Analyzing spatial autocorrelation in species distributions using Gaussian and logit models

By G. Carl, I. Kühn. 2007

123 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/f38279a656d3c32e9455754e52be968b59a1926b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Carsten F. Dormann
2. I. Kühn
3. Joaquín Hortal
4. Jorge M. Lobo
5. Duccio Rocchini
6. Carlo Ricotta
7. A. Jiménez‐Valverde
8. Giovanni Bacaro

In Nature and Landscape Conservation

1. Jorge M. Lobo
2. Joaquín Hortal
3. A. Jiménez‐Valverde
4. Carsten F. Dormann
5. Duccio Rocchini
6. Giovanni Bacaro
7. Carlo Ricotta
8. Szabolcs Lengyel

Citations in the corpus

• Less than eight (and a half) misconceptions of spatial analysis (2012, background): “Anselin and colleagues (Anselin, 1988; Anselin & Florax, 1995; Anselin & Bera, 1998) as well as the (caricature reality) simulations of Dormann et al. (2007), Carl & Kühn (2007, 2008) or Beale et al. (2010) clearly demonstrated that SA does affect coefficient estimates.”
• A practical guide to environmental association analysis in landscape genomics (2015, methodology): “An alternative logistic approach is formalized in generalized estimating equations (GEEs, Carl & Kuhn 2007), an extension of generalized linear models with a logit-link and binomial error distribution that considers spatial autocorrelation within populations.”
• Understanding what controls the spatial distribution of fish populations using a multi-model approach (2011, methodology): “For binomial and other distributions, Generalised Estimating Equation (GEE) seems to be a promising method (Carl and Kühn, 2007).”
• Assessing the validity of autologistic regression (2007, methodology): “A recent study showed that coefficients may be inverted by spatial autocorrelation (Kühn, 2007), demonstrating the need for spatial models.”
• Accounting for uncertainty when mapping species distributions: The need for maps of ignorance (2011, methodology): “On the one hand, each time a different technique or data set is used, the frequent collinearity among variables or their spatial autocorrelation results in different output models and maps (Bini et al., 2009; Carl and Kühn, 2007; Dormann, 2007a; Graham, 2003; see Table 1).”
• Landscape Genomics: Understanding Relationships Between Environmental Heterogeneity and Genomic Characteristics of Populations (2017, methodology): “The earliest EAA methods did not incorporate the neutral genetic structure and simply aimed at testing the association between genotype frequencies and environmental gradients (Joost et al. 2007; Carl and Kühn 2007).”