Evidence map›Paper›PMID 34787932›Full record

ArticleEcological applications : a publication of the Ecological Society of America2022

Assessing dynamic vegetation model parameter uncertainty across Alaskan arctic tundra plant communities.

Eugénie S Euskirchen, Shawn P Serbin, Tobey B Carman, Jennifer M Fraterrigo, Hélène Genet, Colleen M Iversen, Verity Salmon, A David McGuire

Abstract read
In one paragraph

Article in Ecological applications : a publication of the Ecological Society of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Assessing dynamic vegetation model parameter uncertainty across Alaskan arctic tundra plant communities.Ecological applications : a publication of the Ecological Society of America · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Eugénie S EuskirchenInstitute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska, 99775, USA.ORCID 0000-0002-0848-4295
Shawn P SerbinTerrestrial Ecosystem Science & Technology Group, Environmental Sciences Department, Brookhaven National Laboratory, Upton, New York, 11973, USA.ORCID 0000-0003-4136-8971
Tobey B CarmanInstitute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska, 99775, USA.
Jennifer M FraterrigoDepartment of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA.ORCID 0000-0002-0357-1007
Hélène GenetInstitute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska, 99775, USA.ORCID 0000-0003-4537-9563
Colleen M IversenEnvironmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831, USA.ORCID 0000-0001-8293-3450
Verity SalmonEnvironmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831, USA.ORCID 0000-0002-2188-551X
A David McGuireInstitute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, Alaska, 99775, USA.ORCID 0000-0003-4646-0750

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the Arctic region moves into uncharted territory under a warming climate, it is important to refine the terrestrial biosphere models (TBMs) that help us understand and predict change. One fundamental uncertainty in TBMs relates to model parameters, configuration variables internal to the model whose value can be estimated from data. We incorporate a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). We set up different parameterizations of TEM across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula. TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and the stomatal responses to ambient light conditions. Our analysis also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had about equal uncertainty, while heterotrophic respiration had higher uncertainty than the pool of soil C. Our study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.

Indexed as

EcosystemTundraArctic RegionsPlantsSoilUncertaintySoilarctic carbon budgetarctic tundralandscape heterogeneitymodel uncertaintyparameter sensitivityterrestrial biosphere model

Identifiers

PMID34787932
PMCPMC9285828

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.