Depth-resolved vertical distribution of the pteropod <i>Limacina helicina</i> in the Northeast Pacific and its implications for exposure to ocean acidification
Bibliographic record
Abstract
Abstract The pteropod Limacina helicina has become an important bioindicator for the impacts of ocean acidification on marine ecosystems, yet its vertical distribution and diel vertical migration (DVM) patterns remain poorly understood. Understanding these behaviours is critical to accurately predict the risks of ocean acidification to pteropods since the depth ranges they inhabit strongly influence their exposure to water corrosive to aragonite shells (i.e. $\Omega _{Ar} &lt; 1$), given the natural vertical gradients in pH and $\Omega _{Ar}$. To resolve the vertical distribution of L. helicina, we utilized an existing dataset consisting of 179 vertically stratified zooplankton net tows from the Northeast Pacific spanning 1983–2019. Using conventional observational analyses and Bayesian statistical models, we determine and compare the average day and night vertical distributions of two size ranges of L. helicina, plus those of the strong vertical migrator euphausiid Euphausia pacifica and a non-migratory control group of mollusc larvae. We show that the average day and night vertical distributions and mean depths of L. helicina do not differ and closely match those of the non-migratory control, indicating that L. helicina does not perform DVM in this region. Typical mean depths of L. helicina are ∼50–70 m, with $\ge$75% of the population occupying the upper $\sim$100 m, and $\ge$50% being found in the upper $\sim$50 m, regardless of body size and time of day. Given the typical shape of $\Omega _{Ar}$ profiles in the ocean, we estimate that pteropod exposure to low $\Omega _{Ar}$ may be overestimated if calculated using the standard vertically integrated approach (i.e. a homogeneous depth distribution) as opposed to our depth-resolved vertical distribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".