Finding the Largest Brown Trout in the Deepest Stream Water
Abstract
Water depth is an influencing factor for the environment that brown trout choose to live in.
Brown trout avoid shallow water and larger trout choose deeper water. Water depth and fish length data
was collected from four sites on the Black Earth Creek in Wisconsin. This data has been compare in
terms of average water depth of site and average fish length and also by deepest water measurement taken
and longest fish at the sites. The results showed a trend of longer fish being found in deeper water. This
trend was not strong and was found insignificant. A small set of data points and other stream variables
may have lead to the results of this study.
Introduction
Habitat selection in streams varies for fish based on many environmental factors. One
environmental factor that fish show a strong preference for is water depth (Capra et al. 2002). When
thirteen environmental variables are taken into account in a stream study on brown trout, the most
influencing factor on environment choice is water depth (Belaud et al. 2001).
Brown trout avoid shallow areas in streams and larger trout are found in deeper areas of the stream
(Heggens, 2002). The Black Earth Creek has environmental variables that influence brown trout location.
I hypothesized that the largest fish of the brown trout species would be found in the stream sites where the
water depth is the deepest. In this paper I evaluate data collected about water depth and trout length in
order to see if a correlation exists.
Materials and Methods
We collected data from four locations on the Black Earth Creek in Wisconsin. The sites were
Forest-upstream, Forest-downstream, Urban-upstream, and Urban-downstream. At each site a fifty meter
stretch of river was marked off in 10 meter sections. All data collected for each site was within this 50
meter length of river.
Backpack shocker units were used for fish collection. Three sweeps were made of the 50 meter
sections. The fish were netted from each sweep. The fish were identified, weighed, and measured before
being returned to the river. The data used is compiled of the three sweeps at each site.
At each 10 meter marker of the 50 meter section, the width of the river was measured and the
depth was taken at each one-fourth distance across the river. At each marker habitat data was also
collected including data on the river bottom cover, riparian buffer, undercut bank, and surrounding land
use.
All the data was compiled. The deepest depth and largest brown trout measurement for each site
was selected and placed in a graph for observation. The average depth and average brown trout length for
each site was calculated and also placed in a graph for observation. A regression for these two sets of
data was done.
Results
The four river sites placed in order based on average water depth from shallowest to deepest are:
Urban-upstream at 27.3cm, Forest-upstream at 38.3cm, Urban-downstream at 51.67cm, and Forestdownstream at 52.06cm. The river sites in terms of the deepest measurement taken, arranged from
shallowest to deepest remain in the same order as above. The depths are 51cm, 68cm, 76cm, and 82cm
respectively. Table 1 shows the relationship of the sites and the depth data.
The four river sites when placed in order based on average fish length from shortest to longest are:
Urban-upstream at 129.28mm, Forest-upstream at 156.89mm, Forest-downstream at 168.08mm, and
Urban-downstream at 228.76mm. The river sites when arranged from shortest to longest based on the
longest fish measurement take are: Urban-upstream at 239mm, Forest-downstream at 295mm, Forestupstream at 315mm, and Urban-downstream at 410mm.
When the average water depth at each site is graphed along with the average fish length at each
site (Fig 1) there is a trend that as the average water depth of the river sites gets deeper the average fish
length of the river sites get longer. The only river sites where this deeper water-longer fish relationship is
not true is in the river sites with the two deepest average water depths. Here the third longest average fish
length is in the deepest average water depth and the third deepest average water depth is where the longest
average fish length is. The average depth difference between these two sites is 0.39cm, which is
relatively small compared to the range of average depths.
The P-value for the set of averages graphed in Figure 1 is 0.2017 meaning that there is no
significant relationship. The trendline does slope in the direction of a deeper water-larger fish relationship
and has an R-squared value of 0.6373.
When looking at the graph of the deepest measurement at a site graphed with the largest brown
trout at the site (Fig 2) there again is a trend that as the water depth gets deeper the brown trout get longer.
The one site where this relationship does not follow the trend the site with the deepest water. The deepest
water is where the second longest of the longest fish at each site was measured.
The P-value for the set of data graphed in Figure 2 is 0.3826. This means there is no significant
relationship. The trendline has an R squared value of .3811 and the trendline slopes in a positive direction
meaning that in this graph, as the water gets deeper the fish get longer.
Discussion
The sites aligning in the same order from shallowest to deepest with both the average depths and
deepest measurements taken was convenient for showing that the averages and maximums in depth were
both valid for the analysis of the river sites. In both graphs (Fig 1, Fig 2) there was the common trend that
as the water got deeper the fish got longer but the trend shown may not be significant.
In Figure 1 the trendline is steeper and has a larger R-squared value than the trendline in Figure 2.
In this way, the average depths and average fish lengths show a stronger deeper water-longer fish
relationship. The relationship between the calculated averages is more important than the relationship
between the maximums of the sites because the averages are a better representation of each site. The
visual strength shown by the trendline and data points seen in the graph was counteracted because the Pvalue was much above 0.05. The high P-value is likely because the one point that did not follow the trend
was out of place by a good amount in terms of fish length although a less than a 1cm difference in depth
would have placed the point in with the trend of the graph.
When graphing the deepest measurement at the sites with the longest fish at the sites (Fig 2) there
was a much weaker trend. The trendline was sloped less, the R-squared value was lower, and the points
did not show a solid visual trend. The second largest fish was in the deepest water. That does not line up
with the deeper water-larger fish trend. The P-value with a value greater than 0.05 also supports the
insignificance of the trend. The deepest water measurement and the longest brown trout caught are the
extreme data points and the extremes do not represent any experiment well.
I did find what I expected, a trend of deeper water having larger fish, but the trend was week and
labeled insignificant by a regression. I believe that the largest factor leading to this result is the small
number of data points. I only had four data points. With four data points it was hard to reach a solid,
strong, or convincing conclusion. A small number of data points did not well represent the size of brown
trout that were found in the deepest waters of streams.
Within-site comparisons would have been valuable data. My comparisons were among different
sites. In some sites there could be too much variability in depth so that the site is not an accurate example
of any particular depth. To detect real associations, within-site comparisons need to be made in order to
represent a fish population’s local options for water depths (Kaki et al. 1999). The forest and urban
locations for sites were far apart and may have not been options for all the fish. Also without within-site
comparisons the water depths surrounding the tested depth are not represented and could play a large role
in the size of fish found in the tested area.
Factors such as light intensity, availability of cover, temperature, current velocity, river bottom
substrate, flooded vegetation cover, and competition can change any fish’s, including large fish’s,
preference to choose the location of a certain depth of water (Cech et al. 2004, Greenberg et al. 2001).
These factors were all variable in the sites that were tested. If all of these factors were held constant a
better evaluation of depth to fish size could have been made.
I conclude that a second study should be done to research the depth of water-size of fish
relationship for brown trout in stream habitats. The new study would include within-site comparisons of
stream sites. The other values should be held constant within the stream sites so that depth specific data
can be collected and used for evaluation of my hypotheses.