Walleye (Sander vitreus) biological data (length, weight, sex, maturity, and age) from several locations in Lake Erie, October-November of 2003-2014.
Format
A data frame of 33734 observations on the following 10 variables:
- setID
Unique gear (multifilament gill net kegged 6 ft below surface) set identification number.
- loc
Regional location (
1=Toledo to Huron,2=Huron to Fairport Harbor,3=Fairport Harbor to Conneaut).- grid
2.5-minute sampling grid location.
- year
Year of data.
- tl
Total length (mm).
- w
Weight (g). There are several missing values.
- sex
Sex (female, male).
- mat
Maturity (immature, mature).
- age
Age (yrs) from otoliths.
Source
These unpublished data are from the Ohio Department of Natural Resources, Division of Wildlife (via Christopher Vandergoot). Do not use for other than educational purposes without permission from the source. CSV file
Topic(s)
Growth
von Bertalanffy
Weight-Length
Catch curve
Mortality
Maturity
Size Structure
Length Frequency
Condition
Examples
data(WalleyeErie2)
str(WalleyeErie2)
#> 'data.frame': 33734 obs. of 9 variables:
#> $ setID: int 2003001 2003001 2003001 2003001 2003001 2003001 2003001 2003001 2003001 2003001 ...
#> $ loc : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ grid : int 940 940 940 940 940 940 940 940 940 940 ...
#> $ year : int 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 ...
#> $ tl : int 360 371 375 375 375 376 379 379 380 380 ...
#> $ w : int 460 571 507 584 537 553 500 522 525 543 ...
#> $ sex : Factor w/ 2 levels "female","male": 2 2 2 2 2 2 1 2 2 2 ...
#> $ mat : Factor w/ 2 levels "immature","mature": 2 2 2 2 2 2 1 2 2 2 ...
#> $ age : int 2 2 2 2 2 2 2 2 2 2 ...
head(WalleyeErie2)
#> setID loc grid year tl w sex mat age
#> 1 2003001 1 940 2003 360 460 male mature 2
#> 2 2003001 1 940 2003 371 571 male mature 2
#> 3 2003001 1 940 2003 375 507 male mature 2
#> 4 2003001 1 940 2003 375 584 male mature 2
#> 5 2003001 1 940 2003 375 537 male mature 2
#> 6 2003001 1 940 2003 376 553 male mature 2
xtabs(~year+loc+sex,data=WalleyeErie2)
#> , , sex = female
#>
#> loc
#> year 1 2 3
#> 2003 106 169 12
#> 2004 286 367 147
#> 2005 338 820 98
#> 2006 246 1156 343
#> 2007 315 349 65
#> 2008 370 746 191
#> 2009 296 838 270
#> 2010 326 921 175
#> 2011 268 735 398
#> 2012 502 629 169
#> 2013 235 778 94
#> 2014 325 207 0
#>
#> , , sex = male
#>
#> loc
#> year 1 2 3
#> 2003 268 255 19
#> 2004 345 555 139
#> 2005 468 1488 123
#> 2006 448 1705 162
#> 2007 740 967 91
#> 2008 578 1396 168
#> 2009 751 1928 167
#> 2010 564 1320 127
#> 2011 585 1112 324
#> 2012 917 882 148
#> 2013 317 776 137
#> 2014 289 185 0
#>