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Analyzing spatial autocorrelation in species distributions using Gaussian and logit models

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  • Carl, G.
  • Kühn, I.

Abstract

Analyses of spatial distributions in ecology are often influenced by spatial autocorrelation. While methods to deal with spatial autocorrelation in Normally distributed data are already frequently used, the analysis of non-Normal data in the presence of spatial autocorrelation are rarely known to ecologists. Several methods based on the generalized estimating equations (GEE) are compared in their performance to a better known autoregressive method, namely spatially simultaneous autoregressive error model (SSAEM). GEE are further used to analyze the influence of autocorrelation of observations on logistic regression models. Originally, these methods were developed for longitudinal data and repeated measures models. This paper proposes some techniques for application to two-dimensional macroecological and biogeographical data sets displaying spatial autocorrelation. Results are presented for both computationally simulated data and ecological data (distribution of plant species richness throughout Germany and distribution of the plant species Hydrocotyle vulgaris). While for Normally distributed data SSAEM perform better than GEE, GEE provide far better results than frequently used autologistic regressions and remove residual spatial autocorrelation substantially when having binary data.

Suggested Citation

  • Carl, G. & Kühn, I., 2007. "Analyzing spatial autocorrelation in species distributions using Gaussian and logit models," Ecological Modelling, Elsevier, vol. 207(2), pages 159-170.
    • Handle: RePEc:eee:ecomod:v:207:y:2007:i:2:p:159-170
    DOI: 10.1016/j.ecolmodel.2007.04.024
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    References listed on IDEAS

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    1. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
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    Cited by:

    1. Sahar Zarmehri & Ephraim M. Hanks & Lin Lin, 2021. "A Sample Covariance-Based Approach For Spatial Binary Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 220-249, June.
    2. Solaiman Afroughi & Soghrat Faghihzadeh & Majid Jafari Khaledi & Mehdi Ghandehari Motlagh & Ebrahim Hajizadeh, 2011. "Analysis of clustered spatially correlated binary data using autologistic model and Bayesian method with an application to dental caries of 3--5-year-old children," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(12), pages 2763-2774, February.
    3. Dormann, Carsten F., 2007. "Assessing the validity of autologistic regression," Ecological Modelling, Elsevier, vol. 207(2), pages 234-242.
    4. Ranjitkar, Sailesh & Xu, Jianchu & Shrestha, Krishna Kumar & Kindt, Roeland, 2014. "Ensemble forecast of climate suitability for the Trans-Himalayan Nyctaginaceae species," Ecological Modelling, Elsevier, vol. 282(C), pages 18-24.
    5. Haenlein, Michael, 2013. "Social interactions in customer churn decisions: The impact of relationship directionality," International Journal of Research in Marketing, Elsevier, vol. 30(3), pages 236-248.
    6. Marbuah, George & Gren, Ing-Marie & Mckie, Brendan G. & Buisson, Laëtitia, 2021. "Economic activity and distribution of an invasive species: Evidence from night-time lights satellite imagery data," Ecological Economics, Elsevier, vol. 185(C).
    7. repec:gig:joupla:v:3:y:2011:i:2:p:107-140 is not listed on IDEAS
    8. Cerezo, Alexis & Perelman, Susana & Robbins, Chandler S., 2010. "Landscape-level impact of tropical forest loss and fragmentation on bird occurrence in eastern Guatemala," Ecological Modelling, Elsevier, vol. 221(3), pages 512-526.

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