Downstream Modification of Mercury in Diverse River Systems Underscores the Role of Local Conditions in Fish Bioaccumulation
Bibliographic record
Abstract
Abstract Fish consumption advisories for mercury (Hg) are common in rivers, highlighting connections between landscape sources of Hg and downstream fluvial ecosystems. Though watershed conditions can influence concentrations of Hg in smaller streams, how Hg changes downstream through larger rivers and how these changes associate with Hg concentrations in fish is not well understood. Here we present a continuum of concentrations and yields of total mercury (THg) and methylmercury (MeHg) from small tributary systems draining diverse western Canadian headwater landscapes through to major transboundary rivers. We associate these downstream patterns with THg concentrations in tissues of resident fish in major rivers. Mean concentrations and yields of unfiltered THg from over 80 monitored tributaries and major rivers were highly variable in space ranging from 0.28 to 120 ng L −1 and 0.39 to 170 µg ha −1 d −1 , respectively. Using spatial data and a hierarchical cluster analysis, we identified three broad categories of tributary catchment conditions. Linear mixed modeling analysis with water quality variables revealed significantly lower THg concentrations in tributaries draining cordillera-foothills (geometric mean: 0.76 ng L −1 ) regions relative to those draining forested (1.5 ng L −1 ) and agriculturalized landscapes (2.4 ng L −1 ), suggesting that sources and mobility of THg in soils and surface waters were different between landscapes. However, these concentration differences were not sustained downstream in major rivers as local sources and sinks of THg in river channels smoothed differences between landscape types. Extensive fish tissue monitoring in major rivers and ANCOVA analysis found that site-specific, river water THg and MeHg concentrations and local catchment conditions were stronger associates of THg concentrations in fish than broader trends in rivers within and across landscape classes. Consequently, site-specific, targeted monitoring of THg and MeHg concentrations in water and fish is a preferred study design when assessing regional-level patterns in fish tissue concentrations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".