vignettes/R/global4UG.R
In tcarruth/MERA: Method Evaluation and Risk Assessment
# Shared variables
Current_Year<-as.integer(substr(Sys.time(),start=1,stop=4))
# App design
# ----- Fishery answers -----------------------------------------------------------------------------
#F2
M_list<-list("Very short-lived (5 < maximum age < 7)" = "M_60", "Short-lived (7 < maximum age < 10)" = "M_40_60",
"Moderate life span (10 < maximum age < 20)" = "M_20_40", "Moderately long-lived (20 < maximum age < 40)" = "M_10_20",
"Long-lived (40 < maximum age < 80)" = "M_05_10","Very long-lived (80 < maximum age < 160)" = "M_05")
M_mins<- c( 0.56, 0.39, 0.2, 0.098, 0.049, 0.02445)
M_maxes<-c( 0.78, 0.56, 0.39, 0.2, 0.098, 0.049)
#F3
D_list<-list("Crashed (D < 0.05)" = "D_05", "Very depleted (0.05 < D < 0.1)" = "D_05_10",
"Depleted (0.1 < D < 0.15)" = "D_10_15", "Moderately depleted (0.15 < D < 0.3)" = "D_15_30",
"Healthy (0.3 < D < 0.5)" = "D_30_50","Underexploited (0.5 < D)" = "D_50")
D_mins<- c( 0.01, 0.05, 0.1, 0.15, 0.3, 0.5)
D_maxes<-c( 0.05, 0.1, 0.15, 0.3, 0.5, 0.8)
#F4
h_list<-list("Not resilient (steepness < 0.3)" = "h_30", "Low resilience (0.3 < steepness < 0.5)" = "h_30_50",
"Moderate resilence (0.5 < steepness < 0.7)" = "h_50_70", "Resilient (0.7 < steepness < 0.9)" = "h_70_90",
"Very Resilient (0.9 < steepness)" = "h_90")
h_mins<- c( 0.25, 0.3, 0.5, 0.7, 0.9)
h_maxes<-c( 0.3, 0.5, 0.7, 0.9, 0.99)
#F5
FP_list<-list("Stable" = "FP_s","Two-phase" = "FP_gr" ,"Boom-bust" = "FP_bb","Gradual increases"="FP_gi",
"Stable, recent increases"="FP_ri", "Stable, recent declines" = "FP_rd")
M1s<- c(0.2, 0.32, 0.2, 0.15, 0.2, 0.2)
M2s<- c(1.2, 0.79, 0.7, 1.4, 1.2, 0.7)
sd1s<- c(0.075, 0.13, 0.1, 0.05, 0.075, 0.075)
sd2s<- c(0.1, 0.15, 0.18, 0.4, 0.2, 0.2)
h2s<- c(0, 0, 3, 8, 2.5, 0)
bms<- c(F, T, F, F, F, F)
#F6
F_list<-list("Not variable (less than 20% inter-annual change (IAC))" = "F_10", "Variable (maximum IAC between 20% to 50%)" = "F_10_25",
"Highly variable (maximum IAC between 50% and 100%)"="F_25_50")
F_mins<- c(0.05, 0.1, 0.25)
F_maxes<- c(0.1, 0.25, 0.5)
#F7
qh_list<-list("Declining by 2-3% pa (halves every 25-35 years)"="qh_d3_d2","Declining by 1-2% pa (halves every 35-70 years)"="qh_d2_d1",
"Stable -1% to 1% pa (may halve/double every 70 years)"="qh_d1_1","Increasing by 1-2% pa (doubles every 35-70 years)"="qh_1_2",
"Increasing by 2-3% pa (doubles every 25-35 years)"="qh_2_3")
qh_mins<- c(-3, -2, -1, 1, 2)
qh_maxes<-c(-2, -1, 1, 2, 3)
#F8
q_list<-list("Declining by 2-3% pa (halves every 25-35 years)"="q_d3_d2","Declining by 1-2% pa (halves every 35-70 years)"="q_d2_d1",
"Stable -1% to 1% pa (may halve/double every 70 years)"="q_d1_1","Increasing by 1-2% pa (doubles every 35-70 years)"="q_1_2",
"Increasing by 2-3% pa (doubles every 25-35 years)"="q_2_3")
q_mins<- c(-3, -2, -1, 1, 2)
q_maxes<-c(-2, -1, 1, 2, 3)
#F9
LM_list<-list("Very small (0.4 < LM < 0.5)" = "LM_40_50","Small (0.5 < LM < 0.6)"="LM_50_60",
"Moderate (0.6 < LM < 0.7)" = "LM_60_70",
"Moderate to large (0.7 < LM < 0.8)" = "LM_70_80","Large (0.8 < LM < 0.9)"="LM_80_90")
LM_mins<- (4:8)/10
LM_maxes<-(5:9)/10
#F10
sel_list<-list("Very small (0.1 < S < 0.2)" = "sel_10_20","Small (0.2 < S < 0.4)"="sel_20_40",
"Half asymptotic length (0.4 < S < 0.6)" = "sel_40_60",
"Large (0.6 < S < 0.8)" = "sel_60_80","Very large (0.8 < S < 0.9)"="sel_80_90")
sel_mins<- c( 0.1, 0.2, 0.4, 0.6, 0.8)
sel_maxes<-c( 0.2, 0.4, 0.6, 0.8, 0.9)
#F11
dome_list<-list("Asymptotic selectivity (SL = 1)" = "dome_100", "Declining selectivity with length (0.75 < SL < 1)"="dome_75_100",
"Dome-shaped selectivity (0.25 < SL < 0.75)" = "dome_25_75", "Strong dome-shaped selectivity (SL < 0.25)" = "dome_25")
dome_mins<- c(0.98, 0.75, 0.25, 0.05)
dome_maxes<-c(1, 1, 0.75, 0.25)
#F12
DR_list<-list("Low (DR < 1%)"="DR_1","Low - moderate (1% < DR < 10%)"="DR_1_10","Moderate (10% < DR < 30%)"="DR_10_30",
"Moderate - high (30% < DR < 50%)"="DR_30_50", "High (50% < DR < 70%)"="DR_50_70")
DR_mins<- c( 0, 0.01, 0.1, 0.3, 0.5)
DR_maxes<-c( 0.01, 0.1, 0.3, 0.5, 0.7)
#F13
PRM_list<-list("Low (PRM < 5%)"="PRM_5","Low - moderate (5% < PRM < 25%)"="PRM_5_25","Moderate (25% < PRM < 50%)"="PRM_25_50",
"Moderate - high (50% < PRM < 75%)"="PRM_50_75", "High (75% < PRM < 95%)"="PRM_75_95", "Almost all die (95% < PRM < 100%)"="PRM_95_100")
PRM_mins<- c( 0, 0.05, 0.25, 0.5, 0.75, 0.95)
PRM_maxes<-c( 0.05, 0.25, 0.5, 0.75, 0.95, 1)
#F14
sigR_list<-list("Very low (less than 20% inter-annual changes (IAC))"="sigR_10","Low (max IAC of between 20% and 60%)"="sigR_10_30",
"Moderate (max IAC of between 60% and 120%)"="sigR_30_60", "High (max IAC of between 120% and 180%)"="sigR_60_90",
"Very high (max IAC greater than 180%)"="sigR_90")
sigR_mins<- c( 0.05, 0.1, 0.3, 0.6, 0.9)
sigR_maxes<-c( 0.1, 0.3, 0.6, 0.9, 1.2)
#F15
Ah_list<-list("None"="A_1","Small (A < 5%)"="A_1_5", "Small-moderate (5% < A < 10%)" = "A_5_10", "Moderate (10% < A < 20%)" = "A_10_20",
"Large (20% < A < 30%)"="A_20_30","Very large (30% < A < 40%)" = "A_30_40", "Huge (40% < A < 50%)"="A_40_50")
Ah_mins<- c(0.005, 0.01, 0.05, 0.1, 0.2, 0.3, 0.4)
Ah_maxes<-c(0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5)
#F16
Vh_list<-list("Very low (P < 1%)" = "P_1", "Low (1% < P < 5%)" = "P_1_5", "Moderate (5% < P < 10%)" = "P_5_10",
"High (10% < P < 20%)" = "P_10_20", "Fully mixed" = "P_20")
Vh_mins<- c(0.005, 0.01, 0.05, 0.1, 0.2)
Vh_maxes<-c(0.01, 0.05, 0.1, 0.2, 0.5)
#F17
A_list<-list("None"="A_1","Small (A < 5%)"="A_1_5", "Small-moderate (5% < A < 10%)" = "A_5_10", "Moderate (10% < A < 20%)" = "A_10_20",
"Large (20% < A < 30%)"="A_20_30","Very large (30% < A < 40%)" = "A_30_40", "Huge (40% < A < 50%)"="A_40_50")
A_mins<- c(0.005, 0.01, 0.05, 0.1, 0.2, 0.3, 0.4)
A_maxes<-c(0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5)
#F18
V_list<-list("Very low (P < 1%)" = "P_1", "Low (1% < P < 5%)" = "P_1_5", "Moderate (5% < P < 10%)" = "P_5_10",
"High (10% < P < 20%)" = "P_10_20", "Fully mixed" = "P_20")
V_mins<- c(0.005, 0.01, 0.05, 0.1, 0.2)
V_maxes<-c(0.01, 0.05, 0.1, 0.2, 0.5)
#F19
Dh_list<-list("Very low (0.1 < D1 < 0.15)" = "D1_10_15", "Low (0.15 < D1 < 0.3)" = "D1_15_30",
"Moderate (0.3 < D < 0.5)" = "D1_30_50", "High (0.5 < D1)" = "D1_50",
"Asymptotic unfished levels (D1 = 1)" = "D1_100_100")
Dh_mins<- c( 0.1, 0.15, 0.3, 0.5, 1)
Dh_maxes<-c( 0.15, 0.3, 0.5, 1, 1)
# ----- Management answers -----------------------------------------------------------------------------
M1_list<-list("TAC" = "TAC", "TAE" = "TAE",
"Size limit" = "size_limit", "Time-area closures" = "time_area_close")
IB_list<-list("Large underages (40% - 70% of recommended)" = "IB_n30", "Underages (70% - 90% of recommended)" = "IB_n30_n10","Slight underages (90% - 100% of recommended)" = "IB_n10_0",
"Taken exactly (95% - 105% of recommended)"="IB_n5_5","Slight overages (100% - 110% of recommended)"="IB_0_10","Overages (110% - 150% of recommended)"="IB_10_30","Large overages (150% - 200% of recommended)"="IB_30")
IB_mins<- c( 0.4, 0.7, 0.9, 0.95, 1, 1.1, 1.5)
IB_maxes<-c( 0.7, 0.9, 1, 1.05, 1.1, 1.5, 2)
IV_list<-list("Constant (V < 1%)" = "IV_1", "Not variable (1% < V < 5%)" = "IV_1_5","Low variability (5% < V < 10%)" = "IV_5_10",
"Variable (10% < V < 20%)"="IV_10_20","Highly variable (20% < V < 40%)"="IV_20_40")
IV_mins<-c(0.005,0.01,0.05,0.1,0.2)
IV_maxes<-c(0.01,0.05,0.1,0.2,0.4)
IBE_list<-list("Large underages (40% - 70% of recommended)" = "IB_n30", "Underages (70% - 90% of recommended)" = "IB_n30_n10","Slight underages (90% - 100% of recommended)" = "IB_n10_0",
"Taken exactly (95% - 105% of recommended)"="IB_n5_5","Slight overages (100% - 110% of recommended)"="IB_0_10","Overages (110% - 150% of recommended)"="IB_10_30","Large overages (150% - 200% of recommended)"="IB_30")
IBE_mins<- c( 0.4, 0.7, 0.9, 0.95, 1, 1.1, 1.5)
IBE_maxes<-c( 0.7, 0.9, 1, 1.05, 1.1, 1.5, 2)
IVE_list<-list("Constant (V < 1%)" = "IV_1", "Not variable (1% < V < 5%)" = "IV_1_5","Low variability (5% < V < 10%)" = "IV_5_10",
"Variable (10% < V < 20%)"="IV_10_20","Highly variable (20% < V < 40%)"="IV_20_40")
IVE_mins<-c(0.005,0.01,0.05,0.1,0.2)
IVE_maxes<-c(0.01,0.05,0.1,0.2,0.4)
IBSL_list<-list("Much smaller (40% - 70% of recommended)" = "IB_n30", "Smaller (70% - 90% of recommended)" = "IB_n30_n10","Slightly smaller (90% - 100% of recommended)" = "IB_n10_0",
"Taken exactly (95% - 105% of recommended)"="IB_n5_5","Slightly larger (100% - 110% of recommended)"="IB_0_10","Larger (110% - 150% of recommended)"="IB_10_30","Much larger (150% - 200% of recommended)"="IB_30")
IBSL_mins<- c( 0.4, 0.7, 0.9, 0.95, 1, 1.1, 1.5)
IBSL_maxes<-c( 0.7, 0.9, 1, 1.05, 1.1, 1.5, 2)
IVSL_list<-list("Constant (V < 1%)" = "IV_1", "Not variable (1% < V < 5%)" = "IV_1_5","Low variability (5% < V < 10%)" = "IV_5_10",
"Variable (10% < V < 20%)"="IV_10_20","Highly variable (20% < V < 40%)"="IV_20_40")
IVSL_mins<-c(0.005,0.01,0.05,0.1,0.2)
IVSL_maxes<-c(0.01,0.05,0.1,0.2,0.4)
# ----- Data answers -----------------------------------------------------------------------------
D1_list<-list("Historical annual catches (from unfished)" = "ann_cat","Recent annual catches (at least 5 recent years)" = "ann_cat_R", "Historical relative abundance index (from unfished)"= "ind",
"Recent relative abundance index (at least 5 recent years)"= "ind_R",
"Fishing effort" = "fis_eff","Size composition (length samples)" = "siz_com","Age composition (age samples)" = "age_com", "Growth (growth parameters)" = "growth",
"Absolute biomass survey"="cur_bio_sur")
CB_list<-list("Strong under-reporting (30% - 50%)" = "CB_n50_n30", "Under-reporting (10% - 30%)" = "CB_n30_n10","Slight under-reporting (0% - 10%)" = "CB_n10_0",
"Reported accurately (+/- 5%)" = "CB_n5_5","Slight over-reporting (less than 10%)" = "CB_0_10")
CB_mins<- c( 0.5, 0.7, 0.9, 0.95, 1)
CB_maxes<-c( 0.7, 0.9, 1, 1.05, 1.1)
Beta_list<-list("Strong hyperdepletion (2 < Beta < 3)" = "Beta_200_300", "Hyperdepletion (1.25 < Beta < 2)" = "Beta_125_200","Proportional (0.8 < Beta < 1.25)" = "Beta_80_125",
"Hyperstability (0.5 < Beta < 0.8)" = "Beta_50_80","Strong hyperstability (0.33 < Beta < 0.5)"="Beta_33_50")
Beta_mins<- c( 2, 1.25, 0.8, 0.50, 0.33)
Beta_maxes<-c( 3, 2, 1.25, 0.8, 0.50)
Err_list<-list("Perfect" = "Err_perf","Good (accurate and precise)" = "Err_good","Data moderate (some what inaccurate and imprecise)"="Err_Mod",
"Data poor (inaccurate and imprecise)" = "Err_bad")
# ------ Sliders -----------------------------------------------------------------------------------
loc<-list("loc"=1)
stmag<-list("stmag"=1)
co<-list("co"=1)
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# Shared variables
Current_Year<-as.integer(substr(Sys.time(),start=1,stop=4))
# App design
# ----- Fishery answers -----------------------------------------------------------------------------
#F2
M_list<-list("Very short-lived (5 < maximum age < 7)" = "M_60", "Short-lived (7 < maximum age < 10)" = "M_40_60",
"Moderate life span (10 < maximum age < 20)" = "M_20_40", "Moderately long-lived (20 < maximum age < 40)" = "M_10_20",
"Long-lived (40 < maximum age < 80)" = "M_05_10","Very long-lived (80 < maximum age < 160)" = "M_05")
M_mins<- c( 0.56, 0.39, 0.2, 0.098, 0.049, 0.02445)
M_maxes<-c( 0.78, 0.56, 0.39, 0.2, 0.098, 0.049)
#F3
D_list<-list("Crashed (D < 0.05)" = "D_05", "Very depleted (0.05 < D < 0.1)" = "D_05_10",
"Depleted (0.1 < D < 0.15)" = "D_10_15", "Moderately depleted (0.15 < D < 0.3)" = "D_15_30",
"Healthy (0.3 < D < 0.5)" = "D_30_50","Underexploited (0.5 < D)" = "D_50")
D_mins<- c( 0.01, 0.05, 0.1, 0.15, 0.3, 0.5)
D_maxes<-c( 0.05, 0.1, 0.15, 0.3, 0.5, 0.8)
#F4
h_list<-list("Not resilient (steepness < 0.3)" = "h_30", "Low resilience (0.3 < steepness < 0.5)" = "h_30_50",
"Moderate resilence (0.5 < steepness < 0.7)" = "h_50_70", "Resilient (0.7 < steepness < 0.9)" = "h_70_90",
"Very Resilient (0.9 < steepness)" = "h_90")
h_mins<- c( 0.25, 0.3, 0.5, 0.7, 0.9)
h_maxes<-c( 0.3, 0.5, 0.7, 0.9, 0.99)
#F5
FP_list<-list("Stable" = "FP_s","Two-phase" = "FP_gr" ,"Boom-bust" = "FP_bb","Gradual increases"="FP_gi",
"Stable, recent increases"="FP_ri", "Stable, recent declines" = "FP_rd")
M1s<- c(0.2, 0.32, 0.2, 0.15, 0.2, 0.2)
M2s<- c(1.2, 0.79, 0.7, 1.4, 1.2, 0.7)
sd1s<- c(0.075, 0.13, 0.1, 0.05, 0.075, 0.075)
sd2s<- c(0.1, 0.15, 0.18, 0.4, 0.2, 0.2)
h2s<- c(0, 0, 3, 8, 2.5, 0)
bms<- c(F, T, F, F, F, F)
#F6
F_list<-list("Not variable (less than 20% inter-annual change (IAC))" = "F_10", "Variable (maximum IAC between 20% to 50%)" = "F_10_25",
"Highly variable (maximum IAC between 50% and 100%)"="F_25_50")
F_mins<- c(0.05, 0.1, 0.25)
F_maxes<- c(0.1, 0.25, 0.5)
#F7
qh_list<-list("Declining by 2-3% pa (halves every 25-35 years)"="qh_d3_d2","Declining by 1-2% pa (halves every 35-70 years)"="qh_d2_d1",
"Stable -1% to 1% pa (may halve/double every 70 years)"="qh_d1_1","Increasing by 1-2% pa (doubles every 35-70 years)"="qh_1_2",
"Increasing by 2-3% pa (doubles every 25-35 years)"="qh_2_3")
qh_mins<- c(-3, -2, -1, 1, 2)
qh_maxes<-c(-2, -1, 1, 2, 3)
#F8
q_list<-list("Declining by 2-3% pa (halves every 25-35 years)"="q_d3_d2","Declining by 1-2% pa (halves every 35-70 years)"="q_d2_d1",
"Stable -1% to 1% pa (may halve/double every 70 years)"="q_d1_1","Increasing by 1-2% pa (doubles every 35-70 years)"="q_1_2",
"Increasing by 2-3% pa (doubles every 25-35 years)"="q_2_3")
q_mins<- c(-3, -2, -1, 1, 2)
q_maxes<-c(-2, -1, 1, 2, 3)
#F9
LM_list<-list("Very small (0.4 < LM < 0.5)" = "LM_40_50","Small (0.5 < LM < 0.6)"="LM_50_60",
"Moderate (0.6 < LM < 0.7)" = "LM_60_70",
"Moderate to large (0.7 < LM < 0.8)" = "LM_70_80","Large (0.8 < LM < 0.9)"="LM_80_90")
LM_mins<- (4:8)/10
LM_maxes<-(5:9)/10
#F10
sel_list<-list("Very small (0.1 < S < 0.2)" = "sel_10_20","Small (0.2 < S < 0.4)"="sel_20_40",
"Half asymptotic length (0.4 < S < 0.6)" = "sel_40_60",
"Large (0.6 < S < 0.8)" = "sel_60_80","Very large (0.8 < S < 0.9)"="sel_80_90")
sel_mins<- c( 0.1, 0.2, 0.4, 0.6, 0.8)
sel_maxes<-c( 0.2, 0.4, 0.6, 0.8, 0.9)
#F11
dome_list<-list("Asymptotic selectivity (SL = 1)" = "dome_100", "Declining selectivity with length (0.75 < SL < 1)"="dome_75_100",
"Dome-shaped selectivity (0.25 < SL < 0.75)" = "dome_25_75", "Strong dome-shaped selectivity (SL < 0.25)" = "dome_25")
dome_mins<- c(0.98, 0.75, 0.25, 0.05)
dome_maxes<-c(1, 1, 0.75, 0.25)
#F12
DR_list<-list("Low (DR < 1%)"="DR_1","Low - moderate (1% < DR < 10%)"="DR_1_10","Moderate (10% < DR < 30%)"="DR_10_30",
"Moderate - high (30% < DR < 50%)"="DR_30_50", "High (50% < DR < 70%)"="DR_50_70")
DR_mins<- c( 0, 0.01, 0.1, 0.3, 0.5)
DR_maxes<-c( 0.01, 0.1, 0.3, 0.5, 0.7)
#F13
PRM_list<-list("Low (PRM < 5%)"="PRM_5","Low - moderate (5% < PRM < 25%)"="PRM_5_25","Moderate (25% < PRM < 50%)"="PRM_25_50",
"Moderate - high (50% < PRM < 75%)"="PRM_50_75", "High (75% < PRM < 95%)"="PRM_75_95", "Almost all die (95% < PRM < 100%)"="PRM_95_100")
PRM_mins<- c( 0, 0.05, 0.25, 0.5, 0.75, 0.95)
PRM_maxes<-c( 0.05, 0.25, 0.5, 0.75, 0.95, 1)
#F14
sigR_list<-list("Very low (less than 20% inter-annual changes (IAC))"="sigR_10","Low (max IAC of between 20% and 60%)"="sigR_10_30",
"Moderate (max IAC of between 60% and 120%)"="sigR_30_60", "High (max IAC of between 120% and 180%)"="sigR_60_90",
"Very high (max IAC greater than 180%)"="sigR_90")
sigR_mins<- c( 0.05, 0.1, 0.3, 0.6, 0.9)
sigR_maxes<-c( 0.1, 0.3, 0.6, 0.9, 1.2)
#F15
Ah_list<-list("None"="A_1","Small (A < 5%)"="A_1_5", "Small-moderate (5% < A < 10%)" = "A_5_10", "Moderate (10% < A < 20%)" = "A_10_20",
"Large (20% < A < 30%)"="A_20_30","Very large (30% < A < 40%)" = "A_30_40", "Huge (40% < A < 50%)"="A_40_50")
Ah_mins<- c(0.005, 0.01, 0.05, 0.1, 0.2, 0.3, 0.4)
Ah_maxes<-c(0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5)
#F16
Vh_list<-list("Very low (P < 1%)" = "P_1", "Low (1% < P < 5%)" = "P_1_5", "Moderate (5% < P < 10%)" = "P_5_10",
"High (10% < P < 20%)" = "P_10_20", "Fully mixed" = "P_20")
Vh_mins<- c(0.005, 0.01, 0.05, 0.1, 0.2)
Vh_maxes<-c(0.01, 0.05, 0.1, 0.2, 0.5)
#F17
A_list<-list("None"="A_1","Small (A < 5%)"="A_1_5", "Small-moderate (5% < A < 10%)" = "A_5_10", "Moderate (10% < A < 20%)" = "A_10_20",
"Large (20% < A < 30%)"="A_20_30","Very large (30% < A < 40%)" = "A_30_40", "Huge (40% < A < 50%)"="A_40_50")
A_mins<- c(0.005, 0.01, 0.05, 0.1, 0.2, 0.3, 0.4)
A_maxes<-c(0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5)
#F18
V_list<-list("Very low (P < 1%)" = "P_1", "Low (1% < P < 5%)" = "P_1_5", "Moderate (5% < P < 10%)" = "P_5_10",
"High (10% < P < 20%)" = "P_10_20", "Fully mixed" = "P_20")
V_mins<- c(0.005, 0.01, 0.05, 0.1, 0.2)
V_maxes<-c(0.01, 0.05, 0.1, 0.2, 0.5)
#F19
Dh_list<-list("Very low (0.1 < D1 < 0.15)" = "D1_10_15", "Low (0.15 < D1 < 0.3)" = "D1_15_30",
"Moderate (0.3 < D < 0.5)" = "D1_30_50", "High (0.5 < D1)" = "D1_50",
"Asymptotic unfished levels (D1 = 1)" = "D1_100_100")
Dh_mins<- c( 0.1, 0.15, 0.3, 0.5, 1)
Dh_maxes<-c( 0.15, 0.3, 0.5, 1, 1)
# ----- Management answers -----------------------------------------------------------------------------
M1_list<-list("TAC" = "TAC", "TAE" = "TAE",
"Size limit" = "size_limit", "Time-area closures" = "time_area_close")
IB_list<-list("Large underages (40% - 70% of recommended)" = "IB_n30", "Underages (70% - 90% of recommended)" = "IB_n30_n10","Slight underages (90% - 100% of recommended)" = "IB_n10_0",
"Taken exactly (95% - 105% of recommended)"="IB_n5_5","Slight overages (100% - 110% of recommended)"="IB_0_10","Overages (110% - 150% of recommended)"="IB_10_30","Large overages (150% - 200% of recommended)"="IB_30")
IB_mins<- c( 0.4, 0.7, 0.9, 0.95, 1, 1.1, 1.5)
IB_maxes<-c( 0.7, 0.9, 1, 1.05, 1.1, 1.5, 2)
IV_list<-list("Constant (V < 1%)" = "IV_1", "Not variable (1% < V < 5%)" = "IV_1_5","Low variability (5% < V < 10%)" = "IV_5_10",
"Variable (10% < V < 20%)"="IV_10_20","Highly variable (20% < V < 40%)"="IV_20_40")
IV_mins<-c(0.005,0.01,0.05,0.1,0.2)
IV_maxes<-c(0.01,0.05,0.1,0.2,0.4)
IBE_list<-list("Large underages (40% - 70% of recommended)" = "IB_n30", "Underages (70% - 90% of recommended)" = "IB_n30_n10","Slight underages (90% - 100% of recommended)" = "IB_n10_0",
"Taken exactly (95% - 105% of recommended)"="IB_n5_5","Slight overages (100% - 110% of recommended)"="IB_0_10","Overages (110% - 150% of recommended)"="IB_10_30","Large overages (150% - 200% of recommended)"="IB_30")
IBE_mins<- c( 0.4, 0.7, 0.9, 0.95, 1, 1.1, 1.5)
IBE_maxes<-c( 0.7, 0.9, 1, 1.05, 1.1, 1.5, 2)
IVE_list<-list("Constant (V < 1%)" = "IV_1", "Not variable (1% < V < 5%)" = "IV_1_5","Low variability (5% < V < 10%)" = "IV_5_10",
"Variable (10% < V < 20%)"="IV_10_20","Highly variable (20% < V < 40%)"="IV_20_40")
IVE_mins<-c(0.005,0.01,0.05,0.1,0.2)
IVE_maxes<-c(0.01,0.05,0.1,0.2,0.4)
IBSL_list<-list("Much smaller (40% - 70% of recommended)" = "IB_n30", "Smaller (70% - 90% of recommended)" = "IB_n30_n10","Slightly smaller (90% - 100% of recommended)" = "IB_n10_0",
"Taken exactly (95% - 105% of recommended)"="IB_n5_5","Slightly larger (100% - 110% of recommended)"="IB_0_10","Larger (110% - 150% of recommended)"="IB_10_30","Much larger (150% - 200% of recommended)"="IB_30")
IBSL_mins<- c( 0.4, 0.7, 0.9, 0.95, 1, 1.1, 1.5)
IBSL_maxes<-c( 0.7, 0.9, 1, 1.05, 1.1, 1.5, 2)
IVSL_list<-list("Constant (V < 1%)" = "IV_1", "Not variable (1% < V < 5%)" = "IV_1_5","Low variability (5% < V < 10%)" = "IV_5_10",
"Variable (10% < V < 20%)"="IV_10_20","Highly variable (20% < V < 40%)"="IV_20_40")
IVSL_mins<-c(0.005,0.01,0.05,0.1,0.2)
IVSL_maxes<-c(0.01,0.05,0.1,0.2,0.4)
# ----- Data answers -----------------------------------------------------------------------------
D1_list<-list("Historical annual catches (from unfished)" = "ann_cat","Recent annual catches (at least 5 recent years)" = "ann_cat_R", "Historical relative abundance index (from unfished)"= "ind",
"Recent relative abundance index (at least 5 recent years)"= "ind_R",
"Fishing effort" = "fis_eff","Size composition (length samples)" = "siz_com","Age composition (age samples)" = "age_com", "Growth (growth parameters)" = "growth",
"Absolute biomass survey"="cur_bio_sur")
CB_list<-list("Strong under-reporting (30% - 50%)" = "CB_n50_n30", "Under-reporting (10% - 30%)" = "CB_n30_n10","Slight under-reporting (0% - 10%)" = "CB_n10_0",
"Reported accurately (+/- 5%)" = "CB_n5_5","Slight over-reporting (less than 10%)" = "CB_0_10")
CB_mins<- c( 0.5, 0.7, 0.9, 0.95, 1)
CB_maxes<-c( 0.7, 0.9, 1, 1.05, 1.1)
Beta_list<-list("Strong hyperdepletion (2 < Beta < 3)" = "Beta_200_300", "Hyperdepletion (1.25 < Beta < 2)" = "Beta_125_200","Proportional (0.8 < Beta < 1.25)" = "Beta_80_125",
"Hyperstability (0.5 < Beta < 0.8)" = "Beta_50_80","Strong hyperstability (0.33 < Beta < 0.5)"="Beta_33_50")
Beta_mins<- c( 2, 1.25, 0.8, 0.50, 0.33)
Beta_maxes<-c( 3, 2, 1.25, 0.8, 0.50)
Err_list<-list("Perfect" = "Err_perf","Good (accurate and precise)" = "Err_good","Data moderate (some what inaccurate and imprecise)"="Err_Mod",
"Data poor (inaccurate and imprecise)" = "Err_bad")
# ------ Sliders -----------------------------------------------------------------------------------
loc<-list("loc"=1)
stmag<-list("stmag"=1)
co<-list("co"=1)
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