Code required to run the simulations and reproduce the Figures has been uploaded as part of the supplementary material. S1. Implementation of the model in c++. The model can be compiled from the command line using the command [g++ model.cpp -o model] and it can then be run using the command [./model 1 8 500 2000 20.0 37.0 2.0 0.8 0.1 0.1] to generate data for a population comprising n = 8 litters. from How self-organization can guide evolution
datasetposted on 03.11.2016 by Jonathan Glancy, James V. Stone, Stuart P. Wilson
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Self-organization and natural selection are fundamental forces that shape the natural world. Substantial progress in understanding how these forces interact has been made through the study of abstract models. Further progress may be made by identifying a model system in which the interaction between self-organization and selection can be investigated empirically. To this end, we investigate how the self-organizing thermoregulatory huddling behaviours displayed by many species of mammals might influence natural selection of the genetic components of metabolism. By applying a simple evolutionary algorithm to a well-established model of the interactions between environmental, morphological, physiological and behavioural components of thermoregulation, we arrive at a clear, but counterintuitive, prediction: rodents that are able to huddle together in cold environments should evolve a lower thermal conductance at a faster rate than animals reared in isolation. The model therefore explains how evolution can be accelerated as a consequence of relaxed selection, and it predicts how the effect may be exaggerated by an increase in the litter size, i.e. by an increase in the capacity to use huddling behaviours for thermoregulation. Confirmation of these predictions in future experiments with rodents would constitute strong evidence of a mechanism by which self-organization can guide natural selection.