How mobile are fish populations? Diel movement, population turnover, and site fidelity in suckers
Bibliographic record
Abstract
Movement of organisms is an important mechanism controlling an array of processes within ecosystems. Recent analyses suggest that movement is composed of individual displacement (distance moved by individuals) and turnover (proportion of individuals moving). Turnover of individuals is important because it influences population size and structure, as well as interactions among individuals and different species within a habitat. We used stationary antennas and passive integrated transponders tags to monitor individual habitat use, turnover, and displacement of Sonora suckers (Catostomus insignis) and desert suckers (Catostomus clarkii) in the West Fork Gila River, New Mexico, USA. Many tagged fish used our focal stationary antenna reach, but only a subset was consistently present. Population size and turnover rates were variable from day to day. Although some individuals spent the majority of their time within the focal reach, most made extended departures (one or more days) from their home pools. Many individuals displayed fidelity to a particular habitat despite forays elsewhere, returning to the focal reach throughout the study. Diel or short-term movements may explain high turnover rates typically observed and, combined with high site fidelity, may result in the misclassification of individuals as sedentary, despite frequent, potentially short-term, movements into other habitats.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".