Abundance and species diversity hotspots of tracked marine predators across the North American Arctic
Bibliographic record
Abstract
Abstract Aim Climate change is altering marine ecosystems worldwide and is most pronounced in the Arctic. Economic development is increasing leading to more disturbances and pressures on Arctic wildlife. Identifying areas that support higher levels of predator abundance and biodiversity is important for the implementation of targeted conservation measures across the Arctic. Location Primarily Canadian Arctic marine waters but also parts of the United States, Greenland and Russia. Methods We compiled the largest data set of existing telemetry data for marine predators in the North American Arctic consisting of 1,283 individuals from 21 species. Data were arranged into four species groups: (a) cetaceans and pinnipeds, (b) polar bears Ursus maritimus (c) seabirds, and (d) fishes to address the following objectives: (a) to identify abundance hotspots for each species group in the summer–autumn and winter–spring; (b) to identify species diversity hotspots across all species groups and extent of overlap with exclusive economic zones; and (c) to perform a gap analysis that assesses amount of overlap between species diversity hotspots with existing protected areas. Results Abundance and species diversity hotpots during summer–autumn and winter–spring were identified in Baffin Bay, Davis Strait, Hudson Bay, Hudson Strait, Amundsen Gulf, and the Beaufort, Chukchi and Bering seas both within and across species groups. Abundance and species diversity hotpots occurred within the continental slope in summer–autumn and offshore in areas of moving pack ice in winter–spring. Gap analysis revealed that the current level of conservation protection that overlaps species diversity hotspots is low covering only 5% (77,498 km2) in summer–autumn and 7% (83,202 km2) in winter–spring. Main conclusions We identified several areas of potential importance for Arctic marine predators that could provide policymakers with a starting point for conservation measures given the multitude of threats facing the Arctic. These results are relevant to multilevel and multinational governance to protect this vulnerable ecosystem in our rapidly changing world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".