Variability and predictability in a zooplankton community: The roles of disturbance and dispersal
Bibliographic record
Abstract
Disturbances are expected to increase variability and/or decrease predictability in ecosystems, while dispersal (or immigration) may have positive or negative effects on ecosystem stability. We conducted a mesocosm experiment with pelagic zooplankton to examine the independent and interactive effects of a nutrient pulse and immigration from neighbouring lakes on (a) temporal variation in the mesocosm communities and (b) variation among spatial replicates. Surprisingly, nutrient enrichment had no significant effect on among-replicate variability in algal or zooplankton biomass, and it decreased both among-replicate variability in zooplankton community composition and temporal variability of algal biomass (measured as the coefficient of variation). The nutrient pulse increased temporal variation in zooplankton biomass but decreased temporal change in relative species abundances. However, both these effects depended on dispersal, suggesting a context-dependent role of dispersal in community stability. We discuss the importance of considering multiple measures and aspects of community variability.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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 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".