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Enregistrement W2898125119 · doi:10.1002/lob.10266

Scientists Investigate Eutrophication Mystery and find Oligotrophication Instead

2018· article· en· W2898125119 sur OpenAlexaffabout
Naíla Barbosa da Costa

Notice bibliographique

RevueLimnology and Oceanography Bulletin · 2018
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueAquatic Ecosystems and Phytoplankton Dynamics
Établissements canadiensUniversité de Montréal
Organismes subventionnairesnon disponible
Mots-clésEutrophicationTrophic levelTrophic state indexEnvironmental scienceAlgal bloomBloomEcologyWater qualityLake ecosystemBiomanipulationOceanographyNutrientGeographyHydrology (agriculture)EcosystemPhytoplanktonGeologyBiology

Résumé

récupéré en direct d'OpenAlex

Just like doctors measure body temperature to detect fever, limnologists use a lake's trophic status as a “thermometer” to assess its health. The richer in nutrients a lake is, the more likely it is to develop algal or cyanobacterial blooms and, consequently, the worse the water quality. Limnologists have known for a long time that agricultural runoff can lead to lake eutrophication. What is surprising is that we are now detecting blooms in otherwise pristine lakes, located far from human-impacted areas. A remarkable example of this is the 2014 cyanobacterial bloom reported in Dickson Lake (Ontario) – a bloom that remains so far unexplained. Motivated by this mysterious case, Aleksey Paltsev, a PhD candidate in the University of Western Ontario, became curious about how frequently eutrophication occurs in lakes located in relatively undisturbed regions of the Great Lakes Basin. At the 2018 ASLO Meeting in Victoria (BC), he reported on his investigation, in which he followed changes in the trophic status of 12,600 lakes along a 28-year time course. To classify lake trophic status, Paltsev determined chlorophyll a concentrations based on reflectance values of band 3 (corresponding to green light intensity) from Landsat satellite images. “There is something that makes lakes stable and something that forces them to shift from one trophic status to other,” Paltsev says, “we wanted to know what makes some lakes more resilient than others.” For that, he classified lakes into those that were stable (i.e. permanently oligotrophic or eutrophic) and those that were changing from one stable state to another, either because they were becoming more oligotrophic or more eutrophic. His analysis found that more than 5000 lakes could be classified as stable oligotrophic and about 100 as stable eutrophic. A few lakes were unstable, not showing consistent trends toward eutrophication or oligotrophication. Among lakes experiencing a clear shift in stability, surprisingly, he found that more were undergoing oligotrophication (about 3000) than eutrophication (about 2000). Intrigued by this result, Paltsev wanted to explain why such an unexpected phenomenon was happening in the Great Lakes Basin. He then noticed that the amplitude of variation in both oligotrophying and eutrophying lakes was very similar across the time series, hinting that the same broad-scale environmental factors could be driving these processes. Paltsev noted that increased mean temperatures alone could not explain the observed shifts in lakes stability; instead, a combination of environmental factors and lake physical properties were more important defining a lake's fate across the 28-year period studied. He examined the effect of landscape metrics (e.g. percentage of wetland in the catchment), lake morphometry (e.g., lake fetch, width of the littoral zone, and maximum depth) and weather conditions (e.g., precipitation rates) on lake trophic stability. He observed that lakes undergoing eutrophication were relatively deep, had small fetch, wide littoral zone, were surrounded by many wetlands, and were located in watersheds with a decreasing trend in precipitation rates. Lakes undergoing oligotrophication were also relatively deep, but had a medium-size fetch, narrow littoral zone, were surrounded by only a few wetlands and located in watersheds with an increasing trend in precipitation. He concluded that a lake's morphometry and the catchment area influence nutrients inflow and water residence time, consequently impacting nutrient availability to phytoplankton. Bloom-forming algae and cyanobacteria will have more time to uptake nutrients in eutrophying lakes, which are, in general, more connected to the catchment and exhibit low residence time. This study provides clues to solve the mystery of Dickson Lake, and it goes further by showing an unexpected trend of oligotrophication in pristine lakes. However, it is still too early to predict the consequences of the undergoing changes. “Oligotrophication is less dangerous [to water quality] than eutrophication, but we don't know the consequences of it yet. It is really hard to predict how these lakes will react, lakes also have their own system,” Paltsev explains.

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 candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,068
Score d'incertitude au seuil0,480

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 technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,005
Tête enseignante GPT0,189
Écart entre enseignants0,184 · 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; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations1
Publié2018
Routes d'admission2
Résumé présentoui

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