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Addressing biases in citizen science data to document phenology patterns at broad spatial and taxonomic scales
Shifts in the timing of seasonal events (i.e., phenology) are one of the most immediate and apparent responses to global climate change, but data limitations have made examining phenology patterns across greater taxonomic, spatial, and temporal scales challenging. One growing opportunity is leveraging rapidly increasing data resources from citizen science platforms. However, these are biased spatially and taxonomically, potentially leading to erroneous biological conclusions if appropriate data curation and modeling strategies are not used. Here, I will present recent research exploring the novel methods of estimating phenology metrics using incidental citizen science observations. I will the accuracy of phenology estimators across a suite of simulated and empirical examples, and I will also present a case study that showcases a framework that can be used to answer fundamental questions of insect phenology across broad spatial and phylogenetic scales using citizen science records from iNaturalist. Collectively, incidental citizen science data provides a sizable resource for phenological research and continued work to integrate the strengths and weaknesses inherent to these data promises to provide critical insight into pressing ecological issues.

Oct 14, 2021 12:00 PM in Eastern Time (US and Canada)

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