Individual isotopic specializations predict subsequent inter‐individual variation in movement in a freshwater fish
Bibliographic record
Abstract
Abstract Despite many similarities and intuitive links between individual dietary specialization and behavioral inter‐individual variation, these phenomena have been studied in isolation, and empirical data confirming relationships between these intraspecific variance sources are lacking. Here we use stable isotope analysis and acoustic telemetry to test the hypothesis that individual specialization in trophic (δ 15 N) and littoral/pelagic prey reliance (δ 13 C) covary with inter‐individual variation in movement in a group of 34 free‐swimming burbot ( Lota lota ). By performing stable isotope analysis on tissues with differing isotopic turnover rates (anal fin and dorsal muscle), in 24 lethally sampled burbot, we demonstrate that individual specialization in trophic niche (δ 15 N) and littoral/pelagic prey reliance (δ 13 C) occurred within the population. By performing stable isotope analysis on anal fins of a group of telemetry tagged burbot, we were able to show that interactions between trophic niche and littoral/pelagic prey reliance, explained a significant proportion of the subsequent between‐individual variance in mean movement rates. These findings demonstrate an empirical connection between behavioral inter‐individual variation and dietary specialization, thus providing a substantial expansion of our understanding of the wider ecological consequences of these interesting phenomena.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".