Termite-induced heterogeneity in African savanna vegetation: mechanisms and patterns
Bibliographic record
Abstract
Objectives: To (1) assess the strength of evidence for the role of termites in vegetation heterogeneity in African savannas, and (2) identify the mechanisms by which termites induce such heterogeneity. Location: African savannas. Methods: We conducted a review of the literature, a meta-analysis and qualitative systems analysis to identify mechanisms to explain the observed patterns. Results: The review provided evidence for termite-induced heterogeneity in floristic composition and vegetation patterning in savannas across Africa. Termites induced vegetation heterogeneity directly or indirectly through their nest-building and foraging activities, associated nutrient cycling and their interaction with mammalian herbivores and fire. The literature reviewed indicated that termite mounds essentially act as islands of fertility, which are responsible for ecosystem-level spatial heterogeneity in savannas. This was supported by the meta-analysis, which demonstrated that mounds of Ancistrotermes, Macrotermes, Odontotermes (family Macrotermitinae), Cubitermes (family Termitinae) and Trinervitermes (Nasutitermitinae) are significantly enriched in clay (75%), carbon (16%), total nitrogen (42%), calcium (232%), potassium (306%) and magnesium (154%) compared to the surrounding savanna soil. Conclusions: Termite activity is one of the major factors that induce vegetation patterning in African savannas. The implications of this are discussed and research questions for future studies and modelling efforts are indicated.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".