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Knowledge-based and inductive modelling of rough fescue (Festuca altaica, F. campestris and F. hallii) distribution in Alberta, Canada

Abstract:

The distribution of three rough rescue species (Festuca altaica, F. campestris and F. hallii) in Alberta was modelled using knowledge-based and inductive approaches. The first used differences in temperature responses defined in growth cabinet experiments, and simple logical algorithms operating on monthly mean climate surfaces. The second used a point database of surveyed botanical composition to define relationships between the species location and climatic factors. Agreement between the zones defined by knowledge-based logical modelling, inductive modelling from points and interpolation of botanical composition was generally good. Botanical composition was used as an abundance measure to enhance estimates of conditional probability of presence in inductive modelling. Additional peaks in the distribution of conditional probability of presence with certain climate variables allowed the identification of a sub-zone attributable to F. campestris which was smaller than that produced by logical modelling. Modelled zones from both methods agreed with published descriptions of distribution and intergrading between species. Choice of method depends on the relative availability of site data versus the amount of knowledge of species behaviour.