calculate best_f and load into abc

the function returns the same value at various levels of last_f but breaks if I pass the actual last_f (0.427) from the assessment. My guess is that was either a holdover or calculated incorrectly in 2020.

2024/safe/08-abc.Rmd

@@ -116,12 +116,9 @@ Competitors for FHS prey resources include other benthic foragers, like northern
 *Together, the most recent data available suggest there are no apparent ecosystem concerns, although predation pressure may be rising – level 1.*
 
 
-
-
-
 ### Fishery performance
 
-There is no ESP for this stock complex, but we note that the fishery has consistently caught only a small fraction of the ABC (average 16% over last 5 years). We did not examine CPUE trends nor spatial patterns of fishing. There are no changes in the duration of fishing openings. *Altogether, we see no cause for concern and give this consideration a level 1 as well.* 
+There is no ESP for this stock complex, but we note that the fishery has consistently caught only a small fraction of the ABC (averaging less than 20% over the last five years). We did not examine CPUE trends nor spatial patterns of fishing. There are no changes in the duration of fishing openings. *Altogether, we see no cause for concern and give this consideration a level 1 as well.* 
 
 ### Risk Table Summary and ABC recommendation
 
@@ -133,14 +130,25 @@ There is no ESP for this stock complex, but we note that the fishery has consist
 
 #### Overfishing 
 
+The stock is not being subjected to overfishing, because the catch in 2019 (15,858 t) is less than the 2019 OFL (80,918 t). 
+
+
 The official catch estimate for the most recent complete year (`r year-1`) is `r prettyNum(c0,big.mark=",")` t. This is less than the `r year-1` OFL of `r prettyNum(82699,big.mark=",")` t. *The stock is not subject to overfishing.*
 
 #### Overfished (Harvest Scenario 6)
 
+The results of scenarios 6 and 7 above indicate that the stock is not overfished and is not approaching an overfished condition. 
+
+
 The minimum stock size threshold (MSST) for POP is given by the $B_{35\%}$ which is `r prettyNum(b35_25,big.mark=",")` in `r year`. The estimated stock spawning biomass in `r year` is nearly double the MSST at `r prettyNum(ssb_25,big.mark=",")`. *The stock is not overfished*.
 
 #### Approaching Overfished (Harvest Scenario 7)
 
+With regard to assessing the current stock level, the expected stock size in the current year of scenario 6 is 146,307 t, which is higher than B35% (71,280 t), so the stock is not currently overfished. The expected spawning stock size in the year 2033 of scenario 7 (76,046 t) is greater than B35%; thus, the stock is not approaching an overfished condition.
+
+The F that would have produced a catch for `r year-1` equal to the `r year-1` OFL for `r year-1` (`r prettyNum(ofl_23_23, big.mark = ',')`) was `r f_best`.
+
+
 The mean estimated stock spawning biomass in `r year+2` is above the MSST. *The stock is not approaching an overfished state*.
 
 

2024/safe/bsai_fhs_2024.Rmd

@@ -100,7 +100,44 @@ c1 <- pcatch[1] # estimated catch in current year
 c2 <- pcatch[2] # proj catch year + 1
 c3 <- pcatch[3] # proj catch year + 2
 
+# Best F (for SARA files & stock status section) ----
+# Use this year's model to recalculate the F for the previous year 
+## that would have produced the specified OFL for the previous year
+# provide naa, waa, etc from terminal year of model - can rip from Proj
+projdat <- readLines(here::here(year,'model_runs','03b_projection', "projection_data.dat"))
+ages <- 1:as.numeric(unlist(strsplit(projdat[grep("number of ages", projdat)]," "))[1])
+naa_f <-  as.numeric(unlist(strsplit(projdat[29]," "))[ages])
+naa_m <- as.numeric(unlist(strsplit(projdat[30]," "))[ages])
+waa_f <- as.numeric(unlist(strsplit(projdat[23]," "))[ages])  ## fishery WAA
+waa_m <- as.numeric(unlist(strsplit(projdat[24]," "))[ages])
+saa_f <- waa_f <- as.numeric(unlist(strsplit(projdat[26]," "))[ages])
+saa_m <- waa_f <- as.numeric(unlist(strsplit(projdat[27]," "))[ages])
+mort <-  as.numeric(unlist(strsplit(projdat[14]," "))[ages])
+
+ofl_23_23 <- 48161 ## last model's OFL for last year (2023 model for 2023 https://www.npfmc.org/wp-content/PDFdocuments/SAFE/2023/GOAflathead.pdf)
+afscassess::best_f(data = data.frame(age = ages, 
+                                     naa1 = naa_f,
+                                     naa2 = naa_m,
+                                     waa1 = waa_f,
+                                     waa2 = waa_m,
+                                     saa1 = saa_f,
+                                     saa2 = saa_m),
+                   m = mort, m2 = mort,
+                   type = 3, ## two sex, one year
+                   f_ratio= NULL, ## only one gear
+                   last_ofl = ofl_23_23, 
+                   last_f = 0.1
+                   ) %>%
+  strsplit(., " ") %>%
+  unlist() %>%
+  last(.) %>%
+  as.numeric() %>%
+  round(.,3) -> f_best
 ```
+
+
+
+
 
 ```{r, child="01-summary.Rmd", eval = T}
 ```