Is geographical variation in the size of Australian shorebirds consistent with hypotheses on differential migration?
Bibliographic record
Abstract
In differential migrants the members of different age-classes or sex travel to geographically separate non-breeding areas. Here, we test five competing hypotheses explaining differential migration using more than 40 000 records of 22 species of shorebirds (Charadriiformes) occurring at two non-breeding areas at different distance from the breeding grounds and that also differ in climate. We showed that across species, the larger sex was more abundant in south-eastern than in north-western Australia. Size, as indicated by wing-length, was greater in the south-east than in the north-west for both males and females, whereas bill-length showed the opposite pattern. Based on these trends we conclude that the interaction between ambient temperature, body-size and bill-length determines the geographical distribution of shorebirds wintering in Australia. Our findings are not consistent with the resource partitioning, dominance and arrival time hypotheses. This is the first study that disassociates overlapping predictions of competing hypotheses on differential migration, thus contributing to our understanding of the evolution of differential migration in birds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".