Nitrate sources and lake response in high elevation lakes, Uinta Mountains, Utah
Bibliographic record
Abstract
Direct human activities in the Uinta Mountains, Utah, U.S.A., are limited to free-range grazing and recreation, but the larger-scale perturbations of climate warming and atmospheric deposition could also affect these remote sites. As few limnological measurements are available, it is difficult to discern whether changes in the high alpine lake ecosystems are occurring in this area. This study uses a range of paleolimnological and limnological techniques to: (1) identify the timing, nature, and causes of changes in primary production in high elevation Uinta Mountain lakes; (2) pinpoint the relative contributions of different sources of nitrate to these aquatic ecosystems; and (3) use diatom community composition data to determine the nature of lake responses to these large-scale perturbations. The results illustrate that primary production was relatively stable until ~1950 AD when it began to increase. Data from sedimentary delta15N, chlorophyll a, and C:N ratios, indicate that the post-1950 AD increase in primary production resulted from increased nitrate deposition from fertilizers and fossil fuel use. However, we suspect phosphate dust from nearby mining and agricultural activities may also be important. The hypothesis of fertilizer-based enrichment is confirmed by a triple-isotope approach (capital delta-17O, delta18O, and delta15N) to analyzing water and snow nitrates; the results indicate that the dominant source of nitrate inputs to these high elevation sites is atmospheric transport of nitrate- and ammonium-based fertilizers. Atmospherically oxidized nitrate and soil nitrate sources are of secondary importance. Nitrogen deposition is also the primary driver of changes in diatom community composition; these findings are based on synchronous changes in diatom assemblage turnover and delta15N values, and an increase in the nitrophilous diatom species Asterionella formosa. Based on analysis of diatom stratigraphies, canonical correspondence analysis and ß-diversity, it is evident that Uinta Mountain lakes differ in their sensitivity to increased nitrogen deposition; this is corroborated by the changes in primary productivity and delta15N. Our findings are significant in not only understanding the implications of urban and agricultural activities to remote Uinta Mountain lakes, but in enhancing our general understanding of alpine nutrient cycling in the Anthropocene.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".