Benthic and planktonic primary production along a nutrient gradient in Green Bay, Lake Michigan, USA
Bibliographic record
Abstract
Primary production in lakes occurs in both planktonic (water column) and benthic (bottom) habitats. How whole-lake primary production is distributed between these 2 habitats—referred to as autotrophic structure—is a key ecosystem property. Empirical research examining the balance between benthic and planktonic primary production in lakes is scarce, and how autotrophic structure changes across depth, nutrient, water clarity, and biological invasion gradients is unclear. Therefore, we are ill equipped to anticipate ecosystem-level responses to environmental change. We assessed the magnitude of offshore planktonic, nearshore planktonic, and nearshore benthic gross primary production (GPP) along a gradient of nutrients, water clarity, and Cladophora biomass in Green Bay, Lake Michigan, USA, during summer 2010, 2011, and 2012. Benthic and planktonic GPP varied strongly along the trophic gradient. Planktonic GPP increased with nutrients status, whereas benthic GPP decreased. From shore to 10 m depth, autotrophic structure shifted from planktonic dominance near the mouth of the Fox River (95% planktonic) to a mix of benthic and planktonic GPP 35 km from the mouth of the Fox (∼40% benthic). The steep bathymetry at more-distant sites reduced the relative importance of benthic GPP at the whole-ecosystem level. Our work highlights the dual-pathway (i.e., benthic and planktonic) nature of lentic food webs from the perspective of GPP, and shows how both trophic status and bathymetry affect autotrophic structure.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".