Spatial smoothing techniques for the assessment of habitat suitability View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-09

AUTHORS

Thomas Kneib, Jörg Müller, Torsten Hothorn

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. More... »

PAGES

343-364

References to SciGraph publications

  • 2008-06. A Bayesian approach to multi-source forest area estimation in ENVIRONMENTAL AND ECOLOGICAL STATISTICS
  • 1997-06. A Generalized Linear Model Approach to Spatial Data Analysis and Prediction in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 2000. Mixed-Effects Models in Sand S-PLUS in NONE
  • 2000-03. An Improved Model for Spatially Correlated Binary Responses in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 2001. Multivariate Statistical Modelling Based on Generalized Linear Models in NONE
  • 1990-01. Objective recognition of guilds: testing for statistically significant species clusters in OECOLOGIA
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10651-008-0092-x

    DOI

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    DIMENSIONS

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