Spatiotemporal patterns and reliability of bobcat and Canada lynx occurrence records in British Columbia
Bibliographic record
Abstract
Understanding the distribution of a species is imperative for proper management and conservation to occur, especially in the wake of climate change and other biotic and abiotic changes that are currently occurring across the globe. Numerous species are shifting their ranges in response to these changes; tracking these shifts is the first step in conserving current and future habitats. Provincial or state-wide records, as well as citizen science, can be used to estimate broad-scale distributions, whereas camera trapping has become a common tool for detecting species at smaller scales. However, cryptic and elusive species such as mesocarnivores are difficult to monitor. Here, I use bobcats (Lynx rufus) and Canada lynx (L. canadensis) in British Columbia, Canada, as a case study to examine the data sources available for mapping mesocarnivore distributions, use those data to assess whether bobcat and lynx distributions have undergone any shifts over the past century, and determine whether these very similar-looking species can be reliably identified from camera images. I estimated the distributions of bobcats and lynx in BC using five independent data sources. Trapping records, hunting records, vehicle-kill records, trapper surveys that I distributed, and images that I solicited from the public all indicated that bobcats were restricted to southern BC, whereas lynx occurred throughout most of the interior of the province. I used trapping records and trapper surveys to determine whether bobcats or lynx have shifted their range in BC; these data suggest that distributions have remained stable over the past century. Lastly, I measured agreement among experts in classifying images of bobcats and lynx, and revealed that even experts find it difficult to distinguish between bobcats and lynx from camera images; agreement among experts in their classifications of the images was poor, and experts were inconsistent when asked to reclassify images weeks later.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".