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Über dieses Buch

This book is written for ecologists interested in capturing their understandings of how natural systems work in software – to help inform their work and communicate the consequences of proposed management plans. Historically, ecologists had to rely on the skills of trained computer programmers to modeling natural systems, but now a new generation of software is allowing ecologists to directly capture their understandings of systems in software. This book is a compilation of spatially explicit simulation models developed by ecologists and planners without any formal computer programming skills. Readers will be inspired to believe that they too can create similar models of the systems with which they are familiar.



Chapter 1. Never Fear: You Already Model!

Simulation modeling is a valuable decision-support tool for environmental management, but the complexities of model building have historically required the involvement of a computer programming specialist to implement the scientific input of subject matter experts. Many ecologists avoid simulation modeling because of the ongoing perception that it demands excessive project resources and time, plus a very good fit with a computer programmer who typically has no knowledge of the scientific discipline. This chapter illustrates that model building is a natural part of everyday human experience, almost from birth. The implication of that fact is that the arrival of simple, intuitive simulation modeling platforms makes it possible for ecologists (and others) to develop their own sophisticated science-based models without relying on a dedicated computer programmer to write the code. Furthermore, the availability of well-supported, open-source software such as NetLogo ( makes it unnecessary for the novice modeler to expend research project resources on costly proprietary software packages.
James D. Westervelt, Gordon L. Cohen

Chapter 2. A Collaborative Process for Multidisciplinary Group Modeling Projects

Many ecosystem management issues can be illuminated using small, expedient simulation models that may be developed by one or two people. However, modeling more complex or dynamic systems may require larger development teams that include experts from multiple technical disciplines. Such collaborations can produce highly explanatory models with broad possibilities for application, but the process of staffing a development team and coordinating its efforts must be carefully managed and documented. This chapter describes a three-stage development process that encompasses model preparation, construction, and integration. Key concepts include clear identification and communication of modeling objectives; team recruitment, scheduling, and dynamics; conceptualizing the full model and submodels; development of submodels within a functional “dummy” model; and integration, debugging, and compiling of the model. Essential factors for success are clear communication among all project workgroups and careful documentation of development. By adhering to this time-tested process, multidisciplinary teams can use a simple development platform such as NetLogo ( to create rich, dynamic simulations.
James D. Westervelt, Bruce Hannon

Chapter 3. An Introduction to the NetLogo Modeling Environment

This chapter introduces the novice modeler to the open-source NetLogo model development programming language and environment ( The author presents an overview of NetLogo’s technical origins, core capabilities, and key features. Using as a frame of reference the Wolf Sheep Predation model (, which is bundled with the NetLogo download package, the author describes the program interface work areas and their functions. He also interprets examples of the Wolf Sheep Predation code for the novice user to illustrate how the NetLogo programming language is based, to a large extent, on everyday plain language, which makes it easy for the nonprogrammer to learn and use.
David Stigberg

Chapter 4. A Simulation Model of Fire Ant Competition with Cave Crickets at Fort Hood, Texas

Cave cricket populations are essential to the survival of many rare invertebrates that are endemic to the karst regions of Fort Hood, TX. The organic matter they bring into the caves provides an energy source for many karst invertebrates. Red Imported Fire Ants (RIFA) migrating from South America into the southern USA compete for resources with and prey upon cave crickets, which can indirectly threaten certain populations of rare invertebrates. This chapter describes the development of a simple computer model using NetLogo ( that can be localized to support cave-management activities at a variety of locations affected by RIFA. This model incorporates the expertise of natural resources personnel, information from field studies, and digital mapping data to provide spatially explicit simulations of RIFA competition with cave crickets.
Bart Rossmann, Tim Peterson, John Drake

Chapter 5. Spatially Explicit Agent-Based Model of Striped Newt Metapopulation Dynamics Under Precipitation and Forest Cover Scenarios

The striped newt (Notophthalmus perstriatus) is a rare, pond-breeding salamander species threatened by land-use change and recurrent drought in southern Georgia and northern Florida. At Fort Stewart, GA, this species must be successfully managed to ensure long-term population viability. Because little is known about striped newt ecology, it is unclear how to best focus field research to support future land-management initiatives. The authors demonstrate the utility of an agent-based simulation model, developed using NetLogo (, to shed light on striped newt population dynamics that can guide new field studies. The authors model striped newt population dynamics around clusters of temporary breeding ponds at Fort Stewart. In the absence of significant quantitative data, the model enables users to specify and test central but poorly understood dimensions of newt ecology and landscape hydrology. The authors hypothesized that rainfall amounts would be a significant variable affecting newt viability, with reduced rainfall having negative impacts on metapopulations. Simulations tested the effects of changing monthly precipitation rates and percentages of forest canopy cover. The results indicated that rainfall is in fact a significant variable, but percentage of canopy cover is not.
Jennifer L. Burton, Ewan Robinson, Sheng Ye

Chapter 6. Forecasting Gopher Tortoise (Gopherus polyphemus) Distribution and Long-Term Viability at Fort Benning, Georgia

The gopher tortoise (Gopherus polyphemus), once thrived in the southeastern USA, is now at risk due to habitat destruction and fragmentation throughout its native range. The US Army installation at Fort Benning, GA, provides a significant amount of habitat for the gopher tortoise. However, both installation operations and natural processes such as forest canopy growth may disrupt the highly specialized habitat that the gopher tortoise needs to survive. This chapter presents a spatially explicit agent-based simulation model of gopher tortoise population distribution and viability on Fort Benning lands. The model, developed in NetLogo (, incorporates geospatial data for Fort Benning, ecological characteristics of the land, and data on the gopher tortoise life cycle. Because there is little published data on gopher tortoise population dynamics, the model is based on published studies of species individuals. The purpose of this model was to investigate whether useful simulations of tortoise population dynamics and movement may be produced by a model based on robust data on individuals of the species. The authors conclude that the model can produce valid science-based simulations that provide valuable insights on how alternate land-management policies may affect gopher tortoise distribution on Fort Benning.
James D. Westervelt, Bruce MacAllister

Chapter 7. Using Demographic Sensitivity Testing to Guide Management of Gopher Tortoises at Fort Stewart, Georgia: A Comparison of Individual-Based Modeling and Population Viability Analysis Approaches

Population Viability Analysis (PVA) models have been used as a decision-support tool for managing wildlife populations, both game and nongame species. However, PVA models require extensive population-level data; without such data, an individual-based model (IBM) may be a more appropriate tool. The at-risk gopher tortoise (Gopherus polyphemus) is one species of interest about which numerous individual studies have been published, but with little published documentation of population dynamics. Using NetLogo (, the authors developed a spatially explicit IBM for the gopher tortoise population that inhabits Fort Stewart, GA, a US Army installation. The model was used to perform demographic sensitivity analyses and compare the results to sensitivity analyses conducted using a PVA model based on the same combinations of demographic parameters. The comparison showed a significant congruence in results from the two approaches. Several parameters—particularly juvenile and egg-to-age 1 mortality—appeared to disproportionately affect simulation results and are likely to be influenced by habitat manipulation. Based on their results, the authors conclude that IBMs can be useful to perform demographic sensitivity analysis and evaluate the capacity for habitat manipulation alone to provide the means for ensuring long-term persistence of gopher tortoise populations at Fort Stewart.
Tracey D. Tuberville, Kimberly M. Andrews, James D. Westervelt, Harold E. Balbach, John Macey, Larry Carlile

Chapter 8. A Model for Evaluating Hunting and Contraception as Feral Hog Population Control Methods

Feral swine (Sus scrofa) are an invasive species known to feed on small animals, eggs, roots, and herbaceous material. In addition to being a nuisance on managed lands such as Fort Benning, GA, uncontrolled populations of feral swine destroy habitat and elevate the risk of disease for threatened and endangered species that cohabitate the land. This chapter explores the relative effectiveness of controlling feral swine populations with hunting, contraception, and a combination of the two. To study the issue, the authors used NetLogo ( to develop an agent-based simulation model that incorporates digital maps of the subject population’s habitat at Fort Benning. Simulation results supported the hypothesis that the combination of lethal control and oral contraceptive delivery will provide better control of the Fort Benning feral swine population than will either technique alone. Additionally, the model provides a framework for understanding how feral swine interact with the landscape and helps land managers to predict the impacts of proposed control techniques.
Jennifer L. Burton, Marina Drigo, Ying Li, Ariane Peralta, Johanna Salzer, Kranthi Varala, Bruce Hannon, James D. Westervelt

Chapter 9. Spatially Explicit Modeling of Productivity in Pool 5 of the Mississippi River

The restoration and management of large rivers is difficult because such rivers have dynamic ecosystems and complex organic carbon cycles. Furthermore, energy flow is controlled by biotic and abiotic factors, similar to terrestrial systems, and also by hydraulic factors. There are three commonly discussed theories that attempt to describe productivity in large rivers, but none provides a generalized mechanism that can be applied across all rivers and all seasons. This chapter discusses a spatially explicit carbon-cycle model that simulates patterns of productivity in pool 5 of the Mississippi River. The model, developed using NetLogo (, incorporates both ecological and hydraulic processes for the purpose of representing the complexity of the Mississippi River carbon cycle and pinpointing key sources of productivity within it. This model can serve as a simple and effective tool for use by researchers and students who are interested in studying river productivity, and it is readily adaptable to a variety of river ecosystems simply by substituting hydrology inputs such as maps of depth, velocity, and flow direction.
Katherine R. Amato, Benjamin Martin, Aloah Pope, Charles Theiling, Kevin Landwehr, Jon Petersen, Brian Ickes, Jeffrey Houser, Yao Yin, Bruce Hannon, Richard Sparks

Chapter 10. Simulating Gopher Tortoise Populations in Fragmented Landscapes: An Application of the FRAGGLE Model

The gopher tortoise (Gopherus polyphemus) is an at-risk species native to the southeastern United States. It is estimated that gopher tortoise populations have declined by 80% over the past century. The decline can be linked in part to habitat fragmentation, which generally poses a serious threat to biodiversity and is a primary cause of the current high rate of species extinction. The purpose of the model described in this chapter is to provide new insights into the relation between habitat fragmentation and subpopulation mixing. Using the NetLogo simulation modeling platform (, the authors developed a spatially explicit dynamic model that can be adjusted and parameterized to capture the specific life-history and landscape characteristics associated with different species and geographic areas. Simulations capture the effects of habitat fragmentation by showing how an area’s metapopulation will be affected when a new subpopulation is added. Simulation results showed that habitat fragmentation isolates populations that are more susceptible to inbreeding, and it can cause overall decreases in population abundance and genetic diversity, possibly even leading to local extirpation.
Todd BenDor, James D. Westervelt, J. P. Aurambout, William Meyer

Chapter 11. An Individual-Based Model for Metapopulations on Patchy Landscapes-Genetics and Demography (IMPL-GD)

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 ( 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.
Jennifer L. Burton, Richard F. Lance, James D. Westervelt, Paul L. Leberg

Chapter 12. An Implementation of the Pathway Analysis Through Habitat (PATH) Algorithm Using NetLogo

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 (, 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.
William W. Hargrove, James D. Westervelt

Chapter 13. A Technique for Rapidly Forecasting Regional Urban Growth

Excessive suburbanization, often referred to as sprawl, has been rapidly proliferating throughout the USA. In an effort to better understand the social and ecological problems that can arise from sprawling urban growth, planners and researchers increasingly look to simulation models for help. Many established urban growth models are well suited for education and visualization, and they can be used to develop forecasts pertaining to key issues such as where people may be living in the future, how that new living space may affect natural habitats, and how changes to natural drainage may affect both nutrient flows and pollution. Although there is a considerable body of literature on urban growth modeling, the authors of this chapter provide a classroom-level modeling exercise students may use to gain experience with urban growth concepts. In this model, developed using NetLogo (, the drivers of urban growth attractiveness are represented in a geospatially explicit growth attractiveness input map. Growth parameters may be manipulated by adjusting values in the model’s digital maps. Students can use the model to experiment with theory and alternate planning scenarios to gain insight into cause–effect relationships between plans and urban dynamics. As the simulations promote fruitful discussions by urban planning students, further experimentation is encouraged, and refinement of the model itself may be undertaken for educational purposes.
Todd BenDor, James D. Westervelt

Chapter 14. Modeling Intimate Partner Violence and Support Systems

This chapter documents a simulation model developed to examine the dynamics of intimate partner violence (IPV) in a Midwestern US city. IPV is the term for personal abuse among intimate heterosexual partners. It affects all races and income levels, but the individuals at highest risk are African American, immigrant, and lower-income females (Intimate partner violence in the USA, Washington, 2007; Intimate partner violence, Lanham, 2009). Furthermore, cultural and economic factors may influence a woman’s disclosure of IPV, her probability of seeking help, and her utilization of social services. The modeling IPV patterns and policy responses is intended to provide insights that affect decisions about where to locate shelters, for example, or what kinds of support and educational services may be most effective in reducing the recurrence of IPV. The IPV model is a spatially explicit agent-based model developed with NetLogo ( The model was based on sociocultural and economic representations of IPV from the literature and statistics on IPV, crime, and homelessness for Chicago, IL. Simulations enable the user to assess the impacts of different policies and resource-deployment strategies on IPV rates, including the impacts on selected socioeconomic or racial groups. The authors assess the validity of the simulation results and identify areas for improving future versions of the model.
Marina Drigo, Charles R. Ehlschlaeger, Elizabeth L. Sweet


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