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.

新兴的保护范式已从单一物种保护路径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）提供了一种可在量化多重不确定性来源的同时评估生物多样性的研究方法。本研究采用贝叶斯（Bayesian）方法，结合多物种模型对红外相机监测数据（camera trapping data）进行分析，以估算博茨瓦纳北部陆地哺乳动物群落的分布情况，并评估群落、类群及各物种对人为干扰与环境变量的响应。类群划分基于两项生活史特征：体型（分为小型、中型、大型及超大型）与食性（分为食肉动物、杂食动物与植食动物）。本次研究累计开展了6607个相机监测夜的工作，共拍摄到44种哺乳动物。各监测点位的物种丰富度（species richness）估算值介于8至27种之间，物种的平均出现概率为0.32（95%可信区间（95% credible interval）：0.21~0.45）。在群落层面，模型结果显示物种丰富度通常在洪泛平原与草原生境中最高，且随向保护野生动物区域内部的深入程度增加而提升。物种间响应的差异可部分通过本研究的类群划分得到解释。保护区的正向影响在超大型物种与植食动物类群中最为显著，而中型物种在非保护区域的出现概率反而更高。与之相对，草原/洪泛平原覆盖度的正向影响在大型物种与食肉动物类群中最为显著，而在小型物种与植食动物类群中最弱，这表明生境异质性对植食动物多样性具有促进作用。研究总结与应用启示：本研究结果凸显了保护区域与草原在维持非洲南部生物多样性中的核心价值。本研究验证了分层贝叶斯模型在评估群落、类群及单个物种对人为与环境变量响应方面的应用潜力。该研究框架可用于绘制高保护价值区域分布图，并预测土地利用变化带来的生态影响。本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，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，并深化对野生生物群落的科学认知。