Riverine transport and nutrient inputs affect phytoplankton communities in a coastal embayment
Bibliographic record
Abstract
Abstract Rivers often transport phytoplankton to coastal embayments and introduce nutrients that can enrich coastal plankton communities. We investigated the effects of the Nottawasaga River on the nearshore (i.e. within 500 μm of shore) phytoplankton composition along a 10‐km transect of Nottawasaga Bay, Lake Huron in 2015 and 2016. Imaging flow cytometry was used to identify and enumerate algal taxa, which were resolved at sizes larger than small nanoplankton (i.e. >5 μm). Multivariate analysis (perMANOVA and redundancy analysis) and a dilution model were used to examine how nutrients and the transport of algal taxa affected community composition in the bay. Sampling stations with different percentages of river water had significantly different phytoplankton communities. Phytoplankton community composition was also strongly associated with nutrients, including total phosphorus, which also varied with the percentage of river water. The majority of the 51 phytoplankton taxa identified in 2016 had numerical abundances in the bay that could be explained simply by the dilution of incoming river water. Phytoplankton transported from the river had a higher proportion of edible‐sized cells (<30 μm), particularly in summer when colonial cyanobacteria were numerically dominant in the bay. Six taxa were more abundant than expected from the dilution of river water and included some cyanobacteria with late summer maxima. Five of the taxa that were transported from the river were less abundant than expected in the bay. Whereas impacts of fertilisation due to the characteristically higher nutrient concentration in the river are to be expected, the strong and highly correlated effects of transport within the narrow coastal band of this study largely concealed any distinct fertilisation effects. Riverine inputs may strongly influence the nearshore assemblage of phytoplankton in oligotrophic embayments in large lakes, creating hotspots for productivity, species turnover, and trophic dynamics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".