Northern Riches and Rangifer Risks: A review of the Impacts of Resource Extraction for Caribou and Reindeer
Bibliographic record
Abstract
As the global demand for energy continues to rise rapidly, northern ecosystems—i.e. Arctic, subarctic, and boreal regions—are especially at risk due to their rich mineral and hydrocarbon potential. The expansion of infrastructure associated with extractive industries often impacts species and may ultimately contribute to population declines, particularly for those less resilient to environmental changes like Rangifer tarandus. If we aim to support effective conservation and mitigation measures for Rangifer as resource extraction intensifies, there is an urgent need to compile their range of response recorded toward resource extraction. We present a scoping review of 70 studies addressing the impact of mineral and hydrocarbon extraction on Rangifer to synthesize the evidence currently available in the literature, uncover trends in results, and identify remaining knowledge gaps. We recorded effects for various Rangifer populations impacted by resource extraction, with most of the studies concluding that such activities had a significant negative impact on Rangifer, ranging from impacts on 1) distribution and habitat selection, 2) movement and behaviour, 3) forage, contaminants and body condition, and 4) vital rates and demographic. Our work highlights the need to implement long term non-invasive contaminant surveys and to uncover mechanisms linking contaminant levels and behavioural responses to vital rates to better understand the long term impact of these activities on demographic trends.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".