Introduction to Ecological Investigation
Part 1.
Asking Questions:
“Why are things as they are and not otherwise?” – Johannes Kepler
Objective: To develop hypotheses based on your observations and consider
how to test those hypotheses.
Get together in groups of four. Walk into the Arboretum. As you walk
about (or sit in place), observe the organisms and environment around you.
Notice what organisms are abundant, changes in abiotic variables, and
patterns in where organisms occur. Think about how the patterns that you
observe may change over time and space. The objective here is to make you
think about your surroundings, explore your curious mind and formulate
questions that express your curiosity. By observing patterns and questioning
why they exist, you can use them to develop your own hypotheses.
1) List 5- 10 observations (e.g. moss seems to grow more frequently on
the north side of trees.)
2) Choose four of these observations and transform them into questions
that ask why or how these observations came to be. For example,
(assuming data indicate that in fact, moss grows more frequently on
the north side of trees) why does moss grow more frequently on the
north side of trees?
3) Now, transform two of these questions into functional hypotheses. A
functional hypothesis in ecology relates to the cause of the structural
difference observed. E.g. Moss grows on the north side of trees
because it is shaded from the sun. Remember, a hypothesis is a
potential explanation for your observation.
4) Choose one of your hypotheses and identify some predictions that
would test your hypothesis. A prediction states a specific relationship
between variables. If data collect indicate that this relationship
exists, then hypothesis is supported. E.g. moss will be more frequent
in shady areas deep within the forest, than in sunny areas along the
edge of forest. If this is true, then it lends support to the
hypothesis and we have greater confidence that it is correct (note
that the hypothesis is not proven as there is always the possibility
that an alternate hypothesis explains why moss grows on the north
side of trees).
Hypothesis:
Predictions:
Experimental description.
Group members:
Part 2.
Excel Assignment:
Objective: To formulate hypotheses and use appropriate statistical analyses
to test the hypothesis formulated, to use Excel to do data calculation,
presentation, and analysis.
In the paragraphs below, four different aquatic research questions
addressed by students and faculty here in recent years are described. For
each of these scenarios, 1) state the overall hypothesis and give a specific
prediction, 2) use Excel to test that prediction with an appropriate
statistical analysis and create a figure in Excel that displays the results and
3) briefly summarize the findings in a narrative. You should create a Word
document that gives the hypotheses, predictions and statistical results,
displays the figure and provides the findings narrative. (Although you will
prepare your statistical analysis in an Excel file, each group will submit a
SINGLE WORD DOCUMENT via Turnitin.com. Your word document will
include both part 1 and part 2 of this assignment).
Elimia sp. Densities – Ellen Winant
Ellen Winant looked at the densities of a common species of freshwater snail
in the genus Elimia. She hypothesized that snail abundance would be higher
in streams with higher conductivity (a measure of the ion concentration of
the water). Since snails require minerals (primarily calcium) to form their
shells, it seems reasonable to predict that streams with higher ion
availability would support more snails. To test her idea, Ellen sampled snails
in two streams with differing conductivities, Stamp Creek (a low
conductivity stream) and Two Run Creek (a high conductivity stream). In
each stream, she randomly selected 20 points. At each point she used a
Surber sampler to collect snails from a 0.09 m2 area. The data she collected
in August, 2009 is given in the worksheet entitled “Snail Densities”.
Species Richness – Jimi Reece
Jimi Reece was interested in examining the relationship between the number
of fish species in a stream (species richness) and the level of urban
development in the watershed draining that stream. He hypothesized that
as urban development (as measured by road density = the length of roads
per square kilometer) increased, species richness would decrease. A
complicating factor in his analysis was that (all other factors being held
constant) as watershed area increases, the number of species we expect to
find also increases. To account for this, he first conducted a regression
analysis on a large data set from sites throughout the northwest Atlanta
metropolitan area to develop a predictive equation for the number of species
of fish as a function of watershed area. From that same data set, he
selected 16 separate sites with differing levels of urban development. For
each of the sites, he quantified road densities using ARC-INFO, a
geographic information systems software package. He then estimated the
expected species richness based on the watershed size. He then subtracted
the observed value for that site (obtained from field samples) from the
predicted value (obtained from the regression analysis) to generate what is
known as a residual. If the residual is negative, observed species richness is
lower than expected, if it positive, richness is higher than expected. The
residual is the estimate of species diversity (that has been corrected for
watershed area) that you should compared to level of road density. The
data for the sixteen sites he examined is in the worksheet “Urban Fish”.
Lake Allatoona – Dr. Dirnberger
Dr. Dirnberger has conducted a number of differing studies on Lake
Allatoona since he came to Kennesaw. In one of his recent studies, he
realized that it would be useful if he could predict lake temperatures in
August based on the air temperatures experienced over the previous
months. He had a good data set for August lake temperatures (measured at
4 m in the same location in the lake) for the period from 1988 through 1992
and a second set of data from 1997 through 2003. He was able to estimate
the average air temperature for the months of June, July and August from
National Weather Service data. The data appears in the “Lake Allatoona
Temperatures” worksheet. Last summer’s average temperature was 25.91°.
What would Dr. Dirnberger expect the August water temperature to be and
how confident should he be in that prediction?
Sunfish Habitat Preference – Eddie Leonard
Eddie Leonard was interested in the types of habitats used by sunfish in the
genus Lepomis. As a fisherman, he knew that he was more successful
catching sunfish during the day in deep, slow moving areas, but these areas
seemed to be less productive during the night. He wondered if sunfish
actually preferred deeper, slower moving areas during the day. Separately,
he also wondered if sunfish preferred deeper, slower moving areas during
the night. To explore these ideas, he randomly selected 45 points in Stamp
Creek. At each of the 45 points, he measured water depth (in cm) and water
velocity (in cm/s). After taking the measurement, he placed a construction
flag at that location. After all the habitat points were measured and
marked, he entered the stream at the downstream flag and using a mask and
snorkel, did an instantaneous count of the number of sunfish within 1 m of
each of the flags. He did counts both 2 hours before sunset and 2 hours
after sunset (using a dive light). He collected data on two separate
occasions. The data he collected is given in the worksheet entitled “Lepomis
preference”. He divided his depth measurements into shallow (less than 20
cm) and deep (greater than or equal to 20 cm) and his velocity measurements
into slow (less than 10 cm/s) and fast (> 10 cm/s). Combining these gives
four categories, slow-shallow, fast-shallow, slow-deep, fast-deep. The table
entries give the number of habitat points that fall in each of these
categories (available) and the number of sunfish seen using those points
(observed).