Permeability of artificial barriers (fences) for wild boar (Sus scrofa) in Mediterranean mixed landscapes

Abstract

BACKGROUND: Fences are one of the most widespread manmade features in nature, constituting an artificial limitation to the movement of wildlife. To date, their effects on wildlife behavior have been understudied but this knowledge is required to design effective management procedures. Using 21 GPS-monitored wild boar, we evaluated the permeability of different types of fences and described temporal patterns and spatial hotspots for crossing events. A fence's permeability was inferred by the crossing success, i.e., the number of times that animals crossed a barrier vs the number of times they did not cross. The vulnerability of fences at watercourses was explored by assessing whether the frequency of crossings was higher around watercourse intersections than expected by chance.RESULTS: Well-maintained big game proof fences were the most effective in reducing successful wild boar crossings; they were, on average, 30% more efficient than livestock type fences. Crossing success was higher for males than females and during the food shortage period than in the food abundance period. The frequency of crossings around watercourses was higher than expected by chance, especially in moderately and well-maintained big game proof type fences.CONCLUSION: While no fence type was 100% wild boar proof, well-maintained big game proof fences substantially constrained the movement of boar. However, they are vulnerable around watercourses. Managing the conflicts in which this species is involved, such as shared infections and agricultural damage, would require fences that are even more effective than the ones analyzed here, ideally in conjunction with other preventive actions.

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

“…Fence interactions by springbok and eland were dominantly quick, which aligns well with other studies analyzing animalfence interactions based on GPS tracking (Xu et al, 2021;Laguna et al, 2022). In contrast, kudu mostly stayed longer at fences either during stay or trace interactions.…”

“…This habitat configuration might have led kudu, whose home ranges were close to the EF, more towards this fence during foraging movements. Overall, our classification of animal-fence interaction types (see Xu et al, 2021;Laguna et al, 2022) allowed us to identify differences in fence interaction between species with implications for fence management and fence gap planning (cf. Hering et al, 2022).…”

“…Thus, such gaps provide an ideal setup to study the direct effects of fences and fence gaps on animal movements across spatial and temporal scales. While largescale migratory movements of ungulates triggered by seasonal changes in food availability lead to periodic peaks in wildlifefence interactions (e.g., Xu et al, 2021;Hering et al, 2022), many interactions occur daily when home ranges contain fences (e.g., Laguna et al, 2022). At the home range scale, larger herbivores (>1 kg) are severely affected since many species travel long distances to reach forage and water resources (e.g., Bartlam-Brooks et al, 2011;Naidoo et al, 2022).…”

“…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.…”

Don't stop me now: Managed fence gaps could allow migratory ungulates to track dynamic resources and reduce fence related energy loss

“…We chose a 50 m buffer to account for the inaccuracy of the geo-data (fence positions and GPS localizations) and, simultaneously, to avoid misclassification of fence interactions due to a too broad buffer. Further, the 50 m buffer assured comparability to other studies using a similar approach (Xu et al, 2021;Laguna et al, 2022). We calculated the distance to the actual fence-line for each localization.…”