Factors affecting the bacteria-heterotrophic nanoflagellate relationship in oligo-mesotrophic lakes
Bibliographic record
Abstract
The coupling between bacteria and heterotrophic nanoflagellates (HNF) was examined in nine lakes of low productivity for evidence of the effects of various metazooplankton (i.e. rotifers, cladocerans and copepods) on this relationship. We considered the size of cladocerans and, in contrast to most previous across-system studies, the three strata of the water column (i.e. epilimnion, metalimnion and hypolimnion). Rotifers were numerically dominant in all lakes and accounted for 45-84% of total metazooplankton abundance, while the abundance of large cladocerans was relatively low, ranging from 0.066 to 15.2 ind. L 1 . The across-lake relationship between bacteria and HNF was significant in the deeper strata (meta-and hypolimnion) but not in the epilimnion and in the two groups of lakes separated on the basis of their average number of large cladocerans (<5 and >5 ind. L 1 , respectively). The results confirmed the negative impacts of large cladocerans on HNF, but also showed that rotifers, probably through grazing on HNF, may be an important factor causing variation in the bacteria-HNF relationship in unproductive waters. Quadratic models best described the relationships between metazooplankton and the ratio of bacteria to HNF. This ratio seemed to be a result of complex interactions between several factors, including the zooplankton composition and abundance and the depth of the lake. Indeed, this ratio significantly decreased across lakes, with increase in depth. In addition, shallower lakes (having <5 large cladocerans L 1 and fewer Polyarthra vulgaris) tended to have more bacteria and HNF and a higher ratio of bacteria to HNF than deeper lakes (which had >5 large cladocerans L 1 and substantial proportions of P. vulgaris). We suggest that the epilimnion, metalimnion and hypolimnion of lakes be taken into account when analysing the bacteria-HNF relationship as well as the cascading effects of zooplankton on microbial communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".