The use of environmental DNA (eDNA) to detect species in aquatic environments such as ponds and streams is a powerful new technique with many benefits. However, species detection in eDNA-based surveys is likely to be imperfect, which can lead to underestimation of the distribution of a species.
Site occupancy models account for imperfect detection and can be used to estimate the proportion of sites where a species occurs from presence/absence survey data, making them ideal for the analysis of eDNA-based surveys. Imperfect detection can result from failure to detect the species during field work (e.g. by water samples) or during laboratory analysis (e.g. by PCR).
To demonstrate the utility of site occupancy models for eDNA surveys, we reanalysed a data set estimating the occurrence of the amphibian chytrid fungus Batrachochytrium dendrobatidis using eDNA. Our reanalysis showed that the previous estimation of species occurrence was low by 5–10%. Detection probability was best explained by an index of the number of hosts (frogs) in ponds.
Per-visit availability probability in water samples was estimated at 0·45 (95% CRI 0·32, 0·58) and per-PCR detection probability at 0·85 (95% CRI 0·74, 0·94), and six water samples from a pond were necessary for a cumulative detection probability >95%. A simulation study showed that when using site occupancy analysis, researchers need many fewer samples to reliably estimate presence and absence of species than without use of site occupancy modelling.
Our analyses demonstrate the benefits of site occupancy models as a simple and powerful tool to estimate detection and site occupancy (species prevalence) probabilities despite imperfect detection. As species detection from eDNA becomes more common, adoption of appropriate statistical methods, such as site occupancy models, will become crucial to ensure that reliable inferences are made from eDNA-based surveys.