Characterizing dispersal patterns in a threatened seabird with limited genetic structure
Bibliographic record
Abstract
Genetic assignment methods provide an appealing approach for characterizing dispersal patterns on ecological time scales, but require sufficient genetic differentiation to accurately identify migrants and a large enough sample size of migrants to, for example, compare dispersal between sexes or age classes. We demonstrate that assignment methods can be rigorously used to characterize dispersal patterns in a marbled murrelet (Brachyramphus marmoratus) population from central California that numbers approximately 600 individuals and is only moderately differentiated (F(ST) approximately 0.03) from larger populations to the north. We used coalescent simulations to select a significance level that resulted in a low and approximately equal expected number of type I and II errors and then used this significance level to identify a population of origin for 589 individuals genotyped at 13 microsatellite loci. The proportion of migrants in central California was greatest during winter when 83% of individuals were classified as migrants compared to lower proportions during the breeding (6%) and post-breeding (8%) seasons. Dispersal was also biased toward young and female individuals, as is typical in birds. Migrants were rarely members of parent-offspring pairs, suggesting that they contributed few young to the central California population. A greater number of migrants than expected under equilibrium conditions, a lack of individuals with mixed ancestry, and a small number of potential source populations (two), likely allowed us to use assignment methods to rigorously characterize dispersal patterns for a population that was larger and less differentiated than typically thought required for the identification of migrants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".