Machine Learning for Ecology and Sustainable Natural Resource Management (PDF)
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Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often "messy" and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.
Dr. Dawn Magness is a landscape ecologist interested in climate change adaptation, landscape planning, ecological services, and spatial modeling. She earned her M.S.in Fish and Wildlife Science at Texas A & M University and her Ph.D. in the interdisciplinary Resilience and Adaptation Program at the University of Alaska, Fairbanks. Her current projects use multiple methods to assess ecosystem vulnerability to inform strategic adaptation planning. She has conducted research on songbirds, flying squirrels, and American marten.
Dr. Falk Huettmann is a 'digital naturalist' linking computing and the internet with natural history research for global conservation and sustainability. He is a professor of Wildlife Ecology in the Biology & Wildlife Department and Institute of Arctic Biology at the University of Alaska Fairbanks (UAF) where he and many international students run the EWHALE lab. In his lab he studies biodiversity, land- and sea-scapes, the atmosphere, global governance, ecological economics, diseases and new approaches to global sustainability on a pixel-scale. Most of his 200 publications and 7 books are centered on Open Accessand
- 2018, 1st ed. 2018, 441 Seiten, Englisch
- Herausgegeben: Grant Humphries, Dawn R. Magness, Falk Huettmann
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3319969781
- ISBN-13: 9783319969787
- Erscheinungsdatum: 05.11.2018
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- Größe: 15 MB
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