Preparing for tomorrow: A protocol for tree species parameterization in dynamic vegetation models

Projecting long-term forest dynamics under climate change requires models that extend beyond present-day species distributions to capture shifts in composition and structure. Yet, many dynamic vegetation models lack standardized approaches for incorporating climate-relevant or novel species, limiting their predictive capacity. In this study, we introduce a species parameterization protocol grounded in autecological and synecological principles using ecogram-based rankings to support the ecological integration of new tree species into dynamic vegetation models. Rather than relying on absolute trait values, our approach emphasizes relative trait rankings as a flexible framework especially suited for data-limited contexts and scenarios involving novel species assemblages. We implemented the protocol in two vegetation models, successfully parameterizing three ecologically distinct species at stand and landscape scales. The protocol yielded ecologically plausible species behavior under varying climate scenarios, indicating that species rankings, the relative ordering of species based on their ecological performance, rather than finely tuned trait values, are sufficient for reliable projections. By anchoring species in ecological realism without overreliance on site-specific data, this protocol exemplifies good modelling practice, and it enhances transparency, generality, and adaptability, key criteria for robust ecological forecasting. As such, it offers a scalable method to improve vegetation dynamic model responsiveness to climate-driven biodiversity shifts and ecosystem service dynamics. Ultimately, our approach strengthens the role of dynamic vegetation models as practical tools for adaptive forest planning and long-term strategic assessments, especially under growing environmental uncertainty.