Predicted effects of climate change on potential sources of non‐indigenous marine species
Bibliographic record
Abstract
Abstract Aim We compare the present‐day global ocean climate with future climatologies based on Intergovernmental Panel on Climate Change (IPCC) models and examine whether changes in global ocean climate will affect the environmental similarity of New Zealand's (NZ) coastal environments to those of the rest of the world. Our underlying rationale is that environmental changes to source and recipient regions may result in changes to the risk of non‐indigenous species survival and establishment. Location Coastlines of global continents and islands. Methods We determined the environmental similarity (Euclidean distance) between global coastlines and north‐east NZ for 2005 and 2050 using data on coastal seawater surface temperature and salinity. Anticipated climate models from the SRES A1B scenario family were used to derive coastal climatologies for 2050. Results During the next decades, most global regions will experience an increase in coastal seawater surface temperatures and a decline or increase in salinity. This will result in changes in the similarity of other coastal environments to north‐east NZ's coastal areas. Global regions that presently have high environmental similarity to north‐east NZ will variously retain this level of similarity, become more similar or decrease in environmental similarity. Some regions that presently have a low level of similarity will become more similar to NZ. Our models predict a widespread decrease in the seasonal variation in environmental similarity to NZ. Main conclusions Anticipated changes in the global ocean climate have the potential to change the risk of survival and establishment of non‐indigenous marine species arriving to NZ from some global regions. Predicted changes to global human transport networks over the coming decades highlight the importance of incorporating climate change into conservation planning and modelling.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".