GPS data for individual caribou, wolves, moose, and elk that traveled longer distances in western Canada were all significantly correlated with areas of high current densities. The frequency of moose roadkill in New Brunswick was also positively associated with current density, but our map was not able to predict areas of high road mortality for herpetofauna in southern Ontario. The results demonstrate that an upstream modelling approach can be used to characterize functional connectivity for multiple species across a large study area. Our national connectivity map can help governments in Canada prioritize land management decisions to conserve and restore connectivity at both national and regional scales.

Using sentinel nodes to evaluate changing connectivity in a protected areas network

“…by the degree to which they facilitate or impede movement (McRae et al, 2008). For the following analyses, we used the 300-m resolution cost surface of Canada produced by Pither et al (2023), which classifies the landscape according to a cost scheme using 4 values including 1 (low cost), 10, 100, and 1000 (high cost) (Table 1). Bowman et al (2020) showed that current density maps are not sensitive to the absolute costs of a cost surface provided that costs are in the correct rank order.…”

“…This approach, which has been used in many other studies, models landscape connectivity based on degree of naturalness or human modification and makes the assumption that more natural landscapes are less costly for many animals to move through and better facilitate ecological processes (Krosby et al, 2015;Spencer et al, 2010;Theobald et al, 2012). Pither et al (2023) built their cost surface using the most up-to-date spatial data layers including the Canadian Human Footprint (Hirsh-Pearson et al, 2022) and an updated national road layer (Poley et al, 2022). We modified the 300-m cost surface of Pither et al (2023) by adding a fifth lowest cost, which we assigned to natural areas within protected area boundaries under the assumption that these areas are less costly to move through than natural areas outside park boundaries (Spencer et al, 2010; Table 1).…”

“…Pither et al (2023) built their cost surface using the most up-to-date spatial data layers including the Canadian Human Footprint (Hirsh-Pearson et al, 2022) and an updated national road layer (Poley et al, 2022). We modified the 300-m cost surface of Pither et al (2023) by adding a fifth lowest cost, which we assigned to natural areas within protected area boundaries under the assumption that these areas are less costly to move through than natural areas outside park boundaries (Spencer et al, 2010; Table 1). We reasoned that regulation of various human activities, such as resource extraction, hunting, and recreation, within protected areas as well as traditional land use practices and stewardship within Indigenous Protected and Conserved Areas (IPCAs) can provide benefits through decreased disturbance and mortality, allowing wildlife to move more easily through protected and conserved areas (Fryxell et al, 2020;Hebblewhite & Whittington, 2020;Indigenous Circle of Experts, 2018;Obbard et al, 2017;Schuster et al, 2019).…”

“…Often, source and destination nodes have been defined using a priori knowledge about core habitat patches that animals are most likely to move between and in the case of a park-to-park analysis, the parks serve as the nodes. More recently, omnidirectional methods have been developed which shift the nodes outside of the study area and are useful in cases where source and destination locations are unknown or for modelling landscape connectivity without reference to specific node locations (Koen et al, 2014;Phillips et al, 2021;Pither et al, 2023). Placing nodes within park boundaries is the most appropriate method for evaluating park-to-park connectivity; however, omnidirectional methods allow nodes to be independent of the parks network and therefore estimates of connectivity are repeatable over time.…”

“…We conducted these tests on smaller, but real-world landscapes using the 300-m resolution cost surface produced by Pither et al (2023). Next, we tested the ability of our method at a larger scale by modelling connectivity of the protected areas network in the province of Ontario, Canada.…”