Inferred seasonal interaction rewiring of a freshwater stream fish network
Bibliographic record
Abstract
Despite evidence that seasonal variation may lead to the persistence of competing species, studies on the effect of seasonality on community network structures are sparse. Identifying whether seasonal network changes are the result of turnover or rewiring (i.e. rearrangement of interactions among species), also remains understudied in multi‐trophic communities. Using species abundance data for 38 species over three years (from nine sites across central/eastern United States) and a novel tree‐based inference method, we constructed seasonal networks for a multi‐trophic freshwater stream fish community. We found that seasonality influences species interactions, particularly through rewiring (81%) as compared to species turnover (19%). Moreover, the number of rewiring interactions was best explained by fish status as a piscivore/non‐piscivore and species maximum length (R 2 = 0.41). Our findings suggest that rewiring may be a dominant process in stream fish communities experiencing seasonal environments and that traits linked to trophic‐level could be a good indicator of a species contribution to rewiring. As networks dominated by rewiring may be more robust, this network approach could be a valuable conservation tool for identifying which biological relationships must be retained for communities to avoid extinction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".