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Bridging the Atom-to-Global Scale Gap

What is the compelling question or challenge?

How can we quantitatively upscale our extensive knowledge of molecular-to-laboratory-scale mechanisms to more effectively manage and control landscape-to-global scale processes in natural ecosystems?

What do we know now about this Big Idea and what are the key research questions we need to address?

Major research efforts occur largely independently at different spatial scales. At the extremes, research on diverse natural or managed ecosystems at landscape, regional, and global scales provides information on outcomes of integrated processes occurring within these complex systems. Meanwhile, increasingly advanced technologies fuel science on atomic-scale processes of relevance to such systems. Between these extremes, macroscale kinetic and thermodynamic models are used to couple reaction and transport processes to make larger-scale predictions of, for example, water and chemical movement through terrestrial ecosystems. However, we lack systematic tools for directly utilizing molecular-scale knowledge to predict macroscale processes leading to innovative strategies for managing landscape-scale problems that arise.

Terrestrial systems are characterized by hierarchies of intricately interwoven geochemical, physical, and biological processes that are spatially variable when observed at any scale across the earth's surface. Disentangling aggregated or coupled processes within these complex mesoscopic systems is a formidable scientific challenge, in part because of the 26+ orders-of-magnitude scale difference between atomic-level interactions and a landscape that is measurably impacted by human activities. Impactful connections across scales have been made in other fields of science. For example, macroscale (thermodynamic) properties of chemical systems can be predicted from classical or quantum mechanics of atoms within the system using statistical thermodynamics. The "ecological fallacy" based in statistical analysis of behavior of individuals (Robinson, 1950) demonstrates why understanding spatial relationships between variables at a finer spatial scale is the key to understanding the relationships observed in aggregated systems at larger scales. In essence, the finer resolution information can provide a cause-effect explanation for the coarser resolution outcomes, but underlying causes cannot be determined from the coarser resolution information. Consequently, discoveries being made at the atomic, nano and microscales are essential for understanding cause-effect relationships in landscape-scale terrestrial ecosystems. A goal of developing more powerful ways to upscale this information could provide a new branch of predictive geosciences that guide policy and appropriate management practices for mitigating environmental impacts of natural processes and anthropogenic activities.

Developing a unifying scientific theory that serves as a framework for understanding connections between extremes of scales has intellectual merit. Geographical information systems (GIS) have evolved as highly effective tools for managing land use. These systems overlay multiple layers of landscape-scale information to understand, for example, how different geological settings, soil types, land uses, weather patterns, and human influences affect outcomes such as natural ecosystem functions, water quality, agricultural productivity, or urban efficiency. But inputs to GIS systems are at the same spatial scale. At the other extreme, extensive analyses (e.g., spectroscopy and imaging) of chemical reactions involving minerals, mineral-organic complexes, mineral-microbial associations, and other particles that are considered model components of more complex natural systems have provided unprecedented insights on molecular bonding and reaction mechanisms.

Key questions need resolution: Why does small-scale, process-based knowledge commonly break down when applied to larger systems, i.e., why does the whole not equal the sum of the parts? Are our model systems not representative of natural systems? Are there vital, aggregated processes that cannot yet be measured? Can the problem be simplified to identifying a few key components or processes that drive outcomes at all higher scales? Can a theory or model be developed that links all scales in nature?

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