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.

scen1_toadGenerated data for the SRM boreal toad scenario with 80 sites to be used with 2-species modeling.scen2_toad_2NGenerated data for the SRM boreal toad scenario with 160 sites to be used with the dynamic 2-species model.scenario_3_missingGenerated data for the SRM boreal toad scenario with 80 sites and some missing data, as described in the manuscript, to be used with the dynamic 2-species model.scen4_rare_severeGenerated data for rare and susceptible amphibian scenario with 80 sites to be used with dynamic 2-species model.ss_occ_bd_covGenerated data for the SRM boreal toad scenario with 80 sites converted to a single species format to assess how inference changes when the unobservable state is ignored.