Learning from long time series of harvest and population data - Swedish insights for European goose management
Bibliographic record
Abstract
Goose management in Europe is faced by multiple challenges, as some species are declining and in need of conservation actions, while other populations have become very abundant, resulting in calls for increased harvest. Sweden has long-term series of harvest data and counts of breeding and autumn-staging geese. We used national data (indices) for greylag goose, bean goose, and Canada goose to study shifts in temporal trends and correlative patterns, and to infer possible causal links between harvest and population trends. Our study provides an opportunity to guide management given the data collected within the present monitoring, as well as to suggest improvements for future data collection. The populations of greylag and Canada geese increased in Sweden 1979–2018, but this long-term trend included a recent decrease in the latter species. Bean goose breeding index decreased, whilst staging numbers and harvest varied with no clear long-term trend. For Canada goose, our analysis suggests that harvest may affect population growth negatively. For bean goose and greylag goose we could not detect any effect of harvest on autumn counts the following year. We find that the present data and analysis of coherence may suffice as basis for decisions for the current management situation in Sweden with its rather unspecific goals for greylag (very abundant ) and Canada goose (invasive species) populations. However, for management of bean geese, with international concerns of over harvest, data lack crucial information. For future management challenges, with more explicit goals, for all goose species we advocate information that is more precise. Data such as hunting effort, age-structure of goose populations, and mark-recapture data to estimate survival and population size, is needed to feed predictive population models guiding future Swedish and European goose management.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 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".