Spatial‐genetic structuring in a red‐breasted merganser (<i>Mergus serrator</i>) colony in the Canadian Maritimes
Bibliographic record
Abstract
The clustering of kin is widespread across the animal kingdom and two of the primary mechanisms underlying the formation of these patterns in adult kin are (1) philopatric tendencies and (2) actively maintained kin associations. Using polymorphic microsatellites, we had set out to characterize the level of genetic-spatial organization within a colony of female red-breasted mergansers (Mergus serrator) breeding on a series of small barrier islands in Kouchibouguac National Park, NB, Canada. Additionally, using nesting data from this colony, we explored possibilities for the existence of kin associations and/or cooperative interactions between these individuals; specifically in the form of the synchronization of breeding activities (i.e., incubation initiation). Our results include: (1) the detection of broad-scale genetic structuring over the entire colony, as females nesting on separate islands were to some extent genetically distinct; (2) the detection of weak, yet significant, positive spatial autocorrelation of kin at the fine scale, but only in the more densely-populated areas of this colony; and (3) the synchrony of breeding activities among proximally nesting females, apart from any factors of relatedness. While these results confirm the existence of genetic-spatial organization within this colony, the underlying mechanisms producing such a signal are inconclusive.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".