ENSO-driven extreme oscillations in mean sea level destabilise critical shoreline mangroves—An emerging threat
Bibliographic record
Abstract
Recent ENSO-related, extreme low oscillations in mean sea level, referred to as ‘Taimasa’ in Samoa, have destabilised shoreline mangroves of tropical northern Australia, and possibly elsewhere. In 1982 and 2015, two catastrophic Taimasa each resulted in widespread mass dieback of ~76 km2 of shoreline mangroves along 2,000 km of Australia’s Gulf of Carpentaria. For the 2015 event, we determined that a temporary drop in sea level of ~0.4 metres for up to six months duration caused upper zone shoreline mangroves across the region to die from severe moisture deficit and desiccation. The two dramatic collapse events revealed a previously unrecognised vulnerability of semi-arid tidal wetland habitats to more extreme ENSO influences on sea level. In addition, we also observed a relationship between annual sea level oscillations and mangrove forest productivity where seasonal oscillations in mean sea level were co-incident with regular annual mangrove leaf growth during months of higher sea levels (March-May), and leaf shedding during lower sea levels (September-November). The combination of these periodic fluctuations in sea level defined a mangrove ‘Goldilocks’ zone of seasonal productivity during median-scale oscillations, bracketed by critical threshold events when sea levels became unusually low, or high. On the two occasions reported here when sea levels were extremely low, upper zone mangrove vegetation died en masse in synchrony across northern Australia. Such extreme pulse impacts combined with localised stressors profoundly threaten the longer-term survival of mangrove ecosystems and their benefits, like minimisation of shoreline erosion with rising sea levels. These new insights into such critical influences of climate and sea level on mangrove forests offer further affirmation of the urgency for implementing well-considered mitigation efforts for the protection of shoreline mangroves at risk, especially given predictions of future re-occurrences of extreme events affecting sea levels, combined with on-going pressure of rapidly rising sea levels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".