Dry conditions disrupt terrestrial–aquatic linkages in northern catchments
Bibliographic record
Abstract
Aquatic ecosystems depend on terrestrial organic matter (tOM) to regulate many functions, such as food web production and water quality, but an increasing frequency and intensity of drought across northern ecosystems is threatening to disrupt this important connection. Dry conditions reduce tOM export and can also oxidize wetland soils and release stored contaminants into stream flow after rainfall. Here, we test whether these disruptions to terrestrial-aquatic linkages occur during mild summer drought and whether this affects biota across 43 littoral zone sites in 11 lakes. We use copper (Cu) and nickel (Ni) as representative contaminants, and measure abundances of Hyalella azteca, a widespread indicator of ecosystem condition and food web production. We found that tOM concentrations were reduced but correlations with organic soils (wetlands and riparian forests) persisted during mild drought and were sufficient to suppress labile Cu concentrations. Wetlands, however, also became a source of labile Ni to littoral zones, which was linked to reduced abundances of the amphipod H. azteca, on average by up to 70 times across the range of observed Ni concentrations. This reveals a duality in the functional linkage of organic soils to aquatic ecosystems whereby they can help buffer the effects of hydrologic disconnection between catchments and lakes but at the cost of biogeochemical changes that release stored contaminants. As evidence of the toxicity of trace contaminant concentrations and their global dispersion grows, sustaining links among forests, organic soils and aquatic ecosystems in a changing climate will become increasingly important.
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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 | 0.001 |
| 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".