Resource allocation in Marigold seedlings
Objectives:
1) To understand how all organisms are resource limited
2) To formulate testable hypotheses concerning resource limitations in a
plant
3) To use those hypotheses to construct concrete predictions
I. BACKGROUND
All organisms have available to them only the resources in their environment.
For most individuals, some or all resources may be limiting: the resources
are not in overabundance. Thus, for each limiting resource, the individual
must make budgetary decisions: it must allocate the resources amongst the
different expenditures required for survival, growth and reproduction.
The allocation of resources in plants is much easier to quantify than in
animals. The roots, shoots, leaves, and flowers of a plant each have a
distinct function, and yet all these structures require similar resources.
Moreover, unlike animals, a plant individual cannot relocate to a better
environment: the individual must make do with the conditions it finds.
Consider a limitation in nitrogen: the plant must have nitrogen to construct
proteins, and this requirement is expressed in all tissues. If nitrogen is
limiting, then the plant will have to decide how to budget the available
nitrogen among the costs of nutrient acquisition (roots), growth (stems),
energy acquisition (leaves), and reproduction (flowers and seeds).
The variation in resource allocation among plants in different environments
is one of the main sources of non-genetic (environmental) variation in plant
morphology. This is commonly referred to as phenotypic plasticity, and the
ability to respond to the environment in a phenotypically plastic fashion is
heritable, and is a key to the evolutionary success of plants. These
allocation decisions are likely influenced by a variety of factors in addition
to environment. For instance, would we expect the allocation decisions made
by a seedling to be the same as those made by a mature plant of
reproductive age?
As you work through this lab, bear in mind that this forms the basis of the
paper you will write for this course. The more time you spend now on
structuring a good experiment and developing explicit protocols, the easier
your final write-up will be.
II. HYPOTHESES: forming testable predictions
Observation: Plants grown in different places often have different
morphology.
Hypothesis: Differences in plant structure among genetically similar
individuals are due to differences in resource allocation.
Methods: We will test this hypothesis by manipulating seedling density in
dwarf marigolds. You will plant the seedlings 3 weeks prior to the final data
collection, and at planting time you must formulate predictions. The seeds
will be planted at 5/pot or 10/pot. For ease of statistical analysis, you will
need the same number of seeds in each treatment group; this will require
that you plant two pots at the lower density.
What variable(s) do you need to control for (keep the same) in order for the
test to be valid?
Predictions: Make concrete and explicit predictions about what differences
you expect to find between individuals in each treatment for roots, stems,
and leaves. Keep in mind the primary function(s) of each part of the plant.
Roots:
Stem:
Leaves:
Root/Shoot ratio:
III. MATERIALS AND METHODS
Materials: We will supply three 3.5 inch pots, potting soil, and dwarf
marigold seed. Working with your partners, develop a protocol for testing
your predictions. Write all steps here, being explicit about how you will
plant and harvest the seedlings and what variable(s) must be kept constant
across all treatments. Note that this experiment will run over several
weeks, and the course instructors will be sure to water all seedlings.
BE SURE TO MARK YOUR POTS !! They may be moved around during the
weeks and the pots must be labeled so you can identify yours!
Data collection: You will have available to you the following equipment:
balance (scale)
graph paper
ruler
calipers
Working with your partners and your TA, develop a protocol for data
collection, and write it down here:
IV. RESULTS
Before starting with your data collection, take a moment to observe the
seedlings. How many of the seeds you planted have survived? Are there
any apparent differences that you did not plan on measuring that now
appear interesting? Are there any adjustments to your original data
collection protocol that you should make? Note all of these observations
and, particularly importantly, any changes in data collection protocol, here:
Proceed with your data collection, being sure to follow your protocol. This is
the laboratory that will form the basis of your paper, so you must make
careful notes of what you do, and any errors committed.
DATA: Construct two tables below, with measurements tabulated for each
seedling in each treatment group. Leave room to calculate the means and
variances for each measure within each treatment group (see evaluation,
below).
(DATA, continued):
IV. EVALUATION
At this point, you have two sets of numbers. You have the measurements of
seedling morphology from the low density treatment, and the measurements of
morphology from the high density treatment. They may or may not appear very
different, but how can you evaluate those differences in a way that allows you to
test your original predictions?
The conundrum is, are the differences you’ve found between seedlings in each group
due to chance occurrences, or are they due to the experimental treatment?
Biologists use a simple statistical test, Student’s t test, to compare mean values of
different groups (figure 1, lines 1 and 2), in essence testing whether the two groups
are drawn from the same population or from different populations. We can use this
test to determine whether the seedlings are quantitatively different between the
two treatment groups.
Figure 1. The vertical lines indicate the
means for each of the two groups; the
horizontal lines the variation in each.
Clearly, to determine whether the means are different, we must have some
knowledge of the amount of variation in each population. In figure 2, the means are
the same, but the variance is greatly increased. With such high variance, the
differences between the means appears to be much less relevant: it could be due
simply to sampling error (chance sampling of different individuals from the same
population).
Figure 2. The means are the
same as in figure 1, but the
variance in each of the
populations is greatly increased.
To do the Student’s t-test, you must first calculate the mean and standard
deviation (the square root of the variance) within each treatment group for each of
the variables you measured. The mean is calculated as:
__
X =  (x)/n
where  (x)/n means “the sum of all observations (x) divided by the number of
observations (n)
The variance in a treatment group is calculated as
s2 =  (x-mean) 2/n-1
where the right hand portion means “the sum of the squares of the difference
between each measurement and the mean in that treatment divided by the number
of observations in that treatment minus 1”
The standard deviation (s) is the square root of the variance.
When the variance is the same in both treatment groups, then the student t test is
calculated as
__ ___
t = X 1 – X2 / √[(s12+s22)/2]
For this experiment we will assume that the variance is the same. The denominator
in this calculation of t is the pooled variance across both treatments, when sample
sizes are the same. IF sample sizes in the two treatment groups are not the same
(i.e., you had some seed or seedling mortality in one group), then randomly sample
within the larger treatment group to achieve equal sample sizes between the two
groups.
Place your calculations here:
Final steps: Evaluate your predictions. Considering your results, do your data cause
you to accept or to reject your predictions? If you reject your predictions, how
does this affect your initial hypothesis?