Integrating ecosystem metabolism and consumer allochthony reveals nonlinear drivers in lake organic matter processing
Bibliographic record
Abstract
Abstract Lakes process both terrestrial and aquatic organic matter, and the relative contribution from each source is often measured via ecosystem metabolism and terrestrial resource use in the food web (i.e., consumer allochthony). Yet, ecosystem metabolism and consumer allochthony are rarely considered together, despite possible interactions and potential for them to respond to the same lake characteristics. In this study, we compiled global datasets of lake gross primary production (GPP), ecosystem respiration (ER), and zooplankton allochthony to compare the strength and shape of relationships with physicochemical characteristics across a broad set of lakes. GPP was positively related to total phosphorus (TP) in lakes with intermediate TP concentrations (11–75 μg L−1) and was highest in lakes with intermediate dissolved organic carbon (DOC) concentrations. While ER and GPP were strongly positively correlated, decoupling occurred at high DOC concentrations. Lastly, allochthony had a unimodal relationship with TP and related variably to DOC. By integrating metabolism and allochthony, we identified similar change points in GPP and zooplankton allochthony at intermediate DOC (4.5–10 mg L−1) and TP (8–20 μg L−1) concentrations, indicating that allochthony and GPP may be coupled and inversely related. The ratio of DOC:nutrients also helped to identify conditions where lake organic matter processing responded more to autochthonous or allochthonous organic matter sources. As lakes globally face eutrophication and browning, predicting how lake organic matter processing will respond requires an updated paradigm that incorporates nonlinear dynamics and interactions.
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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 | 0.000 | 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) | 0.001 | 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".