James D. Westervelt

1.5 Million Missing Numbers: Overcoming Employment Suppression in County Business Patterns Data

Missing data frustrate research and limit our understanding of regional economies. County Business Patterns annually provides employment data for all U.S. counties and states at the most detailed industrial level, but two out of every three employment statistics are missing. In rural areas, this percentage is higher still. To protect the rights of employers to confidentiality, the U.S. Census Bureau has not disclosed the number of employees in 1.5 million cases in the 2002 data. Instead, it offers a suppression flag that represents an employment range. This article presents a two-stage method for replacing all the flags with employment estimates. Taking advantage of the hierarchical nature of the data both by industry and geography, the first stage identifies the smallest possible range for each suppressed number. Ensuring that employment adds up correctly up and down the industrial and geographical hierarchies, the second stage iteratively adjusts all the estimates until millions of constraints are met. The procedure simultaneously considers all industries in all counties, states, and the nation to produce a complete data set, which is available to the research community on the Internet.

A Technique for Rapidly Forecasting Regional Urban Growth

Recent technological and theoretical advances have helped produce a wide variety of computer models for simulating future urban land-use change. However, implementing these models is often cost prohibitive due to intensive data-collection requirements and complex technical implementation. There is a growing need for a rapid, inexpensive method to project regional urban growth for the purposes of assessing environmental impacts and implementing long-term growth-management plans. We present the Regional Urban Growth (RUG) model, an extensible mechanism for assessing the relative attractiveness of a given location for urban growth within a region. This model estimates development attraction for every location in a rasterized landscape on the basis of proximity to development attractors, such as existing dense development, roads, highways, and natural amenities. RUG can be rapidly installed, parameterized, calibrated, and run on almost any several-county region within the USA. We implement the RUG model for a twelve-county region surrounding the Jordan Lake Reservoir, an impoundment of the Haw River Watershed (North Carolina, USA). This reservoir is experiencing major water-quality problems due to increased runoff from rapid urban growth. We demonstrate the RUG model by testing three scenarios that assume (1) ‘business-as-usual’ growth levels, (2) enforcement of state-mandated riparian buffer regulations, and (3) riparian buffer regulations augmented with forecast conservation measures. Our findings suggest that the RUG model can be useful not only for environmental assessments, stakeholder engagement, and regional planning purposes, but also for studying specific state and regional policy interventions on the direction and location of future growth pressure.

Optimal design of compact and functionally contiguous conservation management areas

Compactness and landscape connectivity are essential properties for effective functioning of conservation reserves. In this article we introduce a linear integer programming model to determine optimal configuration of a conservation reserve with such properties. Connectivity can be defined either as structural (physical) connectivity or functional connectivity; the model developed here addresses both properties. We apply the model to identify the optimal conservation management areas for protection of Gopher Tortoise (GT) in a military installation, Ft. Benning, Georgia, which serves as a safe refuge for this ‘at risk’ species. The recent expansion in the military mission of the installation increases the pressure on scarce GT habitat areas, which requires moving some of the existent populations in those areas to suitably chosen new conservation management areas within the boundaries of the installation. Using the model, we find the most suitable and spatially coherent management areas outside the heavily used training areas.

A Framework for Developing Management Goals for Species at Risk with Examples from Military Installations in the United States

A decision framework for setting management goals for species at risk is presented. Species at risk are those whose potential future rarity is of concern. Listing these species as threatened or endangered could potentially result in significant restrictions to activities in resource management areas in order to maintain those species. The decision framework, designed to foster proactive management, has nine steps: identify species at risk on and near the management area, describe available information and potential information gaps for each species, determine the potential distribution of species and their habitat, select metrics for describing species status, assess the status of local population or metapopulation, conduct threat assessment, set and prioritize management goals, develop species management plans, and develop criteria for ending special species management where possible. This framework will aid resource managers in setting management goals that minimally impact human activities while reducing the likelihood that species at risk will become rare in the near future. The management areas in many of the examples are United States (US) military installations, which are concerned about potential restrictions to military training capacity if species at risk become regulated under the US Endangered Species Act. The benefits of the proactive management set forth in this formal decision framework are that it is impartial, provides a clear procedure, calls for identification of causal relationships that may not be obvious, provides a way to target the most urgent needs, reduces costs, enhances public confidence, and, most importantly, decreases the chance of species becoming more rare.

A dynamic simulation/optimization model for scheduling restoration of degraded military training lands

Intensive use of military vehicles on Department of Defense training installations causes deterioration in ground surface quality. Degraded lands restrict the scheduled training activities and jeopardize personnel and equipment safety. We present a simulation-optimization approach and develop a discrete dynamic optimization model to determine an optimum land restoration for a given training schedule and availability of financial resources to minimize the adverse effects of training on military lands. The model considers weather forecasts, scheduled maneuver exercises, and unique qualities and importance of the maneuver areas. An application of this approach to Fort Riley, Kansas, shows that: i) starting with natural conditions, the total amount of training damages would increase almost linearly and exceed a quarter of the training area and 228 gullies would be formed (mostly in the intensive training areas) if no restoration is carried out over 10 years; ii) assuming an initial state that resembles the present conditions, sustaining the landscape requires an annual restoration budget of

Simulating Land Use Alternatives and Their Impacts on a Desert Tortoise Population in the Mojave Desert, California

957 thousand; iii) targeting a uniform distribution of maneuver damages would increase the total damages and adversely affect the overall landscape quality, therefore a selective restoration strategy may be preferred; and iv) a proactive restoration strategy would be optimal where land degradations are repaired before they turn into more severe damages that are more expensive to repair and may pose a higher training risk. The last finding can be used as a rule-of-thumb for land restoration efforts in other installations with similar characteristics.

An Individual-Based Model for Metapopulations on Patchy Landscapes-Genetics and Demography (IMPL-GD)

Computer-based simulation modeling is becoming an increasingly important tool for ecological research and management. It can provide insights into species– habitat relationships, patterns of habitat in space and time, and the impacts of various activities on animal populations and their environments (Turner et al., 1995). Recently, efforts have been directed toward developing spatially explicit models (Pulliam et al., 1992; Turner et al., 1995) because the spatial distribution and complexity of land characteristics makes it difficult to analyze and simulate a landscape as a whole. Partitioning a landscape into small but connected parcels makes it possible to work with patches of land that can be considered homogeneous (e.g., gridded landscape models). This approach seems especially useful for developing spatially explicit models for endangered species. Our model is one in a series of models developed at the U.S. Army Construction Engineering Research Laboratories (USACERL), Champaign, Illinois to study the processes involved with building dynamic landscape simulation (DLS) models. In this DLS model, we constructed a spatially dynamic habitat model to assess the impacts of military training across time and space on a desert tortoise population (Gopherus agassizii) and its habitat. The desert tortoise was designated as federally threatened in the Mojave Desert in 1990 (Fig. 10.1). It is unevenly distributed over large areas, which makes estimating its population density difficult. Furthermore, it is a long-lived animal with a low reproductive rate, making it highly susceptible to perturbations in the environment (Woodman et al., 1986). Our spatially explicit model for the desert tortoise is a computer-based simulation that runs in simulation time. The model is composed of mathematical, logi10

An Implementation of the Pathway Analysis Through Habitat (PATH) Algorithm Using NetLogo

The model described in this chapter addresses the risk of metapopulation extinction when a habitat parcel is eliminated from a patchy landscape. The authors describe the Individual-Based Model for Metapopulations on Patchy Landscapes-Genetics and Demography (IMPL-GD), an agent-based simulation model developed using NetLogo (http://ccl.northwestern.edu/netlogo/). This model is intended to provide a better understanding of how ecological variables such as landscape physical characteristics, population genetic and demographic traits, and network relationships between habitat parcels relate dynamically to metapopulation viability. The IMPL-GD places generic organisms on a landscape that consists of habitable and non-habitable patches, including traversable but non-habitable terrain. The agents, called “whatsits,” were designed to reflect the characteristics of small, solitary animals that defend small, circular territories in the landscape. They are defined in the model by a unique identification number, age, sex, lineage, and other characteristics. The IMPL-GD model enables the user to rapidly execute thousands of simulations in which a random parcel of habitable terrain is eliminated from the landscape after a given number of time steps and the impact on whatsit population viability is recorded. The output from a large number of IMPL-GD simulations can statistically analyzed to identify associations between the independent ecological variables and quantify their relation to the dependent variable of whatsit survival in the form of a conservation utility index.

Climate Change Impacts and Adaptation on CONUS Military Installations

Habitat connectivity plays a central role in wildlife population viability by increasing the available population size, maintaining gene flow among diverse metapopulations, and facilitating regular migration, dispersal, and recolonization. This chapter documents an agent-based simulation model that can improve our understanding of species migration routes between habitat patches. It is based on the Pathway Analysis Through Habitat (PATH) algorithm, first developed for use on a supercomputer by Hargrove, Hoffman, and Efroymson (2004). Using NetLogo (http://ccl.northwestern.edu/netlogo/), the authors of this chapter created a simplified implementation of PATH that operates on a standard desktop computer. PATH identifies and highlights areas in a landscape that contribute to the natural connections among populations; identifies the metapopulation structure; and indicates the relative strength of connections holding a metapopulation together. A major benefit of this NetLogo implementation of PATH is that it does not require a supercomputer to operate. The model encapsulates essential species migration activities and costs into the bare fundamentals—a binary habitat indicator, a movement parameter, a randomness parameter, an energy-accounting function, and a mortality probability. Simulation results can provide valuable insights to support decisions that promote habitat connectivity for purposes of improved wildlife management.

Quantifying Impacts of Urban Growth Potential on Army Training Capabilities

Military installations must be maintained and managed to provide appropriate training and testing opportunities. As climate changes, natural areas on installations may shift, and the costs to maintain training and testing areas may change. This chapter looks across continental U.S. (CONUS) installations with respect to the habitat and erosion consequences associated with climate forecasts from four Global Climate Models (GCMs). Habitat is important from two perspectives: its ability to support training and testing, and its capacity to meet federal requirements regarding the maintenance of listed threatened and endangered species. That capacity can change due to shifts in weather patterns, flooding, drought potential, and annual temperature patterns. With substantial change, species can be directly affected by invasive species, loss and fragmentation of habitat, or increased disease and predation. Population losses for these species can result in loss of training lands and/or time.