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

• Published in: Environmental management (New York) 2018-09, Vol.62 (3), p.571-583
• Main Authors: Hahus, Ian, Migliaccio, Kati, Douglas-Mankin, Kyle, Klarenberg, Geraldine, Muñoz-Carpena, Rafael
• Format: Article
• Language: English
• Subjects: Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Climate; Cluster Analysis; Conservation areas; Conservation of Water Resources - methods; Earth and Environmental Science; Ecology; Environment; Environmental Management; Florida; Forestry Management; Marshes; Nature Conservation; Quality management; Waste Water Technology; Water conservation; Water Management; Water Pollution Control; Water quality; Water Quality - standards; Water Supply - methods; Wetland management; Wetlands; Wildlife; Wildlife conservation; Wildlife refuges
• ISSN: 0364-152X; 1432-1009
• DOI: 10.1007/s00267-018-1050-5

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.