On the Role of Spatial Clustering Algorithms in Building Species Distribution Models from Community Science Data (Proposals Track) Best Paper: Proposals

Mark Roth (Oregon State University); Tyler Hallman (Swiss Ornithological Institute); W. Douglas Robinson (Oregon State University); Rebecca Hutchinson (Oregon State University)

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Ecosystems & Biodiversity Unsupervised & Semi-Supervised Learning


This paper discusses opportunities for developments in spatial clustering methods to help leverage broad scale community science data for building species distribution models (SDMs). SDMs are tools that inform the science and policy needed to mitigate the impacts of climate change on biodiversity. Community science data span spatial and temporal scales unachievable by expert surveys alone, but they lack the structure imposed in smaller scale studies to allow adjustments for observational biases. Spatial clustering approaches can construct the necessary structure after surveys have occurred, but more work is needed to ensure that they are effective for this purpose. In this proposal, we describe the role of spatial clustering for realizing the potential of large biodiversity datasets, how existing methods approach this problem, and ideas for future work.

Recorded Talk (direct link)