Drones reveal spatial patterning of sympatric Alaskan pinniped species and drivers of their local distributions
Bibliographic record
Abstract
The Arctic and its adjacent ecosystems are undergoing rapid ecological reorganization in response to the effects of global climate change, and sentinel species provide critical updates as these changes unfold. This study leverages emerging remote sensing techniques to reveal fine-scale drivers of distribution and terrestrial habitat use of two sympatric sentinel species of the central Bering Sea, the Pacific harbor seal (Phoca vitulina richardii (Gray, 1864)) and the northern fur seal (Callorhinus ursinus (Linnaeus, 1758)), at non-breeding haul-outs in the Pribilof Islands. We surveyed these species using unoccupied aircraft systems with thermal and visible-light photography, and we applied distributional modeling techniques to quantify the relative influence of habitat characteristics and social dynamics on the local distributions of these species. Drone imagery yielded locations and population counts of each species, and spatial data products allowed quantitative characterization of occupied sites, revealing that conspecific attraction is a driver of local site selection for both species, and Pacific harbor seals and northern fur seals are differentially limited by terrain characteristics. These findings represent new applications of species distribution modeling at local scales, made possible by ultra-high resolution drone surveillance and photogrammetric techniques, which add new spatial context to past observations and future scenarios in this changing ecosystem.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".