Jeffrey Gove

Title: Research Forester
Unit: Climate, Fire, and Carbon Cycle Sciences
Previous Unit: Forest Carbon Dynamics and Estimation for Sustainable Management
Address: Northern Research Station
271 Mast Road
Durham, NH 03824
Phone: 603-868-7667
E-mail: Contact Jeffrey Gove

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Education

• A.A.S. Forest Technology. May 1975, Thompson School of Applied Science, University of New Hampshire.
• B.S. Forestry. December 1977, University of New Hampshire.
• M.S. Forest Mensuration. May 1980. University of New Hampshire.
• M.A. Statistics. December 1992, The Pennsylvania State University.
• Ph.D. Forest Biometrics. August 1989, The Pennsylvania State University.

Civic & Professional Affiliations

Member: Society of American Foresters, American Statistical Association.

Current Research

• Weighted distributions for forest management and research. Weighted distributions provide a theory that brings together many of the stand attributes used in forest management. This theory, while powerful, is straightforward to apply in the context of forest management using statistical software, or packages such as Balance. These methods make it simpler to get compatible estimates from probability-based stand models.
• Optimal methods for developing uneven-aged stand management guidelines.  Based on models of forest growth, these methods can be used to maximize  numerous different objective functions for management; examples include financial objectives, and total biomass. When both the underlying growth and financial models are closely applicable to a given location, the resulting management guides will be optimal with respect to the objective chosen, and provide the basis for better management.
• New methods for sampling down coarse woody debris in forest ecosystems. We continue to develop new methods for the estimation of down coarse woody material; of special interest are the new methods based on probability proportional to size sampling, such as perpendicular distance sampling and its variants, and point relascope sampling. Both field and simulation testing are components of this effort.
• Data assimilation methods in forestry. Data assimilation brings models and sampling together in an optimal (for linear systems) or suboptimal (for nonlinear systems) manner through the use of sequential estimation methods. These probabilistically-based methods are very powerful, and show great potential for use in both forest research and management.

Future Research

• Problems in uneven-age forest management, especially optimal stand diameter distribution models.
• Investigating new methods, and testing and exploring new adaptations of existing methods for sampling forest ecosystems, especially components of down woody material.
• Data assimilation methods for use in forest management and research.

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