The taxonomic and functional biogeographies of phytoplankton and zooplankton communities across boreal lakes
Bibliographic record
Abstract
A bstract Strong trophic interactions link primary producers (phytoplankton) and consumers (zooplankton) in lakes. However, the influence of such interactions on the biogeographical distribution of the taxa and functional traits of planktonic organisms in lakes has never been explicitly tested. To better understand the spatial distribution of these two major aquatic groups, we related composition across boreal lakes (104 for zooplankton and 48 for phytoplankton) in relation to a common suite of environmental and spatial factors. We then directly tested the degree of coupling in their taxonomic and functional distributions across the subset of common lakes. Although phytoplankton composition responded mainly to properties related to water quality while zooplankton composition responded more strongly to lake morphometry, we found significant coupling between their spatial distributions at taxonomic and functional levels based on a Procrustes test. This coupling was not significant after removing the effect of environmental drivers (water quality and morphometry) on the spatial distributions of the two groups. This result suggests that top-down and bottom-up effects (e.g. nutrient concentration and predation) drove trophic interactions at the landscape level. We also found a significant effect of dispersal limitation on the distribution of taxa, which could explain why coupling was stronger for taxa than for traits at the scale of this study, with a turnover of species observed between regions, but no trait turnover. Our results indicate that landscape pelagic food web responses to anthropogenic changes in ecosystem parameters should be driven by a combination of top-down and bottom-factors for taxonomic composition, but with a relative resilience in functional trait composition of lake communities.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".