depletion(catch, effort, method = c("Leslie", "DeLury", "Delury"),
Ricker.mod = FALSE)
## S3 method for class 'depletion':
summary(object, type = c("params", "lm"),
verbose = FALSE, digits = getOption("digits"), ...)
## S3 method for class 'depletion':
coef(object, type = c("params", "lm"),
digits = getOption("digits"), ...)
## S3 method for class 'depletion':
confint(object, parm = c("No", "q", "lm"),
level = conf.level, conf.level = 0.95, digits = getOption("digits"),
...)
## S3 method for class 'depletion':
anova(object, ...)
## S3 method for class 'depletion':
plot(x, xlab = NULL, ylab = NULL, pch = 19,
col.pt = "black", col.mdl = "gray70", lwd = 1, lty = 1,
pos.est = "topright", cex.est = 0.95, ...)removal call (i.e., of class depletion).type="params" (the default) then results for No and q are returned. If type="lm" then results for the underlying linear model are returned.conf.level but used for compatability with generic confint function.depletion call (i.e., of class depletion)."bottomright", "bottom", "bottomleft", "left", "topleft", "Leslie"or"Delury"model was used.lmobject from the fit of CPE on K (Leslie method) or log(CPE) on E (Delury method).deplet in darter (from LobsterPEI and BlueCrab from SMBassLS for N0 to whole numbers, the SE for No to one decimal, q to seven decimals, and the SE of q to at least five decimals.
The Leslie method matches the results of Seber (2002) for N0, q, and the CI for Q but not the CI for N (which was so far off that it might be that Seber's result is incorrect) for the lobster data and the q and CI for q but the NO or its CI (likely due to lots of rounding in Seber 2002) for the Blue Crab data.
The Leslie and DeLury methods match the results of Ricker (1975) for No and Q but not for the CI of No (Ricker used a very different method to compute CIs).Ricker.mod=TRUE then the cumulative catch is modified to be the cumulative catch prior to the sample plus half of the catch of the current sample.
For the DeLury method, a linear regression model of log (catch-per-unit-effort) on cumulative effort is fit. The catchability coefficient (q) is estimated from the negative of the slope and the initial population size (No) is estimated by dividing the intercept as an exponent of e by the catchability coefficient. If Ricker.mod=TRUE then the cumulative effort is modified to be the cumulative effort prior to the sample plus half of the effort of the current sample.
Standard errors for the catchability and population size estimates are computed fronm formulas on page 298 (for Leslie) and 303 (for DeLury) from Seber (2002). Confidence intervals are computed using standard large-sample normal distribution theory with the regression error df.removal for related functionality and deplet in data(SMBassLS)
## Leslie model examples
# no Ricker modification
l1 <- depletion(SMBassLS$catch,SMBassLS$effort,method="Leslie")
coef(l1)
summary(l1)
summary(l1,verbose=TRUE)
confint(l1)
summary(l1,type="lm")
plot(l1)
# with Ricker modification
l2 <- depletion(SMBassLS$catch,SMBassLS$effort,method="Leslie",Ricker.mod=TRUE)
summary(l2)
confint(l2)
plot(l2)
## Delury model examples
# no Ricker modification
d1 <- depletion(SMBassLS$catch,SMBassLS$effort,method="Delury")
coef(d1)
summary(d1)
summary(d1,verbose=TRUE)
confint(d1)
summary(d1,type="lm")
plot(d1)
# with Ricker modification
d2 <- depletion(SMBassLS$catch,SMBassLS$effort,method="Delury",Ricker.mod=TRUE)
summary(d2)
confint(d2)
plot(d2)Run the code above in your browser using DataLab