Population density and territory size in juvenile rainbow trout,<i>Oncorhynchus mykiss</i>: implications for population regulation
Bibliographic record
Abstract
We manipulated population density of juvenile rainbow trout ( Oncorhynchus mykiss ) across a range of realistic densities in artificial stream channels, while controlling food abundance in two different ways: in Experiment 1, the total amount of food was held constant over a threefold increase in density, whereas in Experiment 2, the per capita amount of food was held constant over an eightfold increase in density. We tested the contrasting predictions that territory size (i) is not affected by population density; (ii) decreases with population density as 1/n, where n = the local population size; or (iii) decreases with population density but towards an asymptotic minimum size. In Experiment 1, territory size decreased with increasing population density. With the broader range of densities used in Experiment 2, territory size initially decreased with density and then leveled off at a minimum territory radius of 20–30 cm. Our results suggest an asymptotic minimum size of about 0.2 m 2 for a 5 cm rainbow trout, similar to what is observed for high-density conditions in the wild. This minimum territory size could potentially set an upper limit on local population density and help regulate the population size of stream salmonids.
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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 | 0.001 |
| 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".