Exploring regionalization techniques: Simulation and application in ecology
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Authors
Lohman, Madeleine
Issue Date
2025
Type
Thesis
Language
en_US
Keywords
Alternative Title
Abstract
In this thesis, I examine regionalization algorithms in the context of spatial discretization for ecological research. Regionalization refers to the process in which areal units are created based on spatial relationships between smaller polygons and the similarity or dissimilarity of characteristics between those polygons. Chapter 2 consists of a non-exhaustive literature review detailing multiple regionalization algorithms and cluster optimization or scoring indices, briefly discussing their history, applications within the literature, and the statistical and computational methods underlying their operation. Cluster optimization indices are used to determine how many clusters are best for a certain set of data and a given regionalization algorithm. Chapter 3 applies these algorithms and indices to three simulated datasets, or landscapes, generated with low, medium, and high amounts of variation. There, I compare the performance if different combinations of regionalization algorithms and cluster optimization indices across this range of landscape variation. I demonstrate the tremendous variety of outcomes that different combinations of algorithms and cluster optimization indices can produce. I discuss the differences in clustering between each algorithm/index combination both generally and according to level of variation in the simulated data and compare how similar or dissimilar of results each algorithm produces. Results indicated that the AZP and REDCAP algorithms tended to produce regionalization schemas with the best fit, regardless of the level of variation in the simulated landscape. Among clustering algorithms, the SD index tended to optimize regionalization algorithms to have a number of clusters with the highest goodness of fit. However, this high goodness of fit may result from a large amount of clusters being recommended by the SD index. Chapter 4 uses one of the regionalization algorithms, REDCAP, in with real data to discretize the Prairie Pothole Region in North America. I data relevant to the management of waterfowl species in the area and then discuss the importance of non-statistical science in the evaluation of algorithm results. While often treated as a unified area to waterfowl ecologists, this region shows a tremendous amount of landscape diversity. This diversity may generate differences in wildlife population dynamics across the region. As such, creating discrete spatial units in this area, based on relevant ecological data, may aid ecologists studying waterfowl population trends. The final results indicate that 25 regions in the PPR may exist in which it is reasonable to separately study waterfowl populations.