The Role of Satellite Telemetry Data in 21st Century Conservation of Polar Bears (Ursus maritimus)
Bibliographic record
Abstract
Satellite telemetry (ST) has played a critical role in the management and conservation of polar bears ( Ursus maritimus ) over the last 50 years. ST data provide biological information relevant to subpopulation delineation, movements, habitat use, maternal denning, health, human-bear interactions, and accurate estimates of vital rates and abundance. Given that polar bears are distributed at low densities over vast and remote habitats, much of the information provided by ST data cannot be collected by other means. Obtaining ST data for polar bears requires chemical immobilization and application of a tracking device. Although immobilization has not been found to have negative effects beyond a several-day reduction in activity, over the last few decades opposition to immobilization and deployment of satellite-linked radio collars has resulted in a lack of current ST data in many of the 19 recognized polar bear subpopulations. Here, we review the uses of ST data for polar bears and evaluate its role in addressing 21 st century conservation and management challenges, which include estimation of sustainable harvest rates, understanding the impacts of climate warming, delineating critical habitat, and assessing potential anthropogenic impacts from tourism, resource development and extraction. We found that in subpopulations where ST data have been consistently collected, information was available to estimate vital rates and subpopulation density, document the effects of sea-ice loss, and inform management related to subsistence harvest and regulatory requirements. In contrast, a lack of ST data in some subpopulations resulted in increased bias and uncertainty in ecological and demographic parameters, which has a range of negative consequences. As sea-ice loss due to climate warming continues, there is a greater need to monitor polar bear distribution, habitat use, abundance, and subpopulation connectivity. We conclude that continued collection of ST data will be critically important for polar bear management and conservation in the 21 st century and that the benefits of immobilizing small numbers of individual polar bears in order to deploy ST devices significantly outweigh the risks.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".