Skip to Main Content
Optimization of forest wildlife objectivesAuthor(s): John Hof; Robert Haight
Source: In: Weintraub, Andres; Romero, Carlos; Bjorndal, Trond; Epstein, Rafael; Miranda, Jaime, eds. Handbook of operations research in natural resources: Springer. 405-418.
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
PDF: View PDF (1.48 MB)
DescriptionThis chapter presents an overview of methods for optimizing wildlife-related objectives. These objectives hinge on landscape pattern, so we refer to these methods as "spatial optimization." It is currently possible to directly capture deterministic characterizations of the most basic spatial relationships: proximity relationships (including those that lead to edge effects), habitat connectivity/fragmentation relationships, population growth and dispersal, and patch size/habitat amount thresholds. More complex spatial relationships and stochastic relationships are currently best captured through heuristic manipulation of simulation models. General treatment of stochastic variables in spatial optimization is in its infancy.
- Check the Northern Research Station web site to request a printed copy of this publication.
- Our on-line publications are scanned and captured using Adobe Acrobat.
- During the capture process some typographical errors may occur.
- Please contact Sharon Hobrla, email@example.com if you notice any errors which make this publication unusable.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationHof, John; Haight, Robert. 2007. Optimization of forest wildlife objectives. In: Weintraub, Andres; Romero, Carlos; Bjorndal, Trond; Epstein, Rafael; Miranda, Jaime, eds. Handbook of operations research in natural resources: Springer. 405-418.
KeywordsHabitat connectivity, landscape pattern, reaction-diffusion model, response-surface analysis, search heuristics, simulation optimization, spatial optimization, stochastic population model
- A spatial stochastic programming model for timber and core area management under risk of fires
- A review of operations research models in invasive species management: state of the art, challenges, and future directions
- Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulation
XML: View XML