IBMs are a flexible and powerful tool and very likely to lead to important insights into how system-level properties of ecological systems, for example, stability properties (e.g., resilience), emerge from the interactions of the individuals among each other and with their environment. The approach also poses new challenges, in particular optimization of model complexity, coping with uncertainty in model structure and parameters, formulating the models according to a unifying framework, and implementing, testing, and communicating IBMs according to general standards. First attempts to meet these challenges have been described above, but maturation of the individual-based approach will require more effort and time, probably at least a further decade.
IBMs will continue to be used both in a more pragmatic and a more paradigmatic way. Pragmatic IBMs usually do not refer to adaptive behavior and often are designed to be as compatible with more simple mathematical models as possible. There are certainly many questions where this type of IBM is sufficient, because, as we have seen in the coyote model and gap models described above, for many questions we do not need to refer to adaptive behavior explicitly.
Paradigmatic IBMs, however, are based on the assumption that adaptive behavior, that is, the simple fact that individuals adapt their behavior to their current situation and that they are seeking to maximize fitness, is key to understanding most, if not all, phenomena at the system level. Paradigmatic IBMs are seen as the nucleus of an Individual-based ecology which links individual behavior, including life history and phenotypic plasticity, to system-level structures and dynamics.
In any case, the design, formulation, testing, validation, analysis, and communication of IBMs have to be made more efficient and coherent than they are today. Key to the success of IBMs is also to make ecological modeling less number driven, and more pattern oriented. And we will need reusable standard designs of modeling different life-forms or functional group in order to go beyond populations and model entire communities and ecosystems in an individual-based way.
See also-. Adaptive Agents; Forest Models; Habitat Selection and Habitat Suitability Preferences; Model Development and Analysis; Optimal Foraging Theory; Software; Visualization and Interaction Design for Ecosystem Modeling.
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