Demography, dynamics and data: building confidence for simulating changes in the world's forests

作者：Annemarie Eckes‐Shephard, Arthur Argles, Bogdan Brzeziecki, Peter M. Cox, Martin G. De Kauwe, Adriane Esquivel‐Muelbert, Rosie A. Fisher, G. C. Hurtt, Jürgen Knauer, Charles D. Koven, Aleksi Lehtonen, Sebastiaan Luyssaert, Laura Marqués, Lei Ma, Guillaume Marie, J. R. Moore, Jessica Needham, Stefan Olin, Mikko Peltoniemi, Karl Piltz, Hisashi Sato, Stephen Sitch, Benjamin D. Stocker, Ensheng Weng, Daniel Zuleta, Thomas A. M. Pugh · 发表于：New Phytologist · 年份：2025 · DOI：10.1111/nph.70643 · 被引用次数：7 · 研究领域：Forest ecology and management、Plant Water Relations and Carbon Dynamics、Ecology and Vegetation Dynamics Studies

Summary Vegetation demographic models (VDMs) are advanced tools for simulating forest responses to climate and land‐use changes, and are essential for projecting carbon cycling and large‐scale forest management strategies. Despite their increasing incorporation into Earth System Models, VDMs differ in their demographic assumptions, with no prior quantitative comparison of their performance. We benchmarked nine VDMs against observational data from boreal, temperate and tropical sites, assessing their accuracy in predicting tree growth, carbon turnover, biomass stocks and size distributions. Models were simulated under consistent climate conditions with postdisturbance recovery monitored for at least 420 yr. Postdisturbance carbon recovery trajectories showed significant variability while remaining within observational ranges. Initial regrowth rates varied substantially (0.03–0.60, 0.18–0.70 and 0.35–1.10 kgCm −2 yr −1 for boreal, temperate and tropical sites, respectively), influenced by each model's initial forest state. Models captured mature forest carbon content but showed compensating effects between overestimated growth and underestimated mortality rates. This first multi‐model benchmarking identifies growth and mortality rates as critical calibration targets and highlights the need to refine postdisturbance establishment conditions for model development. We outline specific benchmarking variables needed to improve predictions of forest responses to environmental change.