Landscape suitability and range expansion estimates for the North American Interior Population of trumpeter swans
Bibliographic record
Abstract
ABSTRACT The Interior Population of trumpeter swans ( Cygnus buccinator ) has grown and expanded substantially since reintroduction efforts began in the 1950s. To support management of this population, we developed a landscape suitability model and range expansion estimates (2023 2033). We assessed landscape suitability for breeding trumpeter swans with a use-available modeling design based on GPS collar data (20191iJ2023) and derived home range information from swans in the western Great Lakes Region of the U.S. and Canada. We related occurrence and pseudo-absences to landscape-scale summaries of upland and wetland conditions in a logistic mixed-effects model. We spatially applied the model and incorporated these estimates into a range-expansion model that identified the likelihood of a 50-km grid cell being occupied by 2033. We summarized time-series citizen-science data within cells and related newly occupied range cells and unoccupied cells (2004 2023) to year, survey effort, the amount of suitable breeding landscapes, and distance to previously occupied cells in a logistic regression model. Landscape suitability was positively related to greater amounts of wetland perimeter and foraging areas. New range cells were positively associated with suitable breeding landscapes and survey intensity and negatively associated with year and distance to previously occupied cells. We estimated a 4.4% (95% CI: 2.0-6.9%) annual range expansion rate from 2023 to 2033, with expansion occurring in the Prairie Pothole Region of the Dakotas and the Boreal Shield and James Bay Lowlands of Canada. North Dakota and South Dakota allow tundra swan ( Cygnus columbianus ) hunting but not for trumpeter swans. Our models can be useful for targeting hunter education at a local scale to mitigate unintended take of the similar looking species, allowing for pioneering trumpeter swans to establish persistent populations. Furthermore, our range expansion model can help jurisdictions with no or low breeding trumpeter swan populations anticipate future swan distribution and abundance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".