The interaction of regional and local drivers shapes summer ecosystem metabolism in lakes across Canada
Bibliographic record
Abstract
Abstract Assessments of lake gross primary production (GPP) and respiration (R) and their balance (net ecosystem production, NEP) have been limited to specific watersheds and a limited number of lakes, often along narrow environmental gradients. This is because conventional approaches require either lengthy incubations or the deployment of monitoring equipment, none of which are feasible for large‐scale studies. Here we present a macroscale study of lake metabolism and explore the patterns and drivers of GPP, R, and NEP in lakes across Canada as part of the LakePulse network. We measured summertime water column metabolic rates in 742 lakes, using an oxygen isotopic (δ18O2) approach, which provide an integrative snapshot of mixed‐layer metabolism in stratified lakes, or whole‐lake metabolism in polymictic lakes. The lakes were distributed across the five major Canadian continental drainage basins, covering a wide range of in‐lake, land use, and climatic features. Gross primary production and R varied by four orders of magnitude across lakes and regions, driven by factors such as total phosphorus and nitrogen, dissolved organic carbon, and chlorophyll. Net ecosystem production had a weak but significant positive linear relationship with water column light and a negative relationship with colored dissolved organic matter. Our results reveal systematic differences in regional baseline GPP and R driven by landscape properties such as altitude, and that lake metabolism in some regions may be more sensitive to eutrophication and browning, mediated by regional hydrology, which is itself linked to climate.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".