Spatial smoothing techniques for the assessment of habitat suitability |
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Authors: | Thomas Kneib Jörg Müller Torsten Hothorn |
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Institution: | 1.Institut für Statistik,Ludwig-Maximilians-Universit?t München,Munich,Germany;2.Nationalparkverwaltung Bayerischer Wald,Grafenau,Germany |
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Abstract: | 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 observations is often violated in practice since the points
at which the observations are collected are spatially aligned. 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.
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Keywords: | Bivariate penalised splines Generalised linear models Geostatistics Kriging Spatial autocorrelation |
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