Abstract
AbstractUnderstanding soil systems is critical because they form the structural and nutritional foundation for plants and thus every terrestrial habitat and agricultural system. In this paper, we encourage increased use of mathematical models to drive forward understanding of interactions in soil ecological systems. We discuss several distinctive features of soil ecosystems and empirical studies of them. We explore some perceptions that have previously deterred more extensive use of models in soil ecology and some advances that have already been made using models to elucidate soil ecological interactions. We provide examples where mathematical models have been used to test the plausibility of hypothesized mechanisms, to explore systems where experimental manipulations are currently impossible, or to determine the most important variables to measure in experimental and natural systems. To aid in the development of theory in this field, we present a table describing major soil ecology topics, the theory previously used, and providing key terms for theoretical approaches that could potentially address them. We then provide examples from the table that may either contribute to important incremental developments in soil science or potentially revolutionize our understanding of plant–soil systems. We challenge scientists and mathematicians to push theoretical explorations in soil systems further and highlight three major areas for the development of mathematical models in soil ecology: theory spanning scales and ecological hierarchies, processes, and evolution.
Highlights
Have previously deterred more extensive use of models in soil ecology and some advances that have already been made using models to elucidate soil ecological interactions
We provide examples where mathematical models have been used to test the plausibility of hypothesized mechanisms, to explore systems where experimental manipulations are currently impossible, or to determine the most important variables to measure in experimental and natural systems
Despite advances in technology making complex models more manageable, some of these trade-offs remain (Evans 2012). The consequence of these trade-offs is that while models can be powerful tools to promote understanding and predictability in complex systems, the appropriate model must be selected for the question and system being addressed. Fewer of these developed models have focused on soil systems (Barot et al 2007), and in this paper, we advocate for mathematical models as expressions of quantitative theory, be it empirical or conceptual, as powerful tools for increasing understanding of soil systems
Summary
Until the last few decades, soils were viewed as a unique environment that created challenges for mathematical modeling (Vereecken et al 2016), and there are still fewer modeling papers in soil ecology journals relative to other fields of ecology and evolution (Barot et al 2007). Many practical models (calibrated to specific data sets), such as soil–plant–atmosphere models (SPAM), often focus on abiotic instead of biotic factors (reviewed in Manzoni et al 2013) These models make useful predictions for agricultural systems, which are often relatively simple (e.g., fewer plant species, controlled inputs), but may be less informative about processes in unmanaged systems. The problem of parameter estimation is far from unique to soil systems This may make predictive modeling challenging, but strategic models (which seek to capture key features of a system rather than the fine detail) can identify which mechanisms and parameters have the greatest influence on the system and can explore suites of possible system dynamics under realistic assumptions. This approach may either allow us to put bounds on key parameter values or on the possible ways in which a system may behave
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