Support for Forest Conservation Imperatives: A Robust Approach for Multi-dimensional, Spatially Explicit Resilience Assessment

Forest ecosystems are ecologically, socially, and culturally valuable, and are arguably considered essential to global sustainability. Climate change and altered disturbance regimes are threatening the future of forests around the globe. Many countries are coming together to support and implement conservation and monitoring initiatives to improve future prospects for the restoration and persistence of resilient forest ecosystems. Policies and regulations pertaining to forest conservation require reliable and informative measures of condition and management effectiveness to sustain investments and show progress. Composite indicators provide a promising approach for quantifying forest resilience across scales, yet their development is often inconsistent, lacking theoretical rigor and sensitivity to ecological context. This study presents two transparent, scalable pathways for constructing composite indicators of ecosystem resilience, with a focus on forest resilience in California’s Sierra Nevada. We operationalize the Ten Pillars of Resilience (TPOR) framework and apply two core metric selection methods: Real Core Metrics (RCM) via hierarchical clustering and Synthetic Core Metrics (SCM) via factor analysis, both combined with climate-based ecological stratification, fuzzy logic-based normalization, and optimized metric weighting. Composite indicators were developed using both compensatory and non-compensatory aggregation techniques. Results show that stratified, optimized approaches (RCM and SCM) outperformed a traditional, landscape-wide theoretical model by better capturing ecological variability and avoiding bias from outliers. The SCM approach produced more symmetric and statistically elegant indicators, while RCM indicators retained interpretability through direct ties to tangible forest attributes. We introduce an Information Retention Index (IRI) to quantify the amount of information preserved by the composite indicators and conduct a Monte Carlo-based sensitivity analysis to assess the stability of indicator outputs across methodological choices. Our pathways advance the rigor, transparency, and ecological relevance of composite indicator development, providing a robust foundation for spatially explicit resilience assessments that support adaptive management and conservation investments.