Functional and taxonomic biogeography of phytoplankton and zooplankton communities in relation to environmental variation across the contiguous USA
Bibliographic record
Abstract
For biomonitoring of aquatic ecosystems, the use of coarse group classifications, either taxonomic or functional, has been proposed as an alternative to more highly resolved taxonomic identification. We tested this proposition for phytoplankton and zooplankton using a pan-United States dataset, which also allows us to investigate biogeographic relationships between plankton groups and environmental variables. We used data from 1010 lakes composing the 2012 US National Lakes Assessment and compared relationships derived using genus-level, more aggregated taxonomic resolution and functional types. We examined responses nationally and by ecoregion. Differences in plankton assemblages among ecoregions were detected, especially at genus-level classification. Our analyses show a gradient of altitude and temperature influencing both phytoplankton and zooplankton, and another gradient of nutrients and anthropogenic activity influencing mostly phytoplankton. The overall variation in the planktonic communities explained by environmental variables ranged from 4 to 22%, but together indicated that aggregated taxonomic classification performed better for phytoplankton; for zooplankton, the performance of different classification types depended on the ecoregion. Our analyses also revealed linkages between particular phytoplankton and zooplankton groups, mainly attributable to similar environmental responses and trophic interactions. Overall, the results support the applicability of coarse classifications to infer general responses of plankton communities to environmental drivers.
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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 | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".