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hydra_sim.dat
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hydra_sim.dat
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# This file was created using create_DataFile.R and used all inputs from csv files found in folder:
#createDataFiles_testing/dataInputsHydra
# init_int debug
4
# init_int Nyrs
53
# init_int Nspecies
10
# init_int Nsizebins
5
# init_int Nareas
1
# init_int Nfleets
5
# init_int Nsurveys (e.g. NEFSC spring, NEFSC fall, NEAMAP, etc)
1
# init_number wtconv
1
#
# List of Species in Model
# spinydog
# winterskate
# Aherring
# Acod
# haddock
# yellowtailfl
# winterfl
# Amackerel
# silverhake
# goosefish
#
# init_matrix binwidth(1,Nspecies,1,Nsizebins)
20 20 20 20 30
20 20 20 20 40
5 5 5 5 20
20 20 20 40 50
10 10 20 20 20
10 10 10 10 20
10 10 10 10 10
10 10 10 10 10
10 10 10 10 30
20 20 20 30 40
# init_vector lenwt_a(1,Nspecies)
0.003 0.004 0.01 0.009 0.01 0.008 0.011 0.007 0.007 0.02
# init_vector lenwt_b(1,Nspecies)
3.122 3.317 2.99 3.052 3.068 3.129 3.138 3.319 3.05 2.897
# init_int Nrecruitment_cov
1
# init_int Nmaturity_cov
1
# init_int Ngrowth_cov
1
# init_matrix recruitment_cov(1,Nrecruitment_cov,1,Nyrs)
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
# init_matrix maturity_cov(1,Nmaturity_cov,1,Nyrs)
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
# init_matrix growth_cov(1,Ngrowth_cov,1,Nyrs)
8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9.1611 8.0724 8.9277 8.5853 8.7196 8.4143 9.2862 9.1195 9.6151 9.0835 8.1635 8.4771 8.2231 8.8848 8.7333 8.1252 8.4243 9.3494 9.5417 8.2806 8.7779 9.014 9.8138 9.2278 9.21 9.9467 8.6597 7.9774 8.3691 9.162 8.3644 8.8738 8.9778 9.2916 9.2916 9.2916 9.2916 9.2916
##
rawdata.dat
#
#
# init_3darray obs_effort(1,Nareas,1,Nfleets,1,Nyrs)
# fleet types benthic pelagic longline smallMesh gillnet
# Observed effort. No assessment
302211.9377 302211.9377 357465.5609 344059.4269 334086.4365 304218.8086 236545.5474 201588.2651 237477.9269 234481.0794 215128.8922 223337.3396 225812.4032 209896.8293 244203.787 237064.4294 243573.1316 235167.7761 276100.0949 264747.4119 257470.9005 268005.0889 426128.3724 406296.021 492253.5382 560890.0116 522739.6455 465338.4848 462104.8168 440422.755 503666.5016 433318.9375 390675.4669 370448.8665 306665.796 336675.0529 328785.1533 347796.914 382948.0869 357762.5656 305020.9811 299645.7826 266606.7436 230859.6138 220977.0569 199240.4679 173320.6179 192479.4026 135539.1464 138204.7542 113311.402 101971.2578 96659.17693
1698.308959 1698.308959 5247.357908 7549.406171 3267.318238 970.4113541 66.35394398 694.7350311 261.2459591 805.6171506 303.2179139 190.6892446 687.5298395 4580.638999 2018.220134 18176.02529 4759.804065 2229.787757 22599.85671 10735.91329 4754.278717 4848.377107 8548.687103 13364.20027 9550.807685 13464.2352 8765.506917 13461.38446 6209.131417 4221.728317 3336.560481 1357.700833 26760.27704 2431.870458 1256.64827 1923.979756 762.5532221 2107.239239 411.868592 401.4490315 7165.213778 995.9096413 1612.925163 5398.956616 7176.846785 4868.615896 2744.505226 1644.499624 835.46675 968.5237047 3766.882469 3311.21866 7394.349477
8931.634169 8931.634169 8863.203605 9362.3206 10934.91808 9066.356562 9017.000831 10376.46936 5998.988787 3602.670106 4847.038819 7801.529655 21780.43941 8001.61466 6407.875669 5672.114809 6071.17011 7991.040706 5008.809768 5993.652871 7638.880995 7196.66357 11236.95081 11745.04063 16575.47827 24216.63572 22353.36275 20801.37509 20139.84762 23014.63092 23256.73327 28495.36398 22234.59605 23140.51616 18275.16218 18326.47111 23596.4923 34055.66921 29557.75615 28631.61495 25494.54603 28744.50572 29214.84925 38273.67385 30036.6425 27052.99385 24041.41595 23549.00658 26860.97556 20532.01592 19831.84782 24819.06314 33189.78427
1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06
1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06 1e-06
# init_4darray area1_stomwt(1,Nareas,1,Nspecies,1,Nyrs,Nsizebins)
# read in mean stomach content weight time series from .dat file for intake calculation
# spinydog
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
# winterskate
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
0.093423453 0.939220418 3.915192982 11.46903371 44.88111925
# Aherring
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
0.01 0.03 0.8 1.5 3
# Acod
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
0.035 0.103736364 1.952671779 18.42060702 84.48736889
# haddock
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
0.031 0.096183673 0.939220418 3.915192982 12.07313904
# yellowtailfl
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
# winterfl
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
0.016072464 0.086965517 0.237958824 0.442743017 1.583142857
# Amackerel
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
0.01 0.08 1.3 2 3
# silverhake
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
0.039893347 0.179748344 0.512104803 4.879748476 20.262
# goosefish
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
0.090189781 1.952671779 9.334322368 28.80493233 77.55885308
# init_matrix obs_temp(1,Nareas,1,Nyrs)
# Either observed temperature or manufactured temperature for simulation runs
#1977 to 1997 Georges Bank bottom temp from 2011 ESR (1964-1976 set to 8.0) and 1998 to 2010 Georges Bank bottom temp from 2011 ESR
8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9.1611 8.0724 8.9277 8.5853 8.7196 8.4143 9.2862 9.1195 9.6151 9.0835 8.1635 8.4771 8.2231 8.8848 8.7333 8.1252 8.4243 9.3494 9.5417 8.2806 8.7779 9.014 9.8138 9.2278 9.21 9.9467 8.6597 7.9774 8.3691 9.162 8.3644 8.8738 8.9778 9.2916 9.2916 9.2916 9.2916 9.2916
# init_int yr1Nphase //year 1 N at size estimation phase
-1
# init_int recphase //recruitment parameter estimation phas
-1
# init_int avg_rec_phase //average recruitment estimation phase (could make species specific, currently global)
-1
# init_int avg_F_phase //average fishing mort estimation phase (could make species specific, currently global)
-1
# init_int dev_rec_phase //recruitment deviation estimation phase (could make species specific, currently global)
-1
# init_int dev_F_phase //fishing mort deviation estimation phase (could make species specific, currently global)
-1
# init_int fqphase //fishery q estimation phase
-1
# init_int sqphase //survey q estimation phase
-1
# init_int ssig_phase //survey sigma (obs error) phase
-1
# init_int csig_phase //catch sigma (obs error) phase
-1
# init_matrix recGamma_alpha(1,Nareas,1,Nspecies) //eggprod gamma Ricker model alpha
30 10 0.5 0.01 0.02 0.005 0.03 0.02 1 1
# init_matrix recGamma_shape(1,Nareas,1,Nspecies) //eggprod gamma Ricker model shape parameter
0.8 0.7 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.5
# init_matrix recGamma_beta(1,Nareas,1,Nspecies) //eggprod gamma Ricker model beta
0.00267 0.000233 5e-11 6.25e-14 5.56e-12 2.94e-13 1.14e-12 1.43e-11 5.56e-12 0.000625
# init_matrix recDS_alpha(1,Nareas,1,Nspecies) //SSB Deriso-Schnute model alpha
5 5 5 5 5 5 5 5 5 5
# init_matrix recDS_shape(1,Nareas,1,Nspecies) //SSB Deriso-Schnute model shape parameter
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1
# init_matrix recDS_beta(1,Nareas,1,Nspecies) //SSB Deriso-Schnute model beta
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
# init_matrix recGamSSB_alpha(1,Nareas,1,Nspecies) //SSB gamma alpha
0.438 4.61e+10 141 0.00066 0.0883 6.57e-07 3.88e-12 1.73 0.767 0
# init_matrix recGamSSB_shape(1,Nareas,1,Nspecies) //SSB gamma shape parameter
0.506 -1.49 0.419 0.997 0.685 1.71 2.96 0.54 0.665 0
# init_matrix recGamSSB_beta(1,Nareas,1,Nspecies) //SSB gamma model beta
-9.8e-06 -1.83e-06 4.21e-07 4.11e-07 -1.49e-08 1.87e-06 1.89e-05 -4.8e-08 6.73e-06 0
# init_matrix recRicker_alpha(1,Nareas,1,Nspecies) //SSB Ricker model alpha
0.00433 0.000657 0.144 0.000636 0.00168 0.00304 0.00628 0.00721 0.0323 0.00479
# init_matrix recRicker_shape(1,Nareas,1,Nspecies) //SSB Ricker model shape parameter=1.0, not used
1 1 1 1 1 1 1 1 1 1
# init_matrix recRicker_beta(1,Nareas,1,Nspecies) //SSB Ricker model beta
3.18e-06 4.8e-07 1.19e-06 4.24e-07 1.01e-07 2.97e-07 5.15e-06 8.39e-07 1.57e-05 1.91e-05
# init_matrix recBH_alpha(1,Nareas,1,Nspecies) //SSB Beverton Holt model alpha
0.00421 0.00145 0.519 0.000582 0.000471 0.00249 0.005 0.00783 0.0431 -0.0455
# init_matrix recBH_shape(1,Nareas,1,Nspecies) //SSB Beverton Holt model shape parameter=1.0, not used
1 1 1 1 1 1 1 1 1 1
# init_matrix recBH_beta(1,Nareas,1,Nspecies) //SSB Beverton Holt model beta
2.02e-05 2.86e-06 2.36e-05 7.99e-07 4.53e-08 2.29e-07 5.93e-06 3.23e-06 4.27e-05 -0.000562
# init_matrix recShepherd_alpha //SSB S-R Shepherd 3 param
0.000679278593643755 0.00238040478827991 0.0324947957740724 0.000635431858325005 0.0156398853828967 0.140680357211232 7.80488000825912 12.0397563869223 0.0640580586735159 4.36007075550705
# init_matrix recShepherd_shape //SSB S-R Shepherd 3 param
2 2 1 2 1 1 1 1 2 2
# init_matrix recShepherd_beta //SSB S-R Shepherd 3 param
230602.516354506 219518.204035202 325757.548180872 203654.690435448 11802.5550559253 276.575925532932 1.98036775243744 27.2210491198965 23385.8955613239 142.732519469773
# init_matrix recSHockey_alpha //SSB S-R Hockey 2 param
0.000576796226389063 0.000366601125761476 0.0294117463332349 0.00077946522279607 0.00271650056915626 0.0248646978418339 0.00420975568613284 0.000822971615876735 0.105195171197821 0.000704601959031334
# init_matrix recHpockey_shape //SSB S-R Hockey. S* breakpoint
77045.6785794091 182622.594607581 134763.499981566 53552.9882021775 13760.8475131979 1042.06411097098 3231.95057578212 275704.01102284 6034.22428586082 9930.75389960946
# init_matrix recHockey_beta //SSB S-R Hockey 2 param. This is not used
-0.000576796226389063 -0.000366601125761476 -0.0294117463332349 -0.00077946522279607 -0.00271650056915626 -0.0248646978418339 -0.00420975568613284 -0.000822971615876735 -0.105195171197821 -0.000704601959031334
# init_matrix recSegmented_alpha //SSB S-R Segmented 3 param
0.000232292156141445 0.00046834228083232 0.0249212115442954 0.000318976093164312 0.000785481193694654 0.00503031272648454 0.00636032557911675 0.00109261021591247 0.0897841488637133 0.000591892300930652
# init_matrix recSegmented_shape //SSB S-R Segmented 3 param. Breakpoint
199918.976048948 144801.491933376 172892.060747194 96711.2133513694 166637.418503087 11697.205163847 2216.79532354348 266242.211324248 9115.63244022438 14277.3431698854
# init_matrix recSegmented_beta //SSB S-R Segmented 3 param
-0.000273322986219648 -0.000590427054020243 -0.0294345719166398 -0.000462523592067519 -0.00213335599457685 -0.0205453583826828 -0.00646494132716261 -0.00157818932060981 -0.114272009481592 -0.0011133532839706
# init_ivector rectype(1,Nspecies) //switch for alternate recruitment functions 1=gamma/Ricker, 2=Deriso-Schnute, 9=avg+devs
# 3=SSB gamma, 4=SSB Ricker, 5=SSB Beverton Holt added April 2014,6=Shepherd (added Beet Mar 2017)
8 8 7 8 7 7 7 7 8 8
# init_ivector stochrec(1,Nspecies) //switch for stochastic recruitment. 1 = add error, 0= no error
1 1 1 1 1 1 1 1 1 1
# init_matrix sexratio(1,Nareas,1,Nspecies) // this is proportion females
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
# init_matrix recruitment_covwt(1,Nspecies,1,Nrecruitment_cov) //recruitment covariate weighting factor
0
0
0
0
0
0
0
0
0
0
#//fecundity parameters from .dat file and calculate fecundity at length
# init_matrix fecund_d(1,Nareas,1,Nspecies)
0.4 12 0.00316 0.43326 0.1403 0.000484 0.000484 1.098 0.5 0.3
# init_matrix fecund_h(1,Nareas,1,Nspecies)
0.5 0.005 5 4.047 3.587 5.701 5.701 3.5 3.5 3.3
# init_3darray fecund_theta(1,Nareas,1,Nspecies,1,Nsizebins))
0.0 0.0 0.0 1.0 3.0
0.0 0.0 0.0 2.0 3.0
0.0 0.0 0.0 1.0 1.0
0.0 0.5 1.0 1.2 1.4
0.0 0.5 1.0 1.2 1.4
0.0 1.0 1.0 1.2 1.4
0.0 1.0 1.0 1.5 3.0
0.0 1.0 1.0 1.0 1.8
0.0 0.0 1.0 1.5 1.5
0.0 0.5 1.0 1.2 1.4
# init_matrix maturity_nu(1,Nareas,1,Nspecies)
-5 -5 -34.4725 -5.31 -5.658 -11.6405 -9.2895 -16.7885 -13.166 -10.668
# init_matrix maturity_omega(1,Nareas,1,Nspecies)
0.1 0.1 1.36 0.133 0.1995 0.484 0.3685 0.6495 0.5825 0.2885
# init_matrix maturity_covwt(1,Nspecies,1,Nmaturity_cov) //maturity covariate weighting factor
0
0
0
0
0
0
0
0
0
0
#//growth parameters from .dat file and calculate simple (no cov) prob of growing through length interval
# init_matrix growth_psi(1,Nareas,1,Nspecies)
11.28075301 18.77845121 11.65211497 22.31730855 23.06032547 16.00105119 17.76925648 20.48292586 13.77993268 9.180847155
# init_matrix growth_kappa(1,Nareas,1,Nspecies)
0.688187 0.59567 0.456436561 0.734859369 0.502070649 0.618898136 0.552886341 0.26327238 0.648621251 0.955590716
# init_matrix growth_covwt(1,Nspecies,1,Ngrowth_cov)// growth covariate weighting factor
0
0
0
0
0
0
0
0
0
0
# init_matrix vonB_Linf(1,Nareas,1,Nspecies) //alternate parameterization, vonB growth
99.99 114.1 29.05066434 113.5946212 73.8 44.70963156 56.29613167 43.2563036 41.22438991 84.5
# init_matrix vonB_k(1,Nareas,1,Nspecies) //alternate parameterization, vonB growth
0.1 0.14405 0.45225377 0.197508768 0.376 0.477577158 0.291557309 0.205956533 0.403649534 0.34
# init_vector growthtype //switch for alternate growth types,
#1 power, 2 power/covariates, 3 vonB, 4 vonB covariates
4 4 4 4 2 4 4 4 4 2
# init_number phimax
1
# init_matrix intake_alpha(1,Nareas,1,Nspecies)
0.002 0.002 0.004 0.004 0.004 0.004 0.004 0.004 0.004 0.004
# init_matrix intake_beta(1,Nareas,1,Nspecies)
0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11
# M1 - natural mortality (not explained by model)
# init_3darray M1(1,Nareas,1,Nspecies,1,Nsizebins)
0.02 0.02 0.02 0.02 0.02
0.02 0.02 0.02 0.02 0.02
0.02 0.02 0.02 0.02 0.02
0.02 0.02 0.02 0.02 0.02
0.02 0.02 0.02 0.02 0.02
0.02 0.02 0.02 0.02 0.02
0.02 0.02 0.02 0.02 0.02
0.02 0.02 0.02 0.02 0.02
0.02 0.02 0.02 0.02 0.02
0.02 0.02 0.02 0.02 0.02
# init_3darray isprey(1,Nareas,1,Nspecies,1,Nspecies) //preds in columns, prey in rows
1 0 0 0 0 0 0 0 0 1
1 0 0 0 0 0 0 0 0 0
1 1 0 1 1 0 0 0 1 1
1 1 0 1 1 0 0 0 0 1
1 1 0 0 1 0 0 0 0 1
1 0 0 0 0 0 0 0 0 1
1 0 0 0 0 0 0 0 0 1
1 1 0 1 0 0 0 0 1 1
1 1 0 1 1 0 0 0 1 1
0 0 0 0 0 0 0 0 0 1
# init_matrix preferred_wtratio(1,Nareas,1,Nspecies) //pred sizebins
0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
# init_vector sd_sizepref(1,Nspecies) //pred sizebins
2 2 2 2 2 2 2 2 2 2
# //fishery selectivity pars from dat file, for now not area specific
# init_matrix fishsel_c(1,Nspecies,1,Nfleets) //fishery selectivity c par
#benthic trawl and pelagic trawl and longline
-12.903 -12.060 -12.060 0.000 0.000
-10.000 -10.000 -10.000 0.000 0.000
-4.000 -4.000 -200.000 0.000 0.000
-12.903 -12.060 -12.060 0.000 0.000
-11.677 -10.000 -10.000 0.000 0.000
-15.442 -200.000 -200.000 0.000 0.000
-13.894 -200.000 -200.000 0.000 0.000
-5.000 -5.000 -200.000 0.000 0.000
-8.000 -8.000 -8.000 0.000 0.000
-12.000 -12.000 -12.000 0.000 0.000
# init_matrix fishsel_d(1,Nspecies,1,Nfleets) //fishery selectivity d par
#benthic trawl and pelagic trawl and longline
0.25 0.22 0.22 0.00 0.00
0.25 0.25 0.25 0.00 0.00
0.25 0.25 1.00 0.00 0.00
0.25 0.22 0.22 0.00 0.00
0.25 0.25 0.25 0.00 0.00
0.48 1.00 1.00 0.00 0.00
0.57 1.00 1.00 0.00 0.00
0.25 0.25 1.00 0.00 0.00
0.15 0.25 0.25 0.15 0.00
0.25 0.25 0.25 0.00 0.00
# Following content added after ICES publication by Gaichas et al. 2014
# Made by Andy Beet from Dec 2016 onward
# Equilibrium Biomass. B0(1,Nspecies). Tthese values are obtained by running hydra_sim without any error and zero fishing effort
121616.55 656536.65 613950.1 257314.85 327779.05 183035.8 98749.45 179287.55 45521.86 44091.065
#number of Guilds numGuilds.
4
#Guild Membership guildMembership.
4 4 2 1 3 3 3 2 1 1
#Fleet Membership fleetMembers(1,Nfleets)
1 2 1 3
# AssessmentPeriod. Time period (yrs) to assess guild biomass level
3
# init_int flagLinearRamp. // 0 = step function, 1 = linear function
1
#init_vector minExploitation(1,Nfleets) minimum Exploitation rates imposed by each fleet
1e-05 1e-05 1e-05 1e-05 1e-05
#init_vector maxExploitation(1,Nfleets) maximum Exploitation rates imposed by each fleet
1e-05 1e-05 1e-05 1e-05 1e-05
# init_vector minMaxExploitation(1,2) - [MinExploitation, MaxExploitation
0.05 0.05
# init_vector minMaxThreshold(1,2) - [MinThreshold, MaxThreshold
0.1 0.4
# Nthresholds. number of thresholds used for change in exploitation/fishing - Step function
5
# threshold_percent(1,Nthresholds) threshold %ages (of biomass) when action is taken - Step function
# note that must appear in ascending order
0.1 0.2 0.3 0.4 1e+06
# exploitation_levels(1,Nthresholds). these must pair with the threshold_percent values - Step function
0.05 0.05 0.05 0.05 0.05
# threshold_species(1,Nspecies). Species level detection threshold
0.1 0.1 0 0 0 0 0 0 0 0
# int AssessmentOn. Assessment On or Off
0
# int speciesDetection. include species (in addition to guild) in assessment on or off
0
# int LFI_size. (cm). Threshold to determin a large fish. used in LFI metric
60
# init_number scaleInitialN. used to scale initial yr1N abundances found in .pin file
1
# other food term
50000
#init_matrix effortScaled(1,Nareas,1,Nspecies)
1 1 1 1 1 1 1 1 1 1
# init_4darray discard_Coef(1,Nareas,1,Nspecies,1,Nfleets,1,Nsizebins)
# proportion of each species that is discarded for each fleet(Bottom, Pelagic, Fixed)
# spinydog fleet x sizeclass
1 1 1 1 1
0 0 0 0 0
1 1 1 1 0.68
1 1 1 1 1
0 0 0 0 0
# winterskate fleet x sizeclass
1 1 1 1 1
0 0 0 0 0
0.24 0.24 0.24 0.24 0.24
1 1 1 1 1
0 0 0 0 0
# Aherring fleet x sizeclass
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
# Acod fleet x sizeclass
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
# haddock fleet x sizeclass
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
# yellowtailfl fleet x sizeclass
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
# winterfl fleet x sizeclass
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
# Amackerel fleet x sizeclass
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
# silverhake fleet x sizeclass
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
# goosefish fleet x sizeclass
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
# init_4darray discardSurvival_Coef(1,Nareas,1,1,Nspecies,1,Nfleets,1,Nsizebins)
# proportion of discards that survive being thrown back
# spinydog fleet x sizeclass
0.7 0.7 0.7 0.7 0.7
1 1 1 1 1
0.8 0.8 0.8 0.8 0.8
0.7 0.7 0.7 0.7 0.7
1 1 1 1 1
# winterskate fleet x sizeclass
0.9 0.9 0.9 0.9 0.9
1 1 1 1 1
0.95 0.95 0.95 0.95 0.95
0.9 0.9 0.9 0.9 0.9
1 1 1 1 1
# Aherring fleet x sizeclass
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
# Acod fleet x sizeclass
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
# haddock fleet x sizeclass
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
# yellowtailfl fleet x sizeclass
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
# winterfl fleet x sizeclass
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
# Amackerel fleet x sizeclass
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
# silverhake fleet x sizeclass
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
# goosefish fleet x sizeclass
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
# predOrPrey(1,Nspecies). binary vector indicating predators. inverse = prey
1 1 0 1 0 0 0 0 1 1
# bandwidth_metric. (in yrs) for variance estimate of catch - moving window
5
# init_number baseline_threshold // value of threshold that we stop landing catch. Typically 0.2
0.2
# init_3darray indicator_fishery_q(1,Nareas,1,Nspecies,1,Nfleets)
# which species represent targeted catch. These are used to estmate exploitation rate in assessment
1 1 1 1 1
1 0 1 1 1
1 1 0 1 1
1 0 1 1 1
1 1 1 1 1
1 0 0 1 1
1 0 0 1 1
1 1 0 1 1
1 1 0 1 1
1 0 1 1 1
# AR_parameters(1,3) Survey, recruitment, Catch