edits based on issue #1197

beamer/srg/sensitivity/010-overview.rmd

@@ -3,13 +3,13 @@
 ## Overview
 
 1. Select sensitivity models:
-    - Alternative assumptions for steepness (*h*),
-    - Alternative standard deviations for recruitment ($\sigma_r$),
-    - Remove the age-1 index,
-    - Down-weight fishery age-composition data,
-    - Alternative standard deviations for time-varying selectivity ($\Phi$),
-    - Alternative prior distributions and parameterizations for natural mortality (*M*),
-    - Using the middle of the year to predict maturity-at-age, and
+    - Alternative assumptions for steepness (*h*);
+    - Alternative standard deviations for recruitment ($\sigma_r$);
+    - Remove the age-1 relative index;
+    - Down-weight fishery age-composition data;
+    - Alternative standard deviations for time-varying selectivity ($\Phi$);
+    - Alternative prior distributions and parameterizations for natural mortality (*M*);
+    - Using the middle of the year to predict maturity-at-age; and
     - Adding a relative index of abundance based on environmental-DNA.
 
 2. Retrospectives `r length(retrospective_yrs)` years into the past

beamer/srg/sensitivity/011-sensitivities-to-h-m-sigmar.rmd

@@ -29,7 +29,7 @@ plot_biomass(model_lst = list(sens_models[[1]][[1]],
 ```
 
 
-## Alternative s.d. of recruitment variability, $\sigma_r$; base `r sigma_r`
+## Alternative SD of recruitment variability, $\sigma_r$; base `r sigma_r`
 ```{r sigmar-biomass-fig, fig.height = 4, out.height  = "85%"}
 plot_biomass(model_lst = list(base_model,
                               sens_models[[1]][[4]],
@@ -52,7 +52,7 @@ plot_biomass(model_lst = list(base_model,
         legend.text = element_text(size = 10))
 ```
 
-## Alternative s.d. of recruitment variability, $\sigma_r$; base `r sigma_r`
+## Alternative SD of recruitment variability, $\sigma_r$; base `r sigma_r`
 ```{r sigmar-biomass-fig-rel, fig.height = 4, out.height = "85%"}
 plot_rel_biomass(model_lst = list(base_model,
                                   sens_models[[1]][[4]],

beamer/srg/sensitivity/012-removal-of-age1-index.rmd

@@ -1,6 +1,6 @@
 # Age-1 index
 
-## Removing the age-1 index
+## Removing the age-1 relative index
 ```{r age1-biomass-fig, fig.height = 4, out.height = "85%"}
 plot_biomass(model_lst = list(base_model,
                               sens_models[[2]][[2]]),
@@ -21,7 +21,7 @@ plot_biomass(model_lst = list(base_model,
         legend.text = element_text(size = 10))
 ```
 
-## Removing the age-1 index
+## Removing the age-1 relative index
 ```{r age1-rec-devs-fig, fig.height = 4, out.height = "85%"}
 plot_recdevs(model_lst = list(base_model,
                               sens_models[[2]][[2]]),

beamer/srg/sensitivity/013-weighting.rmd

@@ -5,7 +5,7 @@
 plot_biomass(model_lst = list(base_model,
                               sens_models[[2]][[3]]),
              model_names = c(base_model_name,
-                             sens_models_desc[[2]][2]),
+                             "Down-weight fishery comps"),
              x_expansion = 4,
              ylim = c(0, 5),
              y_breaks = seq(0, 5, 1),

beamer/srg/sensitivity/014-tv-selectivity-phi.rmd

@@ -32,7 +32,7 @@ plot_recdevs(d_obj = d_obj_sens_recdev[[3]],
                  tick_prop = 1.5,
                  vjust_x_labels = -0.25,
                  leg_font_size = 9,
-                 leg_pos = c(0.13, 0.80),
+                 leg_pos = c(0.13, 0.90),
                  inc_means = FALSE,
                  rev_colors = TRUE) +
   theme(axis.title.x = element_text(size = 12),
@@ -50,7 +50,7 @@ plot_survey_index_fits(base_model,
                        x_expansion = 2,
                        tick_prop = 1.5,
                        vjust_x_labels = -0.5,
-                       leg_pos = c(0.74, 0.25),
+                       leg_pos = c(0.74, 0.20),
                        rev_colors = TRUE,
                        leg_font_size = 6,
                        xlim = c(1995, end_yr - 1)) +

beamer/srg/sensitivity/015-m-at-age.rmd

@@ -1,6 +1,6 @@
 # M
 
-## Natural mortality, *M* (base has prior median 0.2, sd 0.1)
+## Natural mortality, *M* (base has prior median 0.2, SD 0.1)
 ```{r m-biomass-fig, fig.height = 4, out.height = "85%"}
 plot_biomass(model_lst = list(base_model,
                               sens_models[[1]][[6]],
@@ -30,7 +30,7 @@ plot_biomass(model_lst = list(base_model,
         legend.text = element_text(size = 10))
 ```
 
-## Natural mortality, *M* (base has prior median 0.2, sd 0.1)
+## Natural mortality, *M* (base has prior median 0.2, SD 0.1)
 ```{r m-biomass-fig-rel, fig.height = 4, out.height = "85%"}
 plot_rel_biomass(model_lst = list(base_model,
                                   sens_models[[1]][[6]],
@@ -63,7 +63,7 @@ plot_rel_biomass(model_lst = list(base_model,
         axis.ticks.length = unit(0.3, "cm"))
 ```
 
-## Natural mortality, *M* (base has prior median 0.2, sd 0.1)
+## Natural mortality, *M* (base has prior median 0.2, SD 0.1)
 ```{r m-par-table, results = "asis"}
 table_param_est(models = list(base_model,
                               sens_models[[1]][[6]],  # [[1]] is base model
@@ -87,7 +87,7 @@ table_param_est(models = list(base_model,
 ```
 
 
-## Natural mortality-at-age (0-1, 2, 3, 4, 5+)
+## Natural mortality at age (0-1, 2, 3, 4, 5+)
 
 
 ```{r matage-sensitivity-biomass-fig, fig.height = 4, out.height = "85%"}
@@ -118,7 +118,7 @@ plot_biomass(model_lst = list(base_model,
 ```
 
 
-## Natural mortality-at-age (0-1, 2, 3, 4, 5+)
+## Natural mortality at age (0-1, 2, 3, 4, 5+)
 
 ```{r matage-sensitivity-biomass-rel, fig.height = 4, out.height = "85%"}
 plot_rel_biomass(model_lst = list(base_model,

beamer/srg/sensitivity/016-maturity-at-age.rmd

@@ -1,6 +1,6 @@
 # Maturity
 
-## Maturity-at-age estimated using middle (182) of the year
+## Maturity at age estimated using middle (182) of the year
 
 ```{r mat-sensitivity-biomass-fig, fig.height = 4, out.height = "85%"}
 

beamer/srg/sensitivity/021-summary.rmd

@@ -9,8 +9,8 @@
   2010s and current increase in biomass due to 2020 and 2021 recruitment.
 * Retrospective squid plots show that since `r assess_yr - 11`, and
   based on medians of the recruitment deviations:
-  - the 2021 cohort is being estimated to be approximately as large as the 2014 cohort,
-  - the 2020 cohort continues to be estimated high, but less than 2014, 2016, and 2021, and
+  - the 2021 cohort is being estimated to be approximately as large as the 2014 cohort;
+  - the 2020 cohort continues to be estimated high, but less than 2014, 2016, and 2021; and
   - the 2022 cohort is estimated to be slightly less than average.
 * Current assessment is generally consistent with recent historical assessments but the 
   relative spawning biomass is less than the 2024 assessment.