Implications of long-term dynamics of fish and zooplankton communities for among-lake comparisons
Bibliographic record
Abstract
Understanding the environmental determinants of lake community composition has been a central pursuit in freshwater ecology. Previous studies have defined community composition based on temporally limited surveys, with the implicit assumption that interannual variation is negligible compared with among-lake variability. Using a long-term data set for fish and zooplankton communities in five north temperate lakes (Wisconsin, USA), we found that interannual, within-lake similarity in species composition (a measure of temporal stability in community composition) generally exceeded community similarity among lakes. Despite these differences, however, the strength of community–environment relationships were found to range widely (2%–99% explained variation) depending on the choice of single-year sample used in the analysis, a result of high temporal coherence in limnological and biological characteristics. Perhaps of greatest concern, interannual similarity in species composition showed consistent relationships with habitat variables commonly used to explain community differences among lakes. Decreasing lake area and shoreline perimeter (indicative of lower habitat heterogeneity) and seepage lakes were associated with low interannual similarity in community composition, thus confounding the ability to differentiate among lake communities according to their habitat characteristics. In light of our results, we offer a number of explicit recommendations for the selection and analysis of community data in future cross-lake studies.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".