Effectiveness of joint species distribution models in the presence of imperfect detection

Joint species distribution models (JSDMs) are a recent development in biogeography and enable the spatial modelling of multiple species and their interactions and dependencies. However, most models do not consider imperfect detection, which can significantly bias estimates. This is one of the first papers to account for imperfect detection when fitting data with JSDMs and to explore the complications that may arise. A multivariate probit JSDM that explicitly accounts for imperfect detection is proposed, and implemented using a Bayesian hierarchical approach. We investigate the performance of the JSDM in the presence of imperfect detection for a range of factors, including varied levels of detection and species occupancy, and varied numbers of survey sites and replications. To understand how effective this JSDM is in practice, we also compare results to those from a JSDM that does not explicitly model detection but instead makes use of  \"collapsed data\". A case study of owls and glide...联合物种分布模型（Joint Species Distribution Models，JSDMs）是生物地理学领域近年来兴起的研究方法，可实现多物种及其相互作用与依存关系的空间建模。然而，绝大多数此类模型未考虑不完全检测（imperfect detection）问题，该因素可能会对模型估计结果造成显著偏倚。本研究属于首批在使用JSDMs拟合数据时纳入不完全检测机制，并探讨由此可能引发的各类复杂情况的学术成果之一。 本文提出一种多元概率单位联合物种分布模型（Multivariate Probit JSDM），并采用贝叶斯分层建模方法完成模型实现。我们针对多种影响因素，探究了不完全检测场景下该JSDM的模型性能，涵盖不同水平的检测概率与物种占据率，以及不同数量的调查样点与重复采样次数。为明确该JSDM在实际应用中的有效性，我们还将其模型结果与另一类未显式建模检测过程、而是采用“压缩数据（collapsed data）”的JSDM所得结果进行了对比。本研究包含一项针对鸮类与滑翔[原文未完整表述]的案例研究。