Arctic marine forest distribution models showcase potentially severe habitat losses for cryophilic species under climate change
Bibliographic record
Abstract
The Arctic is among the fastest-warming areas of the globe. Understanding the impact of climate change on foundational Arctic marine species is needed to provide insight on ecological resilience at high latitudes. Marine forests, the underwater seascapes formed by seaweeds, are predicted to expand their ranges further north in the Arctic in a warmer climate. Here, we investigated whether northern habitat gains will compensate for losses at the southern range edge by modelling marine forest distributions according to three distribution categories: cryophilic (species restricted to the Arctic environment), cryotolerant (species with broad environmental preferences inclusive but not limited to the Arctic environment), and cryophobic (species restricted to temperate conditions) marine forests. Using stacked MaxEnt models, we predicted the current extent of suitable habitat for contemporary and future marine forests under Representative Concentration Pathway Scenarios of increasing emissions (2.6, 4.5, 6.0, and 8.5). Our analyses indicate that cryophilic marine forests are already ubiquitous in the north, and thus cannot expand their range under climate change, resulting in an overall loss of habitat due to severe southern range contractions. The extent of marine forests within the Arctic basin, however, is predicted to remain largely stable under climate change with notable exceptions in some areas, particularly in the Canadian Archipelago. Succession may occur where cryophilic and cryotolerant species are extirpated at their southern range edge, resulting in ecosystem shifts towards temperate regimes at mid to high latitudes, though many aspects of these shifts, such as total biomass and depth range, remain to be field validated. Our results provide the first global synthesis of predicted changes to pan-Arctic coastal marine forest ecosystems under climate change and suggest ecosystem transitions are unavoidable now for some areas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".