Conservation of biological communities requires accurate estimates of abundance for multiple species. Recent advances in estimating abundance of multiple species, such as Bayesian multispecies N-mixture models, account for multiple sources of variation, including detection error. However, false positive errors (misidentification or double counts), which are prevalent in multispecies datasets, remain largely unaddressed. The dependent-double observer method (DDO) is an emerging design-based method that both accounts for detection error and is suggested to reduce the occurrence of false positives because it relies on two observers working collaboratively to identify individuals. To date, the DDO method has not been combined with advantages of multispecies N-mixture models. Here, we derive an extension of a multispecies N-mixture model using the DDO survey method to create a multispecies dependent double-observer abundance model (MDAM). The MDAM uses a hierarchical framework to account for biological and observational processes in a statistically consistent framework while using the accurate observation data from the DDO survey method. We demonstrate that the MDAM accurately estimates abundance of multiple species with simulated and real multispecies data sets.
Results/Conclusions
Simulations showed that the model provides both precise and accurate abundance estimates, with credible interval coverage of the true abundance values at 94.3%. In addition, 92.2% of abundance estimates had a mean absolute percent error between 0 and 20%. We present the MDAM as an important step forward in expanding the applicability of the DDO method to a multispecies setting. Previous implementation of the DDO method suggests the MDAM can be applied to a broad array of biological communities. We suggest that researchers interested in assessing biological communities consider the MDAM as a tool for deriving accurate, multispecies abundance estimates. The MDAM has the flexibility to incorporate long-term, large-scale, and multi-taxa data. It can provide data-driven solutions to reduce cost and effort put into biodiversity monitoring.