Spatial Modeling Assignment

Spatial Modeling Assignment

library(vegan)
data(BCI)
## UTM Coordinates (in metres)
BCI_xy = data.frame(x = rep(seq(625754, 626654, by=100), each=5), 
                    y = rep(seq(1011569,  1011969, by=100), len=50))

1) Examine if there is evidence of spatial dependence in a rare and a common species in the BCI tree dataset

2) Build two generalized linear models to predict the abundance of the species Drypetes standleyi using the abundance of other tree species in the study site. Specifically examine the following species as predictor variables:

sp_ids = c("Cordia.lasiocalyx", "Hirtella.triandra",
           "Picramnia.latifolia", "Quassia.amara",
           "Tabernaemontana.arborea", "Trattinnickia.aspera", 
           "Xylopia.macrantha")

Note renaming the species ids to something a little easier to work with like “sp_a”, “sp_b” will make model construction a little less cumbersome

• Model 1: only include a single species as a predictor variable

• Model 2: include all of the species as predictor variables

With both models examine the spatial dependence of the residuals using the function Variogram. Model the spatial dependence in the residuals using one of the error structures available.

• Did including the spatial error term have a large impact on the coefficients of the model?

• Did including the spatial error terms significantly improve model fit (use function anova to carry out model comparison)?

• Explain why you did or did not observe a difference in the influence of adding the spatial error term between the two models.