Wildlife corridors are often used to connect critical habitat for species protection. Mixed integer programming models have been used in the past to create wildlife corridors, but they lack the capacity to control corridor geometry. We propose an approach that employs path planning techniques from artificial intelligence to account for and control corridor geometry, such as width and length. By combining path planning with network optimization, our approach allows the user to control and optimize the geometric characteristics of wildlife corridors. We illustrate our approach on two realistic landscapes and present numerical results on several computer-generated landscapes. The computational results indicate that this approach is efficient and can address problems controlling corridor geometry that were previously thought intractable. The approach has potential applications in such areas as the selection of routes or barrier construction problems, an example of which is firebreak design.Data are available at https://doi.org/10.1287/opre.2018.1758 .

Forest planners have traditionally used expected growth and yield coefficients to predict future merchantable timber volumes. However, because climate change affects forest growth, the typical forest planning methods using expected value of forest growth can lead to sub-optimal harvest decisions. In this paper, we propose to formulate the harvest planning with growth uncertainty due to climate change problem as a multistage stochastic optimization problem and use sample average approximation (SAA) as a tool for finding the best set of forest units that should be harvested in the first period even though we have a limited knowledge of what future climate will be. The objective of the harvest planning model is to maximize the expected value of the net present value (NPV) considering the uncertainty in forest growth and thus in revenues from timber harvest. The proposed model was tested on a small forest with 89 stands and the numerical results showed that the approach allows to have superior solutions in terms of net present value and robustness in face of different growth scenarios compared to the approach using the expected growth and yield. The SAA method requires to generate samples from the distribution of the random parameter. Our results suggested that a sampling scheme that focuses on generating high number of samples in distant future stages is favorable compared to having large sample sizes for the near future stages. Finally, we demonstrated that, depending on the level of forest growth change, ignoring this uncertainty can negatively affect forest resources sustainability.

ECOSEL is a voluntary market framework for private provision of forest ecosystem services. Multiobjective optimization is used in conjunction with a unique fund

The Washington State Department of Natural Resources (DNR) manages over 800,000 hectares of forested state trust lands and 20,000 kilometers of forest roads in Washington State. Forest harvest and road reconstruction decisions greatly impact the agency’s cash flows and its ability to meet its fiduciary obligations. We introduce a mixed-integer programming model that integrates harvest and road scheduling decisions. We show how DNR embedded the new model in its workflows and applied it to the Upper Clearwater River Landscape in the Olympic Experimental State Forest. We find that the forest valuation of the Upper Clearwater increased by $0.5–$1 million (0.4–1.1 percent) because of the new method, which allowed the DNR to concentrate capital expenditures in support of harvest and road operations in both time and space. This led to a 14.5 percent reduction in the size of the active road network. DNR is now in the process of scaling the new approach to the entire forest estate.