Applying distribution model projections for an uncertain future: the case of the Pacific oyster in UK waters
Bibliographic record
Abstract
ABSTRACT The inherent complexity of the environment is such that attempts to model it must operate under simplifications and assumptions. Considering predictions from alternative models, with a range of assumptions and data requirements, therefore provides a more robust approach. The intractability and uncertainty resulting from a suite of predictions may hinder the application of science in policy, where a single prediction with little ambiguity or uncertainty would be most desirable. Few studies modelling species' distributions attempt to present multi‐model outputs in a format most useful to the non‐modelling community, and none of these have done so for the marine environment. The problem of uncertainty is particularly prevalent in predicting the distribution of invasive alien species under climate change. As invasive alien species are one of the main drivers of biodiversity loss and may incur significant economic costs, the benefit of applying predictions to highlight areas of possible establishment and inform policy and management may be large. An ensemble prediction is used to assess the distribution of suitable habitat for the Pacific oyster, Crassostrea gigas, in UK waters both currently and in the future. The ensemble incorporates predictions from three species distribution models, using data from two global climate models. A method is developed highlighting the agreement of the ensemble, further applying threshold values to retain information from constituent predictions in the final map of agreement. Ensemble predictions made here suggest that Pacific oyster will experience an opening of suitable habitat in northern UK waters, reaching the Faroe Islands and the eastern Norwegian Sea by 2050. Habitat suitability will increase with warming temperatures in the English Channel and Central North Sea for this species. The approaches applied here can be incorporated into risk assessment frameworks for invasive species, as stipulated in the Convention on Biological Diversity. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".