Nutrient availability is the main driver of nanophytoplankton phago-mixotrophy in North American lake surface waters
Bibliographic record
Abstract
Abstract There has been limited research on the abiotic and biotic factors affecting the prevalence of phago-mixotrophy (prevMixo) among nanophytoplankton across freshwater ecosystems. In recent years, large-scale sampling campaigns like the EPA-National Lakes Assessment and the NSERC LakePulse survey have generated surface water community composition data for hundreds of lakes across North America, covering large environmental gradients. We present results from our analyses of the nanophytoplankton community data from these two surveys, focusing on a taxonomic comparison of the mixoplankton communities across ecoregions and multivariate analyses of the environmental drivers of the prevMixo. We identified potentially phago-mixotrophic taxa in the majority of sites and across all ecozones sampled. Lake trophic state was identified as the main predictor of nanophytoplankton resource-acquisition strategy assemblages, with lower prevalence and diversity of mixoplankton communities in more eutrophic lakes. Lake trophic state also controlled the composition of the mixoplankton community and increased total phosphorus levels were associated with a loss of mixoplankton diversity. This study represents the most comprehensive assessment of the prevMixo in lake nanophytoplankton communities to date spanning hundreds of sites and a dozen ecozones.
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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".