From measurement to management: evidence-based practice in natural resource management

ABSTRACT

Ecologists aim to answer fundamental scientific questions regarding the natural world, sometimes with incomplete data. Resource managers make practical decisions about natural resources, nearly always with incomplete data. Our goal is to make research data and results more available to managers who decide which forest stands to harvest and which trees to leave. We are building tools to: 1) represent ecological values and indicators, and relate those to research data, and 2) characterize tree canopy shape. We extend prior NSF work, which resulted in ecology research tools, and "re-purpose" data sets and research results. Our work addresses information technology problems identified by computer scientists, ecologists, and resource managers at government and non-government organizations [1], in particular, data presentation, data quality, and indicators.

REFERENCES

• 1: The Biodiversity and Ecosystem Informatics for Resource Management Report: http://www.evergreen.edu/bdei.
• 2: Borchert, R. and P. B. Tomlinson. 1984. Architecture and crown geometry in Tabebuia rosea (Bignoniaceae)
• 3: Cushing, J. B., N. M. Nadkarni, M. Finch, A. C. S. Fiala, E. Murphy-Hill, L. Delcambre, and D. Maier. 2007. Component-based end-user database design for ecologists. Journal of Intelligent Information Systems. See also http://canopy.evergreen.edu.
• 4: Hallé, F., R. A. Oldeman and P. B. Tomlinson. 1978. Tropical trees and forests: an architectural analysis. Springer-Verlag, New York, NY. American Journal of Botany 71:958--969.
• 5: Van Pelt, R., and N. M. Nadkarni. 2004. Horizontal and vertical distribution of canopy structural elements of Pseudotsuga menziesii forests in the Pacific Northwest. Forest Science 50:326-34

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications

Keywords:
data integration, data quality, data visualization, decision support, ecoinformatics, forest canopy structure, human-computer interaction, indicators, knowledge representation, scaling