Commit a6126235 authored by Lebot Clément's avatar Lebot Clément
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*.RData
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# CPUE_1 #
#############################
# CPUE_1 is an abundance index computed at the regional scale by pooling all the logbooks collected in Brittany each year.
# It represents the number of fish caught per hour of fishing during the first fishing period (fishing period during which only MSW individuals can be caught)
NA NA NA NA NA NA NA NA 0.01346 0.00589 0.00325 NA 0.00603 0.00471 0.00402 0.00413 0.00655 0.00909 0.01521 0.01955 0.0123 0.01676 0.01213 0.00279 0.00758 0.01727 0.01125 0.00596 0.00888 0.0033 0.00402
# END
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#############################
# CPUE_2 #
#############################
# CPUE_2 is an abundance index computed at the regional scale by pooling all the logbooks collected in Brittany each year.
# It represents the number of fish caught per hour of fishing during the second fishing period (fishing period during which only 1SW individuals can be caught)
NA NA NA NA NA NA NA NA 0.0333 0.01729 0.00872 NA 0.0028 0.01746 0.02068 0.01154 0.0056 0.01409 0.03638 0.0063 0.00686 0.00904 0.00845 0.01325 0.02127 0.00663 0.0322 0.01 0.01363 0.02798 0.01455
# END
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#############################
# CPUE_3 #
#############################
# CPUE_3 is an abundance index computed at the regional scale by pooling all the logbooks collected in Brittany each year.
# It represents the number of fish caught per hour of fishing during the third fishing period (fishing period during which only 1SW individuals can be caught)
NA NA NA NA NA NA NA NA 0.02041 0.0115 0.013 NA 0.00747 0.02003 0.0083 0.01873 NA 0.04363 0.06414 0.02205 0.03201 0.03856 0.00284 0.01428 0.00903 0.04767 0.05454 0.02937 0.02561 0.02274 0.01701
# END
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#############################
# E_1 #
#############################
# E_1 is the number of fishing days authorized during the first fishing period
# It is a dataframe with 18 rows and 31columns
# Each column represents one year from 1=1987 to 31=2017
# Each row represents one river :
# 1: Couesnon; 2: Leff; 3: Trieux; 4: Jaudy; 5: Leguer; 6: Yar; 7: Douron; 8: Queffleuth; 9: Penze; 10: Elorn; 11: Mignonne; 12: Aulne; 13: Goyen; 14: Odet; 15: Aven; 16: Elle; 17: Scorff; 18: Blavet;
E_1[,1] E_1[,2] E_1[,3] E_1[,4] E_1[,5] E_1[,6] E_1[,7] E_1[,8] E_1[,9] E_1[,10] E_1[,11] E_1[,12] E_1[,13] E_1[,14] E_1[,15] E_1[,16] E_1[,17] E_1[,18] E_1[,19] E_1[,20] E_1[,21] E_1[,22] E_1[,23] E_1[,24] E_1[,25] E_1[,26] E_1[,27] E_1[,28] E_1[,29] E_1[,30] E_1[,31]
[1,] 101 103 104 105 106 101 102 103 97 99 100 94 95 97 82 99 54 59 96 39 98 68 94 95 46 77 56 53 32 32 24
[2,] 101 103 104 105 106 101 102 103 70 71 72 69 68 70 71 71 72 68 69 70 71 23 69 68 69 55 71 72 69 69 70
[3,] 101 103 104 105 106 101 102 103 70 71 72 69 68 70 41 71 72 38 19 70 71 72 69 68 69 65 50 58 20 64 70
[4,] 101 103 104 105 106 101 102 103 70 71 72 69 68 70 71 71 72 68 69 70 71 72 69 68 69 71 71 72 69 69 70
[5,] 101 103 104 105 106 101 102 103 70 71 72 69 68 70 51 71 72 38 19 28 38 33 25 68 63 71 50 34 14 55 30
[6,] 101 103 104 105 106 101 102 103 70 71 72 69 68 70 71 71 72 68 69 70 71 72 69 68 69 71 71 72 69 0 0
[7,] 101 103 104 105 106 101 102 103 97 71 100 69 68 57 57 58 59 39 57 35 52 59 56 56 57 57 58 59 56 57 57
[8,] 101 103 104 105 106 101 102 103 97 71 72 69 68 57 57 58 59 56 57 57 57 59 56 56 57 57 58 59 56 57 57
[9,] 101 103 104 105 106 101 102 103 97 71 100 69 68 97 57 58 59 56 57 48 57 56 56 56 40 57 58 59 56 57 57
[10,] 101 103 104 105 106 101 102 103 97 58 59 56 56 57 57 58 59 35 57 23 48 45 33 35 27 57 58 59 29 57 57
[11,] 101 103 104 105 106 101 102 103 97 99 100 69 68 57 57 58 59 56 57 57 57 59 56 56 57 57 58 59 56 57 57
[12,] 101 103 104 105 106 101 102 103 97 71 72 69 68 97 98 99 100 95 96 23 52 44 56 48 27 57 58 45 33 7 16
[13,] 101 103 104 105 106 101 102 103 97 71 100 69 68 57 57 58 59 35 57 57 57 59 56 48 40 57 58 59 24 52 57
[14,] 101 103 104 105 106 101 102 103 97 80 91 69 68 57 57 58 59 56 57 57 57 59 56 56 57 57 58 59 56 57 49
[15,] 101 103 104 105 106 101 102 103 97 71 72 69 68 57 57 58 100 66 96 57 52 59 56 56 40 37 58 53 33 57 49
[16,] 101 103 104 105 106 101 102 103 97 99 72 69 68 57 57 58 59 35 57 35 52 41 47 48 47 37 49 50 24 48 49
[17,] 101 103 104 105 106 101 102 103 97 99 100 94 95 97 98 99 100 95 96 97 98 85 79 80 81 83 84 85 70 81 82
[18,] 101 103 104 105 106 101 102 103 97 99 100 94 95 97 98 99 100 95 32 97 98 85 79 80 81 83 84 77 53 81 82
#END
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#############################
# E_2 #
#############################
# E_2 is the number of fishing days authorized during the second fishing period
# It is a dataframe with 18 rows and 31columns
# Each column represents one year from 1=1987 to 31=2017
# Each row represents one river :
# 1: Couesnon; 2: Leff; 3: Trieux; 4: Jaudy; 5: Leguer; 6: Yar; 7: Douron; 8: Queffleuth; 9: Penze; 10: Elorn; 11: Mignonne; 12: Aulne; 13: Goyen; 14: Odet; 15: Aven; 16: Elle; 17: Scorff; 18: Blavet;
E_2[,1] E_2[,2] E_2[,3] E_2[,4] E_2[,5] E_2[,6] E_2[,7] E_2[,8] E_2[,9] E_2[,10] E_2[,11] E_2[,12] E_2[,13] E_2[,14] E_2[,15] E_2[,16] E_2[,17] E_2[,18] E_2[,19] E_2[,20] E_2[,21] E_2[,22] E_2[,23] E_2[,24] E_2[,25] E_2[,26] E_2[,27] E_2[,28] E_2[,29] E_2[,30] E_2[,31]
[1,] 29 29 29 29 29 29 29 61 61 61 77 77 77 77 78 77 77 77 77 77 77 77 77 77 77 77 77 62 52 54 55
[2,] 29 29 29 29 29 29 65 61 34 34 33 34 34 34 33 33 33 33 33 34 33 33 33 33 33 33 33 33 33 33 34
[3,] 29 29 29 29 29 29 61 61 34 34 33 34 34 34 33 33 33 33 33 34 33 33 33 33 33 33 33 33 33 33 34
[4,] 29 29 29 29 29 29 65 61 34 34 33 34 34 34 33 33 33 33 33 34 33 33 33 33 33 33 33 33 33 33 34
[5,] 29 29 29 29 29 29 29 29 34 34 33 34 34 34 33 33 33 33 33 34 33 33 33 33 33 33 33 33 33 33 34
[6,] 29 29 29 29 29 29 29 29 34 34 33 34 34 34 33 33 33 33 33 34 33 33 33 33 33 33 33 33 33 0 0
[7,] 29 29 29 61 61 61 61 61 61 55 61 44 44 46 46 46 46 46 46 46 46 46 77 31 77 77 46 77 45 45 45
[8,] 29 29 29 29 29 29 29 29 29 21 21 21 21 0 46 46 46 46 46 46 46 46 77 77 77 77 46 77 45 45 45
[9,] 29 29 29 61 61 61 61 61 61 44 61 44 44 46 46 46 46 46 46 46 46 46 77 77 77 77 46 77 26 45 45
[10,] 29 29 29 61 61 61 61 61 61 45 45 45 44 45 44 45 45 44 45 45 44 56 45 44 45 44 55 45 45 45 45
[11,] 29 29 29 61 61 61 61 61 61 61 61 44 44 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 26 27 28
[12,] 29 29 29 61 61 61 77 77 77 55 56 56 55 46 46 46 46 46 46 77 77 77 77 77 31 77 47 47 27 28 33
[13,] 29 29 29 61 61 61 61 61 61 44 61 44 44 46 46 46 46 46 46 46 46 77 77 52 45 44 55 45 45 45 45
[14,] 29 29 29 61 61 61 61 61 61 48 59 48 48 46 46 46 46 46 46 46 46 77 77 77 77 77 47 47 27 28 37
[15,] 29 29 29 61 61 61 61 61 61 55 56 56 55 46 46 46 46 46 46 46 77 77 77 77 77 77 47 47 27 28 37
[16,] 29 29 29 61 61 61 61 61 61 77 56 56 55 46 46 46 0 46 46 46 46 62 62 62 62 62 62 62 37 37 37
[17,] 29 29 29 61 61 61 61 77 45 43 49 41 40 46 46 46 0 77 46 46 46 62 62 62 62 62 62 62 62 62 62
[18,] 29 29 29 61 61 61 61 77 45 43 49 41 40 46 46 46 0 77 46 46 46 62 77 62 62 62 62 62 62 62 62
#END
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#############################
# E_3 #
#############################
# E_3 is the number of fishing days authorized during the third fishing period
# It is a dataframe with 18 rows and 31columns
# Each column represents one year from 1=1987 to 31=2017
# Each row represents one river :
# 1: Couesnon; 2: Leff; 3: Trieux; 4: Jaudy; 5: Leguer; 6: Yar; 7: Douron; 8: Queffleuth; 9: Penze; 10: Elorn; 11: Mignonne; 12: Aulne; 13: Goyen; 14: Odet; 15: Aven; 16: Elle; 17: Scorff; 18: Blavet;
E_3[,1] E_3[,2] E_3[,3] E_3[,4] E_3[,5] E_3[,6] E_3[,7] E_3[,8] E_3[,9] E_3[,10] E_3[,11] E_3[,12] E_3[,13] E_3[,14] E_3[,15] E_3[,16] E_3[,17] E_3[,18] E_3[,19] E_3[,20] E_3[,21] E_3[,22] E_3[,23] E_3[,24] E_3[,25] E_3[,26] E_3[,27] E_3[,28] E_3[,29] E_3[,30] E_3[,31]
[1,] 0 0 0 0 0 0 0 0 0 0 0 0 47 45 45 45 0 45 46 45 45 42 45 45 45 45 45 45 11 40 31
[2,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 28 27 28 32 32 32 27 27 27 27 27
[3,] 0 0 0 0 0 0 0 0 36 43 36 36 37 37 37 37 0 35 37 37 28 27 28 32 32 32 27 27 27 27 27
[4,] 0 0 0 0 0 0 0 0 0 0 0 0 0 37 37 37 0 35 37 37 28 27 28 32 32 32 27 27 27 27 27
[5,] 0 0 0 0 0 0 0 0 36 43 36 36 37 37 37 37 0 35 37 37 28 27 28 32 32 32 27 27 27 27 27
[6,] 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 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 0 64 0 0 0 0 0 0 0 0 0 0 31 24 20 19 18 16 39 61 61 61 61
[8,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 47 32 0 28 29 30 31 24 20 19 18 16 39 61 61 61 61
[9,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 31 15 20 19 18 16 39 61 40 61 61
[10,] 0 0 0 0 0 0 0 0 0 64 63 62 62 61 61 45 0 45 45 45 45 61 45 61 61 61 61 61 61 61 61
[11,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24 0 0 0 0 0 0 0 0 0
[12,] 0 0 0 0 0 0 19 18 60 64 63 62 62 46 46 32 0 28 29 45 45 45 45 45 45 45 61 61 61 61 45
[13,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 21 20 0 18 16 15 21 20 18 17
[14,] 0 0 0 0 0 0 0 0 0 21 21 21 21 0 0 0 0 0 0 0 0 21 20 19 18 26 61 61 61 61 45
[15,] 0 0 0 0 0 0 0 0 0 64 63 62 62 46 46 48 0 28 29 30 61 61 45 45 61 61 61 45 61 61 45
[16,] 0 0 0 0 0 0 0 0 0 64 63 62 62 46 47 32 0 35 29 30 30 45 45 45 45 45 45 45 45 45 45
[17,] 0 0 0 0 0 0 0 46 37 37 44 44 44 46 47 32 0 61 30 30 47 45 45 45 45 45 45 45 45 45 45
[18,] 0 0 0 0 0 0 0 46 37 37 44 44 44 46 0 32 0 61 30 30 30 45 45 45 45 45 45 45 45 45 45
#END
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#############################
# I_1 #
#############################
# I_1 is an indicative dataframe notifying whether fishing is prohibited (I_1=0) or authorized (I_1=1) during the first fishing period (i.e. between the 15th of march and the 15th of june)
# It is a dataframe with 18 rows and 31columns
# Each column represents one year from 1=1987 to 31=2017
# Each row represents one river :
# 1: Couesnon; 2: Leff; 3: Trieux; 4: Jaudy; 5: Leguer; 6: Yar; 7: Douron; 8: Queffleuth; 9: Penze; 10: Elorn; 11: Mignonne; 12: Aulne; 13: Goyen; 14: Odet; 15: Aven; 16: Elle; 17: Scorff; 18: Blavet;
I_1[,1] I_1[,2] I_1[,3] I_1[,4] I_1[,5] I_1[,6] I_1[,7] I_1[,8] I_1[,9] I_1[,10] I_1[,11] I_1[,12] I_1[,13] I_1[,14] I_1[,15] I_1[,16] I_1[,17] I_1[,18] I_1[,19] I_1[,20] I_1[,21] I_1[,22] I_1[,23] I_1[,24] I_1[,25] I_1[,26] I_1[,27] I_1[,28] I_1[,29] I_1[,30] I_1[,31]
[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
[2,] 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
[3,] 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
[4,] 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
[5,] 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
[6,] 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 0 0
[7,] 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
[8,] 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
[9,] 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
[10,] 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
[11,] 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
[12,] 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
[13,] 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
[14,] 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
[15,] 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
[16,] 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
[17,] 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
[18,] 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
#END
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#############################
# I_2 #
#############################
# I_2 is an indicative dataframe notifying whether fishing is prohibited (I_2=0) or authorized (I_2=1) during the second fishing period (i.e. between the 16th of june and the 31th of july)
# It is a dataframe with 18 rows and 31columns
# Each column represents one year from 1=1987 to 31=2017
# Each row represents one river :
# 1: Couesnon; 2: Leff; 3: Trieux; 4: Jaudy; 5: Leguer; 6: Yar; 7: Douron; 8: Queffleuth; 9: Penze; 10: Elorn; 11: Mignonne; 12: Aulne; 13: Goyen; 14: Odet; 15: Aven; 16: Elle; 17: Scorff; 18: Blavet;
I_2[,1] I_2[,2] I_2[,3] I_2[,4] I_2[,5] I_2[,6] I_2[,7] I_2[,8] I_2[,9] I_2[,10] I_2[,11] I_2[,12] I_2[,13] I_2[,14] I_2[,15] I_2[,16] I_2[,17] I_2[,18] I_2[,19] I_2[,20] I_2[,21] I_2[,22] I_2[,23] I_2[,24] I_2[,25] I_2[,26] I_2[,27] I_2[,28] I_2[,29] I_2[,30] I_2[,31]
[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
[2,] 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
[3,] 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
[4,] 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
[5,] 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
[6,] 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 0 0
[7,] 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
[8,] 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[9,] 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
[10,] 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
[11,] 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
[12,] 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
[13,] 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
[14,] 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
[15,] 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
[16,] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[17,] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[18,] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#END
\ No newline at end of file
#############################
# I_3 #
#############################
# I_3 is an indicative dataframe notifying whether fishing is prohibited (I_3=0) or authorized (I_3=1) during the second fishing period (i.e. between the 16th of september and the end of october)
# It is a dataframe with 18 rows and 31columns
# Each column represents one year from 1=1987 to 31=2017
# Each row represents one river :
# 1: Couesnon; 2: Leff; 3: Trieux; 4: Jaudy; 5: Leguer; 6: Yar; 7: Douron; 8: Queffleuth; 9: Penze; 10: Elorn; 11: Mignonne; 12: Aulne; 13: Goyen; 14: Odet; 15: Aven; 16: Elle; 17: Scorff; 18: Blavet;
I_3[,1] I_3[,2] I_3[,3] I_3[,4] I_3[,5] I_3[,6] I_3[,7] I_3[,8] I_3[,9] I_3[,10] I_3[,11] I_3[,12] I_3[,13] I_3[,14] I_3[,15] I_3[,16] I_3[,17] I_3[,18] I_3[,19] I_3[,20] I_3[,21] I_3[,22] I_3[,23] I_3[,24] I_3[,25] I_3[,26] I_3[,27] I_3[,28] I_3[,29] I_3[,30] I_3[,31]
[1,] 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[2,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
[3,] 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[4,] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[5,] 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[6,] 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 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
[8,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[9,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
[10,] 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[11,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
[12,] 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[13,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 1 1 1
[14,] 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1
[15,] 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[16,] 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[17,] 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[18,] 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#END
\ No newline at end of file
#############################
# NC_1 #
#############################
# NC_1 is the partially observed abundance of MSW adult returns (Aulne, 1999-2017; Elorn, 2007)
# These observations are supposed to be close to the true number of adults returns (unlike data of the Couesnon cf.Ncensored).
# Hence, a specific modelling was used for these data (see the model)
# It is a dataframe with 18 rows and 31columns
# Each column represents one year from 1=1987 to 31=2017
# Each row represents one river :
# 1: Couesnon; 2: Leff; 3: Trieux; 4: Jaudy; 5: Leguer; 6: Yar; 7: Douron; 8: Queffleuth; 9: Penze; 10: Elorn; 11: Mignonne; 12: Aulne; 13: Goyen; 14: Odet; 15: Aven; 16: Elle; 17: Scorff; 18: Blavet;
NC_1[,1] NC_1[,2] NC_1[,3] NC_1[,4] NC_1[,5] NC_1[,6] NC_1[,7] NC_1[,8] NC_1[,9] NC_1[,10] NC_1[,11] NC_1[,12] NC_1[,13] NC_1[,14] NC_1[,15] NC_1[,16] NC_1[,17] NC_1[,18] NC_1[,19] NC_1[,20] NC_1[,21] NC_1[,22] NC_1[,23] NC_1[,24] NC_1[,25] NC_1[,26] NC_1[,27] NC_1[,28] NC_1[,29] NC_1[,30] NC_1[,31]
[1,] 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 0 0 0 0 0 0
[2,] 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 0 0 0 0 0 0
[3,] 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 0 0 0 0 0 0
[4,] 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 0 0 0 0 0 0
[5,] 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 0 0 0 0 0 0
[6,] 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 0 0 0 0 0 0
[7,] 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 0 0 0 0 0 0
[8,] 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 0 0 0 0 0 0
[9,] 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 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 75 0 0 0 0 0 0 0 0 0 0
[11,] 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 0 0 0 0 0 0
[12,] NA NA NA NA NA NA NA NA NA NA NA NA 46 37 31 39 88 142 92 30 62 34 22 56 168 98 230 231 233 126 186
[13,] 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 0 0 0 0 0 0
[14,] 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 0 0 0 0 0 0
[15,] 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 0 0 0 0 0 0
[16,] 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 0 0 0 0 0 0
[17,] 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 0 0 0 0 0 0
[18,] 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 0 0 0 0 0 0
#END
\ No newline at end of file
#############################
# NC_2 #
#############################
# NC_2 is the partially observed abundance of 1SW adult returns (Aulne, 1999-2017)
# These observations are supposed to be close to the true number of adults returns (unlike data of the Couesnon cf.Ncensored).
# Hence, a specific modelling was used for these data (see the model)
# It is a dataframe with 18 rows and 31columns
# Each column represents one year from 1=1987 to 31=2017
# Each row represents one river :
# 1: Couesnon; 2: Leff; 3: Trieux; 4: Jaudy; 5: Leguer; 6: Yar; 7: Douron; 8: Queffleuth; 9: Penze; 10: Elorn; 11: Mignonne; 12: Aulne; 13: Goyen; 14: Odet; 15: Aven; 16: Elle; 17: Scorff; 18: Blavet;
Nobs_SW[,1] Nobs_SW[,2] Nobs_SW[,3] Nobs_SW[,4] Nobs_SW[,5] Nobs_SW[,6] Nobs_SW[,7] Nobs_SW[,8] Nobs_SW[,9] Nobs_SW[,10] Nobs_SW[,11] Nobs_SW[,12] Nobs_SW[,13] Nobs_SW[,14] Nobs_SW[,15] Nobs_SW[,16] Nobs_SW[,17] Nobs_SW[,18] Nobs_SW[,19] Nobs_SW[,20] Nobs_SW[,21] Nobs_SW[,22] Nobs_SW[,23] Nobs_SW[,24] Nobs_SW[,25] Nobs_SW[,26] Nobs_SW[,27] Nobs_SW[,28] Nobs_SW[,29] Nobs_SW[,30] Nobs_SW[,31]
[1,] 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 0 0 0 0 0 0
[2,] 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 0 0 0 0 0 0
[3,] 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 0 0 0 0 0 0
[4,] 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 0 0 0 0 0 0
[5,] 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 0 0 0 0 0 0
[6,] 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 0 0 0 0 0 0
[7,] 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 0 0 0 0 0 0
[8,] 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 0 0 0 0 0 0
[9,] 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 0 0 0 0 0 0
[10,] 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 0 0 0 0 0 0
[11,] 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 0 0 0 0 0 0
[12,] NA NA NA NA NA NA NA NA NA NA NA NA 553 954 477 278 281 871 243 190 149 46 56 543 398 493 637 405 224 977 916
[13,] 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 0 0 0 0 0 0
[14,] 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 0 0 0 0 0 0
[15,] 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 0 0 0 0 0 0
[16,] 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 0 0 0 0 0 0
[17,] 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 0 0 0 0 0 0
[18,] 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 0 0 0 0 0 0
#END
\ No newline at end of file
#############################
# N_1 #
#############################
# N_1 is the observed abundance of MSW adult returns
# It is a dataframe with 18 rows and 31columns
# Each column represents one year from 1=1987 to 31=2017
# Each row represents one river :
# 1: Couesnon; 2: Leff; 3: Trieux; 4: Jaudy; 5: Leguer; 6: Yar; 7: Douron; 8: Queffleuth; 9: Penze; 10: Elorn; 11: Mignonne; 12: Aulne; 13: Goyen; 14: Odet; 15: Aven; 16: Elle; 17: Scorff; 18: Blavet;
N_1[,1] N_1[,2] N_1[,3] N_1[,4] N_1[,5] N_1[,6] N_1[,7] N_1[,8] N_1[,9] N_1[,10] N_1[,11] N_1[,12] N_1[,13] N_1[,14] N_1[,15] N_1[,16] N_1[,17] N_1[,18] N_1[,19] N_1[,20] N_1[,21] N_1[,22] N_1[,23] N_1[,24] N_1[,25] N_1[,26] N_1[,27] N_1[,28] N_1[,29] N_1[,30] N_1[,31]
[1,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[2,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[3,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[4,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[5,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[6,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[7,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[8,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[9,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[10,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 126 176 218 346 124 217 281 164 134 137
[11,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[12,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[13,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[14,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[15,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[16,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[17,] NA NA NA NA NA NA NA 78 90 87 69 27 93 41 38 22 58 64 130 104 93 87 117 65 215 145 103 135 175 87 143
[18,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
#END
\ No newline at end of file
#############################
# N_2 #
#############################
# N_2 is the observed abundance of 1SW adult returns
# It is a dataframe with 18 rows and 31columns
# Each column represents one year from 1=1987 to 31=2017
# Each row represents one river :
# 1: Couesnon; 2: Leff; 3: Trieux; 4: Jaudy; 5: Leguer; 6: Yar; 7: Douron; 8: Queffleuth; 9: Penze; 10: Elorn; 11: Mignonne; 12: Aulne; 13: Goyen; 14: Odet; 15: Aven; 16: Elle; 17: Scorff; 18: Blavet;
N_2[,1] N_2[,2] N_2[,3] N_2[,4] N_2[,5] N_2[,6] N_2[,7] N_2[,8] N_2[,9] N_2[,10] N_2[,11] N_2[,12] N_2[,13] N_2[,14] N_2[,15] N_2[,16] N_2[,17] N_2[,18] N_2[,19] N_2[,20] N_2[,21] N_2[,22] N_2[,23] N_2[,24] N_2[,25] N_2[,26] N_2[,27] N_2[,28] N_2[,29] N_2[,30] N_2[,31]
[1,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[2,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[3,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[4,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[5,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[6,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[7,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[8,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[9,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[10,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 406 576 368 1138 392 349 1079 465 334 720 482
[11,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[12,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[13,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[14,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[15,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[16,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[17,] NA NA NA NA NA NA NA 505 789 732 451 561 259 313 317 563 229 1115 452 886 481 284 260 764 383 378 601 684 600 621 950
[18,] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
#END
\ No newline at end of file
#############################
# Ncensored_1 #
#############################
# Ncensored_1 is the partially observed abundance of MSW adult returns which is used as censored data in the model (Couesnon, 1996-2015)
# It is used as the lower boundaries of the N_1 distributions.
# Data censored have Ncensored_1 higher than 0.
# It is a dataframe with 18 rows and 31columns
# Each column represents one year from 1=1987 to 31=2017
# Each row represents one river :
# 1: Couesnon; 2: Leff; 3: Trieux; 4: Jaudy; 5: Leguer; 6: Yar; 7: Douron; 8: Queffleuth; 9: Penze; 10: Elorn; 11: Mignonne; 12: Aulne; 13: Goyen; 14: Odet; 15: Aven; 16: Elle; 17: Scorff; 18: Blavet;
Ncensored_1[,1] Ncensored_1[,2] Ncensored_1[,3] Ncensored_1[,4] Ncensored_1[,5] Ncensored_1[,6] Ncensored_1[,7] Ncensored_1[,8] Ncensored_1[,9] Ncensored_1[,10] Ncensored_1[,11] Ncensored_1[,12] Ncensored_1[,13] Ncensored_1[,14] Ncensored_1[,15] Ncensored_1[,16] Ncensored_1[,17] Ncensored_1[,18] Ncensored_1[,19] Ncensored_1[,20] Ncensored_1[,21] Ncensored_1[,22] Ncensored_1[,23] Ncensored_1[,24] Ncensored_1[,25] Ncensored_1[,26] Ncensored_1[,27] Ncensored_1[,28] Ncensored_1[,29] Ncensored_1[,30] Ncensored_1[,31]
[1,] 0 0 0 0 0 0 0 0 0 2 2 3 9 2 11 4 1 8 3 9 0 1 4 41 13 21 7 43 26 0 0
[2,] 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 0 0 0 0 0 0
[3,] 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 0 0 0 0 0 0
[4,] 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 0 0 0 0 0 0
[5,] 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 0 0 0 0 0 0
[6,] 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 0 0 0 0 0 0
[7,] 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 0 0 0 0 0 0
[8,] 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 0 0 0 0 0 0
[9,] 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 0 0 0 0 0 0
[10,] 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 0 0 0 0 0 0
[11,] 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 0 0 0 0 0 0
[12,] 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 0 0 0 0 0 0
[13,] 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 0 0 0 0 0 0
[14,] 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 0 0 0 0 0 0
[15,] 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 0 0 0 0 0 0
[16,] 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 0 0 0 0 0 0
[17,] 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 0 0 0 0 0 0
[18,] 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 0 0 0 0 0 0
#END
\ No newline at end of file
#############################
# Ncensored_2 #
#############################
# Ncensored_2 is the partially observed abundance of 1SW adult returns which is used as censored data in the model (Couesnon, 1996-2015)
# It is used as the lower boundaries of the N_2 distributions.
# Data censored have Ncensored_2 higher than 0.
# It is a dataframe with 18 rows and 31columns
# Each column represents one year from 1=1987 to 31=2017
# Each row represents one river :
# 1: Couesnon; 2: Leff; 3: Trieux; 4: Jaudy; 5: Leguer; 6: Yar; 7: Douron; 8: Queffleuth; 9: Penze; 10: Elorn; 11: Mignonne; 12: Aulne; 13: Goyen; 14: Odet; 15: Aven; 16: Elle; 17: Scorff; 18: Blavet;
Ncensored_2[,1] Ncensored_2[,2] Ncensored_2[,3] Ncensored_2[,4] Ncensored_2[,5] Ncensored_2[,6] Ncensored_2[,7] Ncensored_2[,8] Ncensored_2[,9] Ncensored_2[,10] Ncensored_2[,11] Ncensored_2[,12] Ncensored_2[,13] Ncensored_2[,14] Ncensored_2[,15] Ncensored_2[,16] Ncensored_2[,17] Ncensored_2[,18] Ncensored_2[,19] Ncensored_2[,20] Ncensored_2[,21] Ncensored_2[,22] Ncensored_2[,23] Ncensored_2[,24] Ncensored_2[,25] Ncensored_2[,26] Ncensored_2[,27] Ncensored_2[,28] Ncensored_2[,29] Ncensored_2[,30] Ncensored_2[,31]
[1,] 0 0 0 0 0 0 0 0 0 21 47 137 57 54 128 18 35 50 38 17 20 23 18 84 45 41 33 69 39 0 0
[2,] 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 0 0 0 0 0 0
[3,] 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 0 0 0 0 0 0
[4,] 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 0 0 0 0 0 0
[5,] 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 0 0 0 0 0 0
[6,] 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 0 0 0 0 0 0
[7,] 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 0 0 0 0 0 0
[8,] 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 0 0 0 0 0 0
[9,] 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 0 0 0 0 0 0
[10,] 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 0 0 0 0 0 0
[11,] 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 0 0 0 0 0 0
[12,] 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 0 0 0 0 0 0
[13,] 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 0 0 0 0 0 0
[14,] 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 0 0 0 0 0 0
[15,] 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 0 0 0 0 0 0
[16,] 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 0 0 0 0 0 0
[17,] 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 0 0 0 0 0 0
[18,] 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 0 0 0 0 0 0
#END
\ No newline at end of file
#############################
# Q_1 #
#############################
# Q_1 is the daily flows averaged over the rivers of Britany during the first fishing period (i.e. between the 15th of march and the 15th of june).
# Each item correspond to one year from 1987 to 2017.
5.813246 6.922544 4.300829 2.413026 4.131794 2.657428 3.609785 7.752931 4.16469 2.987703 1.900766 6.354031 6.278684 7.041878 10.97215 3.799222 2.385646 5.204139 3.285993 6.655227 4.762823 6.485789 3.353517 4.185383 1.882476 5.829713 5.903421 4.365072 4.047225 4.618409 2.408577
# END
\ No newline at end of file
#############################
# Q_2 #
#############################
# Q_2 is the daily flows averaged over the rivers of Britany during the second fishing period (between the 16th of june and the 15 of september).
# Each item correspond to one year from 1987 to 2017.
1.677295 1.144708 0.5127763 0.6404067 1.436459 0.7469856 1.174187 1.504545 0.8805419 0.7720455 1.182656 1.271722 1.248816 2.202237 1.348325 1.531352 0.5990431 1.590586 0.8815431 0.8488876 4.976304 1.794486 1.116483 0.7927273 0.6515072 2.108278 1.01012 1.234402 1.073254 1.026579 0.7387321
# END
\ No newline at end of file
#############################
# Q_3 #
#############################
# Q_3 is the daily flows averaged over the rivers of Britany during the third fishing period (between the 16th of september and the end of october).
# Each item correspond to one year from 1987 to 2017.
2.97341 1.598697 0.2948966 0.3374608 0.9271473 1.595016 6.111567 1.504044 1.075103 0.7164263 0.8577743 1.574232 1.44652 2.408339 1.246552 0.7620063 0.5108777 1.255862 0.6447022 0.8711912 1.782069 1.588433 0.8197806 1.712633 0.4581505 2.307085 0.6677116 0.924953 1.02094 0.6063009 1.15768
# END
\ No newline at end of file
#############################
# SRR #
#############################
# SRR is the area of riffle/rapid equivalent available for spawners (unit: 100m²).
# It is a dataframe with 18 rows and 31columns
# Each column represents one year from 1=1987 to 31=2017
# Each row represents one river :
# 1: Couesnon; 2: Leff; 3: Trieux; 4: Jaudy; 5: Leguer; 6: Yar; 7: Douron; 8: Queffleuth; 9: Penze; 10: Elorn; 11: Mignonne; 12: Aulne; 13: Goyen; 14: Odet; 15: Aven; 16: Elle; 17: Scorff; 18: Blavet;
SRR[,1] SRR[,2] SRR[,3] SRR[,4] SRR[,5] SRR[,6] SRR[,7] SRR[,8] SRR[,9] SRR[,10] SRR[,11] SRR[,12] SRR[,13] SRR[,14] SRR[,15] SRR[,16] SRR[,17] SRR[,18] SRR[,19] SRR[,20] SRR[,21] SRR[,22] SRR[,23] SRR[,24] SRR[,25] SRR[,26] SRR[,27] SRR[,28] SRR[,29] SRR[,30] SRR[,31] SRR[,32] SRR[,33] SRR[,34]
[1,] 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94 1107.94
[2,] 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05 723.05
[3,] 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33 2137.33
[4,] 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61 475.61
[5,] 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61 2424.61
[6,] 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04 371.04
[7,] 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23 778.23
[8,] 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17 500.17
[9,] 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61 1097.61
[10,] 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45 1668.45
[11,] 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55 678.55
[12,] 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 2267.55 1931.35 1931.35 1931.35 1931.35
[13,] 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26 490.26
[14,] 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82 2486.82
[15,] 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86 1426.86
[16,] 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68 6375.68
[17,] 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 1899.84 2397.18 2397.18 2397.18 2397.18 2397.18
[18,] 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21 3261.21
#END
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