Replication Data for: Clusters and Gradients for Classifying Land-Use Intensity in Ecological Research

Classifying land-use intensity is central to ecological research and soil health monitoring, yet widely used a priori categories (e.g., conventional', extensive', `semi-natural') risk oversimplifying the diversity of management practices. Here, we tested whether a priori categories, post priori clusters derived from management survey data, or a continuous gradient better captured variation in grassland management. We surveyed 18 Dutch grasslands, recording fertilization, mowing, grazing, and related practices, and compared classification schemes using hierarchical clustering, sparse principal component analysis, and remote-sensing-derived Sentinel-2 Red-Edge Position (S2REP) as an independent proxy of management intensity. A priori and post priori clusters showed moderate agreement (ARI = 0.72), but the weak cluster separation and the lack of an optimal number of clusters supported a gradient structure. Indeed, the gradient approach explained most variation in S2REP (AIC = 94.9, BIC = 102.6), outperforming both categorical schemes. Our results show that land-use intensity is better represented as a continuum rather than discrete categories, providing a more nuanced basis for linking management to ecological outcomes. We advocate gradient-based classification, supported by transparent metrics, as a default approach in ecological and soil health research.