The most recent remeasurement of growth (at approximate total stand age 100 years) from the Black Rock Thinning Trial in western Oregon provides useful information for forest owners interested in accelerating restoration of older forest characteristics in Douglas-fir stands of the Pacific Northwest. Thinnings at several intensities at total stand age of roughly 50 years effectively reset stand growth patterns. With quadratic mean diameters in thinned plots up to 40

Fire spread on forested landscapes depends on vegetation conditions across the landscape that affect the fire arrival probability and forest stand value. Landowners can control some forest characteristics that facilitate fire spread, and when a single landowner controls the entire landscape, a rational landowner accounts for spatial interactions when making management decisions. With multiple landowners, management activity by one may impact outcomes for the others. Various liability regulations have been proposed, and some enacted, to make landowners account for these impacts by changing the incentives they face. In this paper, the effects of two different types of liability regulations are examined – strict liability and negligence standards. We incorporate spatial information into a model of land manager decision-making about the timing and spatial location of timber harvest and fuel treatment. The problem is formulated as a dynamic game and solved via multi-agent approximate dynamic programming. We found that, in some cases, liability regulation can increase expected land values for individual land ownerships and for the landscape as a whole. But in other cases, it may create perverse incentives that reduce expected land value. We also showed that regulations may increase risk for individual landowners by increasing the variability of potential outcomes.

Accounting for externalities generated by fire spread is necessary for managing fire risk on landscapes with multiple owners. In this paper, we determine the optimal management of a synthetic landscape parameterized to represent the ecological conditions of Douglas-fir (Pseudotsuga menziesii) plantations in southwest Oregon. The problem is formulated as a dynamic game, where each agent maximizes their own objective without considering the welfare of the other agents. We demonstrate a method for incorporating spatial information and externalities into a dynamic optimization process. A machine-learning technique, approximate dynamic programming, is applied to determine the optimal timing and location of fuel treatments and timber harvests for each agent. The value functions we estimate explicitly account for the spatial interactions that generate fire risk. They provide a way to model the expected benefits, costs, and externalities associated with management actions that have uncertain consequences in multiple locations. The method we demonstrate is applied to analyze the effect of landscape fragmentation on landowner welfare and ecological outcomes.

This study explores the interrelationship between social capital and poverty, a negative indicator of well-being, in the Western United States. Econometric models that account for the endogeneity of poverty and social capital, spatial dependence, and cross-equation error correlation were used to explore two questions: is the presence of social capital associated with reduced poverty levels and does the presence of poverty impact social capital stocks? We found evidence that communities with higher social capital levels tend to have lower poverty rates and that poverty may pose barriers to social capital formation. This suggests that policies to reduce poverty will be more effective if coupled with policies to support social capital formation. The study's findings are particularly salient for communities in persistent poverty. These results emerged only after accounting for endogeneity and spatial relationships. Because many factors contributing to well-being are jointly determined with well-being and indicators of well-being are frequently spatially clustered, this situation is likely to be more common than has been typically recognized in the literature.

The development of a market for currently non-merchantable forest material, such as harvest residues or small diameter trees, has been suggested as a possible win-win solution that could: (i) provide a material that can be processed in rural communities reeling from changes in the forest products industry and policy environment; (ii) capture more value from timber management activities; and (iii) provide a financial incentive for treatments to reduce wildfire risk or restore forest stands. Modeling the supply of this material with spatially-explicit potential demand locations allows for a realistic analysis of the feasibility of such a market to stimulate rural development. We model multiple scenarios for the utilization of harvest residues within the current forest products market in western Oregon. Sensitivity analysis explored the effects of cost of the depots on feasibility, including policy designed to support depot establishment through subsidies. Scenarios were also used to assess the effects of increases in federal harvest activities. Results suggest that with relatively high biomass prices, there is some potential for investment in depots to aid rural communities in western Oregon, but there is little change in either the overall feasibility or the location of depot establishment under scenarios of increased federal harvest.

Moist coniferous forests have played a significant role in the economic and social well-being of the Pacific Northwest. The region's forest-products industry has evolved in the context of federal forest policy that changed from discussions about selling off federal land to consideration of (1) conservation and sustainable yield; (2) active management and planning; (3) multiple use and preservation; and (4) the present emphasis on ecosystem management and ecological forestry. As the dialogues changed, the alliance between forest industry and forest policy, which was once at the heart of Northwest forest management, also changed.

Forest management in the face of fire risk is a challenging problem because fire spreads across a landscape and because its occurrence is unpredictable. Accounting for the existence of stochastic events that generate spatial interactions in the context of a dynamic decision process is crucial for determining optimal management. This paper demonstrates a method for incorporating spatial information and interactions into management decisions made over time. A machine learning technique called approximate dynamic programming is applied to determine the optimal timing and location of fuel treatments and timber harvests for a fire-threatened landscape. Larger net present values can be achieved using policies that explicitly consider evolving spatial interactions created by fire spread, compared to policies that ignore the spatial dimension of the inter-temporal optimization problem.

Markov Decision Processes (MDPs) are a formulation for optimization problems in sequential decision making. Solving MDPs often requires implementing a simulator for optimization algorithms to invoke when updating decision making rules known as policies. The combination of simulator and optimizer are subject to failures of specification, implementation, integration, and optimization that may produce invalid policies. We present these failures as queries for a visual analytic system (MDPVIS). MDPVIS addresses three visualization research gaps. First, the data acquisition gap is addressed through a general simulator-visualization interface. Second, the data analysis gap is addressed through a generalized MDP information visualization. Finally, the cognition gap is addressed by exposing model components to the user. MDPVIS generalizes a visualization for wildfire management. We use that problem to illustrate MDPVIS and show the visualization's generality by connecting it to two reinforcement learning frameworks that implement many different MDPs of interest in the research community.

Policy analysts wish to visualize a range of policies for large simulator-defined Markov Decision Processes (MDPs). One visualization approach is to invoke the simulator to generate on-policy trajectories and then visualize those trajectories. When the simulator is expensive, this is not practical, and some method is required for generating trajectories for new policies without invoking the simulator. The method of Model-Free Monte Carlo (MFMC) can do this by stitching together state transitions for a new policy based on previously-sampled trajectories from other policies. This off-policy Monte Carlo simulation method works well when the state space has low dimension but fails as the dimension grows. This paper describes a method for factoring out some of the state and action variables so that MFMC can work in high-dimensional MDPs. The new method, MFMCi, is evaluated on a very challenging wildfire management MDP.

Managers of US National Forests must decide what policy to apply for dealing with lightning-caused wildfires. Conflicts among stakeholders (e.g., timber companies, home owners, and wildlife biologists) have often led to spirited political debates and even violent eco-terrorism. One way to transform these conflicts into multi-stakeholder negotiations is to provide a high-fidelity simulation environment in which stakeholders can explore the space of alternative policies and understand the tradeoffs therein. Such an environment needs to support fast optimization of MDP policies so that users can adjust reward functions and analyze the resulting optimal policies. This paper assesses the suitability of SMAC---a black-box empirical function optimization algorithm---for rapid optimization of MDP policies. The paper describes five reward function components and four stakeholder constituencies. It then introduces a parameterized class of policies that can be easily understood by the stakeholders. SMAC is applied to find the optimal policy in this class for the reward functions of each of the stakeholder constituencies. The results confirm that SMAC is able to rapidly find good policies that make sense from the domain perspective. Because the full-fidelity forest fire simulator is far too expensive to support interactive optimization, SMAC is applied to a surrogate model constructed from a modest number of runs of the full-fidelity simulator. To check the quality of the SMAC-optimized policies, the policies are evaluated on the full-fidelity simulator. The results confirm that the surrogate values estimates are valid. This is the first successful optimization of wildfire management policies using a full-fidelity simulation. The same methodology should be applicable to other contentious natural resource management problems where high-fidelity simulation is extremely expensive.

A case-study approach was used to understand the role of social capital in the cycle of adaptive capacity in three rural, forest communities in Washington State. The study reveals social capital to be a critical ingredient in the resolution of diverse community development events. The findings enhance our understanding of the impacts of social capital on community outcomes by dividing the concept into three types-bonding, bridging, and linking social capital. Social capital does not translate linearly into community outcomes. Generally, community members stressed the importance of bridging social capital to achieve community-wide desired outcomes. Yet strong bridging social capital had no potency when linking social capital with key power brokers was absent. Finally, the case-study approach reveals how social capital is created and can be built up or depleted. The findings are applicable to community development practitioners, rural community leaders, and public land managers that interface with forest communities.

Conservation biologists recognize that a system of isolated protected areas will be necessary but insufficient to meet biodiversity objectives. Current approaches to connecting core conservation areas through corridors consider optimal corridor placement based on a single optimization goal: commonly, maximizing the movement for a target species across a network of protected areas. We show that designing corridors for single species based on purely ecological criteria leads to extremely expensive linkages that are suboptimal for multispecies connectivity objectives. Similarly, acquiring the least‐expensive linkages leads to ecologically poor solutions. We developed algorithms for optimizing corridors for multispecies use given a specific budget. We applied our approach in western Montana to demonstrate how the solutions may be used to evaluate trade‐offs in connectivity for 2 species with different habitat requirements, different core areas, and different conservation values under different budgets. We evaluated corridors that were optimal for each species individually and for both species jointly. Incorporating a budget constraint and jointly optimizing for both species resulted in corridors that were close to the individual species movement‐potential optima but with substantial cost savings. Our approach produced corridors that were within 14% and 11% of the best possible corridor connectivity for grizzly bears (Ursus arctos) and wolverines (Gulo gulo), respectively, and saved 75% of the cost. Similarly, joint optimization under a combined budget resulted in improved connectivity for both species relative to splitting the budget in 2 to optimize for each species individually. Our results demonstrate economies of scale and complementarities conservation planners can achieve by optimizing corridor designs for financial costs and for multiple species connectivity jointly. We believe that our approach will facilitate corridor conservation by reducing acquisition costs and by allowing derived corridors to more closely reflect conservation priorities.

Natural resource policymakers and planners increasingly rely on regional and national-level spatial data describing projections of future housing growth, to anticipate development impacts on natural resources and identify policy and planning needs. Such projections have not always been well-grounded in demographic and other factors that influence population and thus housing growth. We develop an empirical model describing population change and housing growth in the rural Midwestern U.S., as a function of demographic transition, socioeconomic factors, and natural amenities. The empirical model is estimated as a set of three equations characterizing: (1) population growth within three age groups, (2) the influence of farmland cover and other county level variables, and (3) household size, housing services, and second home ownership. The estimated population and housing growth models provide a consistent estimate of past change and can be used to project future change. We found age-structure to be an important factor in housing location decisions. Specifically, the influence of natural amenities on both population growth within counties and subsequent housing density changes varies by age group. (C) 2015 Elsevier B.V. All rights reserved.

Solving sequential decision making problems in computational sustainability often requires simulators of ecology, weather, fire, or other complex phenomena. The extreme computational expense of these simulators stymie optimization and interactive visualization of decision rules (policies). This work presents our results in creating an interactive visualization for a wildfire management problem whose simulator normally takes several hours to run. We successfully generate visualizations for a landscape’s development over 100 year time spans within 17 seconds, when the original simulator took several hours.

Researchers in AI and Operations Research employ the framework of Markov Decision Processes (MDPs) to formalize problems of sequential decision making under uncertainty. A common approach is to implement a simulator of the stochastic dynamics of the MDP and a Monte Carlo optimization algorithm that invokes this simulator to solve the MDP. The resulting software system is often realized by integrating several systems and functions that are collectively subject to failures of specification, implementation, integration, and optimization. We present these failures as queries for a computational steering visual analytic system (MDPVIS). MDPVIS addresses three visualization research gaps. First, the data acquisition gap is addressed through a general simulator-visualization interface. Second, the data analysis gap is addressed through a generalized MDP information visualization. Finally, the cognition gap is addressed by exposing model components to the user. MDPVIS generalizes a visualization for wildfire management. We use that problem to illustrate MDPVIS.

Our work is motivated by an important network design application in computational sustainability concerning wildlife conservation. In the face of human development and climate change, it is important that conservation plans for protecting landscape connectivity exhibit certain level of robustness. While previous work has focused on conservation strategies that result in a connected network of habitat reserves, the robustness of the proposed solutions has not been taken into account. In order to address this important aspect, we formalize the problem as a node-weighted bi-criteria network design problem with connectivity requirements on the number of disjoint paths between pairs of nodes. While in most previous work on survivable network design the objective is to minimize the cost of the selected network, our goal is to optimize the quality of the selected paths within a specified budget, while meeting the connectivity requirements. We characterize the complexity of the problem under different restrictions. We provide a mixed-integer programming encoding that allows for finding solutions with optimality guarantees, as well as a hybrid local search method with better scaling behavior but no guarantees. We evaluate the typical-case performance of our approaches using a synthetic benchmark, and apply them to a large-scale real-world network design problem concerning the conservation of wolverine and lynx populations in the U.S. Rocky Mountains (Montana).

Biodiversity underpins ecosystem goods and services and hence protecting it is key to achieving sustainability. However, the persistence of many species is threatened by habitat loss and fragmentation due to human land use and climate change. Conservation efforts are implemented under very limited economic resources, and therefore designing scalable, cost-efficient and systematic approaches for conservation planning is an important and challenging computational task. In particular, preserving landscape connectivity between good habitat has become a key conservation priority in recent years. We give an overview of landscape connectivity conservation and some of the underlying graph-theoretic optimization problems. We present a synthetic generator capable of creating families of randomized structured problems, capturing the essential features of real-world instances but allowing for a thorough typical-case performance evaluation of different solution methods. We also present two large-scale real-world datasets, including economic data on land cost, and species data for grizzly bears, wolverines and lynx.

Forest products here include standing timber and logs, solidwood products (lumber and panels), and fiber products (paper and paperboard). This article describes methods used to model stumpage supply and the markets for logs and processed forest products, the results of empirical studies of demand and supply elasticities, and the nature of forest sector models that link these market components. Stumpage supply models originate primarily from intertemporal utility maximization structures, demand and supply for processed products from cost or profit function analyses. Estimates of short-run own-price elasticities for demand and supply of stumpage are predominantly inelastic. Long-run supply estimates are more elastic due to silvicultural investment options. Short-run product elasticities vary widely with substitution options but are also largely inelastic. Forest products sector models can be classed as 'static' or 'dynamic,' based on their equilibrium solution processes and the ways in which current and expected future market adjustments impact current period market behavior. In applications, the model groups differ markedly in treatment of timber harvest and allocation of harvest to the forest inventory, investment in silviculture and product processing, linkage of forestry and other related sectors such as agriculture, and in their flexibility for policy analysis.

This special issue of Forest Policy and Economics is based on the papers presented and discussions held at the International Conference on the New Frontiers of Forest Economics, June 26–30, 2012 held at ETH, Zurich, Switzerland. This paper discusses the need of new frontiers of forest economics, provides an overview of the special issue, and presents thoughts about new frontiers. The paper suggests that all knowledge of forest economists is conjectural, and without the competition of contradictorily theories forest economics sinks into intellectual poverty. The progress of forest economics will need a never ending fabrication on new and venturous theories for solving problems and strong attempts to refute, to critically assess and discuss, and to test empirically the new theories. The paper discusses three areas for new frontiers of forest economics — integration of sciences using multidisciplinary and transdisciplinary approaches, incorporation and integration of various streams of economics, and answering the unanswered questions by developing new models and methods.