Acidification recovery in a changing climate: Observations from thirty‐five years of stream chemistry monitoring in forested headwater catchments at the Turkey Lakes watershed, Ontario
Bibliographic record
Abstract
Abstract Long‐term ecosystem studies are valuable for understanding integrated ecosystem response to global changes in atmospheric deposition and climate. We examined trends for a 35‐year period (1982/83–2017/18) in concentrations of a range of solutes in precipitation and stream water from nine headwater catchments spanning elevation and surficial geology gradients at the Turkey Lakes watershed (TLW) in northeastern Ontario, Canada. Average annual water year (WY, October to September) concentrations in precipitation significantly declined over the period for sulphate (SO42−), nitrate (NO3−) and chloride (Cl−), while calcium (Ca2+) and potassium (K+) concentrations increased, resulting in a significant pH increase from 4.2 to 5.7. Trends in stream chemistry through time are generally consistent with expectations associated with acidification recovery. Concentration of many stream water solutes (SO42−, Cl−, calcium [Ca2+], magnesium [Mg2+] and NH4+ generally decreased, while others (silica [SiO2] and dissolved organic carbon [DOC]) generally increased. Increases were also observed for alkalinity (six of nine catchments), acid neutralizing capacity ([ANC]; six of nine catchments) and pH (eight of nine catchments), while conductivity declined (six of nine catchments). Variability in trends among catchments are associated with differences in surficial geology and wetland cover. While absolute solute concentrations were generally lower at bedrock dominated high‐elevation catchments compared to till dominated lower elevation catchments, the rate of change of concentration was often greater for high elevation catchments. This study confirms continued, but non‐linear stream chemistry recovery from acidification, particularly at the less buffered high and moderate elevation sites. The heterogeneity of responses among catchments highlights our incomplete understanding of the relative importance of different mechanisms influencing stream chemistry and the consequences for downstream ecosystems.
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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 | 0.001 |
| 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) | 0.000 | 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".