PhorEau: a new process-based model to predict forest functioning, from tree ecophysiology to forest dynamics and biogeography
Bibliographic record
Abstract
Climate change impacts forest functioning and dynamics, yet significant uncertainties persist regarding the interactions between species composition, demographic processes, and environmental drivers. While the effects of climate change on individual plant ecophysiology are better understood, few robust tools integrate these processes dynamically, hindering accurate projections and recommendations for long-term sustainable forest management. Forest gap models strike a balance between complexity and generality and are widely used in predictive forest ecology. However, their lack of explicit representation of critical processes, such as plant phenology and water use, limits their ability to fully capture tree sensitivity to climate change, calling into question the robustness of their future predictions. Therefore, incorporating trait- and process-based representations of climate-sensitive processes within gap models is a crucial step toward generating realistic predictions of forest evolution under climate change. In this study, we coupled the ForCEEPS gap model, validated across a broad range of forest types and environmental conditions in Europe, with two process-based models: a plant phenology model (PHENOFIT) and a plant hydraulics model (SurEAU), each parameterized for the main European tree species. We then evaluated the performance of the resulting PHOREAU model across multiple processes, metrics, and time- and spatial-scales, thereby minimizing the risk of equifinality. PHOREAU demonstrated robust capabilities in predicting fine hydraulic processes at both the forest and stand scales for various species and forest types. This, combined with its enhanced ability to predict stand leaf areas from inventories, led to modest improvements in annual growth predictions compared to the original ForCEEPS model and a strong capacity to predict potential community compositions. By integrating recent advancements in plant hydraulics, phenology, and competition for light and water into a dynamic, individual-based framework, the PHOREAU model bridges the gap between trait diversity and long-term forest productivity and resilience. It offers insights into complex emergent properties and trade-offs linked to diversity effects under extreme climatic events, with significant implications for sustainable forest management strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".