Super-individuals a simple solution for modelling large populations on an individual basis

Abstract

Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. AbstractModelling populations on an individual-by-individual basis has proven to be a fruitful approach. Many complex patterns that are observed on the population level have been shown to arise from simple interactions between individuals. However, a major problem with these models is that the typically large number of individuals needed requires impractically large computation times. The common solution, reduction of the number of individuals in the model, can lead to loss of variation, irregular dynamics, and large sensitivity to the value of random generator seeds. As a solution to these problems, we propose to add an extra variable feature to each model individual, namely the number of real individuals it actually represents. This approach allows zooming from a real individual-by-individual model to a cohort representation or ultimately an all-animals-are-equal view without changing the model formulation. Therefore, the super-individual concept offers easy possibilities to check whether the observed behaviour is an artifact of following a limited number of individuals or of lumping individuals, and also to verify whether individual variability is indeed an essential ingredient for the observed behaviour. In addition the approach offers arbitrarily large computational advantages. As an example the super-individual approach is applied to a generic model of the dynamics of a size-distributed consumer cohort as well as to an elaborate applied simulation model of the recruitment of striped bass.

Spatiotemporal variation in flow-dependent recruitment of long-lived riverine fish: Model development and evaluation

“…The SEIBM-1D SNS has two entities: (1) sturgeon and (2) the river environment. The model keeps track of state variables of the entire life cycle of sturgeon; early life stages (fertilized eggs, yolk-sac larvae, and post-settlement (swim-up) larvae, and age-0 juveniles) are represented as super-individuals (SI YOY , Scheffer et al, 1995), whereas age 1+ juveniles and adults are represented as individual fish. Each SI YOY represents a collection of individuals ("cohorts") from the same mother.…”

Challenges and opportunities for integrating lake ecosystem modelling approaches

“…Three computationally more efficient approaches are presented below: models based on super-individuals (Scheffer et al 1995), physiologically structured population models (De Roos et al 1992) and stage-structured biomass models (De Roos et al 2008). The latter approach could equally well be categorized as a minimal dynamic model and provides a nice bridge between individual-based approaches and simple, unstructured models of consumer-resource interactions in aquatic systems.…”

“…Mortality can modelled as drawing from binomial distributions and acts on this number. This type of model can easily be used in combination with a discrete event queue, which is sometimes used in individual-based models (Scheffer et al 1995). This approach is closely related to that of physiologically structured models, but the implementation differs, and it is easier to scale down to a truly individualbased approach when modelling small populations (Scheffer et al 1995).…”

Individual-based modeling of soil organic matter in NetLogo: Transparent, user-friendly, and open

“…the fixed number of microbes comprised in each SI; cf. Scheffer et al, 1995). In general, shorter time steps and smaller SI favor the advantages of IBM, particularly because the higher resolution reduces averaging or ignoring the effects of local interactions and individual variations .…”