Abstract
The study examines the factors that affect growth of Young of the Year Yellow Perch.
The factors examined were water temperature, density and food abundance. Average
length and weights plotted against growing degree days both produced positive trends
with low R2 values of 0.06 and 0.04 respectively. This very low/absent correlation
agreed with the literature. Average lengths and weights plotted against density
contradicted the literature by showing positive trends with R2 values of 0.07 and 0.14
respectively. A negative trend was expected. Average lengths and weights plotted
against mean conductivity used as a measure of food abundance contradicted the initial
hypothesis by showing positive trends both with the same low R2 value of 0.04. This
indicates a very weak correlation between food abundance and fish growth, which was
not expected from comparisons with the literature as a significant positive correlation was
expected.
Introduction
The growth rate of an organism is dependent on many different factors. These
factors include both abiotic factors such as temperature and biotic factors such as food
abundance. Scientists often seek ways to isolate the effects of various growth factors on
an organism, but have found that in practice it is often difficult to isolate the effects of a
single growth factor.
It is the end result of a number of different growth factors that
ultimately determines the growth rate of an organism. This study examines the effects of
several growth factors on the growth rates of fish. In particular, the species Perca
flavescens, the yellow perch will be used to attempt to answer several questions.
In this study, the young of the year (YOY) yellow perch will be used in order to
examine growth variability in relation to several growth factors. YOY fish are used as an
indicator of growth rates as they provide the most sensitive test, as smaller fish have the
greatest scope for growth and their equal starting size enables easy determination of
growth differences from year to year (Post and McQueen, 1994). The growth variability
in this species is important because studies have shown that small reductions in mean
growth rate can lead to survival decreases of 1-2 orders of magnitude by prolonging the
period of vulnerability to mortality sources (Rice et al, 1993).
The first factor that will be examined is the effect of water temperature on growth
rates. Intuitively, one would expect that years with an overall higher water temperature
would result in increased growth due to higher metabolism and the resulting increased
feeding activity. However, in 1994, Post and McQeen used mean annual water
temperatures and could not determine a positive relationship between these two variables.
As a result of this finding, it is expected that water temperature will be shown to have no
effect on growth rates. The second factor that will be examined is the effects of perch
density on growth rate. Studies have shown that as density levels increase, competition
for available food increases, with a resulting decrease in the available food per fish, thus
resulting in less mass being added to each one (Carlander, 1997). The two types of
competition that affect the perch are exploitative competition and interference
competition. Exploitative competition occurs when a fish eats a prey item, which reduces
that prey items availability to other fish. Interference competition occurs when a fish
forces another fish away from a prey item, therefore reducing the availability of the prey
item to the fish that was forced away. There is a direct behavioral interaction involved in
this type of competition. Studies have also shown that competitively superior fishes can
force weaker fishes out of more productive high quality habitats, which can affect
growth rates (Post, Johannes and McQueen, 1997). It is expected that increasing density
will limit growth rates. The third factor to be examined is the effects of food abundance
on the yellow perch. It has been shown in the literature, inconsistently however, that
there is a positive relationship between food abundance and fish growth (Boisclair and
Lagett, 1987). Lower densities of prey items tend to limit growth as the number of prey
consumed per fish is decreased (Confer et al, 1990). It is expected that a positive
relationship will be found between food abundance and fish growth.
Procedure
Length and weight measurements were taken for yellow perch from Gull Lake for
the years 1990-99 using seine haul data and trap net data. The measurements were made
in September or October depending on the year. The year 1994 is not applicable due to
absence of collection for the year, and the 1999 data is incomplete, containing only 33
fish from seine hauls. Weight data is missing for the year 1996 due to missing fish data.
Mean yearly lengths and weights were calculated, as well as standard deviations for
average weight and length measurements for each year. Frequency histograms were
plotted to allow for distinctions for age classes. The maximum cutoff for YOY yellow
perch was assumed to be 90 mm. It is assumed that all fish above 90mm will be age 1+
fish, and will not included in the study. Measurements for the year 1990 included 82 fish
with unknown data, thus these fish were excluded to allow for accurate results.
The density of yellow perch was estimated on a catch per effort basis. In
calculating this, only seine haul data was used to allow for consistency in the data. The
second factor of water temperature was estimated using air temperature. Growing degree
days were used as an indicator of water temperature. A growing degree day is a unit of
physiological time where the organism is assumed to be advancing in its life cycle, or,
growing. Cumulative growing degree days were added for each year from April 1st to
Sept 30th as this period includes the start and finish of the spawning period. In addition,
these are the warmest months where growth is expected to occur. The formula to
calculate growing degree days is as follows:
[ (Tmax + Tmin)/2-Tbase]
where Tmax is the daily maximum temperature in degrees Celsius, and Tmin is the
minimum daily temperature. Tbase is the base temperature, in this case 12oC, which it
used because growth occurs from 9-12oC, and this takes into account the beginning and
end of spawning, therefore, it is assumed that above 12oC YOY perch will be present and
growing. An average value of cumulative growing degree days was then taken for each
year. To avoid negative values in the calculation, all negative values were counted as
zero. 1990-1996 growing degree data came from the air temperature values taken from
the Toronto Pearson International Airport website. 1996-1999 data came from the
National Climatic Data Center website, also at the Toronto Pearson Airport station.
The third factor of food abundance was indirectly measured by taking average
conductivity values for each year. Conductivity was used as an indirect measure of total
lake nutrient inputs, which is related to plankton abundance. Thus, it served as an
indirect measure of food abundance for the fish, as no other data related to food
abundance was available.
Results
Figure 1 shows yearly temperature trends from April 1st to Sept. 30th, for the
years 1990-99, excluding 1994. Yearly fluctuations are observed, with high years being
1991 and 1995. The lowest year was 1997. Figure 2 a. and 2b. show frequency
histograms of perch length for the years 1990-1999, excluding 94 data. The 1999 data is
incomplete. The distribution of lengths shows a normal distribution with the exception of
the incomplete 1999 data, with a higher concentration of fish in the range of 60-70mm.
The 1999 data is skewed to the left, with a higher concentration of fish in the right hand
portion of the graph. Figure 3 shows the yearly growing degree days for the time period
analyzed. Inspection shows that the years 1991 and 1995 show the highest number of
growing degree day while the years 1997 and 1992 show the lowest. No general trend in
growing degree days can be seen for the last 10 years in the area of Toronto Pearson
airport. Figure 4 show the graphs of growing degree days versus average length and
weight respectively. The length graph shows an overall positive trend, with the
coefficient of determination (R2) value equal to 0.06. The weight graph also shows an
overall positive trend, with the R2 value equal to 0.04. Figure 5 shows the results of
density measured in catch per effort versus average length and weight for each year. The
length graph shows an overall positive trend, with an R2 value of 0.07. The weight graph
shows a similar positive trend, with an R2 value of 0.14. It should be noted that the
addition of 1999 data for the density graphs altered the R2 value by a disproportionate
amount than what would normally be expected. The R2 value for the length variable was
0.22 prior to the addition of 1999 data, while the R2 value was 0.39 prior to the addition
of 1999 weight data. Figure 6 shows plots of average lake conductivity versus average
perch length and weight for each year. The length graph shows a positive trend with an
R2 value of 0.04. The average weight graph also shows a positive trend, with an R2 value
of 0.04.
Discussion
The study analyzed the limiting factors on growth of YOY yellow perch. The
factors examined were water temperature, perch density and food abundance. Figure 4
shows the plots of average lengths and weights of perch per year versus the total number
of growing degree days above 12oC for each year. Although overall positive trends are
seen for both plots, which would indicate a positive effect of water temperature on fish
growth, the coefficients of determination are very low in both cases. The R2 value of
0.060 for the weight plot and 0.043 for the length plot indicate that only a very small
portion of the variability in weight or length respectively can be attributed to variations in
water temperature. This result is in agreement with a study conducted by Post and
McQueen where mean annual water temperatures were used to examine this correlation
and no significant effect on growth could be attributed to variations in water temperature
(1994). The low R2 values indicate that other biological factors play a role in the growth
of fish in addition to water temperature. One possibility affecting the strength of the
correlation is a phenomenon known as prey splitting. This occurs in midsummer as
zooplankton levels decline, the young perch switch from feeding mainly on plankton to
feeding on benthos. It has been found that growth rates decline after the switch possibly
because the young perch are not as effective at searching for and capturing these new
prey items (Post and McQueen, 1994). In addition, factors such as density and food
abundance will also have an effect on the correlation, with both density and food
abundance expected to inversely affect growth. The problem may be that it is extremely
difficult to isolate for the effects of one variable such as water temperature while
examining its effects on a biological factor such as growth that is dependent on many
different factors all interacting with one another.
This study contradicted the expectation from the literature for the effects of
density on fish growth, as is evident from inspection of Figure 5. The figure suggests that
as density increases, fish growth increases. However, the literature has shown that as fish
densities increase, the amount of available food decreases due to increased predation, and
thus less food is consumed per fish, which results in decreased growth (Carlander, 1997).
The R2 values for length and weight are both low, 0.07 and 0.14 respectively. It should
be noted that prior to the addition of the 1999 incomplete data, the R2 values for length
and weight were much higher, 0.22 and 0.39 respectively. The reason for this occurrence
is the addition of the small sample size of 1999 fish drastically affects the results since
the high and low extreme values of length and weight have much more of an affect of the
average for the 1999 year since the denominator in the average calculation is much lower.
This means that the 1999 average value will be much higher than if the data was
complete and the sample size was much higher. This is in fact true as the maximum
values for both graphs occur at the 1999 year. As a result of this incomplete data set and
the effects that it has on the observed R2 value, the data analyzed will be the data set prior
to the addition of the 1999 data, thus, an overall positive trend is observed with R2 values
of 0.22 for length and 0.39 for weight. These previous results indicate that density has a
positive affect on yellow perch growth, although the moderate to low R2 values indicate
that density effects are not measured in isolation. The unexpected positive relationship
may be due to several factors. The first of which is that yellow perch are a schooling
species, and therefore may benefit from social interactions and prey avoidance
(Hergrender and Hasler, 1968). The second factor that may account for the observed
result is cohort splitting. This occurs as density increases, competitively superior fish
force weaker fish out of the more productive littoral zone (Post, Johannes and McQueen,
1997). Since collection occurred in the littoral zone, as densities of YOY increase each
year, there will be a larger number of more productive fish residing in the littoral zone,
which will be collected each year. These fish will therefore have a higher average length
and weight. Thus, a year with a higher number of superior fish will have a higher
average length and weight than fish collected from the previous year, which results in a
higher growth rate in comparison between the two years. A third factor that is evident
from this finding is that the cost of intraspecific competition is a negative one for the
perch.
The third factor examined was food abundance, measured indirectly using
conductivity data. Conductivity was used as it is an indirect measurement of nutrient
inputs to the lake water. This in turn is related to food abundance, since nutrient inputs
can be used to measure total lake productivity (Abbey and Mackay, 1991). The literature
has shown, inconsistently however, that there is positive relationship between the
quantity of food consumed and fish growth (Boisclair and Lagett, 1987). Figure 6 shows
the length and weight plots for this variable in the study. Upon inspection of these two
graphs, one can see that R2 value is very low at only 0.04, even though there is an overall
positive trend. The very weak correlation is in contradiction with the literature. There
are several possible reasons for this. The first reason comes from the fact that increased
food abundance may not necessarily translate to increase food consumption . This occurs
as benthic invertebrates are often difficult for fish to detect due to substrate refuge and
cryptic coloration, therefore making feeding difficult (Williams and Feltmate, 1992) . A
second more likely explanation occurs because of the conductivity data itself. For some
years, conductivity averages consisted conductivity readings over the entire depth range
of the lake, while during other years only a few readings were available. This results in
certain years having a much more accurate indicator of the actual mean conductivity of
the lake for that point in time, while others have an average between two numbers. For
example, the year 1996 had only one value, this is not an average at all, merely a value at
one depth. This inconsistency in the data set could account for the poor correlation
observed. A third possible explanation for the poor correlation comes from the strength
of the indirect relationship between conductivity and food abundance. If this relationship
is not strongly correlated, the data used in the study will not have a strong correlation to
fish growth. A possible way to improve this part of the study would be to measure total
phosphorus levels of the lake, as this has been shown to be an strong indicator of total
lake productivity, which would then be strongly correlated with food abundance (Abbey
and Mackay, 1991).
Works Cited
Post, J.R and McQueen, D.J. (1994) Variability in first year growth of Yellow Perch
(Perca falvescens): Prediction from a simple model, observations and experiment.
Canadian Journal of Aquatic Science. 51: 2501-2512.
Rice, A., Miller, J., Rose,K., Crowder,L., Marschall,E., Trebitz, A., DeAngelis, D.
(1993). Growth rate variation and larval survival : Inferences from an individual
–based size dependent prediction model. Canadian Journal of Fisheries and
Aquatic Sciences 50: 133-142.
Carlander, K. D (1997). Handbook of Freshwater Fishery Biology. Vol. 3. Iowa State
University Press, Iowa.
Post, J.R, Johannes, M.R, and McQueen, D.J. (1997). Evidence of density-dependent
cohort splitting in age-0 yellow perch (Perca flavescens): potential behavioral
mechanisms and population level consequences. Canadian Journal of Fisheries
and Aquatic Sciences. 54. 867-865.
Boisclair D. and Leggett, W.C. (1989). Among Population Variability of Fish Growth:1.
Influence of the Quantity of Food Consumed. Canadian Journal of Fisheries and
Aquatic Sciences. 46. 457-467.
Confer, J.L, et al. (1990). Influence of Prey Abundance on species and Size selection by
Young Yellow Perch (Perca flavescens). Canadian Journal of Fisheries and
Aquatic Sciences. 47. 882-887.
Hergendrader, G.L, and Hasler, A.D (1968). Influence of changing seasons on schooling
behavior of Yellow Perch. Journal of Fisheries Research Board of Canada. 25.
711-716.
Abbey, D.H, and Mackay, W.C.(1991). Prediction the growth of age 0 yellow perch from
measures of whole-lake productivity. Freshwater Biology. 26. 519-25
Williams, D.D and Feltmate, B.W. (1992). Aquatic Insects. Cab international. Pgs. 238-9.
Summary of Changes made to Previous Project
- variable of food abundance added (via conductivity data)
- all graph axes modified to produce as many axes constant as possible
for proper comparisons
- 1999 data added/ histogram produced
- spreadsheet reorganized and unmodified/non-necessary sheets hidden
to avoid clutter
- graph of weight vs. Degree Days redone (error in data)
- 1997-1999 degree day data re-entered – problems with previous data
- added 1998/99 temperature graph
- regression equations displayed on same graph rather than separate ones
Take Home Final Exam
Ron Federchuk
ID#: 971 900 390
BIO 332Y
Professor N. Collins
April 18th 2000