Spatial modelling of non-target exposure to anticoagulant rodenticides can inform mitigation options in two boreal predators inhabiting areas with intensive oil and gas development
Bibliographic record
Abstract
Intensive industrial development occurs in the ecologically significant boreal forest, including oil and gas development in northern Alberta, Canada. This forest is home to many highly-valued animal species including fisher (Pekania pennanti; formerly Martes pennanti) and American marten (Martes americana). Second-generation anticoagulant rodenticides (SGARs) are commonly used near human infrastructure in developed areas to control and reduce damage from rodent pests. High body burdens of SGARs in rodent prey pose risks of secondary poisoning for fisher and marten that readily consume rodents. The objective of this research was to determine if fisher and marten living in anthropogenically-disturbed areas of northern Alberta showed evidence of SGAR exposure. Fisher and marten carcasses were collected from the region, aged, sexed, and liver samples were analysed for rodenticides using liquid-chromatography mass spectrometry (LCMS). SGARs were found in the livers of non-target fisher and marten. As SGARs were found in the livers of fisher with sufficient frequency for complete statistical analysis, analyses including ANOVA, linear regression, and spatial cluster analyses were used to assess spatial patterns exhibited by fisher exposure frequencies against potential explanatory variables such as boreal anthropogenic disturbances and land cover classes. Additionally, companies operating in the region were surveyed to identify their current rodent control measures in an effort to verify the results of the spatial analyses. This is the first study to demonstrate non-target SGAR exposure of fisher and marten in Canada. Exposure frequency in fisher exhibited clustering, which showed the strongest relationships to factors including total boreal disturbances, number of oil sands mines, and broadleaf forest cover. The spatial methods used in this paper provide tools to develop local interventions for mitigation and conservation efforts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".