Termites Create Spatial Structure And Govern Ecosystem Function By Affecting N-2 Fixation In An East African Savanna
Bibliographic record
Abstract
The mechanisms by which even the clearest of keystone or dominant species exert community-wide effects are only partially understood in most ecosystems. This is especially true when a species or guild influences community-wide interactions via changes in the abiotic landscape. Using stable isotope analyses, we show that subterranean termites in an East African savanna strongly influence a key ecosystem process: atmospheric nitrogen fixation by a monodominant tree species and its bacterial symbionts. Specifically, we applied the N-15 natural abundance method in combination with other biogeochemical analyses to assess levels of nitrogen fixation by Acacia drepanolobium and its effects on co-occurring grasses and forbs in areas near and far from mounds and where ungulates were or were not excluded. We find that termites exert far stronger effects than do herbivores on nitrogen fixation. The percentage of nitrogen derived from fixation in Acacia drepanolobium trees is higher (55-80%) away from mounds vs. near mounds (40-50%). Mound soils have higher levels of plant available nitrogen, and Acacia drepanolobium may preferentially utilize soil-based nitrogen sources in lieu of fixed nitrogen when these sources are readily available near termite mounds. At the scale of the landscape, our models predict that termite/soil derived nitrogen sources influence >50% of the Acacia drepanolobium trees in our system. Further, the spatial extent of these effects combine with the spacing of termite mounds to create highly regular patterning in nitrogen fixation rates, resulting in marked habitat heterogeneity in an otherwise uniform landscape. In summary, we show that termite-associated effects on nitrogen processes are not only stronger than those of more apparent large herbivores in the same system, but also occur in a highly regular spatial pattern, potentially adding to their importance as drivers of community and ecosystem structure.
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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 | 0.001 |
| 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".