Sea-Level Rise and Species Conservation in Bangladesh’s Sundarbans Region
Bibliographic record
Abstract
This paper develops a methodology for identifying high-priority species conservation areas in Bangladesh’s Sundarbans region, an UNESCO World Heritage site, considering both species vulnerability and the likelihood of inundation by future sea-level rise (SLR). Our species vulnerability analysis develops a composite spatial vulnerability indicator based on total species counts, endangered species counts, endemicity, and four measures of extinction risk from the high-resolution range maps and conservation status assessments for 378 terrestrial vertebrate species provided by IUCN Bangladesh, IUCN International and BirdLife International.We extend the analysis by identifying areas where protection will fail if they are inundated by SLR in this century. We project SLR by 2100 at 120 cm, near the upper bound of the current consensus, and develop digital maps of the Sundarbans region that incorporate alternative assumptions about interim subsidence (8 cm, 35 cm) and deposition of alluvial sediment (0 cm, 40 cm). We overlay these maps with our composite species vulnerability map to produce SLR-risk-adjusted maps for priority assessment.While it would be highly desirable to protect all species of Sundarbans, resource scarcity may necessitate focusing protection on the highest-priority areas. Our analysis indicates that the highest-priority conservation status should be assigned to Sundarbans core region that has both high species vulnerability and the lowest likelihood of inundation in this century. We also identify other critical areas in four echelons of descending priority, depending upon their likelihood of inundation by sea-level rise. We hope that our methodology will contribute to cost-effective conservation management in the Sundarbans region.
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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 | 0.001 | 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 | 0.000 |
| 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".