Climate‐driven spatial and temporal patterns in peatland pool biogeochemistry
Bibliographic record
Abstract
Abstract Peatland pools are freshwater bodies that are highly dynamic aquatic ecosystems because of their small size and their development in organic‐rich sediments. However, our ability to understand and predict their contribution to both local and global biogeochemical cycles under rapidly occurring environmental change is limited because the spatiotemporal drivers of their biogeochemical patterns and processes are poorly understood. We used (1) pool biogeochemical data from 20 peatlands in eastern Canada, the United Kingdom, and southern Patagonia and (2) multi‐year data from an undisturbed peatland of eastern Canada, to determine how climate and terrain features drive the production, delivering and processing of carbon (C), nitrogen (N), and phosphorus (P) in peatland pools. Across sites, climate (24%) and terrain (13%) explained distinct portions of the variation in pool biogeochemistry, with climate driving spatial differences in pool dissolved organic C (DOC) concentration and aromaticity. Within the multi‐year dataset, DOC, carbon dioxide (CO2), total N concentrations, and DOC aromaticity were highest in the shallowest pools and at the end of the growing seasons, and increased gradually from 2016 to 2021 in relation to a combination of increases in summer precipitation, mean air temperature for the previous fall, and number of extreme summer heat days. Given the contrasting effects of terrain and climate, broad‐scale terrain characteristics may offer a baseline for the prediction of small‐scale pool biogeochemistry, while broad‐scale climate gradients and relatively small year‐to‐year variations in local climate induce a noticeable response in pool biogeochemistry. These findings emphasize the reactivity of peatland pools to both local and global environmental change and highlight their potential to act as widely distributed climate sentinels within historically relatively stable peatland ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".