A Solution to Treat Mixed-Type Human Datasets from Socio-Ecological Systems

Abstract

Abstract Coupled human and natural systems (CHANS) are frequently represented by large datasets with varied data including continuous, ordinal, and categorical variables. Conventional multivariate analyses cannot handle these mixed data types. In this paper, our goal was to show how a clustering method that has not before been applied to understanding the human dimension of CHANS: a Gower dissimilarity matrix with partitioning around medoids (PAM) can be used to treat mixed-type human datasets. A case study of land managers responsible for invasive plant control projects across rivers of the southwestern U.S. was used to characterize managers’ backgrounds and decisions, and project properties through clustering. Results showed that managers could be classified as “federal multitaskers” or as “educated specialists”. Decisions were characterized by being either “quick and active” or “thorough and careful”. Project goals were either comprehensive with ecological goals or more limited in scope. This study shows that clustering with Gower and PAM can simplify the complex human dimension of this system, demonstrating the utility of this approach for systems frequently composed of mixed-type data such as CHANS. This clustering approach can be used to direct scientific recommendations towards homogeneous groups of managers and project types.