Abstract for: Balancing Forest Ecosystem Services under Climate Change - A Multi-Model Approach

Forests cover 31% of Earth's total land area, provide essential ecosystem services but face increasing threat from climate change and natural disturbances. The conventional management often prioritizes economic benefits over long-term ecological sustainability. We investigate temporal dynamics of timber production, carbon sequestration and biodiversity conservation under different management interventions and environmental disturbances in Olympic Experimental State Forest(OESF); to balance economic outputs and ecological resilience to inform adaptive forest management strategies. We employ multi-model approach combining spatially explicit LANDIS-II model with STELLA system dynamics. In LANDIS-II, we simulate forest succession, disturbances, and management over 100-years across the landscape, assessing short-(0-25years) and long-term (26-100years) impacts. STELLA model incorporates LANDIS-II outputs and socio-economic factors to explore system-wide effect and feedback loops. We examine management strategies (intensive, none, multiscale) under CMIP6 climate-scenarios, drawing upon extensive literature and data (OESF, FIA) to inform metrics (carbon-biodiversity-timber). We anticipate that our multi-model approach will yield several key insights. These include: (1) the revelation of complex feedback loops between forest structure, biodiversity, carbon storage, and economic outputs, with potential tipping points under extreme climate and disturbance scenarios; (2) we expect our results to suggest that gradient management approach emerges as a promising strategy for balancing multiple objectives, including economic viability and ecological integrity. This study will advance adaptive management strategies for forests in a changing climate and demonstrate the value of SD for addressing complex ecological and economic challenges. Our study bridges science-practice gaps and provides a user-friendly platform to promote evidence-based forest management. Through feedback loops, causal mapping, and scenario analysis, it enables stakeholders to explore management strategies trade-offs, such as harvest intensity and conservation. Authors acknowledge using AI-powered writing assistants to enhance vocabulary and grammar.