Accounting for imperfect detection when estimating species-area relationships and beta-diversity

Ecologists have historically quantified fundamental biodiversity patterns, including species-area relationships (SARs) and beta diversity, using observed species counts. However, imperfect detection may often bias derived community metrics and subsequent community models. Although several statistical methods claim to correct for imperfect detection, their performance in species-area and β-diversity research remains unproven. We examine inaccuracies in the estimation of SARs and β-diversity parameters that emerge from imperfect detection, and whether such errors can be mitigated using a non-parametric diversity estimator (iNEXT.3D) and Multi-Species Occupancy Models (MSOMs). We simulated 28,350 sampling regimes of 2835 fragmented communities, varying the mean and standard deviation of species detection probabilities, and the number of sampling repetitions. We then quantified the bias, accuracy, and precision of derived estimates of model coefficients for SARs and the effects of patch area on β-diversity (pairwise Sørensen similarity). Imperfect detection biased estimates of all evaluated parameters, particularly when mean detection probabilities were low, and there were few sampling repetitions. Observed counts consistently underestimated species richness and SAR z-values, and overestimated SAR c-values; iNEXT.3D and MSOMs only partially resolved these biases. iNEXT.3D provided the best estimates of SAR z-values, although MSOM estimates were generally comparable. All three methods accurately estimated pairwise Sørensen similarity in most circumstances, but only MSOMs provided unbiased estimates of the coefficients of models examining covariate effects on β-diversity. Even when using iNEXT.3D or MSOMs, imperfect detection consistently caused biases in SAR coefficient estimates, calling into question the robustness of previous SAR studies. Furthermore, the inability of observed counts and iNEXT.3D to estimate β-diversity model coefficients resulted from a systematic, area-related bias in Sørensen similarity estimates. Importantly, MSOMs corrected for these biases in β-diversity assessments, even in suboptimal scenarios. Nonetheless, as estimator performance consistently improved with increasing sampling repetitions, the importance of appropriate sampling effort cannot be understated.

Passive sampling hypothesis did not shape microbial species-area relationships in open microcosm systems

The passive sampling hypothesis is one of the most important hypotheses used to explain the mechanism of species-area relationships (SAR) formation. This hypothesis has not yet been experimentally validated due to the confusion between passive sampling (a larger area may support more colonists when fully sampled) and sampling effects (more sampling effort will result in increased species richness when sampling is partial). In this study, we created an open microcosm system with homogeneous habitat, consistent total resources, and biodiversity background using Chinese paocai soup, a fermented vegetable, as a substrate. We made efforts to entirely exclude the influence of sampling effects and to exclusively obtain microorganisms from dispersal using microcosm and high-throughput sequencing techniques. However, in this study, passive sampling based on dispersal failed to shape SAR, and community differences were predominantly caused by species replacement, with only minor contributions from richness differences. Ecological processes including extinction and competitive exclusion, as well as underlying factors like temporal scales and the small island effects, are very likely to have been involved in the studied system. To elucidate the mechanism of SAR development, future studies should design experiments to validate the involvement of dispersal independently.