Modelling the biogeographic boundary shift of <i>Calanus finmarchicus</i> reveals drivers of Arctic Atlantification by subarctic zooplankton
Bibliographic record
Abstract
Biological communities in the Arctic are changing through the climate-driven encroachment of subarctic species. This "Atlantification" extends to keystone Calanoid copepods, as the small-bodied Calanus finmarchicus increases in abundance in areas where it overlaps with larger Arctic congeners. The environmental factors that are facilitating this shift, whether related to optimal conditions in temperature or seasonality, remain unclear. Assessing these drivers at an Arctic-wide scale is necessary to predict future ecosystem change and impacts. Here we have compiled range-wide occurrences of C. finmarchicus and a suite of seasonal biophysical climatologies to build a boreo-Arctic ecological niche model. The data set was divided into two eras, 1955-1984 and 1985-2017, and an optimized MaxEnt model was used to predict the seasonal distribution of the abiotic niche of C. finmarchicus in both eras. Comparing outputs between eras reveals an increase in habitat suitability at the Arctic range edge. Large and significant increases in suitability are predicted in the regions of the Greenland, Labrador, and Southern Barents Seas that have experienced reduced sea-ice cover. With the exception of the Barents Sea, these areas also show a seasonal shift in the timing of peak habitat suitability toward an earlier season. Our findings suggest that the Atlantification of Arctic zooplankton communities is accompanied by climate-driven phenology changes. Although seasonality is a critical constraint to the establishment of C. finmarchicus at Arctic latitudes, earlier sea-ice retreat and associated productivity is making these environments increasingly favorable for this subarctic 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".