Simulated glacial and interglacial vegetation across Africa: implications for species phylogenies and trans‐African migration of plants and animals
Bibliographic record
Abstract
Abstract The paleoenvironmental context of plant and animal species evolution (including glacial migrations and population separations) is based on a very patchy and incomplete paleo‐phytogeographic record. It was our objective, therefore, to provide an additional source for paleovegetation comparison by presenting simulations from a state‐of‐the‐art fully coupled earth system model (HadCM3LC). We simulated potential paleovegetation distributions following pre‐Industrial and last glacial maximum (LGM) climate forcing for the continent of Africa. Our LGM simulations indicate that tropical broadleaf forest was not severely displaced by expanding grasslands within central Africa, although the outer extent of closed forest decreases, particularly in the north. Our simulations indicate that the structure of glacial forests may have been much different from today, in that LGM simulations indicate that forests were likely characterized by lower leaf area indexes, lower tree heights and lower vegetation carbon content. On the other hand, warmer interglacial climate (like our pre‐Industrial climate scenario) results in simulated expansion of tropical forest from coast to coast across central Africa that we postulate could have acted as a barrier to plant and animal species migrations. We suggest that our modeling experiments have implications for the interpretation of phylogenetic data, including that of our own species, Homo sapiens sapiens .
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".