Effects of wasting disease on sea star populations in southern California: Variations over a 40-year period
Bibliographic record
Abstract
ABSTRACT Sea star wasting disease has been described for asteroid populations from many parts of the world and for numerous species. The causative agents are generally unknown. However, within the Southern California Bight, a Vibrio bacterium appears to have been the cause of wasting disease in the 1980s, and probably the 1990s, during warm water episodes associated with El Ninos. Although a densovirus was implicated in the mass mortality of one species of subtidal sea star during the geographically widespread epizootics that began in 2013, the etiology appears to vary among species, locations, and time periods, and in some cases may not even be associated with a pathogen. We have been studying subtidal benthic communities in southern California since the 1970s and have documented the population effects of wasting disease epizootics associated with warm water El Niño events from 1980 through 2020. Until 2015, population declines coincident with wasting disease were followed by various degrees of recovery, whereas after 2015 there has been little or no recovery at our study sites. Prior to 2013, wasting disease in the northeast Pacific was only reported from locations in the Gulf of California, the southern California bight, and the coast of Vancouver Island, British Columbia. Since 2013, epizootics of wasting disease among sea stars have been observed along the entire west coast of North America. We speculate that the lack of recovery after 2015 may be due to a reduction in larval supply caused by the greater geographical extent of the disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".