data_kerinci: Times of 'capture' of large mammals

Example dataR Documentation

Times of 'capture' of large mammals

Description

Times of capture of large mammals in camera traps in Kerinci Seblat National Park, Indonesia.

Usage

data(kerinci)

Format

A data frame with 1098 rows and three columns:

Zone

A number indicating which of four zones the record comes from.

Sps

A factor indicating which species was observed: boar (wild pig), clouded leopard, golden cat, macaque, muntjac, sambar deer, tapir, or tiger.

Time

The time of the observation on a scale of 0 to 1, where 0 and 1 both correspond to midnight

Source

Ridout, M.S. and Linkie, M. (2009) Estimating overlap of daily activity patterns from camera trap data. Journal of Agricultural, Biological and Environmental Statistics, 14, 322-337.

https://www.kent.ac.uk/smsas/personal/msr/overlap.html

Examples

data(kerinci)
str(kerinci)
# Time is in days, ie. 0 to 1:
range(kerinci$Time)
# Convert to radians:
timeRad <- kerinci$Time * 2*pi

# Extract data for tiger and tapir for Zone3:
spsA <- timeRad[kerinci$Zone == 3 & kerinci$Sps == 'tiger']
spsB <- timeRad[kerinci$Zone == 3 & kerinci$Sps == 'tapir']

# Plot the data:
overlapPlot(spsA, spsB)  # Tapir are mainly nocturnal
overlapPlot(spsA, spsB, xcenter="midnight")
legend('topleft', c("Tiger", "Tapir"), lty=c(1, 2), col=c("black", "blue"), bty='n')

# Check sample sizes:
length(spsA)
length(spsB)
# If the smaller sample is less than 50, Dhat1 gives the best estimates, together with
# confidence intervals from a smoothed bootstrap with norm0 or basic0 confidence interval.

# Calculate estimates of overlap:
( Dhats <- overlapEst(spsA, spsB) )  # or just get Dhat1
( Dhat1 <- overlapEst(spsA, spsB, type="Dhat1") )

# Do 999 smoothed bootstrap values:
bs <- bootstrap(spsA, spsB, 999, type="Dhat1")
mean(bs)
hist(bs)
abline(v=Dhat1, col='red', lwd=2)
abline(v=mean(bs), col='blue', lwd=2, lty=3)

# Get confidence intervals:
bootCI(Dhat1, bs)['norm0', ]
bootCI(Dhat1, bs)['basic0', ]

mikemeredith/overlap documentation built on Nov. 26, 2022, 7:11 p.m.