An uncertain future for woodland caribou (Rangifer tarandus caribou): The impact of climate change on winter distribution in Ontario
Bibliographic record
Abstract
Habitat alteration and climate change are two important environmental stressors posing increasing threats to woodland caribou, Rangifer tarandus caribou, in Ontario. Our first objective was to identify the importance of linear features, habitat, and climate on the occurrence of woodland caribou during the winter season using over 30 years of records (1980-2012). Our second objective was to forecast the impacts of climate change on the future occurrence and range of woodland caribou. Woodland caribou occurrence and environmental data collected during 1980 to 2012 were obtained from the Ontario Ministry of Natural Resources (OMNR). Logistic regression models were used to identify the importance of linear features, habitat, and climate on woodland caribou. We then forecast future caribou occurrences using 126 future climate projections. Woodland caribou preferred coniferous forests and mixed forests that tended to be associated with increased lichen coverage, and regions with colder winters. Woodland caribou also avoided anthropogenically disturbed regions, such as areas associated with high road density or developed areas. Caribou range extent was projected to contract by 57.2-100% by 2050 and 58.9-100% by 2070. Furthermore, all 126 climate change scenarios forecast a range loss of at least 55% for woodland caribou in Ontario by 2050. We project complete loss of woodland caribou in Ontario if winter temperatures increase by more than 5.6°C by 2070. We found that woodland caribou in Ontario are sensitive to changes in climate and forecasted that an average of 95% of Ontario’s native woodland caribou could become extirpated by 2070. The greatest extirpations were projected to occur in the northernmost regions of Ontario as well as northeastern Ontario, while regions in western Ontario were projected to have the lowest rates of extirpation. This underscores the importance of mitigating greenhouse gases as a means to protect this iconic 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".