A multistate dynamic site occupancy model for spatially aggregated sessile communities

Inferring transition probabilities among ecological states is fundamental to community ecology because, based on Markovian dynamics models, these probabilities provide quantitative predictions about community composition and estimates of various properties that characterize community dynamics. Markov community models have been applied to sessile organisms because such models facilitate estimation of transition probabilities by tracking species occupancy at many fixed observation points over multiple periods of time. Estimation of transition probabilities of sessile communities seems easy in principle but may still be difficult in practice because resampling error (i.e., a failure to resample exactly the same location at fixed points) may cause significant estimation bias. Previous studies have developed novel analytical methods to correct for this estimation bias. However, they did not consider the local structure of community composition induced by the aggregated distribution of organisms that is typically observed in sessile assemblages and is very likely to affect observations. In this study, we developed a multistate dynamic site occupancy model to estimate transition probabilities that accounts for resampling errors associated with local community structure. The model applies a nonparametric multivariate kernel smoothing methodology to the latent occupancy component to estimate the local state composition near each observation point, which is assumed to determine the probability distribution of data conditional on the occurrence of resampling error. By using computer simulations, we confirmed that an observation process that depends on local community structure may bias inferences about transition probabilities. By applying the proposed model to a real dataset of intertidal sessile communities, we also showed that estimates of transition probabilities and of the properties of community dynamics may differ considerably when spatial dependence is taken into account. Our approach can even accommodate an anisotropic spatial correlation of species composition, and may serve as a basis for inferring complex nonlinear ecological dynamics.