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Record W4290079100 · doi:10.1016/j.envpol.2022.119810

Effects of forest management on mercury bioaccumulation and biomagnification along the river continuum

2022· article· en· W4290079100 on OpenAlexafffund
Lauren Negrazis, Karen A. Kidd, Maitane Erdozain, Erik J. S. Emilson, Carl P. J. Mitchell, Michelle A. Gray

Bibliographic record

VenueEnvironmental Pollution · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsThe Scarborough HospitalCanadian Forest ServiceUniversity of New BrunswickUniversity of TorontoNatural Resources CanadaMcMaster University
FundersCanadian Forest ServiceNatural Sciences and Engineering Research Council of CanadaJarislowsky Foundation
KeywordsBiomagnificationTrophic levelEnvironmental scienceBiotaEcologyMethylmercuryFood webBioaccumulationHydrology (agriculture)BiologyGeology

Abstract

fetched live from OpenAlex

Forest management can alter the mobilization of mercury (Hg) into headwater streams and its conversion to methylmercury (MeHg), the form that bioaccumulates in aquatic biota and biomagnifies through food webs. As headwater streams are important sources of organic materials and nutrients to larger systems, this connectivity may also increase MeHg in downstream biota through direct or indirect effects of forestry on water quality or food web structure. In this study, we collected water, seston, food sources (biofilm, leaves, organic matter), five macroinvertebrate taxa and fish (slimy sculpin; Cottus cognata) at 6 sites representing different stream orders (1–5) within three river basins with different total disturbances from forestry (both harvesting and silviculture). Methylmercury levels were highest in water and some food sources from the basin with moderate disturbance (greater clearcutting but less silviculture). Water, leaves, stoneflies and fish increased in MeHg or total Hg along the river continuum in the least disturbed basin, and there were some dissipative effects of forest management on these spatial patterns. Trophic level (δ15N) was a significant predictor of MeHg (and total Hg in fish) within food webs across all 18 sites, and biomagnification slopes were significantly lower in the basin with moderate total disturbance but not different in the other two basins. The elevated MeHg in lower trophic levels but its reduced trophic transfer in the basin with moderate disturbance was likely due to greater inputs of sediments and of dissolved organic carbon that is more humic, as these factors are known to both increase transport of Hg to streams and its uptake in primary producers but to also decrease MeHg bioaccumulation in consumers. Overall, these results suggest that the type of disturbance from forestry affects MeHg bioaccumulation and trophic transfer in stream food webs and some longitudinal patterns along a river continuum.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.212
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2022
Admission routes2
Has abstractyes

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