Judy Cushing

Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology

Most efforts to harness the power of big data for ecology and environmental sciences focus on data and metadata sharing, standardization, and accuracy. However, many scientists have not accepted the data deluge as an integral part of their research because the current scientific method is not scalable to large, complex datasets. Here, we explain how integrating a data-intensive, machine learning approach with a hypothesis-driven, mechanistic approach can lead to a novel knowledge, learning, analysis system (KLAS) for discovery and problem solving. Machine learning leads to more efficient, user-friendly analytics as the streams of data increase while hypothesis-driven decisions lead to the strategic design of experiments to fill knowledge gaps and to elucidate mechanisms. KLAS will transform ecology and environmental sciences by shortening the time lag between individual discoveries and leaps in knowledge by the scientific community, and will lead to paradigm shifts predicated on open access data and analytics in a machine learning environment.

Biodiversity and ecosystem informatics

The field of Biodiversity and Ecosystem Informatics (BDEI) brings together computer scientists, biologists, natural resource managers, and others who wish to solve real-world challenges while advancing the underlying ecological, computer, and information sciences. The potential for synergies among these disciplines is high, because our need to understand complex, ecosystem-scale processes requires the solution to many groundbreaking technological problems. Fortunately, we are beginning to see increased support for applied computer science and information technology research in the context of environmental problem-solving. In July, 2001, the National Science Foundation (NSF), in collaboration with the United States Geological Survey (USGS), and the National Aeronautics and Space Administration (NASA), invited proposals for high-risk, small-scale planning and incubation activities to catalyze innovation and rapid advances in this new research community. The papers included in this special issue are selected, peer-reviewed summaries from principal investigators involved in this first NSF BDEI effort. These papers provide an overview of this emerging area and remind us that computer and information science and engineering play a crucial role in creating the technologies from which advances in the natural sciences evolve.

Co-producing software for complex environmental data visualization

Environmental scientists, land managers, and policy actors are increasingly presented with high-stakes high-uncertainty problems stemming from human-ecosystem interactions. To address these problems, scientists and managers frequently use models that produce enormous geospatial and temporal datasets that are constantly modified. To help make sense of this complex and changing data, we are immersed in a co-production effort where software engineers and environmental scientists collaborate on the development of visualization software. Preliminary findings suggest that this software co-production process could help build legitimacy for the information it produces, with potential implications for generating actionable science for policy and governance.

Visualizing valley wind flow

The field of micrometeorology is primarily concerned with smaller-scale meteorological phenomena, specifically those which occur within the lowest atmospheric layer called the Atmospheric Boundary Layer (ABL). The primary defining characteristic of the ABL is that wind dynamics within this layer are influenced by the Earths topography, as well as time-dependent temperature changes in the Earths surface. In forests and connected valleys, weak-wind flows transport moisture, heat, gases and potential contaminants, directly impacting adjacent ecosystems [Thomas et al. 2012].