Table_1_Untangling Effects of Human Disturbance and Natural Factors on Mortality Risk of Migratory Caribou.docx
Bibliographic record
Abstract
<p>Human disturbances are rapidly increasing in northern and Arctic regions, raising concerns about the recovery and persistence of declining caribou (Rangifer tarandus) populations. Yet, the consequences of behavioral responses toward human disturbances on vital rates rarely have been investigated. Herein, we assessed the cumulative and instantaneous effects of human disturbances (roads, human settlements, mines and mining exploration) at different temporal scales on the mortality risk of 254 GPS- collared migratory caribou monitored in two herds, the Rivière-aux-Feuilles (RFH) and Rivière-George (RGH) herds, in northern Québec and Labrador, Canada. We also assessed the relative importance of human disturbances on caribou mortality risk compared with non-anthropogenic factors, including habitat use by caribou, predation risk by gray wolves (Canis lupus), and local weather conditions. Human disturbances alone, exclusive of hunting, had a limited impact on mortality risk of caribou. Repeated exposure to disturbances did not have detectable effects on mortality risk during the early life period (1−7 years old), but more abundant precipitation (RFH) or the use of areas with a higher predation risk (RGH) did so. At the seasonal scale, non-anthropogenic factors, particularly the use of highly selected habitat by caribou and air temperature, had a greater effect than anthropogenic factors on the mortality risk in the RFH. Caribou of the RFH using more frequently higlhy selected habitats decreased their chance of mortality during winter, whereas individuals using warmer areas during summer faced a higher risk of mortality. At the daily scale, we observed that anthropogenic and non-anthropogenic factors generally had either no effect on the daily risk of mortality, or their effects were undistinguishable from the effect of latitude, with which they were highly correlated. The only exception was for the RFH in winter, for which the daily risk of mortality increased 10 folds for each 10-km increment closer to industrial disturbances. Although the impacts of human disturbances on caribou survival were limited to specific regions and areas, we nevertheless detected a negative effect on survival on the RFH, even at the currently low level of human development. Our study highlights the importance of assessing effects of human disturbances at various spatiotemporal scales, and of considering the relative influence of other non-anthropogenic factors to fully understand drives of wildlife populations.</p>
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".