Boiani M V, Dupont P, Bischof R, Milleret C, Friard O, Geary M, Avanzinelli E, von Hardenberg A, Marucco F
Department of Biological Sciences Conservation Biology Research Group, University of Chester Chester UK.
Faculty of Environmental Sciences and Natural Resource Management Norwegian University of Life Sciences Ås Norway.
Ecol Evol. 2024 Aug 21;14(8):e70204. doi: 10.1002/ece3.70204. eCollection 2024 Aug.
The ongoing expansion of wolf () populations in Europe has led to a growing demand for up-to-date abundance estimates. Non-invasive genetic sampling (NGS) is now widely used to monitor wolves, as it allows individual identification and abundance estimation without physically capturing individuals. However, NGS is resource-intensive, partly due to the elusive behaviour and wide distribution of wolves, as well as the cost of DNA analyses. Optimisation of sampling strategies is therefore a requirement for the long-term sustainability of wolf monitoring programs. Using data from the 2020-2021 Italian Alpine wolf monitoring, we investigate how (i) reducing the number of samples genotyped, (ii) reducing the number of transects, and (iii) reducing the number of repetitions of each search transect impacted spatial capture-recapture population size estimates. Our study revealed that a 25% reduction in the number of transects or, alternatively, a 50% reduction in the maximum number of repetitions yielded abundance estimates comparable to those obtained using the entire dataset. These modifications would result in a 2046 km reduction in total transect length and 19,628 km reduction in total distance searched. Further reducing the number of transects resulted in up to 15% lower and up to 17% less precise abundance estimates. Reducing only the number of genotyped samples led to higher (5%) and less precise (20%) abundance estimates. Randomly subsampling genotyped samples reduced the number of detections per individual, whereas subsampling search transects resulted in a less pronounced decrease in both the total number of detections and individuals detected. Our work shows how it is possible to optimise wolf monitoring by reducing search effort while maintaining the quality of abundance estimates, by adopting a modelling framework that uses a first survey dataset. We further provide general guidelines on how to optimise sampling effort when using spatial capture-recapture in large-scale monitoring programmes.
欧洲狼（）种群的持续扩张导致对最新数量估计的需求不断增加。非侵入性基因采样（NGS）现在被广泛用于监测狼，因为它无需实际捕获个体就能进行个体识别和数量估计。然而，NGS资源密集，部分原因是狼的行为难以捉摸、分布广泛，以及DNA分析的成本。因此，优化采样策略是狼监测项目长期可持续性的必要条件。利用2020 - 2021年意大利阿尔卑斯山狼监测的数据，我们研究了（i）减少基因分型样本数量、（ii）减少样带数量以及（iii）减少每个搜索样带的重复次数如何影响空间捕获 - 重捕种群数量估计。我们的研究表明，样带数量减少25%，或者最大重复次数减少50%，得到的数量估计与使用整个数据集得到的估计相当。这些修改将使样带总长度减少2046公里，总搜索距离减少19628公里。进一步减少样带数量会导致数量估计低至15%，精度低至17%。仅减少基因分型样本数量会导致数量估计更高（5%）且精度更低（20%）。对基因分型样本进行随机抽样会减少每个个体的检测次数，而对搜索样带进行抽样则会使检测总数和检测到的个体数的减少不太明显。我们的工作表明，通过采用使用首个调查数据集的建模框架，在保持数量估计质量的同时减少搜索工作量来优化狼监测是可行的。我们还进一步提供了在大规模监测项目中使用空间捕获 - 重捕时如何优化采样工作量的一般指导原则。
