A field test of whether herbivores mitigate the effects of plant invasions on soil biodiversity
Bibliographic record
Abstract
Research Highlight: Marco Fioratti Junod, Irene Cordero, Nadia Chinn, Jennifer Firn, Julia Holmes, Marcus Klein, Gabrielle Lebbink, Uffe N. Nielsen, Martin Schütz, Stephan Zimmermann, Anita C. Risch. (2025). Herbivory mediates the response of below-ground food webs to invasive grasses. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.70113. A recent paper by Junod et al. (2025) tested whether herbivores could mitigate the effects of invasive African lovegrass on soil biodiversity in Australian grasslands. They found that herbivores, including livestock, native mammals and invertebrates, acted to reverse four of the 13 negative effects that African lovegrass had on soil biodiversity and function. Their work demonstrated two mechanisms through which herbivores may have acted-reducing plant litter and increasing soil temperature. The paper also highlights the difficulty of predicting the response of soil biodiversity to change because of unclear interdependencies among organisms. General techniques such as food web models, isotopic tracers and more detailed study of the lives of soil organisms will help us bridge this knowledge gap.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".