Skip to contents

Gopher tortoises shell remains

gopherdat2.Rd

Data of fresh Gopher tortoise shell remains (Ozgul et al. 2009) .

Usage

data("gopherdat2")

Format

A data frame with 30 observations on the following 7 variables.

Site

a factor representing the sampling site. There are 10 levels:

  • BS (Big Shoals State Park).

  • CB (Camp Blanding Wildlife Management Area).

  • Cent (privately-owned property in central Florida).

  • CF (Cecil Field/Branan Field Wildlife and Environmental Area).

  • FC (Fort Cooper State Park).

  • FE (Flying Eagle Wildlife Management Area).

  • GH (Gold Head Branch State Park).

  • Old (Perry Oldenburg Wildlife and Environmental Area).

  • Ord (Ordway-Swisher Biological Station).

  • TE (Tenoroc Fish Management Area).

year

a numeric vector of the sampling year.

shells

a numeric vector of the number of shells found.

Area

a numeric vector representing site area (units unknown).

density

a numeric vector representing population density.

prev

a numeric vector representing the seroprevalence (frequency of antibodies to disease) of M. agassizi.

Details

Details of the study is described in (Ozgul et al. 2009) as follows: "The fieldwork was conducted between 2003 and 2006, during late spring/summer (May–September). Systematic surveys were conducted to locate tortoise burrows and shell remains from deceased tortoises at study sites and consisted of a line of four to eight observers spaced 10 m apart walking parallel transects across the study area."

Source

(Ozgul et al. 2009)

References

Ozgul A, Oli MK, Bolker BM, Perez-Heydrich C (2009). “Upper respiratory tract disease, force of infection, and effects on survival of gopher tortoises.” Ecological Applications, 19(3), 786-798. doi:10.1890/08-0219.1 .

Examples

## Simple model gives a singular fit:
gopher_glmer <- glmer(shells ~ factor(year) + prev + offset(log(Area))
                   + (1|Site), data = gopherdat2, family = "poisson")
#> boundary (singular) fit: see help('isSingular')
## The site-level variance for this model is indeed zero:
VarCorr(gopher_glmer)
#>  Groups Name        Std.Dev.
#>  Site   (Intercept) 0       
## So a Poisson GLM gives the same answer here:
gopher_glm <- glm(shells ~ factor(year) + prev + offset(log(Area)),
                  data = gopherdat2, family = "poisson")
all.equal(fixef(gopher_glmer), coef(gopher_glm))
#> [1] TRUE