Factors Leading to Increased Algal Production in Mountain Lakes: A Paleolimnological Perspective from the Uinta Mountains, Utah, USA
Bibliographic record
Abstract
Mountain lakes are often remote, located in environments that experience cold temperatures, high incident solar and ultraviolet radiation, and prolonged ice and snow cover. They are, therefore, frequently dilute and oligotrophic. Together these factors can affect mountain lake ecosystem structure, diversity, and productivity. However, distant human activities resulting in atmospheric pollution, as well as more local disturbances, such as fish stocking, potentially increase nutrient inputs and alter mountain lake ecosystems. Our research addresses how these human activities have altered algal production in Uinta Mountain (Utah, USA) lakes. Sedimentary chlorophyll a and its derivatives were measured using visible reflectance spectroscopy in short sediment cores from a total of 12 lakes, including both alpine and subalpine lakes, to determine trends in algal production. All sediment cores were dated using 210Pb and 14C dating, and the records were shown to extend back 300 to 500 years. Our results show that regardless of whether lakes were stocked or not, algal production remained virtually unchanged until 1950 when it increased dramatically in most lakes. The widespread distribution of the sites points to a regional stressor, such as atmospheric deposition of nutrients, as being the main cause for increased algal production. Additional analyses, including diatoms and C and N isotopes, measured in sediments from some lakes support this finding. The few lakes where algal production trends differed showed either that algal production had changed little overtime or that it was variable throughout the record. Although speculative, the lake that showed unchanged algal production is surrounded by a wetland that may have contributed nitrogen to the lake throughout the record meaning that additional nitrogen had little effect on algal production. Lakes with more variable algal production were subalpine lakes. The variable trend may point to more complex pathways and transport of nitrogen from the catchment to the lakes at lower elevation sites. Our findings show that remote mountain lakes, which typically are important water resources and biodiversity hotspots, are rapidly changing as a result of human activities, but not all of these lakes are responding in the same way. To effectively protect mountain lakes it will be important to identify and quantify influential factors affecting lake response to anthropogenic stressors.
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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".