CS 594 / EEB 504 - Spring 2005
Special Topics (Jointly offered by the Departments of Computer Science
and Ecology and Evolutionary Biology)
Computational Science for Natural Resource Management
Time: Mondays 3:35-5:30
Place: Claxton 205. Note - room has changed
Course numbers: CS594 Section#28446, EEB504 Section#33827 2 credit hours
Dr. Michael Berry, Professor and Interim Head of Computer Science
Dr. Louis Gross, Professor of Ecology and Evolutionary Biology and Mathematics
Dr. Dali Wang Research Assistant Professor of Computer Science
Note: Initial class gathering will be Thursday Jan. 13 at 3:35PM in Claxton 211 for those who are available at that time. Contact Dr. Gross if you will be attending the course but cannot attend this initial gathering.
Natural resource management has become a field in which a scientific basis plays a key role in public policy decisions. Policy in areas such as preserve design, harvest management, water flow control, land-use regulation, and control of invasive species all require input from the best available science in conjunction with the public decision process. Technological advances have led to the availability of large data sets, from remote sensing as well as from field and laboratory observations, which serve as a basis for the science of resource management. This general field sometimes is called "EcoInformatics". Computational capability has expanded so that quite complex computer-based models link the biotic and abiotic components of natural systems to potential management decisions. There is thus a need for resource managers to be aware of the computational science tools now available to them, and those that may become readily available in the near future. Similarly, there is a need for computational scientists to be aware of the potential applications of their expertise in numerous areas that have great potential public impact.
Provide a survey of computational science for students and practitioners of natural resource management in the context of applications that affect public policy.
For computationally-trained attendees, provide an overview of the main approaches used in natural resource management, and how computational science can contribute to this.
Topic coverage includes: data structures and databases for geographic data, including the various metadata standards associated with these; basic modeling methods for spatial analysis of data, including dealing with temporal/dynamic effects; decision support tools including those with explicit spatial components and associated optimization approaches; peer-to-peer computing and applications; grid-computing and applications; linking physical and biotic models of natural systems.
Key case studies to be included in the course are biodiversity modeling (see Lifemapper.org for one distributed computing application in this area), regional landuse simulation (see RSIM for one spatially-explicit simulation project) and multimodeling for restoration (see atlss.org and the GEM website for one grid-computing application to Everglades restoration).
Participation in this course requires approval of the instructors. Potential
participants with mainly quantitative/computational backgrounds should
contact Dr. Berry while those with mainly biological or natural resource backgrounds should contact Dr. Gross .
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