Reliable estimates of the impacts and costs of biological invasions are critical to developing credible management, trade and regulatory policies. Worldwide, forests and urban trees provide important ecosystem services as well as economic and social benefits, but are threatened by non-native insects. More than 450 non-native forest insects are established in the United States but estimates of broad-scale economic impacts associated with these species are largely unavailable. We developed a novel modeling approach that maximizes the use of available data, accounts for multiple sources of uncertainty, and provides cost estimates for three major feeding guilds of non-native forest insects. For each guild, we calculated the economic damages for five cost categories and we estimated the probability of future introductions of damaging pests. We found that costs are largely borne by homeowners and municipal governments. Wood- and phloem-boring insects are anticipated to cause the largest economic impacts by annually inducing nearly $1.7 billion in local government expenditures and approximately $830 million in lost residential property values. Given observations of new species, there is a 32% chance that another highly destructive borer species will invade the U.S. in the next 10 years. Our damage estimates provide a crucial but previously missing component of cost-benefit analyses to evaluate policies and management options intended to reduce species introductions. The modeling approach we developed is highly flexible and could be similarly employed to estimate damages in other countries or natural resource sectors.

Over the past 15 years the endangered eastern timber wolf (Canis lupus lycaon) has been slowly recolonizing northern Wisconsin and, more recently, upper Michigan, largely by dispersing from Minnesota (where it is listed as threatened). We have used geographic information systems (GISs) and spatial radiocollar data on recolonizing wolves in northern Wisconsin to assess the importance of factors in defining favorable wolf habitat. We built a multiple logistic regression model applied to the northern Great Lakes states to estimate the amount and spatial distribution of favorable wolf habitat at the regional landscape scale. Our results suggest that areas with high probability of favorable habitat are more extensive than previously estimated in the northern Great Lake States. Several variables were significant in comparing new pack areas in Wisconsin to nonpack areas, including land ownership class, land cover type, road density, human population, and spatial landscape indices such as fractal dimension (land cover patch boundary complexity), land cover type contagion, landscape diversity, and landscape dominance. Road density and fractal dimension were the most important predictor variables in the logistic regression models. The results indicate that public forest land and private industrial forest land are both important in managing for a broad‐ranging animal such as the wolf. Our data portray favorable habitat that is highly fragmented along development corridors in northern Wisconsin, which may be responsible for the slow growth of the wolf population. Upper Michigan, which is just beginning to be colonized by wolves, has very large, contiguous areas of likely habitat approaching the importance of those in northeastern Minnesota. If continuing development or wolf control restrict dispersing wolves from moving from Minnesota to Wisconsin, and Wisconsin habitat becomes more marginal through further fragmentation, Michigan has the potential to maintain a significant wolf population independent of Minnesota and serve as a source population for Wisconsin. However, a simple island/corridor model of wolf habitat in Wisconsin does not seem to apply. Wolves apparently move throughout the landscape, across many unfavorable areas, but establishment success is restricted to higher quality habitat. Source‐sink dynamics may be operating here, and they suggest that reduction of the Minnesota population in the near term may affect recovery in Wisconsin and Michigan. Our analysis is an example of use of long‐term monitoring data and large‐scale cross‐boundary regional analysis that must be done to solve complex spatial questions in resource management and conservation.

Expanding human population and economic growth have lead to large-scale conversion of natural habitat to human-dominated landscapes with consequent large-scale declines in biodiversity. Conserving biodiversity, while at the same time meeting expanding human needs, is an issue of utmost importance. In this paper we develop a spatially explicit landscape-level model for analyzing the biological and economic consequences of alternative land-use patterns. The spatially-explicit biological model incorporates habitat preferences, area requirements and dispersal ability between habitat patches for terrestrial vertebrate species to predict the likely number of species that will be sustained on the landscape. The spatially explicit economic model incorporates site characteristics and location to predict economic returns in a variety of potential land uses. We use the model to search for efficient land-use patterns that maximize biodiversity conservation objectives for a given level of economic returns, and vice-versa. We apply the model to the Willamette Basin, Oregon, USA. By thinking carefully about the arrangement of activities, we find land-use patterns that sustain high biodiversity and economic returns. Compared to the current land-use pattern, we show that both biodiversity conservation and the value of economic activity could be increased substantially.

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Cost-effective surveillance strategies are needed for efficient responses to biological invasions and must account for the trade-offs between surveillance effort and management costs. Less surveillance may allow greater population growth and spread prior to detection, thereby increasing the costs of damages and control. In addition, surveillance strategies are usually applied in environments under continual invasion pressure where the number, size and location of established populations are unknown prior to detection. We develop a novel modeling framework that accounts for these features of the decision and invasion environment and determines the long term sampling effort that minimises the total expected costs of new invasions. The optimal solution depends on population establishment and growth rates, sample sensitivity, and sample, eradication, and damage costs. We demonstrate how to optimise surveillance systems under budgetary constraints and find that accounting for spatial heterogeneity in sampling costs and establishment rates can greatly reduce management costs.

Optimal any-aged management problems for mixed-species stands have been solved for the first time. Problem formulation calls for periodic planting and harvesting controls to be applied without constraints on the stand age or size structure over time; classical definitions of both even- and uneven-aged management are, thus, subsets of this general any-aged management definition. The solution technique is a derivative-free, coordinate-search process called the method of Hooke and Jeeves. The optimizer incorporates without modification the Stand Prognosis Model, a single-tree simulator that is used extensively in the western United States. This paper focuses on sensitivity analysis and performance of the optimizer on problems with both short and long time horizons and with different definitions of the harvest controls. Superior harvest regimes are found by defining harvest controls with relatively wide diameter-class boundaries and with broad species groups. In all cases examined, there is variability in present value and harvest pattern associated with the choice of the starting point for the optimizer. The cause of these multiple optima is a nonconvex and discontinuous response surface produced by the single-tree simulator. This points out the need to examine the results from several random starts before making conclusions about optimal timber harvesting.