Spatial Smoothing Techniques for the Assessment of Habitat Suitability

Precise knowledge about factors influencing the habitat suitability
of a certain species forms the basis for the implementation of
effective programs to conserve biological diversity. Such knowledge
is frequently gathered from studies relating abundance data to a
set of influential variables in a regression setup. In particular,
generalised linear models are used to analyse binary
presence/absence data or counts of a certain species at locations
within an observation area. However, one of the key assumptions of
generalised linear models, the independence of the observations is
often violated in practice since the points at which the
observations are collected are spatially aligned. While several
approaches have been developed to analyse and account for spatial
correlation in regression models with normally distributed
responses, far less work has been done in the context of
generalised linear models. In this paper, we describe a general
framework for semiparametric spatial generalised linear models that
allows for the routine analysis of non-normal spatially aligned
regression data. The approach is utilised for the analysis of a
data set of synthetic bird species in beech forests, revealing that
ignorance of spatial dependence actually may lead to false
conclusions in a number of situations.