Complexity in Ecology and Conservation: Mathematical, Statistical, and Computational Challenges
BioSciencePublished 1 January 2005Open access
Jessica L. Green, Alan Hastings, Peter Arzberger, Francisco J. Ayala, Kathryn L. Cottingham, Kim Cuddington
Citations154
SJR quartileQ1
SJR score2.37
SNIP2.73
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TL;DR
Examples from a wide array of research areas in population biology and community ecology highlight the importance of fostering synergistic ties across disciplines for current and future research and application.
Abstract
Creative approaches at the interface of ecology, statistics, mathematics, informatics, and computational science are essential for improving our understanding of complex ecological systems. For example, new information technologies, including powerful computers, spatially embedded sensor networks, and Semantic Web tools, are emerging as potentially revolutionary tools for studying ecological phenomena. These technologies can play an important role in developing and testing detailed models that describe real-world systems at multiple scales. Key challenges include choosing the appropriate level of model complexity necessary for understanding biological patterns across space and time, and applying this understanding to solve problems in conservation biology and resource management. Meeting these challenges requires novel statistical and mathematical techniques for distinguishing among alternative ecological theories and hypotheses. Examples from a wide array of research areas in population biology and community ecology highlight the importance of fostering synergistic ties across disciplines for current and future research and application.
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