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Creating multithemed ecological regions for macroscale ecology: Testing a flexible,repeatable, and accessible clustering method
Authors:Kendra Spence Cheruvelil  Shuai Yuan  Katherine E Webster  Pang‐Ning Tan  Jean‐François Lapierre  Sarah M Collins  C Emi Fergus  Caren E Scott  Emily Norton Henry  Patricia A Soranno  Christopher T Filstrup  Tyler Wagner
Institution:1. Department of Fisheries and Wildlife & Lyman Briggs College, Michigan State University, East Lansing, MI, USA;2. Department of Computer Science & Engineering, Michigan State University, East Lansing, MI, USA;3. School of Natural Sciences, Trinity College Dublin, Dublin, 2, Ireland;4. Département de Sciences Biologiques, Université de Montréal, Pavillon Marie‐Victorin, Montréal, QC, Canada;5. Center for Limnology, University of Wisconsin, Madison, WI, USA;6. Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA;7. National Ecological Observatory Network, Boulder, CO, USA;8. Division of Outreach and Engagement, Oregon State University, Corvallis, OR, USA;9. Large Lakes Observatory & Minnesota Sea Grant, University of Minnesota Duluth, Duluth, MN, USA;10. U.S. Geological Survey, Pennsylvania Cooperative Fish & Wildlife Research Unit, Pennsylvania State University, University Park, PA, USA
Abstract:Understanding broad‐scale ecological patterns and processes often involves accounting for regional‐scale heterogeneity. A common way to do so is to include ecological regions in sampling schemes and empirical models. However, most existing ecological regions were developed for specific purposes, using a limited set of geospatial features and irreproducible methods. Our study purpose was to: (1) describe a method that takes advantage of recent computational advances and increased availability of regional and global data sets to create customizable and reproducible ecological regions, (2) make this algorithm available for use and modification by others studying different ecosystems, variables of interest, study extents, and macroscale ecology research questions, and (3) demonstrate the power of this approach for the research question—How well do these regions capture regional‐scale variation in lake water quality? To achieve our purpose we: (1) used a spatially constrained spectral clustering algorithm that balances geospatial homogeneity and region contiguity to create ecological regions using multiple terrestrial, climatic, and freshwater geospatial data for 17 northeastern U.S. states (~1,800,000 km2); (2) identified which of the 52 geospatial features were most influential in creating the resulting 100 regions; and (3) tested the ability of these ecological regions to capture regional variation in water nutrients and clarity for ~6,000 lakes. We found that: (1) a combination of terrestrial, climatic, and freshwater geospatial features influenced region creation, suggesting that the oft‐ignored freshwater landscape provides novel information on landscape variability not captured by traditionally used climate and terrestrial metrics; and (2) the delineated regions captured macroscale heterogeneity in ecosystem properties not included in region delineation—approximately 40% of the variation in total phosphorus and water clarity among lakes was at the regional scale. Our results demonstrate the usefulness of this method for creating customizable and reproducible regions for research and management applications.
Keywords:constrained spectral clustering  ecoregions  geospatial variables  lake  landscape  macroecology  macrosystems  regional spatial scale  regionalization  spatial heterogeneity
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