Literature DB >> 29704044

Using Cluster Analysis to Compartmentalize a Large Managed Wetland Based on Physical, Biological, and Climatic Geospatial Attributes.

Ian Hahus1, Kati Migliaccio2, Kyle Douglas-Mankin3, Geraldine Klarenberg2, Rafael Muñoz-Carpena2.   

Abstract

Hierarchical and partitional cluster analyses were used to compartmentalize Water Conservation Area 1, a managed wetland within the Arthur R. Marshall Loxahatchee National Wildlife Refuge in southeast Florida, USA, based on physical, biological, and climatic geospatial attributes. Single, complete, average, and Ward's linkages were tested during the hierarchical cluster analyses, with average linkage providing the best results. In general, the partitional method, partitioning around medoids, found clusters that were more evenly sized and more spatially aggregated than those resulting from the hierarchical analyses. However, hierarchical analysis appeared to be better suited to identify outlier regions that were significantly different from other areas. The clusters identified by geospatial attributes were similar to clusters developed for the interior marsh in a separate study using water quality attributes, suggesting that similar factors have influenced variations in both the set of physical, biological, and climatic attributes selected in this study and water quality parameters. However, geospatial data allowed further subdivision of several interior marsh clusters identified from the water quality data, potentially indicating zones with important differences in function. Identification of these zones can be useful to managers and modelers by informing the distribution of monitoring equipment and personnel as well as delineating regions that may respond similarly to future changes in management or climate.

Entities:  

Keywords:  Cluster analysis; Ecosystem management; Everglades; Wetlands

Mesh:

Year:  2018        PMID: 29704044     DOI: 10.1007/s00267-018-1050-5

Source DB:  PubMed          Journal:  Environ Manage        ISSN: 0364-152X            Impact factor:   3.266


  1 in total

1.  Conductivity as a tracer of agricultural and urban runoff to delineate water quality impacts in the northern Everglades.

Authors:  Matthew C Harwell; Donatto D Surratt; Dorianne M Barone; Nicholas G Aumen
Journal:  Environ Monit Assess       Date:  2008-01-26       Impact factor: 2.513

  1 in total

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