Caught in the mesh: roads and their network‐scale impediment to animal movement

Abstract

Roads have a pervasive multi‐faceted influence on ecosystems, including pronounced impacts on wildlife movements. In recognition of the scale‐transcending impacts of transportation infrastructure, ecologists have been encouraged to extend the study of barrier impacts from individual roads and animals to networks and populations. In this study, we adopt an analytical representation of road networks as mosaics of landscape tiles, separated by roads. We then adapt spatial capture–recapture analysis to estimate the propensity of wildlife to stay within the boundaries of the road network tiles (RNTs) that hold their activity centres. We fit the model to national non‐invasive genetic monitoring data for brown bears Ursus arctos in Sweden and show that bears had up to 73% lower odds of using areas outside the network tile of their home range centre, even after accounting for the effect of natural barriers (major rivers) and the decrease in utilization with increasing distance from a bear's activity centre. Our study highlights the pronounced landscape‐level barrier effect on wildlife mobility and, in doing so, introduces a novel and flexible approach for quantifying contemporary fragmentation from the scale of RNTs and individual animals to transportation networks and populations.

Effects of habitat quality and access management on the density of a recovering grizzly bear population

“…Increased human access into the backcountry, most often through resource extraction roads, increases human–bear conflict and thus increases bear mortality in both hunted and unhunted populations (Falcucci et al., ; McLellan, ; McLellan & Shackleton, ; Nielsen, Herrero, et al., ). In addition to direct mortality near roads, perceived risks by bears may decrease foraging efficiency (Hertel et al., ), and alter activity patterns (Martin, Basille, Moorter, Kindberg, & Swenson, ; McLellan & Shackleton, ; Northrup et al., ) and movements (Bischof, Steyaert, & Kindberg, ; Roever, Boyce, & Stenhouse, ), thus potentially reducing habitat effectiveness. Our work builds on these mechanisms and links the effects of roads to reduced grizzly bear density.…”

Genetic tagging in the Anthropocene: scaling ecology from alleles to ecosystems

“…For example, Fuller et al (2015) extended their SCR density-habitat model to estimate limits to mink connectivity, uncovering strong connectivity among stream networks and the negative impacts of human development. Similarly, Bischof et al (2017) show that brown bears place home range centers in largely unroaded landscapes and resist using habitat that involves crossing a road. Bischof's results are especially striking due to increasing global road densities and the fragmentation of wilderness (Ibisch et al 2016, Potapov et al 2017).…”

“…), and roads can greatly reduce connectivity for many taxa (Holderegger and Di Giulio , Bischof et al. ). Researchers are seeking rapid and cost‐effective approaches to quantify factors influencing animal movement and to identify ways to mitigate the negative effects of roads and other disturbances.…”

“…Similarly, Bischof et al. () show that brown bears place home range centers in largely unroaded landscapes and resist using habitat that involves crossing a road. Bischof's results are especially striking due to increasing global road densities and the fragmentation of wilderness (Ibisch et al.…”

A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture

“…Spatial capture–recapture (SCR) models are now routinely used in ecological studies to estimate density of animal populations using encounter history data from recognizable individuals (Borchers & Efford, ; Efford, ; Royle & Young, ). More recently, extensions of SCR have been used to investigate many aspects of spatial ecology (Royle, Fuller, & Sutherland, ), including dispersal and survival (Ergon & Gardner, ; Schaub & Royle, ), landscape connectivity (Sutherland, Fuller, & Royle, ), habitat fragmentation (Bischof, Steyaert, & Kindberg, ), landscape conservation management (Morin, Fuller, Royle, & Sutherland, ), and epidemiology (Muneza et al, ). The basic principle of SCR models is the use of spatial patterns of individual detection/non‐detections to estimate detection probability as a function of the distance from individual activity centers.…”