Mercury in biota from Florida’s freshwater lakes and rivers: a review of current research and emerging challenges
Bibliographic record
Abstract
A notable amount of aquatic mercury (Hg) research has been done in northern ecosystems but less is known about Hg dynamics in the sub-tropics. Our collective understanding of Hg bioaccumulation is particularly lacking in freshwater lakes and rivers across Florida, USA, which is dominated by uniquely shallow and polymictic solution lakes, as well as black-water rivers. To understand the state of information and direct future research and management efforts, we conducted a literature review of the primary research available on Hg dynamics in biota from inland freshwater ecosystems across Florida. We notably excluded research from the greater Everglades National Park (which includes the upgradient water conservation areas), a unique wetland system that has been well researched. In total, 42 peer-reviewed papers, conference proceedings, and student theses were found with data on methyl or total Hg in biota from Floridian lakes, rivers, or wetlands. These sources were notably dated, and much of the data used, even in more recent research, was >20 years old. Nevertheless, the collective research report elevated Hg concentrations across biotic groups, when compared to other regions in the United States, but lower than in the greater Everglades region. There was also significant spatial and/or temporal variability in the concentrations of both total Hg (THg) and methyl Hg (MeHg). Communal drivers of this variability included measures of biological productivity, precipitation, and the abundance of wetlands influencing a given waterbody. Notably, sulfate and dissolved organic matter, well studied drivers of Hg methylation and bioaccumulation in northern systems and the greater Everglades region, were absent from much of this past modeling; the limited research available suggest complex effects that warrant further study. Broader knowledge gaps and future challenges (e.g., the impact of urbanization, lake management practices, and invasive species) are also discussed herein, specifically focusing on how they may impact Hg cycling and direct future monitoring, research, or mitigation efforts. Such challenges are impacting freshwater ecosystem globally. This, coupled with the potential for other lakes to resemble Florida’s warm and productive systems due to climate change, make the findings of our review broadly applicable.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".