An unseen synchrony or recurrent resource pulse opportunity? linking fisheries with aeroecology
Bibliographic record
Abstract
Abstract Understanding insect and fish interactions from a spatial and temporal perspective can have implications on large‐scale phenology in freshwater systems, yet current information is limited. We employed a novel approach of combining information from acoustic telemetry for six freshwater fish species and weather radar to assess the phenology of mayfly emergence and foraging patterns of freshwater fish. We hypothesized that freshwater fish conduct synchronous movements with annual mayfly hatches as a pulse resource opportunity. Generalized additive models were developed to assess movement distance as a function of species and time; before, during, and after annual mayfly hatch events. A cross‐section abundance index was also employed to quantify dynamics of aerial mayflies. Hatch dynamics revealed nocturnal emergence behaviour with annual variations in intensity, spatial extent, and origin. We found that the hatch was likely a pulse resource feeding opportunity for channel catfish, common carp, freshwater drum, and walleye instead of a synchronized feeding event. Bigmouth buffalo and lake sturgeon utilized riverine habitat away from the hatch and did not likely forage on the emerging mayflies. Remote sensing of fishes and emergent insects using our approach is the first attempt at bridging the capabilities of fisheries ecology and aeroecology to advance movement ecology.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".