Centre for Environmental Sciences, UHasselt - Hasselt University, Diepenbeek, Belgium; Data Science Institute, UHasselt -Hasselt University, Diepenbeek, Belgium; Research Institute for Nature and Forest, Brussels, Belgium.
Data Science Institute, UHasselt -Hasselt University, Diepenbeek, Belgium; Leuven Biostatistics and statistical Bioinformatics Centre, KU Leuven, Leuven, Belgium.
Sci Total Environ. 2024 Feb 10;911:168546. doi: 10.1016/j.scitotenv.2023.168546. Epub 2023 Nov 17.
Increasing human-wild boar interactions have led to damage to agricultural crops, traffic collisions and disease transmissions. Dividing natural areas in zones with differential hunting pressure is one of the currently adopted management strategies. However, the effectiveness of this approach is under debate. Hence, there is a need to better understand how to mitigate negative human-wild boar interactions effectively. Camera traps are cost-efficient, and non-invasive tools to monitor animal populations. N-mixture models can reliably estimate spatial variation in relative abundances when animals are imperfectly detected and/or cannot be individually identified. Thus, they are useful tools to infer the impacts of several factors on the land-use intensity of wild boar, based on camera trap data. In a nature area in central Belgium, we compare "summer" (April-September) land-use intensity of wild boar from 2018 until 2021 between three zones: a hunting free core zone, a winter hunting zone where hunting only takes place between November and March, and a year-round hunting zone. The latter is also close to the forest edge, agricultural crops and settlements. We compare spatial abundance models that capture these zone effects, or attractive effects of croplands, repulsive effects of hunting and repulsive effects of non-lethal human disturbances. We reveal between zone differences in wild boar land-use intensities across all summers. Additionally, we find that non-lethal human disturbance and croplands also explain variation in wild boar land-use intensity, but do not find negative associations with hunting locations. Our results suggest that the effects of zoning on wild boar land-use patterns are relevant in medium-sized natural areas. Moreover, we identify the need to install additional cameras outside of the managed area in order to assess the impacts of hunting in combination with non-lethal human activities on wild boar to mitigate negative human-wild boar interactions in the future.
人与野猪的互动增加导致了农业作物受损、交通事故和疾病传播。将自然区域划分为具有不同狩猎压力的区域是目前采用的管理策略之一。然而,这种方法的有效性存在争议。因此,需要更好地了解如何有效地减轻人与野猪的负面互动。相机陷阱是一种经济高效且非侵入性的工具,可以监测动物种群。N-混合模型可以在动物不完全被检测到和/或无法单独识别的情况下可靠地估计相对丰度的空间变化。因此,它们是基于相机陷阱数据推断几个因素对野猪土地利用强度影响的有用工具。在比利时中部的一个自然区域,我们在三个区域(无狩猎核心区、冬季狩猎区和全年狩猎区)比较了 2018 年至 2021 年期间野猪的“夏季”(4 月至 9 月)土地利用强度。后者也靠近森林边缘、农田和定居点。我们比较了捕捉这些区域效应或农田吸引力效应、狩猎排斥效应和非致命人类干扰排斥效应的空间丰度模型。我们揭示了所有夏季野猪土地利用强度的区域差异。此外,我们发现非致命的人类干扰和农田也解释了野猪土地利用强度的变化,但没有发现与狩猎地点的负面关联。我们的研究结果表明,分区对野猪土地利用模式的影响在中等大小的自然区域中是相关的。此外,我们发现需要在管理区域外安装额外的相机,以评估狩猎与非致命人类活动相结合对野猪的影响,以便将来减轻人与野猪的负面互动。