Our knowledge of microbial processes—who is responsible for what,the rates at which they occur,and the substrates consumed and products produced—is imperfect for many if not most taxa,but even less is known about ho...Our knowledge of microbial processes—who is responsible for what,the rates at which they occur,and the substrates consumed and products produced—is imperfect for many if not most taxa,but even less is known about how microsite processes scale to the ecosystem and thence the globe.In both natural and managed environments,scaling links fundamental knowledge to application and also allows for global assessments of the importance of microbial processes.But rarely is scaling straightforward:More often than not,process rates in situ are distributed in a highly skewed fashion,under the influence of multiple interacting controls,and thus often difficult to sample,quantify,and predict.To date,quantitative models of many important processes fail to capture daily,seasonal,and annual fluxes with the precision needed to effect meaningful management outcomes.Nitrogen cycle processes are a case in point,and denitrification is a prime example.Statistical models based on machine learning can improve predictability and identify the best environmental predictors but are—by themselves—insufficient for revealing process-level knowledge gaps or predicting outcomes under novel environmental conditions.Hybrid models that incorporate well-calibrated process models as predictors for machine learning algorithms can provide both improved understanding and more reliable forecasts under environmental conditions not yet experienced.Incorporating trait-based models into such efforts promises to improve predictions and understanding still further,but much more development is needed.展开更多
基金support was provided by the Great Lakes Bioenergy Research Center,US Department of Energy,Office of Science,Office of Biological and Environmental Research(Award DE‐SC0018409)the National Science Foundation Long‐term Ecological Research Program(DEB 2224712)at the Kellogg Biological Station,the USDA Long‐term Agroecosystem Research Network program,and by Michigan State University AgBioResearch.
文摘Our knowledge of microbial processes—who is responsible for what,the rates at which they occur,and the substrates consumed and products produced—is imperfect for many if not most taxa,but even less is known about how microsite processes scale to the ecosystem and thence the globe.In both natural and managed environments,scaling links fundamental knowledge to application and also allows for global assessments of the importance of microbial processes.But rarely is scaling straightforward:More often than not,process rates in situ are distributed in a highly skewed fashion,under the influence of multiple interacting controls,and thus often difficult to sample,quantify,and predict.To date,quantitative models of many important processes fail to capture daily,seasonal,and annual fluxes with the precision needed to effect meaningful management outcomes.Nitrogen cycle processes are a case in point,and denitrification is a prime example.Statistical models based on machine learning can improve predictability and identify the best environmental predictors but are—by themselves—insufficient for revealing process-level knowledge gaps or predicting outcomes under novel environmental conditions.Hybrid models that incorporate well-calibrated process models as predictors for machine learning algorithms can provide both improved understanding and more reliable forecasts under environmental conditions not yet experienced.Incorporating trait-based models into such efforts promises to improve predictions and understanding still further,but much more development is needed.
