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Uniting the scales of microbial biogeochemistry with trait‐based modelling
Author(s) -
Wan Joe,
Crowther Thomas W.
Publication year - 2022
Publication title -
functional ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.272
H-Index - 154
eISSN - 1365-2435
pISSN - 0269-8463
DOI - 10.1111/1365-2435.14035
Subject(s) - trait , biogeochemical cycle , biogeochemistry , ecology , biology , ecosystem , scale (ratio) , carbon cycle , computer science , geography , cartography , programming language
Below‐ground microbial communities drive some of Earth's largest biogeochemical fluxes, yet they represent a major source of uncertainty in global biogeochemical models. This review synthesizes recent advances in trait‐based soil carbon modelling in order to identify how empirical observations of microbial traits can inform the next generation of soil carbon models. We identify four key perspectives from which trait‐based models have investigated the role of microbes in soil carbon fluxes, ranging from the largest to the smallest scales of biological organization: (i) Earth system models, which have recently begun to incorporate microbial traits at a global scale; (ii) ecosystem models, which relate microbial carbon cycling to other trophic levels and element cycles; (iii) models from community ecology, which link theories of species diversity to ecosystem cycles; and (iv) models of fine‐scale physiology, which mechanistically represent traits at the individual level. Highlighting the contributions of diverse trait‐based modelling approaches, we caution that this diversity makes it challenging to link perspectives at different scales. The meaning of a trait depends both on the structure of the model in which it occurs and on the scale treated by the model. Thus, reapplying a fine‐scale trait at a broader scale may make incorrect predictions, an issue we illustrate quantitatively using model simulations. With these challenges in mind, we highlight several ways to synthesize the scales of microbial biogeochemical modelling: (i) quantitatively, using mathematical scaling techniques, (ii) empirically, by applying experiments to test relationships between scales and (iii) conceptually, by identifying key traits and processes across scales. Taking full advantage of trait‐based modelling, ecologists will thus be able to incorporate multiple perspectives to better predict carbon cycling in a changing world. Read the free Plain Language Summary for this article on the Journal blog.