An interpretable framework for scoring forest structural complexity in the Acadian Forest

Abstract

Forest structural complexity links biodiversity, productivity, and stability, yet its multidimensional, correlated attributes hinder reproducible comparison and operational diagnosis. Using inventory data from permanent plots across eight Acadian forest properties in Canada, we developed a management-oriented framework based on 21-plot scale attributes and a 2×2 design crossing two attribute sets (Raw-21 and Stat-Subset) with two weighting schemes (rule-based rank weights and a PCA–RF surrogate), all harmonized to a common semantic direction and 0–100 scale. An empirical baseline (M0) quantified rank rearrangements caused by methodological choice.

Raw-21 showed severe collinearity and tail instability, whereas Stat-Subset improved robustness through screening and denoising. The PCA–RF surrogate generalized well. Rank extremes were stable across models, but mid-ranked forests were method-sensitive. Rule-based weighting preserved broader mechanism coverage, whereas learning-based weighting concentrated on dominant proxies. Diagnostic outputs linked higher complexity to shifts from basic structural framework attributes toward large-diameter and deadwood elements, supporting targeted prescriptions and spatial zoning.