Intraspecific interference in a tropical stream shredder guild
Bibliographic record
Abstract
The structure of stream communities is typically thought to be driven by stochastic events such as floods, in contrast with communities in many other systems in which biotic interactions have a major role. However, it is possible that biotic interactions are important in some situations in streams, especially where resources are limited and physical influences are stable for substantial periods. Leaf litter – the main energy source and a distinct habitat in forest streams – constitutes a patchy resource where biotic interactions among and within consumer species are likely to occur. The intraspecific interference in four leaf-eating species (shredders), common in Australian tropical streams, was experimentally examined – Anisocentropus kirramus (Trichoptera : Calamoceratidae), Lectrides varians and Triplectides gonetalus (Trichoptera : Leptoceridae) and Atalophlebia sp. (Ephemeroptera : Leptophlebiidae). All four species showed some degree of intraspecific interference, indicated by lowered leaf breakdown rates when density increased. Breakdown rates per capita decreased exponentially for all species with increased density, with slight differences among species. These differences were more evident when body size was taken into account, with breakdown rates depressed at lower densities for the two species with larger body sizes, T. gonetalus and Atalophlebia sp. Overall breakdown rates did not always increase with higher densities, because they were compensated for by depressed individual breakdown rates. Our results indicate that intraspecific interference can be an important mechanism regulating leaf breakdown in streams.
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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 | 0.000 | 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".