Testing atlantisom: generate standard survey length and age comps

Introduction

This page documents initial testing of the atlantisom package in development at https://github.com/r4atlantis/atlantisom using three different Atlantis output datasets. Development of atlantisom began at the 2015 Atlantis Summit in Honolulu, Hawaii, USA.

The purpose of atlantisom is to use existing Atlantis model output to generate input datasets for a variety of models, so that the performance of these models can be evaluated against known (simulated) ecosystem dynamics. Atlantis models can be run using different climate forcing, fishing, and other scenarios. Users of atlantisom will be able to specify fishery independent and fishery dependent sampling in space and time, as well as species-specific catchability, selectivty, and other observation processes for any Atlantis scenario. Internally consistent multispecies and ecosystem datasets with known observation error characteristics will be the atlantisom outputs, for use in individual model performance testing, comparing performance of alternative models, and performance testing of model ensembles against “true” Atlantis outputs.

Initial testing was conducted by S. Gaichas using R scripts in the R folder of this repository that are titled “PoseidonTest_[whatwastested].R”. Initial tests are expanded and documented in more detail in these pages. C. Stawitz improved and streamlined the setup and intialization sections.

Setup

First, you will want to set up libraries and install atlantisom if you haven’t already. This document assumes atlantisom is already installed. For complete setup and initialization, please see TrueBioTest.

This document is written in in R Markdown (Allaire et al. 2019), and we use several packages to produce the outputs (Wickham and Henry 2019; Wickham et al. 2019; Wickham et al. 2018; Müller 2017; Pedersen 2019; Arnold 2019).

library(tidyr)
require(dplyr)
library(ggplot2)
library(data.table)
library(here)
library(ggforce)
library(ggthemes)
library(atlantisom)

Initialize input files and directories, read in “truth”

Abbreviated here; for a full explanation please see TrueBioTest. This document assumes that atlantisom::run_truth has already completed and stored an .RData file in the atlantis output model directory.

initCCA <- FALSE
initNEUS <- TRUE
initNOBA <- FALSE

if(initCCA){
  d.name <- here("atlantisoutput","CalCurrentSummitScenario1")
  functional.groups.file <- "CalCurrentV3Groups.csv"
  biomass.pools.file <- "DIVCalCurrentV3_BIOL.nc"
  biol.prm.file <- "CalCurrentV3_Biol.prm"
  box.file <- "CalCurrentV3_utm.bgm"
  initial.conditions.file <- "DIVCalCurrentV3_BIOL.nc"
  run.prm.file <- "CalCurrentV3_run.xml"
  scenario.name <- "CCV3"
}

if(initNEUS){
  d.name <- here("atlantisoutput","NEUStest20160303")
  functional.groups.file <- "NeusGroups.csv" 
  biomass.pools.file <- ""
  biol.prm.file <- "at_biol_neus_v15_DE.prm"
  box.file <- "neus30_2006.bgm"
  initial.conditions.file <- "inneus_2012.nc"
  run.prm.file <- "at_run_neus_v15_DE.xml"
  scenario.name <- "neusDynEffort_Test1_"
}

if(initNOBA){
  d.name <- here("atlantisoutput","NOBACERESGlobalSustainability")
  functional.groups.file <- "nordic_groups_v04.csv" 
  biomass.pools.file <- "nordic_biol_v23.nc"
  biol.prm.file <- "nordic_biol_incl_harv_v_007_3.prm"
  box.file <- "Nordic02.bgm"
  initial.conditions.file <- "nordic_biol_v23.nc"
  run.prm.file <- "nordic_run_v01.xml"
  scenario.name <- "nordic_runresults_01"
}
# NOBA note: output filenames in CCA and NEUS begin with "output" and the run_truth function is written to expect this. Need to check if default Atlantis output file nomenclature has changed or if NOBA is a special case. For now, NOBA filenames have been changed to include prefix "output"

#Load functional groups
funct.groups <- load_fgs(dir=d.name,
                         file_fgs = functional.groups.file)
#Get just the names of active functional groups
funct.group.names <- funct.groups %>% 
  filter(IsTurnedOn == 1) %>%
  select(Name) %>%
  .$Name

if(initCCA) {
  d.name <- here("atlantisoutput","CalCurrentSummitScenario1")
  truth.file <- "outputCCV3run_truth.RData"
  load(file.path(d.name, truth.file))
  CCAresults <- result
} 

if(initNEUS) {
  d.name <- here("atlantisoutput","NEUStest20160303")
  truth.file <- "outputneusDynEffort_Test1_run_truth.RData" 
  load(file.path(d.name, truth.file))
  NEUSresults <- result
}

if(initNOBA){
  d.name <- here("atlantisoutput","NOBACERESGlobalSustainability")
  truth.file <- "outputnordic_runresults_01run_truth.RData" 
  load(file.path(d.name, truth.file))
  NOBAresults <- result
}

Simulate a survey part 3: sample for length and age composition

Here we will compare the effects of survey timing, spatial coverage, and survey selectivity on the sampled (cohort) age comps. This section uses the atlantisom::create_survey() and atlantisom::sample_fish() to get a biological sample dataset. From that dataset, several other functions can be run:

• age samples from sample_ages
• length samples from sample_lengths (TO BE WRITTEN)
• weight samples from sample_weights (TO BE WRITTEN)
• diet samples from sample_diet

To create a survey, the user specifies the timing of the survey, which species are captured, the spatial coverage of the survey, the species-specific survey efficiency (“q”), and the selectivity at age for each species.

The following settings should achieve a survey that samples all Atlantis model output timesteps, all fish and shark species, and all model polygons, with perfect efficiency and full selectivity for all ages:

# should return a perfectly scaled survey 
effic1 <- data.frame(species=funct.group.names,
                     efficiency=rep(1.0,length(funct.group.names)))

# should return all lengths fully sampled (Atlantis output is 10 age groups per spp)
# BUT CHECK if newer Atlantis models can do age-specific outputs
selex1 <- data.frame(species=rep(funct.group.names, each=10),
                     agecl=rep(c(1:10),length(funct.group.names)),
                     selex=rep(1.0,length(funct.group.names)*10))

# should return all model areas
boxpars <- load_box(d.name, box.file)
boxall <- c(0:(boxpars$nbox - 1))

# these are model specific, generalized above
# if(initCCA) boxall <- c(0:88) 
# if(initNEUS) boxall <- c(0:29)
# if(initNOBA) boxall <- c(0:59) 

# should return all model output timesteps; need to generalize
# if(initCCA) timeall <- c(0:100) 
# if(initNEUS) timeall <- c(0:251)
# if(initNOBA) timeall <- c(0:560) 

# generalized
runpar <- load_runprm(d.name, run.prm.file)
noutsteps <- runpar$tstop/runpar$outputstep
stepperyr <- if(runpar$outputstepunit=="days") 365/runpar$toutinc

timeall <- c(0:noutsteps)
  
# define set of species we expect surveys to sample (e.g. fish only? vertebrates?)
# for ecosystem indicator work test all species, e.g.
survspp <- funct.group.names 

# to keep plots simpler, currently hardcoded for vertebrate/fished invert groups
# if(initCCA) survspp <- funct.group.names[c(1:44, 59:61, 65:68)] 
# if(initNEUS) survspp <- funct.group.names[1:21]
# if(initNOBA) survspp <- funct.group.names[1:36]

# for length and age groups lets just do fish and sharks
# NOBA model has InvertType, changed to GroupType in file, but check Atlantis default
if(initNOBA) funct.groups <- rename(funct.groups, GroupType = InvertType)

survspp <- funct.groups$Name[funct.groups$IsTurnedOn==1 &
                           funct.groups$GroupType %in% c("FISH", "SHARK")]

#if(initCCA) survspp <- survspp[!survspp %in% "Pisciv_T_Fish"]

Here we use create_survey on the numbers output of run_truth to create the survey census of age and length composition.

# this uses result$nums, but we are not creating a biomass index this time, so we don't need a weight at age conversion

if(initCCA) datN <- CCAresults$nums
if(initNEUS) datN <- NEUSresults$nums
if(initNOBA) datN <- NOBAresults$nums

survey_testNall <- create_survey(dat = datN,
                                 time = timeall,
                                 species = survspp,
                                 boxes = boxall,
                                 effic = effic1,
                                 selex = selex1)

# consider saving this interim step if it takes a long time go generate

Next, get true annual (cohort) age comp from this census survey based on run truth. (is there a standard Atlantis output I can compare this to as we did for biomass?)

# what is true composition? need annual by species, use code from sample_fish
# do tidyly
dat2 <- survey_testNall %>%
  group_by(species, agecl, time) %>%
  summarize(numAtAge = sum(atoutput))

#dat<-survey_testNall
#dat2 <- aggregate(dat$atoutput,list(dat$species,dat$agecl,dat$time),sum)
#names(dat2) <- c("species","agecl","time","numAtAge")

totN <- dat2 %>%
  group_by(species, time) %>%
  summarize(totN = sum(numAtAge))

#totN <- aggregate(dat2$numAtAge,list(dat2$species,dat2$time),sum )
#names(totN) <- c("species","time","totN")

dat2totN <- merge(dat2, totN)

# ageclcomp <- ggplot(dat2totN, aes(x=agecl, y=numAtAge/totN, col=time)) +
#   geom_point()
# 
# ageclcomp + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 1, scales="free")
# ageclcomp + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 2, scales="free")
# ageclcomp + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 3, scales="free")
# ageclcomp + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 4, scales="free")
# ageclcomp + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 5, scales="free")

We know from the previous test that the sample_fish function with the maximum possible effective sample size compares favorably (or perfectly) to true annual age comp calculated above as a test. Now we examine the effects of changing survey timing, survey area, survey selectivity, and fish sample size on age comps.

The standard survey approach is described here. First, we map species into general groups for each model:

# make defaults that return a standard survey, implement in standard_survey
# users need to map their species groups into these general ones
#   large pelagics/reef associated/burrowers/otherwise non-trawlable
#   pelagics
#   demersals
#   selected flatfish

if(initCCA) { #Sarah's CCA Grouping
  nontrawl <- c("Shark_C","Yelloweye_rockfish","Benthopel_Fish","Pisciv_S_Fish",
                "Pisciv_T_Fish","Shark_D","Shark_P")
  pelagics <- c("Pisciv_V_Fish","Demersal_S_Fish","Pacific_Ocean_Perch","Mesopel_M_Fish",
                "Planktiv_L_Fish","Jack_mackerel","Planktiv_S_Fish","Pacific_sardine",
                "Anchovy","Herring","Pisciv_B_Fish")
  demersals <- c("Demersal_P_Fish","Planktiv_O_Fish","Demersal_D_Fish",
                 "Demersal_DC_Fish","Demersal_O_Fish","Darkblotched_rockfish",
                 "Demersal_F_Fish","Demersal_E_Fish","Bocaccio_rockfish",
                 "Demersal_B_Fish","Shark_R","Mesopel_N_Fish","Shark_B","Spiny_dogfish",
                 "SkateRay")
  selflats <- c("Pisciv_D_Fish", "Arrowtooth_flounder","Petrale_sole")
}

if(initNEUS) { # Sarah's NEUS Grouping
  nontrawl <- c("Pisciv_T_Fish", "Shark_D", "Shark_P", "Reptile", "Mesopel_M_Fish")
  pelagics <- c("Planktiv_L_Fish", "Planktiv_S_Fish", "Benthopel_Fish", "Pisciv_S_Fish")
  demersals <- c("Pisciv_D_Fish", "Demersal_D_Fish","Demersal_E_Fish", 
                 "Demersal_S_Fish","Demersal_B_Fish","Demersal_DC_Fish",
                 "Demersal_O_Fish","Demersal_F_Fish",
                 "Shark_B", "SkateRay")
  selflats <- c("Pisciv_B_Fish")
}

if(initNOBA) { # Sarah's NOBA Grouping
  nontrawl <- c("Sharks_other", "Pelagic_large","Mesop_fish")
  pelagics <- c("Pelagic_small","Redfish_other","Mackerel","Haddock",
                "Saithe","Redfish","Blue_whiting","Norwegian_ssh","Capelin")
  demersals <- c("Demersals_other","Demersal_large","Flatfish_other","Skates_rays",
                 "Green_halibut","North_atl_cod","Polar_cod","Snow_crab")
  selflats <- c("Long_rough_dab")
}

We use the following specifications for our default standard bottom trawl survey, including survey cv by species group:

# general specifications for bottom trawl survey, with items defined above commented out to avoid wasting time loading already loaded files:
#   once per year at mid year
#   could generalize from the run.prm file: 
# runpar <- load_runprm(d.name, run.prm.file)
# noutsteps <- runpar$tstop/runpar$outputstep
# stepperyr <- if(runpar$outputstepunit=="days") 365/runpar$toutinc
#   take midpoint of 0, steps per year to start seq and go to max time by steps per year
midptyr <- round(median(seq(1,stepperyr)))

annualmidyear <- seq(midptyr, noutsteps, stepperyr)

#   ~75-80% of boxes (leave off deeper boxes?)
# boxpars <- load_box(d.name, box.file)
boxsurv <- c(2:round(0.8*(boxpars$nbox - 1)))

#   define bottom trawl mixed efficiency
ef.nt <- 0.01 # for large pelagics, reef dwellers, others not in trawlable habitat
ef.pl <- 0.1  # for pelagics
ef.dm <- 0.7  # for demersals
ef.fl <- 1.1  # for selected flatfish

# bottom trawl survey efficiency specification by species group
effnontrawl <- data.frame(species=nontrawl, efficiency=rep(ef.nt,length(nontrawl)))
effpelagics <- data.frame(species=pelagics, efficiency=rep(ef.pl,length(pelagics)))
effdemersals <- data.frame(species=demersals, efficiency=rep(ef.dm,length(demersals)))
effselflats <- data.frame(species=selflats, efficiency=rep(ef.fl,length(selflats)))

efficmix <- bind_rows(effnontrawl, effpelagics, effdemersals, effselflats)

#   mixed selectivity (using 10 agecl for all species)
#     flat=1 for large pelagics, reef dwellers, others not in trawlable habitat
#     sigmoid 0 to 1 with 0.5 inflection at agecl 3 for pelagics, reaching 1 at agecl 5, flat top
#     sigmoid 0 to 1 with 0.5 inflection at agecl 5 for most demersals and flatfish, reaching 1 at agecl 7, flat top
#     dome shaped 0 to 1 at agecl 6&7 for selected demersals, falling off to 0.7 by agecl 10

sigmoid <- function(a,b,x) {
  1 / (1 + exp(-a-b*x))
}

# survey selectivity specification by species group
selnontrawl <- data.frame(species=rep(nontrawl, each=10),
                          agecl=rep(c(1:10),length(nontrawl)),
                          selex=rep(1.0,length(nontrawl)*10))
selpelagics <- data.frame(species=rep(pelagics, each=10),
                          agecl=rep(c(1:10),length(pelagics)),
                          selex=sigmoid(5,1,seq(-10,10,length.out=10)))
seldemersals <- data.frame(species=rep(demersals, each=10),
                          agecl=rep(c(1:10),length(demersals)),
                          selex=sigmoid(1,1,seq(-10,10,length.out=10)))
selselflats <- data.frame(species=rep(selflats, each=10),
                          agecl=rep(c(1:10),length(selflats)),
                          selex=sigmoid(1,1,seq(-10,10,length.out=10)))

selexmix <- bind_rows(selnontrawl, selpelagics, seldemersals, selselflats)

# use this constant 0 cv for testing
surv_cv_0 <- data.frame(species=survspp, cv=rep(0.0,length(survspp)))

#   define bottom trawl survey cv by group
cv.nt <- 1.0 # for large pelagics, reef dwellers, others not in trawlable habitat
cv.pl <- 0.5  # for pelagics
cv.dm <- 0.3  # for demersals
cv.fl <- 0.3  # for selected flatfish

# specify cv by species groups
surv_cv_nontrawl <- data.frame(species=nontrawl, cv=rep(cv.nt,length(nontrawl)))
surv_cv_pelagics <- data.frame(species=pelagics, cv=rep(cv.pl,length(pelagics)))
surv_cv_demersals <- data.frame(species=demersals, cv=rep(cv.dm,length(demersals)))
surv_cv_selflats <- data.frame(species=selflats, cv=rep(cv.fl,length(selflats)))

surv_cv_mix <- bind_rows(surv_cv_nontrawl, surv_cv_pelagics, surv_cv_demersals, surv_cv_selflats)

This generates the standard survey once per year, most areas, with mixed efficiency and selectivity.

# this uses result$nums, but we are not creating a biomass index this time, so we don't need a weight at age conversion

if(initCCA) datN <- CCAresults$nums
if(initNEUS) datN <- NEUSresults$nums
if(initNOBA) datN <- NOBAresults$nums

survey_testNstd <- create_survey(dat = datN,
                                 time = annualmidyear,
                                 species = survspp,
                                 boxes = boxsurv,
                                 effic = efficmix,
                                 selex = selexmix)

# consider saving this interim step if it takes a long time to generate

Now we sample fish with no error from the standard survey:

# setting the effN higher than actual numbers results in sampling all

# this one is high but not equal to total for numerous groups
effNhigh <- data.frame(species=survspp, effN=rep(1e+8, length(survspp)))

comptesthigh <- sample_fish(survey_testNstd, effNhigh)
names(comptesthigh) <- c("species","agecl","polygon", "layer","time","numAtAgesamp")

comptesttot <- aggregate(comptesthigh$numAtAgesamp,list(comptesthigh$species,comptesthigh$time),sum )
names(comptesttot) <- c("species","time","totsamp")

comptestprop <- merge(comptesthigh, comptesttot)

And compare this to the true age comp above to see the impact of only standard survey sampling on age comps:

# compare individual years, these proportions at age should not match
comparecomps <- ggplot() +
  geom_point(data=subset(dat2totN, time==min(annualmidyear)), aes(x=agecl, y=numAtAge/totN, color="true"), alpha = 0.3) +
  geom_point(data=subset(comptestprop, time==min(annualmidyear)), aes(x=agecl, y=numAtAgesamp/totsamp, color="samp"), alpha = 0.3) +
  theme_tufte() +
  theme(legend.position = "top") +
  labs(colour=paste0(scenario.name, " start"))

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 1, scales="free")

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 2, scales="free")

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 3, scales="free")

if(initCCA) comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 4, scales="free")
#comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 5, scales="free")


comparecomps <- ggplot() +
  geom_point(data=subset(dat2totN, time==annualmidyear[length(annualmidyear)/2]), aes(x=agecl, y=numAtAge/totN, color="true"), alpha = 0.3) +
  geom_point(data=subset(comptestprop, time==annualmidyear[length(annualmidyear)/2]), aes(x=agecl, y=numAtAgesamp/totsamp, color="samp"), alpha = 0.3)+
  theme_tufte() +
  theme(legend.position = "top") +
  labs(colour=paste0(scenario.name, " midpoint"))

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 1, scales="free")

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 2, scales="free")

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 3, scales="free")

if(initCCA) comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 4, scales="free")
#comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 5, scales="free")


comparecomps <- ggplot() +
  geom_point(data=subset(dat2totN, time==annualmidyear[length(annualmidyear)]), aes(x=agecl, y=numAtAge/totN, color="true"), alpha = 0.3) +
  geom_point(data=subset(comptestprop, time==annualmidyear[length(annualmidyear)]), aes(x=agecl, y=numAtAgesamp/totsamp, color="samp"), alpha = 0.3)+
  theme_tufte() +
  theme(legend.position = "top") +
  labs(colour=paste0(scenario.name, " end"))

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 1, scales="free")

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 2, scales="free")

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 3, scales="free")

if(initCCA) comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 4, scales="free")
#comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 5, scales="free")

Now we run sample_fish with a more realistic biological sample size for each species, to see the combined impact of the standard survey and biological sampling:

# sample_fish uses an effN effective sample size for multinomial
# but this should be the REAL sample size taken (annual average over whole area)
# sample size of fish for lengths--a fairly large number
# a proportion of this is used for ages below
# assign by groups as above

#   define n fish for biological sampling by group
#   this could easily be a vector or time series, constant here
ns.nt <- 25 # for large pelagics, reef dwellers, others not in trawlable habitat
ns.pl <- 1000  # for pelagics
ns.dm <- 1000  # for demersals
ns.fl <- 1000  # for selected flatfish

effNnontrawl <- data.frame(species=nontrawl, effN=rep(ns.nt,length(nontrawl)))
effNpelagics <- data.frame(species=pelagics, effN=rep(ns.pl,length(pelagics)))
effNdemersals <- data.frame(species=demersals, effN=rep(ns.dm,length(demersals)))
effNselflats <- data.frame(species=selflats, effN=rep(ns.fl,length(selflats)))

effNmix <- bind_rows(effNnontrawl, effNpelagics, effNdemersals, effNselflats)

comptestmix <- sample_fish(survey_testNstd, effNmix)
names(comptestmix) <- c("species","agecl","polygon", "layer","time","numAtAgesamp")

comptestmixtot <- aggregate(comptestmix$numAtAgesamp,list(comptesthigh$species,comptesthigh$time),sum )
names(comptestmixtot) <- c("species","time","totsamp")

comptestmixprop <- merge(comptestmix, comptestmixtot)

We can compare true (cohort) age comp (“true”), survey sampled age comp with no subsampling effect (“samphigh”), and age comp from biological subsamling aboard the survey (“sampreal”):

# compare individual years, these proportions at age wont match
comparecomps <- ggplot() +
  geom_point(data=subset(dat2totN, time==min(annualmidyear)), aes(x=agecl, y=numAtAge/totN, color="true"), alpha = 0.3) +
  geom_point(data=subset(comptestprop, time==min(annualmidyear)), aes(x=agecl, y=numAtAgesamp/totsamp, color="samphigh"), alpha = 0.3) +
  geom_point(data=subset(comptestmixprop, time==min(annualmidyear)), aes(x=agecl, y=numAtAgesamp/totsamp, color="sampreal"), alpha = 0.3) +
  theme_tufte() +
  theme(legend.position = "top") +
  labs(colour=paste0(scenario.name, " start"))

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 1, scales="free")

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 2, scales="free")

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 3, scales="free")

if(initCCA) comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 4, scales="free")
#comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 5, scales="free")

#ggsave("stdsurvcomp_realeffN_time0.png", width=11, height=11)

comparecomps <- ggplot() +
  geom_point(data=subset(dat2totN, time==annualmidyear[length(annualmidyear)/2]), aes(x=agecl, y=numAtAge/totN, color="true"), alpha = 0.3) +
  geom_point(data=subset(comptestprop, time==annualmidyear[length(annualmidyear)/2]), aes(x=agecl, y=numAtAgesamp/totsamp, color="samphigh"), alpha = 0.3) +
  geom_point(data=subset(comptestmixprop, time==annualmidyear[length(annualmidyear)/2]), aes(x=agecl, y=numAtAgesamp/totsamp, color="sampreal"), alpha = 0.3) +
  theme_tufte() +
  theme(legend.position = "top") +
  labs(colour=paste0(scenario.name, " midpoint"))

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 1, scales="free")

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 2, scales="free")

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 3, scales="free")

if(initCCA) comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 4, scales="free")
#comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 5, scales="free")

#ggsave("stdsurvcomp_realeffN_time100.png", width=11, height=11)

comparecomps <- ggplot() +
  geom_point(data=subset(dat2totN, time==annualmidyear[length(annualmidyear)]), aes(x=agecl, y=numAtAge/totN, color="true"), alpha = 0.3) +
  geom_point(data=subset(comptestprop, time==annualmidyear[length(annualmidyear)]), aes(x=agecl, y=numAtAgesamp/totsamp, color="samphigh"), alpha = 0.3) +
  geom_point(data=subset(comptestmixprop, time==annualmidyear[length(annualmidyear)]), aes(x=agecl, y=numAtAgesamp/totsamp, color="sampreal"), alpha = 0.3) +
  theme_tufte() +
  theme(legend.position = "top") +
  labs(colour=paste0(scenario.name, " end"))

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 1, scales="free")

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 2, scales="free")

comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 3, scales="free")

if(initCCA) comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 4, scales="free")
#comparecomps + facet_wrap_paginate(~species, ncol=3, nrow = 3, page = 5, scales="free")

#ggsave("stdsurvcomp_realeffN_time250.png", width=11, height=11)

With these biological sample sizes (~1000 fish per year in the whole area) the impact of biological subsampling is minimal compared with the impact of survey length selectivity. Smaller biological sample sizes (~25 fish per year for nontrawl category) show more difference from the survey sampled age comp.

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2019. Rmarkdown: Dynamic Documents for R. https://CRAN.R-project.org/package=rmarkdown.

Arnold, Jeffrey B. 2019. Ggthemes: Extra Themes, Scales and Geoms for ’Ggplot2’. https://CRAN.R-project.org/package=ggthemes.

Müller, Kirill. 2017. Here: A Simpler Way to Find Your Files. https://CRAN.R-project.org/package=here.

Pedersen, Thomas Lin. 2019. Ggforce: Accelerating ’Ggplot2’. https://CRAN.R-project.org/package=ggforce.

Wickham, Hadley, and Lionel Henry. 2019. Tidyr: Easily Tidy Data with ’Spread()’ and ’Gather()’ Functions. https://CRAN.R-project.org/package=tidyr.

Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, and Kara Woo. 2018. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.

Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2019. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.