Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models
Information on species' habitat associations and distributions, across a wide range of spatial and temporal scales, are 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 signaled as an increasingly important source to fill in data gaps where information is needed to make comprehensive and
... ust inferences on species distributions. However, there are perceived trade-offs of combining highly structured, scientific survey data with largely unstructured, citizen-science data. As a result, the focus of most methodological advances to combine these sources of information has been on treating these sources as independent. The degree to which each source of information is allowed to directly inform a common underlying process depends on the perceived quality of the data. We explore these trade-offs by applying a simplified approach of filtering citizen-science data to resemble structured survey data, and analyze 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. 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. The practical approach we have shown for data integration can be 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 processing and filtering method we present can be used to integrate other types of data (e.g. camera traps) with more localized efforts (e.g. research projects), ultimately improving our ecological knowledge on the distribution and habitat associations of species of conservation concern worldwide.