Sandy beach ecosystem and species red listing highlight priorities for beach conservation and restoration
Bibliographic record
Abstract
Sandy beach ecosystems and species are often referred to as ‘threatened’ or even ‘endangered’. These terms carry specific meaning in the context of IUCN Red Listing, and although the expert judgement is largely correct, rarely are there formal assessments to back up the claims that beach ecosystems and species are at risk. Our aim was to undertake the first ecosystem and species red listing for sandy beaches and their macrofauna. The IUCN Red List of Ecosystems criteria were applied to the 12 sandy beach ecosystem types in South Africa, using ecosystem maps, data on pressures to beaches, and structured expert assessment. The IUCN Red List of Species criteria were applied to 20 macrofauna species, using data from GBIF, iNaturalist, and field sampling, maps of threats to beaches, literature, and expert opinion. Three ecosystem types are Endangered, with a further four types being Near Threatened, and the remaining five being Least Concern. Of the 20 species assessed, four are Endangered ( Tylos capensis , Tylos granulatus , Acanthoscelis ruficornis, Donax serra ), three are Near Threatened ( Africorchestia quadrispinosa, Capeorchestia capensis , Pachyphaleria capensis ), and the remaining 13 are Least Concern. Notably, six of the seven threatened and Near Threatened species are supralittoral animals, and the other is harvested. We propose doing these analyses worldwide because systematic red listing can benchmark the risk of beach ecosystem and biodiversity loss, and highlight priorities for conservation and restoration, especially given the goals and targets in the Kunming-Montreal Global Biodiversity Framework.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".