Viral ecology of lakes : a descriptive and ecological study of viruses that infect phytoplankton
Bibliographic record
Abstract
Since the 'discovery' of the high abundance of viruses in aquatic environments, it has been generally assumed that viruses in lakes are similar to those in oceans. I directly compared these two systems using a large, robust data set. Viral abundance was significantly different among the surveyed environments. The relationship between viral and bacterial abundance indicated a fundamental difference between lakes and oceans, and suggested that viruses infecting phytoplankton may be more important in lakes. Molecular techniques (PCR & DGGE) were used to document spatial and temporal variations in the richness of viruses that infect eukaryotic phytoplankton (Phycodnaviridae) in lakes at the Experimental Lakes Area (ELA). Phycodnavirus richness was highest in the eutrophic lake, and during the spring/early summer in all the lakes. Viral richness was closely associated with phytoplankton abundance and composition. As a result, richness was influenced by trophic status, while patterns of richness were affected by regional climatic conditions. Phylogenetic analysis of environmental Phycodnavirus DNA polymerase (pol) sequences indicated that freshwater Phycodnaviruses are genetically different from cultured isolates and marine environmental sequences. A genetic distance analysis indicated that pol sequences > 7 % different infected different host species. Therefore, the 20 different freshwater sequences likely infected nine different hosts. Multivariate statistics identified seven possible phytoplankton hosts, including chlorophytes, chrysophytes, diatoms and dinoflagellates. Finally, the modified dilution experiment was evaluated as an approach for estimating viral-mediated phytoplankton mortality in two lakes at the ELA. Experiments resulted in non-significant apparent growth rate regressions. While a model analysis, indicated that the method was sensitive to poorly constrained parameters such as burst size and length of the lytic cycle, making it unsuitable for estimating mortality rates in these lakes. These studies indicate that Phycodnaviridae are a genetically rich and dynamic component of lakes. Their richness is influenced by both the chemical and physical components of their environment. Although the presence of these viruses indicates that they are a source of phytoplankton mortality, the magnitude of their impact on structuring phytoplankton communities awaits methodological advances. Nonetheless, these findings support the view that viruses infecting phytoplankton are ecologically important components of lake ecosystems.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".