Assessing models for estimating future forest biodiversity and ecosystem service provision in support of Integrative Forest Management

Integrative Forest Management (IFM) seeks to balance biodiversity and ecosystem service provision through climate-resilient, adaptive, and diverse forests. Implementing IFM requires models that can project forest responses to changing conditions and management. However, uncertainty remains regarding the suitability of different models for supporting IFM and the influence of model choice on projected biodiversity and ecosystem service indicators. We evaluated three models: LandClim, 3PG mix , and dyn-ET, representing process-based, hybrid, and empirical approaches, for their ability to capture IFM principles. Using harmonized inputs, we simulated timber provision, carbon storage, deadwood volume, post-hoc index (structural and compositional diversity), and recreational value under two climate scenarios, four management scenarios, and three forest types. All models captured individual IFM principles, but none represented its full integrative and adaptive nature. Model choice strongly influenced projection magnitude and direction, often exceeding the effects of climate or management scenarios. For instance, changes in cumulative carbon storage between moderate (SSP2–4.5) and severe (SSP5–8.5) climate scenarios ranged from –3.7% to +4.4% across models, with similarly large differences under alternative management and forest type scenarios. These divergences primarily reflect differences in model structure, process and climate representation, and management implementation. No model adequately represented climate extremes or dynamically adaptive management. Our results highlight that model selection critically shapes management recommendations and that model choice must align with decision contexts for meaningful decision-support. Advancing modeling approaches is essential to support policy frameworks promoting IFM. Hence, model development should prioritize climate sensitivity, flexibility, and interoperability, while balancing complexity and usability to support IFM.