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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
3
# init_int Nyrs
53
# init_int Nspecies
10
# init_int Nsizebins
5
# init_int Nareas
1
# init_int Nfleets
5
# 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
# init_3darray obs_survey_biomass(1,Nareas,1,Nspecies,1,Nyrs)
# THESE ARE FROM ATLANTIS AND SHOULD NOT BE USED IN FITTING: REPLACE WITH SURVEY DATA
323000 319000 323000 319000 314000 311000 313000 322000 336000 356000 392000 420000 453000 490000 526790.4511 564224.1819 603763.1846 644112.1309 685405.0426 717264.437 744810.2684 767269.7318 789731.1329 979954.8372 985479.7632 1000515.39 1023820.644 1049917.425 1078689.475 1224782.882 1201531.237 1187721.183 1173335.965 1164290.95 1156423.711 1203591.62 1187939.493 1174918.714 1164941.359 1200004.681 1189195.422 1184021.859 1294204.862 1256708.157 1225214.335 1195731.486 1168301.268 1144856.825 1123438.211 1104724.898 1093997.021 1076095.912 1134127.98
9410000 12300000 9410000 12300000 14500000 16300000 17500000 18700000 19900000 20400000 21100000 21900000 21800000 22200000 22389484.64 21541868.79 21308407.29 21106745.51 19753412.52 19068476.03 18336451.86 16946254.55 16085421.76 15539140.69 14373546.52 13589341.82 12868604.76 12205122.25 11590939.66 11095728.29 10650485.21 10228070.52 9914450.771 9593714.214 9226765.459 8993661.686 8695970.038 8469437.076 8215500.63 8037542.722 7923579.623 7859274.16 7793980.251 7766567.353 7730183.132 7673258.065 7680214.841 7663712.502 7639406.148 7647605.541 7695455.734 7708465.565 7723009.839
2990000 2600000 2990000 2600000 2140000 1700000 1320000 1020000 797000 669000 542000 474000 449000 494000 397879.1304 340716.2376 668887.0067 1043678.761 1353107.697 1528923.948 1662839.113 2168901.571 2146757.576 2110259.217 2066905.624 1997122.391 1918926.596 1844719.955 1774274.858 1854537.106 1770558.237 1704440.713 2202171.471 2420113.423 2192195.592 2768020.319 2431448.968 2208835.034 1989203.981 2273224.524 2071026.551 1827409.665 2549296.709 2640433.293 2173768.218 1805002.122 1732532.5 1456583.104 1298397.594 1354090.011 1218542.647 1132961.528 1077122.363
2780000 2760000 780000 2760000 2830000 3120000 3600000 3790000 3670000 3550000 3390000 3220000 3070000 2910000 2737093.239 2562230.292 2422916.681 2248162.082 2076421.787 1934455.033 1809640.639 1682423.136 1565607.065 1472716.507 1384334.903 1304268.332 1208027.313 1118064.936 1049797.21 1021879.48 996187.0565 969111.1091 964196.5441 951633.7046 933072.8176 923235.353 902985.3448 898395.8245 904571.6742 922382.4579 951800.3942 991600.3143 1033636.653 1077381.561 1094595.04 1123754.607 1158401.041 1212525.154 1254172.55 1280904.006 1297075.675 1323815.465 1340320.514
15600000 8450000 15600000 8450000 6880000 5920000 5220000 4620000 4180000 3780000 3460000 3200000 2970000 2770000 2562865.865 2366401.154 2194211.941 2006930.446 1841819.638 1685845.274 1519315.666 1346066.252 1166658.314 1020225.763 855224.0936 720173.1192 582471.6408 472304.2216 430130.0256 411155.5352 389327.8552 370041.5408 342710.1672 323673.312 301050.4808 287829.1432 267124.0296 258782.7376 259546.7064 276478.7496 302874.6512 346545.6024 394847.14 433622.4544 482953.0112 538099.0856 594398.9088 654471.8024 713936.6392 745711.5048 788509.3488 842049.5296 878392.6168
4910000 3570000 4910000 3570000 3700000 3510000 3190000 3040000 2900000 2760000 2590000 2510000 2360000 2230000 2043972.691 1943792.262 1792332.27 1659130.24 1499836.28 1336590.934 1220177.382 1099276.746 974208.4785 864464.1207 751584.2098 664304.7629 575998.152 517228.6477 521243.9249 540774.7838 529957.6171 507232.2293 506323.0172 511517.8128 487396.6615 468755.3561 469841.496 470524.6337 492237.6016 526443.6352 565947.6723 611201.8616 636001.2359 647349.1859 663803.4676 689059.9053 716930.9422 742295.5024 768397.2617 790945.7218 803183.2252 833482.1041 845360.8373
1363490 1422230.513 1363490 1422230.513 1279323.126 1176870.487 1082473.348 1030758.899 944591.7848 897859.5285 826456.2842 765167.4087 703410.8561 648700.8199 580096.8205 517803.0529 458068.556 414293.7095 377658.0967 322050.884 303428.3376 295897.7824 277219.3328 243318.881 231144.2788 226624.3094 210239.2501 195689.4483 217203.957 226427.9669 195858.5211 203399.9842 199169.0748 201838.7882 196143.4905 219414.1743 197286.0951 204249.4385 198415.0648 208915.3013 221993.8974 232466.8641 228546.8303 252680.6033 240331.4744 243872.4579 248249.2608 244715.0947 252139.2978 251893.8696 252676.5128 256330.666 262387.2886
2140000 2660000 2140000 2660000 2080000 2580000 2830000 2070000 1520000 1620000 1540000 1410000 1310000 813000 516919.3752 436598.2114 669578.0949 1056394.152 1002190.669 905406.5194 1099483.41 1970538.619 2853338.45 2443469.084 3450339.652 2777447.163 2400255.884 2072301.839 2785817.557 2444118.714 2015734.9 2787888.252 2100936.835 1829818.983 2535466.488 3130151.247 2542633.785 3428029.998 3328807.6 4009914.487 3149511.923 3955657.58 2982027.114 3021793.431 3511015.926 2637640.001 3380942.525 3960918.726 4553936.672 5237088.609 5793643.948 6420934.137 4921421.97
5e+06 5264460 5e+06 5264460 5027540 4480820 3926235 3448180 3041910 2667910 2452015 2097655 1796295 1544535 1298900 1030890 1465390 1955675 2325605 2452155 2443455 2379600 2237180 2093985 1851325 1644920 1433840 1264085 1167880 1107045 1068050 1035855 1021370 1004030 980235 981910 967180 986035 989970 1051535 1112725 1179575 1248480 1314330 1415845 1477605 1519510 1621495 1702530 1769795 1794900 1838910 1904070
5e+05 533187 5e+05 533187 627923 758534.5 843375.5 854665 862953 874340.5 877886.5 883578.5 877285.5 885015.5 871092 870090.5 875358 889216 884812 885856 872234.5 880019 870902 865407 864961 866824 851914.5 844535.5 825003.5 824347 820541.5 819458.5 809473.5 820131 816845.5 814717.5 809854.5 812368.5 804822 812431.5 809281.5 804658 805165 813186 803146 804741 796595 798917 795012.5 808069 796351 804116 790638
# init_3darray obs_catch_biomass(1,Nareas,1,Nspecies,1,Nyrs)
# THESE ARE FROM ASSESSMENTS see Catches.xls placeholder for real catch data
0 0 235 611 746 695 10006 2714 4562 9303 5765 11643 24063 18902 24676 22671 17341 8131 1534 6271 5354 10192 7147 5968 5361 6107 4506 4484 4987 6676 17788 15183 18987 23311 21744 24365 28279 19825 22962 23690 18394.5 15038 13791.5 11635 14020 19691 21488 27078 20307 20307 20307 20307 20307
28.10134136 28.10134136 28.10134136 28.10134136 25.82967604 32.47640049 25.99794754 60.32533459 30.03646367 43.41404833 58.55848379 53.25793138 72.27261146 73.11396898 67.39273781 95.99889368 94.40031438 124.7733211 254.5947873 414.1161742 518.3603716 276.3018114 360.6899713 742.6662879 698.9998324 642.2081994 829.6626561 1211.30243 1778.377402 5626.729029 9459.427731 9160.564769 10396.30523 10290.98024 7129.806617 6029.269719 11239.28154 8264.169956 10282.73404 9018.409484 10414.12905 10403.1419 10657.11487 11714.51909 11918.39455 9751.847933 10553.9367 10553.9367 10553.9367 10553.9367 10553.9367 10553.9367 10553.9367
0 0 0 0 173640 94601 185200 275764 445656 371155 306423 333692 248526 254500 210502 202643 115338 83612 72732 81048 99445 85622 44447 33230 46660 33352 40220 49957 53617 55843 55407 80435 92749 76881 63700 106185 117419 124099 108623 110853 109141 120575 93222 102472 94724 93738 104001 81519 84681 103556 68454 81104 81104
10853 14731 23486 27189 25165 38333 53134 36752 43136 37939 25652 28179 25059 28923 27331 25008 19926 27367 35751 39264 48771 47649 61172 53473 39836 42422 26927 32191 42044 34472 44592 39214 30454 24794 15855 9102 9743 11807 9884 10998 9770 14023 11386 9054 5415 4895 4788 6588 5297 5100 4159 4159 4159
40877 46650 54004 54846 64086 150362 121274 51469 40923 22252 11300 10862 5866 5429 4450 5606 4484 10994 22516 19647 27638 25011 17627 12009 10394 7943 6846 6997 6689 4915 5574 6997 6244 4668 4827 2442 4131 3833 5665 6357 8711 11788 13258 12827 18253 21814 15989 16815 21021 23126 25903 25903 25903
5900 5700 7700 16690 19814 19448 13741 15307 18321 21271 21410 15610 18039 16953 17211 16750 14988 10639 6944 6935 7539 6979 12520 11989 6280 3267 3474 3580 2759 1783 4089 2564 5299 4300 4158 1135 1700 2464 3985 4963 7341 7419 5663 6562 6815 3851 2109 1662 1504 1806 1160 1169 1169
0 0 0 0 1747 1851 2333 2454 2138 2633 2824 4286 4608 3068 2315 3053 2034 3798 3510 3319 4227 4290 3338 4203 4182 2432 2110 3138 3278 2343 2442 2310 2056 1873 1150 842 1554 1562 1569 1235 2027 2413 2558 3328 3026 2347 1125 1039 1179 2013 1544 1544 1544
0 0 7978 9092 13405 16533 23496 34181 90495 135917 234872 382794 415830 436609 367534 309951 259052 80209 28345 33042 25545 30806 27548 32559 40638 71609 70692 80394 82492 73961 82996 70155 36366 31424 31187 27424 37547 38449 34548 29927 20480 37826 62133 79543 108886 108886 108886 108886 108886 108886 108886 108886 108886
45543 39688 79002 73924 94462 45279 47808 33371 41378.94 24054.96 27527.97 36398.22 25223.95 32090.95 20682 39874 13634 12457 12609 3415 4730 7054 7569 7954 10880 10859 10856 7765 8574 6963 8335 7311 6730 5050 4140 3224 4443 3045 2738 4190 2952 3868 3106 2006 1165 890 941 1764 788 1232 1123 1169.6 1215.32
0 0 0 0 45 37 299 539 451 258 199 213 437 710 1197 1853 2236 3137 3889 4014 4390 4087 4701 4646 4935 5373 4979 5874 6008 7323 6535 6726 7954 11530 11335 13500 12789 11051 8205 9918 11743 16245 15457 16309 14062 11159 7187 5276 4393 3783 5159.75 4652.9375 4497.171875
# 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
0
# init_int recphase //recruitment parameter estimation phas
0
# init_int avg_rec_phase //average recruitment estimation phase (could make species specific, currently global)
0
# init_int avg_F_phase //average fishing mort estimation phase (could make species specific, currently global)
0
# init_int dev_rec_phase //recruitment deviation estimation phase (could make species specific, currently global)
0
# init_int dev_F_phase //fishing mort deviation estimation phase (could make species specific, currently global)
0
# init_int fqphase //fishery q estimation phase
0
# init_int sqphase //survey q estimation phase
0
# init_int ssig_phase //survey sigma (obs error) phase
0
# init_int csig_phase //catch sigma (obs error) phase
0
# 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