Inferring neutral biodiversity parameters using environmental DNA data sets

The DNA present in the environment is a unique and increasingly exploited source of information for conducting fast and standardized biodiversity assessments for any type of organisms. The datasets resulting from these surveys are however rarely compared to the quantitative predictions of biodiversity models. In this study, we simulate neutral taxa-abundance datasets, and artificially noise them by simulating noise terms typical of DNA-based biodiversity surveys. The resulting noised taxa abundances are used to assess whether the two parameters of Hubbell's neutral theory of biodiversity can still be estimated. We find that parameters can be inferred provided that PCR noise on taxa abundances does not exceed a certain threshold. However, inference is seriously biased by the presence of artifactual taxa. The uneven contribution of organisms to environmental DNA owing to size differences and barcode copy number variability does not impede neutral parameter inference, provided that the number of sequence reads used for inference is smaller than the number of effectively sampled individuals. Hence, estimating neutral parameters from DNA-based taxa abundance patterns is possible but requires some caution. In studies that include empirical noise assessments, our comprehensive simulation benchmark provides objective criteria to evaluate the robustness of neutral parameter inference.