Modelling broad‐scale wolverine occupancy in a remote boreal region using multi‐year aerial survey data
Bibliographic record
Abstract
Abstract Aim We used data from aerial surveys of wolverine tracks collected in seven winters over a 10‐year period (2003–2012) within a 574,287 km2 study area to evaluate the broad‐scale pattern of wolverine occurrence across a remote northern boreal forest region, identifying areas of high and low occupancy. Location Northern Ontario, Canada. Taxon Wolverine (Gulo gulo Linnaeus, 1758). Methods We collected wolverine tracks and observations in 100‐km2 hexagonal survey units, making a total of 6,664 visits to 3,039 units, visiting each 1–9 times. We used hierarchical Bayesian occupancy modelling to model wolverine occurrence, and included covariates with the potential to affect detection and/or occupancy probability of wolverines. Results we detected wolverines on 946 visits, 14.2% of total visits. Probability of detecting a wolverine varied among years and between the two ecozones in the study area. Wolverine occupancy was negatively related to two important covariates, the geographical coordinate Easting and thawing degree‐days. A site occupancy probability map indicated that wolverine occupancy probabilities were highest, and standard error lowest, in the western and northern portions of the study area. Main conclusions The occupancy framework enabled us to use observation data from tracks of this elusive, wide‐ranging carnivore over a vast, remote area while explicitly considering detectability and spatial autocorrelation, yielding a map of probable wolverine distribution in northern Ontario that would not be possible using other methods of detection across a large region. With resource development pressures increasing in this globally significant region in the face of a changing climate, it is important to monitor changes in distribution of species like wolverines that have low population growth rates, large spatial requirements and sensitivity to human disturbance. This study demonstrates a relatively cost‐effective and non‐invasive alternative to monitoring based on wolverine harvest records, which have not been available since 2009 in Ontario due to changes in the provincial regulatory regime for this threatened species.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".