Ecologist-Developed Spatially-Explicit Dynamic Landscape Models

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 (http://ccl.northwestern.edu/netlogo/) makes it unnecessary for the novice modeler to expend research project resources on costly proprietary software packages.

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 (http://ccl.northwestern.edu/netlogo/) to create rich, dynamic simulations.

This chapter introduces the novice modeler to the open-source NetLogo model development programming language and environment (http://ccl.northwestern.edu/netlogo/). 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 (http://ccl.northwestern.edu/netlogo/models/WolfSheepPredation), 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.

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 (http://ccl.northwestern.edu/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.

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 (http://ccl.northwestern.edu/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.

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.

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.

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 (http://ccl.northwestern.edu/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.