Effects of re‐oligotrophication and climate warming on plankton richness and community stability in a deep mesotrophic lake
Bibliographic record
Abstract
We studied the effects of re‐oligotrophication and climate warming on plankton richness and community stability over a period of 30 years in the deep mesotrophic Lake Zurich (Switzerland). We assembled monthly time‐series of phytoplankton and zooplankton taxonomic richness, phytoplankton functional groups (species with similar functional traits) and physico‐chemical environmental descriptors (temperature, conductivity, pH, P‐PO 4 3− , N‐NO 3 − , light absorption). We used multiple linear regression to test: 1) the effect of environmental variability over time and depth on the accrual of plankton richness; and 2) the relative effect of richness and environmental variability on the stability of plankton. Environmental change was characterised by increase in temperature, decrease in phosphorus levels, reduced temporal variability of both, and higher heterogeneity of phosphorus over depth (spatial heterogeneity). These conditions occurred concurrently with accrual in plankton taxonomic and functional richness. Increase in temperature and spatial heterogeneity were the best predictors of phytoplankton richness, while phytoplankton richness and spatial heterogeneity had the strongest effects on zooplankton richness. Temporal stability in phytoplankton biovolume was mainly affected by variability in phosphorus and temperature, while zooplankton abundance levels were more strongly linked to fluctuations in nitrogen, temperature and phytoplankton biovolumes. Our analysis highlights that climate warming and re‐oligotrophication may favour an increase in spatial (depth) heterogeneity in the water column of deep lakes, enhancing the potential for phytoplankton species co‐existence and an increase in plankton richness. Our analysis also suggests that the intensity of fluctuations in key environmental variables can be a better predictor of plankton community stability then average richness.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".