Adaptive agents (AA) provide a realistic framework for ecosystem simulation, evolving ecosystem structures and behaviours by emerging, submerging, interacting and evolving ecological entities.
Individual-based AA prove applicable to a spatially explicit simulation of highly simplified terrestrial food webs.
State variable-based AA where evolutionary computation is embodied appear to be relevant for simulations of aquatic food webs dynamics and plankton species interactions.
Embodiment of evolutionary computation in adaptive agents for aquatic species or functional groups can be achieved by evolving predictive rules (ER), differential equations (EDE) or artificial neural networks (ANN) from a diverse lake database.
Ecosystem simulation by state variable-based adaptive agents gains resilience to environmental change from an agent bank providing alternative agents for same species or functional groups evolved from a diverse lake database.
The presented concepts are currently tested by means of a multivariate time-series database for nine lakes different in climate, eutrophication and morphology.