Mystery of the Disappearing Dogfish: Transboundary Analyses Reveal Steep Population Declines Across the Northeast Pacific With Little Evidence for Regional Redistribution
Bibliographic record
Abstract
ABSTRACT Quantifying broad‐scale population trends and distribution change is critical for effective management and conservation of marine species, particularly under climate change. However, fragmented regional survey data often hinder such efforts for transboundary populations. A prime example is Pacific Spiny Dogfish ( Squalus suckleyi , Squalidae), a small shark with a remarkably slow life history and wide‐ranging distribution. Dogfish are now caught incidentally but were heavily fished along the Pacific US–Canada coast ≈80 years ago. Reports on local population trends have conflicted along the coast, suggesting that movement between regions may be responsible. We fit spatiotemporal models integrating data from 10 surveys to synthesise trends in biomass, abundance, distribution and thermal niche for dogfish across their entire eastern Pacific Ocean range. Prior to 2003, Alaskan biomass increased through the 1990s whereas California to British Columbia indices were variable and imprecise. However, during 2003–2023, we found a coastwide 51% (95% CI: 38%–61%) decline in dogfish biomass with mature females and immature dogfish showing the largest proportional declines. Regionally, declines were steepest for the US West Coast (71%–85%) and Canada (58%–82%), while Alaska showed less severe declines (13%–54%). Off the US West Coast, dogfish shifted into deeper waters as temperatures in their habitat increased, but these patterns do not explain the coastwide declines. Our results suggest population declines are primarily driven by reduced abundance rather than between‐region movement, indicating elevated coastwide conservation concern and helping focus investigations of causal mechanisms.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".