This is an adaptation of the Best F Excel spreadsheet forwarded to assessment authors from Grant Thompson is 2020.
Use this year’s model to recalculate the F for the previous year that would have produced the specified OFL for the previous year.
Data should be from this year’s assessment in a dataframe.
age, naa, waa, saa.age, naa, waa, saa1, saa2.age, naa1, naa2, waa1, waa2, saa1, saa2.age, naa1, naa2, waa1, waa2, saa11, saa21, saa12, saa22,formatting numbers are sex-gear (e.g., sex1/gear1, sex2/gear1, sex1/gear2…)
m: natural mortality from current modelm2: natural mortality from current model if using a 2 sex modellast_ofl: the OFL from the previous assessmentf_ratio: the final ratio of catch between gears in the previous assessment, as estimated by this year’s modeltype:
The function is in the groundfishr package and available from GitHub.
# install.packages("devtools")
devtools::install_github("BenWilliams-NOAA/groundfishr")
# load ----
library(groundfishr)
# data ----
oneone <- data.frame(age = 1:9,
naa = c(15.27, 10.85, 0.24, 8.72, 2.49, 10.66, 3.92, 3.71, 2.6),
waa = c(0.69, 1.11, 1.54, 1.95, 2.32, 2.64, 2.91, 3.41, 3.33),
saa = c(0.01, 0.1 , 0.57, 0.94, 0.99, 1, 1, 1, 1))
onetwo <- data.frame(age = 1:9,
naa = c(15.27, 10.85, 0.24, 8.72, 2.49, 10.66, 3.92, 3.71, 2.6),
waa = c(0.69, 1.11, 1.54, 1.95, 2.32, 2.64, 2.91, 3.41, 3.33),
saa01 = c(0.01, 0.1 , 0.57, 0.94, 0.99, 1, 1, 1, 1),
saa01 = c(0.01, 0.06, 0.34, 0.82, 0.97, 1, 1, 1, 1))
# data from excel spreadsheet
twotwo = structure(list(age = 2:31,
naa_1 = c(7.63679, 5.42537, 0.121917, 4.35873, 1.24303,
5.32974, 1.95914, 1.854, 1.30156, 1.41362,
0.735815, 1.33366, 0.500973, 2.8615, 0.538878,
0.874829, 1.19386, 0.033371, 0.506956, 0.167269,
0.117931, 0.0480766, 0.342494, 0.00281983,
0.247283, 0.0431833, 0.0294041, 0.0182076,
0.0811841,0.549864),
naa_2 = c(7.63679, 5.41544, 0.121735, 4.40234, 1.26591,
5.44178, 1.99886, 1.88773, 1.32467, 1.44008,
0.749866, 1.35731, 0.509176, 2.90783, 0.54674,
0.883593, 1.20234, 0.0335946, 0.508423,
0.167075, 0.117353, 0.0478421, 0.348337,
0.00302884, 0.285272, 0.0533892, 0.0379721,
0.023669, 0.10561, 0.642394),
waa_1 = c(0.971715586, 1.456610048, 1.877666845,
2.216248989, 2.476126923, 2.669769481,
2.811274969, 2.913338963, 2.986303936,
3.038149188, 3.074833056, 3.100713565,
3.118935223, 3.131746354, 3.140744588,
3.147060361, 3.151491197, 3.1545986,
3.156777345, 3.158304712, 3.159375318,
3.160125696, 3.1606516, 3.161020166,
3.161278459, 3.161459467, 3.161586315,
3.161675206, 3.161737498, 3.161781151),
waa_2 = c(0.916819778, 1.477123278, 2.052195835,
2.598202155, 3.090971733, 3.520478614,
3.885686957, 4.190661834, 4.441940672,
4.646893835, 4.812779804, 4.946254777,
5.053162923, 5.138490103, 5.20640581,
5.260347147, 5.303117714, 5.336986373,
5.363778285, 5.384954987, 5.401682719,
5.414889559, 5.425312496, 5.433535815,
5.440022136, 5.445137382, 5.449170763,
5.452350711, 5.454857574, 5.456833673),
saa_11 = c(0.00889783, 0.0976488, 0.566051, 0.940203,
0.994751, 0.999562, 0.999964, 0.999997, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1),
saa_21 = c(0.00718349, 0.058106, 0.344685, 0.817671,
0.974513, 0.996942, 0.99964, 0.999958,
0.999995, 0.999999, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1),
saa_12 = c(0.120336, 0.367107, 0.617893, 0.815247, 0.941289,
0.998866, 1, 0.959358, 0.890888, 0.806329,
0.71479, 0.622865, 0.534999, 0.453925,
0.381086, 0.317002, 0.26157, 0.21429,
0.174438, 0.141185, 0.113681, 0.0911072,
0.072704, 0.0577917, 0.0457734, 0.0361345,
0.0284382, 0.0223178, 0.0174684, 0.0136391),
saa_22 = c(0.014480753, 0.134767549, 0.381791759,
0.664627671, 0.886024982, 0.997733286, 1,
0.920367772, 0.793681429, 0.650166456,
0.510924744, 0.387960808, 0.28622393,
0.206047906, 0.145226539, 0.100490268,
0.068418865, 0.045920204, 0.030428616,
0.019933204, 0.01292337, 0.008300522,
0.005285872, 0.003339881, 0.002095204,
0.001305702, 0.000808731, 0.000498084,
0.000305145, 0.000186025)),
class = "data.frame", row.names = c(NA, -30L))
# best f ----
# type 1
best_f(m = 0.1, last_ofl = 15, data = oneone, type = 1)
#> [1] "Type 1 corresponding F_OFL = 0.20656413725415"
# type 2
best_f(m = 0.1, last_ofl = 15, data = onetwo, type = 2, f_ratio = 0.1)
#> [1] "Type 2 corresponding F_OFL = 0.203441686254522"
# type 3
best_f(m = 0.12, last_ofl = 15, data = twotwo, type = 3, m2 = 0.08)
#> [1] "Type 3 corresponding F_OFL = 0.084369061115298"
# type 4
best_f(m = 0.12, last_ofl = 15, data = twotwo, type = 4, f_ratio = 0.1, m2 = 0.08)
#> [1] "Type 4 corresponding F_OFL = 0.0870928631480975"Note the type 4 model produces the same output as the spreadsheet.