Sampling errors create bias in Markov models for community dynamics: the problem and a method for its solution

Abstract

Repeated, spatially explicit sampling is widely used to characterize the dynamics of sessile communities in both terrestrial and aquatic systems, yet our understanding of the consequences of errors made in such sampling is limited. In particular, when Markov transition probabilities are calculated by tracking individual points over time, misidentification of the same spatial locations will result in biased estimates of transition probabilities, successional rates, and community trajectories. Nonetheless, to date, all published studies that use such data have implicitly assumed that resampling occurs without error when making estimates of transition rates. Here, we develop and test a straightforward maximum likelihood approach, based on simple field estimates of resampling errors, to arrive at corrected estimates of transition rates between species in a rocky intertidal community. We compare community Markov models based on raw and corrected transition estimates using data from Endocladia muricata-dominated plots in a California intertidal assemblage, finding that uncorrected predictions of succession consistently overestimate recovery time. We tested the precision and accuracy of the approach using simulated datasets and found good performance of our estimation method over a range of realistic sample sizes and error rates.