Data from: Using camera trapping and hierarchical occupancy modelling to evaluate the spatial ecology of an African mammal community

Emerging conservation paradigms have shifted from single to multi-species approaches focused on sustaining biodiversity. Multi-species hierarchical occupancy modelling provides a method for assessing biodiversity while accounting for multiple sources of uncertainty. We analysed camera trapping data with multi-species models using a Bayesian approach to estimate the distributions of a terrestrial mammal community in northern Botswana and evaluate community, group, and species-specific responses to human disturbance and environmental variables. Groupings were based on two life-history traits: body size (small, medium, large and extra-large) and diet (carnivore, omnivore and herbivore). We photographed 44 species of mammals over 6607 trap nights. Camera station-specific estimates of species richness ranged from 8 to 27 unique species, and species had a mean occurrence probability of 0·32 (95% credible interval = 0·21–0·45). At the community level, our model revealed species richness was generally greatest in floodplains and grasslands and with increasing distances into protected wildlife areas. Variation among species’ responses was explained in part by our species groupings. The positive influence of protected areas was strongest for extra-large species and herbivores, while medium-sized species actually increased in the non-protected areas. The positive effect of grassland/floodplain cover, alternatively, was strongest for large species and carnivores and weakest for small species and herbivores, suggesting herbivore diversity is promoted by habitat heterogeneity. Synthesis and applications. Our results highlight the importance of protected areas and grasslands in maintaining biodiversity in southern Africa. We demonstrate the utility of hierarchical Bayesian models for assessing community, group and individual species’ responses to anthropogenic and environmental variables. This framework can be used to map areas of high conservation value and predict impacts of land-use change. Our approach is particularly applicable to the growing number of camera trap studies world-wide, and we suggest broader application globally will likely result in reduced costs, improved efficiency and increased knowledge of wildlife communities.

新兴保护范式已从单一物种保护转向以维持生物多样性为核心的多物种保护路径。多物种分层占用模型（multi-species hierarchical occupancy modelling）为评估生物多样性提供了可行手段，同时可量化多来源不确定性。本研究采用贝叶斯方法（Bayesian approach）下的多物种模型分析红外相机监测数据，以估算博茨瓦纳北部陆地哺乳动物群落的分布格局，并评估群落、物种类群及单个物种对人为干扰与环境变量的响应。物种类群基于两类生活史特征划分：一是体型（分为小型、中型、大型与超大型），二是食性（分为食肉动物、杂食动物与植食动物）。本次研究累计获得6607个相机有效监测夜的影像记录，共监测到44种哺乳动物。各相机站点的物种丰富度估计值介于8至27种之间，物种的平均出现概率为0.32（95%可信区间为0.21~0.45）。在群落尺度上，模型结果显示物种丰富度在泛滥平原与草原生境中普遍最高，且随进入保护野生动物区的距离增加而升高。物种间的响应差异可部分通过预设的物种类群得到解释：保护区的正向影响在超大型物种与植食动物群体中最为显著，而中型物种在非保护区域的出现概率反而更高；草原与泛滥平原生境的正向效应则在大型物种与食肉动物群体中最为突出，在小型物种与植食动物群体中最弱，这表明生境异质性可促进植食动物多样性。研究总结与应用启示：本研究结果凸显了保护区与草原生境在维持非洲南部生物多样性中的关键作用。同时，本研究验证了分层贝叶斯模型在评估群落、物种类群及单个物种对人为与环境变量响应方面的实用性。该分析框架可用于绘制高保护价值区域的空间分布图，并预测土地利用变化带来的生态影响。我们的研究方法尤其适用于全球范围内日益增多的红外相机监测研究，且我们认为，在全球范围内推广该方法有望降低研究成本、提升研究效率，并深化对野生动物群落的科学认知。