Understanding the dynamics of fish spawning phenology and habitat in a changing ecosystem using a long-term ichthyoplankton monitoring dataset
Bibliographic record
Abstract
As ecosystems change, understanding the consequences for fish population dynamics and habitat use is essential for resource management. Using white perch ( Morone americana) survey data on early life stages collected during a long-term ichthyoplankton monitoring program in the Hudson River (New York, USA), an ecosystem under immense pressure from climate change, ecological shifts, and anthropogenic activities, we evaluated the drivers of changes in egg abundance and spawning habitat between 1980 and 2017. Results indicated that egg abundance is associated nonlinearly with temperature, conductivity, discharge, depth, location, and the week of year. We also found that egg abundance has declined within the river over time. Additionally, shifts in hotspots of spawning activity were identified, including evidence that the lower extent of spawning habitat has moved upriver since 1980. This study indicates that the dynamics of white perch early life histories and spawning habitat are changing. It also highlights the utility of a long-term monitoring program for broadening our understanding of fish ecology in the age of big data in changing ecosystems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".