Relations of Angler Catch and Per Unit Effort and Yield to Limnological Variables in Wisconsin Lakes
File(s)
Date
1995-05Author
Potter, David F.
Publisher
University of Wisconsin-Stevens Point, College of Natural Resources
Metadata
Show full item recordAbstract
Linear predictive models were developed for Wisconsin
lakes that use easily-measured limnological variables to
predict summer angler CPUE (number of fish caught/angler
hour) and yield (number of fish harvested/ha) for six
species of sport fish: muskellunge (Esox masquinongy),
northern pike (Esox lucius), largemouth bass (Micropterus
salmoides), smallmouth bass (Micropterus dolomieu), walleye
(Stizostedion vitreum), and yellow perch (Perea flavescens).
Creel and limnological data for 163 Wisconsin lakes were
partitioned into two data sets to develop (108 lakes) and
validate (86 lakes) predictive models (31 lakes overlapped).
To reveal important relations, model development lakes
were first classified according to similarities among seven
limnological variables: area of adjacent wetlands, surface
area, conductivity, growing season, mean depth, shoreline
development factor, and percent of the surface area less
than 1 meter deep. K-means cluster analysis produced five
lake clusters that accounted for 42% of the variation in the
limnological data. Discriminant analysis re-classified the
lakes into five lake groups with an overall
misclassification rate of 15%. These lake groups were
different from each other: Group 1 contained lakes that were
typically the smallest and shallowest; Group 2 were deepest;
Group 3 had the highest conductivity, ·lowest sinuosity, and
longest growing seasons; Group 4 were the largest with the
greatest amount of adjacent wetlands and highest sinuosity;
Group 5 had lakes with the shortest growing season.
Indicator variable regression analysis developed, and
tested for differences among, lake-group predictive models.
In most cases, the initial predictive models for lake groups
did not differ from each other. In all, 17 species-specific
models explained from 6 to about 72% of the variation in
CPUE and yield from five limnological variables: area,
conductivity, growing season, mean depth, and percent
shallow depth.
Model validation lakes were classified with the
discriminant functions, and the mean square of predictive
errors (MSPR) was derived for comparison with the mean
square of errors (MSE) of initial models. Four of the 17
models did not have significant differences between MSPR's
and MSE's, and were thus validated.
Data sets were then pooled to derive a second set of
more robust models. Nine models were developed; however,
they only accounted for 4 - 29% of the variation with the
same five predictors, and did not detect differences in
slopes and/or intercepts among lake groups.
Although most of the regression models are of little
practical use in predicting species-specific CPUE and yield,
they provide a framework for further investigations that may
involve use of different dependent variables, incorporating
more elaborate independent variables, and classifying lakes
differently. The lake classification delineated lake groups
that were limnologically different, and therefore, may serve
other management applications.