Structuring features of lake districts: landscape controls on lake chemical responses to drought
Bibliographic record
Abstract
1. Within a lake district of relatively homogeneous geomorphology, the responses of lakes to climate are influenced by the complexity of the hydrogeologic setting, position in the landscape, and lake‐specific biological and physical features. We examined lake chemical responses to drought in surface water‐ and groundwater‐dominated districts to address two general questions. (1) Are spatial patterns in chemical dynamics among lakes uniform and synchronous within a lake district, suggesting broad geomorphic controls; variable in a spatially explicit pattern, with synchrony related to landscape position, suggesting hydrologic flowpath controls; or spatially unstructured and asynchronous, suggesting overriding control by lake‐specific factors? (2) Are lake responses to drought a simple function of precipitation quantity or are they dictated by more complex interactions among climate, unique lake features, and hydrologic setting? 2. Annual open‐water means for epilimnetic concentrations of chloride, calcium, sulfate, ANC, DOC, total nitrogen, silica, total phosphorus, and chlorophyll a measured between 1982 and 1995 were assembled for lakes in the Red Lake and ELA districts of north‐western Ontario, the Muskoka – Dorset district in south‐central Ontario, and the Northern Highland district of Wisconsin. Within each district, we compared responses of lakes classified by landscape position into highland or lowland, depending on relative location within the local to regional hydrologic flow system. Synchrony, defined as a measure of the similarity in inter‐annual dynamics among lakes within a district, was quantified as the Pearson product‐moment correlation ( r ) between two lakes with observations paired by year. To determine if solute concentrations were directly related to interannual variations in precipitation quantity, we used regression analysis to fit district‐wide slopes describing the relationship between each chemical variable and annual (June to May) and October to May (Oct–May) precipitation. 3. Among lakes in each of the three Ontario districts, the pattern of chemical response to interannual shifts in precipitation was spatially uniform. In these surface water‐ dominated districts, solute concentrations were generally a simple function of precipitation. Conservative solutes, like calcium and chloride, tended to be more synchronous and were negatively related to precipitation. Solutes such as silica, total phosphorus, and chlorophyll a , which are influenced by in‐lake processes, were less synchronous and relationships with precipitation tended to be positive or absent. 4. In the groundwater‐dominated Northern Highland lakes of Wisconsin, we observed spatial structure in drought response, with lowland lakes more synchronous than highland lakes. However, there was no evidence for a direct relationship between any solute and precipitation. Instead, increases in the concentration of the conservative ion calcium during drought were not followed by a symmetrical return to pre‐drought conditions when precipitation returned to normal or above‐average values. 5. For calcium, time lags in recovery from drought appeared related to hydrologic features in a complex way. In the highland Crystal Lake, calcium concentrations tracked lake stage inversely, with a return to pre‐drought concentrations and lake stage five years after the drought. This pattern suggests strong evaporative controls. In contrast, after five years of normal precipitation, calcium in the lowland Sparkling Lake had not returned to pre‐drought conditions despite a rebound in lake stage. This result suggests that calcium concentrations in lowland lakes were controlled more by regional groundwater flowpaths, which track climatic signals more slowly. 6. Temporal dynamics driven by climate were most similar among lakes in districts that have a relatively simple hydrology, such as ELA. Where hydrologic setting was more complex, as in the groundwater‐dominated Northern Highland of Wisconsin, the expression of climate signals in lakes showed lags and spatial patterns related to landscape position. In general, we expect that landscape and lake‐specific factors become increasingly important in lake districts with more heterogeneous hydrogeology, topography or land use. These strong chemical responses to climate need to be considered when interpreting the responses of lakes to other regional disturbances.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".