Social Effects on Annual Fitness in Red Squirrels
Bibliographic record
Abstract
When resources are limited, mean fitness is constrained and competition can cause genes and phenotypes to enhance an individual's own fitness while reducing the fitness of their competitors. Negative social effects on fitness have the potential to constrain adaptation, but the interplay between ecological opportunity and social constraints on adaptation remains poorly studied in nature. Here, we tested for evidence of phenotypic social effects on annual fitness (survival and reproductive success) in a long-term study of wild North American red squirrels (Tamiasciurus hudsonicus) under conditions of both resource limitation and super-abundant food resources. When resources were limited, populations remained stable or declined, and there were strong negative social effects on annual survival and reproductive success. That is, mean fitness was constrained and individuals had lower fitness when other nearby individuals had higher fitness. In contrast, when food resources were super-abundant, populations grew and social constraints on reproductive success were greatly reduced or eliminated. Unlike reproductive success, social constraints on survival were not significantly reduced when food resources were super-abundant. These findings suggest resource-dependent social constraints on a component of fitness, which have important potential implications for evolution and adaptation.
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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 | 0.000 |
| 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".