Reviving extinct Mediterranean forest communities may improve ecosystem potential in a warmer future
Bibliographic record
Abstract
The Mediterranean Basin is the region of Europe most vulnerable to negative climate‐change impacts, including forest decline, increased wildfire, and biodiversity loss. Because humans have affected Mediterranean ecosystems for millennia, it is unclear whether the region's native ecosystems were more resilient to climate change than current ecosystems, and whether they would provide sustainable management options if restored. We simulated vegetation with the L and C lim model, using present‐day climate as well as future climate‐change scenarios, in three representative areas that encompass a broad range of Mediterranean conditions and vegetation types. Sedimentary pollen records that document now‐extinct forests help to validate the simulations. Forests modeled under present climate closely resemble the extinct forests when human disturbance is limited; under future scenarios, characterized by increased temperatures and decreased precipitation, extinct forests are projected to re‐emerge. When combined with modeling, paleoecological evidence reveals the potential of native vegetation to re‐establish under current and future climate conditions, and provides a template for novel management strategies to maintain forest productivity and biodiversity in a warmer and drier future.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".