Mass mortality events of echinoderms: Global patterns and local consequences
Bibliographic record
Abstract
Wildlife mass mortality events can have profound ecological consequences and may be becoming more frequent or severe due to climate change, anthropogenic factors or other stressors.Mortality events involving echinoderms are of particular concern because of the important role echinoderms play in structuring marine ecosystems.In this thesis I explore the local consequences of a widespread sea star mortality event, and investigate the global trends in echinoderm mass mortality events.I found that the mass mortality of the sunflower sea star Pycnopodia helianthoides, which began in the summer of 2013 as a result of a wasting syndrome, resulted in a trophic cascade involving urchins and kelp at the local scale (i.e., Howe Sound, BC).A global review of reports of echinoderm dieoffs revealed that these events have not become more frequent or extensive since 1897.However, disease and climate change may be playing an increasing role.This study provides some of the first evidence of subtidal community shifts following sea star wasting syndrome, and highlights the need for consistent and comprehensive documentation of echinoderm population trends in the literature to increase our understanding of mass mortality events.
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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 | 0.000 |
| 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 | 0.001 |
| 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".