MétaCan
Menu
Retour à la cohorte
Enregistrement W2744968415 · doi:10.2134/csa2017.62.0803

Wildland Fire Impacts on Mercury in Fish

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

Notice bibliographique

RevueCSA News · 2017
Typearticle
Languehu
DomaineEnvironmental Science
ThématiqueFire effects on ecosystems
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEnvironmental scienceBiogeochemical cycleWilderness areaEcosystemFire ecologyFire regimeGeographyForestryHydrology (agriculture)WildernessEcologyEngineering

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,169
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,007

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,250
Écart entre enseignants0,236 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueCSA NewsMême sujetFire effects on ecosystemsTravaux en français237 207