Mutualism, herbivory, and invasive ants as seasonally dependent drivers of root surface area in a foundational savanna ant–plant
Bibliographic record
Abstract
Many plants are defended from herbivory by costly insect mutualists. Understanding positive associations between plants and mutualists requires a whole-plant perspective including roots. We hypothesized that root surface area increases with mutualist activity (to a saturation threshold) and recent rainfall but that this relationship shifts when herbivores are excluded. We also hypothesized that invasive ants limit root surface area and that mutualism breakdown driven by invaders blunts root responses to rainfall and herbivore exclusion. Using minirhizotrons (est. 2021), we surveyed root surface area of ant-acacias during a dry (2022) and then a wet (2023) season. Study plots either excluded or permitted vertebrate browsers, within a natural experiment comparing mutualist-defended ant-acacias to those invaded by a mutualism-disrupting ant. Root area increased with mutualist activity to a threshold, but this positive association was less apparent during rainy periods. Megabrowser exclusion increased overall root area but reduced the threshold for a positive association with mutualist activity and reduced the steepness of the root area-rainfall correlation. Ant-invaded acacias had smaller root areas that correlated less steeply with rainfall. Positive associations between insect defense and root area were thus contingent on rainfall, herbivory, and biotic invasion, drivers that are shifting under global change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".