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.

Spatial aggregation and specificity of incidents with wildlife make tea plantations in southern India potential buffers with protected areas

Many wildlife species survive in human-modified landscapes and understanding the opinions of those who share space with wildlife will aid conservation efforts. Using a questionnaire, we assessed the presence of 12 mammal species in 78 tea plantations in the Nilgiris, southern India. We obtained data on (i) plantation size, location, and elevation, (ii) species presence over a year, (iii) type and number of wildlife incidents caused, (iv) financial cost of wildlife damage, and (v) support for wildlife conservation. We used a generalized linear model to assess whether the distance to protected areas, elevation, and plantation size influenced species presence and the effect of these variables and wildlife incidents on support for conservation. Among all species reported, Bonnet Macaque, Wild Boar, and Porcupine were the most widespread, and the former two and the Gaur reportedly caused >50% of damages. Crop damage was the most frequent (74%, n = 244), whereas livestock predation, attacks on people, and infrastructure damage constituted <10% of incidents reported. The cost of wildlife damage was negligible for 72 estates and significant for six. The number of species increased with proximity to protected areas, with increasing elevation and plantation area. Plantation management (62%) supported wildlife conservation, and support increased with decreasing plantation size, increasing distance to protected areas, and with a higher number of species reported, but decreased with increasing incidents of wildlife damage. Mitigating impacts of a few widely distributed species that cause disproportionate damage and compensating those that incur disproportionately high costs could increase support for conservation. Education and awareness programs for the plantation community can further help increase support and participation in wildlife conservation activities. Plantations can thus serve as supplementary habitats for wildlife in regions where hard boundaries between protected areas and human settlements prevail.

Evaluation of damage, made by wild animals to agricultural fields, using different methods

The paper presents experimental studies of different techniques (measurements directly in the sample plots and remote measurements, such as high-resolution satellite images or images made by a quadcopter drone) for estimation of damage, made by wild ungulates in agricultural fields. The practical experience gained during the research was analysed. It has been found that for the assessment of the damage caused by wild ungulates, the most suitable layout of sample plots is systematic, covering the whole area evenly. When the sample plots covered 0.2% of the total surveyed area, the error of the estimated wildlife damage was in a range of ±3.8%; when the sample plots covered 0.3% of the total surveyed area, the error of the estimated wildlife damage was in a range of ±2.1%. The measurements of 10 sample plots in loco took 33–40 min, with a team of three assessors. Evaluation works done with a quadcopter drone are faster (10 ha field shot in 20 min and analysis of data in 40 min) and require only one specialist to operate the drone. It was proved that high-resolution satellite images, done in Sentinel missions, are not suitable for estimation of damage, made by wild ungulates in agricultural fields.