Mapping socio‐ecological systems in Idaho: Spatial patterns and analytical considerations

Abstract

Policy interest in socio‐ecological systems has driven attempts to define and map socio‐ecological zones (SEZs), that is, spatial regions, distinguishable by their conjoined social and bio‐geo‐physical characteristics. The state of Idaho, USA, has a strong need for SEZ designations because of potential conflicts between rapidly increasing and impactful human populations, and proximal natural ecosystems. Our Idaho SEZs address analytical shortcomings in previously published SEZs by: (1) considering potential biases of clustering methods, (2) cross‐validating SEZ classifications, (3) measuring the relative importance of bio‐geo‐physical and social system predictors, and (4) considering spatial autocorrelation. We obtained authoritative bio‐geo‐physical and social system datasets for Idaho, aggregated into 5‐km grids = 25 km2, and decomposed these using principal components analyses (PCAs). PCA scores were classified using two clustering techniques commonly used in SEZ mapping: hierarchical clustering with Ward's linkage, and k‐means analysis. Classification evaluators indicated that eight‐ and five‐cluster solutions were optimal for the bio‐geo‐physical and social datasets for Ward's linkage, resulting in 31 SEZ composite types, and six‐ and five‐cluster solutions were optimal for k‐means analysis, resulting in 24 SEZ composite types. Ward's and k‐means solutions were similar for bio‐geo‐physical and social classifications with similar numbers of clusters. Further, both classifiers identified the same dominant SEZ composites. For rarer SEZs, however, classification methods strongly affected SEZ classifications, potentially altering land management perspectives. Our SEZs identify several critical regions of social–ecological overlap. These include suburban interface types and a high desert transition zone. Based on multinomial generalized linear models, bio‐geo‐physical information explained more variation in SEZs than social system data, after controlling for spatial autocorrelation, under both Ward's and k‐means approaches. Agreement (cross‐validation) levels were high for multinomial models with bio‐geo‐physical and social predictors for both Ward's and k‐means SEZs. A consideration of historical drivers, including indigenous social systems, and current trajectories of land and resource management in Idaho, indicates a strong need for rigorous SEZ designations to guide development and conservation in the region. Our analytical framework can be broadly applied in SES research and applied in other regions, when categorical responses—including cluster designations—have a spatial component.