Individual differences exceed species differences in the movements of a river fish community
Bibliographic record
Abstract
Abstract Repeatable individual differences often account for large proportions of intraspecific variation in animal movements. However, meta-population models have continued to rely on single species-level and season-specific species-level means for movement prediction. Here, we test the hypothesis that repeatable individual differences can account for a similar proportion of movement distance variation as species differences. We used radio telemetry to generate repeated measures of movement from 504 hetero-specific fish. We tracked 5 large bodied fish species (Salvelinus confluentus, Oncorhynchus mykiss, Prosopium williamsoni, Thymallus arcticus, and Sander vitreus) in the upper reaches of the Peace River, British Columbia, Canada, over 8 years. We applied a hierarchical framework to partition repeatability of movement distances at the intra- and interspecific biological levels, and among short-term (within-season) and long-term (across seasons and years) temporal levels. Our results show that long-term movement distance repeatability was higher at the intraspecific level than at the interspecific level, demonstrating that animal personality can account for more variation in movement than species differences. These findings provide a novel, community level demonstration of the importance of individual variation, highlighting the predictive gains associated with a shift in the focus of spatial ecology, away from species mean and seasonal species-level mean predictive approaches, towards a spatial behavioral types-based predictive approach.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".