Global variation in zooplankton niche divergence across ocean basins
Bibliographic record
Abstract
Modelling responses to climate change assume zooplankton populations remain similar over time with little adaptation (niche conservatism). Oceanic barriers, genetic, phenotypic variation and species interactions in cosmopolitan species could drive niche divergence within species. We assess niche divergence among 224 globally distributed species across the seven main ocean basins. There were 357 diverged niches out of 828 ocean basin comparisons. The proportion of diverged niches varied both across and within phyla. Copepoda (156 of 223 species) were used to test for niche divergence between same species populations across different environmental gradients. Global niche divergence was found to be more likely for species in colder temperatures and near shore environments. Opposing temperature responses were found for four comparisons which may relate to the different connectivity patterns between them. This study demonstrates adaptive potential across environmental-niche gradients, which must be considered when modelling population responses to climate change.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".