Density‐dependent habitat selection and the ideal free distribution in marine fish spatial dynamics: considerations and cautions
Bibliographic record
Abstract
Abstract Current methods and theory used in the study of the spatial dynamics of marine fish are problematic. Positive relationships between population abundance and occupied area are typically interpreted as evidence of density‐dependent habitat selection. However, both abundance and area may co‐vary with an un‐parameterized variable, such as a density‐independent effect. In addition, if density‐dependent habitat selection is present, population growth rates in optimal habitats would be expected to be lower than in marginal habitats. This same pattern can also evolve from a large‐scale, spatially autocorrelated change in a density‐independent factor. The theory underlying density‐dependent habitat selection, the ideal free distribution, can be tautological when no a priori information of how habitat suitability changes with density is known. In this case, an ideal free distribution can be defined for any pattern of habitat‐specific population growth rates. However, these problems are not insurmountable and solutions may be found by considering spatial variation in proxies of fitness and explicitly allowing for the relative importance of habitat selection (density dependent) and environmental (density independent) effects to vary with spatial scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".