Biological and nutrient responses to catchment disturbance and warming in small lakes near the Alaskan tundra–taiga boundary
Bibliographic record
Abstract
Understanding effects of recent climate warming and changes in catchment conditions on nutrient cycling and the biology of shallow subarctic lakes is necessary to predict their evolution. Here, we use multiple analytical methods on sediment cores to identify effects of change in catchment conditions on nutrient availability and biotic assemblages in two subarctic lakes on the Seward Peninsula (Alaska, USA). We compare limnological and biotic responses to flooding and expansion of a thermokarst lake basin (late 1950s), increased shrub growth in the catchment of another lake (since the mid-1980s), and regional warming (since the late 1970s). Among these three environmental drivers, the largest biotic responses occurred because of flooding and expansion of the thermokarst lake. An increase in the nitrogen isotope composition and decline in organic carbon isotope composition in sediments are interpreted to reflect an elevated supply of dissolved inorganic carbon and nitrogen. This was associated with significant shifts in composition of chironomid and diatom assemblages. In contrast, increases in particulate organic carbon and nitrogen from enhanced shrub growth had less influence on the biota. Declines in cold-water biotic indicators typical of warming lakes in Arctic regions occurred several decades after catchment-induced changes to the nutrient supply in both systems. This indicates that initial lake catchment condition may mediate lake-specific changes in nutrient cycling and aquatic productivity within regions undergoing warming.
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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 | 0.000 | 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".