update

2023_ASSESSMENT/DATA_EXPLORE/Cummulative_catch_plots.r

@@ -12,7 +12,7 @@ akfin = DBI::dbConnect(odbc::odbc(), "akfin",
 
 
 
-Plot_CUMMULATIVE<-function(species="'PCOD'",FMP_AREA="'BSAI'",subarea="'BS'",syear=2020)
+Plot_CUMMULATIVE<-function(species="'PCOD'",FMP_AREA="'BSAI'",subarea="'BS'",syear=2018)
  {
   library(RODBC)
   library(data.table)
@@ -47,9 +47,9 @@ Plot_CUMMULATIVE<-function(species="'PCOD'",FMP_AREA="'BSAI'",subarea="'BS'",sye
 
   grid<-data.table(expand.grid(YEAR=unique(W1$YEAR),FMP_GEAR=unique(W1$FMP_GEAR),WEEK=0:52))
 
-  grid2<-grid[YEAR==2021 & WEEK<42]
-  grid<-grid[YEAR<2021]
-  grid<-rbind(grid,grid2)
+  #grid2<-grid[YEAR==2021 & WEEK<42]
+  #grid<-grid[YEAR<2021]
+  #grid<-rbind(grid,grid2)
   W1=merge(W1,grid,all=T)
   W1[is.na(TONS)]$TONS<-0.00
 
@@ -62,12 +62,15 @@ Plot_CUMMULATIVE<-function(species="'PCOD'",FMP_AREA="'BSAI'",subarea="'BS'",sye
    }
 
   cYear<-year(Sys.time())
-
-  d<- ggplot(data=W1,aes(x=WEEK,y=CUM,color=factor(YEAR),shape=factor(YEAR)))
+  nc=(length(unique(W1$YEAR)))-1
+  okabe <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
+  colors=c(okabe[1:nc],'black')
+  shapes=c(as.numeric(14+(1:nc)),1)
+    d<- ggplot(data=W1,aes(x=WEEK,y=CUM,color=factor(YEAR),shape=factor(YEAR)))
   d<- d+geom_point()+geom_path(aes(group=YEAR))#+geom_line(data=W1[YEAR==cYear],color="black",size=1)
-  d<-d+facet_wrap(~FMP_GEAR,scale="free_y")
-  d<-d+theme_bw()+theme(axis.text.x = element_text(hjust=0, angle = 90))
-  d<-d+labs(title=paste0(species," Area = ",FMP_AREA," Subarea = ",subarea), y="Cummulative Catch (t)",color="Year")
+  d<-d+facet_wrap(~FMP_GEAR,scale="free_y")+scale_color_manual(values=colors)
+  d<-d+theme_bw()+theme(axis.text.x = element_text(hjust=0, angle = 90))+scale_shape_manual(values=shapes)
+  d<-d+labs(title=paste0(species," Area = ",FMP_AREA," Subarea = ",subarea), y="Cummulative Catch (t)",shape="Year",color="Year")
   print(d)
 }
 

2023_ASSESSMENT/Functions/DATA_EXPLORE/INSEASON_GRAPHS.R

@@ -131,7 +131,7 @@ windows()
   d<-d+geom_boxplot()
   d<-d+facet_wrap(~YEAR)
    d<-d+xlab("Year")+ylab("log(CPUE)")
-   d<-d+ggtitle(paste0("CPUE by month for BS longline"))
+   d<-d+ggtitle(paste0("CPUE by month for BS longline"))+theme_bw()
   d
   windows()
   d<-ggplot(data_10[GEAR%in% c("BOTTOM TRAWL")&NMFS_AREA%in%c(500:539)],aes(as.factor(MONTH),log(CPUE_W),group=as.factor(MONTH)))
@@ -331,7 +331,7 @@ data$FREQUENCY<-as.numeric(data$FREQUENCY)
 #data<-data[(data$WEIGHT/data$OFFICIAL_TOTAL_CATCH)>0.3]
 #dataL<-data[GEAR_TYPE%in%c(6,8)]
 
-x6<-untable(data[,c(1:23)],num=data$FREQUENCY)
+x6<-reshape::untable(data[,c(1:23)],num=data$FREQUENCY)
 
 #data<-rbind(dataT,dataL)
 #x6<-untable(data[,c(1:21)],num=data$FREQUENCY)
@@ -357,35 +357,35 @@ x6[NMFS_AREA%in%c(610)]$AREA<-"Western GOA"
 x6[NMFS_AREA%in%c(620,630)]$AREA<-"Central GOA"
 x6[NMFS_AREA%in%c(540:544)]$AREA<-"AI"
 
-d<-ggplot(x6[YEAR==2018], aes(x=LENGTH)) + geom_histogram(binwidth=1)
+d<-ggplot(x6[YEAR==2024], aes(x=LENGTH)) + geom_histogram(binwidth=1)
 d<-d+facet_wrap(~GEAR)
 
 
-d<-ggplot(x6[YEAR%in%c(2022,2023)&NMFS_AREA %in% c(500:539)&GEAR_TYPE%in%c(1,6,8)], aes(x=LENGTH,fill=factor(AREA),y=..density..)) + geom_histogram(binwidth=1)
+d<-ggplot(x6[YEAR%in%c(2023,2024)&NMFS_AREA %in% c(500:539)&GEAR_TYPE%in%c(1,6,8)], aes(x=LENGTH,fill=factor(AREA),y=..density..)) + geom_histogram(binwidth=1)
  d<-d+facet_wrap(YEAR~GEAR)
  d<-d+xlim(0,120)
  d<-d+xlab("Fork Length (cm)")
  d
 
 
 
-d<-ggplot(x6[YEAR%in%c(2022,2023)&NMFS_AREA %in% c(500:539)&GEAR_TYPE%in%c(8)&MONTH %in% c("01","02","03")], aes(x=LENGTH,fill=AREA,y=..density..)) + geom_histogram(binwidth=1)
+d<-ggplot(x6[YEAR%in%c(2023,2024)&NMFS_AREA %in% c(500:539)&GEAR_TYPE%in%c(8)&MONTH %in% c("01","02","03")], aes(x=LENGTH,fill=AREA,y=..density..)) + geom_histogram(binwidth=1)
  d<-d+facet_wrap(YEAR~AREA)
  d<-d+xlim(0,120)
  d<-d+xlab("Fork Length (cm)")
  d
 
 
 
-d<-ggplot(x6[YEAR%in%c(2022,2023)&NMFS_AREA %in% c(500:539)&GEAR_TYPE%in%c(1)&MONTH %in% c("01","02","03")], aes(x=LENGTH,fill=AREA,y=..density..)) + geom_histogram(binwidth=1, fill='purple')
+d<-ggplot(x6[YEAR%in%c(2023,2024)&NMFS_AREA %in% c(500:539)&GEAR_TYPE%in%c(1)&MONTH %in% c("01","02","03")], aes(x=LENGTH,fill=AREA,y=..density..)) + geom_histogram(binwidth=1, fill='purple')
  d<-d+facet_wrap(YEAR~AREA)
  d<-d+xlim(0,120)
  d<-d+xlab("Fork Length (cm)")
  d
 
 
 
-d<-ggplot(x6[YEAR%in%c(2022,2023)&NMFS_AREA %in% c(500:539)&GEAR_TYPE%in%c(1)&MONTH %in% c("01","02","03")], aes(x=LENGTH,fill=AREA,y=..density..)) + geom_histogram(binwidth=1)
+d<-ggplot(x6[YEAR%in%c(2023,2024)&NMFS_AREA %in% c(500:539)&GEAR_TYPE%in%c(1)&MONTH %in% c("01","02","03")], aes(x=LENGTH,fill=AREA,y=..density..)) + geom_histogram(binwidth=1)
  d<-d+facet_wrap(YEAR~AREA)
  d<-d+xlim(0,120)
  d<-d+xlab("Fork Length (cm)")

docs/2024_ASSESSMENT/Functions/DATA_EXPLORE/Catch_map.r

@@ -17,7 +17,7 @@ FROM
     INNER JOIN norpac.atl_lov_gear_type ON obsint.debriefed_haul.gear_type = norpac.atl_lov_gear_type.gear_type_code
 WHERE
     obsint.debriefed_spcomp.species = 202
-    AND obsint.debriefed_spcomp.year > 2019
+    AND obsint.debriefed_spcomp.year > 1990
     AND obsint.debriefed_haul.nmfs_area > 500
     AND obsint.debriefed_haul.nmfs_area < 539
     AND norpac.atl_lov_gear_type.geartype_form = 'H'"
@@ -30,7 +30,7 @@ WHERE
          dplyr::rename_all(toupper) %>%
         data.table()
 
-CATCH<-read.csv("CATCH.CSV")
+#CATCH<-read.csv("CATCH.CSV")
 CATCH<-data.table(CATCH)
 CATCH$CPUE=0
 CATCH[GEAR_TYPE%in%c(6,8)]$CPUE<-CATCH[GEAR_TYPE%in%c(6,8)]$EXTRAPOLATED_WEIGHT/CATCH[GEAR_TYPE%in%c(6,8)]$TOTAL_HOOKS_POTS
@@ -41,8 +41,159 @@ CATCH1<-data.frame(CATCH)
 
 make_idw_map_FSH(x = subset(CATCH1,YEAR==2021&GEAR_TYPE%in%c(1:4)), # Pass data as a data frame
              region = "bs.all", # Predefined bs.all area
-             set.breaks = "Jenks", # Gets Jenks breaks from classint::classIntervals()
+             set.breaks = c(0,5,10,15,30,900), # Gets Jenks breaks from classint::classIntervals()
              in.crs = "+proj=longlat", # Set input coordinate reference system
              out.crs = "EPSG:3338", # Set output coordinate reference system
              grid.cell = c(20000, 20000), # 20x20km grid
-             key.title = "test")
+             key.title = "test")
+
+
+
+
+
+
+
+
+  library(sf)
+library(raster)
+library(ggplot2)
+
+
+# Load the shapefile
+shapefile <- st_read("suvevey_area.shp")
+
+# Load your data
+
+# Load the shapefile
+shapefile <- st_read("suvevey_area.shp")
+akland<- st_read("akland2.shp")
+sgrid<-st_read("survey_grid.shp")
+
+grat<-st_read("graticule.shp")
+bathym<-st_read("bathymetry.shp")
+
+
+# Load your data
+data <- CATCH
+
+# Convert your data to a sf object
+data_sf <- st_as_sf(data, coords = c("LONGITUDE", "LATITUDE"), crs = 4326)
+
+# Create a grid of polygons with a resolution of 5 units (change this to your desired resolution)
+grid <- st_make_grid(shapefile, cellsize = 0.5, square = TRUE)
+
+
+grid<-sgrid
+# Convert the grid to a sf object
+grid_sf <- st_sf(geometry = st_sfc(grid))
+
+grid_sf<-st_as_sfc(geometry = st_sfc(grid))
+
+# Join the data to the grid
+grid_with_data <- st_join(grid_sf, data_sf)
+
+
+
+
+# Group by grid cell and sum the EXTRAPOLATED_WEIGHT
+grid_with_data <- grid_with_data %>%
+  group_by(geometry, YEAR) %>%
+  summarise(EXTRAPOLATED_WEIGHT = sum(EXTRAPOLATED_WEIGHT, na.rm = TRUE))
+
+
+
+grid_with_data1 <- subset(grid_with_data, !is.na(YEAR))
+grid_with_data1 <- subset(grid_with_data1, YEAR %in% c(2010,2017:2023))
+
+
+# Plot the data
+
+akland_cropped <- st_crop(akland, grid_sf)
+
+bathym_cropped <- st_crop(bathym, grid_sf)
+
+trans_power <- scales::trans_new(
+  name = "power",
+  transform = function(x) x^(1/5),
+  inverse = function(x) x^5
+)
+
+
+min_weight <- min(grid_with_data1$EXTRAPOLATED_WEIGHT/1000, na.rm = TRUE)
+max_weight <- max(grid_with_data1$EXTRAPOLATED_WEIGHT/1000, na.rm = TRUE)
+
+# Generate breaks at regular intervals across the range
+breaks <- round(seq(from = min_weight, to = max_weight, length.out = 5))
+
+ggplot(grid_with_data1) +
+  geom_sf(aes(fill = EXTRAPOLATED_WEIGHT/1000), color = NA) +
+  geom_sf(data = akland_cropped, fill = "gray50", color = NA) +
+  geom_sf(data = shapefile, fill=NA,color = 'gray20',linetype=1) +
+  scale_fill_gradient(low="light blue",high= "red", trans = "sqrt", breaks = breaks) +
+  theme_bw(base_size=20) +
+  facet_wrap(~YEAR,ncol=4)+labs(fill="Observed catch (t)")
+
+
+
+
+
+
+centroids <- st_centroid(grid_with_data1)
+
+
+# Define a function to calculate the weighted centroid of a group
+weighted_centroid_group <- function(data, weight) {
+  # Calculate the weighted coordinates
+  weighted_coords <- st_coordinates(data$geometry) * weight
+  # Calculate the total weight
+  total_weight <- sum(weight)
+  # Calculate the weighted centroid
+  centroid <- colSums(weighted_coords) / total_weight
+  # Convert the centroid to an sf point
+  centroid_sf <- st_as_sf(as.data.frame(t(centroid)), coords = c("X", "Y"), crs = st_crs(data$geometry))
+  return(centroid_sf)
+}
+
+# Group by YEAR and calculate the weighted centroid of each group
+centroids_by_year <- centroids %>%
+  group_by(YEAR) %>%
+  group_map(~ weighted_centroid_group(.x, .x$EXTRAPOLATED_WEIGHT))
+
+
+
+# Collapse the list into a single sf object
+centroids_by_year_sf <- do.call(rbind, centroids_by_year)
+years <- 1991:2023
+centroids_by_year_sf$YEAR<-years
+
+
+
+centroids_by_year_df <- as.data.frame(st_coordinates(centroids_by_year_sf))
+
+# Add the YEAR column
+centroids_by_year_df$YEAR <- centroids_by_year_sf$YEAR
+
+
+
+target_crs <- "+proj=utm +zone=2 +datum=WGS84 +units=m +no_defs"
+
+# Transform the CRS of the sf object
+centroids_by_year_sf_utm <- st_transform(centroids_by_year_sf, target_crs)
+
+# Convert the sf object to a data frame
+centroids_by_year_df_utm <- as.data.frame(st_coordinates(centroids_by_year_sf_utm))
+
+centroids_by_year_df_utm$YEAR <- centroids_by_year_sf_utm$YEAR
+
+
+
+
+ggplot(centroids_by_year_df_utm,aes(x=YEAR,y=Y/1000))+geom_point(size=2)+geom_line()+theme_bw(base_size=16)+labs(x='Year',y='Northings (km)')
+
+
+
+  ggplot(centroids_by_year_sf)+
+  geom_sf(aes(color = factor(YEAR))) +
+   geom_sf(data = akland_cropped, fill = "gray50", color = NA) +
+   geom_sf(data = shapefile, fill=NA,color = 'gray20',linetype=1) +
+   theme_bw()+labs(title='Fishery Center of Gravity',color='Year')

docs/2024_ASSESSMENT/NOVEMBER_MODELS/FIGURES/R4SS_FIGURES/MODEL23.1.0.d/plots/_SS_output.html

@@ -94,8 +94,8 @@ <h2><a name="Home">Home</h2>
 3.30.21.00;_safe;_compile_date:_Feb 10 2023;_Stock_Synthesis_by_Richard_Methot_(NOAA)_using_ADMB_13.1</p>
 
 <p><b>r4ss info:<br></b>
-Version: 1.47.0<br>Date: NULL<br>Built: R 4.2.2; ; 2023-03-02 02:48:27 UTC; windows<br>RemoteType: github<br>RemoteHost: api.github.com<br>RemoteRepo: r4ss<br>RemoteUsername: r4ss<br>RemotePkgRef: r4ss/r4ss<br>RemoteRef: HEAD<br>RemoteSha: 420b187f5d78ff696d39363c214a0ef16d99c462<br><p><b>Starting time of model:</b>
-Thu Oct 19 23:03:43 2023</p>
+Version: 1.49.2<br>Date: NULL<br>Built: R 4.3.2; ; 2024-07-12 16:02:11 UTC; windows<br>RemoteType: github<br>RemoteHost: api.github.com<br>RemoteRepo: r4ss<br>RemoteUsername: r4ss<br>RemoteRef: HEAD<br>RemoteSha: b6976cd6317d75dddbad9012ca788cd8777254eb<br><p><b>Starting time of model:</b>
+Tue Oct 8 10:00:00 2024</p>
 
 <p><b>Warnings (from file warnings.sso):</b></p>
 

docs/2024_ASSESSMENT/NOVEMBER_MODELS/FIGURES/R4SS_FIGURES/MODEL23.1.0.d/plots/_SS_output_AgeComp.html

@@ -95,12 +95,11 @@ <h2><a name="AgeComp">AgeComp</h2>
 <br><i><small>file: <a href='comp_agefit__aggregated_across_time.png'>comp_agefit__aggregated_across_time.png</a></small></i>
 <p align=left><a href='comp_agefit__multi-fleet_comparison.png'><img src='comp_agefit__multi-fleet_comparison.png' border=0 width=500 alt=''></a><br>Pearson residuals,  comparing across fleets <br> 
 Closed bubbles are positive residuals (observed > expected) and open bubbles are negative residuals (observed < expected).<br><i><small>file: <a href='comp_agefit__multi-fleet_comparison.png'>comp_agefit__multi-fleet_comparison.png</a></small></i>
-<p align=left><a href='comp_agefit_flt2mkt0_page1.png'><img src='comp_agefit_flt2mkt0_page1.png' border=0 width=500 alt=''></a><br>Age comps, whole catch, Survey (plot 1 of 2).<br><br>'N adj.' is the input sample size after data-weighting adjustment. N eff. is the calculated effective sample size used in the McAllister-Ianelli tuning method.<br><i><small>file: <a href='comp_agefit_flt2mkt0_page1.png'>comp_agefit_flt2mkt0_page1.png</a></small></i>
-<p align=left><a href='comp_agefit_flt2mkt0_page2.png'><img src='comp_agefit_flt2mkt0_page2.png' border=0 width=500 alt=''></a><br>Age comps, whole catch, Survey (plot 1 of 2).<br><br>'N adj.' is the input sample size after data-weighting adjustment. N eff. is the calculated effective sample size used in the McAllister-Ianelli tuning method. (plot 2 of 2)<br><i><small>file: <a href='comp_agefit_flt2mkt0_page2.png'>comp_agefit_flt2mkt0_page2.png</a></small></i>
-<p align=left><a href='comp_agefit_residsflt2mkt0_page2.png'><img src='comp_agefit_residsflt2mkt0_page2.png' border=0 width=500 alt=''></a><br>Pearson residuals, whole catch, Survey (max=2.54) (plot 2 of 2) <br> 
-Closed bubbles are positive residuals (observed > expected) and open bubbles are negative residuals (observed < expected).<br><i><small>file: <a href='comp_agefit_residsflt2mkt0_page2.png'>comp_agefit_residsflt2mkt0_page2.png</a></small></i>
-<p align=left><a href='comp_agefit_sampsize_flt2mkt0.png'><img src='comp_agefit_sampsize_flt2mkt0.png' border=0 width=500 alt=''></a><br>N-EffN comparison, Age comps, whole catch, Survey<br><i><small>file: <a href='comp_agefit_sampsize_flt2mkt0.png'>comp_agefit_sampsize_flt2mkt0.png</a></small></i>
-<p align=left><a href='comp_agefit_data_weighting_TA1.8_Survey.png'><img src='comp_agefit_data_weighting_TA1.8_Survey.png' border=0 width=500 alt=''></a><br>WARNING: this figure is based on multinomial likelihood and has not been updated to account for Dirichlet-Multinomial likelihood and the sample size adjustment associated with the estimated log(<i>&#920</i>) parameters.<br><br> Mean age for Survey with 95% confidence intervals based on current samples sizes.<br>Francis data weighting method TA1.8: thinner intervals (with capped ends) show result of further adjusting sample sizes based on suggested multiplier (with 95% interval) for age data from Survey:<br>0.9984 (0.7291-1.7331) <br><br>For more info, see<br> <blockquote>Francis, R.I.C.C. (2011). Data weighting in statistical fisheries stock assessment models. <i>Can. J. Fish. Aquat. Sci.</i> 68: 1124-1138.  <a href=https://doi.org/10.1139/f2011-025> https://doi.org/10.1139/f2011-025</a> </blockquote><br><i><small>file: <a href='comp_agefit_data_weighting_TA1.8_Survey.png'>comp_agefit_data_weighting_TA1.8_Survey.png</a></small></i>
-<p align=left><a href='comp_gstagefit_flt2mkt0.png'><img src='comp_gstagefit_flt2mkt0.png' border=0 width=500 alt=''></a><br>Excluded age comps, whole catch, Survey.<br><br>'N adj.' is the input sample size after data-weighting adjustment. N eff. is the calculated effective sample size used in the McAllister-Ianelli tuning method.<br><i><small>file: <a href='comp_gstagefit_flt2mkt0.png'>comp_gstagefit_flt2mkt0.png</a></small></i>
-<p align=left><a href='comp_gstagefit_residsflt2mkt0.png'><img src='comp_gstagefit_residsflt2mkt0.png' border=0 width=500 alt=''></a><br>Pearson residuals, whole catch, Survey (max=NA) <br> 
+<p align=left><a href='comp_agefit_flt2mkt0.png'><img src='comp_agefit_flt2mkt0.png' border=0 width=500 alt=''></a><br>Age comps, whole catch, survey.<br><br>'N adj.' is the input sample size after data-weighting adjustment. N eff. is the calculated effective sample size used in the McAllister-Ianelli tuning method.<br><i><small>file: <a href='comp_agefit_flt2mkt0.png'>comp_agefit_flt2mkt0.png</a></small></i>
+<p align=left><a href='comp_agefit_residsflt2mkt0.png'><img src='comp_agefit_residsflt2mkt0.png' border=0 width=500 alt=''></a><br>Pearson residuals, whole catch, survey (max=2.81) <br> 
+Closed bubbles are positive residuals (observed > expected) and open bubbles are negative residuals (observed < expected).<br><i><small>file: <a href='comp_agefit_residsflt2mkt0.png'>comp_agefit_residsflt2mkt0.png</a></small></i>
+<p align=left><a href='comp_agefit_sampsize_flt2mkt0.png'><img src='comp_agefit_sampsize_flt2mkt0.png' border=0 width=500 alt=''></a><br>N-EffN comparison, Age comps, whole catch, survey<br><i><small>file: <a href='comp_agefit_sampsize_flt2mkt0.png'>comp_agefit_sampsize_flt2mkt0.png</a></small></i>
+<p align=left><a href='comp_agefit_data_weighting_TA1.8_survey.png'><img src='comp_agefit_data_weighting_TA1.8_survey.png' border=0 width=500 alt=''></a><br>Mean age for survey with 95% confidence intervals based on current sample sizes.<br>Francis data weighting method TA1.8: thinner intervals (with capped ends) show result of further adjusting sample sizes based on suggested multiplier (with 95% interval) for age data from survey:<br>1.0016 (0.7204-1.7837) <br>For more info, see  <a href=https://doi.org/10.1139/f2011-025> Francis (2011)</a>.<br><i><small>file: <a href='comp_agefit_data_weighting_TA1.8_survey.png'>comp_agefit_data_weighting_TA1.8_survey.png</a></small></i>
+<p align=left><a href='comp_gstagefit_flt2mkt0.png'><img src='comp_gstagefit_flt2mkt0.png' border=0 width=500 alt=''></a><br>Excluded age comps, whole catch, survey.<br><br>'N adj.' is the input sample size after data-weighting adjustment. N eff. is the calculated effective sample size used in the McAllister-Ianelli tuning method.<br><i><small>file: <a href='comp_gstagefit_flt2mkt0.png'>comp_gstagefit_flt2mkt0.png</a></small></i>
+<p align=left><a href='comp_gstagefit_residsflt2mkt0.png'><img src='comp_gstagefit_residsflt2mkt0.png' border=0 width=500 alt=''></a><br>Pearson residuals, whole catch, survey (max=NA) <br> 
 Closed bubbles are positive residuals (observed > expected) and open bubbles are negative residuals (observed < expected).<br><i><small>file: <a href='comp_gstagefit_residsflt2mkt0.png'>comp_gstagefit_residsflt2mkt0.png</a></small></i>

docs/2024_ASSESSMENT/NOVEMBER_MODELS/FIGURES/R4SS_FIGURES/MODEL23.1.0.d/plots/_SS_output_Bio.html

@@ -106,5 +106,3 @@ <h2><a name="Bio">Bio</h2>
 <p align=left><a href='bio23_timevarygrowthcontour_sex1.png'><img src='bio23_timevarygrowthcontour_sex1.png' border=0 width=500 alt=''></a><br>Contour plot of time-varying growth<br><i><small>file: <a href='bio23_timevarygrowthcontour_sex1.png'>bio23_timevarygrowthcontour_sex1.png</a></small></i>
 <p align=left><a href='bio24_time-varying_L_at_Amin_Fem_GP_1.png'><img src='bio24_time-varying_L_at_Amin_Fem_GP_1.png' border=0 width=500 alt=''></a><br>Time-varying mortality and growth parameters<br><i><small>file: <a href='bio24_time-varying_L_at_Amin_Fem_GP_1.png'>bio24_time-varying_L_at_Amin_Fem_GP_1.png</a></small></i>
 <p align=left><a href='bio24_time-varying_Richards_Fem_GP_1.png'><img src='bio24_time-varying_Richards_Fem_GP_1.png' border=0 width=500 alt=''></a><br>Time-varying mortality and growth parameters<br><i><small>file: <a href='bio24_time-varying_Richards_Fem_GP_1.png'>bio24_time-varying_Richards_Fem_GP_1.png</a></small></i>
-<p align=left><a href='bio24_time-varying_AgeKeyParm2.png'><img src='bio24_time-varying_AgeKeyParm2.png' border=0 width=500 alt=''></a><br>Time-varying mortality and growth parameters<br><i><small>file: <a href='bio24_time-varying_AgeKeyParm2.png'>bio24_time-varying_AgeKeyParm2.png</a></small></i>
-<p align=left><a href='bio24_time-varying_AgeKeyParm3.png'><img src='bio24_time-varying_AgeKeyParm3.png' border=0 width=500 alt=''></a><br>Time-varying mortality and growth parameters<br><i><small>file: <a href='bio24_time-varying_AgeKeyParm3.png'>bio24_time-varying_AgeKeyParm3.png</a></small></i>