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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.
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