Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method

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.

[1]  James Bailey,et al.  Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance , 2010, J. Mach. Learn. Res..

[2]  Julian D. Olden,et al.  A framework for hydrologic classification with a review of methodologies and applications in ecohydrology , 2012 .

[3]  P. Soranno,et al.  Regional variability among nonlinear chlorophyll—phosphorus relationships in lakes , 2014 .

[4]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[5]  Peng Gao,et al.  Regionalization of forest pattern metrics for the continental United States using contiguity constrained clustering and partitioning , 2012, Ecol. Informatics.

[6]  Pang-Ning Tan,et al.  Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse , 2015, GigaScience.

[7]  Raúl Ramos Lobo,et al.  Supervised regionalization methods: A survey , 2006 .

[8]  W. Hargrove,et al.  Potential of Multivariate Quantitative Methods for Delineation and Visualization of Ecoregions , 2004, Environmental management.

[9]  Tomasz F. Stepinski,et al.  Pattern-based Regionalization of Large Geospatial Datasets Using Complex Object-based Image Analysis , 2015, ICCS.

[10]  J. Lee,et al.  Multivariate Analysis of the Ecoregion Delineation for Aquatic Systems , 2002, Environmental management.

[11]  Jonathan J. Cole,et al.  Patterns and regulation of dissolved organic carbon: An analysis of 7,500 widely distributed lakes , 2007 .

[12]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[13]  Songlin Fei,et al.  Evidence of biotic resistance to invasions in forests of the Eastern USA , 2015, Landscape Ecology.

[14]  Kendra Spence Cheruvelil,et al.  Multiscale landscape and wetland drivers of lake total phosphorus and water color , 2011 .

[15]  Ian B. Marshall,et al.  A national framework for monitoring and reporting on environmental sustainability in Canada , 1996, Environmental monitoring and assessment.

[16]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[17]  Jonathan V. Higgins,et al.  A Freshwater Classification Approach for Biodiversity Conservation Planning , 2005 .

[18]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[19]  William W. Hargrove,et al.  Using multivariate clustering to characterize ecoregion borders , 1999, Comput. Sci. Eng..

[20]  F. P. Kapinos,et al.  Hydrologic unit maps , 1987 .

[21]  William W. Hargrove,et al.  A continental strategy for the National Ecological Observatory Network , 2008 .

[22]  Kendra Spence Cheruvelil,et al.  Grouping Lakes for Water Quality Assessment and Monitoring: The Roles of Regionalization and Spatial Scale , 2008, Environmental management.

[23]  Robert Perciasepe,et al.  National Strategy For The Development Of Regional Nutrient Criteria , 1998 .

[24]  Wolfgang Lucht,et al.  Forest resilience and tipping points at different spatio‐temporal scales: approaches and challenges , 2015 .

[25]  David A Gauthier,et al.  Toward a Scientifically Rigorous Basis for Developing Mapped Ecological Regions , 2004, Environmental management.

[26]  G. Allen,et al.  Freshwater Ecoregions of the World: A New Map of Biogeographic Units for Freshwater Biodiversity Conservation , 2008 .

[27]  J. Omernik Ecoregions of the Conterminous United States , 1987 .

[28]  R. Preziosi,et al.  The spatial structure of the physical environment , 1993, Oecologia.

[29]  Martin D. Buhmann,et al.  Radial Basis Functions: Theory and Implementations: Preface , 2003 .

[30]  F. Klijn,et al.  Ecoregions and ecodistricts: Ecological regionalizations for the Netherlands' environmental policy , 1995 .

[31]  T. Loveland,et al.  Ecoregions and Ecoregionalization: Geographical and Ecological Perspectives , 2004, Environmental management.

[32]  Craig A. Stow,et al.  Long-Term Citizen-Collected Data Reveal Geographical Patterns and Temporal Trends in Lake Water Clarity , 2014, PloS one.

[33]  J. Omernik,et al.  Developing a Spatial Framework of Common Ecological Regions for the Conterminous United States , 2001, Environmental management.

[34]  P. Soranno,et al.  Multi-scaled drivers of ecosystem state: quantifying the importance of the regional spatial scale. , 2013, Ecological applications : a publication of the Ecological Society of America.

[35]  T. Bailey Spatial Analysis: A Guide for Ecologists , 2006 .

[36]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[37]  Robert S. Thompson,et al.  Topographic, Bioclimatic, and Vegetation Characteristics of Three Ecoregion Classification Systems in North America: Comparisons Along Continent-wide Transects , 2004, Environmental management.

[38]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[39]  Pang-Ning Tan,et al.  Constrained spectral clustering for regionalization: Exploring the trade-off between spatial contiguity and landscape homogeneity , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[40]  Catherine A. Sugar,et al.  Finding the Number of Clusters in a Dataset , 2003 .

[41]  Robert H. Whittaker,et al.  Vegetation of the Great Smoky Mountains , 1956 .

[42]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[43]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[44]  P. Soranno,et al.  Macrosystems ecology: understanding ecological patterns and processes at continental scales , 2014 .