Hydroclimate Variability Affects Habitat‐Specific (Open Water and Littoral) Lake Metabolism
Bibliographic record
Abstract
Abstract Lakes integrate the signals of changing hydroclimate in their surrounding basin, which substantially influence gross primary production (GPP), respiration (R), and net ecosystem production (NEP). Most research focuses only on the changes to the open water habitat despite the littoral habitat's importance to lakes and its sensitivity to hydroclimatic variability. We analyze how years with different ice‐out dates and snow water equivalent (SWE) triggered different metabolism responses in the open water and littoral habitats of a subalpine lake. The dry (early ice‐out date and low SWE) and wet (late ice‐out date and high SWE) years had lower GPP and R rates in both habitats compared to the average hydroclimatic year. Furthermore, consumer biomass decreased during dry and wet years. GPP and R reduced the most in the littoral habitat. Consequently, the relative contribution to total lake GPP and R of the littoral habitat decreased, and the contribution of the open water habitat increased during the dry and wet years. We demonstrate that hydroclimatic conditions affect productivity and consumer biomass and show that within‐lake habitats do not experience equivalent responses to climate forcing. Our study has implications for how ecosystem scientists and managers quantify the absolute and relative contributions of the littoral habitat to whole lake production in the context of climate variation.
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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 | 0.000 | 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) | 0.001 | 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".