Fjord oceanographic dynamics provide refuge for critically endangered <i>Pycnopodia helianthoides</i>
Bibliographic record
Abstract
Disease outbreaks as a driver of wildlife mass mortality events have increased in magnitude and frequency since the 1940s. Remnant populations, composed of individuals that survived mass mortality events, could provide insight into disease dynamics and species recovery. The sea star wasting disease (SSWD) epidemic led to the rapid >90% decline of the sunflower star Pycnopodia helianthoides . We surveyed the biomass density of P. helianthoides on the central British Columbia coast before, during and after the arrival of SSWD by conducting expert diver surveys in shallow subtidal habitats from 2013 to 2023. We found a rapid decline in biomass density following the onset of SSWD in 2015. Despite consistent recruitment post-outbreak to sites associated with outer islands, we found repeated loss of large adult individuals over multiple years. Within nearby fjord habitats, we found remnant populations composed of large adult P. helianthoides . The interaction of temperature and salinity with the biomass density of P. helianthoides varied by location, with high biomass density associated with higher temperatures in the outer islands and with lower temperatures and higher salinity in the fjords. These patterns suggest that fjords provide refuge from consequences of SSWD and protecting these populations could be imperative for the species.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".