Harvesting zooplankton with submersible pumps does not affect water quality, stabilizes phytoplankton and zooplankton populations, and provides a supplemental food source for fry
Bibliographic record
Abstract
ABSTRACT Objective The objective of this study was to quantify the cascading effects on the pond ecology and water quality that result from mechanically harvesting large zooplankton (>250 µm) and to track the nutritional value of zooplankton over time. Methods Eight 0.04-ha ponds, containing no fish, were selected. In four ponds, a 373-W submersible pump was suspended in the water column and allowed to pump continuously for 4 d per week (treatment ponds) to collect zooplankton in 250-µm mesh bags. The other four ponds contained no submersible pump and were used as control ponds. The harvested zooplankton were subjected to proximate analyses to determine their base nutritional makeup. Weekly samples were analyzed for water quality, phytoplankton, and zooplankton. Results The nutritional value of zooplankton varied over time and was negatively influenced by ostracod abundance. There was no overall influence of treatment on water quality or total phytoplankton or zooplankton abundance; however, the effects for sampling date and the interaction of treatment and sampling date were significant. Over time, Cyanophyta continually increased in the control ponds but decreased in the treatment ponds. Chlorophyta decreased in the control ponds but slightly increased in the treatment ponds. Generally, zooplankton abundance increased throughout the summer in all the ponds. However, the treatment ponds typically had lower abundances, on average, than the control ponds. Zooplankton abundance was also much more variable in the control ponds than in the treatment ponds. Conclusion Zooplankton maintain adequate nutritional value over time as long as ostracod density remains low. The results of this study show that harvest pressure from capturing zooplankton resulted in the stabilization of the phytoplankton and zooplankton populations, particularly as the study moved further into the summer and communities were established. This shows promise as an effective method to harvest zooplankton for small-scale feed supplementation while improving the pond community by increasing Chlorophyta populations, reducing the variance in zooplankton populations, and reducing the variance and possibly the density of Cyanophyta.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".