Spatial Optimization in Ecological Applications

Spatial Optimization in Ecological Applications

Spatial Optimization in Ecological Applications

Spatial Optimization in Ecological Applications

Synopsis

Whether discussing habitat placement for the northern spotted owl or black-tailed prairie dog or strategies for controlling exotic pests, this book explains how capturing ecological relationships across a landscape with pragmatic optimization models can be applied to real world problems. Using linear programming, Hof and Bevers show how it is possible for the researcher to include many thousands of choice variables and many thousands of constraints and still be quite confident of being able to solve the problem in hand with widely available software. The authors' emphasis is to preserve optimality and explore how much ecosystem function can be captured, stressing the solvability of large problems such as those in real world case studies.

Excerpt

When we completed Spatial Optimization for Managed Ecosystems (Hof and Bevers 1998), we immediately identified two shortcomings. First, although we suggested the use of optimization models for developing theoretical hypotheses about ecosystems, we presented no examples, so the point may have been lost. in this book, we include one section devoted to this topic and point to theoretical hypotheses throughout. Second, and more important, we feared that some readers of our last book might conclude that capturing ecological spatial relationships in optimization models requires the use of esoteric integer and nonlinear solution methods, implying that these relationships can be captured only heuristically or in small “toy models.” in this book, we focus on capturing ecological relationships across a landscape with pragmatic optimization models that can be applied to real-world problems. We use linear programming primarily but also include two formulations for integer programming that are “integer-friendly.” the model in chapter 14 is nonlinear but is still readily solvable.

Using linear programming makes it possible to include many thousands of choice variables and many thousands of constraints and still be confident of being able to solve problems with widely available software. To capture ecological relationships in linear programs, we think of the problem in terms of discrete difference equations, combined with the production system activity analysis applied in, for example, traditional timber harvest scheduling models. Even with this approach, we must often make simplifying assumptions about ecosystem function. the alternative approach would be to start with a more complex (e.g., nonlinear and nonconvex) model and investigate the use of heuristic procedures to approach optimized solutions (Boston 1999; Jager . . .

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.