Multi‐species occupancy models as robust estimators of community richness

Abstract

Abstract Understanding patterns of diversity is central to ecology and conservation, yet estimates of diversity are often biased by imperfect detection. In recent years, multi‐species occupancy models (MSOM) have been developed as a statistical tool to account for species‐specific heterogeneity in detection while estimating true measures of diversity. Although the power of these models has been tested in various ways, their ability to estimate gamma diversity—or true community size, N is a largely unrecognized feature that needs rigorous evaluation. We use both simulations and an empirical dataset to evaluate the bias, precision, accuracy and coverage of estimates of N from MSOM compared to the widely applied iChao2 non‐parametric estimator. We simulated 5,600 datasets across seven scenarios of varying average occupancy and detectability covariates, as well as varying numbers of sites, replicates and true community size. Additionally, we use a real dataset of surveys over 9 years (where species accumulation reached an asymptote, indicating true N), to estimate N from each annual survey. Simulations showed that both MSOM and iChao2 estimators are generally accurate (i.e. unbiased and precise) except under unideal scenarios where mean species occupancy is low. In such scenarios, MSOM frequently overestimated N. Across all scenarios, MSOM estimates were less certain than iChao2, but this led to over‐confident iChao2 estimates that showed poor coverage. Results from the real dataset largely confirmed the simulation findings, with MSOM estimates showing greater accuracy and coverage than iChao2. Community ecologists have a wide choice of analytical methods, and both iChao2 and MSOM estimates of N are substantially preferable to raw species counts. The simplicity of non‐parametric estimators has obvious advantages, but our results show that in many cases, MSOM may provide superior estimates that also account more accurately for uncertainty. Both methods can show strong bias when average occupancy is very low, and practitioners should show caution when using estimates derived from either method under such conditions.