Soil fauna: key to new carbon models

Abstract

Abstract. Soil organic matter (SOM) is key to maintaining soil fertility, mitigating climate change, combatting land degradation, and conserving above-and below-ground biodiversity and associated soil processes and ecosystem services. In order to derive management options for maintaining these essential services provided by soils, policy makers depend on robust, predictive models identifying key drivers of SOM dynamics. Existing SOM models and suggested guidelines for future SOM modelling are defined mostly in terms of plant residue quality and input and microbial decomposition, overlooking the significant regulation provided by soil fauna. The fauna controls almost any aspect of organic matter turnover, foremost by regulating the activity and functional composition of soil microorganisms and their physical-chemical connectivity with soil organic matter. We demonstrate a very strong impact of soil animals on carbon turnover, increasing or decreasing it by several dozen percent, sometimes even turning C sinks into C sources or vice versa. This is demonstrated not only for earthworms and other larger invertebrates but also for smaller fauna such as Collembola. We suggest that inclusion of soil animal activities (plant residue consumption and bioturbation altering the formation, depth, hydraulic properties and physical heterogeneity of soils) can fundamentally affect the predictive outcome of SOM models. Understanding direct and indirect impacts of soil fauna on nutrient availability, carbon sequestration, greenhouse gas emissions and plant growth is key to the understanding of SOM dynamics in the context of global carbon cycling models. We argue that explicit consideration of soil fauna is essential to make realistic modelling predictions on SOM dynamics and to detect expected non-linear responses of SOM dynamics to global change. WePublished by Copernicus Publications on behalf of the European Geosciences Union. 566 J. Filser et al.: Soil fauna: key to new carbon models present a decision framework, to be further developed through the activities of KEYSOM, a European COST Action, for when mechanistic SOM models include soil fauna. The research activities of KEYSOM, such as field experiments and literature reviews, together with dialogue between empiricists and modellers, will inform how this is to be done.

Pedotransfer Functions in Earth System Science: Challenges and Perspectives

“…The ambition here is to show how PTFs can be improved to better parameterize soil processes by better quantifying rate parameters in biogeochemical processes related to soil carbon (e.g., Q10 and first‐order rate constants), nitrogen (e.g., mineralization constants), and biotic and biodiversity‐related processes. The latter has been largely neglected as there is a rising awareness of the different soil biotic processes and their role in the C, N, and general nutrient cycling in terrestrial ecosystems (Filser, et al, ).…”

Soil fauna diversity increases CO2 but suppresses N2O emissions from soil

“…Specifically, the close relationship between soil‐derived greenhouse gas (GHG) emissions and soil processes such as biogeochemical cycling of C and N may result in feedback mechanisms that can either amplify or diminish the initial climate forcing (Crowther et al, ; Van Nes et al, ). As part of the soil biodiversity, soil fauna species can have a major impact on this relationship through their influence on biogeochemical cycling of nutrients (Coulis et al, ; Filser et al, ; Sauvadet, Chauvat, Brunet, & Bertrand, ; Wall et al, ), plant productivity (Liiri, Setala, Haimi, Pennanen, & Fritze, ; Van Groenigen et al, ), and soil‐derived GHG emissions (Lubbers, Van Groenigen, et al, ).…”

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

“…The goal of these models is to predict the dependent variable (e.g., decomposition; Parton, Schimel, Cole, & Ojima, ). Explicit trait‐based models, such as those developed for the simulation of microbial communities (e.g., Allison, ) and faunal communities (Filser et al., ), require extensive knowledge of the intra‐ and interspecific trait variation along environmental gradients and their effects on ecosystem pools and fluxes. Two major advantages of this approach are: (a) the explicit parameterization of traits allows for measured values as direct model input and (b) complex interactions between organisms are allowed and may lead to emergent properties, such as top‐down or bottom‐up regulation of food web structure.…”

“…Increasingly, more nuanced models are possible due to better understanding of the role of faunal groups and availability of more comprehensive data on traits of these groups at different spatial and temporal scales. Evidence from soil food web models indicates that inclusion of plant, microbial, and soil faunal traits and their interactions is imperative to improve the predictive power of biogeochemical models (Allison, ; Faucon et al., ; Filser et al., ; Funk et al., ; Wieder, Bonan, & Allison, ). To move forward, we propose that gaps in knowledge of measuring and understanding functional traits must be addressed and general principles must be identified.…”