Identifying Ecological Knowledge and Research Gaps via the African Database of Savanna Protected Areas ( <scp>ADSPA</scp> )
Bibliographic record
Abstract
ABSTRACT Aim Despite their extent (40° of latitude and 50° of longitude), research in African savannas is dominated by a few heavily studied areas. We gathered data from African savanna protected areas to (i) evaluate their contributions to the primary literature, (ii) identify environmental groupings with respect to climate, soils, and landscape variables, and (iii) analyze the determinants of tree cover and fire within groupings. Location Africa. Methods We extracted climate, soil, topography, hydrology, elephant, fire, and tree cover data from polygon boundaries for 244 African savanna protected areas. The polygon layers and data were assembled into a novel geodatabase: African Database of Savanna Protected Areas (ADSPA). Cluster analysis identified natural bioclimatic groupings and structural equation modelling was used to analyse and compare the drivers of fire and tree cover within and across clusters. Results Previous literature disproportionately focused on a few savannas: 46% of savanna publications came from 2% of protected areas. Cluster analysis identified five bioclimatic groups: (1) African hot mesic savannas, (2) African cool mesic fertile savannas, (3) West African hot semi‐arid savannas, (4) Southern African semi‐arid savannas, and (5) Kalahari arid savannas. Current savanna science in protected areas is biased toward the Southern African semi‐arid and African cool mesic fertile savannas, while hot mesic, hot semi‐arid, and arid savannas are underrepresented. Climate and soils were strongly associated with tree cover and fire across protected areas, but no significant biome‐wide effects of fire on tree cover emerged. However, tree cover was negatively related to fire in the hot mesic savanna cluster. Main Conclusions Significant biogeographic and ecological variation within African savannas highlights the need for research across the entire breadth of the biome, especially West Africa. We stress the need for spatially explicit, Africa‐wide, data on mammalian herbivore biomass to better assess the importance of this variable for savanna functioning.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".