Weak interactions between strong interactors in an old-field ecosystem: Control of nitrogen cycling by coupled herbivores and detritivores
Bibliographic record
Abstract
Interactions between herbivores and detritivores are common in greenhouse and laboratory experiments. Such interactions are thought to cause feedbacks in real ecosystems where the combined actions of these animals create either high or low nutrient cycling rates. There is limited evidence from factorial field experiments to support these expectations. We present the results of a three-year experiment wherein we factorially manipulated grasshopper herbivores and earthworm detritivores in an old-field ecosystem and tested for significant interaction effects on plants, nitrogen mineralization, and microorganisms. Then, we used a dynamical systems model built and parameterized for the study ecosystem to test the theoretical strength of these interactions. We predicted that grasshoppers and earthworms would have a positive interaction effect on plant growth and nitrogen cycling by driving plant community change. We found neither evidence for interaction effects on any of the variables we measured nor a consistent change in the composition of the plant community even though the individual effects of grasshoppers and earthworms were as expected. Our dynamical systems model made the same prediction across a broad section of parameter space (e.g., feeding rates, death rates, etc) and after longer term simulations. Our results suggest that interactions between herbivores and detritivores are only likely in situ when animals have exceptionally high individual effects on ecosystems and where the exogenous forces driving plant community change and soil biogeochemical fluxes are weak.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".