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Enregistrement W2109408742 · doi:10.1093/biosci/biu209

Plants Duke It Out in a Warming Arctic

2015· article· en· W2109408742 sur OpenAlexaboutno aff
Lesley Evans Ogden

Notice bibliographique

RevueBioScience · 2015
Typearticle
Langueen
DomaineEarth and Planetary Sciences
ThématiqueClimate change and permafrost
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTundraArctic vegetationShrubArcticEcologyEnvironmental sciencePermafrostEcosystemDominance (genetics)Global warmingClimate changeArctic ecologyGeographyBiology

Résumé

récupéré en direct d'OpenAlex

Global warming has significantly influenced tundra ecosystems; thawing permafrost, increasing fire frequency, and altering vegetation. Arctic plant communities are in flux, duking it out for dominance in a changing world. Alongside changes in temperature and precipitation, myriad environmental and biological factors may be influencing plant growth from local to regional scales. Researchers are collaborating to document and predict Arctic vegetation shifts, particularly the phenomenon of shrub encroachment, or shrubification. What do we know about vegetation change in a warming Arctic? Are shrubs taking over, and where are the knowledge gaps? Above latitudinal and altitudinal tree lines, shrubs like birch (Betula), willow (Salix), and alder (Alnus) are often the tallest plants. Their dominance appears to be increasing. Since a May 2001 Nature paper by Matthew Sturm and colleagues drew attention to this phenomenon, research has burgeoned, with shrub encroachment highlighted in the March 2014 Intergovernmental Panel on Climate Change report. Shrubs, explains Isla Myers-Smith, can restructure tundra ecosystems directly and indirectly by altering ecosystem function, creating feedback mechanisms that further advance their own growth and range. Species such as the dwarf birch (Betula nana) can take advantage of warmer temperatures and augmented nutrients by increasing the height and density of their canopy cover. Shrubification may reduce the growth potential of other species as shrub foliage limits competitors’ access to light and modifies snow depth, hydrology, nutrient exchange, carbon balance, albedo, and energy flux. These changes may make the Arctic more like it was millennia ago. “The Arctic was a lot shrubbier in the past,” says Myers-Smith, a Chancellor's -fellow in the Global Change Research Group at the University of Edinburgh. One line of evidence is lake sediments. Sedimentary charcoal (from fires) and pollen indicate that shrubs were a more dominant tundra vegetation in times past, between 6000 and 14,000 years ago. Growth ring analysis from long-lived Arctic shrubs shows that woody species respond to changing conditions. Growth varies among years, often correlating closely with climate variables, especially summer temperature, explains Myers-Smith. More evidence for shrubification comes from tundra experiments. Queen's University's Tara Zamin and her colleagues, for example, erected greenhouses during summers at Daring Lake, in Canada's Northwest Territories, measuring plant responses after 6–8 years. Birch (Betula glandulosa) apical stems grew 2.5 times as much on experimentally warmed than on control patches (doi:10.1088/1748-9326/7/3/034027). “Deciduous shrubs are… able to respond to changes in their community,” says Zamin. In another experiment at the same site, with greenhouses as one treatment and nutrient supplementation as another, they found different effects with evergreen shrubs. Aboveground evergreen biomass increased by 66 percent on plots with greenhouse warming, but decreased by 70 percent on plots with high-level nutrient additions. Their results demonstrate that ongoing Arctic vegetation change is neither uniform nor simple (doi:10.1111/1365-2745.12237). The monitoring of “unmanipulated” (except by climate change) sites with satellite data, repeat photography, and intensively studied quadrats contributes another line of evidence. On Herschel Island, in the Canadian Arctic, Myers-Smith uses repeat photography, vegetation monitoring, and tree ring assessment, noting a dramatic shift. Compared with photographs -dating back to the 1890s, when whalers occupied the site, shrubs have increased in size and height, growing from isolated patches to almost continuous cover. The monitoring is ongoing. In 2014, she was surprised to measure an individual with a height of 1 meter, a giant twice as tall as most others. “We named him Gunther, the tallest shrub on Herschel Island,” she says. Gunther grew 20 centimeters in one summer. Shrubs are expanding throughout the Arctic. But as Sarah Elmendorf and her coauthors point out, it is not happening uniformly (doi:10.1038/nclimate1465). New results from the “shrub hub” research team indicate that climate-sensitive shrubification is a relatively consistent trend in the European Arctic, whereas the Canadian and US Arctic response is more variable. Early indications are that, in addition to temperature, water availability is crucial. At drier Arctic sites, shrubs appear less able to respond. Shifting herbivore communities are also being investigated, with early indications of winners and losers. Willow-eating moose may be moving northward beyond the tree line, whereas lichen-loving caribou, already in decline in some areas, may face less food as their preferred forage is crowded and shaded out by shrubs. In the Yukon, shrubification is changing the landscape for food, cover, and visibility. Ground squirrels, usually associated with open tundra, are behaving differently. They are “less willing to stay at foraging patches as long in shrubbier habitats,” explains Helen Wheeler, a postdoc at Aarhus University in Denmark. With many unanswered questions about the effects of shrubification on other plant and animal species, the wider tundra ecosystem, and climate feedbacks, the large international research collaborations are continuing. “There's so much we don't know,” says Wheeler. Myers-Smith agrees, adding that we may be in for some surprises.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,030
Score d'incertitude au seuil0,059

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0050,001
Communication savante0,0030,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,002

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 Opus0,150
Tête enseignante GPT0,289
Écart entre enseignants0,139 · 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 source (Gemma direct ou Codex distillé), 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

Citations3
Publié2015
Routes d'admission1
Résumé présentoui

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