rbs: Red-backed salamander _Plethodon cinereus_ data.

In jaroyle/oSCR: Multi-Session Sex-Structured Spatial Capture-Recapture Models

Red-backed salamander Plethodon cinereus data.

Description

The rbs data is the data analyzed by Sutherland et al. (2016) in their Herpetologia paper and presented again in the official oSCR publication (Sutherland et al. 2019). This is a four-session data set where sessions are independent artificial cover object (ACO) plots in Ithaca, NY, USA.

Usage

data("rbs_ecography")

Format

The data contains an encounter data file (rbs.edf) and 4 trap deployment files ( rbs.tdf1, rbs.tdf2, rbs.tdf3, rbs.tdf4) that are used to build objects necessary for oSCR.fit.

Source

Sutherland, C., Munoz, D. J., Miller, D. A., & Grant, E. H. C. (2016). Spatial capture-recapture: a promising method for analyzing data collected using artificial cover objects. Herpetologica, 72(1), 6-12.

Sutherland, C., Royle, J. A., & Linden, D. W. (2019). oSCR: a spatial capture-recapture R package for inference about spatial ecological processes. Ecography, 42(9), 1459-1469.

Ecography supplement: http://www.ecography.org/appendix/ecog-04551

Examples

library(oSCR)
data(rbs_ecography)
ls()

# trap deployment (session 4)
str(rbs.tdf4)

# encounter data
str(rbs.edf)

# prepare data for analysis
rbs.data <- data2oscr(
  edf = rbs.edf,                     #the EDF
  sess.col = 1,                      #session column
  id.col = 2,                        #individual column
  occ.col = 3,                       #occasion column
  trap.col = 4,                      #trap column
  sex.col = 5,                       #sex column (optional)
  tdf = list(rbs.tdf1, rbs.tdf2,     #list of TDFs
             rbs.tdf3, rbs.tdf4),
  K = c(7,5,6,4),                    #occasion vector
  ntraps = c(50,50,50,50),           #no. traps vector
  trapcov.names = c("jday","jday2"), #covariate names
  tdf.sep = "/",                     #char used separator
  sex.nacode = "U")                  #unknown sex code

names(rbs.data)
rbs.sf <- rbs.data$scrFrame
names(rbs.sf)
rbs.sf

# plot the encounters
par(mfrow=c(2,2), mar=c(2,2,2,2), oma=c(0,0,0,0))
plot(rbs.sf,jit = 2)

# make the state space
rbs.ss <- make.ssDF(scrFrame = rbs.sf, buffer = 4, res=0.5)
str(rbs.ss)

# plot the state space
par(mfrow=c(2,2), mar=c(0,0,0,0), oma=c(0,0,0,0))
plot(ssDF = rbs.ss, scrFrame = rbs.sf)

# fit a model (ART ~100 min)
m1 <- oSCR.fit(
  model = list(D~1,                    #density
               p0~b + jday + jday2,    #detection
               sig~1),                 #space use
  scrFrame = rbs.sf, ssDF = rbs.ss)
m1

jaroyle/oSCR documentation built on Sept. 23, 2023, 12:46 p.m.