Genetic relatedness shapes social dynamics in a threatened finch: implications for population assessment
Bibliographic record
Abstract
Tropical granivorous finches often form large flocks around resources. The composition of these flocks, whether they are random groups of individuals or comprise related birds travelling together, is currently unknown. To bridge this knowledge gap, we combined high-frequency location tracking with comprehensive genetic sequencing to investigate the relationship between pairwise association strength and genetic relatedness in Gouldian finches, Erythrura gouldiae , as they ranged across the landscape. We found that birds captured within close proximity were more genetically similar than birds captured further away and generally moved across the landscape together. These findings show that for the Gouldian finch, within-flock associations are influenced by genetic relatedness, and we argue that forming sibling subgroups may assist young Gouldian finches to optimally locate resources across this harsh landscape. Maintaining stable and consistent flock compositions as they move across the landscape and visit waterholes may have implications for estimating population size for Gouldian finches. This is because it will reduce the likelihood of single bird revisits and double counts within the same sampling window during waterhole surveys. By considering the flock as a distinct unit, reasonably accurate estimates could be made of a local population, and repeated counts on consecutive days could be assumed to provide reliable and replicable abundance estimates. • Integrating genetics with biotelemetry reveals social dynamics in flocking birds. • Genetic relatedness is correlated with association strength in Gouldian finches. • Gouldian finches captured close together are more likely to travel together. • Counting surveys should consider that flocks might be more stable than expected.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".