Assessing the validity of autologistic regression

By Carsten F. Dormann. 2007

138 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/a27dae99c4ada5663850847bafa0e5d9e4cb9655

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Understanding what controls the spatial distribution of fish populations using a multi-model approach (2011, background): “(2006), Wintle and Bardos (2006) and Dormann et al. (2007). The following section is restricted to the presentation of models that appear to be particularly suited to deal with the modelling of fish population spatial distribution.”
Methods to account for spatial autocorrelation in the analysis of species distributional data : a review (2007, background): “In either case, spatial autocorrelation can make a large difference for statistical inference based on spatial data (for review see Dormann, 2007a, b, c; for drastic cases of this effect see Tognelli and Kelt 2004 and Kühn 2007). How to interpret these differences, especially the shifts in parameter”
The performance of state-of-the-art modelling techniques depends on geographical distribution of species. (2009, background): “…example, Seoane et al. (2005) showed that the predictive power of regression trees (RT) was highest when modelling species with high prevalence, and Dormann (2007) and Reineking and Schröder (2006) showed that different autologistic regression methods were severely impacted by the prevalence of…”
Predicting the distribution of vulnerable marine ecosystems in the deep sea using presence-background models (2014, background): “%) of published SDM studies did not implement methods to account for its effects (Dormann, 2007a), and to date no studies conducted for the deep-sea have explicitly addressed the effects of SA on model predictions.”
Do pathogen spillover, pesticide use, or habitat loss explain recent North American bumblebee declines? (2012, methodology): “Although our analysis involved spatial data, we used standard nonspatial logistic regression because analysis of residuals from our nonspatial models indicated that spatial autocorrelation in the residuals was weak and limited to short distances (see supporting information) and, even in the presence”
Residual spatial autocorrelation in macroecological and biogeographical modeling: a review (2019, background): “In sampling, these are termed “artifacts” in that they are not a result of the environment but rather caused by the researcher (Dormann, 2007a; Crase et al., 2014).”