Habitat associations of marine predators in the northern California Current during the low productivity downwelling season
Bibliographic record
Abstract
Introduction Eastern Boundary Upwelling Systems are some of the most productive marine ecosystems in the world. Little is known about habitat associations and spatial distributions of marine predators during seasonal periods of low productivity because there are few at-sea surveys during this period. During low productivity or prey scarcity, predators consuming similar prey in the same time and space may compete for limited resources, or they may avoid competition by exploiting different habitats or occupying separate spaces (i.e. niche partitioning). In this study, we examined habitat associations and niche partitioning of marine predators during the low-productivity winter downwelling season of the northern California Current Ecosystem (CCE). Methods Seabird and marine mammal counts were continuously collected during systematic at-sea surveys during February–March/April in the northern California Current across four years (2006, 2008, 2009, and 2012). We examined seabird and marine mammal distributions in relation to seven habitat characteristics [i.e., sea surface temperature (°C), salinity, depth (m), seafloor slope (%), distance from shore (km), and distance from the 100 m and 200 m isobaths (km)]. We used a non-parametric multivariate analysis [i.e. canonical correspondence analysis (CCA)] to quantify species’ habitat associations and directional distribution ellipses to explore overlap in species core winter habitat. Results Results show 49 seabird and ten marine mammal species inhabit the CCE during this low productivity period, including endangered southern resident killer whales (Orcinus orca). Seabirds and marine mammals exhibited significant but low overlap in habitat associations (i.e. weak niche partitioning) and similar habitat associations to summer studies. Discussion We also found that some species with similar foraging strategies showed asymmetrical spatial range overlap (i.e. common murre (Uria aalge) and parakeet auklet (Aethia psittacula)), which may mean that expected increased competition due to climate change can negatively affect some species more than others. Given that climate change is leading to increased frequencies, intensities, and durations of marine heat waves during winter months, addressing the winter ecology knowledge gap will be important to understanding how climate change is going to affect species that reside in or migrate through the northern California Current during the low productivity downwelling season.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".