Population variation in density‐dependent growth, mortality and their trade‐off in a stream fish
Bibliographic record
Abstract
Abstract Important variation in the shape and strength of density‐dependent growth and mortality is observed across animal populations. Understanding this population variation is critical for predicting density‐dependent relationships in natural populations, but comparisons amongst studies are challenging as studies differ in methodologies and in local environmental conditions. Consequently, it is unclear whether: (a) the shape and strength of density‐dependent growth and mortality are population‐specific; (b) the potential trade‐off between density‐dependent growth and mortality differs amongst populations; and (c) environmental characteristics can be related to population differences in density‐dependent relationships. To elucidate these uncertainties, we manipulated the density (0.3–7 fish/ ) of young‐of‐the‐year brook trout (Salvelinus fontinalis) simultaneously in three neighbouring populations in a field experiment in Newfoundland, Canada. Within each population, our experiment included both spatial (three sites per stream) and temporal (three consecutive summers) replication. We detected temporally consistent population variation in the shape of density‐dependent growth (negative linear and negative logarithmic), but not for mortality (positive logarithmic). The strength of density‐dependent growth across populations was reduced in sections with a high percentage of boulder substrate, whereas density‐dependent mortality increased with increasing flow, water temperature and more acidic pH. Neighbouring populations exhibited different mortality‐growth trade‐offs: the ratio of mortality‐to‐growth increased linearly with increasing density at different rates across populations (up to 4‐fold differences), but also increased with increasing temperature. Our results are some of the first to demonstrate temporally consistent, population‐specific density‐dependent relationships and trade‐offs at small spatial scales that match the magnitude of interspecific variation observed across the globe. Furthermore, key environmental characteristics explain some of these differences in predictable ways. Such population differences merit further attention in models of density dependence and in science‐based management of animal populations.
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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 | 0.000 | 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".