Seasonality in Southern Ocean isoscapes
Bibliographic record
Abstract
Polar marine ecosystems are particularly vulnerable to the effects of climate change. Warming temperatures, freshening seawater and disruption to sea ice formation potentially all have detrimental cascading effects on food webs. New approaches are needed to better understand spatio-temporal interactions among biogeochemical processes at the base of Southern Ocean food webs, and how these interactions vary seasonally. In marine systems, isoscapes (models of the spatial variation in the stable isotopic composition) of carbon and nitrogen identify the spatial expression of varying biogeochemical processes on nutrient utilization by phytoplankton. Isoscapes also provide a baseline for interpreting stable isotope compositions of higher trophic level animals in movement, migration and diet research. Here we produce carbon and nitrogen isoscapes across the entire Southern Ocean (>40°S) using surface particulate organic matter (POM) isotope data, collected from multiple sources over the past 50 years and throughout the annual cycle. We use Integrated Nested Laplace Approximation (INLA)-based approaches to predict mean annual isoscapes and four seasonal isoscapes using a suite of environmental data as predictor variables. Clear spatial gradients in δ 13 C and δ 15 N values were predicted across the Southern Ocean, consistent with previous statistical and mechanistic isoscape views of isotopic variability in this region. We identify strong seasonal variability in both carbon and nitrogen isoscapes, with key implications for the use of static or annual average isoscape baselines in animal studies attempting to document seasonal migratory or foraging behaviours.
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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 | 0.001 |
| 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 | 0.000 |
| 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".