A Geographic Network Automata Approach for Modeling Dynamic Ecological Systems

Abstract

Landscape connectivity networks are composed of nodes representing georeferenced habitat patches that link together based on a species’ maximum dispersal distance. These static representations cannot capture the complexity in species dispersal where the network of habitat patch nodes changes structure over time as a function of local dispersal dynamics. Therefore, the objective of this study is to integrate geographic information, complexity, and network science to propose a novel Geographic Network Automata (GNA) modeling approach for the simulation of dynamic spatial ecological networks. The proposed GNA modeling approach is applied to the emerald ash borer (EAB) forest insect infestation using geospatial data sets from Michigan, U.S.A. and simulates the evolution of the EAB spatiotemporal dispersal network structures across a large regional scale. The GNA model calibration and sensitivity analysis are performed. The simulated spatial network structures are quantified using graph theory measures. Results indicate that the spatial distribution of habitat patch nodes across the landscape in combination with EAB dispersal processes generate a highly connected small‐world dispersal network that is robust to node removal. The presented GNA model framework is general and flexible so that different types of geospatial phenomena can be modeled, providing valuable insights for management and decision‐making.

Agent-Based Modeling and the City: A Gallery of Applications

“…The flexibility that the agent-based modeling approach provides has allowed such models to be used in a diverse set of applications. These range from archeology (Axtell et al 2002), agriculture (Hailegiorgis et al 2018), basketball (Oldham and Crooks 2019), crime (Malleson et al 2013), diseases (Perez and Dragicevic 2009), disasters (Jumadi et al 2018), invasive species (Anderson and Dragićević 2018), to urban growth (Xie and Yang 2011), housing markets (Geanakoplos et al 2012), gentrification (Jackson et al 2008), slum formation (Patel et al 2018), and traffic (Manley and Cheng 2018). So, while agent-based modelers have been utilizing geographical data in their models, what has changed is the growth of data and ways of integrating such data within models (which will be discussed more in Sect.…”

Complex spatial networks: Theory and geospatial applications

“…In all the aforementioned approaches that integrate network theory and complex systems modeling, network structure is introduced into the model from which network dynamics and processes operate, but the subsequent evolution of the network structure as a function of network dynamics is rarely explored. In order to fill this gap and directly simulate and explore the tightly coupled relationship between network dynamics and network structure and subsequently network evolution, Anderson and Dragićević (2020) propose a novel complex modeling framework that unites GAS and network science called Geographic Network Automata (GNA). The developed approach builds on concepts presented in nonspatial network automata modeling approaches (Sayama & Laramee, 2009) but instead in geospatial applications where geographic space is explicit (Anderson & Dragićević, 2020, b).…”

“…Furthermore, the GNA modeling approach facilitates the simulation of a variety of complex spatial systems as dynamic evolving networks that can be measured, characterized, and analyzed using graph theory. The developed GNA framework is applied to represent and characterize spatial patterns and dynamics of insect infestation at the regional scale across the state of Michigan, USA (Anderson & Dragićević, 2020). The results from these models inform the ways in which the spatial structure of the landscape in combination with EAB infestation dynamics makes EAB eradication particularly challenging and are useful to forecast, quantify, and help mitigate adverse environmental impacts of infestation spread.…”

Future Developments in Geographical Agent‐Based Models: Challenges and Opportunities

“…Agent‐based models were first formally proposed in the early 1990s (e.g., Epstein and Axtell 1996) but their lineage goes back much further to the development of models of individual locational decision‐making in the 1950s and 1960s in the influential work of Hagerstrand (1953), Donnelly et al (1964), and Schelling (1969) among others. They are now reaching a point of acceptance as a research tool across the geographical and social sciences, exploring such phenomena as epidemiology (Shook and Wang 2015), invasive species (Anderson and Dragićević 2020), settlement patterns (Bura et al 1996), and segregation (Benenson and Hatna 2011) (see Polhill et al 2019 for further discussion on the applications of ABMs and their use in policy).…”