Shifts in algal dominance in freshwater experimental ponds across differing levels of macrophytes and nutrients
Bibliographic record
Abstract
Abstract Excess nutrient loading into ponds and shallow lakes can lead to undesirable algal growth and a shift to a turbid state. Previous work has suggested that such an ecosystem transition may be mediated by the biotic constituents of the habitat and food web; however, earlier experiments have been conducted at coarse temporal resolution and have typically used a single initial density of macrophytes, a key structural component of ponds and shallow lakes. To address these gaps, we tested the hypotheses that experimental ponds with lower macrophyte densities and more rapid increases in nutrient loading would shift to phytoplankton dominance, whereas higher macrophyte densities and slower, lower concentration nutrient inputs would maintain a clear state. Ponds containing plankton and juvenile fish were assigned to treatments with none, low, or high macrophyte densities, and weekly, high or biweekly (i.e., fortnightly), low nutrient inputs. Using additive mixed‐effects models, we demonstrated that temporal trajectories of phytoplankton biomass were explained by macrophyte density in interaction with biomass of important zooplankton grazers (Bosminidae, Sididae, and Daphniidae), as well as with pH and time. Phytoplankton biomass followed a convex unimodal trajectory in ponds with no or low macrophytes (muted in the latter), and minimal increases in high macrophyte treatments. Declines in phytoplankton attributable to top‐down control likely freed resources for periphyton and metaphyton, which subsequently became abundant in ponds without and with macrophytes, respectively. Our results demonstrate that high densities of macrophytes, combined with herbivory and competition for light between phytoplankton and metaphyton, enhance resilience of the clear water state to the undesirable effects associated with eutrophication.
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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 | 0.000 | 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 | 0.000 |
| 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 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".