Spatial ecology of conflicts: unravelling patterns of wildlife damage at multiple scales (original) (raw)
Related papers
A Framework for Estimating Human-Wildlife Conflict Probabilities Conditional on Species Occupancy
Frontiers in conservation science, 2021
Managing human-wildlife conflicts (HWCs) is an important conservation objective for the many terrestrial landscapes dominated by humans. Forecasting where future conflicts are likely to occur and assessing risks to lives and livelihoods posed by wildlife are central to informing HWC management strategies. Existing assessments of the spatial occurrence patterns of HWC are based on either understanding spatial patterns of past conflicts or patterns of species distribution. In the former case, the absence of conflicts at a site cannot be attributed to the absence of the species. In the latter case, the presence of a species may not be an accurate measure of the probability of conflict occurrence. We present a Bayesian hierarchical modeling framework that integrates conflict reporting data and species distribution data, thus allowing the estimation of the probability that conflicts with a species are reported from a site, conditional on the species being present. In doing so, our model corrects for both false-positive and false-negative conflict reporting errors. We provide study design recommendations using simulations that explore the performance of the model under a range of conflict reporting probabilities. We applied the model to data on wild boar (Sus scrofa) space use and conflicts collected from the Central Terai Landscape (CTL), an important tiger conservation landscape in India. We found that tolerance for wildlife was a predictor of the probability with which farmers report conflict with wild boars from sites not used by the species. We also discuss useful extensions of the model when conflict data are verified for potential false-positive errors and when landscapes are monitored over multiple seasons.
A landscape of overlapping risks for wolf-human conflict in Wisconsin, USA
Journal of Environmental Management, 2019
Managing risk requires an adequate understanding of risk-factors that influence the likelihood of a particular event occurring in time and space. Risk maps can be valuable tools for natural resource managers, allowing them to better understand spatial characteristics of risk. Risk maps can also support risk-avoidance efforts by identifying which areas are relatively riskier than others. However, risks, such as human-carnivore conflict, can be diverse, multi-faceted, and overlapping in space. Yet, efforts to describe risk typically focus on only one aspect of risk. We examined wolf complaints investigated in Wisconsin, USA for the period of 1999-2011. We described the spatial patterns of four types of wolf-human conflict: livestock depredation, depredation on hunting hounds, depredation on non-hound dogs, and human health and safety concerns (HHSC). Using predictive landscape models and discriminant functions analysis, we visualized the landscape of risk as a continuous surface of overlapping risks. Each type of conflict had its own unique landscape signature; however, the probability of any type of conflict increased closer to the center of wolf pack territories and with increased forest cover. Hunting hound depredations tended to occur in areas considered to be highly suitable wolf habitat, while livestock depredations occurred more regularly in marginal wolf habitat. HHSC and non-hound dog depredations were less predictable spatially but tended to occur in areas with low housing density adjacent to large wildland areas. Similar to other research evaluating the risk of human-carnivore conflict, our data suggests that human-carnivore conflict is most likely to occur where humans or human property and large carnivores co-occur. However, identifying areas of co-occurrence is only marginally valuable from a conservation standpoint and could be described using spatially-explicit human and carnivore data without complex analytical approaches. These results challenge our traditional understanding of risk and the standard approach used in describing risk. We suggest that a more comprehensive understanding of the risk of human-carnivore conflict can be achieved by examining the spatial and non-spatial factors influencing risk within areas of co-occurrence and by describing the landscape of risk as a continuous surface of multiple overlapping risks.
Innovative conservation tools are greatly needed to reduce livelihood losses and wildlife declines resulting from human–carnivore conflict. Spatial risk modeling is an emerging method for assessing the spatial patterns of predator–prey interactions , with applications for mitigating carnivore attacks on livestock. Large carnivores that ambush prey attack and kill over small areas, requiring models at fine spatial grains to predict livestock depredation hot spots. To detect the best resolution for predicting where carnivores access livestock, we examined the spatial attributes associated with livestock killed by tigers in Kanha Tiger Reserve, India, using risk models generated at 20, 100, and 200-m spatial grains. We analyzed land-use, human presence, and vegetation structure variables at 138 kill sites and 439 random sites to identify key landscape attributes where livestock were vulnerable to tigers. Land-use and human presence variables contributed strongly to predation risk models, with most variables showing high relative importance (≥0.85) at all spatial grains. The risk of a tiger killing livestock increased near dense forests and near the boundary of the park core zone where human presence is restricted. Risk was nonlinearly related to human infrastructure and open vegetation, with the greatest risk occurring 1.2 km from roads, 1.1 km from villages, and 8.0 km from scrubland. Kill sites were characterized by denser, patchier, and more complex vegetation with lower visibility than random sites. Risk maps revealed high-risk hot spots inside of the core zone boundary and in several patches in the human-dominated buffer zone. Validation against known kills revealed predictive accuracy for only the 20 m model, the resolution best representing the kill stage of hunting for large carnivores that ambush prey, like the tiger. Results demonstrate that risk models developed at fine spatial grains can offer accurate guidance on landscape attributes livestock should avoid to minimize human–carnivore conflict.
2015
Predation risk is known to evoke behavioural responses in prey animals, and prey are often faced with a trade-off between lowering their risk to predation and acquiring resources. This situation becomes more complex in a multi-predator landscape, especially if those predators employ different hunting strategies, and induce different spatial patterns of risk. In this study, the spatial patterns of predation risk that roe deer face from humans, as well as their natural predators, lynx and wolves, were identified. Using the natural experiment provided by the return of large predators and the coinciding decline in hunting mortality, the behavioural responses of roe deer to shifting patterns of predation risk were examined. Using predation and hunting mortality locations, combined with used locations from the same 149 roe deer, predation risk to each predator was related to habitat and infrastructure attributes. Mostly in line with predictions, agricultural lands were found to present th...
The Sustainability of Agro-Food and Natural Resource Systems in the Mediterranean Basin, 2015
Over the last few years, wildlife damages to the agricultural sector have shown an increasing trend at the global scale. Fragile rural areas are more likely to suffer because marginal lands, which have little potential for profit, are being increasingly abandoned. Moreover, public administrations have difficulties to meet the growing requests for crop damage compensations. There is therefore a need to identify appropriate measures to control this growing trend. The specific aim of this research is to understand this phenomenon and define specific and effective action tools. In particular, the proposed research involves different steps that start from the historic analysis of damages and result in the mapping of risk levels using different tests (ANOVA, PCA and spatial correlation) and spatial models (MCE-OWA). The subsequent possibility to cluster risk results ensures greater effectiveness of public actions. The results obtained and the statistical consistency of applied parameters ensure the strength of the analysis and of costeffectiveness parameters.
Patterns of Human–Brown Bear Conflict in the Urban Area of Brașov, Romania
Sustainability
Human–bear conflicts are increasing in number due to deforestation, urban expansion, tourism, and habitat invasion by humans. Our study was conducted in Brașov, a picturesque city in central Romania. Brașov is surrounded by forests and has significant tourist traffic, but also much uncollected garbage and many garbage containers, which attract brown bears. We recorded human–bear conflicts in four districts (Răcădău, the Historic Centre, Noua, and Gară) between 2004 and 2018, finding 55 cases in total, of which in 19 cases involving people, 4 people were killed and 32 were injured. In 36 cases, there were no human victims. We mapped the locations of human–bear conflicts and garbage containers, then analysed their space–time location and human victims in terms of behavioural factors. The altitudes at which brown bears were identified ranged from 580 to 790 m, whereas bears were found in the city at distances of between 100 and 2600 m from the forest. The highest frequency of human–bea...
Ecological implications from spatial patterns in human-caused brown bear mortality
Wildlife Biology, 2016
Humans are important agents of wildlife mortality, and understanding such mortality is paramount for effective population management and conservation. However, the spatial mechanisms behind wildlife mortality are often assumed rather than tested, which can result in unsubstantiated caveats in ecological research (e.g. fear ecology assumptions) and wildlife conservation and/or management (e.g. ignoring ecological traps). We investigated spatial patterns in humancaused mortality based on 30 years of brown bear Ursus arctos mortality data from a Swedish population. We contrasted mortality data with random locations and global positioning system relocations of live bears, as well as between sex, age and management classes ('problem' versus 'no problem' bear, before and after changing hunting regulations), and we used resource selection functions to identify potential ecological sinks (i.e. avoided habitat with high mortality risk) and traps (i.e. selected habitat with high mortality risk). We found that human-caused mortality and mortality risk were positively associated with human presence and access. Bears removed as a management measure were killed in closer proximity to humans than hunter-killed bears, and supplementary feeding of bears did not alter the spatial structure of human-caused bear mortality. We identified areas close to human presence as potential sink habitat and agricultural fields (oat fields in particular) as potential ecological traps in our study area. We emphasize that human-caused mortality in bears and maybe in wildlife generally can show a very local spatial structure, which may have far-reaching population effects. We encourage researchers and managers to systematically collect and geo-reference wildlife mortality data, in order to verify general ecological assumptions and to inform wildlife managers about critical habitat types. The latter is especially important for vulnerable or threatened populations.
Conservation Biology, 2004
Many carnivore populations escaped extinction during the twentieth century as a result of legal protections, habitat restoration, and changes in public attitudes. However, encounters between carnivores, livestock, and humans are increasing in some areas, raising concerns about the costs of carnivore conservation. We present a method to predict sites of human-carnivore conflicts regionally, using as an example the mixed forest-agriculture landscapes of Wisconsin and Minnesota (U.S.A.). We used a matched-pair analysis of 17 landscape variables in a geographic information system to discriminate affected areas from unaffected areas at two spatial scales (townships and farms). Wolves (Canis lupus) selectively preyed on livestock in townships with high proportions of pasture and high densities of deer (Odocoileus virginianus) combined with low proportions of crop lands, coniferous forest, herbaceous wetlands, and open water. These variables plus road density and farm size also appeared to predict risk for individual farms when we considered Minnesota alone. In Wisconsin only, farm size, crop lands, and road density were associated with the risk of wolf attack on livestock. At the level of townships, we generated two state-wide maps to predict the extent and location of future predation on livestock. Our approach can be applied wherever spatial data are available on sites of conflict between wildlife and humans. Predicción de Conflicto Humano-Carnívoro: un Modelo Espacial Basado en 25 Años de Datos de Depredación de Ganado por Lobos Resumen: Muchas poblaciones de carnívoros lograron evitar la extinción durante el siglo veinte debido a protecciones legales, restauración de hábitat y cambios en las actitudes del público. Sin embargo, los encuentros entre carnívoros, ganado y humanos están incrementando en algunasáreas, lo cual es causa de preocupación en cuanto a los costos de la conservación de carnívoros. Presentamos un método para predecir los sitios de conflictos humanos -carnívoro a nivel regional, utilizando como ejemplo los paisajes mixtos de bosques-agricultura de Wisconsin y Minnesota (E. U. A.). Utilizamos un análisis apareado de 17 variables del paisaje en un sistema § §Current address: Living Landscapes Program, Wildlife Conservation Society, Treves et al. Predicting Human-Carnivore Conflict 115
Grizzly bear (Ursus arctos) populations across their range are being threatened by anthropogenic development and associated increases in human-caused mortality. However, details surrounding the impact of cumulative human effects are not yet fully understood, as prior research has focused primarily on habitat selection of individual disturbance features, rather than the spatio-temporal dynamics of aggregated disturbance processes. We used grizzly bear relative-abundance information from a DNA population inventory alongside a GIS database of human footprint dynamics to gain insight into the relationships between human disturbance features and the spatial distribution of grizzly bears in west-central Alberta, Canada: a landscape experiencing heavy resource development. We used candidate model-selection techniques and zero-inflated Poisson regression models to test competing hypotheses about disturbance processes, neighborhood effect and landscape characteristics. The best model explained about 57% of the overall variation in relative grizzly bear abundance. Areas with lower 'disturbance exposure' (i.e. high mean distance to new disturbances over time), lower 'neighborhood disturbance' (i.e. disturbance density around those areas), and higher 'availability of regenerating forest', were associated with higher bear abundance. In addition, areas located further away from an adjacent protected area exhibited a higher probability of 'excess absences', accounting indirectly for the cumulative effects of disturbance and the history of human-caused mortality. Our results suggest that managing the spatio-temporal exposure of grizzly bears to new disturbance features may be an important consideration for conserving this species in rapidly changing landscapes.