Climate Sensitivity of Lentic Mountain Ecosystems
Bibliographic record
Abstract
Lentic freshwater ecosystems are threatened by unprecedented global change. Often considered sentinels for change, lentic ecosystems like lakes and ponds are particularly sensitive and vulnerable to the effects of global change because they respond rapidly to changes in the environment and integrate information from their surrounding catchment within their ecosystem. These sensitive lentic ecosystems are increasingly threatened by climate-driven shifts like warming air temperatures, increasing hydroclimatic variability, and changing ice phenologies. Additionally, global-change phenomena such as increasingly extreme wildfire activity further threaten these ecosystems. In particular, mountain lentic ecosystems are experiencing some of the world’s greatest rates of change in air temperature and precipitation regimes, respond strongly to climate forcing, and may be particularly sensitive to global change. In this dissertation, I investigated the climate sensitivity of lentic ecosystems in three primary ways. First, we quantified lake exposure to wildfire smoke across North America, and reviewed the known and theoretical impacts of that exposure. Then, we investigated how wildfire smoke affects lake and pond temperature and ecosystem metabolism across a watershed. Finally, we addressed the impact of hydroclimatic variability on lake zooplankton assemblage, abundance, and diversity. We found that the physical, biological, and chemical processes in lakes likely all respond to wildfire smoke exposure, and that in small, oligotrophic mountain lakes and ponds, smoke reduces water temperatures and ecosystem metabolism. These studies highlight that as wildfires increase in frequency and intensity, smoke from those fires have the potential to impact lentic ecosystem processes from local to continental scales. We also found that while much of the research on climate impacts focuses on the effects of warming, climate change-driven extremes in hydroclimate significantly determines lake zooplankton community abundance, biomass, and diversity. With increasingly extreme variability in hydroclimate, mountain lake zooplankton communities may undergo major shifts in assemblage and abundance. As we face increasing challenges driven by both climate change and human behavior, lakes and ponds can serve as key indicators of change in an ever-changing world.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".