Estimating bobcat and Canada lynx distributions in British Columbia
Bibliographic record
Abstract
ABSTRACT Understanding the distribution of a species is useful before undertaking management and conservation actions. Distribution estimates provide ecological insights about a species, and help frame the scope and scale of research questions. However, compiling reliable distribution information is a challenge for elusive mesocarnivores such as bobcats (Lynx rufus) and Canada lynx (L. canadensis). In British Columbia, Canada, bobcats and lynx are key mesocarnivores ecologically and are important furbearers, but their distributions are poorly understood. We compiled and compared 5 independent sources of bobcat and lynx records in British Columbia to gain a better understanding of their provincial distributions: trapping records, hunting records, vehicle‐kill records, trapper surveys, and images solicited from the public. Our objectives were to compare bobcat and lynx distributions derived from each of these data sources, and provide reliable estimates for the distribution of each species in British Columbia between 2008 and 2017. Although each method has unique advantages and limitations, all data sources indicated similar distributions, and each data source provided unique locations for the final distribution maps that we derived. Bobcats were restricted to the southern half of British Columbia, whereas lynx occurred across most of the interior of the province. Bobcat and lynx distributions broadly overlapped in southern British Columbia, but image detections generally occurred at higher elevations for lynx than bobcats. We demonstrate the utility of combining multiple data sources when estimating species distributions, and highlight the usefulness of citizen science in such studies. © 2018 The Wildlife Society.
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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 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".