Differing drivers of decline within a migratory metapopulation has implications for future conservation
Bibliographic record
Abstract
Abstract Researchers generally ascribe demographic drivers in a single sub‐population and presume they are representative. With this information, practitioners implement blanket conservation measures across metapopulations to reverse declines. However, such approaches may not be appropriate in circumstances where sub‐populations are spatiotemporally segregated and exposed to different environmental variation. The Greenland White‐fronted Goose, Anser albifrons flavirostris, is an Arctic‐nesting migrant that largely comprises two sub‐populations (delineated by northerly and southerly breeding areas in west Greenland). The metapopulation has declined since 1999 but this trend is only mirrored in one sub‐population and the causes of this disparity are unclear. Here we compare the drivers and trends of productivity in both sub‐populations using population‐ and individual‐level analysis. We examined how temperature and precipitation influenced population‐level reproductive success over 37 years and whether there was a change in the relationship when metapopulation decline commenced. In addition, we used biologging devices to remotely classify incubation events for 86 bird‐years and modelled how phenology and environmental conditions influenced individual‐level nest survival. Correlations between reproductive success and temperature/precipitation on the breeding grounds have weakened for both sub‐populations. This has resulted in lower reproductive success for the northerly, but not southerly breeding sub‐population, which at the individual‐level appears to be driven by lower nest survival. Earlier breeding ground arrival and less precipitation during incubation increased nest survival in the northerly breeding population, while no factors examined were important for the southerly breeding sub‐population. This suggests reproductive success is driven by different factor(s) in the two sub‐populations. Demographic rates and their environmental drivers differ between the sub‐populations examined here and consequently we encourage further decomposition of demography within metapopulations. This is important for conservation practitioners to consider as bespoke conservation strategies, targeting different limiting factors, may be required for different sub‐populations.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".