Investigating the Effect of Spatial Distribution and Spatiotemporal Information on Speciation using Individual-Based Ecosystem Simulation

Morteza Mashayekhi ., Robin Gras .


In this paper, we investigate the impact of species’
spatial and spatiotemporal distribution information on
speciation, using an individual-based ecosystem simulation
(Ecosim). For this purpose, using machine learning techniques,
we try to predict if one species will split in near future. Because of
the imbalanced nature of our dataset we use smote algorithm to
make a relatively balanced dataset to avoid dismissing the minor
class samples. Experimental results show very good predictions
for the test set generated from the same run as the learning set. It
also shows good results on test sets generated from different runs
of Ecosim. We also observe superior results when we use, for the
learning set, a run with more species compare to a run with less
species. Finally we can conclude that spatial and spatiotemporal
information are very effective in predicting speciation.

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