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

Ellen L. Fry, Jonathan R. De Long, Lucía ÁlvarezGarrido, Nil Alvarez, Yolima Carrillo, Laura Castañeda-Gómez, Mathilde Chomel, Marta Dondini, John E. Drake, Shun Hasegawa, Sara Hortal, Benjamin G. Jackson, Mingkai Jiang, Jocelyn M. Lavallee, Belind M. Medlyn, Jennifer Rhymes, Brajesh K. Singh, Pete Smith, Ian C. Anderson, Richard D. BardgettElizabeth M. Baggs, David Johnson

Research output: Contribution to journalArticle

10 Citations (Scopus)
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Abstract

1. 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 synthesise 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.
2. One promising approach to synthesise 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 function 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.
3. 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.
4. 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.
Original languageEnglish
Pages (from-to)146-157
Number of pages12
JournalMethods in Ecology and Evolution
Volume10
Issue number1
Early online date8 Oct 2018
DOIs
Publication statusPublished - Jan 2019

Keywords

  • above‐belowground interactions
  • biodiversity
  • carbon and nitrogen cycling
  • climate change
  • community weighted means
  • effect and response traits
  • intra- and interspecific variation
  • mycorrhizae
  • above-belowground interactions

ASJC Scopus subject areas

  • Ecological Modelling
  • Ecology, Evolution, Behavior and Systematics

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    Fry, E. L., De Long, J. R., ÁlvarezGarrido, L., Alvarez, N., Carrillo, Y., Castañeda-Gómez, L., Chomel, M., Dondini, M., Drake, J. E., Hasegawa, S., Hortal, S., Jackson, B. G., Jiang, M., Lavallee, J. M., Medlyn, B. M., Rhymes, J., Singh, B. K., Smith, P., Anderson, I. C., ... Johnson, D. (2019). Using plant, microbe and soil fauna traits to improve the predictive power of biogeochemical models. Methods in Ecology and Evolution, 10(1), 146-157. https://doi.org/10.1111/2041-210X.13092