Detecting population regulation via a nonlinear time series model
Jonathan Alsop
Injury Prevention Research Unit, University of Otago, New Zealand
Abstract
Testing for the presence of density dependence in a time series of
population counts is problematic. Often linear time series models have
been used as bases for testing for population regulation. A non-linear
model has been devised which incorporates these simple models, but
allows for a 'stronger' form of density dependence. This model can be
readily extended to include additional lag terms and extraneous
variables. Under certain parameter conditions the model is
chaotic. This behaviour is examined via bifurcation diagrams and
estimates of the Lyapunov exponent.