Broad geographic variation in age- and sex-dependent origin of harvested eurasian wigeon (Mareca penelope) revealed by stable-hydrogen (δ2H) isotope analyses of feathers
Bibliographic record
Abstract
Abstract Migratory waterfowl are a harvested resource shared among multiple European countries, exposing them to potential overexploitation. Management of take is challenging since the life cycle of migratory waterfowl consists of several stages distributed among several locations, with possible spatio-temporal overlap among populations with differing population trends. Successful harvest management in such situations requires knowledge about the connections between breeding and non-breeding locations, and where birds are harvested. Breeding populations of Eurasian wigeon ( Mareca penelope ) are declining in Finland, underlining the need for more effective harvest management. Relative proportions and temporal distribution of local breeding birds and migrants from a larger Russian breeding population within the Finnish hunting bag has been unknown to date. We studied spatio-temporal origins of Finnish harvested wigeon by measuring stable-hydrogen ( δ 2 H) isotope values from legally harvested birds. We modelled the changes in δ 2 H values of the feather samples within the hunting season using Gaussian processes and found that the origin of harvested wigeon in Finland changed during the hunting season and differed by age and sex. In juveniles and adult females but not in adult males, origin of harvested birds shifted from local and possibly western Russian birds to more long-distance migratory birds during the harvest season. These patterns likely reflected sex- and age-specific differences in migratory behaviour of Eurasian wigeon in the East Atlantic flyway, which can be used to guide future management and conservation of this species through the implementation of spatio-temporal harvest regulation.
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 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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".