Food quality influences density-dependent fitness, but not always density-dependent habitat selection, in red flour beetles (Coleoptera: Tenebrionidae)
Bibliographic record
Abstract
Abstract Density-dependent habitat selection models inherently rely on the negative relationship between population density and mean fitness in different habitats. Habitats differing in quality, such as different food sources or habitat structure, can have different strengths of density-dependent relationships, which can then affect patterns of density dependence in habitat selection. We tested the hypothesis that density dependence in fitness dictates the patterns in density-dependent habitat selection: individuals should prefer higher-quality habitat over lower-quality habitat. We used controlled experiments with red flour beetles (Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae)) to measure density dependence of fitness and to examine density-dependent habitat selection by beetles in wheat (Triticum Linnaeus (Poaceae)), corn (Zea mays Linnaeus (Poaceae)), and soy (Glycine max (Linnaeus) Merrill (Fabaceae)) flour habitats. Despite large differences in fitness between habitats (fitness was the highest in wheat flour, lower in corn flour, and zero in soy flour), beetles showed only weak preference for wheat over corn flour and for corn over soy flour, but showed strong preference for wheat over soy flour. These preferences were the strongest at low density. This study gives insight into the relationship between habitat quality and density-dependent habitat selection in flour beetles.
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 | 0.000 | 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".