Climate change winner in the deep sea? Predicting the impacts of climate change on the distribution of the glass sponge Vazella pourtalesii
Bibliographic record
Abstract
Shallow-water sponges are often cited as being ‘climate change winners’ due to their resiliency against climate change effects compared to other benthic taxa. However, little is known of the impacts of climate change on deep-water sponges. The deep-water glass sponge Vazella pourtalesii is distributed off eastern North America, forming dense sponge grounds with enhanced biodiversity on the Scotian Shelf off Nova Scotia, Canada. While the strong natural environmental variability that characterizes these sponge grounds suggests this species is resilient to a changing environment, its physiological limitations remain unknown, and the impact of more persistent anthropogenic climate change on its distribution has never been assessed. We used Random Forest and generalized additive models to project the distribution of V. pourtalesii in the northwest Atlantic using environmental conditions simulated under moderate and worst-case CO2 emission scenarios. Under future (2046-2065) climate change, the suitable habitat of V. pourtalesii will increase up to 4 times its present-day size and shift into deeper waters and higher latitudes, particularly in its northern range where ocean warming will serve to improve the habitat surrounding this originally sub-tropical species. However, not all areas projected as suitable habitat in the future will realistically be populated, and the reduced likelihood of occurrence in its core habitat on the Scotian Shelf suggests that the existing Vazella sponge grounds may be negatively impacted. An effective monitoring programme will require tracking changes in the density and distribution of V. pourtalesii at the margins between core habitat and where losses and gains were projected.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".