Abstract
Computational ecology is an emerging interdisciplinary discipline founded mainly on modeling and simulation methods for studying ecological systems. Among the existing modeling formalisms, the individual‐based modeling is particularly well suited for capturing the complex temporal and spatial dynamics as well as the nonlinearities arising in ecosystems, communities, or populations due to individual variability. In addition, being a bottom‐up approach, it is useful for providing new insights on the local mechanisms which are generating some observed global dynamics. Of course, no conclusions about model results could be taken seriously if they are based on a single model execution and they are not analyzed carefully. Therefore, a sound methodology should always be used for underpinning the interpretation of model results. The sensitivity analysis is a methodology for quantitatively assessing the effect of input uncertainty in the simulation output which should be incorporated compulsorily to every work based on in‐silico experimental setup. In this article, we present R/Repast a GNU R package for running and analyzing Repast Simphony models accompanied by two worked examples on how to perform global sensitivity analysis and how to interpret the results.
Highlights
The individual-based modeling is being established progressively as a main-stream and valuable tool for modeling complex processes in many distinct areas of knowledge, ranging from social science, economics to any flavor of computational and systems science such as biology, ecology and so on [1]
The possibility of incorporating many details comes with the cost of models with a high complexity level, containing many rules and parameters for which the exact values are, in many cases, hard or impossible to determine experimentally, that is what is know as parameter uncertainty
Model calibration is the task of estimate the set of values for input parameters of some simulation model which provides the best fitting to any empirical data set available for the system under study [3]
Summary
The individual-based modeling is being established progressively as a main-stream and valuable tool for modeling complex processes in many distinct areas of knowledge, ranging from social science, economics to any flavor of computational and systems science such as biology, ecology and so on [1]. According to [4] most of Individual-based models published tends to omit the systematic calibration and sensitivity analysis tasks, due to the fact that modelers practitioners do not have the specific knowledge to implement or use the required methods. It seems to be clear, that the availability of simple and user friendly tools for experiment design and analysis would help modelers to improve the formal quality of their models. We will describe the RRepast package functionalities, the most significant API elements, as well as a worked example for illustrating the basic use case of the package
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