Seasonal variation in marine bird distribution in the northeast Pacific Ocean
Bibliographic record
Abstract
Human activities have profound influences on marine ecosystems. Marine birds are particularly sensitive to these impacts and, given their ease of observation and diverse life histories, often represent good indicators of ecosystem health. Conserving marine birds and their ecosystems requires robust predictions of species distribution to help mitigate human disturbance in areas where large aggregations of diverse species occur. We modelled variation in marine bird species diversity (Shannon-Wiener Index) and taxonomic family level probability of occurrence to map the intensity and extent of highly diverse ‘hotspots’ in Canada’s Pacific Exclusive Economic Zone. To do so, we paired 20 yr of survey data from the North Pacific Pelagic Seabird Database (1997-2017) and remote sensing data describing marine conditions and local geography (sea surface temperature, chlorophyll a, bathymetry, distance to shore, and benthic substrate type). These data were used to illustrate how seasonality within years and the El Niño-Southern Oscillation (ENSO) across years influenced spatial patterns in diversity. Hotspots were most persistent in Hecate Strait, off the west coast of Vancouver Island, and surrounding the Scott Islands in most seasons. Changes in hotspot locations and intensity were observed across seasons and within season under varying ENSO conditions. Our results provide a template for mapping marine species distribution for the purpose of identifying hotspots of diversity, and thereby facilitate planning to minimize harmful impacts in highly diverse and dynamic systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".