This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.3389/fenvs.2023.1171194. This is version 1 of this Preprint.
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Ecosystem management is integral to the future of soils, yet anthropogenic drivers represent a key source of uncertainty in ecosystem models. First- and new-generation soil models formulate many soil pools using first-order decomposition, which tends to generate simpler yet numerous parameters. Systems or complexity theory, developed across various scientific and social fields, may help improve robustness of soil models, by offering consistent assumptions about system openness, potential dynamic instability and distance from commonly assumed stable equilibria, as well as new analytical tools for formulating more generalized model structures that reduce parameter space and yield a wider array of possible model outcomes, such as quickly shrinking carbon stocks with pulsing or lagged respiration. This paper builds on recent perspectives of soil modeling to ask how various soil functions can be better understood by applying a complex systems lens. We synthesized previous literature reviews with concepts from non-linear dynamical systems in theoretical ecology and soil sciences more broadly to identify areas for further study that may help improve the robustness of soil models under the uncertainty of human activities and management. Three broad dynamical concepts were highlighted: soil variable memory or state-dependence, oscillations, and tipping points or hysteresis. These themes represent less intuitive yet key dynamics that can emerge after assuming nuanced observations, such as reversibility of organo-mineral associations, dynamic aggregate- and pore hierarchies, persistent wet-dry cycles, higher-order microbial community and predator-prey interactions, cumulative legacy land use history, and social management interactions and/or cooperation. We discuss how these aspects may contribute useful analytical tools, metrics, and frameworks that help integrate the uncertainties in future soil states, ranging from micro- to regional scales, including those indirectly affected by human activities and management decisions. Overall, this study highlights the potential benefits of incorporating spatial heterogeneity and dynamic instabilities into future model representations of whole soil processes. Additionally, it advocates for transdisciplinary collaborations between natural and social scientists, extending research into anthropedology and biogeosociochemistry, to better integrate and understand longer-term anthropogenic drivers of soil processes, potentially from soil structural dynamics to microbial community and food web ecology.
Agriculture, Applied Mathematics, Biogeochemistry, Biology, Dynamic Systems, Earth Sciences, Ecology and Evolutionary Biology, Life Sciences, Longitudinal Data Analysis and Time Series, Research Methods in Life Sciences, Soil Science, Sustainability
Complex Systems, nonlinear oscillator dynamics, soil system modeling, Hysteresis, critical tipping point transition
Published: 2023-03-21 19:26
Last Updated: 2023-03-21 23:26
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Data and Code Availability Statement: