Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models

Abstract

Abstract Aim Information on species’ habitat associations and distributions, across a wide range of spatial and temporal scales, is a fundamental source of ecological knowledge. However, collecting information at relevant scales is often cost prohibitive, although it is essential for framing the broader context of more focused research and conservation efforts. Citizen science has been signalled as an increasingly important source to fill in data gaps where information is needed to make comprehensive and robust inferences on species distributions. However, there are perceived trade‐offs of combining highly structured, scientific survey data with largely un‐structured, citizen science data. Methods We explore these trade‐offs by applying a simplified approach of filtering citizen science data to resemble structured survey data and analyse both sources of data under a common framework. To accomplish this, we integrated high‐resolution survey data on shorebirds in the northern Central Valley of California with observations in eBird for the entire region that were filtered to improve their quality. Results The integration of survey data with the filtered citizen science data resulted in improved inference and increased the extent and accuracy of distribution models on shorebirds for the Central Valley. The structured surveys improved the overall accuracy of ecological inference over models using citizen science data only by increasing the representation of data collected from high‐quality habitats for shorebirds. Main conclusions The practical approach we have shown for data integration can also be used to improve the efficiency of designing biological surveys in the context of larger, citizen science monitoring efforts, ultimately reducing the financial and time expenditures typically required of monitoring programs and focused research. The simple method we present can be used to integrate other types of data with more localized efforts, ultimately improving our ecological knowledge on the distribution and habitat associations of species of conservation concern worldwide.