Elevated methylmercury in High Arctic<i>Daphnia</i>and the role of productivity in controlling their distribution
Bibliographic record
Abstract
Abstract Mercury is a contaminant of concern in polar regions due to long‐range atmospheric transport of this metal from southern latitudes followed by intense deposition on snow. We surveyed zooplankton in 16 lakes and ponds in the Canadian Arctic Archipelago (74–76°N) to determine methylmercury (MeHg) content and the role of environmental characteristics and taxonomic composition on accumulation processes. Zooplankton communities containingDaphnia(mainlyD. middendorffiana) had on average five times the MeHg content of copepod‐dominated communities. The percent biomass ofDaphniabest explained MeHg variation in bulk zooplankton compared with water chemistry and morphometric variables. Water‐column concentrations of MeHg were low at most study sites (mainly ≤0.07 ng L−1), andDaphniastrongly bioaccumulated mercury through species‐specific processes. As we observedDaphniain more productive water bodies (i.e., ponds, a eutrophied lake), we then tested the role of productivity in determining the distribution of this keystone herbivore using a broad‐scale literature dataset of 47 High Arctic lakes (65–77°N).Daphniadensity was positively related to the amount of organic carbon in the water column in both dissolved and particulate fractions [dissolved organic carbon (DOC) partial ,P< 0.001; particulate organic carbon (POC) partial ,P=0.032]. The strong influence of DOC suggests that bacterial production is an important energy source for ArcticDaphnia. Our findings indicate that productivity influences the MeHg content of zooplankton communities through its control of species composition; specifically, low productivity limits the presence of mercury‐richDaphniain many copepod‐dominated lakes of the High Arctic. Aquatic productivity is expected to increase with climate warming, and we present a conceptual model that predicts how environmental drivers could extend the distribution ofDaphniain lakes and alter the movement of mercury in food webs of the Canadian High Arctic.
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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 | 0.000 |
| 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".