Assessment of ‘top‐down’ and ‘bottom‐up’ forces as determinants of rotifer distribution among lakes in Ontario, Canada
Bibliographic record
Abstract
Predation and food supply are generally perceived as important determinants of spatial and temporal variations of populations. The population dynamics of freshwater rotifers have been well researched in this aspect. However, their spatial variations have attracted less attention and have not been studied by simultaneously considering both predation and food supply. We studied spatial variations of rotifer abundance among 34 Canadian boreal lakes. A large part of the variance of rotifer abundance was associated with variables related to trophic status including chlorophyll a and total phosphorus. However, abundances of mesozooplankton such as potential predators and competitors did not correlate with rotifer abundance and did not explain the residual of the regression between rotifer abundance and chlorophyll a . The results of the present study indicated that variation in rotifer abundance among lakes was caused by ‘bottom‐up’ forces related to food supply and not by ‘top‐down’ predatory interactions. This provides a contrast to previous empiric and experimental studies that reported that temporal variations of rotifer abundance were mainly regulated by ‘top‐down’ interactions. This discrepancy suggests that overall differences in rotifer abundance among lakes are mainly determined by ‘bottom‐up’ forces while temporal changes in single lakes are shaped by ‘top‐down’ forces. Meanwhile, the composition of rotifer species was correlated with mesozooplankton abundance as well as trophic status. Rotifer species with long spines or rigid loricae were found in the lakes where mesozooplankton were abundant, which suggests that defensive morphology could have affected the rotifer species distribution among the study lakes.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".