| Summary: | Citizen science projects provide a vast amount of biological data that can be used to model population trends of species. Robust statistical modeling techniques are necessary to account for multiple sources of bias inherent to the data. One such citizen science project, eBird, is an online database of avian checklist data entered by birdwatchers from discrete locations and visits. The eBird dataset may be large enough to fill information gaps left by other monitoring programs if biases in the data are modeled appropriately and if the models can be validated against reliable survey data. We compared eBird and North American Breeding Bird Survey (BBS) data from southern Ontario to determine if patterns in annual indices and long-term trends were similar for 22 species that reach the northern limit of their range in that region. Mixed-effects models were used to address varying observer skill and uneven geographic coverage in eBird, and the number of species per checklist was used as a covariate to represent effort and to accommodate historic lists lacking effort information. The average Pearson's correlation coefficient between eBird and BBS annual indices across species was 0.35, and the correlation between trends estimated from the annual indices was 0.72. eBird data generally agreed with BBS data with the exception of two common species that showed opposite trends, several species with low detection rates, and for two species with little long-term change in occurrence based on BBS data. Our results suggest that eBird data can be used to generate long-term trends that could complement data from traditional surveys, yet more work is needed to understand circumstances that lead to disagreement between eBird and other surveys.
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