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Record W2744968415 · doi:10.2134/csa2017.62.0803

Wildland Fire Impacts on Mercury in Fish

2017· article· hu· W2744968415 on OpenAlexaboutno aff
Tracy Hmielowski

Bibliographic record

VenueCSA News · 2017
Typearticle
Languagehu
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceBiogeochemical cycleWilderness areaEcosystemFire ecologyFire regimeGeographyForestryHydrology (agriculture)WildernessEcologyEngineering

Abstract

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Prescribed burn. Source: USDA Forest Service. Wildland fire is a natural part of forest ecosystems in the Great Lakes region. In wilderness areas like Boundary Waters Canoe Wilderness Area (BWCWA) of the Superior National Forest, managers use prescribed fire to mimic this natural process. Prescribed fire can also be used as a tool to mitigate wildfire risk. This mitigation approach was used in northern Minnesota after a “derecho” event (line of intense, widespread, and fast-moving windstorms) on 4 July 1999. The straight-line winds toppled trees, damaged buildings, and blocked roads across North Dakota, northern Minnesota, southern Ontario and Quebec, and into New England. Randy Kolka, SSSA member and Research Soil Scientist with the USDA Forest Service, explains that as part of the response effort, the USDA Forest Service implemented a plan to treat areas impacted by the blowdown using prescribed fire. The objective of the prescribed fires was to consume some of the newly available fuel, thereby reducing the severity and spread of any wildfires that started in the affected region. Yellow perch. Source: USDA. Fire (prescribed fire or wildfires) also has broader effects on an ecosystem, including impacting nutrient and biogeochemical cycles. For example, mercury (Hg) stored in the soil can be volatilized as a gas and become part of a larger global cycle, emitted and either re-deposited locally throughout the landscape in smoke particulates, or transported in runoff to lakes and streams following the fire. The release of Hg from the soil and into waterways has been shown to lead to an increase in Hg present in fish. According to Kolka, shallow soils in this region mean that precipitation can “get to the lakes faster” and potentially carry Hg that has been released by fire into the waterways. Given the concern over human exposure to Hg through the consumption of fish, it is important to understand how fire influences bioaccumulation of Hg in the food chain. Impact of blowdown on the Boundary Waters Canoe Wilderness Area following a straight-line wind event on 4 July 1999. As a result, the Superior National Forest initiated a prescribed burn program to address the additional fuel loads but also had concerns about mercury pollution to the lakes. Photo courtesy of the Superior National Forest. Read the full study in the Journal of Environmental Quality at http://bit.ly/2uwekXX. To determine if wildland fires in the region were influencing Hg cycling, data were collected from watersheds of two small lakes in the BWCWA in Minnesota from 2004 to 2012, and findings were presented in the Journal of Environmental Quality (http://bit.ly/2uwekXX). The lakes, Everett and Thelma, have similar watersheds and surrounding topography. The Everett Lake watershed experienced two fire events during the study period, a low-severity prescribed fire in 2004, and a moderate severity wildfire in 2007. Through the course of the study, researchers collected soil, water, and fish data. Samples of the soil organic horizon (O horizon) were analyzed for carbon content, organic matter, and total Hg content. For both lakes throughout the study, water was measured for pH, dissolved oxygen, temperature, and Secchi disk depth. Water samples were also analyzed for P, N, total organic C, and Hg. Lake water levels were also monitored. Fish were sampled in the spring each year, and the authors report data for yellow perch. The mass, length, and age of fish were determined and were analyzed for Hg content. The authors report that the two watersheds had similar soil C and Hg levels before the fires. After the wildfire in 2007 at Everett Lake, the watershed soil C in the O horizon decreased by approximately 26% and soil Hg by approximately 19%. And although lake productivity and nutrients increased in the growing season after the 2007 wildfire, this increase was observed in both lakes and was, therefore, unrelated to the wildfire event. Throughout the study, fish size and Hg concentrations fluctuated. The highest levels of Hg concentration were observed at Everett Lake in 2006 and 2010, and the lowest levels were observed in 2012. The 2010 increase was significant, but again, this trend was observed in both lakes, and the researchers concluded it was not an effect of fire. The authors determined fish Hg levels throughout this study were driven by other factors, such as the air temperature during spring hatching and lake water levels. Kolka says the researchers have “confidence in the results” given the long-term nature of the data collection, which captured two fire events and variation in the environmental conditions. While this study did not find wildland fire to have impacts on fish Hg levels, that does not mean fire in this region will never impact lake chemistry or fish Hg concentrations. Kolka explains that these two fires captured by this study were low to moderate in severity. In the event of a high-severity wildfire, or a fire that burned a greater proportion of the watershed and surrounding landscape, the release of Hg from soil could be much higher and lead to an increase in fish Hg concentration. Continued monitoring, of both fire severity and fish populations, may reveal conditions under which fish Hg concentrations increase after a fire. But at this point, there is no evidence that low-to-moderate severity prescribed fires or wildfires will have a negative impact on waterways or fish Hg concentrations in the BWCWA.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.007

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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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