Assessing coastal zooplankton in the St. Lawrence estuary: spatio-temporal patterns of taxonomic and functional biodiversity
Bibliographic record
Abstract
Abstract Biodiversity assessment promotes information on the state of an ecosystem. Zooplankton, as a sentinel group at the basis of aquatic food webs, are, thus, an important component to monitor for ecosystem conservation and management. For the first time, we characterized biodiversity of coastal zooplankton along the shallow Northern shoreline of the lower St. Lawrence estuary (LSLE) using an integrated taxonomic and trait-based approach. For 3 years (2019–2021), in July and October, the zooplankton community and environmental parameters were sampled at < 35 m depth. Mesozooplankton were identified at the lowest possible taxonomic level and assigned functional traits. Community structure and diversities revealed high spatio-temporal variations among three different geographic sectors and between seasons, mainly driven by water temperature, Chlorophyll-a concentration and less by salinity. Hotspots of taxonomic and functional diversities occurred in different sectors in the same month, underlining the complementarity of the two approaches. Seasonal shifts in functional diversity hotspots highlight how environmental variability affects biodiversity beyond taxonomic metrics alone. The results of our study in the LSLE establish a first robust baseline to improve our understanding of zooplankton dynamics in the coastal LSLE, to allow future tracking of ongoing change due to the increase of anthropogenic activities and climate changes and to support future monitoring efforts.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".