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.
Bibliographical noteFunding Information:
We thank Nicole Smith, Ed Bissell, and Scott Stopyak for their efforts with the LAGOS-NE database design, population, and management; Nick Skaff for his help with LAGOS-NEGEO QAQC and efforts with the wetland connectivity metrics; Farzan Masrourshalmani for transferring the MatLab code (created by SY) to R and drafting documentation; and Joseph Stachelek for assistance cleaning up the code and posting it to GitHub. We thank the entire CSI Limnology team for their support and input along the way. Financial support was provided by the National Science Foundation Macrosystems Biology Program in the Emerging Frontiers Division of the Biological Sciences Directorate (EF-1065786, EF-1065649, and EF-1065818) and the USDA National Institute of Food and Agriculture, Hatch project 176820. SMC was supported by an NSF Postdoctoral Research Fellowship in Biology (DBI-1401954). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
© 2017 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
- constrained spectral clustering
- geospatial variables
- regional spatial scale
- spatial heterogeneity