A sharp floristic discontinuity revealed by the biogeographic regionalization of African savannas
Bibliographic record
Abstract
Abstract Aim In tropical Africa, savannas cover huge areas, have high plant species richness and are considered as a major natural resource for most countries. There is, however, little information available on their floristics and biogeography at the continental scale, despite the importance of such information for our understanding of the drivers of species diversity at various scales and for effective conservation and management. Here, we collated and analysed floristic data from across the continent in order to propose a biogeographical regionalization for African savannas. Location We collated floristic information (specifically woody species lists) for 298 samples of savanna vegetation across Africa, extending from 18° N to 33° S and from 17° W to 48° E. Taxa We focused on native woody species. Methods We used ordination and clustering to identify the floristic discontinuities and gradual transitions across African savannas. Floristic relationships, specificity and turnover, within and between floristic clusters, were analysed using a (dis‐)similarity‐based approach. Results We identified eight floristic clusters across African savannas which in turn were grouped into two larger macro‐units. Ordinations at species and genus levels showed a clear differentiation in woody species composition between the North/West macro‐unit and the South/East macro‐unit. This floristic discontinuity matches to the High (i.e. N&W) and Low (S&E) division of Africa previously proposed by White ( ) and which tracks climatic and topographical variation. In the N&W savannas, the floristic gradient determined by rainfall was partitioned into the Sudanian (drier) and Guinean (wetter) clusters. Within the highly heterogeneous S&E savannas and woodlands, six clusters were identified: Ugandan, Ethiopian, Mozambican, Zambezian, Namibian and South African. Main conclusions The proposed pan‐African classification of savannas and woodlands might assist the development of coordinated management and conservation policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".