Department of Anthropology & Archaeology, University of Calgary, Calgary, Alberta, Canada.
Faculty of Sciences, Technologies, and the Environment, University of Mahajanga, Mahajanga, Madagascar.
Am J Primatol. 2021 Jul;83(7):e23270. doi: 10.1002/ajp.23270. Epub 2021 May 19.
Primate species face growing risks of extinction throughout the world. To better protect their populations, effective monitoring techniques are needed. The goal of this study was to evaluate the use of arboreal camera traps and occupancy modeling as conservation tools for threatened lemur species. This project aimed to (1) estimate the occupancy and detection probabilities of lemur species, (2) investigate factors potentially affecting lemur habitat use, and (3) determine whether ground or arboreal cameras are better for surveying lemur assemblages. We conducted camera trapping research in five forest fragments (total trap nights = 1770; 900 arboreal trap nights (134 photo events); 870 ground trap nights (2 photo events)) and reforestation areas (total trap nights = 608; 1 photo event) in Kianjavato, Madagascar from May to September 2019. We used arboreal trap data from fragments to estimate occupancy for five species: the red-fronted brown lemur (Eulemur rufifrons; ψ = 0.54 ± SD 0.03), Jolly's mouse lemur (Microcebus jollyae; ψ = 0.14 ± 0.17), the greater dwarf lemur (Cheirogaleus major; ψ = 0.42 ± 0.30), the red-bellied lemur (Eulemur rubriventer; ψ = 0.24 ± 0.03), and the black-and-white ruffed lemur (Varecia variegata; ψ = 0.24 ± 0.08). Tree diameter, elevation, distance to village, and canopy connectivity were important predictors of occupancy, while camera height, canopy connectivity, fragment ID, and fragment size predicted detection. Arboreal cameras recorded significantly higher species richness compared with ground cameras. We suggest expanded application of arboreal camera traps in future research, but we recommend longer trapping periods to better sample rarer species. Overall, arboreal camera trapping combined with occupancy modeling can be a highly efficient and useful approach for monitoring and predicting the occurrence of elusive lemur species and has the potential to be effective for other arboreal primates and canopy taxa across the globe.
灵长类物种在世界各地面临着越来越大的灭绝风险。为了更好地保护它们的种群，需要有效的监测技术。本研究的目的是评估树栖相机陷阱和占有模型作为保护濒危狐猴物种的工具。该项目旨在：(1) 估计狐猴物种的占有和检测概率；(2) 研究可能影响狐猴栖息地利用的因素；(3) 确定地面或树栖相机更适合调查狐猴组合。我们在马达加斯加的 Kianjavato 进行了五个森林片段（总陷阱夜数=1770；900 个树栖陷阱夜（134 张照片事件）；870 个地面陷阱夜（2 张照片事件））和再造林区（总陷阱夜数=608；1 张照片事件）的相机诱捕研究，时间为 2019 年 5 月至 9 月。我们使用片段中的树栖陷阱数据来估计五种物种的占有情况：红额褐狐猴（Eulemur rufifrons；ψ=0.54±0.03）、乔利氏鼠狐猴（Microcebus jollyae；ψ=0.14±0.17）、大侏儒狐猴（Cheirogaleus major；ψ=0.42±0.30）、红腹狐猴（Eulemur rubriventer；ψ=0.24±0.03）和黑白毛领狐猴（Varecia variegata；ψ=0.24±0.08）。树径、海拔、离村庄的距离和树冠连通性是占有情况的重要预测因子，而相机高度、树冠连通性、片段 ID 和片段大小预测了检测情况。树栖相机的记录物种丰富度明显高于地面相机。我们建议在未来的研究中扩大应用树栖相机陷阱，但我们建议延长诱捕期，以更好地采样稀有的物种。总体而言，树栖相机陷阱结合占有模型可以成为监测和预测难以捉摸的狐猴物种出现的高效且有用的方法，并且有可能对全球其他树栖灵长类动物和树冠类群有效。
