Surprising widespread Cymodocea nodosa occurrence along Israel’s Mediterranean coast and Implications for Seagrass Conservation in a hotspot of climate change
Bibliographic record
Abstract
Cymodocea nodosa is a temperate seagrass that grows in shallow and sheltered waters of the Mediterranean Sea. Although it is found in both the western and eastern basins, it was thought to be absent from the extremely warm and salty waters along the Israeli coastline, the most eastern part of the Mediterranean. We conducted methodical, seasonal, towed-diver surveys along the Mediterranean coast of Israel, recording position, depth, presence/absence of C. nodosa, seabed characteristics, and habitat complexity. We used general additive models (GAMs) to understand how the combination of depth, latitude (space), and season (time), explained the distribution of local meadows. We then compared the habitat affinity of these Israeli meadows with other sites in the Eastern Mediterranean by conducting a systematic literature review and using Species Distribution Models (SDMs). Underwater surveys unveiled the extensive distribution of C. nodosa over a narrow depth range of 8-21m (with peak occurrence at 14m) in exposed habitats. These locations are distinct from other Eastern Mediterranean populations, in which C. nodosa is found in shallower and sheltered habitats. SDMs confirmed the increase in the geographical range also reflects an increase in realized niche breadth into higher values of temperatures, salinity, and current velocity. Considering that the eastern tip of the Mediterranean is a climate change hotspot, finding C. nodosa populations surviving these harsh conditions holds implications for seagrass conservation and restoration in the entire Mediterranean. However, the low density of observed meadows suggests that these populations require careful monitoring to prevent local extirpation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".