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.

物种分布建模已从依赖平衡假设，转向明确识别系统的暂态动态。这一转变催生了更为复杂的建模技术，但针对存在不可观测状态的生态系统，这类动态模型的性能尚未得到评估。本研究的动机源自新兴传染病壶菌病（Chytridiomycosis）所带来的影响——该疾病会导致全球多个两栖物种种群数量下降。本研究以该宿主-病原体系统为通用范例，首先阐释了若未将病原体动态纳入建模流程，会得到具有误导性的推论；尤其当病原体无法在宿主缺失的情况下被调查监测时，这一问题尤为突出。研究发现，当病原体仅能通过宿主检出且病原体信息被作为协变量纳入模型时，传统建模技术会低估该病原体对宿主物种分布及种群动态的影响强度。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——尤其当采样设计受限于物种的自然历史特征或采样技术时，该方法的优势更为显著。当使用多状态占有模型且系统存在不可观测状态时，参数估计结果会受到模型复杂度、数据稀疏性以及系统内在动态的影响。研究表明，即便样本量充足，诸多纳入了生命率季节变化的模型仍可能无法生成合理的估计结果，这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统计推断，并据此提出了可规避此类问题的研究设计建议。此外，我们主张使用时变协变量来阐释数据中的时间趋势，并建议开发适配系统生物学特征的采样技术，在可行条件下消除不可观测状态。