Modelling the effects of habitat on self-thinning, energy equivalence, and optimal habitat structure for juvenile trout
Bibliographic record
Abstract
Self-thinning theory predicts that decline in density with increasing individual mass should match the exponent of the metabolism–body mass relationship (∼0.9 in salmonids). However, self-thinning assumes energy equivalence (constant energy available to a cohort as it ages), which may be unrealistic for mobile taxa. I evaluate this assumption using a bioenergetic–stream habitat model to assess the sensitivity of available energy and self-thinning slopes to changes in habitat structure (percent pool). Self-thinning slopes across three age-classes of juvenile trout (young of the year, 1+, and 2+) were sensitive to both modelled habitat structure and density-independent mortality rates. Density-independent overwinter mortality generated self-thinning curves similar to those expected from metabolic allometry, even without habitat limitation (density-dependent mortality). Energy available to sympatric cohorts was unequal under most habitat configurations because of size-based differences in swimming performance that affected habitat availability and interference competition (dominance) that allowed resource monopolization by older cohorts. The optimal habitat structure that maximized abundance of the 2+ age-class (and best approximated energy equivalence) was ∼40% pool, but this value was sensitive to density-independent mortality rate and assumptions about the effect of the pool to riffle ratio on invertebrate prey production.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".