Depletion-Based Stock Reduction Analysis(DB-SRA) from DLMtool

EwE without environmental effects and without fleet dynamics

Case 0: stock assessment base run

  • B0

  • Case 0: scenario A agg_png 2

  • Case 0: scenario B

agg_png 2

  • Case 0: scenario C
    • Add 11 years of equilibrium catches (catch in 1985) to the start of the catch
    • Try a sequence of Dep (0.1 - 0.9 with interval of 0.05), BMSY_B0 (0.1 - 0.9 with interval of 0.05), and FMSY_M (0.1 - 2.0 with an interval of 0.05) and find the scenario that has the lowest sum of squared differences

[1] 2012 [1] “_lowCV” agg_png 2

Linear regression models from cases 1 - 5 using “true” values from EwE

  • Case 1: Link Atlantic Multidecadal Oscillation Index with menhaden biomass estimates and adjust projections: AMO is an indicator of climate conditions and would affect recruitment variability of menhaden-like species

  • Case 2: Link Palmer drought severity index with menhaden biomass estimates and adjust projections: PSDI is a long-term indicator of drought conditions and it reflects river discharge and precipitation

  • Case 3: Link biomass of Striped bass from the EwE with menhaden biomass estimates and adjust projections because bass is a major predator

  • Case 4: Link fishing effort of menhaden with menhaden biomass estimates and adjust projections

  • Case 5: Link catch per unit effort of menhaden with menhaden biomass estimates and adjust projections

  • Linear regression models from case 1 - 5 (Lag = 1)

    • True biomass of menhaden-like species as functions of AMO, PDSI, biomass of striped bass, fishing effort of menhaden, and menhaden CPUE

Status of indicators (SOI)

  • If stock-indicator relationship is positive, \(SOI_{y} = \frac{I_{y}-I_{y}^{min}}{I_{y}^{max}-I_{y}^{min}}\)

  • If stock-indicator relationship is negative, \(SOI_{y} = 1-\frac{I_{y}-I_{y}^{min}}{I_{y}^{max}-I_{y}^{min}}\)

    where \(I_{y}\) represents indicator value in year y. \(I_{y}^{min}\) and \(I_{y}^{max}\) represent the minimum and maximum values of \(I\) from the time series.

Adjust projections

  • If \(\frac{B2008_{i}}{BMSY} > 1\), \(F^{'}_{i} = FMSY^{min} + SOI2008 \times (FMSY^{max}-FMSY^{min})\)

  • If \(\frac{B2008_{i}}{BMSY} \le 1\) and \(\frac{B2008_{i}}{BMSY} > 0.5\), \(F^{'}_{i} = SOI2008 \times \frac{B2008_{i}}{BMSY} \times FMSY_{i}\)

  • If \(\frac{B2008_{i}}{BMSY} \le 0.5\), \(F^{'}_{i} = 0\)

where \(i\) represents individual iterations

Cases 1-5 are based on the settings from Scenario A

  • Slope values from linear regression models
case projection_year amo pdsi bassB menhadenC menhadenE menhadenCPUE bassCPUE herringCPUE menhadenV
scenario A 2013 -0.18~lm 0.04~lm -2.75472075895165e-06*^~lm -6.16683246867721e-07*^lm -0.01*~lm 3.36491387850548e-06~lm -0.000117200849096584*^~lm -2.12632008581731e-06*~lm -1.28948990509768e-09*^lm
scenario A 2014 -0.23~lm 0.05lm -2.99872684832955e-06*^lm -6.36800110943955e-07*lm -0.01*~lm 3.7877519875523e-06~lm -0.000132441970872483*^~lm -2.18411707970607e-06*lm -1.34730335758057e-09*^lm
scenario A 2015 -0.17~lm 0.03lm -2.26807922319263e-06*^lm -4.73309802676105e-07*^lm -0.00491484315425542*~lm 2.53806237649955e-06~lm -0.000101164201744157*^~lm -1.64449672962529e-06*~lm -9.87638723824728e-10*^lm
scenario A 2016 -0.01~lm 0.04~lm -1.31197573513994e-06*^~lm -3.16455438413169e-07*^~lm -0.00150459795266021*~lm 8.64588373479455e-08~lm -0.0000524801266483754*~lm -1.05134914342197e-06*~lm -6.20820338765002e-10*^~lm
scenario A 2017 -0.00250580549889982~lm 0.04~lm -1.59497805823587e-06*^~lm -3.88289542601425e-07*^~lm -0.0020363125083822*~lm 3.07840905875729e-07~lm -0.0000642309693665358*lm -1.318639767975e-06*~lm -7.66832148379424e-10*^~lm

Cases 1-5 are based on the settings from Scenario B

  • Slope values from linear regression models
case projection_year amo pdsi bassB menhadenC menhadenE menhadenCPUE bassCPUE herringCPUE menhadenV
scenario B 2013 -0.17~lm 0.04~lm -2.70388812833018e-06*^~lm -6.08118385035436e-07*^lm -0.01*~lm 3.11103925120482e-06~lm -0.000114190015373927*^~lm -2.09127097982624e-06*~lm -1.26696509985797e-09*^lm
scenario B 2014 -0.21~lm 0.04lm -2.86015939909818e-06*^lm -6.09420755149238e-07*lm -0.01*~lm 3.42306189653325e-06~lm -0.000125415245553059*^~lm -2.09144565211668e-06*~lm -1.28193604628433e-09*^lm
scenario B 2015 -0.2~lm 0.02lm -2.66117721348385e-06*^lm -5.52997483344579e-07*^lm -0.01*~lm 2.97950096422674e-06~lm -0.000118385444798629*^~lm -1.92807954114117e-06*lm -1.15702445256259e-09*^lm
scenario B 2016 0.00457123400475641~lm 0.05~lm -1.12276824703988e-06*^~lm -2.79361116695719e-07*^lm -0.00101801468193004~lm -5.33348337242373e-08~lm -0.0000440154359546925*lm -9.48149412397354e-07*~lm -5.40833753954504e-10*^~lm
scenario B 2017 -0.00332271595763167lm 0.04lm -1.63425175006211e-06*^~lm -3.93420322321291e-07*^~lm -0.00205623346324939*~lm 2.67015685655826e-07lm -0.0000653576394309592*lm -1.331878438285e-06*~lm -7.7452054562306e-10*^~lm

Cases 1-5 are based on the settings from Scenario C

  • Slope values from linear regression models
case projection_year amo pdsi bassB menhadenC menhadenE menhadenCPUE bassCPUE herringCPUE menhadenV
scenario C 2013 -1.4*~lm -0.44~lm -2.95403994254439e-06~lm -2.90150648354698e-07~lm -0.01*~lm 0.0000268766178805404*^lm -0.000186184080536338*~lm -4.51749577363195e-06*lm -7.81257445598303e-10~lm
scenario C 2014 -1.23*~lm -0.56~lm -2.45334499105909e-06~lm -2.75116140251877e-07~lm -0.01*~lm 0.0000236575181555933*lm -0.00014285832730643~lm -4.62000924948801e-06*~lm -7.36041749529826e-10~lm
scenario C 2015 -1.28*~lm -0.29~lm -2.12527081714333e-06~lm -2.0793253948901e-07~lm -0.01*~lm 0.0000246284266149071*^lm -0.000160557834817018*~lm -4.03147715664648e-06*~lm -5.87717757683979e-10~lm
scenario C 2016 -0.69*lm 0.02~lm 2.16705312291998e-06~lm 7.51685175638862e-07*~lm -0.01*lm 8.61642643433072e-06lm 6.8579218748617e-06~lm 4.14827420616682e-06*~lm 1.16171121231616e-09~lm
scenario C 2017 -1.32*~lm -0.36~lm -1.79586703794368e-06~lm -5.86659452563516e-08lm -0.01*~lm 0.0000252980601377657*^lm -0.000152372711764025*~lm -3.75084460368537e-06*lm -2.92131121014362e-10lm
  • Projections

[DATA NOT ENOUGH] at 7 NA [DATA NOT ENOUGH] at 7 NA [DATA NOT ENOUGH] at 7 NA [DATA NOT ENOUGH] at 7 NA [DATA NOT ENOUGH] at 7 NA [DATA NOT ENOUGH] at 7 NA [DATA NOT ENOUGH] at 7 NA [DATA NOT ENOUGH] at 7 NA [DATA NOT ENOUGH] at 7 NA [DATA NOT ENOUGH] at 7 NA [DATA NOT ENOUGH] at 7 NA [DATA NOT ENOUGH] at 7 NA Average_Catch B_K B_BMSY Average_Biomass Max 351862 0.7169 1.23e+06 2137774 Min 0 0.0000 0.00e+00 0 OM+OM FMSY 204207 0.3049 5.21e+05 570693 OM+DBSRA FMSY 154573 0.4505 7.70e+05 744602 OM+AMO Fadj 0 0.7169 1.23e+06 1051243 OM+PDSI Fadj 0 0.7169 1.23e+06 1051243 OM+Bass Biomass Fadj 0 0.7169 1.23e+06 1051243 OM+Menhaden Catch Fadj 0 0.7169 1.23e+06 1051243 OM+Menhaden Effort Fadj 0 0.7169 1.23e+06 1051243 OM+Menhaden CPUE Fadj 0 0.7169 1.23e+06 1051243 OM+Bass CPUE Fadj 0 0.7169 1.23e+06 1051243 OM+Herring CPUE Fadj 0 0.7169 1.23e+06 1051243 OM+Menhaden Value Fadj 0 0.7169 1.23e+06 1051243 DBSRA EM+DBSRA FMSY 351862 0.0987 1.40e-01 2137774 DBSRA EM+AMO Fadj 0 0.0983 1.41e-01 824383 DBSRA EM+PDSI Fadj 0 0.0985 1.41e-01 882987 DBSRA EM+Bass Biomass Fadj 0 0.0978 1.40e-01 941813 DBSRA EM+Menhaden Catch Fadj 0 0.0975 1.40e-01 950221 DBSRA EM+Menhaden Effort Fadj 0 0.0978 1.40e-01 821049 DBSRA EM+Menhaden CPUE Fadj 0 0.0984 1.41e-01 880031 DBSRA EM+Bass CPUE Fadj 0 0.0985 1.41e-01 857855 DBSRA EM+Herring CPUE Fadj 0 0.0975 1.40e-01 884146 DBSRA EM+Menhaden Value Fadj 0 0.0976 1.40e-01 843719 Bonanza_Period Collapse_Period Max 5 0 Min 0 5 OM+OM FMSY 0 2 OM+DBSRA FMSY 0 2 OM+AMO Fadj 0 1 OM+PDSI Fadj 0 1 OM+Bass Biomass Fadj 0 1 OM+Menhaden Catch Fadj 0 1 OM+Menhaden Effort Fadj 0 1 OM+Menhaden CPUE Fadj 0 1 OM+Bass CPUE Fadj 0 1 OM+Herring CPUE Fadj 0 1 OM+Menhaden Value Fadj 0 1 DBSRA EM+DBSRA FMSY 0 5 DBSRA EM+AMO Fadj 0 5 DBSRA EM+PDSI Fadj 0 5 DBSRA EM+Bass Biomass Fadj 0 5 DBSRA EM+Menhaden Catch Fadj 0 5 DBSRA EM+Menhaden Effort Fadj 0 5 DBSRA EM+Menhaden CPUE Fadj 0 5 DBSRA EM+Bass CPUE Fadj 0 5 DBSRA EM+Herring CPUE Fadj 0 5 DBSRA EM+Menhaden Value Fadj 0 5 agg_png 2

EwE with environmental effects and without fleet dynamics

EwE with environmental effects and fleet dynamics