Towards a better understanding of potential impacts of climate change on marine species distribution: a multiscale modelling approach
Bibliographic record
Abstract
Abstract Aim In this paper, we applied the concept of ‘hierarchical filters’ in community ecology to model marine species distribution at nested spatial scales. Location Global, M editerranean S ea and the G ulf of G abes ( T unisia). Methods We combined the predictions of bioclimatic envelope models ( BEMs ) and habitat models to assess the current distribution of 20 exploited marine species in the G ulf of G abes. BEMs were first built at a global extent to account for the full range of climatic conditions encountered by a given species. Habitat models were then built using fine‐grained habitat variables at the scale of the G ulf of G abes. We also used this hierarchical filtering approach to project the future distribution of these species under both climate change (the A 2 scenario implemented with the M editerranean climatic model NEMOMED 8) and habitat loss (the loss of P osidonia oceanica meadows) scenarios. Results The hierarchical filtering approach predicted current species geographical ranges to be on average 56% smaller than those predicted using the BEMs alone. This pattern was also observed under the climate change scenario. Combining the habitat loss and climate change scenarios indicated that the magnitude of range shifts due to climate change was larger than from the loss of P . oceanica meadows. Main conclusions Our findings emphasize that BEMs may overestimate current and future ranges of marine species if species–habitat relationships are not also considered. A hierarchical filtering approach that accounts for fine‐grained habitat variables limits the uncertainty associated with model‐based recommendations, thus ensuring their outputs remain applicable within the context of marine resource management.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".