Identifying differences in roadless areas in Canada based on global, national, and regional road datasets

Abstract

Roads are an overwhelming component of the global human footprint and their absence helps identify intact areas with high ecological value. Road‐free areas are decreasing globally, making accurate estimation of their location and size of great importance. Identification of such regions requires accurate data, but substantial variability exists in road network datasets created and maintained at different spatial scales. We compared estimates of road length, density, and roadless areas across Canada, which contains a high proportion of the world's remaining undisturbed and road‐free areas. Global‐ and national‐scale datasets included, on average, only 11%–14% of roads represented in regional‐scale data or volunteered geographic information (VGI), with the most pronounced differences in less‐developed areas. Regional‐scale datasets, with the lowest estimates of amount of roadless area and smallest mean roadless patch size, are likely the most complete road datasets but are not available for all jurisdictions, limiting their national‐scale utility. VGI provides a national‐scale alternative but still lacks many low‐use roads. Available global and national datasets have insufficient information for accurate assessments of roadless areas in Canada, which will require detailed, consistent subnational datasets assembled and maintained by each province and territory in a coordinated fashion to achieve national coverage.

Predicting areas important for ecological connectivity throughout Canada

“…Eight of those layers were from the Canadian Human Footprint (CHF) [ 36 ] and included built environments, nighttime lights, croplands, pasturelands, dams and reservoirs, mining, oil and gas, and forestry areas. We used a recently developed national road layer for Canada [ 37 ] that includes resource-access roads, along with a national railway layer. For the buffer area outside of Canada, we used six layers from the Global Human Footprint [ 38 ] (GHF): built environments, nighttime lights, croplands, pasturelands, railways, and roads.…”

Integrating carbon stocks and landscape connectivity for nature‐based climate solutions

“…Circuit theory models require a movement cost surface as an input, which represents anthropogenic and natural landscape features by the degree to which they impede or facilitate movement of animals. The cost surface used by Pither et al ( 2021 ) was produced using the most up to date spatial data including the Canadian human footprint (Hirsh‐Pearson et al, 2022 ) and a recently developed national road layer (Poley et al, 2022 ). In their study, the authors modeled terrestrial connectivity (i.e., landscape connectivity relevant to terrestrial, non‐volant fauna), such that natural features like forests and wetlands represented a low cost to movement, while roads and cities as well as large water bodies and mountains represented a high cost to movement (for more details see Pither et al, 2021 ).…”

Guidelines for using A global standard for the identification of Key Biodiversity Areas: version 1.2

“…Preliminary analyses may be based on readily available global and/ or preferably regional-level cumulative human pressure datasets (e.g., roads and infrastructure; Sanderson et al, 2002;Venter et al, 2016;Poley et al, 2022).…”