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Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method
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, usi...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5415510/ https://www.ncbi.nlm.nih.gov/pubmed/28480004 http://dx.doi.org/10.1002/ece3.2884 |
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author | Cheruvelil, Kendra Spence Yuan, Shuai Webster, Katherine E. Tan, Pang‐Ning Lapierre, Jean‐François Collins, Sarah M. Fergus, C. Emi Scott, Caren E. Henry, Emily Norton Soranno, Patricia A. Filstrup, Christopher T. Wagner, Tyler |
author_facet | Cheruvelil, Kendra Spence Yuan, Shuai Webster, Katherine E. Tan, Pang‐Ning Lapierre, Jean‐François Collins, Sarah M. Fergus, C. Emi Scott, Caren E. Henry, Emily Norton Soranno, Patricia A. Filstrup, Christopher T. Wagner, Tyler |
author_sort | Cheruvelil, Kendra Spence |
collection | PubMed |
description | 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 km(2)); (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. |
format | Online Article Text |
id | pubmed-5415510 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-54155102017-05-05 Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method Cheruvelil, Kendra Spence Yuan, Shuai Webster, Katherine E. Tan, Pang‐Ning Lapierre, Jean‐François Collins, Sarah M. Fergus, C. Emi Scott, Caren E. Henry, Emily Norton Soranno, Patricia A. Filstrup, Christopher T. Wagner, Tyler Ecol Evol Original Research 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 km(2)); (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. John Wiley and Sons Inc. 2017-03-26 /pmc/articles/PMC5415510/ /pubmed/28480004 http://dx.doi.org/10.1002/ece3.2884 Text en © 2017 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Research Cheruvelil, Kendra Spence Yuan, Shuai Webster, Katherine E. Tan, Pang‐Ning Lapierre, Jean‐François Collins, Sarah M. Fergus, C. Emi Scott, Caren E. Henry, Emily Norton Soranno, Patricia A. Filstrup, Christopher T. Wagner, Tyler Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method |
title | Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method |
title_full | Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method |
title_fullStr | Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method |
title_full_unstemmed | Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method |
title_short | Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method |
title_sort | creating multithemed ecological regions for macroscale ecology: testing a flexible, repeatable, and accessible clustering method |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5415510/ https://www.ncbi.nlm.nih.gov/pubmed/28480004 http://dx.doi.org/10.1002/ece3.2884 |
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