Floristic evidence for alternative biome states in tropical Africa
Bibliographic record
Abstract
The idea that tropical forest and savanna are alternative states is crucial to how we manage these biomes and predict their future under global change. Large-scale empirical evidence for alternative stable states is limited, however, and comes mostly from the multimodal distribution of structural aspects of vegetation. These approaches have been criticized, as structure alone cannot separate out wetter savannas from drier forests for example, and there are also technical challenges to mapping vegetation structure in unbiased ways. Here, we develop an alternative approach to delimit the climatic envelope of the two biomes in Africa using tree species lists gathered for a large number of forest and savanna sites distributed across the continent. Our analyses confirm extensive climatic overlap of forest and savanna, supporting the alternative stable states hypothesis for Africa, and this result is corroborated by paleoecological evidence. Further, we find the two biomes to have highly divergent tree species compositions and to represent alternative compositional states. This allowed us to classify tree species as forest vs. savanna specialists, with some generalist species that span both biomes. In conjunction with georeferenced herbarium records, we mapped the forest and savanna distributions across Africa and quantified their environmental limits, which are primarily related to precipitation and seasonality, with a secondary contribution of fire. These results are important for the ongoing efforts to restore African ecosystems, which depend on accurate biome maps to set appropriate targets for the restored states but also provide empirical evidence for broad-scale bistability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".