Top‐down and bottom‐up control of litter decomposers in streams
Bibliographic record
Abstract
Summary Detritivores preferentially consume certain aquatic hyphomycete species while rejecting others. Fungal identity may therefore be a crucial factor determining stream food‐web structure and complexity and extend the impact of microbial diversity to effects up through the food web. In this study, we examined if shredder feeding is affected by the identity of fungi on leaves (bottom‐up effects) and if preferences of shredders for particular fungi affect the composition of fungal assemblages (top‐down effects). Oak leaf discs were conditioned in microcosms with six individual fungal species previously reported as highly palatable (P), unpalatable/rejected (R) and intermediate (I). Additionally, three microcosms were inoculated with three mixtures of four fungal species, each consisting of a different subset of the six species. Colonised discs were offered to three detritivores with different feeding strategies: Proasellus sp. (Isopoda), Echinogammarus meridionalis (Amphipoda) and Schizopelex festiva (Trichoptera). When offered leaves colonised by single fungal species, consumption rates by E. meridionalis and S. festiva were higher on I, followed by P and, finally, R species. Consumption rates by Proasellus sp. were similar across fungal treatments. Consumption rates by the three invertebrates were also similar across all fungal multispecies treatments, suggesting that invertebrate preferences for, or rejection of, a given fungal species may be masked when it grows in proximity to other fungi. Composition and structure of fungal communities were not significantly affected by the feeding of any of the three invertebrates. Our results suggest that certain combinations of fungal and detritivore species result in unpredictable bottom‐up and top‐down effects in stream food webs.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".