## commands used to create ovenCH from the input files
## "netsites0509.txt" and "ovencapt.txt"
## for information only - these files not distributed
# netsites0509 <- read.traps(file = "netsites0509.txt",
# skip = 1, detector = "proximity")
# temp <- read.table("ovencapt.txt", colClasses=c("character",
# "character", "numeric", "numeric", "character"))
# ovenCHp <- make.capthist(temp, netsites0509, covnames = "Sex")
# ovenCHp <- reduce(ovenCHp, dropunused = FALSE) # drop repeat detections
par(mfrow = c(1,5), mar = c(1,1,4,1))
plot(ovenCHp, tracks = TRUE, varycol = TRUE)
par(mfrow = c(1,1), mar = c(5,4,4,2) + 0.1) ## defaults
counts(ovenCHp, "n")
if (FALSE) {
## trimmed version of data - for consistency with earlier versions
ovenCH <- reduce(ovenCHp, outputdetector = "multi", dropunused = FALSE)
## array constant over years, so build mask only once
## "pdot" type is no longer favoured, but kept here for consistency
ovenmask <- make.mask(traps(ovenCH)[["2005"]], type = "pdot",
buffer = 400, spacing = 15, detectpar = list(g0 = 0.03,
sigma = 90), nocc = 10)
## fit constant-density model
ovenbird.model.1 <- secr.fit(ovenCH, mask = ovenmask)
## fit temporal trend in density (Session capitalized)
ovenbird.model.D <- secr.fit(ovenCH, mask = ovenmask,
model = list(D ~ Session))
## fit temporal trend in density, Dlambda parameterization (Efford 2025 Appendix J)
ovenbird.model.Dl <- secr.fit(ovenCH, mask = ovenmask,
model = list(D ~ 1), details = list(Dlambda = TRUE))
predictDlambda(ovenbird.model.Dl)
## compare compatible pre-fitted models
AIC(ovenbird.model.1, ovenbird.model.D)
}
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