Density: Density Estimation with Telemetry

In RMark: R Code for Mark Analysis

Density Estimation with Telemetry

Description

The annotated code below is a companion to A Gentle Introduction to Program MARK, Chapter 20: Density Estimation (http://www.phidot.org/software/mark/docs/book/pdf/chap20.pdf). It requires the file "Density.txt", which is the RMark analog to the example Density Estimation input file distributed with MARK. These are simulated data intended to mimic a study of small mammals, such as deer mice, sampled at 2 sites (habitat types), A and B. Each habitat type was sampled with a (10 x 10) live-trapping grid (10m trap spacing). There are 5 occasions. In addition to marking each mouse with an individually identifiable ear tag, 50 percent of the individuals captured were fitted with a small VHF transmitter. These radio-tagged individuals were located once during the day and once at night for 5 days immediately after mark-recapture sampling (n = 10 locations total per animal) and each location was recorded as in or out of the study site. The single covariate we recorded is the distance to the edge (DTE) of the of the site from the mean trap location of each individual (i.e., compute the mean trap location for each individual captured >1 time, then compute the minimum distance from this mean location to the edge of the site).

Format

A data frame with 32 observations of 5 variables

ch

a character vector containing the capture history for 5 occasions

TotalLocations

The total number of telemetry locations if telemetered; otherwise a .

TotalIn

total number of locations in the original site if telemetered; otherwise a .

Site

the original site A or B

DTE

distance to the edge of the site when originally caught

Author(s)

Jake Ivan

Examples

#Read in Density Estimation input file specific to RMark

#Add 2 covariates that will be used for threshhold models - see below & p. 20-14, 20-15
#Specify data type - use "Densitypc" for this example, which is "Density Estimation with
# Huggins p and c". Could also use "DensityRanpc" (Huggins p and c with random effects),
#"DensityHet" (Huggins heterogeniety with pi and p), "DensityFHet" (Huggins full
#heterogeneity with pi and p) and DensityFHet (Huggins Full heterogeniety with pi, p, and c).
#Be sure to specify areas argument in process.data for this model. It will not run if you don't
# give it the area of each study site

data(Density)
#Create variables for threshhold model - see below & p. 20-14, 20-15
Density$Thresh15 <- ifelse(Density$DTE<15, Density$DTE, 15)
#Create variables for threshhold model - see below & p. 20-14, 20-15
Density$Thresh25 <- ifelse(Density$DTE<25, Density$DTE, 25)
data_proc <- process.data(Density, model="Densitypc", groups = c("Site"), areas=rep(0.81,2))
data_ddl <- make.design.data(data_proc)

#Run model p(.)p~(.) from p. 20-9, 20-10. View results.
p_dot <- list(formula = ~1, share=TRUE)
ptilde_dot <- list(formula = ~1)
model1 <- mark(data_proc,data_ddl,model.parameters=list(p=p_dot,ptilde=ptilde_dot),delete=TRUE)

#Run models p(site)p~(.) and p(.)p~(site) as indicated on p. 20-11. View results.
p_site <- list(formula = ~1 + Site, share=TRUE)
ptilde_site <- list(formula = ~1 + Site)
model2 <- mark(data_proc, data_ddl, model.parameters=list(p=p_dot, ptilde=ptilde_site),
                  delete=TRUE)
model3 <- mark(data_proc, data_ddl, model.parameters=list(p=p_site, ptilde=ptilde_dot),
                    delete=TRUE)


#Run model p(DTE)p~(DTE) as indicated on p. 20-12. View results.
p_DTE <- list(formula = ~1 + DTE, share=TRUE)
ptilde_DTE <- list(formula= ~1 + DTE)
model4 <- mark(data_proc, data_ddl, model.parameters = list(p=p_DTE, ptilde=ptilde_DTE),
                  delete=TRUE)


#Compute Model Selection Table that appears on p. 20-12. View results.
ModSelTable <- collect.models(type="Densitypc")
ModSelTable

#Run Threshhold models from p. 20-15.
p_DTE_Thresh15 <- list(formula = ~1 + Thresh15, share=TRUE)
p_DTE_Thresh25 <- list(formula = ~1 + Thresh25, share=TRUE)
model5 <- mark(data_proc, data_ddl, model.parameters = list(p=p_DTE_Thresh15, ptilde=ptilde_DTE)
               ,delete=TRUE)
model6 <- mark(data_proc, data_ddl, model.parameters = list(p=p_DTE_Thresh25, ptilde=ptilde_DTE)
               ,delete=TRUE)


#Re-compute Model Selection Table that appears on p. 20-16
ModSelTable <- collect.models(type="Densitypc")
ModSelTable

RMark documentation built on Aug. 14, 2022, 1:05 a.m.