Machine Learning for Ecology and Sustainable Natural Resource Management

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Ecologists and natural resource managers face complex decisions amid a rapidly changing environment due to climate change, urban sprawl, and other factors. The rise of Geographic Information System (GIS) technology, online data availability, and remote sensing has led to large, complex datasets that are often messy and challenging to interpret. Basic artificial intelligence algorithms, particularly machine learning, are powerful tools that can significantly impact the life sciences. In ecology, these algorithms assist resource managers in synthesizing information to better understand intricate ecological systems. Machine learning has diverse applications, notably in data exploration for generating hypotheses, predicting ecological patterns in space and time, and recognizing patterns for ecological sampling. It enables predictive assessments even when variable relationships are unclear. When traditional methods fall short, machine learning can reveal insights into ecosystem complexity that were previously unattainable. Despite its potential, many ecologists have yet to incorporate machine learning into their scientific processes. This volume emphasizes how these techniques can enhance traditional methodologies in the field, offering a pathway to improved ecological understanding and management.