Centre for Environmental Sciences, UHasselt, Diepenbeek, Belgium.
Research Institute Nature and Forest, Brussels, Belgium.
Sci Rep. 2023 Sep 27;13(1):16169. doi: 10.1038/s41598-023-43184-w.
Knowledge on animal abundances is essential in ecology, but is complicated by low detectability of many species. This has led to a widespread use of hierarchical models (HMs) for species abundance, which are also commonly applied in the context of nature areas studied by camera traps (CTs). However, the best choice among these models is unclear, particularly based on how they perform in the face of complicating features of realistic populations, including: movements relative to sites, multiple detections of unmarked individuals within a single survey, and low detectability. We conducted a simulation-based comparison of three HMs (Royle-Nichols, binomial N-mixture and Poisson N-mixture model) by generating groups of unmarked individuals moving according to a bivariate Ornstein-Uhlenbeck process, monitored by CTs. Under a range of simulated scenarios, none of the HMs consistently yielded accurate abundances. Yet, the Poisson N-mixture model performed well when animals did move across sites, despite accidental double counting of individuals. Absolute abundances were better captured by Royle-Nichols and Poisson N-mixture models, while a binomial N-mixture model better estimated the actual number of individuals that used a site. The best performance of all HMs was observed when estimating relative trends in abundance, which were captured with similar accuracy across these models.
动物丰度知识在生态学中至关重要，但由于许多物种的检测率低，情况变得复杂。这导致了广泛使用层次模型（HMs）来描述物种丰度，这些模型也常用于相机陷阱（CTs）研究的自然区域。然而，这些模型中哪种是最佳选择尚不清楚，特别是在面对现实种群的复杂特征时，包括：与地点的相对运动、在单次调查中对未标记个体的多次检测以及检测率低等。我们通过生成根据双变量 Ornstein-Uhlenbeck 过程移动的未标记个体组，并使用 CT 进行监测，基于模拟比较了三种 HMs（Royle-Nichols、二项式 N 混合和泊松 N 混合模型）。在一系列模拟场景下，没有一种 HMs 始终能准确地产生丰度。然而，尽管存在个体偶然重复计数的情况，泊松 N 混合模型在动物确实在地点之间移动时表现良好。Royle-Nichols 和泊松 N 混合模型更能捕获绝对丰度，而二项式 N 混合模型更能估计使用地点的实际个体数量。当估计丰度的相对趋势时，所有 HMs 的表现最佳，这些模型在估计相对趋势时具有相似的准确性。
