Back-calculated lengths-at-age for Walleye from Lake Mille Lacs, 2000-2011.
Source:R/WalleyeML.R
WalleyeML.RdBack-calculated lengths-at-age for Walleye (Sander vitreus) from Lake Mille Lacs. Walleye were captured by Minnesota Department of Natural Resources personnel in fishery-independent gillnets (five multifilament nylon mesh panels with each panel measuring 15.2 m wide and 1.8 m high; bar-measure mesh sizes of the panels were 19.1, 25.4, 31.7, 38.1, and 50.8 mm) set in the fall (mid September to early October) from 2000 to 2011.
Format
A data frame of 14583 observations on the following 9 variables:
- ID
A unique fish identification number.
- Year
Year of data.
- Sex
Sex (female, male).
- Est.Age
Estimated (from otoliths) age (yrs) at capture.
- TL
Total length (mm).
- Scale.Rad
Total scale radius (mm) at capture.
- Dist.Ann
Scale radius (mm) to annulus given in
BC.Age.- BC.Age
Annulus or previous age.
- BC.Len
Back-calculated length at
BC.Age. Lengths were back-calculated using the Scale-Proportional Hypothesis method.
Source
These unpublished data are from the Minnesota Department of Natural Resources, Section of Fisheries (via Melissa Treml). Do not use for other than educational purposes without permission from the source. CSV file
Examples
data(WalleyeML)
str(WalleyeML)
#> 'data.frame': 14583 obs. of 9 variables:
#> $ ID : Factor w/ 3146 levels "2000.10001.F",..: 1 1 1 1 2 2 3 3 3 3 ...
#> $ Year : int 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 ...
#> $ Sex : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Est.Age : int 4 4 4 4 2 2 9 9 9 9 ...
#> $ TL : num 452 452 452 452 343 ...
#> $ Scale.Rad: num 5.2 5.2 5.2 5.2 3.2 3.2 6.4 6.4 6.4 6.4 ...
#> $ Dist.Ann : num 1.2 2.4 3.7 4.5 1.3 2.4 1.5 2.5 3.5 4.5 ...
#> $ BC.Age : int 1 2 3 4 1 2 1 2 3 4 ...
#> $ BC.Len : num 99.4 205.2 319.8 390.4 135.5 ...
head(WalleyeML)
#> ID Year Sex Est.Age TL Scale.Rad Dist.Ann BC.Age BC.Len
#> 1 2000.10001.F 2000 F 4 452.1 5.2 1.2 1 99.36
#> 2 2000.10001.F 2000 F 4 452.1 5.2 2.4 2 205.18
#> 3 2000.10001.F 2000 F 4 452.1 5.2 3.7 3 319.82
#> 4 2000.10001.F 2000 F 4 452.1 5.2 4.5 4 390.37
#> 5 2000.10002.F 2000 F 2 342.9 3.2 1.3 1 135.47
#> 6 2000.10002.F 2000 F 2 342.9 3.2 2.4 2 255.56
xtabs(~Year+Est.Age+Sex,data=WalleyeML)
#> , , Sex = F
#>
#> Est.Age
#> Year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 19
#> 2000 45 96 63 132 165 114 189 176 189 30 44 180 26 0 0 0 0 0
#> 2001 23 64 66 28 95 138 112 96 99 140 22 24 195 0 0 0 17 19
#> 2002 14 30 150 132 90 198 385 272 297 300 0 0 0 0 0 0 0 0
#> 2003 15 22 21 60 75 66 105 120 0 0 0 0 0 0 0 0 0 0
#> 2004 15 40 36 32 75 120 112 120 0 0 0 0 0 0 0 0 0 0
#> 2005 20 40 60 24 45 90 105 120 0 0 0 0 0 0 0 0 0 0
#> 2006 15 30 42 64 20 6 105 120 0 0 0 0 0 0 0 0 0 0
#> 2007 14 38 21 60 80 18 0 104 0 0 0 0 0 0 0 0 0 0
#> 2008 16 32 45 60 75 90 0 0 0 0 0 0 0 3 0 0 0 0
#> 2009 14 32 48 60 80 90 105 48 0 0 0 0 0 0 0 0 0 0
#> 2010 11 32 45 72 85 18 0 0 0 0 0 0 0 0 0 0 0 0
#> 2011 16 30 48 48 60 66 21 80 0 0 0 0 9 0 0 0 0 0
#>
#> , , Sex = M
#>
#> Est.Age
#> Year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 19
#> 2000 58 80 63 88 105 54 182 192 171 20 66 204 39 0 15 48 0 0
#> 2001 29 60 72 24 95 90 91 88 81 140 33 24 130 0 0 0 34 19
#> 2002 5 20 171 88 55 102 224 136 252 220 0 0 0 0 0 0 0 0
#> 2003 15 22 36 60 65 102 56 120 0 0 0 0 0 0 0 0 0 0
#> 2004 15 40 36 24 75 36 70 32 0 0 0 0 0 0 0 0 0 0
#> 2005 20 40 60 8 5 78 35 32 0 0 0 0 0 0 0 0 0 0
#> 2006 15 30 48 56 20 12 77 40 0 0 0 0 0 0 0 0 0 0
#> 2007 16 34 36 60 80 6 0 8 0 0 0 0 0 0 0 0 0 0
#> 2008 15 30 42 32 80 90 0 0 0 0 0 0 0 0 0 0 0 0
#> 2009 15 30 42 64 15 90 105 16 0 0 0 0 0 0 0 0 0 0
#> 2010 13 30 45 60 50 6 0 0 0 0 0 0 0 0 0 0 0 0
#> 2011 13 28 45 44 55 24 7 40 0 0 0 0 0 0 0 0 0 0
#>