Ocean warming and multiple source populations increase the threat of an invasive bryozoan to kelp beds in the northwest Atlantic Ocean
Bibliographic record
Abstract
Climate change is expected to create more favourable climatic conditions for many invasive species, increasing their abundance and range. One such invasive species is Membranipora membranacea, an epiphytic bryozoan causing defoliation of kelp beds in the northwest Atlantic Ocean (NWA). The impact of M. membranacea is directly linked to its abundance, which is anticipated to increase due to climate change. Additionally, further range expansion may threaten Arctic kelp beds in the future. We constructed a species distribution model (SDM) to predict the abundance of M. membranacea in the NWA under present and future climate scenarios. We also assessed the effect of a possible additional invasion of M. membranacea from populations in Norway. The projected future abundance distribution of M. membranacea in the NWA differed substantially depending on the future climate scenario employed, but the bryozoan was predicted to occur in the Arctic at low abundances regardless of the scenario. However, we also found that populations of M. membranacea in Norway achieve much higher abundances at lower temperatures than NWA populations and could pose a dire threat to kelp beds in the NWA and southern Canadian Arctic if introduced in these regions. Although the SDMs performed well under internal validation, estimating the impact of M. membranacea is complicated by the context-dependent response of kelp communities to coverage by the bryozoan. Nonetheless, this study provides valuable predictions of the response of an ecologically significant invasive species to climate change with findings of broader relevance to the study of other invasive organisms.
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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 | 0.001 |
| 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".