Concepts & Synthesis

Abstract

Two of the most prominent frameworks to develop in ecology over the past decade are metacommunity ecology, which seeks to characterize multispecies distributions across space, and occupancy modeling, which corrects for imperfect detection in an effort to better understand species occurrence patterns Although their goals are complementary, metacommunity theory and statistical occupancy modeling methods have developed independently. For instance, the elements of metacommunity structure (EMS) framework uses species occurrence data to classify metacommunity structure and link it to underlying environmental gradients. While the efficacy of this approach relies on the quality of the data, few studies have considered how imperfect detection, which is widespread in ecological surveys and the major focus of occupancy modeling, affects the outcome. We introduce a framework that integrates multispecies occupancy models with the current EMS framework, detection error-corrected EMS (DECEMS). This method offers two distinct advantages. First, DECEMS reduces bias in characterizing metacommunity structure by using repeated surveys and occupancy models to disentangle species-specific occupancy and detection probabilities, ultimately bringing metacommunity structure classification into a more probabilistic framework. Second, occupancy modeling allows estimation of species-specific responses to environmental covariates, which will increase our ability to link species-level effects to metacommunity-wide patterns. After reviewing the EMS framework, we introduce a simple multispecies occupancy model and show how DECEMS can work in practice, highlighting that detection error often causes EMS to assign incorrect structures. To emphasize the broader applicability of this approach, we further illustrate that DECEMS can reduce the rate of structure misclassification by more than 20% in some cases, even proving useful when detection error rates are quite low (;10%). Integrating occupancy models and the EMS framework will lead to more accurate descriptions of metacommunity structure and to a greater understanding of the mechanisms by which different structures arise.