Structural realism, emergence, and predictions in next-generation ecological modelling: Synthesis from a special issue

Abstract

The two main challenges of ecological modelling are to yield more general understanding and theory and to provide testable and robust predictions. To achieve this, emergence, structural realism, and prediction have to become key elements of designing models. In the special issue “Next-generation ecological modelling”, which is dedicated to Donald DeAngelis on the occasion of his 70th birthday, 16 contributions present and discuss main features of next-generation ecological modelling. One key feature is to base the description of individuals’ behaviour and interactions on first principles rooted in energetic or evolutionary theory. To cope with increasing model complexity, standardization, separate testing of alternative submodels against multiple output patterns, and documenting these tests will be required. Including micro-evolution is essential to capture organisms’ response to changing conditions. Functional types may be used instead of species for representing communities. Model analysis will be challenging, but robustness analysis, which tries to break models’ explanations, can help to tell signals from noise and identify general mechanisms underlying the internal organization of ecological systems. Ultimately, next-generation modelling should aim at developing general theory to better understand stability properties and mechanisms. This understanding then can provide the basis for restoring, maintaining, or strengthening the resilience of ecosystems and supporting sustainable management of natural resources.