BIOL 130, Spring 2015
1
Energy Balance and Trophic Status in
Fish: Intro to Excel
Introduction
This is a data set from this paper: D. A.
Arrington, K. O. Winemiller, W. F.
Loftus, and S. Akin. 2002. "How often
do fishes 'run on empty'? Ecology 83 (8):
2145:2151. This study grew out of a
similar one on lizards in which the
proportion of animals with empty
stomachs was used as an index of
"instantaneous energy balance". In that
study the researchers wanted to know
whether lizards alternate between states
of feast or famine in contrast to keeping
a positive energy balance (which means
they consume more energy than they
use) all the time.
Arrington et al. (2002) wanted to look at
the energy balance question with
animals with different feeding modes such as ones that eat insects vs. ones that
eat other animals. They used fish in their
study because these animals display a
wide range of trophic (feeding)
specializations. They measured the
number of fish with empty stomachs in a
very large data set - 36,875 individual
fish of 254 species collected from
Africa, South America, Central America,
and North America. They only included
samples with >10 individuals in the
study; average sample size was 145.
The researchers examined the relationship
between "trophic status" (what an animal
eats) and number of fish with empty
stomachs. (Since they were looking at the
feast vs, famine question, they were
interested in fish with empty stomachs).
They classified fish into 4 categories piscivores (carnivores that eat other fish),
omnivores (eat everything), invertivores
(eat invertebrate animals), and
algivores/detritivores (eat algae and
detritus). They also looked at
reproductive behavior of the fish and
found that within the piscivores a
disproportionate number provided
parental care (e.g. mouth brooding - the
fish takes care of baby fish in its mouth!
This certainly would limit what the fish
could eat).
Arrington et al. conclude that animals
able to put surplus energy into lipids and
other storages can tolerate the energy
costs associated with high-cost
reproduction and also survive stress
periods. (Under which of the four
feeding strategy would fish more
likely be able to store fats? Which fish
have "high-cost" feeding strategies).
They state that their results "reveal a
potential influence of feeding frequency
and energy balance on life history
evolution."
BIOL 130, Spring 2015
Methods
1. Make an Excel graph by opening the
data set in Excel:

Go over the Excel
spreadsheet to make sure you
understand the headings, the
terms, and the question that
the scientists were asking.

Next, highlight the columns
from the words "Trophic
Category" and Percent" down
to the end of the numbers.

To make this bar graph
figure, go to the Insert tab
and select “Column Chart”.
2. Now examine the figure to interpret
the data. What are your conclusions?
Is there anything more you would
like to know? It’s pretty messy so
we’ll clean it up.
3. First, we’ll sort the data and then
copy and paste the data we need to
another worksheet.
4. To sort the data, highlight the blank
cell in the upper left of the
spreadsheet – this should highlight
all the data.
5. Choose the Data tab on the top of the
spreadsheet, and choose Sort. (Data
 Sort). Sort by Trophic Category.
This will put all the Algi/Detrivores
together, as well as the other classes.
6. Copy the data for each Trophic
Category into another worksheet, and
give each Trophic Category its own
column.
7. Now we can clean it up by
calculating the average and the
standard deviations for each class!
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To calculate the average and
standard deviations:
= average(highlighted cells)
= stdev(highlighted cells)
8. Make a graph of only the averages
for each Trophic Category. We’ll use
the standard deviations to give our
graph error bars. Highlight all
category labels and the averages.
Click Insert  column 2D
Column
9. Make sure that the Chart is
highlighted and click on Chart tools
 Layout. You’ll want to add some
axis labels, especially on the y-axis.
Click on Axis titles and add both a
vertical and horizontal title.
10. To add error bars, click on a bar on
the graph to highlight all the bars.
Click on Error Bars on the same
Layout tab. Go to “More error bar
options” and choose Custom (all the
way at the bottom). Highlight your
calculated Standard Deviations in the
worksheet for both positive and
negative error bars!
So, are there significant differences
among the categories? Are some Trophic
Categories hungrier than others?
ANOVA
After you have made a graph and
visually inspected your variable of
choice for the three trophic categories,
you may want to determine if there are
statistically significant differences
between them. You can do this for one
variable at a time (e.g. percent with
empty stomachs) using a one-way
Analysis of Variance (ANOVA).
BIOL 130, Spring 2015
ANOVA is a type of statistical
test that is often used to compare sample
groups (when there are more than 2
categories*** see t-test when there is
only 2 groups). ANOVA considers all of
the variation that exists within your data
set and determines how much of that
variation can be explained by any
grouping variables that you have
identified. It then calculates the
probability that you would observe a
similar distribution of variation by
chance alone (the P value). If that value
is very small (i.e. < 0.05), it is likely that
there are real differences among the
groups that you identified.
Excel has the capability to
perform several types of ANOVA, the
simplest of which is a one-way ANOVA.
You can use a one-way ANOVA to see
if there are differences in percent empty
stomachs for each trophic category.
To do an ANOVA, use the data
with each Trophic Category in its own
column. Note that the length of the four
columns will be different; this is because
there are different numbers of species in
each group.
Choose Data / Data Analysis /
ANOVA: single factor from the menus.
From the next menu, give the
block that holds your data as the ‘Input
range’ and check to make sure ‘columns’
is checked after ‘Grouped by:’. If your
column labels are highlighted click the
“Labels in First Row” box. Under
‘Output Options’ click ‘Output Range’
and indicate the upper left corner of an
empty area of your spreadsheet for the
range (this is where the results of the
ANOVA will be put), and leave the
other settings at their default values.
The output from the ANOVA
will include an F statistic and a P value.
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A large value for F indicates that a large
proportion of the variation in your data
can be explained by the grouping
variable (the Trophic Category). A small
value for P indicates that it is very
unlikely that you would see these
differences among your groups by
chance alone. Typically a P value < 0.05
is considered statistically significant,
however it is important to remember that
you are likely to see a difference of that
magnitude 1 times in 20, even if there
were no real differences among your
groups.
t-test***
T-tests are simplified versions of
ANOVA - they are appropriate when you
are comparing only 2 data categories,
such as light versus dark, wet versus dry,
etc. Again, t-tests compare the variation
within the data to the variation between
data groups. Excel can also do t-tests,
but since they are simpler you have more
choices of the tests to be done.
 Use t-test: paired 2 sample for
means when you have paired
samples (the same plant ) and you
want to know if the average value of
your collected data changed between
2 sampling periods.
 Use t-test: two-sample assuming
equal variances when you don't
have paired samples and you want to
know if they are significantly
different from each other. The equal
variances part means that your data
aren't all that noisy - you can tell by
looking at the standard deviations. If
both data sets have similar standard
deviations (same magnitude) you can
use this test.
BIOL 130, Spring 2015
 Use t-test: two-sample assuming
unequal variances for a similar case
to above, except your standard
deviations are much different from
each other.
TODAY'S ASSIGNMENT
Come up with another question based on
these data. Tell me what it is, make the
graph and do the analysis, and tell me
what you found. You can email me the
file (jschnurr@wells.edu)!
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