A data-driven framework for evaluating categorical versus gradient representations of land-use intensity

Classifying land-use intensity is central to ecological research and soil health monitoring, yet commonly used a priori categories (e.g. “conventional”, “extensive”, “semi-natural”) may oversimplify the diversity of management practices. Such simplification can obscure continuous variation in management intensity that more accurately reflects underlying ecological processes. Here, we present a data-driven analytical workflow to evaluate alternative representations of land-use intensity based solely on management data, prior to linking management to ecological responses. The framework combines unsupervised clustering, gradient-based ordination, and quantitative diagnostics to assess whether management intensity in a given system is better represented as discrete categories, emergent clusters, or gradients. Using management survey data from 18 Dutch grasslands, we compared a priori land-use classes with post priori clusters and a gradient derived from multivariate management variables. Clustering revealed only weak separation among fields with different category labels, while the gradient captured a smoother pattern of management variation and aligned more closely with an independent remote-sensing proxy. Model comparison further indicated that the gradient-based representation provided a slightly better representation of management intensity than categorical alternatives. Our results demonstrate how data-driven evaluation of classification structure can inform the choice of representation for land-use intensity, reducing the risk of imposing arbitrary categories on continuous management variation. The proposed workflow is broadly applicable to ecological contexts where heterogeneous management or monitoring data must be translated into accurate variables, including environmental monitoring and ecosystem assessment.