Spatial ecology of conflicts: unravelling patterns of wildlife damage at multiple scales

The Spatial Ecology of Nuisance Crocodiles: Movement Patterns of Relocated American Crocodiles (Crocodylus acutus) in Guanacaste, Costa Rica

Anthropogenic alterations of the environment have increased, highlighting the need for human-wildlife coexistence and conflict mitigation. Spatial ecology, and the use of passive satellite movement technology in particular, has been used to identify patterns in human-wildlife conflict as a function of shared resources that present potential for dangerous situations. Here, we aim to remotely identify patterns indicative of human-crocodile conflict in Guanacaste, Costa Rica by exploring site fidelity and diverse modes of movement (i.e., land and water) across space between nuisance (relocated) and non-nuisance (wild) crocodiles. Advanced satellite remote sensing technology provided near-constant movement data on individuals at the regional scale. Telonics Iridium SeaTrkr-4370-4 transmitters were used with modified crocodilian fitting. Results indicate that relocated crocodiles exhibited large-scale movements relative to wild crocodiles. Nuisance relocated crocodiles either returned to the area of nuisance or potentially attempted to in short time frames. The results presented here highlight the need for alternative management strategies that facilitate relocation efficacy.

Identifying the Risk Regions of Wild Boar (Sus scrofa) Incidents in China

The objectives of this study were to identify the risk regions of wild boar incidents in China and to draw a risk map. Risk maps can be used to plan the prioritization of preventive measures, increasing management effectiveness from both a short- and a long-term perspective. We used a web crawler (web information access technology) to obtain reports of wild boar incidents from China's largest search engine (Baidu) and obtained 196 valid geographic locations of wild boar incidents from the reports. Subsequently, a system of environmental variables-with climate, topography, landscape, and human disturbance as the main variable types-was constructed, based on human-land-system thinking. Finally, the Maxent model was applied to predict the risk space of wild boar incidents in China by integrating the geographic location information for wild boar incidents with the environmental variables. We observed that the types of environmental variables that contributed to wild boar incidents were in the descending order of climate (40.5%) > human disturbance (25.2%) > landscape (24.4%) > topography (9.8%). Among the 14 environmental variables, annual precipitation, the GDP index, and the mean annual temperature were the main environmental variables. The distance from woodland, distance from cultivated land, and elevation were the secondary environmental variables. The response curves of the environmental variables demonstrated that the highest probability of wild boar incidents occurred when the annual average temperature was 16 °C, the annual precipitation was 800 mm, and the altitudes were 150 m and 1800 m. The probability of wild boar incidents decreased with an increase in the distance from cultivated and forested land, and increased sharply and then levelled off with an increase in the GDP index. Approximately 12.18% of China was identified as being at a high risk of wild boar incidents, mainly on the eastern side of the Huhuanyong Line.

Planning for wolf-livestock coexistence: landscape context predicts livestock depredation risk in agricultural landscapes

Extensive pastoral livestock systems in Central Europe provide multiple ecosystem services and support biodiversity in agricultural landscapes but their viability is challenged by livestock depredation (LD) associated with the recovery of wolf populations. Variation in the spatial distribution of LD depends on a suite of factors, most of which are unavailable at the appropriate scales. To assess if LD patterns can be predicted sufficiently with land use data alone at the scale of one federal state in Germany, we employed a machine-learning-supported resource selection approach. The model used LD monitoring data, and publicly available land use data to describe the landscape configuration at LD and control sites (resolution 4 km * 4 km). We used SHapley Additive exPlanations to assess the importance and effects of landscape configuration and cross-validation to evaluate the model performance. Our model predicted the spatial distribution of LD events with a mean accuracy of 74%. The most influential land use features included grassland, farmland and forest. The risk of livestock depredation was high if these three landscape features co-occurred with a specific proportion. A high share of grassland, combined with a moderate proportion of forest and farmland, increased LD risk. We then used the model to predict the LD risk in five regions; the resulting risk maps showed high congruence with observed LD events. While of correlative nature and lacking specific information on wolf and livestock distribution and husbandry practices, our pragmatic modelling approach can guide spatial prioritisation of damage prevention or mitigation practices to improve livestock-wolf coexistence in agricultural landscapes.

Patterns of livestock loss associated with a recolonizing wolf population in Germany

Predation on livestock presents a daunting challenge for human–carnivore coexistence in agricultural landscapes. In Germany, the recolonization of wolves is ongoing and its consequences are insufficiently understood. Knowledge about which livestock species are susceptible to wolf predation, which farm types are predisposed to attacks by wolves, and when predation on livestock occurs is valuable for mitigating stakeholder conflicts. To this end, we analyzed 14 years of monitoring data and assessed the livestock prey spectrum, identified correlates between predation on livestock, farm type and livestock category, and described temporal patterns of livestock loss caused by a recolonizing wolf population in the state of Brandenburg (Germany). Among a total of 1387 recorded cases, 42% were unequivocally attributed to wolves (SCALP criteria C1 and C2) and 12% of cases were not caused by wolves. The number of head of livestock killed during a single wolf attack was mediated by farm type and livestock species; losses per event were greater in full-time farms vs. other farm types and greater in sheep, farmed deer and other livestock species, compared to cattle. While sheep were the most commonly killed livestock species, the increase in wolf territories over the investigation period was associated with a widening of the domestic prey species spectrum. Count regression models provided evidence for the increasing frequency of predation events over the 14-year period, along with an exponential increase in wolf territories. Predation on livestock occurred throughout the year, yet seasonality of events was evident and differed across livestock categories. Predation on sheep peaked in the fall, coinciding with the post-weaning period of wolf offspring. Predation on cattle peaked in the spring, coinciding with the cattle calving period. These results call for renewed investment in the implementation of prevention methods for all susceptible domestic species, particularly during times of elevated predation risk.

Assessing the spatial-temporal patterns of conflicts between humans and Asiatic black bears (Ursus thibetanus) around the Gaoligongshan Nature Reserve, China

Conflicts between humans and Asiatic black bears (Ursus thibetanus) are widespread in Asia and pose challenges to human-bear coexistence. Identifying effective mitigation measures requires a thorough understanding of human-bear conflicts (HBC). We assessed spatial-temporal patterns of HBC and their impact factors around the Baoshan Section of the Gaoligongshan Nature Reserve (GNNR) between 2012 and 2020. The results suggested that crop raiding by bears occurred most commonly, followed by beehive loss, livestock depredation, and human casualties. HBC hotspots occurred near the protected area where local people frequently encountered bears. The landscapes with lower elevation and human density were at higher risk of HBC. Furthermore, villages with more fragmented forests or less fragmented croplands were more vulnerable to HBC. The differences in agricultural structures contributed to the diverse composition of HBC between the two regions. In addition, crop raiding by bears decreased significantly, probably due to the changing landscape composition and configuration derived from human behaviors, yet livestock depredation and beehive loss increased. Our findings indicated the complex interrelationship between the environment, bears, and humans, which could guide the implementation of mitigation measures. We recommend multiple approaches based on a social-ecological system to mitigate HBC.

Spatial ecology of conflicts: unravelling patterns of wildlife damage at multiple scales

Human encroachment into natural habitats is typically followed by conflicts derived from wildlife damage to agriculture and livestock. Spatial risk modelling is a useful tool to gain the understanding of wildlife damage and mitigate conflicts. Although resource selection is a hierarchical process operating at multiple scales, risk models usually fail to address more than one scale, which can result in the misidentification of the underlying processes. Here, we addressed the multi-scale nature of wildlife damage occurrence by considering ecological and management correlates interacting from household to landscape scales. We studied brown bear (Ursus arctos) damage to apiaries in the North-eastern Carpathians as our model system. Using generalized additive models, we found that brown bear tendency to avoid humans and the habitat preferences of bears and beekeepers determine the risk of bear damage at multiple scales. Damage risk at fine scales increased when the broad landscape context also favoured damage. Furthermore, integrated-scale risk maps resulted in more accurate predictions than single-scale models. Our results suggest that principles of resource selection by animals can be used to understand the occurrence of damage and help mitigate conflicts in a proactive and preventive manner.