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Using plant, microbe, and soil fauna traits to improve the predictive power of biogeochemical models. / Fry, Ellen L.; De Long, Jonathan R. (Corresponding author); Álvarez Garrido, Lucía; Alvarez, Nil; Carrillo, Yolima; Castañeda-Gómez, Laura; Chomel, Mathilde; Dondini, Marta; Drake, John E.; Hasegawa, Shun; Hortal, Sara; Jackson, Benjamin G.; Jiang, Mingkai; Lavallee, Jocelyn M.; Medlyn, Belinda E.; Rhymes, Jennifer; Singh, Brajesh K.; Smith, Pete; Anderson, Ian C.; Bardgett, Richard D.; Baggs, Elizabeth M.; Johnson, David.

In: Methods in Ecology and Evolution, Vol. in press, No. 0, 2019, p. 146-157.

Research output: Contribution to journal/periodicalArticleScientificpeer-review

Harvard

Fry, EL, De Long, JR, Álvarez Garrido, L, Alvarez, N, Carrillo, Y, Castañeda-Gómez, L, Chomel, M, Dondini, M, Drake, JE, Hasegawa, S, Hortal, S, Jackson, BG, Jiang, M, Lavallee, JM, Medlyn, BE, Rhymes, J, Singh, BK, Smith, P, Anderson, IC, Bardgett, RD, Baggs, EM & Johnson, D 2019, 'Using plant, microbe, and soil fauna traits to improve the predictive power of biogeochemical models' Methods in Ecology and Evolution, vol. in press, no. 0, pp. 146-157. https://doi.org/10.1111/2041-210X.13092

APA

Fry, E. L., De Long, J. R., Álvarez Garrido, L., Alvarez, N., Carrillo, Y., Castañeda-Gómez, L., ... Johnson, D. (2019). Using plant, microbe, and soil fauna traits to improve the predictive power of biogeochemical models. Methods in Ecology and Evolution, in press(0), 146-157. https://doi.org/10.1111/2041-210X.13092

Vancouver

Fry EL, De Long JR, Álvarez Garrido L, Alvarez N, Carrillo Y, Castañeda-Gómez L et al. Using plant, microbe, and soil fauna traits to improve the predictive power of biogeochemical models. Methods in Ecology and Evolution. 2019;in press(0):146-157. https://doi.org/10.1111/2041-210X.13092

Author

Fry, Ellen L. ; De Long, Jonathan R. ; Álvarez Garrido, Lucía ; Alvarez, Nil ; Carrillo, Yolima ; Castañeda-Gómez, Laura ; Chomel, Mathilde ; Dondini, Marta ; Drake, John E. ; Hasegawa, Shun ; Hortal, Sara ; Jackson, Benjamin G. ; Jiang, Mingkai ; Lavallee, Jocelyn M. ; Medlyn, Belinda E. ; Rhymes, Jennifer ; Singh, Brajesh K. ; Smith, Pete ; Anderson, Ian C. ; Bardgett, Richard D. ; Baggs, Elizabeth M. ; Johnson, David. / Using plant, microbe, and soil fauna traits to improve the predictive power of biogeochemical models. In: Methods in Ecology and Evolution. 2019 ; Vol. in press, No. 0. pp. 146-157.

BibTeX

@article{1c4da4abcd0c462796449ab61ef767e6,
title = "Using plant, microbe, and soil fauna traits to improve the predictive power of biogeochemical models",
abstract = "Abstract Process-based models describing biogeochemical cycling are crucial tools to understanding long-term nutrient dynamics, especially in the context of perturbations, such as climate and land-use change. Such models must effectively synthesize ecological processes and properties. For example, in terrestrial ecosystems, plants are the primary source of bioavailable carbon, but turnover rates of essential nutrients are contingent on interactions between plants and soil biota. Yet, biogeochemical models have traditionally considered plant and soil communities in broad terms. The next generation of models must consider how shifts in their diversity and composition affect ecosystem processes. One promising approach to synthesize plant and soil biodiversity and their interactions into models is to consider their diversity from a functional trait perspective. Plant traits, which include heritable chemical, physical, morphological and phenological characteristics, are increasingly being used to predict ecosystem processes at a range of scales, and to interpret biodiversity?ecosystem functional relationships. There is also emerging evidence that the traits of soil microbial and faunal communities can be correlated with ecosystem functions such as decomposition, nutrient cycling, and greenhouse gas production. Here, we draw on recent advances in measuring and using traits of different biota to predict ecosystem processes, and provide a new perspective as to how biotic traits can be integrated into biogeochemical models. We first describe an explicit trait-based model framework that operates at small scales and uses direct measurements of ecosystem properties; second, an integrated approach that operates at medium scales and includes interactions between biogeochemical cycling and soil food webs; and third, an implicit trait-based model framework that associates soil microbial and faunal functional groups with plant functional groups, and operates at the Earth-system level. In each of these models, we identify opportunities for inclusion of traits from all three groups to reduce model uncertainty and improve understanding of biogeochemical cycles. These model frameworks will generate improved predictive capacity of how changes in biodiversity regulate biogeochemical cycles in terrestrial ecosystems. Further, they will assist in developing a new generation of process-based models that include plant, microbial, and faunal traits and facilitate dialogue between empirical researchers and modellers.",
keywords = "above-belowground interactions, biodiversity, carbon and nitrogen cycling, climate change, community weighted means, effect and response traits, intra- and interspecific variation, mycorrhizae, international",
author = "Fry, {Ellen L.} and {De Long}, {Jonathan R.} and {{\'A}lvarez Garrido}, Luc{\'i}a and Nil Alvarez and Yolima Carrillo and Laura Casta{\~n}eda-G{\'o}mez and Mathilde Chomel and Marta Dondini and Drake, {John E.} and Shun Hasegawa and Sara Hortal and Jackson, {Benjamin G.} and Mingkai Jiang and Lavallee, {Jocelyn M.} and Medlyn, {Belinda E.} and Jennifer Rhymes and Singh, {Brajesh K.} and Pete Smith and Anderson, {Ian C.} and Bardgett, {Richard D.} and Baggs, {Elizabeth M.} and David Johnson",
note = "6660, TE; Data Archiving: no data: {"}This work does not contain any data.{"}",
year = "2019",
doi = "10.1111/2041-210X.13092",
language = "English",
volume = "in press",
pages = "146--157",
journal = "Methods in Ecology and Evolution",
issn = "2041-210X",
publisher = "John Wiley and Sons Ltd",
number = "0",

}

RIS

TY - JOUR

T1 - Using plant, microbe, and soil fauna traits to improve the predictive power of biogeochemical models

AU - Fry, Ellen L.

AU - De Long, Jonathan R.

AU - Álvarez Garrido, Lucía

AU - Alvarez, Nil

AU - Carrillo, Yolima

AU - Castañeda-Gómez, Laura

AU - Chomel, Mathilde

AU - Dondini, Marta

AU - Drake, John E.

AU - Hasegawa, Shun

AU - Hortal, Sara

AU - Jackson, Benjamin G.

AU - Jiang, Mingkai

AU - Lavallee, Jocelyn M.

AU - Medlyn, Belinda E.

AU - Rhymes, Jennifer

AU - Singh, Brajesh K.

AU - Smith, Pete

AU - Anderson, Ian C.

AU - Bardgett, Richard D.

AU - Baggs, Elizabeth M.

AU - Johnson, David

N1 - 6660, TE; Data Archiving: no data: "This work does not contain any data."

PY - 2019

Y1 - 2019

N2 - Abstract Process-based models describing biogeochemical cycling are crucial tools to understanding long-term nutrient dynamics, especially in the context of perturbations, such as climate and land-use change. Such models must effectively synthesize ecological processes and properties. For example, in terrestrial ecosystems, plants are the primary source of bioavailable carbon, but turnover rates of essential nutrients are contingent on interactions between plants and soil biota. Yet, biogeochemical models have traditionally considered plant and soil communities in broad terms. The next generation of models must consider how shifts in their diversity and composition affect ecosystem processes. One promising approach to synthesize plant and soil biodiversity and their interactions into models is to consider their diversity from a functional trait perspective. Plant traits, which include heritable chemical, physical, morphological and phenological characteristics, are increasingly being used to predict ecosystem processes at a range of scales, and to interpret biodiversity?ecosystem functional relationships. There is also emerging evidence that the traits of soil microbial and faunal communities can be correlated with ecosystem functions such as decomposition, nutrient cycling, and greenhouse gas production. Here, we draw on recent advances in measuring and using traits of different biota to predict ecosystem processes, and provide a new perspective as to how biotic traits can be integrated into biogeochemical models. We first describe an explicit trait-based model framework that operates at small scales and uses direct measurements of ecosystem properties; second, an integrated approach that operates at medium scales and includes interactions between biogeochemical cycling and soil food webs; and third, an implicit trait-based model framework that associates soil microbial and faunal functional groups with plant functional groups, and operates at the Earth-system level. In each of these models, we identify opportunities for inclusion of traits from all three groups to reduce model uncertainty and improve understanding of biogeochemical cycles. These model frameworks will generate improved predictive capacity of how changes in biodiversity regulate biogeochemical cycles in terrestrial ecosystems. Further, they will assist in developing a new generation of process-based models that include plant, microbial, and faunal traits and facilitate dialogue between empirical researchers and modellers.

AB - Abstract Process-based models describing biogeochemical cycling are crucial tools to understanding long-term nutrient dynamics, especially in the context of perturbations, such as climate and land-use change. Such models must effectively synthesize ecological processes and properties. For example, in terrestrial ecosystems, plants are the primary source of bioavailable carbon, but turnover rates of essential nutrients are contingent on interactions between plants and soil biota. Yet, biogeochemical models have traditionally considered plant and soil communities in broad terms. The next generation of models must consider how shifts in their diversity and composition affect ecosystem processes. One promising approach to synthesize plant and soil biodiversity and their interactions into models is to consider their diversity from a functional trait perspective. Plant traits, which include heritable chemical, physical, morphological and phenological characteristics, are increasingly being used to predict ecosystem processes at a range of scales, and to interpret biodiversity?ecosystem functional relationships. There is also emerging evidence that the traits of soil microbial and faunal communities can be correlated with ecosystem functions such as decomposition, nutrient cycling, and greenhouse gas production. Here, we draw on recent advances in measuring and using traits of different biota to predict ecosystem processes, and provide a new perspective as to how biotic traits can be integrated into biogeochemical models. We first describe an explicit trait-based model framework that operates at small scales and uses direct measurements of ecosystem properties; second, an integrated approach that operates at medium scales and includes interactions between biogeochemical cycling and soil food webs; and third, an implicit trait-based model framework that associates soil microbial and faunal functional groups with plant functional groups, and operates at the Earth-system level. In each of these models, we identify opportunities for inclusion of traits from all three groups to reduce model uncertainty and improve understanding of biogeochemical cycles. These model frameworks will generate improved predictive capacity of how changes in biodiversity regulate biogeochemical cycles in terrestrial ecosystems. Further, they will assist in developing a new generation of process-based models that include plant, microbial, and faunal traits and facilitate dialogue between empirical researchers and modellers.

KW - above-belowground interactions

KW - biodiversity

KW - carbon and nitrogen cycling

KW - climate change

KW - community weighted means

KW - effect and response traits

KW - intra- and interspecific variation

KW - mycorrhizae

KW - international

U2 - 10.1111/2041-210X.13092

DO - 10.1111/2041-210X.13092

M3 - Article

VL - in press

SP - 146

EP - 157

JO - Methods in Ecology and Evolution

JF - Methods in Ecology and Evolution

SN - 2041-210X

IS - 0

ER -

ID: 9409504