Projected climate change and limited dispersal potential threaten the seahorse species Hippocampus hippocampus and Hippocampus guttulatus
Bibliographic record
Abstract
Abstract Climate change is predicted to drive species redistribution, altering the composition of marine communities and affecting ecosystem functioning. There is increasing evidence that fish distributions follow a trend of poleward expansion, while realised niches often decrease in range size. This study aimed to assess the influence of oceanographic connectivity and intrinsic dispersal mechanisms on changes in suitable habitats of the European seahorses ( Hippocampus hippocampus and Hippocampus guttulatus ) from the present (2010–2020) until the end of the century (2090–2100). Habitat suitability was assessed using Ensemble Species Distribution Modelling under two Shared Socio-Economic Pathways (SSP2 and SSP5) coupled with biophysical modelling to estimate oceanographic connectivity. The results show that intrinsic dispersal mechanisms and oceanographic connectivity limit the northward range expansion, while predicted habitat loss remained constant in all dispersal scenarios, with the highest habitat loss forecasted in the Mediterranean and northern Africa. Consequently, considering restricted dispersal, H. guttulatus was projected to experience a negative range change (or habitat contraction [-45% (SSP5)] as opposed to an expansion with unlimited dispersal (standard model) [+ 6% (SSP5)]. On the other hand, for H. hippocampus , a more limited range expansion [+ 8% (SSP5)] was forecasted compared to the standard model [+ 17% (SSP5)]. In addition, the lower emission scenario (SSP2) resulted in half of the habitat losses, while maintaining climate refugia for H. guttulatus populations in the north Mediterranean, Iberian Atlantic Coast, and Black Sea. This study highlights the importance of considering dispersal abilities when assessing climate change impacts on marine species distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".