Mapping the Habitats of the Red Sea Plume: Merging Expert and Community‐Contributed Data in a Changing Climate
Bibliographic record
Abstract
ABSTRACT The red seaweed Asparagopsis taxiformis (common name: red sea plume) is attracting global attention because of its ability to reduce methane emissions in livestock systems. However, its habitat and distribution within the Great Barrier Reef (GBR)—one of the world's most iconic marine ecosystems—remain largely unexplored, posing challenges for conservation and the sustainable development of the seaweed industry. To help bridge this gap, we used habitat suitability modelling to identify areas in the GBR with favourable environmental conditions for A. taxiformis . We combined traditional and community‐contributed data with marine spatial datasets to generate a predictive model using a machine learning approach (MaxEnt). Our findings indicate that A. taxiformis may occupy a broad habitat range along the GBR, spanning nearshore and offshore areas from the northern to southern sectors, albeit with some gaps. These potential habitats include areas with no previous records. Highly suitable habitats were found in areas with water depth of less than 20 m, minimum average seawater velocities of 0.3–0.5 m s −1 , and minimum photosynthetically active radiation levels of 25–28 E m −2 day −1 . Future projections suggest that more areas will become more suitable by 2050, possibly indicating habitat expansion. The identification of unreported potential habitats of A. taxiformis in the GBR provides a foundation for targeted monitoring and adaptive conservation and management strategies at both species and ecosystem 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".