Seasonality of zooplankton communities in the Hudson Bay system
Bibliographic record
Abstract
The strong seasonality of sub-Arctic seas needs to be considered to understand their ecosystems. The Hudson Bay system undergoes strong seasonal changes in 1) sea ice conditions, alternating between complete ice cover in winter and open water in summer; 2) river discharge, which peaks in the spring and influences the stratification of the bay; and 3) surface circulation that consists of a weak double gyre system in spring and summer and a cyclonic system in the autumn. Recent studies that included data collected during spring have shown that the annual primary productivity in the Hudson Bay system is higher than previously reported. Similarly, the regional zooplankton assemblages have been studied mostly in late summer, possibly leading to an underestimation of the annual secondary production. Here, we use data collected during five one to six week-long expeditions of the CCGS Amundsen in the Hudson Bay system between 2005 and 2018 to describe the seasonality in mesozooplankton assemblages and investigate how it depends on environmental variables. In general, small pan-Arctic and boreal copepods such as Microcalanus spp., Oithona similis and Pseudocalanus spp. dominated the assemblages. From spring to summer, the relative abundance of the Arctic-adapted Calanus hyperboreus and Calanus glacialis decreased, while the proportion of the boreal Pseudocalanus spp. and Acartia spp. increased. The day of the year and the ice break-up date explained most of the variation in mesozooplankton assemblages. Physical processes explained most of the species distribution in spring, while the lack of lipid-rich zooplankton species in late summer and autumn, especially in coastal regions, suggests some top-down control. This lack of lipid-rich zooplankton late in the season contrasts with other seasonally ice-covered seas. More data are needed to fully understand the implications of these dynamics under climate change, but this study establishes a baseline against which future changes can be compared.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".