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Agent-based models (ABMs) and state-and-transition simulation models (STSMs) have proven useful for understanding processes underlying social-ecological systems and evaluating practical questions about how systems might respond to different scenarios. ABMs can simulate a variety of agents (i.e., autonomous units, such as wildlife, people, or viruses); agent characteristics, decision-making, adaptive behavior, and mobility; and interactions between agents and their environment. STSMs are flexible and intuitive stochastic models of landscape dynamics that can track scenarios and landscape attributes, and integrate diverse data types. Both can be run spatially and track metrics of management success. Due to the complementarity...
This software code was developed to estimate the probability that individuals found at a geographic location will belong to the same genetic cluster as individuals at the nearest empirical sampling location for which ancestry is known. POPMAPS includes 5 main functions to calculate and visualize these results (see Table 1 for functions and arguments). Population assignment coefficients and a raster surface must be estimated prior to using POPMAPS functions (see Fig. 1a and b). With these data in hand, users can run a jackknife function to choose an optimal parameter combination that reconstructs empirical data best (Figs. 2 and S2). Pertinent parameters include 1) how many empirical sampling localities should be...


    map background search result map search result map Coupled Agent-Based and State-and-Transition Simulation Model Coupled Agent-Based and State-and-Transition Simulation Model