From d53a844cdebed67f857a629063bc43ca23a5d00c Mon Sep 17 00:00:00 2001 From: Jim Ianelli Date: Mon, 9 Oct 2023 23:22:21 -0700 Subject: [PATCH] more dat-file work --- 2023_runs/README.md | 1 + 2023_runs/dat/README.md | 19 ++ 2023_runs/dat/comp.R | 23 ++ 2023_runs/dat/cov_2023.dat | 41 +++ 2023_runs/dat/cov_2023_orig.dat | 41 +++ 2023_runs/dat/pm_23.0.dat | 437 +++++++++++++++++++++++++++++ 2023_runs/dat/rescale_cov.R | 102 +++++++ 2023_runs/dat/selvar23.dat | 60 ++++ 2023_runs/dat/ssb23.csv | 57 ++++ 2023_runs/dat/temp_paul23.dat | 471 ++++++++++++++++++++++++++++++++ 2023_runs/dat/wtage2023.dat | 174 ++++++++++++ 2023_runs/sep23/pm.dat | 2 +- doc/references.bib | 111 ++++++++ 13 files changed, 1538 insertions(+), 1 deletion(-) create mode 100644 2023_runs/dat/comp.R create mode 100644 2023_runs/dat/cov_2023.dat create mode 100644 2023_runs/dat/cov_2023_orig.dat create mode 100644 2023_runs/dat/pm_23.0.dat create mode 100644 2023_runs/dat/rescale_cov.R create mode 100644 2023_runs/dat/selvar23.dat create mode 100644 2023_runs/dat/ssb23.csv create mode 100644 2023_runs/dat/temp_paul23.dat create mode 100644 2023_runs/dat/wtage2023.dat diff --git a/2023_runs/README.md b/2023_runs/README.md index ce7ec89..8e5db00 100644 --- a/2023_runs/README.md +++ b/2023_runs/README.md @@ -7,6 +7,7 @@ sep23 is the base selected change model from september which has: - spawning biomass weight-at-age set to the RE model estimates from fishery data for the A-season - the new AVO time series - modest process error in ATS selectivity variability + - Pete Hulson's publication on effective sample size for bottom-trawl survey data diff --git a/2023_runs/dat/README.md b/2023_runs/dat/README.md index 406b859..2e810d2 100644 --- a/2023_runs/dat/README.md +++ b/2023_runs/dat/README.md @@ -1,5 +1,24 @@ # Notes +## 2023 November assessment + +[x] Create fixed-effects fishery wt-age datafile "wtage2023.dat" (also done in script "wt.R" in WtAgeRE, but calls on specimen data from survey and sampler data from fishery) +[x] Generated random-effect SSB wt-at-age (done in script "wt.R" in WtAgeRE) +[x] Expand Paul Spencer's input files to work for 2023 (but placeholders only) +[ ] Put SSB wt-at-age into appropriate input datafile +[ ] Update fishery catch +[ ] Update fishery catch-at-age +[ ] Update survey cpue so that design-based estimator can be used from scratch +[ ] Update BTS design based estimates +[x] Update BTS Covariance matrix ("cov_2023.dat") +[ ] Add in Hulson's BTS sample size +[ ] Update AVO time series +[ ] Update ATS age composition for the 2022 data (previously used BTS ALK) + + +pm_22.dat: Full final dataset with + +## September work Changed "std_tune.dat" to "obs_err_init.dat" to accommodate adding in datafiles that have tuned stdevs (in directory "tune") Covariance matrix is derived from Stan's DD algorthm before VAST diff --git a/2023_runs/dat/comp.R b/2023_runs/dat/comp.R new file mode 100644 index 0000000..42095a8 --- /dev/null +++ b/2023_runs/dat/comp.R @@ -0,0 +1,23 @@ + +library(tidyverse) +library(ggplot2) +library(ggthemes) +library(ggridges) + +df <- data.frame(read.table("agew.dat")) +names(df) <- 1:15 +df$Year <- (1982:2018) +df.l <- gather(df,"Age","N",-Year) %>% mutate(Source="DB") +df2 <- data.frame(read.table("age1.dat",header=TRUE)) +# put the two in one dataframe +df <- rbind(df2,df.l) + +# Compute proportions +dfp <- df %>% group_by(Year,Source) %>% mutate(Age=as.numeric(Age),Proportion=N/sum(N)) %>% filter(Year<=1999) + +# Plot +ggplot(dfp,aes(x=Age,y=as.factor(Year),height = Proportion, fill=Source,color=Source)) + + geom_density_ridges2(stat = "identity",scale = 3, alpha = .1)+ + xlim(c(1,15)) + ylab("Year") + xlab("Age (years)") + scale_y_discrete(limits=rev(levels(as.factor(dfp$Year)))) + theme_few() + geom_density_ridges(stat = "binline", bins=10, scale = 0.95)+ + diff --git a/2023_runs/dat/cov_2023.dat b/2023_runs/dat/cov_2023.dat new file mode 100644 index 0000000..7d9020a --- /dev/null +++ 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0.696932986 0.808573285 0.934792241 0.983716243 1.110090232 0.896256915 1.619041974 1.289666455 +0.031532787 0.113172734 0.240187623 0.392892849 0.543011597 0.639746947 0.712191862 0.745851368 0.782381229 0.901469147 1.094850025 0.92357505 1.072474776 1.892101509 1.416936706 +0.033327848 0.133008776 0.25604884 0.39670786 0.563822385 0.679883647 0.805020762 0.936518401 1.006467236 1.034434591 1.142940509 1.09697609 1.508178288 1.440500871 1.309022423 +0.023417064 0.115008316 0.276888956 0.459929374 0.569257426 0.692991123 0.76798241 0.857368045 0.913345976 0.987011448 1.022179787 1.104971366 1.048272624 1.070253326 1.349568682 +0.019380752 0.101459822 0.244144755 0.378145671 0.526992229 0.652066615 0.763603851 0.846668019 0.933519833 0.971437494 1.001150919 1.149534691 1.211687201 1.281049807 1.179917849 +0.018495649 0.087193364 0.279247415 0.437187833 0.582488803 0.686632399 0.788236316 0.870999726 0.970100191 1.102708516 1.105671451 1.236948448 1.235486845 1.749460306 1.230626606 +0.022553568 0.083533376 0.21397106 0.406607915 0.575800608 0.689063242 0.805223493 0.981970849 0.968320224 1.262557586 1.247212431 1.246648968 1.389705798 1.638032697 1.246968301 +0.020319991 0.10850146 0.241958619 0.416450696 0.646619245 0.785332663 0.950143453 1.030621575 1.064024628 1.328355453 1.326541881 1.547037133 1.556585854 1.536816208 1.743715961 +0.031689084 0.117343148 0.221257593 0.441148338 0.565233181 0.72191307 0.936799438 1.336564857 1.574484153 1.622437222 1.692529159 1.895356839 1.926997647 1.941451524 1.96177442 +0.027062065 0.095919642 0.196687891 0.375678579 0.532483569 0.689808725 0.838139801 0.928383216 1.269596435 1.267111449 1.328308063 1.387740759 1.461337291 1.764743441 1.757660864 +0.025225422 0.134561038 0.22362502 0.394297251 0.547275951 0.694533734 0.762828456 0.997097865 1.142014088 1.26636429 1.444106539 1.711001125 1.903016304 1.794556846 1.776686924 +0.033300215 0.109915022 0.265899823 0.480980012 0.538858085 0.63233835 0.696644128 0.785593495 0.846709044 0.960479213 1.166773547 1.369473936 1.623201894 1.684791209 1.738218 +0.029279013 0.113887513 0.251122675 0.40643369 0.512022355 0.595795685 0.678600156 0.721862861 0.81782518 0.874899121 0.977607696 1.044707584 1.15193339 1.389053393 1.626173395 +0.029279013 0.113887513 0.251122675 0.40643369 0.512022355 0.595795685 0.678600156 0.721862861 0.81782518 0.874899121 0.977607696 1.044707584 1.15193339 1.389053393 1.626173395 +0.029279013 0.113887513 0.251122675 0.40643369 0.512022355 0.595795685 0.678600156 0.721862861 0.81782518 0.874899121 0.977607696 1.044707584 1.15193339 1.389053393 1.626173395 +#bottom_temp +0.801569974 1.308777926 0.887 0.963 0.660695373 1.363870605 0.937346639 1.263771226 1.036109015 1.218350075 0.770514845 1.31695717 0.565789349 0.646254864 1.447577778 1.095249208 1.475999401 0.149055698 0.870974168 1.049198637 1.431904869 1.629112326 1.467596046 1.499882273 0.651583608 0.644704453 0.407246675 0.439671593 0.439671593 0.439671593 0.439671593 0.439671593 0.439671593 0.439671593 0.4 0.4 0.4 0.4 0.4 0.4 +#n_age_err +1 +#age_err +0.994 0.00576 1.72e-14 0 0 0 0 0 0 0 0 0 0 0 0 +0.0176 0.965 0.0176 1.32e-10 0 0 0 0 0 0 0 0 0 0 0 +3.02e-08 0.0355 0.929 0.0355 3.02e-08 0 0 0 0 0 0 0 0 0 0 +1.38e-15 1.06e-06 0.057 0.886 0.057015091 1.06e-06 0 0 0 0 0 0 0 0 0 +4e-23 1.07e-12 1.25e-05 0.08 0.84 0.080011094 1.25e-05 1.07e-12 0 0 0 0 0 0 0 +2.6e-30 4.3e-19 1.28e-10 7.42e-05 0.103 0.794 0.102942042 7.42e-05 1.28e-10 0 0 0 0 0 0 +5.84e-37 2.15e-25 4.21e-16 4.5e-09 0.000281 0.125 0.75 0.124855101 0.000281265 4.5e-09 0 0 0 0 0 +4.98e-43 2.22e-31 1.21e-21 8.05e-14 6.83e-08 0.000784 0.145 0.708 0.145168358 0.000783758 6.83e-08 8.05e-14 0 0 0 +1.53e-48 5.72e-37 4.96e-27 1.01e-18 4.86e-12 5.73e-07 0.00176 0.164 0.669 0.163535451 0.001756272 5.73e-07 4.86e-12 0 0 +1.53e-53 3.84e-42 3.73e-32 1.42e-23 2.12e-16 1.27e-10 3.13e-06 0.00336 0.18 0.634 0.179774235 0.003356355 3.13e-06 1.27e-10 0 +4.45e-58 6.52e-47 5.64e-37 2.88e-28 8.78e-21 1.6e-14 1.78e-09 1.24e-05 0.00569 0.194 0.60093454 0.193827561 0.00569276 1.24e-05 1.78e-09 +3.32e-62 2.65e-51 1.75e-41 9.64e-33 4.43e-25 1.71e-18 5.57e-13 1.56e-08 3.86e-05 0.00881 0.206 0.570831873 0.205734376 0.008811112 3.86e-05 +5.73e-66 2.39e-55 1.1e-45 5.57e-37 3.13e-29 1.95e-22 1.36e-16 1.06e-11 9.5e-08 9.93e-05 0.0127 0.215603453 0.543204737 0.215603453 0.012794178 +2.08e-69 4.47e-59 1.34e-49 5.65e-41 3.33e-33 2.76e-26 3.22e-20 5.34e-15 1.26e-10 4.33e-07 0.00022 0.017275673 0.223589352 0.517828659 0.24108567 +1.46e-72 1.62e-62 3.07e-53 9.94e-45 5.52e-37 5.26e-30 8.63e-24 2.44e-18 1.2e-13 1.03e-09 1.57e-06 0.000433888 0.022448549 0.229871987 0.747244002 +#nlbins +50 +#olc_fsh +356.3017 191.3244 175.9986 272.3212 300.429 379.9534 1060.6808 1871.753 3406.311 8008.779 14866.69 28191.33 47829.85 75761.34 118991.21 160278.74 187798.99 195005.81 161925.57 120952.47 81832.51 58525.5 48529.8 53112.68 63932.6 85089.87 99836.23 108665.82 106007.88 95931.45 84187.29 68509.41 54431.39 41751.01 29627.829 23569.507 16344.513 12779.3747 8947.8269 6733.8239 5031.319 3361.0409 2788.0421 1906.3801 1438.2577 870.6747 733.1553 561.2517 392.6394 298.4975 +#age_len +1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0.99 0.00949 2.22e-05 2.22e-05 3.44e-07 8.89e-09 6.42e-10 3.83e-11 1.89e-12 7.67e-14 2.55e-15 1.11e-16 0 0 0 0 0 0 0 0 0 0 0 0 0 +0.18 0.234 0.271 0.196 0.0746 0.0241 0.011827654 0.005148318 0.001985807 0.000678747 0.000205576 5.52e-05 1.31e-05 2.76e-06 5.16e-07 9.45e-08 5.14e-09 6.64e-11 5.32e-13 2.66e-15 0 0 0 0 0 +0.00166 0.00913 0.0377 0.105 0.14 0.118 0.127 0.123 0.108 0.085342971 0.061163853 0.039625368 0.023206067 0.012285068 0.005878939 0.003161575 0.000799915 7.79e-05 5.1e-06 2.24e-07 6.61e-09 1.31e-10 1.73e-12 1.53e-14 0 +2.23e-06 3.45e-05 0.000382 0.00289 0.00901 0.0142 0.0258 0.0427 0.0641 0.0873 0.107985505 0.121217889 0.123525185 0.114269917 0.095961197 0.101110882 0.064927079 0.019725952 0.004103568 0.000584149 5.69e-05 3.78e-06 1.72e-07 5.32e-09 1.14e-10 +2.71e-08 5.22e-07 7.65e-06 8.2e-05 0.000357 0.000736 0.00172 0.00373 0.00747 0.0138 0.0236 0.037274811 0.054378943 0.073305192 0.09131201 0.160683414 0.217651952 0.166756652 0.0937213 0.038633431 0.011677831 0.002587688 0.000420211 5e-05 4.65e-06 +8.28e-10 1.75e-08 2.92e-07 3.73e-06 1.93e-05 4.59e-05 0.000123 0.00031 0.000728 0.0016 0.00327 0.00627 0.01122592 0.018785279 0.029377785 0.070216606 0.149284186 0.197599775 0.200435248 0.155803709 0.092806707 0.042358647 0.014811937 0.003967519 0.000958662 +5.3e-11 1.16e-09 2.06e-08 2.88e-07 1.64e-06 4.24e-06 1.23e-05 3.38e-05 8.71e-05 0.000212 0.000485 0.00104 0.00212 0.004052571 0.007295945 0.021165228 0.060018578 0.112301599 0.165884729 0.193449022 0.178104165 0.129456795 0.074285304 0.033649677 0.016336399 +1.11e-11 2.24e-10 3.81e-09 5.24e-08 2.99e-07 7.82e-07 2.32e-06 6.53e-06 1.74e-05 4.4e-05 0.000106 0.00024 0.000517 0.001058055 0.002051498 0.006626105 0.022217206 0.051214138 0.09572002 0.145060566 0.178255928 0.177620793 0.143515469 0.094026717 0.081699131 +1.41e-11 2.2e-10 3.03e-09 3.49e-08 1.76e-07 4.27e-07 1.2e-06 3.2e-06 8.19e-06 2e-05 4.67e-05 0.000104 0.000222 0.0004529 0.00088199 0.002899936 0.010186588 0.025417434 0.052999645 0.092356586 0.134501839 0.163705067 0.16652285 0.141567555 0.208101202 +2.43e-11 3.02e-10 3.43e-09 3.33e-08 1.49e-07 3.34e-07 8.81e-07 2.23e-06 5.43e-06 1.27e-05 2.86e-05 6.18e-05 0.000128 0.000257 0.000493 0.001607239 0.005672796 0.014542307 0.031884034 0.059789887 0.095897088 0.131556698 0.154367141 0.154929241 0.348763235 +5.09e-11 5.12e-10 4.87e-09 4.03e-08 1.61e-07 3.35e-07 8.31e-07 1.99e-06 4.6e-06 1.03e-05 2.22e-05 4.63e-05 9.31e-05 0.000181 0.00034 0.001083139 0.003753385 0.009593984 0.021360052 0.041422901 0.069971294 0.102954544 0.131953561 0.147315726 0.469890563 +1.18e-10 9.82e-10 7.95e-09 5.69e-08 2.04e-07 3.96e-07 9.28e-07 2.11e-06 4.64e-06 9.9e-06 2.05e-05 4.11e-05 7.98e-05 0.00015 0.000275 0.00085008 0.002856071 0.007155724 0.01584685 0.031020046 0.053673089 0.082089791 0.110979571 0.132623461 0.562320358 +2.89e-10 2.01e-09 1.41e-08 8.85e-08 2.88e-07 5.22e-07 1.16e-06 2.5e-06 5.26e-06 1.07e-05 2.13e-05 4.11e-05 7.72e-05 0.000141 0.00025 0.000748308 0.002422054 0.005891576 0.012820811 0.024959709 0.043471722 0.067736108 0.094423906 0.117758886 0.629215673 +2.36e-10 1.36e-09 8.28e-09 4.58e-08 1.36e-07 2.34e-07 4.97e-07 1.03e-06 2.09e-06 4.15e-06 8.02e-06 1.52e-05 2.8e-05 5.05e-05 8.89e-05 0.000265358 0.000866491 0.002164024 0.004921893 0.010194751 0.019230809 0.033036712 0.051686365 0.073643837 0.803790944 +#test +1234567 diff --git a/2023_runs/dat/rescale_cov.R b/2023_runs/dat/rescale_cov.R new file mode 100644 index 0000000..9ba843a --- /dev/null +++ b/2023_runs/dat/rescale_cov.R @@ -0,0 +1,102 @@ +radian +1 +install.packages("MBESS") +install.packages("GGally") +library(MBESS) +library(GGally) +#c1 <- as.matrix(read.table("dat/cov_biom.dat",header=F,as.is=T)) +#c1 <- as.matrix(read.table("cov22_orig.dat",header=F,as.is=T)) +#c1 <- as.matrix(read.csv("stan_cov_matrix_2022_db.csv",header=F,as.is=T)) +#c1 <- as.matrix(read.table("stan22.dat",header=F,as.is=T)) +c1 <- as.matrix(read.table("stan_2022_cov.dat",header=F,as.is=T)) +#c1 <- as.matrix(read.table("stan_cov_21.dat",header=F,as.is=T)) +#c1 <- as.matrix(read.table("stan_cov_21.dat",header=F,as.is=T)) +#c1 <- as.matrix(read.csv("varcov.csv",header=F,as.is=T)) +#c1 <- as.matrix(read.table("dat/cov_biom.dat",header=F,as.is=T)) +#c1 <- as.matrix(read.table("dat/rescaled_cov_2018.dat",header=F,as.is=T)) +#c1 <- as.matrix(read.table("dat/rescaled_cov_2018_fitsrv.dat",header=F,as.is=T)) +#c1 <- as.matrix(read.table("dat/thorCov.dat",header=F,as.is=T)) + +########################## +#c1 <- as.matrix(read.table("cov_biom19.dat",header=F,as.is=T)) +c1 +d1 <- sqrt(diag(c1)) +#d2 <- as.vector(d1/1e6) #kg +d2 <- as.vector(d1/1e3) +#d2 <- as.vector(d1) +c2 <- cov2cor(c1) +c3 <- sweep(sweep(c2, 1L, d2, "*"), 2L, d2, "*") +all.equal(c3,c1) +?write.table +q +c3[1:4,1:4 ] + +write.table(c3,file="cov_2022.dat",row.names=F,col.names=F) +########################## + + set up a model with recruitment set to 1, to generate yield per recruit and spawning biomass per recruit (spr). That just uses selectivity, + fishing rate, size-at-age, and maturity at age +- Calculate equilibrium recruitment using + +spr = 9.197e-7 +- based on that, I can get total yield (ypr times rinf) spawning biomss (spr * rinf), etc. +alpha = exp(-4.43) +beta = exp(-6.42) +ssb = seq(0,1000,100) +plot(ssb,alpha*ssb*exp(-beta*ssb)) + +# note here the 1e^ 6 is to reflect that the stock recruit function predicts millions of recruits. I could have easily folded that into alpha. +rinf = log(alpha * spr * 1E6) / (beta * spr) +rinf + + +c1 <- as.matrix(read.table("dat/thorCov.dat",header=F)) +df <- as.numeric(read.table("dat/vast_biom.dat",header=F,as.is=T)) +names(c1)= 1:38 +names(df)= 1:37 +m <- as.data.frame(mvrnorm(n = 1000, df, c1, tol = 1e-6, empirical = FALSE, EISPACK = FALSE)) +colnames(m) <- 1982:2018 +ggpairs(m,columns=3:7) +ggpairs(m,columns=33:37) +pm <- ggpairs(tips, 2:3) +data(tips, package = "reshape") +pm <- ggpairs(tips[, 1:3]) +p_(pm) +pm <- ggpairs(tips, 1:3, columnLabels = c("Total Bill", "Tip", "Sex")) +p_(pm) +d1 <- sqrt(diag(c1)) +d2 <- as.vector(d1/1000) +c2 <- cov2cor(c1) +c3 <- sweep(sweep(c2, 1L, d2, "*"), 2L, d2, "*") +all.equal(c3,c1) +write.table(c3,file="rescaled_cov_2019.dat") + +c1 <- as.matrix(read.csv("dat/thorCov_In.csv",header=F,as.is=T)) +c1 <- as.matrix(read.csv("Cov_Index.csv",header=F,as.is=T)) +d1 <- sqrt(diag(c1)) +d2 <- as.vector(d1/1000) +c2 <- cov2cor(c1) +c3 <- sweep(sweep(c2, 1L, d2, "*"), 2L, d2, "*") +all.equal(c3,c1) +write.table(c3,file="dat/thorCov.dat",row.names=F,col.names=F) +d2 <- as.vector(d1/1000) +n <- length(d2) +d2[n] <- d2[n]*.5 + +c2 <- cov2cor(c1) +c3 <- sweep(sweep(c2, 1L, d2, "*"), 2L, d2, "*") +all.equal(c3,c1) +write.table(c3,file="dat/rescaled_cov_2018_fitsrv.dat") + +c1 <- as.matrix(read.csv("varcov.csv",header=F,as.is=T)) +d1 <- sqrt(diag(c1)) +d2 <- as.vector(d1/1000) +n <- length(d2) +d2[n] <- d2[n]*.5 +c2 <- cov2cor(c1) +c3 <- sweep(sweep(c2, 1L, d2, "*"), 2L, d2, "*") +all.equal(c3,c1) +write.table(c3,file="dat/rescaled_cov_2018_fitsrv.dat") + +q() +n \ No newline at end of file diff --git a/2023_runs/dat/selvar23.dat b/2023_runs/dat/selvar23.dat new file mode 100644 index 0000000..febe50c --- /dev/null +++ b/2023_runs/dat/selvar23.dat @@ -0,0 +1,60 @@ +1964 0 0 0 +1965 0.5 0 0 +1966 0.5 0 0 +1967 0.5 0 0 +1968 0.5 0 0 +1969 0.5 0 0 +1970 0.5 0 0 +1971 0.5 0 0 +1972 0.5 0 0 +1973 0.5 0 0 +1974 0.5 0 0 +1975 0.5 0 0 +1976 0.5 0 0 +1977 0.5 0 0 +1978 0.5 0 0 +1979 0.5 0 0 +1980 0.5 0 0 +1981 0.5 0 0 +1982 0.5 0 0 +1983 0.5 0 0 +1984 0.5 0 0 +1985 0.5 0 0 +1986 0.5 0 0 +1987 0.5 0 0 +1988 0.5 0 0 +1989 0.5 0 0 +1990 0.5 0 0 +1991 0.5 0 0 +1992 0.5 0 0 +1993 0.5 0 0 +1994 0.5 0 0 +1995 0.5 0 0.138 +1996 0.5 0 0.138 +1997 0.5 0 0.138 +1998 0.5 0 0.138 +1999 0.5 0 0.138 +2000 0.5 0 0.138 +2001 0.5 0 0.138 +2002 0.5 0 0.138 +2003 0.5 0 0.138 +2004 0.5 0 0.138 +2005 0.5 0 0.138 +2006 0.5 0 0.138 +2007 0.5 0 0.138 +2008 0.5 0 0.138 +2009 0.5 0 0.138 +2010 0.5 0 0.138 +2011 0.5 0 0.138 +2012 0.5 0 0.138 +2013 0.5 0 0.138 +2014 0.5 0 0.138 +2015 0.5 0 0.138 +2016 0.5 0 0.138 +2017 0.5 0 0.138 +2018 0.5 0 0.138 +2019 1.9 0 0.138 +2020 1.9 0 0.138 +2021 0 0 0.138 +2022 0 0 0.138 +2023 0 0 0.138 diff --git a/2023_runs/dat/ssb23.csv b/2023_runs/dat/ssb23.csv new file mode 100644 index 0000000..c6b987d --- /dev/null +++ b/2023_runs/dat/ssb23.csv @@ -0,0 +1,57 @@ +year,3,4,5,6,7,8,9,10,11,12,13,14,15,source +1970,0.311072,0.427458,0.55009,0.674562,0.797415,0.916081,1.028772,1.134344,1.232157,1.321963,1.403799,1.477905,1.544656,Predicted +1971,0.307228,0.452313,0.576279,0.701144,0.82365,0.941422,1.052838,1.156889,1.253046,1.341142,1.421276,1.49373,1.558912,Predicted +1972,0.321568,0.433307,0.585158,0.711117,0.834228,0.952199,1.063499,1.167202,1.262849,1.350331,1.429793,1.501553,1.566041,Predicted +1973,0.313585,0.464989,0.584425,0.738544,0.862507,0.980459,1.091068,1.193594,1.287737,1.373516,1.451177,1.521113,1.58381,Predicted +1974,0.329852,0.457189,0.6163,0.738007,0.890127,1.008925,1.119505,1.221329,1.314283,1.398545,1.47449,1.542613,1.603475,Predicted +1975,0.298991,0.579313,0.720039,0.883094,1.001329,1.144476,1.250469,1.345787,1.430982,1.506773,1.573952,1.633328,1.685689,Predicted +1976,0.332728,0.441983,0.729979,0.872966,1.034031,1.147123,1.28293,1.380174,1.465961,1.541319,1.607317,1.664999,1.71534,Predicted +1977,0.294369,0.63373,0.759139,1.051895,1.190692,1.340931,1.438571,1.555963,1.633143,1.698221,1.752966,1.798972,1.837634,Predicted +1978,0.324975,0.425699,0.772108,0.899593,1.190521,1.324595,1.468092,1.557697,1.666336,1.73448,1.790565,1.836587,1.874294,Predicted +1979,0.32522,0.483367,0.592591,0.941505,1.066786,1.352017,1.477959,1.611767,1.690814,1.788555,1.845853,1.891417,1.92743,Predicted +1980,0.309436,0.567516,0.738667,0.851723,1.197264,1.31383,1.586623,1.697742,1.815398,1.877776,1.958924,2.000129,2.030383,Predicted +1981,0.29829,0.499527,0.767809,0.941965,1.052375,1.391079,1.497887,1.759051,1.857499,1.962077,2.011437,2.079959,2.109153,Predicted +1982,0.28816,0.424556,0.632569,0.902848,1.075247,1.181115,1.513338,1.612421,1.865168,1.954929,2.05086,2.091833,2.152377,Predicted +1983,0.338035,0.465312,0.611216,0.82203,1.089842,1.25587,1.352645,1.674029,1.761304,2.001863,2.079492,2.163657,2.193437,Predicted +1984,0.333252,0.4394,0.572117,0.719624,0.929027,1.193194,1.354018,1.444591,1.759219,1.839519,2.073137,2.144034,2.221794,Predicted +1985,0.369905,0.47498,0.588734,0.723692,0.869227,1.073532,1.330424,1.482577,1.563703,1.868579,1.939175,2.163378,2.22532,Predicted +1986,0.304231,0.439473,0.548282,0.663136,0.797126,0.940158,1.140893,1.393528,1.541044,1.617383,1.917496,1.98347,2.203279,Predicted +1987,0.349399,0.40203,0.542521,0.652877,0.76637,0.896842,1.034853,1.229605,1.475721,1.616508,1.68615,1.979767,2.039562,Predicted +1988,0.321568,0.430801,0.487801,0.62958,0.738802,0.849367,0.97566,1.108692,1.298017,1.538533,1.673746,1.737981,2.026454,Predicted +1989,0.275451,0.412535,0.52665,0.585088,0.725601,0.831551,0.937447,1.058175,1.185143,1.368209,1.602495,1.731666,1.790154,Predicted +1990,0.28947,0.422648,0.567631,0.684074,0.740464,0.875682,0.974076,1.070966,1.181882,1.298723,1.47171,1.696219,1.816089,Predicted +1991,0.303474,0.455308,0.597385,0.744991,0.859126,0.909551,1.036256,1.124504,1.21034,1.309847,1.415331,1.577302,1.791333,Predicted +1992,0.340274,0.393124,0.549769,0.693264,0.839622,0.950532,0.996355,1.117575,1.199848,1.279516,1.372883,1.472413,1.62872,Predicted +1993,0.392955,0.516439,0.578744,0.738174,0.879218,1.019239,1.121106,1.156151,1.265629,1.335782,1.403385,1.485052,1.57345,Predicted +1994,0.361138,0.534251,0.665318,0.729857,0.887321,1.023282,1.156051,1.249273,1.2749,1.374656,1.435133,1.493352,1.566091,Predicted +1995,0.316379,0.471433,0.650466,0.783277,0.846281,0.999777,1.130077,1.256098,1.341968,1.360007,1.45221,1.505361,1.556611,Predicted +1996,0.333901,0.390836,0.549886,0.730096,0.861871,0.922196,1.071871,1.197615,1.318673,1.399421,1.41236,1.499618,1.548064,Predicted +1997,0.354105,0.444891,0.507783,0.668588,0.847253,0.975036,1.029664,1.172548,1.290894,1.404315,1.477463,1.48303,1.563275,Predicted +1998,0.309331,0.400946,0.494246,0.557878,0.718031,0.895011,1.020389,1.072152,1.211914,1.327037,1.437251,1.507287,1.509895,Predicted +1999,0.329153,0.431508,0.529679,0.624911,0.686843,0.842601,1.01331,1.131213,1.174832,1.306188,1.412945,1.515044,1.57736,Predicted +2000,0.300434,0.408751,0.515378,0.614808,0.708932,0.768001,0.919673,1.085512,1.19811,1.236252,1.362158,1.463627,1.560697,Predicted +2001,0.337797,0.453002,0.569508,0.678547,0.775854,0.86449,0.915727,1.058065,1.213734,1.315836,1.34353,1.459302,1.551131,Predicted +2002,0.331896,0.44944,0.570636,0.688907,0.796393,0.889684,0.972589,1.016996,1.151892,1.29988,1.394336,1.414615,1.523333,Predicted +2003,0.345542,0.441977,0.565429,0.688366,0.805105,0.908631,0.996271,1.072442,1.109511,1.236834,1.377283,1.464428,1.477751,Predicted +2004,0.333214,0.498176,0.602803,0.728668,0.849481,0.96073,1.056421,1.134723,1.200719,1.227287,1.344158,1.474469,1.551969,Predicted +2005,0.277321,0.403093,0.571805,0.677536,0.802429,0.920728,1.02839,1.119806,1.19345,1.254639,1.276422,1.388651,1.514547,Predicted +2006,0.307204,0.443658,0.578357,0.749699,0.853116,0.972025,1.081786,1.179272,1.2596,1.3218,1.371598,1.382333,1.484051,Predicted +2007,0.306246,0.492451,0.638847,0.776474,0.945239,1.041992,1.151392,1.24982,1.334957,1.402541,1.452055,1.489549,1.488579,Predicted +2008,0.305784,0.457111,0.651412,0.800194,0.935722,1.09906,1.188069,1.288239,1.376611,1.451368,1.50862,1.548115,1.576076,Predicted +2009,0.351046,0.532902,0.696418,0.894309,1.03993,1.167289,1.318968,1.394083,1.479114,1.55186,1.611064,1.653231,1.678375,Predicted +2010,0.269194,0.446664,0.633652,0.79868,0.995241,1.137422,1.259873,1.405702,1.474443,1.552896,1.619094,1.671947,1.708072,Predicted +2011,0.246138,0.431514,0.617696,0.80725,0.970019,1.160741,1.29459,1.40711,1.54212,1.599693,1.66703,1.722446,1.765044,Predicted +2012,0.277195,0.394309,0.587637,0.776162,0.963654,1.121093,1.30421,1.428994,1.531637,1.656452,1.703878,1.761374,1.807428,Predicted +2013,0.344036,0.437337,0.563045,0.758905,0.945201,1.126933,1.276152,1.449471,1.563581,1.655206,1.769055,1.805845,1.853221,Predicted +2014,0.410493,0.488791,0.58986,0.717857,0.911703,1.092792,1.267094,1.407456,1.571127,1.675277,1.756989,1.861224,1.888867,Predicted +2015,0.373447,0.448184,0.528505,0.63017,0.757642,0.950132,1.129287,1.301282,1.439133,1.60021,1.701779,1.780988,1.882841,Predicted +2016,0.29962,0.434905,0.512939,0.594232,0.695042,0.820304,1.009639,1.185034,1.352933,1.486555,1.643423,1.74091,1.816236,Predicted +2017,0.335214,0.417055,0.558643,0.638534,0.718192,0.814778,0.934012,1.116163,1.283729,1.443549,1.569129,1.718197,1.808264,Predicted +2018,0.357598,0.430726,0.517694,0.660791,0.739354,0.815576,0.907259,1.02065,1.196434,1.357429,1.510708,1.629944,1.772977,Predicted +2019,0.433823,0.546216,0.629467,0.719417,0.85989,0.931668,0.998208,1.078352,1.179169,1.341976,1.490055,1.630805,1.738123,Predicted +2020,0.374715,0.48507,0.600214,0.684275,0.773512,0.912141,0.981288,1.044693,1.121421,1.218713,1.37801,1.522685,1.660197,Predicted +2021,0.302947,0.426239,0.539358,0.655317,0.738661,0.826045,0.962029,1.028024,1.087995,1.161178,1.254941,1.410816,1.552236,Predicted +2022,0.340108,0.446412,0.577403,0.692791,0.806753,0.884937,0.964956,1.092163,1.148595,1.198696,1.262054,1.346289,1.493099,Predicted +2023,0.319566,0.490613,0.604994,0.738365,0.851658,0.960207,1.030665,1.101476,1.218651,1.264729,1.304523,1.357884,1.432609,Predicted +2024,0.313754,0.449219,0.627224,0.743655,0.87522200000000006,0.983852,1.085745,1.148271,1.21044,1.318695,1.355893,1.387076,1.432245,Predicted +2025,0.313754,0.443407,0.58583,0.765885,0.880512,1.007415,1.10939,1.203351,1.257234,1.310483,1.409859,1.438446,1.461437,Predicted diff --git a/2023_runs/dat/temp_paul23.dat b/2023_runs/dat/temp_paul23.dat new file mode 100644 index 0000000..5255f7b --- /dev/null +++ b/2023_runs/dat/temp_paul23.dat @@ -0,0 +1,471 @@ +# Data file for SST temperature, Consumption estimates, and spatial distribution of pollock and ATF +# To be consistent with 2020 pollock model, extend data (where appropriate) by one year +# with PLACEHOLDER VALUES, TO BE FINALIZED LATER + +# Summer SST (1963 to 2019, from Kearny hindcast (B10K-K20_CORECFS)) +# Summer SST (1963 to 2020, from Kearny hindcast (B10K-K20_CORECFS)) +9.156797693 8.853836656 9.024034595 9.36530107 10.45591739 9.78034906 10.06023983 9.129846 8.293262 9.578705 8.92675 9.747115 9.103794 9.255449 10.710193 10.172035 10.442624 10.083121 10.634558 9.290167 10.595447 10.274514 9.532808 9.344515 10.528864 9.94015 10.438807 9.892196 10.311824 9.296852 10.526982 9.760453 9.896575 9.380796 10.160248 9.045305 8.650129 9.27298 9.346503 10.527505 10.942544 11.709321 10.916911 9.360558 9.718449 9.030152 8.813312 9.262602 8.343 7.988732 9.296986 11.891247 10.442589 12.086488 9.945973 10.195702 11.760843 11 11 11 +# the number of non-pollock predators +2 +# the number of pollock predators +0 +# the abundance of the predators (millions, rows 1 = atf< 40 cm, row 2 = atf >= 40 cm, col = years (1964-2020)) +279.942 263.156 248.838 237.309 232.838 237.939 259.565 319.413 615.746 650.992 575.300 455.462 314.884 198.877 131.237 145.531 270.296 480.287 531.046 522.431 708.743 759.786 781.147 1032.219 1238.928 1346.196 1324.263 1149.172 1002.239 832.082 718.024 662.320 767.787 800.677 850.433 1092.355 1248.308 1299.061 1423.407 1460.651 1425.633 1316.788 1306.568 1228.328 1106.883 1012.786 893.258 793.306 773.338 869.873 921.653 901.745 881.964 1655.333 1769.465 926.185 1000.00 1000 1000 1000 +145.033 143.486 140.828 137.303 133.016 128.134 123.090 118.431 114.785 113.121 115.291 130.455 152.629 179.090 186.558 170.024 141.671 113.012 92.419 83.924 93.712 119.353 146.077 167.474 184.421 209.929 239.879 276.093 328.538 380.777 408.841 410.752 391.786 365.755 339.683 322.376 318.110 323.410 337.077 363.318 399.442 435.097 465.278 491.439 505.163 505.291 498.146 486.771 467.316 440.347 410.543 383.923 367.593 366.432 374.673 359.419 350.00 350 350 350 +# number of strata for computing spatial predation +5 +# strata index numbers +1020 30 40 50 60 +# the number of ages which are preyed upon +3 +# the ages which are preyed upon +1 2 3 +# distribution of age 1 pollock across strata, by year (rows) +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.22824 0.12391 0.31353 0.00471 0.32962 +0.053100424 0.158232561 0.325286708 0.047911089 0.415469218 +0.234586014 0.061901024 0.377765234 0.001437464 0.324310264 +0.222415876 0.111479245 0.310030569 0.00589687 0.35017744 +0.62616141 0.028870861 0.218398435 0.00027064 0.126298653 +0.322226113 0.155598397 0.333470566 0.002567808 0.186137115 +0.14189301 0.182171439 0.550407479 0.003889663 0.121638408 +0.104494783 0.076364944 0.114525884 0.001599518 0.703014871 +0.252957721 0.091862578 0.36368667 0.000375566 0.291117465 +0.195587127 0.13797882 0.244961664 0.002537561 0.418934828 +0.165280753 0.138211547 0.171849032 0.000837337 0.523821331 +0.22230492 0.040090997 0.357601641 0.021623632 0.358378809 +0.366588618 0.132313984 0.300063928 0.000113162 0.200920309 +0.136529061 0.151661303 0.000207909 0.000679575 0.710922151 +0.060881844 0.052761352 0.348704186 0.00155437 0.536098249 +0.09201895 0.199737889 0.233568799 0.025799594 0.448874767 +0.19399862 0.088766153 0.152502674 0.005966179 0.558766373 +0.281245593 0.109503306 0.315533636 0.003085623 0.290631842 +0.112828397 0.081878268 0.1894678 0.004502393 0.611323142 +0.270027103 0.110326212 0.366656586 0.001219317 0.251770781 +0.094628635 0.501074398 0.149176419 0.010392225 0.244728323 +0.245357733 0.13745755 0.34008228 0.002600797 0.274501641 +0.113430177 0.284954433 0.350196217 0.000148719 0.251270453 +0.154450631 0.050585061 0.296142025 0.00018997 0.498632314 +0.179048403 0.204541417 0.380339558 0.003516477 0.232554146 +0.455424031 0.172642619 0.145135378 0.009106983 0.21769099 +0.338583519 0.065532272 0.436971399 0.001107411 0.157805399 +0.286552565 0.035122198 0.594686547 0 0.083638691 +0.27368364 0.093144485 0.385473529 0.001421851 0.246276495 +0.231762969 0.085295748 0.487559893 0.000218076 0.195163314 +0.311616246 0.087451369 0.337669323 0.000174441 0.263088621 +0.271123375 0.034596597 0.546187658 1.0806E-07 0.148092262 +0.209483325 0.188320063 0.257992218 0.012742092 0.331462301 +0.175331389 0.218520419 0.353748061 0.000681026 0.251719106 +0.135664925 0.084475836 0.193555381 0.00149351 0.584810348 +0.156065801 0.025450013 0.413044783 4.01895E-07 0.405439001 +0.102621158 0.094345255 0.494985051 0.000442148 0.307606388 +0.440216481 0.07045412 0.297775323 0.000566618 0.190987458 +0.442832046 0.164824028 0.178769807 0.002152572 0.211421548 +0.442832046 0.164824028 0.178769807 0.002152572 0.211421548 +0.442832046 0.164824028 0.178769807 0.002152572 0.211421548 +0.442832046 0.164824028 0.178769807 0.002152572 0.211421548 +0.442832046 0.164824028 0.178769807 0.002152572 0.211421548 +# distribution of age 2 pollock across strata, by year (rows) +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.04599 0.10336 0.30293 0.00980 0.53791 +0.02881309 0.026100692 0.27486357 0.008883133 0.661339515 +0.001670803 0.024294771 0.145040665 0.096416217 0.732577545 +0.007170161 0.055632629 0.17192726 0.022300045 0.742969905 +0.003501511 0.114715372 0.014086636 0.028238985 0.839457496 +0.003146285 0.158026989 0.182399023 0.016568736 0.639858967 +0.006486934 0.305647517 0.117642202 0.065170302 0.505053046 +0.001485011 0.012343982 0.008052232 0.002770485 0.97534829 +0.002179193 0.056004195 0.209177457 0.004095275 0.728543879 +0.021274573 0.014440196 0.050710658 0.000220602 0.913353971 +0.03424826 0.05917312 0.027412488 0.001864024 0.877302109 +0.066432982 0.092983416 0.117670633 0.038707346 0.684205623 +0.261670505 0.07910027 0.519954799 0.00133317 0.137941256 +0.01571181 0.11767071 0.479135128 0.000146135 0.387336216 +0.001148847 0.037402891 0.006461906 0.00109113 0.953895227 +0.214655793 0.128555937 0.275887434 0 0.380900836 +0.037622421 0.049695804 0.037788804 0.003790962 0.87110201 +0.046428545 0.124274691 0.300820216 0.002521737 0.525954811 +0.000167903 0.084037232 0.139405214 0.004069038 0.772320612 +0.009083841 0.124719368 0.467208359 0.000704418 0.398284014 +0.117985329 0.468674144 0.305997679 5.02956E-07 0.107342345 +0.006450615 0.08719783 0.387023215 0.014425274 0.504903065 +0.005315784 0.084671077 0.746907499 1.5479E-05 0.163090161 +0.007481356 0.106507677 0.576786574 0.012740504 0.296483888 +0.040380207 0.050145205 0.292329743 8.30823E-07 0.617144014 +0.029171015 0.017044656 0.39697345 0.000411108 0.556399771 +0.004775437 0.109906049 0.089670762 0.001099103 0.794548649 +0.054227833 0.083132788 0.235498125 0.011723091 0.615418163 +0.017972867 0.139963282 0.105536624 0.000291891 0.736235335 +0.001599218 0.234302402 0.539420635 0.000517448 0.224160297 +0.026484898 0.110331206 0.408045448 0.001498836 0.453639612 +0.06234121 0.141851333 0.492924627 0.004008342 0.298874489 +0.016100509 0.091991555 0.228911478 0.000132221 0.662864236 +0.030234072 0.096949167 0.688833842 0.016551853 0.167431066 +0.184717339 0.10113436 0.27992363 0.000343938 0.433880733 +0.011107496 0.072314606 0.37079782 0.001025677 0.5447544 +0.104116475 0.200319167 0.550917624 0.00577183 0.138874904 +0.21384358 0.029229159 0.489102749 0.003039815 0.264784696 +0.050580193 0.037112001 0.780272003 1.20664E-06 0.132034596 +0.050580193 0.037112001 0.780272003 1.20664E-06 0.132034596 +0.050580193 0.037112001 0.780272003 1.20664E-06 0.132034596 +0.050580193 0.037112001 0.780272003 1.20664E-06 0.132034596 +0.050580193 0.037112001 0.780272003 1.20664E-06 0.132034596 +# distribution of age 3 pollock across strata, by year (rows) +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.03000 0.21072 0.21861 0.03078 0.50988 +0.030530999 0.119957979 0.383492766 0.006729566 0.45928869 +5.13183E-07 0.060023623 0.225353739 0.055760871 0.658861253 +0.006254645 0.071861239 0.010276108 0.160238932 0.751369076 +0.000329313 0.183964125 0.009559363 0.303551438 0.502595761 +0.005774043 0.21094265 0.050790797 0.030034374 0.702458137 +0.00045265 0.278480162 0.088705885 0.068703786 0.563657517 +0.000538841 0.129314926 0.08298238 0.018069004 0.76909485 +0.000619991 0.026330741 0.107439003 0.014851236 0.850759028 +0.004061206 0.105452304 0.052717159 0.020890804 0.816878528 +0.007278566 0.10848901 0.011729493 0.003541759 0.868961172 +0.000120762 0.436748635 0.054369192 0.043185757 0.465575655 +0.031375366 0.64365643 0.097268853 0.086017096 0.141682256 +0.000333649 0.246982092 0.080600583 0.005773361 0.666310315 +0.001642774 0.080644862 0.159199276 7.53368E-05 0.758437751 +0.010942617 0.467284397 0.114114519 0 0.407658467 +0.002732981 0.157959901 0.457070832 0.014121985 0.368114302 +0.080707568 0.204257675 0.315839211 0.001314613 0.397880933 +8.67068E-05 0.041897407 0.059080678 0.008712399 0.89022281 +0.000398568 0.130853672 0.243264091 0.005476932 0.620006738 +0.047152425 0.272402771 0.252227088 0.001203874 0.427013842 +0.00334559 0.148262448 0.380017283 0.042337164 0.426037516 +0.000478283 0.376684798 0.341853988 0.024050686 0.256932245 +0.00069116 0.313727018 0.29792528 0.05582104 0.331835503 +0.003961498 0.095899679 0.126918487 1.06661E-06 0.77321927 +0.036683346 0.256101413 0.277391358 0.000357302 0.429466581 +0.001368989 0.127270315 0.04068109 0.001615673 0.829063934 +0.03789455 0.23207458 0.382386998 0.092994218 0.254649654 +0.000411879 0.036172256 0.017544171 0.011767083 0.934104612 +0.000999617 0.395679579 0.07197461 0.001428377 0.529917818 +0.013223249 0.156767671 0.165421165 0.006267775 0.65832014 +0.010373822 0.403783311 0.296800423 0.040701008 0.248341437 +0.016903701 0.101736591 0.320511572 0.000439169 0.560408967 +0.069578591 0.245569271 0.44027702 0.002966698 0.241608421 +0.089079765 0.561506289 0.170316938 0.007304332 0.171792676 +0.127905752 0.232724677 0.417170718 0.002081358 0.220117495 +0.023813069 0.20398643 0.677233016 0.007747902 0.087219582 +0.404821581 0.037257244 0.409037079 0.01114742 0.137736677 +0.067221378 0.104837436 0.617666255 0.01244343 0.197831501 +0.067221378 0.104837436 0.617666255 0.01244343 0.197831501 +0.067221378 0.104837436 0.617666255 0.01244343 0.197831501 +0.067221378 0.104837436 0.617666255 0.01244343 0.197831501 +0.067221378 0.104837436 0.617666255 0.01244343 0.197831501 +# distribution of atf less 40 cm across strata, by year (rows) +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00823 0.37123 0.08358 0.34939 0.18757 +0.00000 0.16927 0.18587 0.27264 0.37222 +0.00000 0.38809 0.12661 0.25541 0.22989 +0.00000 0.31648 0.07723 0.33811 0.26818 +0.01112 0.25323 0.08190 0.29891 0.35485 +0.00520 0.27654 0.06445 0.48527 0.16854 +0.01403 0.41275 0.10628 0.21742 0.24951 +0.00906 0.33290 0.15041 0.26790 0.23973 +0.02379 0.36354 0.14709 0.26581 0.19977 +0.01283 0.32266 0.09500 0.35404 0.21547 +0.00911 0.51437 0.06690 0.30292 0.10670 +0.00000 0.35840 0.04002 0.40231 0.19927 +0.01576 0.48394 0.09144 0.29217 0.11669 +0.00987 0.35848 0.13804 0.33503 0.15859 +0.00000 0.27056 0.11883 0.37963 0.23098 +0.00000 0.25808 0.05217 0.46766 0.22209 +0.00753 0.24022 0.08448 0.41598 0.25179 +0.01342 0.41310 0.06108 0.30558 0.20684 +0.00383 0.40491 0.03849 0.29261 0.26016 +0.00000 0.31844 0.04729 0.48077 0.15350 +0.00951 0.45766 0.03313 0.37332 0.12638 +0.01114 0.52624 0.03471 0.35794 0.06996 +0.02453 0.58037 0.09349 0.20892 0.09269 +0.01560 0.51672 0.11528 0.25756 0.09483 +0.01048 0.50756 0.15390 0.22769 0.10035 +0.00000 0.43417 0.07968 0.34821 0.13794 +0.00000 0.31091 0.06809 0.39913 0.22188 +0.00000 0.36830 0.05873 0.42034 0.15263 +0.00554 0.23363 0.03164 0.53177 0.19743 +0.00961 0.33534 0.04951 0.41450 0.19104 +0.00697 0.32432 0.02805 0.48987 0.15079 +0.00000 0.25344 0.02578 0.41263 0.30815 +0.00212 0.24883 0.01017 0.52688 0.21200 +0.00765 0.34748 0.03753 0.43586 0.17147 +0.01564 0.49224 0.05800 0.29414 0.13998 +0.04539 0.45078 0.12537 0.20044 0.17802 +0.00000 0.43914 0.11217 0.32425 0.12444 +0.01297 0.40688 0.18578 0.30327 0.09110 +0.00000 0.41679 0.10158 0.31991 0.16172 +0.00000 0.41679 0.10158 0.31991 0.16172 +0.00000 0.41679 0.10158 0.31991 0.16172 +0.00000 0.41679 0.10158 0.31991 0.16172 +0.00000 0.41679 0.10158 0.31991 0.16172 +# distribution of atf gt 40 cm across strata, by year (rows) +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00280 0.22191 0.05262 0.30002 0.42264 +0.00000 0.17296 0.01236 0.38499 0.42969 +0.00000 0.26979 0.03709 0.40041 0.29271 +0.00000 0.20404 0.00000 0.35291 0.44305 +0.00000 0.12796 0.04503 0.36188 0.46513 +0.00000 0.14487 0.00000 0.36129 0.49384 +0.00369 0.16825 0.03289 0.31510 0.48008 +0.00000 0.25483 0.00329 0.30494 0.43694 +0.00000 0.18935 0.04060 0.21756 0.55249 +0.00000 0.11930 0.00524 0.31900 0.55647 +0.00000 0.20127 0.01514 0.37808 0.40551 +0.00000 0.21687 0.00751 0.40782 0.36779 +0.00889 0.22317 0.03203 0.30620 0.42971 +0.00970 0.29159 0.02653 0.35387 0.31831 +0.00000 0.33090 0.00000 0.36549 0.30362 +0.00588 0.33716 0.03228 0.32546 0.29921 +0.00000 0.22637 0.02369 0.42520 0.32474 +0.00000 0.28403 0.03851 0.26073 0.41673 +0.00000 0.22408 0.00000 0.52660 0.24931 +0.00000 0.15952 0.02830 0.26067 0.55151 +0.00000 0.20856 0.03181 0.29773 0.46190 +0.00000 0.14715 0.03950 0.26312 0.55023 +0.01053 0.24200 0.07916 0.15130 0.51701 +0.00632 0.26276 0.04664 0.26409 0.42019 +0.01598 0.28890 0.04479 0.23747 0.41286 +0.00401 0.35263 0.02344 0.30968 0.31025 +0.00000 0.27310 0.04066 0.29596 0.39028 +0.00000 0.23105 0.04274 0.30900 0.41721 +0.00000 0.16252 0.03301 0.36316 0.44131 +0.00286 0.21401 0.06592 0.24755 0.46966 +0.00200 0.25297 0.09077 0.21455 0.43971 +0.00000 0.16695 0.03304 0.27772 0.52229 +0.00000 0.15341 0.04140 0.32523 0.47997 +0.00000 0.15638 0.08509 0.21802 0.54050 +0.00000 0.16828 0.09603 0.17646 0.55923 +0.01231 0.18459 0.19672 0.15008 0.45631 +0.00756 0.19767 0.15248 0.25117 0.39112 +0.01082 0.29657 0.26082 0.19713 0.23466 +0.00591 0.32694 0.21512 0.22320 0.22882 +0.00591 0.32694 0.21512 0.22320 0.22882 +0.00591 0.32694 0.21512 0.22320 0.22882 +0.00591 0.32694 0.21512 0.22320 0.22882 +0.00591 0.32694 0.21512 0.22320 0.22882 +# the strata area for getting predatuion by area (thousands of sq km) +118.8982651 103.3002273 107.8222474 38.7924363 94.56232837 +## things to fit for estimating predation +# ATF consumption (ages 1-3) +# number of years of estimates of ATF consumption, by predator +11 26 +# years for which we have consumption estimatates +1983 1986 1990 2000 2001 2002 2006 2007 2008 2013 2014 +1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2006 2007 2009 2010 2011 2012 2013 2014 2015 2016 2017 +# consumption estimates (kilotons, row 1 = ATF < 40 cm, row 2 = ATF >= 40 cm) +92.42127731 254.9051043 462.0560324 177.3533664 571.8149268 394.9250921 285.4527615 407.3188444 93.51040347 311.8751716 178.1255589 +211.2031491 480.567643 534.6239051 550.6581612 480.4897172 432.4062727 177.9912012 390.9200764 326.6838292 311.107004 587.2882102 521.3420953 493.3565449 497.0190322 457.825571 184.3334696 263.0000969 354.8011986 560.7280808 689.5554756 632.3501607 735.3018615 928.5737963 712.9893128 358.9252958 128.4894649 +# catch proportions (ages 1-3, by weight) for ATF < 40 cm by year +0.85273 0.11976 0.02751 +0.78317 0.21683 0.00000 +0.83050 0.14559 0.02391 +0.62284 0.37716 0.00000 +0.71557 0.27751 0.00692 +0.18473 0.47193 0.34334 +0.98059 0.01941 0.00000 +0.92475 0.07525 0.00000 +0.20714 0.66475 0.12811 +0.99200 0.00800 0.00000 +0.58381 0.35492 0.06127 +# catch proportions (ages 1-3) for ATF >= 40 cm by year +0.14387 0.41604 0.44010 +0.43292 0.47727 0.08981 +0.27736 0.63473 0.08792 +0.03934 0.46767 0.49299 +0.25223 0.34153 0.40624 +0.06814 0.35137 0.58049 +0.05962 0.23588 0.70449 +0.36850 0.33874 0.29277 +0.30532 0.39612 0.29856 +0.05777 0.44040 0.50183 +0.03755 0.31741 0.64504 +0.10478 0.29525 0.59997 +0.25009 0.36604 0.38387 +0.04694 0.50200 0.45106 +0.08945 0.26378 0.64677 +0.21550 0.15835 0.62615 +0.22470 0.23619 0.53911 +0.11684 0.19976 0.68340 +0.21531 0.47051 0.31418 +0.21198 0.26212 0.52590 +0.03573 0.36834 0.59593 +0.75592 0.11137 0.13271 +0.30535 0.53760 0.15705 +0.12984 0.32507 0.54509 +0.06937 0.43064 0.49999 +0.12120 0.31362 0.56518 +# the sample sizes for the consmption age comps (rows = pred, col= predation year) +50 50 50 50 50 50 50 50 50 50 50 +50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 +# the C_a parameter for max compsumption rate +0.125 0.125 +# the C_a parameter for max compsumption rate +-0.199 -0.199 +# TCM parameter for max consumption temperature function +26 26 +# TC0 parameter for max consumption temperature function +20.512 20.512 +# CQ parameter for max consumption temperature function +2.497 2.497 +# the temperature by strata (from ACLIM_hindcast, years before 1982 are an average) +4.360253542 2.154195273 0.815437707 4.019796208 2.403877604 +4.360253542 2.154195273 0.815437707 4.019796208 2.403877604 +4.360253542 2.154195273 0.815437707 4.019796208 2.403877604 +4.360253542 2.154195273 0.815437707 4.019796208 2.403877604 +4.360253542 2.154195273 0.815437707 4.019796208 2.403877604 +4.360253542 2.154195273 0.815437707 4.019796208 2.403877604 +4.348134 2.3094573 -0.05061286 3.982983 2.074454 +2.497424 1.0066836 -0.42451541 3.461386 1.641736 +2.43973 0.6739367 -0.29773958 3.427912 1.973702 +4.058981 2.0488988 -0.10633813 4.337568 1.616081 +3.679798 0.5825206 0.41988467 3.0938 2.861539 +2.378325 0.718827 -0.2302499 3.705854 1.410105 +1.80307 0.2482798 -0.69947089 3.392617 2.132737 +4.393788 3.496997 1.30841686 5.364371 3.216115 +6.853687 3.8493368 1.93916972 4.679671 3.193864 +7.419447 4.5004021 3.37059741 5.085538 3.970347 +5.980524 3.5512466 1.52759133 4.68736 3.197166 +6.389247 3.8612869 2.20875674 5.01554 3.415832 +4.563606 2.0958243 2.17098285 4.390188 3.356535 +5.742435 3.7383416 1.28955181 4.995722 3.149884 +3.13395 1.9426122 0.04890082 4.233712 2.078837 +2.416033 1.7111861 0.79992939 4.334784 2.313044 +3.204918 1.8154874 0.14405379 4.114489 2.580028 +5.803015 3.6893004 1.61156194 5.139355 3.254879 +3.792504 1.7708332 0.75251748 4.060348 2.842735 +5.482235 2.6499909 2.20979032 3.973223 2.770019 +4.872173 2.6369295 0.68734487 4.170253 2.691204 +4.66501 2.4070466 0.20180992 3.94385 2.196864 +3.835402 1.3124922 -0.14128389 4.240232 1.515181 +6.969419 3.4238643 1.46426981 4.619817 3.085612 +4.741561 2.8153967 0.07686012 4.546096 2.158367 +3.620375 1.436322 0.04249977 3.403692 2.086332 +5.519602 2.5694187 1.63553084 3.760486 2.793872 +3.85777 1.2840163 1.33023494 3.18101 2.05341 +5.654973 3.2493535 1.99822017 4.245158 3.239017 +2.456438 0.8172659 0.21199293 3.323887 1.990556 +4.563776 1.9286108 0.93291589 3.532171 2.167298 +4.056245 2.6275127 1.25515774 4.124464 2.398734 +5.623387 2.5536491 1.83504781 3.814038 2.269554 +5.451068 2.9506266 2.3822157 4.654725 3.016224 +5.195829 2.6429888 1.4975723 4.031718 2.500495 +5.246613 2.910676 1.50302283 4.277883 2.516058 +3.735287 1.9141921 0.33831724 3.856958 2.213868 +2.886063 0.7841668 -0.36544597 2.85212 1.722475 +2.587398 0.7343266 -0.24733105 2.916441 1.576841 +3.853648 0.5890507 -0.16641168 3.146446 1.289574 +2.358028 0.5709882 -0.13665044 3.436174 2.141433 +3.237983 1.4533411 0.03288059 3.438194 2.131028 +2.96874 0.2116222 -0.55937721 2.522293 1.460926 +3.37734 0.8447977 -0.11282829 3.431688 1.541359 +5.075854 3.079051 1.3058004 4.183044 2.33708 +5.729849 3.5010319 0.92952536 4.970757 2.007836 +6.984331 4.4355349 2.36446119 5.211475 2.845619 +3.787157 1.4556509 0.85187968 3.638727 2.389669 +6.022971 3.499845 2.79231094 4.494286 3.252263 +6.720991108 3.911308965 2.687099921 4.78243868 3.131752405 +6.720991108 3.911308965 2.687099921 4.78243868 3.131752405 +6.720991108 3.911308965 2.687099921 4.78243868 3.131752405 +6.720991108 3.911308965 2.687099921 4.78243868 3.131752405 +6.720991108 3.911308965 2.687099921 4.78243868 3.131752405 +# mean weight of the predator groups (g) +210.946 211.943 211.352 209.033 202.754 193.198 179.514 158.547 110.908 138.766 192.961 264.054 336.013 376.550 337.639 194.540 106.058 94.749 131.061 177.539 163.244 170.183 176.032 153.328 150.616 162.464 185.128 223.077 244.019 253.923 241.451 218.882 175.656 171.441 174.254 156.741 158.744 172.019 176.526 187.222 196.966 210.180 206.676 208.995 215.691 219.090 228.204 229.410 210.223 178.230 171.172 185.453 200.602 128.812 143.285 194.677 195.0 195.0 195 195 +1641.212 1659.523 1682.611 1708.961 1738.114 1768.684 1797.055 1818.348 1826.221 1810.966 1756.017 1587.846 1352.105 1262.357 1303.898 1421.890 1577.294 1746.132 1877.951 1850.127 1588.961 1342.234 1246.091 1222.209 1201.228 1175.084 1165.318 1140.064 1116.420 1132.322 1189.513 1274.026 1375.087 1474.068 1561.433 1610.041 1612.332 1586.060 1540.150 1472.930 1409.522 1374.325 1364.788 1370.340 1396.310 1433.547 1470.987 1507.717 1552.116 1605.346 1662.007 1712.330 1730.439 1703.508 1667.113 1704.940 1700.00 1700 1700 1700 +# Cmax for functional respose (for avg wgt atf, at avg temperature across EBS shelf from 1982-2019) [UPDATED] +3.586083938 2.382786007 +# Copepod time series +# phase to estimate +-5 +# nyrs +15 +# Yrs of copepod +2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2014 2015 2016 2018 +# +# VAST estimates of copepod +824 762 2445 1663 2249 3623 23657 9733 12294 15393 30504 7023 2420 3896 5321 +# SE of VAST ests of copepod +404 360 856 556 708 721 9140 2236 2542 2986 5625 1709 740 1223 1899 +# +1234567 diff --git a/2023_runs/dat/wtage2023.dat b/2023_runs/dat/wtage2023.dat new file mode 100644 index 0000000..07a71b7 --- /dev/null +++ b/2023_runs/dat/wtage2023.dat @@ -0,0 +1,174 @@ +# Datafile created in R +#log_sd_coh +-1.9814 +#log_sd_yr +-0.92356 +#cur_yr +2023 +#styr +1970 +#endyr +2025 +#N_data_sets +2 +#N_yrs_data_sets +32 +41 +#fshry_yrs +1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 +#survey_yrs +1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2021 2022 2023 +#stage +3 +#endage +15 +#fishery +0.2882931 0.4851677 0.6058415 0.7293716 0.844097 0.8826935 1.016319 1.124358 1.14108 1.231533 1.221871 1.294797 1.251613 +0.3968031 0.4646974 0.6511901 0.7136756 0.8185426 0.9857865 1.03042 1.199786 1.236505 1.26924 1.19314 1.35743 1.431386 +0.4947278 0.6120715 0.6518174 0.774858 0.9344757 1.062411 1.197734 1.240419 1.423335 1.539832 1.575729 1.608702 1.507688 +0.4000505 0.6516264 0.7323901 0.7458028 0.7269725 1.070046 1.379894 1.324863 1.33502 1.409007 1.39704 1.277838 1.369793 +0.3856115 0.5052645 0.7285221 0.8432449 0.8472956 0.9695919 1.231843 1.296302 1.400678 1.401951 1.392233 1.094948 1.305507 +0.3356064 0.4450744 0.6843644 0.796765 0.9483794 0.9555191 1.025466 1.099698 1.418002 1.489233 1.520591 1.701901 1.601963 +0.3267175 0.47687 0.5590455 0.7475644 0.8888078 1.074089 1.095178 1.235658 1.286725 1.399735 1.561271 1.363416 1.337754 +0.3693837 0.5893025 0.6183761 0.621628 0.7795683 1.040016 1.169286 1.275858 1.316131 1.427718 1.448363 1.436974 1.528371 +0.4044558 0.5070159 0.6433917 0.7022943 0.7286319 0.8936622 1.037729 1.252724 1.223968 1.42242 0.9948698 1.40474 1.238645 +0.3529669 0.5256566 0.6299162 0.7317384 0.7797076 0.8066376 0.9679051 1.014843 1.252876 1.286418 1.108137 1.083882 1.358762 +0.3289871 0.5047765 0.6679117 0.7855035 0.9638143 0.9856404 1.061105 1.132863 1.319515 1.411108 1.568255 1.472392 1.494921 +0.3849426 0.5098224 0.6673429 0.798844 0.9108518 1.025741 1.112964 1.101521 1.283517 1.442173 1.578749 1.289712 1.567803 +0.4892412 0.5487842 0.6515612 0.768839 0.8630036 0.9527843 1.08565 1.201529 1.211577 1.194453 1.374064 1.354663 1.709442 +0.4084855 0.5838909 0.6412472 0.7598723 0.8876315 0.9243766 1.035505 1.176252 1.126812 1.166779 1.309497 1.253624 1.184688 +0.3510699 0.5082272 0.6410912 0.7417098 0.880134 0.9599554 1.061691 1.074204 1.21628 1.267985 1.217488 1.075505 1.342282 +0.3055219 0.448377 0.6063994 0.7553307 0.8576654 0.9586313 1.06031 1.116617 1.190103 1.218201 1.279677 1.384137 1.417078 +0.349006 0.5074331 0.6423453 0.7829344 0.9605336 1.100324 1.192178 1.266491 1.326893 1.487726 1.443788 1.728544 1.511781 +0.3282221 0.5187135 0.6526894 0.7739367 0.8999054 1.054227 1.116566 1.288861 1.452421 1.527705 1.56043 1.873552 1.644688 +0.3396064 0.5251301 0.7047681 0.8786265 0.9994171 1.130492 1.398287 1.479225 1.557881 1.576152 1.806939 2.025702 2.222079 +0.380773 0.4899893 0.6675303 0.9090469 1.114265 1.276856 1.373608 1.585756 1.679019 1.923453 1.947904 2.077039 2.271161 +0.2903063 0.5082767 0.6655524 0.8094414 0.9714593 1.223768 1.342173 1.513015 1.58174 1.623338 2.079569 1.707231 2.242237 +0.2706083 0.4094757 0.6432135 0.8241379 0.9739199 1.169735 1.302818 1.509451 1.598869 1.636675 1.680013 2.031152 2.062133 +0.2895651 0.4421383 0.564462 0.7805717 1.130108 1.281472 1.439581 1.684725 1.827352 1.785734 1.933904 2.159083 2.182183 +0.3188213 0.4541317 0.6168786 0.7512559 0.8938563 1.156345 1.306716 1.386355 1.669198 1.773348 1.704242 1.623389 2.215234 +0.4039953 0.4622115 0.5705796 0.690256 0.7860738 0.8867081 1.140721 1.195296 1.315406 1.670917 1.389203 1.559162 2.600077 +0.4085589 0.5311169 0.557025 0.645972 0.7318207 0.799736 0.9405046 1.043411 1.177987 0.78776 0.9111782 1.683923 1.42947 +0.4083074 0.498516 0.6502866 0.6937211 0.7518517 0.827407 0.893922 0.9111547 1.027504 0.9613184 1.359071 1.40474 1.56049 +0.3772452 0.4667525 0.5731819 0.7336999 0.8088135 0.85317 0.9063508 1.038827 0.9355938 1.110072 1.359071 1.40474 1.129641 +0.4219116 0.5652466 0.6432253 0.7593976 0.8784833 0.9622758 1.006798 1.064675 1.034985 1.181892 0.7541725 1.40474 1.56049 +0.3872785 0.5215788 0.6319834 0.7155715 0.7992112 0.9550814 1.005954 1.040258 1.189235 1.072451 1.208377 0.9607388 1.56049 +0.393063 0.4798559 0.5737146 0.6897824 0.7568733 0.8413162 1.010658 1.129811 1.159749 1.26933 1.2145 1.40474 1.56049 +0.440141 0.5060703 0.5739125 0.7240341 0.837484 0.8832588 0.9836968 0.9723265 1.271469 1.044234 1.234535 0.9103079 1.56049 +#survey +0.1810319 0.3401026 0.4112848 0.7774897 1.050631 1.187602 1.401295 1.566716 2.23455 1.990129 1.903567 1.523858 2.945983 +0.2092313 0.3422084 0.5362144 0.7737986 1.015548 1.452548 1.405551 1.667173 1.528755 1.577053 2.013324 2.126792 1.834381 +0.2152697 0.3142335 0.43248 0.6026432 0.9256616 1.313141 1.268111 1.473242 1.985148 1.687624 1.904739 1.426142 2.122132 +0.2286039 0.3684811 0.4809657 0.7150139 0.9098834 1.212701 1.723102 1.436339 1.532255 1.776502 2.037585 1.652323 2.634223 +0.1689697 0.309573 0.4136977 0.6076133 0.7667573 1.017531 1.304328 1.649841 1.276107 1.38089 1.982826 2.233434 2.222914 +0.2531875 0.3317592 0.4221824 0.5468054 0.7180407 0.8486852 1.008414 1.261388 1.580836 1.612165 2.201604 2.044836 2.394967 +0.2809457 0.3304929 0.4457183 0.4939646 0.5898163 0.814366 0.9080141 1.039845 1.229934 1.284693 1.570747 0.6864434 1.740335 +0.1689137 0.277038 0.3709714 0.5491282 0.6619114 0.8368121 1.027491 1.000039 1.115422 1.013625 1.260065 1.14383 1.10276 +0.1873976 0.3558652 0.4784745 0.5448059 0.6143028 0.7348851 1.029256 0.9793391 1.023303 1.187875 0.8545945 1.390105 1.714438 +0.2006172 0.3330148 0.5654191 0.6500294 0.7765332 0.8556218 1.015925 1.104574 1.287471 1.370091 1.347148 1.732752 1.691324 +0.2548529 0.3997084 0.4639046 0.5700713 0.7560974 0.7706575 0.9289464 1.007886 1.13785 1.520173 1.538978 1.426394 1.562768 +0.2495186 0.4086956 0.4643654 0.5480458 0.6618757 0.7831976 0.986841 0.9990959 1.148662 1.285484 1.508885 1.57648 1.90897 +0.2117956 0.4009507 0.5361996 0.6720164 0.6482305 1.0463 1.165821 1.106631 1.219348 1.240532 1.367204 1.436164 1.450571 +0.16853 0.3625568 0.4781399 0.648494 0.6237462 0.785126 0.9108941 1.280941 1.222215 1.254045 1.399422 1.421749 1.740114 +0.1510669 0.2997016 0.4865756 0.5831852 0.7577896 0.8198246 0.9793578 1.02341 1.350087 1.461438 1.487486 1.639299 1.963853 +0.1888442 0.2768783 0.3816938 0.5304666 0.6735189 0.7760232 0.9964968 0.9658604 1.211315 1.464772 1.089919 1.566396 1.951038 +0.2128021 0.3323016 0.4460319 0.5192406 0.8110831 0.8870327 1.077657 1.292644 1.591888 1.414569 1.515568 1.669169 1.905074 +0.216293 0.3508504 0.3922503 0.5263055 0.6157379 0.8816969 1.037549 1.008256 1.278596 1.13221 1.66606 1.759817 2.192308 +0.2191448 0.3981239 0.470044 0.5209674 0.7221631 0.7593688 0.9249255 1.034916 1.235953 1.338122 1.781745 1.625572 2.047887 +0.2014172 0.3555048 0.6167273 0.7294372 0.7499372 1.000419 0.9818262 1.030861 1.277821 1.41905 1.473561 1.765574 1.539117 +0.2686227 0.402463 0.5438607 0.6849798 0.7128001 0.9042623 1.006335 1.053446 1.009736 1.139011 1.474134 1.409576 2.108251 +0.3416501 0.4189941 0.647751 0.7098316 0.8864087 0.8687491 1.123543 1.239424 1.268921 1.284637 1.368589 1.747321 1.780308 +0.283823 0.5157383 0.5950073 0.7497722 0.8949268 0.9315299 1.11922 1.02684 1.278627 1.551575 1.53706 2.375173 1.712005 +0.23067 0.3941576 0.5393634 0.6983441 0.8585392 0.9305516 0.9925457 1.222539 1.383086 1.229117 1.400692 1.504434 1.681657 +0.1787955 0.4573273 0.6045092 0.6790878 0.7886901 0.8657822 1.057634 1.171102 1.278715 1.336333 1.668646 1.518887 1.703814 +0.2944949 0.4929926 0.637474 0.8095432 0.928332 1.059545 1.001948 1.314502 1.308706 1.268207 1.416606 1.36723 1.385353 +0.2198369 0.4911264 0.6009138 0.7304209 0.8573038 0.9460209 0.9874546 1.153566 1.639522 1.372385 1.708225 1.539505 1.681943 +0.2413689 0.5087423 0.688201 0.8147338 1.009672 1.067613 1.120889 1.358999 1.449254 1.768965 1.76668 2.049176 2.48598 +0.2441986 0.4957358 0.6574479 0.8040582 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0.229 0.283 0.173 0.187 0.268 0.4 0.8 +0.116 0.139 0.147 0.166 0.188 0.19 0.26 0.3 0.306 0.351 0.118 0.208 0.8 +0.124 0.143 0.149 0.185 0.173 0.233 0.271 0.292 0.408 0.293 0.4 0.276 0.8 +0.1475881 0.1164835 0.124714 0.147987 0.197786 0.2287728 0.2546931 0.275483 0.2918495 0.3448886 0.4054791 0.4978875 0.2037008 diff --git a/2023_runs/sep23/pm.dat b/2023_runs/sep23/pm.dat index 63e91f7..44fb620 100644 --- a/2023_runs/sep23/pm.dat +++ b/2023_runs/sep23/pm.dat @@ -6,5 +6,5 @@ control.dat ../dat/cov_2022.dat ../dat/wtage2022b.dat ../dat/surveycpue.dat -../dat/temp_paul22.dat +../dat/temp_paul23.dat ../dat/q_bts.dat diff --git a/doc/references.bib b/doc/references.bib index 09d1d0a..b2251dd 100644 --- a/doc/references.bib +++ b/doc/references.bib @@ -1,3 +1,114 @@ + +@article{levine2019, +title = {Don’t work too hard: Subsampling leads to efficient analysis of large acoustic datasets}, +journal = {Fisheries Research}, +volume = {219}, +pages = {105323}, +year = {2019}, +issn = {0165-7836}, +doi = {https://doi.org/10.1016/j.fishres.2019.105323}, +url = {https://www.sciencedirect.com/science/article/pii/S0165783619301705}, +author = {Mike Levine and Alex {De Robertis}}, +keywords = {Acoustic data analysis, Acoustic trawl surveys, Subsampling, Walleye pollock}, +abstract = {Echo-integration measurements have been traditionally made from dedicated fisheries survey vessels, but extensive measurements from moorings, autonomous vehicles, and fishing vessels are increasingly available. Processing these data by traditional means developed for well-staffed fisheries surveys can be prohibitively time-consuming, which has limited their use. Automated processing methods exist to efficiently handle these large datasets; however, as compared to post-processing by trained analysts, these methods require substantial expertise and methodological development, and they often produce less certain results. Here, we evaluate the use of subsampling, which takes advantage of the spatial correlation common in many fish populations, to improve the efficiency of traditional processing methods while retaining a high level of precision. We subsampled data from an eastern Bering Sea walleye pollock (Gadus chalcogrammus) acoustic-trawl survey and compared estimates of pollock backscatter from subsamples to those from the full survey. Over a survey-wide scale, processing < 5% of the data resulted in estimates within 5% of those from processing the full survey. This suggests that in some applications there may be diminishing returns associated with exhaustively processing large spatially correlated datasets. We present an example that applies this simple approach by subsampling archived echosounder data from chartered fishing vessels to prioritize the areas surveyed in future surveys, which would not have been feasible without subsampling. When averaged values over large scales (e.g. over a survey domain) are required, precise echo integration estimates can be obtained with modest effort by processing relatively small subsamples of a dataset.} +} +@techreport{hulson2023, + title = {Bottom Trawl Survey Age and Length Composition Input Sample Sizes for Stocks Assessed with Statistical Catch-at-Age Assessment Models at the Alaska Fisheries Science Center}, + author = {Hulson, P-J. F. and Williams, B. C. and Siskey, M. R. and Bryan, M. D. and Conner, J.}, + institution = {Alaska Fisheries Science Center (U.S.). and Auke Bay Laboratories (Juneau, Alaska). and Alaska Fisheries Science Center (U.S.). Resource Assessment and Conservation Engineering Division. and Alaska Fisheries Science Center (U.S.). Resource Ecology and Fisheries Management Division. and Washington (State). Department of Fish and Wildlife.}, + year = {2023}, + series = {NOAA Technical Memorandum NMFS-AFSC}, + number = {470}, + doi = {10.25923/8pxq-bs48}, + url = {https://doi.org/10.25923/8pxq-bs48}, +} + +@article{derobertis2012, +title = {Distribution of fish and macrozooplankton in ice-covered and open-water areas of the eastern Bering Sea}, +journal = {Deep Sea Research Part II: Topical Studies in Oceanography}, +volume = {65-70}, +pages = {217-229}, +year = {2012}, +note = {Understanding Ecosystem Processes in the Eastern Bering Sea}, +issn = {0967-0645}, +doi = {https://doi.org/10.1016/j.dsr2.2012.02.005}, +url = {https://www.sciencedirect.com/science/article/pii/S0967064512000094}, +author = {Alex {De Robertis} and Edward D. Cokelet}, +keywords = {Acoustics, Eastern Bering Sea, Sea ice, Temperature, Walleye pollock, , Icebreaker}, +abstract = {The eastern Bering Sea shelf is a productive ecosystem with extensive commercial fisheries. Although the area is well-studied during summer months, little is known about the abundance and distribution of fish and macrozooplankton during periods of seasonal ice cover. The use of an icebreaker during the Bering Sea Ecosystem Study (BEST) provided a platform for spring acoustic surveys of fish and zooplankton in ice-covered areas for the first time. Icebreaker measurements were complemented with observations from conventional vessels during spring and summer. In spring, very little backscatter from fish (dominated by walleye pollock, Theragra chalcogramma) was observed in the ice-covered northern areas where near-bottom waters were cold (<∼0.5°C), including areas where walleye pollock are abundant in summer. The majority of fish were observed within 40km (and often slightly inside) the ice edge over similar seafloor depths as in summer. Together, these observations suggest that pollock, a dominant component of the ecosystem, shift their distribution to a more restricted geographic area in spring, following the ice edge southeast along the bathymetry, away from areas of cold water and extensive ice cover, then reoccupying these areas in summer. In contrast, acoustic backscatter attributed to zooplankton (likely dominated by euphausiids) was more evenly distributed, and less restricted by water temperature and ice cover. The implications of this seasonal shift in fish distribution are uncertain, but this may affect predator–prey interactions by reducing overlap of pollock with euphausiids, an important prey source, while increasing overlap of adult and juvenile pollock and potentially increasing cannibalism.} +} + + +@article{hulson2013, +author = {Hulson, Peter-John F. and Quinn, Terrance J. and Hanselman, Dana H. and Ianelli, James N.}, +title = {Spatial modeling of Bering Sea walleye pollock with integrated age-structured assessment models in a changing environment}, +journal = {Canadian Journal of Fisheries and Aquatic Sciences}, +volume = {70}, +number = {9}, +pages = {1402-1416}, +year = {2013}, +doi = {10.1139/cjfas-2013-0020}, +URL = { https://doi.org/10.1139/cjfas-2013-0020 }, +eprint = { https://doi.org/10.1139/cjfas-2013-0020 } , + abstract = { Climate change may affect the spatial distribution of fish populations in ways that would affect the accuracy of spatially aggregated age-structured assessment models. To evaluate such scenarios, spatially aggregated models were compared with spatially explicit models using simulations. These scenarios were based on hypothetical climate-driven distribution shifts and reductions in mean recruitment of walleye pollock (Gadus chalcogrammus) in the eastern Bering Sea. Results indicate that biomass estimates were reasonably accurate for both types of estimation models and precision improved with the inclusion of tagging data. Bias in some aggregated model scenarios could be attributed to unaccounted-for process errors in annual fishing mortality rates and was reduced when estimating effective sample size or time-varying selectivity. Spatially explicit models that allow estimation of variability in movement and ontogenetic parameters (specified as a random walk process) were shown to be feasible, whereas models that misspecified ontogenetic movement and climate change effects resulted in biased biomass and movement parameter estimates. These results illustrate that more complex models may characterize processes better but may be less robust for management advice. } +} + + + +@article{polyanichko2022, + author = {Polyanichko, V.I. and Kuznetsov, M.Y.}, + title = {Distribution and abundance of walleye pollock \emph{Theragra chalcogramma} in the northwestern Bering Sea in the summer-fall period of 2020 and their interannual variability}, + journal = {Izvestiya TINRO}, + year = {2022}, + volume = {202}, + number = {4}, + pages = {793-809}, + doi = {10.26428/1606-9919-2022-202-793-809}, + language = {Russian} +} +@article{wyllie-echeverria1998, +author = {WYLLIE-ECHEVERRIA, TINA and WOOSTER, WARREN S.}, +title = {Year-to-year variations in Bering Sea ice cover and some consequences for fish distributions}, +journal = {Fisheries Oceanography}, +volume = {7}, +number = {2}, +pages = {159-170}, +keywords = {Aleutian Low Pressure System, Arctic cod, cold pool, decadal variability, sea ice, walleye pollock}, +doi = {https://doi.org/10.1046/j.1365-2419.1998.00058.x}, +url = {https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1365-2419.1998.00058.x}, +eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1046/j.1365-2419.1998.00058.x}, +abstract = {The southernmost extension of winter ice cover varies interannually and on longer time scales, reflecting large-scale changes in driving forces, especially in the position and intensity of the winter Aleutian Low Pressure System. A conspicuous pattern is alternating warm and cool periods of several years' duration. These variations in sea ice cover are reflected in the character of a subsurface cold pool, formed as stratification isolates the deeper cold waters from surface exchanges. The cold pool is better developed and more extensive in summers that follow deep southward penetration of winter sea ice. Interannual and decadal-scale variations in the distributions of some fish stocks reflect those of ice and thermal conditions. In particular, the distribution of walleye pollock, Theragra chalcogramma, varies significantly with multiannual cool and warm years while Arctic cod, Boreogadus saida, is only present within the cold pool. The relation among climate variations, sea ice cover, subsurface thermal conditions, and fish distribution provides information on how climate affects marine ecosystems and may also have practical application in predicting fish distributions.}, +year = {1998} +} + + +@article{mueter2007, + ISSN = {10510761}, + URL = {http://www.jstor.org/stable/40062132}, + abstract = {Seasonal ice cover creates a pool of cold bottom water on the eastern Bering Sea continental shelf each winter. The southern edge of this cold pool, which defines the ecotone between arctic and subarctic communities, has retreated ~230 km northward since the early 1980s. Bottom trawl surveys of fish and invertebrates in the southeastern Bering Sea (1982-2006) show a coincident reorganization in community composition by latitude. Survey catches show community-wide northward distribution shifts, and the area formerly covered by the cold pool has seen increases in total biomass, species richness, and average trophic level as subarctic fauna have colonized newly favorable habitats. Warming climate has immediate management implications, as 57% of variability in commercial snow crab (Chionoecetes opilio) catch is explained by winter sea ice extent. Several measures of community distribution and structure show linear relationships with bottom temperature, suggesting warming climate as the primary cause of changing biogeography. However, residual variability in distribution not explained by climate shows a strong temporal trend, suggesting that internal community dynamics also contribute to changing biogeography. Variability among taxa in their response to temperature was not explained by commercial status or life history traits, suggesting that species-specific responses to future warming will be difficult to predict.}, + author = {Franz J. Mueter and Michael A. Litzow}, + journal = {Ecological Applications}, + number = {2}, + pages = {309--320}, + publisher = {Ecological Society of America}, + title = {Sea Ice Retreat Alters the Biogeography of the Bering Sea Continental Shelf}, + urldate = {2023-10-09}, + volume = {18}, + year = {2008} +} + + +@article{hulson2017, + author = {Hulson, Peter-John F. and Hanselman, Dana H. and Shotwell, S. Kalei}, + title = {Investigations into the distribution of sample sizes for determining age composition of multiple species}, + journal = {Fishery Bulletin}, + year = {2017}, + issn = {115:326-342}, + url = {https://spo.nmfs.noaa.gov/content/fishery-bulletin/investigations-distribution-sample-sizes-determining-age-composition}, + doi = {10.7755/FB.115.3.4}, + note = {Contact author: Peter-John F. Hulson, Email: pete.hulson@noaa.gov} +} + @article{maunder2020, title = {The need for spatio-temporal modeling to determine catch-per-unit effort based indices of abundance and associated composition data for inclusion in stock assessment models}, journal = {Fisheries Research},