Benthic algae compensate for phytoplankton losses in large aquatic ecosystems
Bibliographic record
Abstract
Anthropogenic activities can induce major trophic shifts in aquatic systems, yet we have an incomplete understanding of the implication of such shifts on ecosystem function and on primary production (PP) in particular. In recent decades, phytoplankton biomass and production in the Laurentian Great Lakes have declined in response to reduced nutrient concentrations and invasive mussels. However, the increases in water clarity associated with declines in phytoplankton may have positive effects on benthic PP at the ecosystem scale. Have these lakes experienced oligotrophication (a reduction of algal production), or simply a shift in autotrophic structure with no net decline in PP? Benthic contributions to ecosystem PP are rarely measured in large aquatic systems, but our calculations based on productivity rates from the Great Lakes indicate that a significant proportion (up to one half, in Lake Huron) of their whole-lake production may be benthic. The large declines (5-45%) in phytoplankton production in the Great Lakes from the 1970s to 2000s may be substantially compensated by benthic PP, which increased by up to 190%. Thus, the autotrophic productive capacity of large aquatic ecosystems may be relatively resilient to shifts in trophic status, due to a redirection of production to the near-shore benthic zone, and large lakes may exhibit shifts in autotrophic structure analogous to the regime shifts seen in shallow 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".