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Compute meta-analytic estimates of instantaneous and conditional natural mortality

Source: R/est_natmort.R
est_natmort.Rd

Several methods may be used to estimate instantaneous (M) and conditional natural mortality (cm) from other types of data, especially those saved in the life history parameters vector/list from makeLH.

Usage

est_natmort(lhparms = NULL, method = "rFAMS", incl.avg = FALSE, ...)

Arguments

lhparms

A named vector or string returned by lhparms.

method

A string that indicates what methods to use to estimate M (see metaM).

incl.avg

A logical that indicates whether the average cm should be computed from the estimated M of all methods.

...

Option arguments for parameter values required by methods using parameters other than those in lhparms. See examples.

Value

A data.frame with the following items:

  • method: The name for the method within the function (as given in method).

  • M: The estimated instantaneous natural mortality rate (from metaM)

  • cm: The estimated conditional natural mortality rate (computed directly from M).

  • givens: A string that contains the input values required by the method to estimate M.

Details

The default methods to use are all of those listed in Mmethods that use some of the life history parameters required by makeLH. These methods are not all equally useful or robust, so the user may want to select a subset of them for use after learning more about them. See references in metaM.

Other methods that require parameters other than those required by makeLH can be used by providing the name of the method in method and the required parameters as arguments, as defined in metaM. See metaM for more details and the examples below for an example.

Author

Derek Ogle

Examples

# An example lhparm as would be returned from makeLH
tmp <- list(N0=100,tmax=15,Linf=500,K=0.3,t0=-0.5,LWalpha=-5.16,LWbeta=3.1)

# All methods in metaM() that use those life history parameters
est_natmort(tmp)
#>            method          M         cm                  givens
#> 1       HoenigNLS 0.41002265 0.33636478                 tmax=15
#> 2         HoenigO 0.29543534 0.25579246                 tmax=15
#> 3        HoenigO2 0.29631969 0.25645032                 tmax=15
#> 4        HoenigLM 0.36126963 0.30320890                 tmax=15
#> 5    HewittHoenig 0.28133333 0.24522330                 tmax=15
#> 6           tmax1 0.34060000 0.28865661                 tmax=15
#> 7       PaulyLNoT 0.47024562 0.37515122          K=0.3, Linf=50
#> 8              K1 0.50760000 0.39806150                   K=0.3
#> 9              K2 0.56300000 0.43050200                   K=0.3
#> 10      HamelCope 0.36000000 0.30232367                 tmax=15
#> 11       JensenK1 0.45000000 0.36237185                   K=0.3
#> 12       JensenK2 0.65100000 0.47847601                   K=0.3
#> 13 AlversonCarney 0.19872105 0.18022146          tmax=15, K=0.3
#> 14   ChenWatanabe 0.07181977 0.06930138 tmax=15, K=0.3, t0=-0.5

# Same but including the average in the last row
est_natmort(tmp,incl.avg=TRUE)
#>            method          M         cm                  givens
#> 1       HoenigNLS 0.41002265 0.33636478                 tmax=15
#> 2         HoenigO 0.29543534 0.25579246                 tmax=15
#> 3        HoenigO2 0.29631969 0.25645032                 tmax=15
#> 4        HoenigLM 0.36126963 0.30320890                 tmax=15
#> 5    HewittHoenig 0.28133333 0.24522330                 tmax=15
#> 6           tmax1 0.34060000 0.28865661                 tmax=15
#> 7       PaulyLNoT 0.47024562 0.37515122          K=0.3, Linf=50
#> 8              K1 0.50760000 0.39806150                   K=0.3
#> 9              K2 0.56300000 0.43050200                   K=0.3
#> 10      HamelCope 0.36000000 0.30232367                 tmax=15
#> 11       JensenK1 0.45000000 0.36237185                   K=0.3
#> 12       JensenK2 0.65100000 0.47847601                   K=0.3
#> 13 AlversonCarney 0.19872105 0.18022146          tmax=15, K=0.3
#> 14   ChenWatanabe 0.07181977 0.06930138 tmax=15, K=0.3, t0=-0.5
#> 15        AVERAGE 0.37552622 0.30586468                        

# Selecting just one method
est_natmort(tmp,method="HoenigNLS")
#>      method         M        cm  givens
#> 1 HoenigNLS 0.4100226 0.3363648 tmax=15

# Selecting several methods
est_natmort(tmp,method=c("HoenigNLS","HoenigO","HoenigO2","HoenigLM"))
#>      method         M        cm  givens
#> 1 HoenigNLS 0.4100226 0.3363648 tmax=15
#> 2   HoenigO 0.2954353 0.2557925 tmax=15
#> 3  HoenigO2 0.2963197 0.2564503 tmax=15
#> 4  HoenigLM 0.3612696 0.3032089 tmax=15

# A method that uses a parameter not usually in lhparms
est_natmort(tmp,method="QuinnDeriso",PS=0.05)
#>        method         M        cm           givens
#> 1 QuinnDeriso 0.1997155 0.1810363 PS=0.05, tmax=15

# Selecting all Hoenig methods using Mmethods from FSA
est_natmort(tmp,method=FSA::Mmethods("Hoenig"))
#>          method         M        cm  givens
#> 1     HoenigNLS 0.4100226 0.3363648 tmax=15
#> 2       HoenigO 0.2954353 0.2557925 tmax=15
#> 3      HoenigOF 0.2793944 0.2437585 tmax=15
#> 4      HoenigOM 0.3594796 0.3019605 tmax=15
#> 5      HoenigOC 0.2409592 0.2141263 tmax=15
#> 6      HoenigO2 0.2963197 0.2564503 tmax=15
#> 7     HoenigO2F 0.2568301 0.2265003 tmax=15
#> 8     HoenigO2M 0.3521442 0.2968213 tmax=15
#> 9     HoenigO2C 0.3110774 0.2673428 tmax=15
#> 10     HoenigLM 0.3612696 0.3032089 tmax=15
#> 11 HewittHoenig 0.2813333 0.2452233 tmax=15

# Over-riding the Linf param in parameters list, but others from tmp
est_natmort(tmp,method="PaulyLNoT")              # Linf from tmp
#>      method         M        cm         givens
#> 1 PaulyLNoT 0.4702456 0.3751512 K=0.3, Linf=50
est_natmort(tmp,Linf=1000/10,method="PaulyLNoT") # Linf from Linf= arg
#>      method         M        cm          givens
#> 1 PaulyLNoT 0.3740975 0.3120902 K=0.3, Linf=100