Temporal, spatial, and taxonomic patterns of crustacean zooplankton variability in unmanipulated north‐temperate lakes
Bibliographic record
Abstract
We quantified the spatial and temporal variability of crustacean zooplankton abundance at annual time steps with 261 lake‐years of data from 22 lakes in three regions of central North America. None of these lakes had been experimentally manipulated. Using a nested three‐way analysis of variance, we apportioned variance among years, regions, lakes, and their interactions for 10 functional groups and 4 larger taxonomic aggregates. We proposed that relative variation in the abundance of zooplankton would be greatest among regions and lakes and least among years. We also explored how variability differed among functional groups and changed with taxonomic aggregation. Spatial sources of variation dominated the analysis, but a large interaction between lakes and years indicated that time cannot be ignored. Regional variation was half that found among lakes. Relative variance components differed widely among functional groups, which indicates that species will differ in their response to environmental controls and sensitivity to perturbation. Total explained variation also differed widely among zooplankton and decreased with increasing aggregation of taxa. Whether choosing ecological indicators or designing experiments and monitoring programs, these results clearly show that large‐scale temporal and spatial variability will be an important consideration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".