Advancing statistical models to reveal the effect of dissolved oxygen on the spatial distribution of marine taxa using thresholds and a physiologically based index
Bibliographic record
Abstract
The rapid pace of ocean change has prompted a need to forecast likely future species distributions. Species distribution models are often categorized as either correlative (statistical) or mechanistic, and each has limitations both for advancing understanding and for prediction. Here we sought to benefit from mechanistic understanding of how and why low dissolved oxygen affects species' distributions by applying physiologically informed statistical models to the spatial distribution of sablefish Anoplopoma fimbria , a deep‐dwelling commercially important groundfish. We fit spatial models to trawl‐survey data on catch rate, local temperature and dissolved oxygen, and estimated parameters of the metabolic index, which provided a way to express the temperature‐dependence of oxygen tolerance. We fit generalized linear mixed effects models with Gaussian random fields to capture the latent spatially fixed variables, and included both linear and breakpoint functions for pO 2 and the metabolic index. The best fitting models all included breakpoint effects of pO 2 , and the estimated threshold value of 0.05 atm is close to levels in laboratory studies where metabolism begins to decline. Models based on the metabolic index were not as well supported as those that included pO 2 , likely because of the decrease in temperature and slight increase in pO 2 at deep (> 800 m) depths. These findings illustrate that statistical models of species distributions can be improved by incorporating knowledge of how physiological mechanisms operate. Furthermore, they illustrate that even species with high tolerance for low dissolved oxygen may undergo species distribution shifts in the face of growing oxygen depletion in coastal ocean ecosystems.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".