Climate-Driven Legacies in Simulated Microbial Communities Alter Litter Decomposition Rates

Abstract

The mechanisms underlying diversity-functioning relationships have been a consistent area of inquiry in biogeochemistry since the 1950s. Though these mechanisms remain unresolved in soil microbiomes, many approaches at varying scales have pointed to the same notion—composition matters. Confronting the methodological challenge arising from the complexity of microbiomes, this study used the model DEMENTpy, a trait-based modeling framework, to explore trait-based drivers of microbiome-dependent litter decomposition. We parameterized DEMENTpy for five sites along a climate gradient in Southern California, United States, and conducted reciprocal transplant simulations analogous to a prior empirical study. The simulations demonstrated climate-dependent legacy effects of microbial communities on plant litter decomposition across the gradient. This result is consistent with the previous empirical study across the same gradient. An analysis of community-level traits further suggests that a 3-way tradeoff among resource acquisition, stress tolerance, and yield strategies influences community assembly. Simulated litter decomposition was predictable with two community traits (indicative of two of the three strategies) plus local environment, regardless of the system state (transient vs. equilibrium). Although more empirical confirmation is still needed, community traits plus local environmental factors (e.g., environment and litter chemistry) may robustly predict litter decomposition across spatial-temporal scales. In conclusion, this study offers a potential trait-based explanation for climate-dependent community effects on litter decomposition with implications for improved understanding of whole-ecosystem functioning across scales.

Microbial growth in soil

“…A single population of plants can suppress N availability 48 , and a single population of animals can modify soil disturbance 49 . The tremendous biodiversity of soil microorganisms means that individual microbial populations should also have profound influence on ecosystems 50,51 . New methods that quantify the growth rates of individual microbial taxa 24,[39][40][41] are promising avenues for developing quantitative links between specific microbial taxa and soil processes.…”

Eco‐evolutionary insights into microbial litter decomposition

“…Additionally, competitive interactions within the community could lead to unexpected emergent community properties and behaviors, including community‐level outcomes that do not reflect optimal biomass accumulation or growth rates at the community scale, as has been demonstrated in forests (Dybzinski et al ., 2014). The optimality approach provides exciting opportunities for further investigation of when the community‐level eco‐evolutionary optimization drives unexpected outcomes or deviates from models that explicitly consider microbial community functional diversity (Wang & Allison, 2022; Shao et al ., 2023), thus better guiding the selection of microbial modeling approaches.…”

Embracing fine‐root system complexity in terrestrial ecosystem modeling

“…A notable aboveground shift in vegetation structure is from the big‐leaf approach using a single‐layer canopy to explicitly modeling multi‐layer canopy structures (e.g., Bonan et al, 2021; Norman, 1993; Sinclair et al, 1976; Yan et al, 2017), and further to a paradigm simulating vegetation dynamics in the spatial and vertical structuring by age and size (e.g., Fisher et al, 2018; Kohyama, 1993; Moorcroft et al, 2001; Shugart, 1984; Shugart et al, 2018; Wang et al, 2016). A similar belowground example is the shift from simulating the tremendously complex soil microbiomes first implicitly (e.g., Parton et al, 1987), then as a “big microbe” (e.g., Allison et al, 2010), and more recently as a constellation of a few discrete functional groups (e.g., Wieder et al, 2015) or of even continuous hypothetical individuals using a trait‐based approach (Wang & Allison, 2022) in a structurally explicit soil environment (Wang et al, 2019). Resonating with this trend of complexity, our demonstration added quantitative support to the conceptual, theoretical, and empirical arguments for TAM.…”