Bio 326: Homework 3: Due December 1st 9:30 am; 50 points

Background Information
Cedar Creek Natural History Area is a 2200 hectare (1 hectare =
2.47 acres) experimental ecological reserve that includes
prairie/savanna habitat and prairie plants growing in abandoned
agricultural fields. Prairies in Minnesota are dominated by
bunchgrasses such as little bluestem, big bluestem, and Indian
grass, and also include a large number of species of forbs such as
lupine, purple prairie clover, milkweeds, and goldenrods. Savanna
consists of scattered trees within a continuous stand of prairie
grassland. Fire is common in these systems and the trees are
restricted to a few fire-resistant species, primarily bur oaks.

The Biodiversity/Productivity Experiment
This experiment was set up to examine the effects of manipulating
species richness on plant productivity and biomass. Human impacts
are driving many species locally and globally extinct. These human
impacts include habitat loss or fragmentation, habitat
modifications such as pollution and introductions of exotic
species, and climate change. Fragmentation or fast shifts in
climate may limit the number of species that occur in a habitat
simply because the seed of that species cannot reach that area. Our
experiment is analogous to this situation in that we allow the seed
of many species to reach some areas, and restrict the number of
species able to reach other areas. We do not focus on the loss of a
particular species, but rather ask whether the loss of biodiversity
has any general, predictable effects.

In particular, we focus on whether biodiversity affects plant
productivity. Productivity is the amount of plant biomass produced
on a given area of land, over a given amount of time. If a
grassland were being used to produce hay, its annual productivity
would be the amount of hay that it could produce each year. We use
aboveground plant biomass as a measure of annual productivity. In
our system productivity approximates biomass because no aboveground
plant biomass from the previous year survives to the current year.
Each year, aboveground plant biomass either dies and decomposes or
is consumed in the spring fires we set. Productivity is an
important ecosystem trait, as all higher trophic levels depend upon
it directly or indirectly as a food resource. In addition,
maximizing productivity is a goal of many pasture, forestry, and
agricultural systems, and it is possible that insights about the
effects of biodiversity can be applied to some of these
systems.

In 1993 the vegetation and seed bank were removed from an
abandoned agricultural field. In spring of 1994, 168 plots, each 9
m x 9 m, were seeded to contain 1, 2, 4, 8, or 16 grassland-savanna
species. All plots received, in total, 10 g m-2 of seed in May 1994
and 5 g m-2 in May 1995, with seed mass divided equally among
species. Treatments were maintained by weeding 3 or 4 times/year.
Plots were sampled in mid-August for aboveground living plant
biomass by clipping, drying, and weighing four 0.1 x 3.0 m
vegetation strips per plot from 1996 through 1999, and eight strips
per plot in 2000. Different areas were sampled each year.

Description of the dataset behind the graph:
The x axis shows the number of species planted in a plot. In this
experiment, the treatment is the number of species planted, which
ranged from 1 to 16. Each treatment was applied to dozens of plots
(ranging from 29 to 39 depending on the treatment). The different
colors represent different years, from 1996 to 2002. The y axis
gives the value for aboveground biomass, averaged across all the
plots of a given treatment. The error bars give the standard
errors. The standard error is a measure of “variance,” and tells us
how much the average value is expected to vary if the experiment
were to be run again.
Small standard error bars indicate that our confidence in the
accuracy of the estimate is high and large standard error bars
indicate that our confidence is low.
We will made a scatter plot graph to visualize the relationship
between biomass and species richness and to help us interpret our
data. Although there are many types of graphs to choose from, the
two primarily used by scientists are the “scatter plot” and the
“column.” Species richness is the experimentally manipulated
variable, referred to as the predictor variable. By convention, we
use the horizontal, or x-axis, to indicate our predictor variable,
and the vertical or y-axis to represent our response variable.
Species richness is a variable that can be represented
quantitatively, and we therefore choose a scatter plot to graph our
data. If we had categorical data (e.g., plots characterized simply
as “low,” “medium,” and “high” diversity) we would choose a column
graph.

Questions:
Look at the graph and describe the patterns that you see. How would
you describe the pattern to another student looking at the graph
for the first time?
How does the relationship between biomass and species richness
change over time? How does it stay the same? What is the advantage
of having data from more than one or two years?
What conclusions can you draw, or hypotheses can you make, about
the effect of the loss of biodiversity in natural systems? What are
the problems that need to be considered when extrapolating the
results of this experiment to natural systems?

© 2004 – Joe Fargione, David Tilman and the Ecological Society of
America – TIEE, Volume 2
Copyright Statement. Teaching Issues and Experiments in Ecology
(TIEE) is a project of the Education and Human Resources Committee
of the Ecological Society of America. This page was originally
published on 15 August 2004 and was last revised 15 August
2004.