The devil to pay: a cost of mutualism with <i>Myrmelachista schumanni</i> ants in ‘devil's gardens’ is increased herbivory on <i>Duroia hirsuta</i> trees
Bibliographic record
Abstract
'Devil's gardens' are nearly pure stands of the myrmecophyte, Duroia hirsuta, that occur in Amazonian rainforests. Devil's gardens are created by Myrmelachista schumanni ants, which nest in D. hirsuta trees and kill other plants using formic acid as an herbicide. Here, we show that this ant-plant mutualism has an associated cost; by making devil's gardens, M. schumanni increases herbivory on D. hirsuta. We measured standing leaf herbivory on D. hirsuta trees and found that they sustain higher herbivory inside than outside devil's gardens. We also measured the rate of herbivory on nursery-grown D. hirsuta saplings planted inside and outside devil's gardens in ant-exclusion and control treatments. We found that when we excluded ants, herbivory on D. hirsuta was higher inside than outside devil's gardens. These results suggest that devil's gardens are a concentrated resource for herbivores. Myrmelachista schumanni workers defend D. hirsuta against herbivores, but do not fully counterbalance the high herbivore pressure in devil's gardens. We suggest that high herbivory may limit the spread of devil's gardens, possibly explaining why devil's gardens do not overrun Amazonian rainforests.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".