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

Area of Science:

• Wetland Ecology
• Geospatial Analysis
• Conservation Science

Background:

• Managed wetlands require effective spatial zonation for optimal resource management.
• Understanding spatial heterogeneity is crucial for ecological monitoring and conservation efforts.
• Arthur R. Marshall Loxahatchee National Wildlife Refuge faces challenges in managing its diverse wetland ecosystems.

Purpose of the Study:

• To compartmentalize Water Conservation Area 1 using geospatial attributes.
• To compare hierarchical and partitional clustering methods for wetland zonation.
• To identify management zones based on physical, biological, and climatic data.

Main Methods:

• Hierarchical cluster analysis (average linkage) and partitional cluster analysis (partitioning around medoids) were employed.
• Geospatial data encompassing physical, biological, and climatic factors were utilized.
• Cluster results were compared for spatial aggregation, evenness, and outlier identification.

Main Results:

• Partitional methods yielded more evenly sized and spatially aggregated clusters.
• Hierarchical methods proved effective in identifying outlier regions.
• Geospatial clusters showed similarity to water quality-based clusters, with geospatial data enabling finer subdivision.

Conclusions:

• Geospatial zonation provides a valuable tool for wetland management and ecological modeling.
• Subdivision of marsh areas based on geospatial attributes can reveal functional differences.
• This approach supports informed decisions regarding monitoring, resource allocation, and climate change adaptation.