Termites and ungulates affect arbuscular mycorrhizal richness and infectivity in a semiarid savanna
Bibliographic record
Abstract
In savanna ecosystems, mound-building termites and ungulate herbivores profoundly affect the abundance and diversity of aboveground organisms. Yet, surprisingly little is known about how these two groups interact to impact belowground communities. Using the Kenya Long-term Exclosure Experiment (KLEE), where ungulate herbivores have been excluded for over 15 years, we examined how the presence of termites and ungulate herbivore exclusion affected species richness, community composition, and infectivity of arbuscular mycorrhizal fungi (AMF). We also measured plant richness and soil nutrients to examine how the effects of termites and ungulate exclusion may indirectly impact AMF communities. AMF richness and infectivity and plant richness were significantly lower on termite mounds than in off-mound areas. AMF infectivity and plant richness were significantly higher in off-mound areas, especially where herbivores had access. Our results revealed a strong suppressive effect of termites on AMF communities that was not enhanced or ameliorated by the presence of ungulate herbivores. Herbivores, by contrast, enhanced the relationship between plants and their fungal symbionts but only in the absence of the suppressive effects of termites. Our results underscore the importance of multiple drivers affecting the patterns of both above- and below-ground communities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".