Methods to account for spatial autocorrelation in the analysis of species distributional data : a review
read more
Citations
Environmental suitability models predict population density, performance and body condition for microendemic salamanders.
Humid tropical rain forest has expanded into eucalypt forest and savanna over the last 50 years
Exploring the effects of spatial autocorrelation when identifying key drivers of wildlife crop-raiding
Urban bumblebees are smaller and more phenotypically diverse than their rural counterparts.
Variation in dry grassland communities along a heavy metals gradient.
References
Generalized Linear Models
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Modern Applied Statistics with S
Longitudinal data analysis using generalized linear models
Mixed-Effects Models in S and S-PLUS
Related Papers (5)
Novel methods improve prediction of species' distributions from occurrence data
Frequently Asked Questions (12)
Q2. What are the typical examples of ecological data with normally distributed errors?
Typical examples of ecological data with normally distributed errors include abundance, species richness, or functional diversity per unit area, crop yield and catch per unit effort.
Q3. What can be done to achieve the prediction of values within the parameter and spatial range?
i.e. the prediction of values within the parameter and spatial range, can be achieved by several of the presented methods.
Q4. What is the way to decompose a connectivity matrix?
Either binary or distance-based connectivity matrices can be decomposed, offering a great deal of flexibility regarding topology and transformations.
Q5. What are the main reasons for the use of Bayesian methods?
Bayesian methods are also a generally more suitable tool for inference in data sets with many missing values, or when accounting for detection probabilities (Gelfand et al. 2005, Kühn et al. 2006).
Q6. why is the eigenvector extraction limited to 7000 observations?
Due to numerical precision regarding the eigenvector extraction of large matrices (Bai et al. 1996) the method is limited to ca 7000 observations depending on platform and software (but see Griffith 2000a, for solutions based on large binary connectivity matrices).
Q7. What was the weight matrix used to simulate the spatially correlated errors oi?
A weight matrix W was used to simulate the spatially correlated errors oi using weights according to the distance between data points.
Q8. What are the two models that are used to model the error generating process?
CAR and SAR, on the other hand, model the error generating process and operate with weight matrices that specify the strength of interaction between neighbouring sites.
Q9. What are the advantages of Bayesian methods for the analyses of species distribution data?
Bayesian methods for the analyses of species distribution data are more flexible; they can be more easily extended to include more complex structures (Latimer et al. 2006).
Q10. What is the main argument for spatial autocorrelation in species distribution models?
While Lennon (2000) and others (Tognelli and Kelt 2004, Jetz et al. 2005, Dormann 2007b, Kühn 2007) argue that spatial autocorrelation in species distribution models may well bias coefficient estimation, Diniz-Filho et al. (2003) and Hawkins et al. (2007) found non-spatial model to be robust and unbiased for several data sets.
Q11. What are the constraints placed on the variance-covariance matrix?
Some restrictions are placed upon the resulting variance-covariance matrix a: a) it must be symmetric, and b) it must be positive definite.
Q12. What is the argument that the use of spatial parameters at least helps to derive better models?
One might therefore argue that, while taking the autocorrelation structure as constant adds one more assumption, the use of spatial parameters at least helps to derive better models.