Data from: Inferential biases linked to unobservable states in complex occupancy models

Modeling of species distributions has undergone a shift from relying on equilibrium assumptions to recognizing transient system dynamics explicitly. This shift has necessitated more complex modeling techniques, but the performance of these dynamic models has not yet been assessed for systems where unobservable states exist. Our work is motivated by the impacts of the emerging infectious disease chytridiomycosis, a disease of amphibians that associated with declines of many species worldwide. Using this host-pathogen system as a general example, we first illustrate how misleading inferences can result from failing to incorporate pathogen dynamics into the modeling process, especially when the pathogen is difficult or impossible to survey in the absence of a host species. We found that traditional modeling techniques can underestimate the effect of a pathogen on host species occurrence and dynamics when the pathogen can only be detected in the host, and pathogen information is treated as a covariate. We propose a dynamic multistate modeling approach that is flexible enough to account for the detection structures that may be present in complex multistate systems, especially when the sampling design is limited by a species’ natural history or sampling technology. When multistate occupancy models are used and an unobservable state is present, parameter estimation can be influenced by model complexity, data sparseness, and the underlying dynamics of the system. We show that, even with large sample sizes, many models incorporating seasonal variation in vital rates may not generate reasonable estimates, indicating parameter redundancy. We found that certain types of missing data can greatly hinder inference, and we make study design recommendations to avoid these issues. Additionally, we advocate the use of time-varying covariates to explain temporal trends in the data, and the development of sampling techniques that match the biology of the system to eliminate unobservable states when possible.

物种分布建模领域已实现研究范式的转变：从依赖平衡假设，转向明确识别暂态系统动力学。这一转变催生了更为复杂的建模技术，但这类动态模型的性能尚未在存在不可观测状态的生态系统中得到评估。本研究的研究动机源于新兴传染病壶菌病（chytridiomycosis）的影响——该病害侵袭两栖动物，已在全球范围内导致诸多两栖物种种群衰退。我们以该宿主-病原系统作为通用范例，首先阐明：若建模过程未纳入病原动态，尤其当宿主缺失时难以甚至无法调查病原的情况下，会得出误导性的推断结论。研究发现，当仅能通过宿主检测到病原，且将病原信息作为协变量纳入模型时，传统建模技术会低估病原对宿主物种出现及种群动态的影响。为此，我们提出一种动态多状态建模方法，该方法具备足够灵活性，可适配复杂多状态系统中可能存在的检测结构，尤其适用于受物种自然史或采样技术限制的采样设计场景。当采用多状态占据模型（occupancy model）且存在不可观测状态时，参数估计会受到模型复杂度、数据稀疏性以及系统潜在动态的影响。研究表明，即便样本量充足，诸多纳入了生命率季节性变化的模型仍可能无法生成合理的参数估计结果，提示存在参数冗余问题。我们还发现，特定类型的缺失数据会严重阻碍推断过程，并据此提出了可规避此类问题的研究设计建议。此外，本研究倡导使用时变协变量来阐释数据中的时间趋势，并呼吁开发适配系统生物学特征的采样技术，以便在可行条件下消除不可观测状态。