Towards marine microbiome community bioindicators for monitoring Arctic plankton in response to climate change
Bibliographic record
Abstract
The Arctic is being heavily impacted by climate change. The air temperature is rising more than 2 times faster than the rest of the globe, sea ice cover is shrinking every year and we are observing increased freshwater inputs from melting coastal glaciers (Ardyna & Arrigo, 2020).We attempt to quantify the impact of these evolving parameters on the Arctic marine microbiome, which is at the base of the marine food web and ecosystems. In particular, we develop meta-omics-based bioindicators, targeting bacterial plankton, and investigating community growth rates as a key ecological trait to study the global plankton response to environmental changes. This adaptive phenotypic trait, which has been shown to consistently vary with water temperature in non-Arctic bacteria (Abreu et al., 2023), can be estimated for single organisms and can also be averaged to capture the evolution of an entire community.Here, we compare multiple methods linking omics data to growth rate and explore how the relationship between growth and temperature behaves in Arctic waters. We demonstrate its robustness by quantifying the impact of nutrients on the temperature-growth rate relationship. Finally, we integrate data from ocean and sea ice ecosystems collected during large-scale Arctic campaigns (i.e., TOPC, FRAM, MOSAIC, Refuge-Arctic) in order to decipher the spatiotemporal distribution of this relationship in the Arctic.Altogether, this work will be instrumental to understand the various local responses of the Arctic microbiome ecosystems to contrasted perturbations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".