| Summary: | Predation by grey wolves Canis lupus has been identified as an important cause of boreal woodland caribou Rangifer tarandus caribou mortality, and it has been hypothesized that wolf use of human-created linear features such as seismic lines, pipelines and roads increases movement, resulting in higher kill rates. We tested if wolves select linear features and whether movement rates increased while travelling on linear features in north-eastern Alberta and north-western Saskatchewan using 5-min GPS (Global Positioning System) locations from twenty-two wolves in six packs. Wolves selected all but two linear feature classes, with the magnitude of selection depending on feature class and season. Wolves travelled two to three times faster on linear features compared to the natural forest. Increased average daily travelling speed while on linear features and increased proportion of steps spent travelling on linear features increased net daily movement rates, suggesting that wolf use of linear features can increase their search rate. Synthesis and applications. Our findings that wolves move faster and farther on human-created linear features can inform mitigation strategies intended to decrease predation on woodland caribou, a threatened species. Of the features that can realistically be restored, mitigation strategies such as silviculture and linear deactivation (i.e. tree-felling and fencing) should prioritize conventional seismic lines (i.e. cleared lines used for traditional oil and gas exploration) and pipelines, as they were selected by wolves and increased travelling speed, before low-impact seismic lines. Zip file of RSF Data and Step DataData collected using GPS collars. RSF data includes 10 random/GPS location. Step data are lines connecting sequential 5 minute locations. RSF data: Data collected using GPS collars. Fields include a unique ID field (SEQID), animal identifier field (DEVICE_ID), season, year, if the GPS location is on (1) or off (0) a linear feature (LF), what type of feature (LF_TYPE), ...
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