An evaluation of isotopic (δ<sup>2</sup>H) methods to provide estimates of avian breeding and natal dispersal
Bibliographic record
Abstract
Abstract Natal and breeding dispersal represents an important component of animal demography and metapopulation theory. This phenomenon also has implications for conservation and management because understanding movements of individuals potentially allows the identification of key habitats that may be acting as population sources or sinks. Intrinsic markers such as stable isotope abundance in tissues that can be associated with provenance can provide a coarse but pragmatic solution to understanding such movements. Different methodologies have been proposed to quantify natal and breeding dispersal by using stable isotope analyses of keratinous tissues (hair, feathers), each of them with their own advantages and limitations. Here, we compared results provided by four different methods to estimate dispersal (three already published and one novel) in animals using stable isotope measurements. We used a single large dataset of feather δ2H values from golden‐winged warblers (Vermivora chrysoptera) representing five different populations breeding in North America to compare model results. We propose one method as the most adequately supported by data, and we used this method to demonstrate how biological factors explaining dispersal status can be identified and geographical origins of immigrants inferred. Our results point to a generalized methodological approach to using stable isotope data to study immigration and dispersal in birds and other animals.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".