-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathyfs_captions.txt
213 lines (108 loc) · 10.7 KB
/
yfs_captions.txt
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
1. `r table_nums("catch",display="cite")`
2. `r table_nums("catageprop",display="cite")`
3. `r table_nums("mature",display="cite")`
4. `r table_nums("retdis",display="cite")`
5. `r table_nums("retdiscat",display="cite")`
6. `r table_nums("wtagetab",display="cite")`
7. `r table_nums("Srvbio_CI",display="cite")`
8. `r table_nums("biomCI",display="cite")`
9. `r table_nums("NBS_bio",display="cite")`
10. `r table_nums("natagesrv",display="cite")`
11. `r table_nums("occurrence",display="cite")`
12`. `r table_nums("rescat",display="cite")`
13. `r table_nums("liketab",display="cite")`
14. `r table_nums("pars",display="cite")`
15. `r table_nums("Mcomparison",display="cite")`
16. `r table_nums("fmort",display="cite")`
16. `r table_nums("FSBCI",display="cite")`
`r table_nums("rec",display="cite")`
17. `r table_nums("ABCOFL",display="cite")`
16. `r table_nums("projection",display="cite")`
17. `r table_nums("bycatch1",display="cite")`
17. `r table_nums("bycatch2",display="cite")`
17. `r table_nums("bycatch3",display="cite")`
17. `r table_nums("eco",display="cite")`
Mean length and weight at age for Yellowfin Sole (unsmoothed), females presented first, followed by males.
1.. `r figure_nums("NBS",display="cite")`
2.. `r figure_nums("NBSEBSlens",display="cite")`
3.. `r figure_nums("spawnplot",display="cite")`
4.. `r figure_nums("wtage",display="cite")`
5.. `r figure_nums("black",display="cite")`
6.. `r figure_nums("tempanom",display="cite")`
7.. `r figure_nums("catchfig",display="cite")`
8.. `r figure_nums("obs_sizecomp",display="cite")`#not done should be 4.2
9.. `r figure_nums("pie_bymonth",display="cite")`
10.. `r figure_nums("fishmo",display="cite")` #not done shold be 4.4
11.. `r figure_nums("cpueline",display="cite")`
12.. `r figure_nums("cpuefig",display="cite")` 392
13.. `r figure_nums("srvCI",display="cite")`
14.. `r figure_nums("cpuediff",display="cite")`
15.. `r figure_nums("ricker1",display="cite")`
16.. `r figure_nums("ricker2",display="cite")`
17.. `r figure_nums("srvsel",display="cite")`
18.. `r figure_nums("fshsel",display="cite")`
19.. `r figure_nums("plotq",display="cite")`
20.. `r figure_nums("sexratio",display="cite")`
21.. figure_nums("srv_agecomp1",display="cite")`
22.. figure_nums("srv_agecomp2",display="cite")`
23.. figure_nums("fsh_agecomp1",display="cite")`
24.. figure_nums("fsh_agecomp2",display="cite")`
25.. `r figure_nums("srvbio_fit",display="cite")`
26.. `r figure_nums("mcmcplots",display="cite")`
27.. `r figure_nums("BioFSBCI",display="cite")`
28.. `r figure_nums("fishingavgF",display="cite")` **
29.. `r figure_nums("recfig",display="cite")`
30.. `r figure_nums("retro1",display="cite")`
31.. `r figure_nums("retro2",display="cite")`
32.. `r figure_nums("phaseplane",display="cite")`
Model 14_1 has been the preferred model for the past 5 assessments, and operates by fixing $M$ at 0.12 for both sexes and estimating $q$ using the relationship between survey catchability and the annual average water temperature at the sea floor (from survey stations at less than 100 m). The other models used in the evaluation represented various combinations of estimating $M$ or $q$ as free parameters with different amounts of uncertainty in the parameter estimates (Wilderbuer et al. 2010). The results are detailed in those assessments and are not repeated here except for the following observations.
fig.5_cap = figure_nums(name="fig_5",caption = "Yellowfn sole CPUE (catch per unit effort in kg/ha) from the annual Bering Sea shelf trawl surveys, 1982-2018.")
p=ggplot(data=retro,aes(x=Year,y=Female_Spawning_Biomass,ymin=LI,ymax=UI))+
geom_ribbon(aes(fill=year),alpha=0.3)+geom_line(aes(color=year))+
ylab("Female Spawning Biomass (t)")+theme_bw()+theme(legend.text=element_text(size=11),axis.text=element_text(size=9),axis.title.x = element_text(size=14),axis.title.y = element_text(size=14))+
scale_x_continuous(breaks = seq(1954,2018, by = 2))+theme(axis.text.x=element_text(angle=90,hjust=-1))
fig.10_cap = figure_nums(name="fig_10",caption = "Yellowfin Sole length-at-age anomalies, for males and females, and bottom temperature anomalies. Correspondence in these residuals is apparent with a 2-3 year lag effect from the mid-1990s to 2017. Late 1980s and early 1990s pattern may be a density-dependent response in growth from the large 1981 and 1983 year-classes.")
fig.11_cap = figure_nums(name="fig_11",caption = "Table of temperature anomalies (second row at top as bars) and observed values by age and year. Shadings within the matrix reflects relative weight-at-age (within a row) with darker red heavier than average.")
fig.12_cap = figure_nums(name="fig_12",caption = "Average bottom water temperature from stations less than or equal to 100 m in the Bering Sea trawl survey (bars) and the stock assessment model estimate of q for each year 1982-2017.")
fig.13_cap = figure_nums(name="fig_13",caption = "Fit of the Ricker (1958) stock recruitment model to two distinct stock recruitment time-series data sets, blue line full data set and red line 1978-2012 data set (top panel), and the fit to the assessment preferred model (model 18_1), lower panel).")
fig.14_cap = figure_nums(name="fig_14",caption = "Posterior distributions of Fmsy for the two models considered in the stock productivity analysis.")
fig.15_cap = figure_nums(name="fig_15",caption = "Estimated fishery selectivity by age and year for males (panel a.) and females (panel b.).")
fig.16_cap = figure_nums(name="fig_16",caption = "Model fit to the survey biomass estimates (top left panel), model estimate of the full selection fishing mortality rate throughout the time-series (top right panel), model estimate of total biomass (middle left panel), the model estimate of survey selectivity (middle right panel) and the estimate of female spawning biomass (bottom left panel).
")
fig.17_cap = figure_nums(name="fig_17",caption = "Stock assessment model fit to the time-series of fishery and survey age composition, by sex.")
fig.18_cap = figure_nums(name="fig_18",caption = "Comparison of the fit to the survey biomass using a fixed q and the q-bottom temperature relationship.")
fig.19_cap = figure_nums(name="fig_19",caption = "Year class strength of age 5 Yellowfin Sole estimated by the stock assessment model. The dotted line is the average of the estimates from 62 years of recruitment.")
fig.20_cap = figure_nums(name="fig_20",caption = "Posterior distributions of some important parameters estimated by the preferred stock assessment model (from mcmc integration).")
fig.22_cap = figure_nums(name="fig_22",caption = "Projection of Yellowfin Sole female spawning biomass (1,000s t) at the average full- selection F from the past 5 years (0.104) through 2032 with B40% and Bmsy levels indicated (top panel). Tier 1 projection of Yellowfin Sole ABC assuming 150,000 t catch in each future year for models 14_1 (base model) and 18_1 (new base model) (bottom panel).")
totallike=sum(YFS$wt_like,YFS$age_likeihood_for_survey,YFS$age_likelihood_for_fishery,YFS$catch_likelihood,YFS$selectivity_likelihood,YFS$survey_likelihood,YFS$recruitment_likelilhood)
\begin{table}[ht]
\centering
\begin{tabular}{lrr|rr}
\hline
& \multicolumn{2}{c|}{Model 18.2} & \multicolumn{2}{c}{Model 18.1} \\
Quantity & `r thisyr+1` &`r thisyr+2` & `r thisyr+1` &`r thisyr+2` \\
\hline
$M$ (natural mortality rate) & 0.12 & 0.12 & 0.12, 0.135 & 0.12, 0.135 \\
Tier & 1a & 1a & 1a & 1a \\
Projected total (age 6+) biomass (t) & `r formatC(extra_sd2$GM_Biom[1]*1000,format="d",big.mark=",")` & `r formatC(extra_sd2$GM_Biom[2]*1000,format="d",big.mark=",")` t & `r formatC(extra_sd1$GM_Biom[1]*1000,format="d",big.mark=",")` t & `r formatC(extra_sd1$GM_Biom[2]*1000,format="d",big.mark=",")` t \\
Projected female spawning biomass (t) & `r formatC(extra_sd2$SSB[1]*1000,format="d",big.mark=",")` t & `r formatC(extra_sd2$SSB[2]*1000,format="d",big.mark=",")`t & `r formatC(extra_sd1$SSB[1]*1000,format="d",big.mark=",")` t & `r formatC(extra_sd1$SSB[2]*1000,format="d",big.mark=",")` t \\
$\:\:\:\:\:\:B_{100\%}$ & `r B0_2` & `r B0_2` & `r B0_1` t & `r B0_1` t \\
$\:\:\:\:\:\:B_{MSY\%}$ & `r formatC(1000*extra_sd2$Bmsy[1],format="d",big.mark=",")` & `r formatC(1000*extra_sd2$Bmsy[1],format="d",big.mark=",")` & `r formatC(1000*extra_sd1$Bmsy[1],format="d",big.mark=",")` t & `r formatC(1000*extra_sd1$Bmsy[1],format="d",big.mark=",")` t \\
$F_{OFL}$ & `r round(extra_sd2$AM_Fmsyr[1],3)` & `r round(extra_sd2$AM_Fmsyr[2],3)` & `r round(extra_sd1$AM_Fmsyr[1],3)` & `r round(extra_sd1$AM_Fmsyr[2],3)` \\
$maxF_{ABC}$ & `r round(extra_sd2$HM_Fmsyr[1],3)` & `r round(extra_sd2$HM_Fmsyr[2],3)` & `r round(extra_sd1$HM_Fmsyr[1],3)` & `r round(extra_sd1$HM_Fmsyr[2],3)` \\
$F_{ABC}$ & `r round(extra_sd2$HM_Fmsyr[1],3)` & `r round(extra_sd2$HM_Fmsyr[2],3)` & `r round(extra_sd1$HM_Fmsyr[1],3)` & `r round(extra_sd1$HM_Fmsyr[2],3)` \\
$OFL$ & `r formatC(1000*extra_sd2$OFL_AM[1],format="d",big.mark=",")` & `r formatC(1000*extra_sd2$OFL_AM[2],format="d",big.mark=",")` & `r formatC(1000*extra_sd1$OFL_AM[1],format="d",big.mark=",")` t & `r formatC(1000*extra_sd1$OFL_AM[2],format="d",big.mark=",")`t \\
$maxABC$ & `r formatC(1000*extra_sd2$ABC_HM[1],format="d",big.mark=",") ` & `r formatC(1000*extra_sd2$ABC_HM[2],format="d",big.mark=",") ` & `r formatC(1000*extra_sd1$ABC_HM[1],format="d",big.mark=",") ` t & `r formatC(1000*extra_sd1$ABC_HM[2],format="d",big.mark=",") ` t \\
$ABC$ & `r formatC(1000*extra_sd2$ABC_HM[1],format="d",big.mark=",") ` & `r formatC(1000*extra_sd2$ABC_HM[2],format="d",big.mark=",") ` & `r formatC(1000*extra_sd1$ABC_HM[1],format="d",big.mark=",") ` t & `r formatC(1000*extra_sd1$ABC_HM[2],format="d",big.mark=",") ` t \\
\hline
Status & `r thisyr-1` & `r thisyr` & `r thisyr-1` & `r thisyr` \\
\hline
Overfishing & No & n/a & No & n/a \\
Overfished & n/a & No & n/a & No \\
Approaching overfished & n/a & No & n/a & No \\
\hline
\end{tabular}
\begin{tablenotes}
\item Projections were based on xxx.
\end{tablenotes}
\end{table}