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Record W2109408742 · doi:10.1093/biosci/biu209

Plants Duke It Out in a Warming Arctic

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

Bibliographic record

VenueBioScience · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsnot available
Fundersnot available
KeywordsTundraArctic vegetationShrubArcticEcologyEnvironmental sciencePermafrostEcosystemDominance (genetics)Global warmingClimate changeArctic ecologyGeographyBiology

Abstract

fetched live from 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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.150
GPT teacher head0.289
Teacher spread0.139 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2015
Admission routes1
Has abstractyes

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