Is it the road or the fence? Influence of linear anthropogenic features on the movement and distribution of a partially migratory ungulate

Abstract

Background Anthropogenic linear features change the behavior and selection patterns of species, which must adapt to these ever-increasing features on the landscape. Roads are a well-studied linear feature that alter the survival, movement, and distribution of animals. Less understood are the effects of fences on wildlife, though they tend to be more ubiquitous across the landscape than roads. Even less understood are potential indirect effects when fences are found in tandem with roads along transportation corridors. Methods We assessed how the spatial configuration of fences and roads effect the movement (crossing effect) and distribution (proximity effect) of a partially migratory pronghorn population (Antilocapra americana) on the grasslands of southern Alberta, Canada. We used data from 55 collared pronghorn within a step-selection function framework to assess the influence of 4 linear features: (1) pasture fences, (2) roads not fenced, (3) roads fenced on one side, and (4) roads fenced on both sides on the selection pattern of migratory and resident animals. We examined whether steps along a movement pathway (i.e., crossing effect) were influenced by the type of linear feature animals attempted to cross and, whether these features affected the distribution of pronghorn (i.e., proximity effect) across the landscape. Results The top model for crossing effect for both movement tactics contained all 4 linear features and land cover. Regression coefficients were negative for all linear features, indicating that individuals were less likely to chose steps that crossed linear features. For the proximity effect, migrant animals avoided all linear features except roads fenced on both sides, where they selected areas closer to this feature. Resident animals, on the other hand, were found closer to pasture fences but further from roads without fences. Conclusions Our results indicate that both fences and roads are indirectly affecting pronghorn resource use spatially and behaviorally, whether each linear feature is found separately or in tandem. Modifying existing fences and roads to account for responses to these distinct linear features could facilitate more successful crossing opportunities and/or shifts in distribution. Allowing pronghorn to freely move across the landscape will maintain functional connectivity to ensure population persistence of this endemic ungulate.

Effects of fences and fence gaps on the movement behavior of three southern African antelope species

“…In the case of non-cross encounters, a crossing might not have been intended, and the animals performed more feeding bouts already, leading to lower speeds. Nevertheless, in both cases, the increased speed during the interaction suggests an apparent alteration of the ungulates' movement behavior, which has been demonstrated in other studies as well (e.g., You et al, 2013;Prokopenko et al, 2017;Jones et al, 2022). Fence interactions of the type stay included sequences of low speeds by definition; hence, we found decreased speeds at fences during such events in many cases.…”

“…Our results highlight that a systematic review of animal-fence interaction type classification in terms of the tracking data's temporal resolution, as well as a consistent and appropriate nomenclature, is needed to improve the comparability of studies. Many studies show that linear, anthropogenic landscape features, such as roads and fences, affect the movements of terrestrial animals (e.g., Jones et al, 2022) and negatively influence space use (e.g., Robb et al, 2022). Individual movement strategies, the spatial density of such features, and their permeability determine the severity of the features' effects on the animals.…”

“…This risk manifests indirectly by either hindering animal access to crucial resources (e.g., Spinage, 1992) or artificially increasing the hunting success of predators near fence lines (e.g., Dupuis-Desormeaux et al, 2016b;Prugh et al, 2019), or directly through entanglement (e.g., Mbaiwa and Mbaiwa, 2006;Safner et al, 2021). Despite much emphasis on such potentially fatal consequences, the impact of most fence interactions is more nuanced: For instance, altered movement behavior leads to overexploitation of food resources [Loxodonta africana (elephant) in southern Africa, Vanak et al, 2010], narrowed or obstructed corridors during seasonal migrations [Odocoileus hemionus (mule deer) in North America, Sawyer et al, 2013], reduced foraging periods [Procapra przewalskii (Przewalski's gazelle) in East Asia, You et al, 2013], or an alteration of space use in the proximity of fences [Antilocapra americana (pronghorn) in North America, Jones et al, 2022].…”

“…Therefore, improved knowledge of the direct effects of fences and passable fence sections (from here on referred to as "fence gaps") on ungulate movement becomes essential. Fence and fence gap planning, as well as policymaking, require a fundamental understanding of how ungulates interact with fences, how locomotion is affected, and what ultimate consequences emerge, especially concerning the energetic loss faced by the animals (e.g., Hering et al, 2022;Jones et al, 2022) and consequences for the populations (e.g., Van Moorter et al, 2020). One step towards disentangling the complexity of fence effects on wildlife is to evaluate the behavior of ungulates at fences and fence gaps.…”

“…Xu et al (2021) quantified pronghorn and mule deer fence interactions based on hourly GPS-telemetry data in the rangelands of Wyoming, USA, and identified primarily quick interactions ("cross" or "bounce"), even though "average movement", "tracing", "back-and-forth", and "trapped" behaviors were also reported. While these advances contributed significantly to the fence ecology framework suggested by McInturff et al (2020), studies examining the alteration of ungulate movements directly at fences are scarce (e.g., Goddard et al, 2001;Gulsby et al, 2011;Nandintsetseg et al, 2019;Laguna et al, 2022;Jones et al, 2022). Recently, high-resolution animal tracking data with more than two localizations per hour have become available, allowing the derivation of improved statistics on animal-fence interactions.…”