Larger body size leads to greater female beluga fitness at the southern periphery of their range
Bibliographic record
Abstract
Identifying phenotypic characteristics of evolutionarily fit individuals provides important insight into the evolutionary processes that cause range shifts with climate warming. Female beluga whales (Delphinapterus leucas) from the Canadian high Arctic (BB) residing in the core region of the species’ geographic range are 14% larger than their conspecifics at the southern periphery in Hudson Bay (HB). We investigated the causal mechanism for this north (core)-south (periphery) difference as it relates to fitness by combining morphometric data with ovarian corpora counted in female reproductive tracts. We found evidence for reproductive senescence in older HB females from the southern peripheral population but not for BB whales. Female beluga whale fitness in the more-northern BB increased faster with age (48% partial variation explained) versus a more gradual slope (25%) in HB. In contrast, body length in HB female beluga accounted for five times more of the total variation in fitness compared to BB whales. We speculate that female HB beluga fitness was more strongly linked with body length due to higher density, as larger body size provides survival advantages during seasonal food limitations. Understanding the evolutionary mechanism of how fitness changes will assist conservation efforts in anticipating and mitigating future challenges to peripheral populations.
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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) | 0.004 | 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".