Partial diel vertical migration of sympatric vendace (<i>Coregonus albula</i>) and Fontane cisco (<i>Coregonus fontanae</i>) is driven by density dependence
Bibliographic record
Abstract
Recent studies have indicated that in fish populations performing diel vertical migrations (DVM), some individuals do not migrate but reflect a resident phenotype, a pattern named as partial DVM. I present data on fish densities and the proportion of residents in Lake Stechlin (Germany) as obtained by annual midwater trawling over four discrete depths during nighttime over 8 years. The lake is inhabited by the sympatric vendace (Coregonus albula) and Fontane cisco (Coregonus fontanae). The proportion of vendace residents increased with the density of vendace, whereas the proportion of Fontane cisco residents declined with increasing density, indicating that density plays a role in the migration patterns for both species, but in opposite directions. There were almost no differences in mean size, size-frequency distributions, or Fulton condition factor between resident or migrant parts of the populations in both species. However, the proportion of dry mass in wet mass, which indicates individual nutritional status, had a tendency to be lower in migrants than in residents in both species in the years 2011, 2012, and 2013. These data suggest that density dependence may be an important factor that modifies the proportion of residents in vertically migrating fish populations. In contrast, length-dependent predation vulnerability or systematic individual differences in nutritional status were not strongly supported as potential predictors of the proportion of residents. It needs to be discussed whether partial DVM is conceptually similar to partial seasonal migrations of fish, or whether DVM reflects variants of the ideal free distribution, which are inherently density-dependent.
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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 | 0.000 |
| 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".