Diet and landscape characteristics drive spatial patterns of mercury accumulation in a high-latitude terrestrial carnivore
Bibliographic record
Abstract
Limited information exists on mercury concentrations and environmental drivers of mercury bioaccumulation in high latitude terrestrial carnivores. Spatial patterns of mercury concentrations in wolverine (Gulo gulo, n = 419) were assessed across a 1,600,000 km2 study area in relation to landscape, climate, diet and biological factors in Arctic and boreal biomes of western Canada. Hydrogen stable isotope ratios were measured in wolverine hair from a subset of 80 animals to assess the spatial scale for characterizing environmental conditions of their habitat. Habitat characteristics were determined using GIS methods and raster datasets at two scales, the collection location point and a 150 km radius buffer, which was selected based on results of a correlation analysis between hydrogen stable isotopes in precipitation and wolverine hair. Total mercury concentrations in wolverine muscle ranged >2 orders of magnitude from 0.01 to 5.72 μg/g dry weight and varied geographically, with the highest concentrations in the Northwest Territories followed by Nunavut and Yukon. Regression models at both spatial scales indicated diet (based on nitrogen stable isotope ratios) was the strongest explanatory variable of mercury concentrations in wolverine, with smaller though statistically significant contributions from landscape variables (soil organic carbon, percent cover of wet area, percent cover of perennial snow-ice) and distance to the Arctic Ocean coast. The carbon and nitrogen stable isotope ratios of wolverine muscle suggested greater mercury bioaccumulation could be associated with feeding on marine biota in coastal habitats. Landscape variables identified in the modelling may reflect habitat conditions which support enhanced methylmercury transfer to terrestrial biota. Spatially-explicit estimates of wet atmospheric deposition were positively correlated with wolverine mercury concentrations but this variable was not selected in the final regression models. These landscape patterns provide a basis for further research on underlying processes enhancing methylmercury uptake in high latitude terrestrial food webs.
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 | 0.000 |
| 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) | 0.001 | 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".