The Scientific Method Steps in the Scientific Method There is a great deal of variation in the specific techniques scientists use explore the natural world. However, the following steps characterize the majority of scientific investigations: Step 1: Make observations Step 2: Propose a hypothesis to explain observations Step 3: Test the hypothesis with further observations or experiments Step 4: Analyze data Step 5: State conclusions about hypothesis based on data analysis Each of these steps is explained briefly below, and in more detail later in this section. Step 1: Make observations A scientific inquiry typically starts with observations. Often, simple observations will trigger a question in the researcher's mind. Example: A biologist frequently sees monarch caterpillars feeding on milkweed plants, but rarely sees them feeding on other types of plants. She wonders if it is because the caterpillars prefer milkweed over other food choices. Step 2: Propose a hypothesis The researcher develops a hypothesis (singular) or hypotheses (plural) to explain these observations. A hypothesis is a tentative explanation of a phenomenon or observation(s) that can be supported or falsified by further observations or experimentation. Example: The researcher hypothesizes that monarch caterpillars prefer to feed on milkweed compared to other common plants. (Notice how the hypothesis is a statement, not a question as in step 1.) Step 3: Test the hypothesis The researcher makes further observations and/or may design an experimentto test the hypothesis. An experiment is a controlled situation created by a researcher to test the validity of a hypothesis. Whether further observations or an experiment is used to test the hypothesis will depend on the nature of the question and the practicality of manipulating the factors involved. Example: The researcher sets up an experiment in the lab in which a number of monarch caterpillars are given a choice between milkweed and a number of other common plants to feed on. Step 4: Analyze data The researchersummarizes and analyzes the information, or data, generated by these further observations or experiments. Example: In her experiment, milkweed was chosen by caterpillars 9 times out of 10 over all other plant selections. Step 5: State conclusions The researcher interprets the results of experiments or observations and forms conclusions about the meaning of these results. These conclusions are generally expressed as probability statements about their hypothesis. Example: She concludes that when given a choice, 90 percent of monarch caterpillars prefer to feed on milkweed over other common plants. Often, the results of one scientific study will raise questions that may be addressed in subsequent research. For example, the above study might lead the researcher to wonder why monarchs seem to prefer to feed on milkweed, and she may plan additional experiments to explore this question. For example, perhaps the milkweed has higher ...

1. The Scientific Method
Steps in the Scientific Method
There is a great deal of variation in the specific techniques
scientists use explore the natural world. However, the following
steps characterize the majority of scientific investigations:
Step 1: Make observations
Step 2: Propose a hypothesis to explain observations
Step 3: Test the hypothesis with further observations or
experiments
Step 4: Analyze data
Step 5: State conclusions about hypothesis based on data
analysis
Each of these steps is explained briefly below, and in more
detail later in this section.
Step 1: Make observations
A scientific inquiry typically starts with observations. Often,
simple observations will trigger a question in the researcher's
mind.
Example: A biologist frequently sees monarch caterpillars
feeding on milkweed plants, but rarely sees them feeding on
other types of plants. She wonders if it is because the
caterpillars prefer milkweed over other food choices.
Step 2: Propose a hypothesis
The researcher develops a hypothesis (singular) or hypotheses
(plural) to explain these observations. A hypothesis is a
tentative explanation of a phenomenon or observation(s) that
can be supported or falsified by further observations or
experimentation.
Example: The researcher hypothesizes that monarch caterpillars
prefer to feed on milkweed compared to other common plants.
(Notice how the hypothesis is a statement, not a question as in
step 1.)
Step 3: Test the hypothesis
The researcher makes further observations and/or may design an

2. experimentto test the hypothesis. An experiment is a controlled
situation created by a researcher to test the validity of a
hypothesis. Whether further observations or an experiment is
used to test the hypothesis will depend on the nature of the
question and the practicality of manipulating the factors
involved.
Example: The researcher sets up an experiment in the lab in
which a number of monarch caterpillars are given a choice
between milkweed and a number of other common plants to feed
on.
Step 4: Analyze data
The researchersummarizes and analyzes the information, or
data, generated by these further observations or experiments.
Example: In her experiment, milkweed was chosen by
caterpillars 9 times out of 10 over all other plant selections.
Step 5: State conclusions
The researcher interprets the results of experiments or
observations and forms conclusions about the meaning of these
results. These conclusions are generally expressed as
probability statements about their hypothesis.
Example: She concludes that when given a choice, 90 percent of
monarch caterpillars prefer to feed on milkweed over other
common plants.
Often, the results of one scientific study will raise questions
that may be addressed in subsequent research. For example, the
above study might lead the researcher to wonder why monarchs
seem to prefer to feed on milkweed, and she may plan additional
experiments to explore this question. For example, perhaps the
milkweed has higher nutritional value than other available
plants.
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The Scientific Method Flowchart
The steps in the scientific method are presented visually in the
following flow chart. The question raised or the results obtained
at each step directly determine how the next step will proceed.
Following the flow of the arrows, pass the cursor over each blue

3. box. An explanation and example of each step will appear. As
you read the example given at each step, see if you can predict
what the next step will be.
Activity: Apply the Scientific Method to Everyday Life
Use the steps of the scientific method described above to solve
a problem in real life. Suppose you come home one evening and
flick the light switch only to find that the light doesn’t turn on.
What is your hypothesis? How will you test that hypothesis?
Based on the result of this test, what are your conclusions?
Follow your instructor's directions for submitting your
response.
The above flowchart illustrates the logical sequence of
conclusions and decisions in a typical scientific study. There
are some important points to note about this process:
1. The steps are clearly linked.
The steps in this process are clearly linked. The hypothesis,
formed as a potential explanation for the initial observations,
becomes the focus of the study. The hypothesis will determine
what further observations are needed or what type of experiment
should be done to test its validity. The conclusions of the
experiment or further observations will either be in agreement
with or will contradict the hypothesis. If the results are in
agreement with the hypothesis, this does not prove that the
hypothesis is true! In scientific terms, it "lends support" to the
hypothesis, which will be tested again and again under a variety
of circumstances before researchers accept it as a fairly reliable
description of reality.
2. The same steps are not followed in all types of research.
The steps described above present a generalized method
followed in a many scientific investigations. These steps are not
carved in stone. The question the researcher wishes to answer
will influence the steps in the method and how they will be
carried out. For example, astronomers do not perform many
experiments as defined here. They tend to rely on observations

4. to test theories. Biologists and chemists have the ability to
change conditions in a test tube and then observe whether the
outcome supports or invalidates their starting hypothesis, while
astronomers are not able to change the path of Jupiter around
the Sun and observe the outcome!
3. Collected observations may lead to the development of
theories.
When a large number of observations and/or experimental
results have been compiled, and all are consistent with a
generalized description of how some element of nature operates,
this description is called a theory. Theories are much broader
than hypotheses and are supported by a wide range of evidence.
Theories are important scientific tools. They provide a context
for interpretation of new observations and also suggest
experiments to test their own validity. Theories are discussed in
more detail in another section.
Recommended Reading
· "A Method of Enquiry" by George Kneller, in Science and Its
Ways of Knowing.
· "The So-called Scientific Method" by Henry H. Bauer,
in Science and Its Ways of Knowing.
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The Scientific Method in Detail
In the sections that follow, each step in the scientific method is
described in more detail.
Step 1: Observations
Observations in Science
An observation is some thing, event, or phenomenon that is
noticed or observed. Observations are listed as the first step in
the scientific method because they often provide a starting
point, a source of questions a researcher may ask. For example,
the observation that leaves change color in the fall may lead a
researcher to ask why this is so, and to propose a hypothesis to
explain this phenomena. In fact, observations also will provide
the key to answering the research question.
In science, observations form the foundation of all hypotheses,

5. experiments, and theories. In an experiment, the researcher
carefully plans what observations will be made and how they
will be recorded. To be accepted, scientific conclusions and
theories must be supported by all available observations. If new
observations are made which seem to contradict an established
theory, that theory will be re-examined and may be revised to
explain the new facts. Observations are the nuts and bolts of
science that researchers use to piece together a better
understanding of nature.
Observations in science are made in a way that can be precisely
communicated to (and verified by) other researchers. In many
types of studies (especially in chemistry, physics, and biology),
quantitative observations are used. A quantitative observation is
one that is expressed and recorded as a quantity, using some
standard system of measurement. Quantities such as size,
volume, weight, time, distance, or a host of others may be
measured in scientific studies.
Some observations that researchers need to make may be
difficult or impossible to quantify. Take the example of color.
Not all individuals perceive color in exactly the same way. Even
apart from limiting conditions such as colorblindness, the way
two people see and describe the color of a particular flower, for
example, will not be the same. Color, as perceived by the human
eye, is an example of a qualitative observation.
Qualitative observations note qualities associated with subjects
or samples that are not readily measured. Other examples of
qualitative observations might be descriptions of mating
behaviors, human facial expressions, or "yes/no" type of data,
where some factor is present or absent. Though the qualities of
an object may be more difficult to describe or measure than any
quantities associated with it, every attempt is made to minimize
the effects of the subjective perceptions of the researcher in the
process. Some types of studies, such as those in the social and
behavioral sciences (which deal with highly variable human
subjects), may rely heavily on qualitative observations.
Question: Why are observations important to science?

6. Limits of Observations
Because all observations rely to some degree on the senses
(eyes, ears, or steady hand) of the researcher, complete
objectivity is impossible. Our human perceptions are limited by
the physical abilities of our sense organs and are interpreted
according to our understanding of how the world works, which
can be influenced by culture, experience, or education.
According to science education specialist, George F. Kneller,
"Surprising as it may seem, there is no fact that is not colored
by our preconceptions" ("A Method of Enquiry," from Science
and Its Ways of Knowing[Upper Saddle River: Prentice-Hall
Inc., 1997], 15).
Observations made by a scientist are also limited by the
sensitivity of whatever equipment he is using. Research findings
will be limited at times by the available technology. For
example, Italian physicist and philosopher Galileo Galilei
(1564–1642) was reportedly the first person to observe the
heavens with a telescope. Imagine how it must have felt to him
to see the heavens through this amazing new instrument! It
opened a window to the stars and planets and allowed new
observations undreamed of before.
In the centuries since Galileo, increasingly more powerful
telescopes have been devised that dwarf the power of that first
device. In the past decade, we have marveled at images from
deep space, courtesy of the Hubble Space Telescope, a large
telescope that orbits Earth. Because of its view from outside the
distorting effects of the atmosphere, the Hubble can look 50
times farther into space than the best earth-bound telescopes,
and resolve details a tenth of the size (Seeds, Michael
A., Horizons: Exploring the Universe, 5th ed. [Belmont:
Wadsworth Publishing Company, 1998], 86-87).
Construction is underway on a new radio telescope that
scientists say will be able to detect electromagnetic waves from
the very edges of the universe! This joint U.S.-Mexican project
may allow us to ask questions about the origins of the universe
and the beginnings of time that we could never have hoped to

7. answer before. Completion of the new telescope is expected by
the end of 2001.
Although the amount of detail observed by Galileo and today's
astronomers is vastly different, the stars and their relationships
have not changed very much. Yet with each technological
advance, the level of detail of observation has been increased,
and with it, the power to answer more and more challenging
questions with greater precision.
Question: What are some of the differences between a casual
observation and a 'scientific observation'?
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Step 2: The Hypothesis
A hypothesis is a statement created by the researcher as a
potential explanation for an observation or phenomena. The
hypothesis converts the researcher's original question into a
statement that can be used to make predictions about what
should be observed if the hypothesis is true. For example, given
the hypothesis, "exposure to ultraviolet (UV) radiation increases
the risk of skin cancer," one would predict higher rates of skin
cancer among people with greater UV exposure. These
predictions could be tested by comparing skin cancer rates
among individuals with varying amounts of UV exposure. Note
how the hypothesis itself determines what experiments or
further observations should be made to test its validity. Results
of tests are then compared to predictions from the hypothesis,
and conclusions are stated in terms of whether or not the data
supports the hypothesis. So the hypothesis serves a guide to the
full process of scientific inquiry.
The Qualities of a Good Hypothesis
· A hypothesis must be testable or provide predictions that are
testable. It can potentially be shown to be false by further
observations or experimentation.
· A hypothesis should be specific. If it is too general it cannot
be tested, or tests will have so many variables that the results
will be complicated and difficult to interpret. A well-written
hypothesis is so specific it actually determines how the

8. experiment should be set up.
· A hypothesis should not include any untested assumptions if
they can be avoided. The hypothesis itself may be an
assumption that is being tested, but it should be phrased in a
way that does not include assumptions that are not tested in the
experiment.
· It is okay (and sometimes a good idea) to develop more than
one hypothesis to explain a set of observations. Competing
hypotheses can often be tested side-by-side in the same
experiment.
Question: Why is the hypothesis important to the scientific
method?
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Activity: Recognizing Good Hypotheses
Which of the following represents the best hypothesis? Click to
select one, then check your answer.
Cultures of the bacteria E. coli grow well in a lighted incubator
maintained at 90°F. A culture of E. coli was accidentally left
uncovered overnight on a laboratory bench where it was dark
and temperatures fluctuated between 65°F and 68°F. When the
technician returned in the morning, all the cells were dead.
Which of the following statements is the best hypothesis to
explain why the cells died, based on this observation?
A.
Some factor caused the E. coli cells to die.
B.
E. coli cells will die within 8 hours if it is exposed to
temperatures below 80°F.
C.
E. coli cells were killed by the combined effects of lack of
light, lack of moisture, exposure to oxygen, and lower than
optimal temperatures.

9. D.
E. coli cells were killed intentionally by someone who entered
the lab during the night, possibly a janitor.
Bottom of Form
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Step 3: Testing the Hypothesis
A hypothesis may be tested in one of two ways: by making
additional observations of a natural situation, or by setting up
an experiment. In either case, the hypothesis is used to make
predictions, and the observations or experimental data collected
are examined to determine if they are consistent or inconsistent
with those predictions. Hypothesis testing, especially through
experimentation, is at the core of the scientific process. It
is how scientists gain a better understanding of how things
work.
Testing a Hypothesis by Observation
Some hypotheses may be tested through simple observation. For
example, a researcher may formulate the hypothesis that the sun
always rises in the east. What might an alternative hypothesis
be? If his hypothesis is correct, he would predict that the sun
will rise in the east tomorrow. He can easily test such a
prediction by rising before dawn and going out to observe the
sunrise. If the sun rises in the west, he will have disproved the
hypothesis. He will have shown that it does not hold true in
every situation. However, if he observes on that morning that
the sun does in fact rise in the east, he has not proven the
hypothesis. He has made a single observation that is consistent
with, or supports, the hypothesis. As a scientist, to confidently
state that the sun will always rise in the east, he will want to
make many observations, under a variety of circumstances. Note
that in this instance no manipulation of circumstance is required
to test the hypothesis (i.e., you aren't altering the sun in any
way).
Testing a Hypothesis by Experimentation
An experiment is a controlled series of observations designed to

10. test a specific hypothesis. In an experiment, the researcher
manipulates factors related to the hypothesis in such a way that
the effect of these factors on the observations (data) can be
readily measured and compared. Most experiments are an
attempt to define a cause-and-effect relationship between two
factors or events—to explain why something happens. For
example, with the hypothesis "roses planted in sunny areas
bloom earlier than those grown in shady areas," the experiment
would be testing a cause-and-effect relationship between
sunlight and time of blooming.
A major advantage of setting up an experiment versus making
observations of what is already available is that it allows the
researcher to control all the factors or events related to the
hypothesis, so that the true cause of an event can be more easily
isolated. In all cases, the hypothesis itself will determine the
way the experiment will be set up. For example, suppose my
hypothesis is "the weight of an object is proportional to the
amount of time it takes to fall a certain distance." How would
you test this hypothesis?
The Qualities of a Good Experiment
Experiments can vary considerably depending upon the
hypothesis that is being tested. However, most experiments have
the following elements in common.
· The experiment must be conducted on a group of subjects that
are narrowly defined and have certain aspects in common. This
is the group to which any conclusions must later be confined.
(Examples of possible subjects: female cancer patients over age
40, E. coli bacteria, red giant stars, the nicotine molecule and
its derivatives.)
· All subjects of the experiment should be (ideally) completely
alike in all ways except for the factor or factors that are being
tested. Factors that are compared in scientific experiments are
called variables. A variable is some aspect of a subject or event
that may differ over time or from one group of subjects to
another. For example, if a biologist wanted to test the effect of
nitrogen on grass growth, he would apply different amounts of

11. nitrogen fertilizer to several plots of grass. The grass in each of
the plots should be as alike as possible so that any difference in
growth could be attributed to the effect of the nitrogen. For
example, all the grass should be of the same species, planted at
the same time and at the same density, receive the same amount
of water and sunlight, and so on. The variable in this case
would be the amount of nitrogen applied to the plants. The
researcher would not compare differing amounts of nitrogen
across different grass species to determine the effect of nitrogen
on grass growth. What is the problem with using different
species of plants to compare the effect of nitrogen on plant
growth?
There are different kinds of variables in an experiment. A factor
that the experimenter controls, and changes intentionally to
determine if it has an effect, is called an independent variable.
A factor that is recorded as data in the experiment, and which is
compared across different groups of subjects, is called
a dependent variable. In many cases, the value of the dependent
variable will be influenced by the value of an independent
variable. The goal of the experiment is to determine a cause-
and-effect relationship between independent and dependent
variables—in this case, an effect of nitrogen on plant growth. In
the nitrogen/grass experiment, (1) which factor was the
independent variable? (2) Which factor was the dependent
variable?
· Nearly all types of experiments require a control group and an
experimental group. The control group generally is not changed
in any way, but remains in a "natural state," while
theexperimental group is modified in some way to examine the
effect of the variable which of interest to the researcher. The
control group provides a standard of comparison for the
experimental groups. For example, in new drug trials, some
patients are given a placebo while others are given doses of the
drug being tested. The placebo serves as a control by showing
the effect of no drug treatment on the patients. In research

12. terminology, the experimental groups are often referred to
as treatments, since each group is treated differently. In the
experimental test of the effect of nitrogen on grass growth, what
is the control group?In the example of the nitrogen experiment,
what is the purpose of a control group?
· In research studies a great deal of emphasis is placed on
repetition. It is essential that an experiment or study include
enough subjects or enough observations for the researcher to
make valid conclusions. The two main reasons why repetition is
important in scientific studies are (1) variation among subjects
or samples and (2) measurement error.
Variation among Subjects
There is a great deal of variation in nature. In a group of
experimental subjects, much of this variation may have little to
do with the variables being studied, but could still affect the
outcome of the experiment in unpredicted ways. For example, in
an experiment designed to test the effects of alcohol dose levels
on reflex time in 18- to 22-year-old males, there would be
significant variation among individual responses to various
doses of alcohol. Some of this variation might be due to
differences in genetic make-up, to varying levels of previous
alcohol use, or any number of factors unknown to the
researcher.
Because what the researcher wants to discover is average dose
level effects for this group, he must run the test on a number of
different subjects. Suppose he performed the test on only 10
individuals. Do you think the average response calculated would
be the same as the average response of all 18- to 22-year-old
males? What if he tests 100 individuals, or 1,000? Do you think
the average he comes up with would be the same in each case?
Chances are it would not be. So which average would you
predict would be most representative of all 18- to 22-year-old
males?
A basic rule of statistics is, the more observations you make,
the closer the average of those observations will be to the
average for the whole population you are interested in. This is

13. because factors that vary among a population tend to occur most
commonly in the middle range, and least commonly at the two
extremes. Take human height for example. Although you may
find a man who is 7 feet tall, or one who is 4 feet tall, most men
will fall somewhere between 5 and 6 feet in height. The more
men we measure to determine average male height, the less
effect those uncommon extreme (tall or short) individuals will
tend to impact the average. Thus, one reason why repetition is
so important in experiments is that it helps to assure that the
conclusions made will be valid not only for the individuals
tested, but also for the greater population those individuals
represent.
"The use of a sample (or subset) of a population, an event, or
some other aspect of nature for an experimental group that is
not large enough to be representative of the whole" is called
sampling error (Starr, Cecie, Biology: Concepts and
Applications, 4th ed. [Pacific Cove: Brooks/Cole, 2000],
glossary). If too few samples or subjects are used in an
experiment, the researcher may draw incorrect conclusions
about the population those samples or subjects represent.
Use the jellybean activity below to see a simple demonstration
of samping error.
Directions: There are 400 jellybeans in the jar. If you could not
see the jar and you initially chose 1 green jellybean from the
jar, you might assume the jar only contains green jelly beans.
The jar actually contains both green and black jellybeans. Use
the "pick 1, 5, or 10" buttons to create your samples. For
example, use the "pick" buttons now to create samples of 2, 13,
and 27 jellybeans. After you take each sample, try to predict the
ratio of green to black jellybeans in the jar. How does your
prediction of the ratio of green to black jellybeans change as
your sample changes?
Conclusion
Measurement Error
The second reason why repetition is necessary in research

14. studies has to do with measurement error. Measurement error
may be the fault of the researcher, a slight difference in
measuring techniques among one or more technicians, or the
result of limitations or glitches in measuring equipment. Even
the most careful researcher or the best state-of-the-art
equipment will make some mistakes in measuring or recording
data. Another way of looking at this is to say that, in any study,
some measurements will be more accurate than others will. If
the researcher is conscientious and the equipment is good, the
majority of measurements will be highly accurate, some will be
somewhat inaccurate, and a few may be considerably inaccurate.
In this case, the same reasoning used above also applies here:
the more measurements taken, the less effect a few inaccurate
measurements will have on the overall average.
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Step 4: Data Analysis
In any experiment, observations are made, and often,
measurements are taken. Measurements and observations
recorded in an experiment are referred to as data. The data
collected must relate to the hypothesis being tested. Any
differences between experimental and control groups must be
expressed in some way (often quantitatively) so that the groups
may be compared. Graphs and charts are often used to visualize
the data and to identify patterns and relationships among the
variables.
Statistics is the branch of mathematics that deals with
interpretation of data. Data analysis refers to statistical methods
of determining whether any differences between the control
group and experimental groups are too great to be attributed to
chance alone. Although a discussion of statistical methods is
beyond the scope of this tutorial, the data analysis step is
crucial because it provides a somewhat standardized means for
interpreting data. The statistical methods of data analysis used,
and the results of those analyses, are always included in the
publication of scientific research. This convention limits the
subjective aspects of data interpretation and allows scientists to

15. scrutinize the working methods of their peers.
Why is data analysis an important step in the scientific method?
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Step 5: Stating Conclusions
The conclusions made in a scientific experiment are particularly
important. Often, the conclusion is the only part of a study that
gets communicated to the general public. As such, it must be a
statement of reality, based upon the results of the experiment.
To assure that this is the case, the conclusions made in an
experiment must (1) relate back to the hypothesis being tested,
(2) be limited to the population under study, and (3) be stated as
probabilities.
The hypothesis that is being tested will be compared to the data
collected in the experiment. If the experimental results
contradict the hypothesis, it is rejected and further testing of
that hypothesis under those conditions is not necessary.
However, if the hypothesis is not shown to be wrong, that does
not conclusively prove that it is right! In scientific terms, the
hypothesis is said to be "supported by the data." Further testing
will be done to see if the hypothesis is supported under a
number of trials and under different conditions.
If the hypothesis holds up to extensive testing then the
temptation is to claim that it is correct. However, keep in mind
that the number of experiments and observations made will only
represent a subset of all the situations in which the hypothesis
may potentially be tested. In other words, experimental data
will only show part of the picture. There is always the
possibility that a further experiment may show the hypothesis to
be wrong in some situations. Also, note that the limits of
current knowledge and available technologies may prevent a
researcher from devising an experiment that would disprove a
particular hypothesis.
The researcher must be sure to limit his or her conclusions to
apply only to the subjects tested in the study. If a particular
species of fish is shown to consume their young 90 percent of
the time when raised in captivity, that doesn't necessarily mean

16. that all fish will do so, or that this fish's behavior would be the
same in its native habitat.
Finally, the conclusions of the experiment are generally stated
as probabilities. A careful scientist would never say,
"drug x kills cancer cells;" she would more likely say,
"drug x was shown to destroy 85 percent of cancerous skin cells
in rats in lab trials." Notice how very different these two
statements are. There is a tendency in the media and in the
general public to gravitate toward the first statement. This
makes a terrific headline and is also easy to interpret; it is
absolute. Remember though, in science conclusions must be
confined to the population under study; broad generalizations
should be avoided. The second statement is sound science.
There is data to back it up. Later studies may reveal a more
universal effect of the drug on cancerous cells, or they may not.
Most researchers would be unwilling to stake their reputations
on the first statement.
As a student, you should read and interpret popular press
articles about research studies very carefully. From the text, can
you determine how the experiment was set up and what
variables were measured? Are the observations and data
collected appropriate to the hypothesis being tested? Are the
conclusions supported by the data? Are the conclusions worded
in a scientific context (as probability statements) or are they
generalized for dramatic effect? In any researched-based
assignment, it is a good idea to refer to the original publication
of a study (usually found in professional journals) and to
interpret the facts for yourself.
Activity: Interpretation of a Science Study as Presented in the
Popular Media
Read the article below, and then answer the questions that
follow. Follow your instructor's directions for submitting your
responses.
E. Coli Kills Cancer
Cancer is often fought with chemotherapy, and the effects of
these toxic drugs can be excruciating. But Canadian researchers

17. have discovered that a familiar, yet potent toxin can actually
shrink brain tumors in less than 48 hours with no apparent ill
effects.
The cancer-fighting chemical is verotoxin, which is produced by
the ubiquitous E.coli bacteria. This toxin, which causes
diarrhea, was injected into human brain tumors implanted in
mice. It not only shrank the tumors, but none of the tumors
reappeared.
How can a substance dangerous in the stomach not be dangerous
to the brain cells? "What is important is the amount of the
toxin," says Dr. Clifford Lingwood of the Hospital for Sick
Children in Toronto. Just a little bit of it won't hurt you, but the
more you're exposed to the sicker you'll get. The idea is to find
a level that is harmless to the animal as a whole, but deadly to
the cancer cells. A study of baboons measured how much
verotoxin it would take to make an ape sick. Animals given
small doses showed no side effects, nor did the mice in
Lingwood's study.
Lingwood says that the verotoxin stops the growth of new blood
vessels. "Tumor cells are particularly susceptible," he explains,
because the tumors are marked by a specific glycolipid, a
receptor that acts as a gateway into the cell.
The verotoxin finds the glycolipids on tumors and the blood
vessels that surround the tumor cells. It attaches itself to the
receptor and causes the cells to commit suicide. Verotoxins
ignore normal, non-cancerous brain cells, which don't contain
the receptor. With the toxin attacking both its outer membrane
and its food supply, the brain tumor shrivels almost
immediately after treatment begins. In cancer cells in Petri
dishes, "You can see a significant difference in 90 minutes,"
says Lingwood.
Lingwood's results were reported in the June issue of the
journal, Oncology Research.
—Martha Heil
Posted 7/19/1999
Answer the following questions:

18. 1. What experiments did the scientists perform?
2. What hypothesis was tested?
3. What data were collected?
4. What were the conclusions of the study?
5. Was the data collected sufficient to support the conclusions
made?
6. Was any important information about how the study was
performed left out of the article? If so, what information is
missing?
7. Is the title of this article an accurate statement of the study’s
findings? Why or why not?
Return to top of page
Qualities of a Good Experiment
The following are qualities of a good experiment:
· narrowly defined subjects
· all subjects treated alike except for the factor or variable
being studied
· a control group is used for comparison
· measurements related to the factors being studied are carefully
recorded
· enough samples or subjects are used so that conclusions are
valid for the population of interest
· conclusions made relate back to the hypothesis, are limited to
the population being studied, and are stated in terms of
probabilities
Recommended Reading
· For more detailed information on experiments and different
types of experimental designs see Chapter 2, "Establishing
Causal Links," of A Beginner's Guide to the Scientific
Method by Stephen S. Carey.

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Facilitation of afforestation by Lupinus nootkatensis and by
black plastic
mulch in south-west Iceland
Dennis A. Riege a; Adalsteinn Sigurgeirsson b
a University of Maryland University College, USA b Icelandic
Forest Research, Reykjavík, Iceland
First published on: 30 November 2009
To cite this Article Riege, Dennis A. and Sigurgeirsson,
Adalsteinn(2009) 'Facilitation of afforestation by Lupinus
nootkatensis and by black plastic mulch in south-west Iceland',
Scandinavian Journal of Forest Research, 24: 5, 384 —
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ORIGINAL ARTICLE
Facilitation of afforestation by Lupinus nootkatensis and by
black
plastic mulch in south-west Iceland
DENNIS A. RIEGE
1
& ADALSTEINN SIGURGEIRSSON
2

21. 1
University of Maryland University College, PSC 482, Box 178,
FPO AP 96362, USA, and
2
Icelandic Forest Research,
Mógilsá, IS-116 Reykjavı́k, Iceland
Abstract
Afforestation has proven difficult in south-west Iceland in a
region of degraded soils and high winds. Experiments at
Keflavik International Airport began in 2002 to examine
whether Nootka lupine (lupin; Lupinus nootkatensis) or black
plastic
mulch facilitates establishment of Sitka spruce (Picea
sitchensis), Hooker willow (Salix hookeriana) or downy birch
(Betula
pubescens) by ameliorating microsite conditions. By 2008, both
lupine and black plastic mulch facilitated growth of all
species at most plots. However, survival of spruce and birch
seedlings decreased where dense lupine was accompanied by
dense grass (but not in dense lupine alone). This indirect
mechanism (nurse plant stimulation of competitor species)
differs
from prior models of shifts in balance from facilitation to
competition under the stress-gradient hypothesis. Hooker
willow
performed best in both lupine and plastic mulch. However, in
areas without dense grass, Sitka spruce continued successful
growth and has potential for longer term afforestation. Planting
seedlings into shallow excavations in lupine improved
growth of willow and birch but not spruce. For afforestation in
south-west Iceland, it is recommended that a mix of tree
seedlings be transplanted directly into young lupine stands with

22. sparse grass cover, with shelterbelts of seedlings planted into
black plastic mulch along the stand edges.
Keywords: Betula pubescens, competition, Nootka lupine, Picea
sitchensis, Salix hookeriana, stress-gradient hypothesis.
Introduction
One plant species may have both facilitative and
inhibitory effects on another species, depending on
densities and circumstances (Callaway & Walker,
1997; Holmgren et al., 1997). Callaway and Walker
(1997) proposed a model that stated that facilitative
effects will increase under conditions of abiotic stress
and will decrease in a natural succession as stress
diminishes over time. Subsequent studies generally
support this stress-gradient hypothesis (Gómez-
Aparicio et al., 2004; Lortie & Callaway, 2005;
Michalet, 2005; Veblen, 2008). In Iceland, Aradóttir
(2004) found that Nootka lupine (lupin; Lupinus
nootkatensis) had competitive as well as facilitative
effects on tree seedling establishment of native

23. downy birch (Betula pubescens) and that the compe-
titive effects increased as lupine cover expanded. In
the present study, experiments were initiated at
Keflavik International Airport, Iceland in 2002 to
exploit a window of opportunity to plant tree
seedlings in young lupine�grass meadows. The
timing allowed for examination of whether lupine
facilitation might be superseded by competition as
lupine density increased, in accord with the stress-
gradient hypothesis.
Iceland faces a formidable set of difficulties
compared with other countries practicing afforesta-
tion (Óskarsson & Sigurgeirsson, 2001). The climate
is windy with low temperatures during the growing
season. Soils are cold, wet and deficient in nitrogen.
Frost heaving is common. Icelandic soils are of
volcanic origin and most are andisols (Arnalds
et al., 1995). Because of the volcanic origin and
fine textures, the soils are highly susceptible to wind

24. erosion. Human activity since the settlement of
Iceland 1100 years ago has resulted in soil erosion
and desertification of large areas (Magnússon,
1997). The Sudurnes peninsula, protruding into
the Atlantic Ocean, is an area of strong winds that
has lost much of its original topsoil. The winds,
Correspondence: D. A. Riege, University of Maryland
University College, PSC 482, Box 178, FPO AP 96362, USA. E-
mail: [email protected]
Scandinavian Journal of Forest Research, 2009; 24: 384�393
(Received 21 October 2008; accepted 11 June 2009)
ISSN 0282-7581 print/ISSN 1651-1891 online # 2009 Taylor &
Francis
DOI: 10.1080/02827580903117404
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eroded soils, salt spray and paucity of soil nitrogen
have made attempts at afforestation particularly
difficult. Today, most of Sudurnes is semi-barren
or has a low cover of moss or heath. For several
decades, Nootka lupine, introduced from Alaska,
has been widely and successfully used for revegeta-
tion of desertified lands in Iceland (Arnalds &
Runolfsson, 2004). At Keflavik Airport in Sudurnes
a vegetation restoration program has resulted in
establishment of several hundred hectares of lupine

27. and grass cover. Nootka lupine improves sites for
seedling establishment by nitrogen fixation, addition
of organic material and amelioration of microclimate
(Myrold & Huss-Danell 2003; Magnússon et al.,
2004; Mattson et al. 2007). Magnússon et al. (2004)
reported that lupine stands in Iceland may start to
degenerate after 15�20 years, to be replaced by
grassland. Establishment of forest cover at the
airport will accomplish a management goal to re-
place low ground cover with taller vegetation, in
order to reduce habitat for colonial nesting birds that
are an aircraft strike hazard. Development of meth-
ods that promote afforestation in south-west Iceland
will be useful to counter regional soil erosion and to
establish windbreaks around human habitation.
This study also examined the facilitation potential
of black plastic mulch, which has been shown to be
effective in promoting tree seedling establishment,
especially in low-quality sites (Green et al., 2003).

28. Plastic mulch inhibits competition from herbaceous
cover, increases soil temperature and moisture, and
ameliorates the problem of frost heaving (Flint &
Childs, 1987; Tarara, 2000; Green et al., 2003). In
field trials in Iceland, plastic mulch has benefited
seedling establishment of willow (Salix) species
(Sigurgeirsson, 2000).
Sitka spruce (Picea sitchensis), Hooker willow
(Salix hookeriana) and downy birch have all shown
potential for afforestation in south-west Iceland and
were chosen for these experiments. However, with-
out facilitation under the climatically harsh condi-
tions near the coast and on nutrient-poor soils (as
are common in Sudurnes), plantings of these species
often fail (Riege & Sigurgeirsson, unpublished data).
Sitka spruce should be competitive with lupine,
owing to its shade tolerance (Burns & Honkala,
1990) and evergreen habit. In southern Iceland,

29. Sitka spruce seedlings have performed well within
lupine cover (Óskarsson & Sigurgeirsson, 2004). In
the USA, growth of Sitka spruce seedlings in old
fields was better under bracken fern (Pteridium
aquilinum) cover than in the open (Riege & del
Moral, 2004). Both lupine and bracken die back
over winter. Native Salix species (S. phylicifolia and
S. lanata) in Iceland are inherently slow growing and
seldom reach more than 1�2 m in height except
under favorable, sheltered conditions. Hence, fast
growing introduced species, such as Hooker willow
and feltleaf willow (S. alaxensis) from Alaska, are
favored in Iceland when the goal is to achieve
rapid forest cover, shelter and soil protection
(Sigurgeirsson, 2000). At present, clones of Hooker
and feltleaf willow are predominantly used for
shelterbelts in southern Iceland. Downy birch (Be-
tula pubescens) is the only native tree species that

30. forms natural woodlands in Iceland. In some regions
of Iceland, it can be an aggressive pioneer that can
rapidly colonize denuded soils and derelict land, if
protected from sheep grazing. Lupine may be a more
effective facilitator of spruce colonization than Hoo-
ker willow or downy birch, which are pioneer
deciduous trees that are adapted to open growth
(Franklin & Dyrness, 1973; Sveinbjörnsson et al.,
1993). Plastic mulch may be more effective than
lupine for establishment of willow and birch. The
present experimental sites included patches of lupine
of varying density that were established in both semi-
barren and moss�heath cover. This allowed the
effects of cover type and lupine density on facilita-
tion to be investigated.
The regional aim of this study was to improve
afforestation methods in a difficult boreal, maritime
environment. Experiments were designed to test the

31. hypotheses that survival and growth of transplanted
tree seedlings are facilitated by lupine and by black
plastic mulch under variable site conditions. The
study also examined whether planting seedlings in a
shallow excavation within lupine further improves
facilitation. A broader goal was to utilize the varying
cover densities of lupine and grass to investigate
whether results followed the stress-gradient hypoth-
esis, in which competitive effects increase to the
detriment of facilitative effects as plant density
increases.
Materials and methods
Study sites
Keflavik International Airport is located in the
south-west corner of Iceland on the Sudurnes
Peninsula (Figure 1). Climate is moderated by the
Gulf Stream and can be characterized as temperate,
maritime and windy. Mean January temperature is

32. 08C, July 108C (US Naval Air Station Keflavik
Meterologic Office Climate Summary, 1949�1995).
Mean annual precipitation is 1074 mm, dispersed
throughout the year. Mean wind speed is 6 m s
�1
,
with extreme winds to 39 m s
�1
. Soils at the airport
are highly eroded and vegetation is sparse as a result
of centuries of human destruction of woodlands and
overgrazing on the peninsula (Steindórsson, 1957).
Afforestation facilitation by lupine 385
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A vegetation restoration program at Keflavik
International Airport has resulted in many areas of
lupine cover. Five locations were chosen in 2002 for
experimental sites and identified as FB, F5, F2, F3
and FA in the Airport Geographic Information
System. FB, F5 and F2 were mostly barren before
revegetation. F3 was covered with grass�moss before
the restoration treatment, with a thin sod developed
in some areas. FA was representative of large tracts
of moss�heath cover in the region, dominated by
mixtures of Racomitrium moss, crowberry (Empetrum
nigrum) and heather (Calluna vulgaris). F5, F2 and
F3 received a restoration treatment in 1998�1999 of

35. seeding with a 25 kg ha
�1
mixture of 45% tufted
hairgrass (Deschampsia caespitosa), 45% Bering hair-
grass (D. beringensis), 10% annual ryegrass (Lolium
multiflorum) and 3.5 kg ha
�1
lupine. This was fol-
lowed by an application of chicken manure at
30 t ha
�1
. FB and FA were pilot sites in the restora-
tion program where lupine was not sown as seed, but
transplanted as seedlings in 1996 at a density of
1500 ha
�1
, along with a 15 kg ha
�1
hairgrass mix,
followed by an application of 20 t ha
�1

36. of organic
materials. During July�August 2002, the percentage
cover of major vegetation types within the lupine
patches at each site was estimated by a sample of
nine 1 m
2
quadrats located in a stratified-random
pattern.
Experimental design
Tree seedlings appropriate to the region were sup-
plied by the Sudurlandsskógar afforestation project
of Selfoss, Iceland: containerized seedlings of 1-year-
old Hooker willow (clone Katla, Iceland), downy
birch (provenance Reykjarhóll, Iceland) and 1-year
and 2-year Sitka spruce (provenance Taraldsøy,
Norway). Salix can be easily propagated vegetatively
with unrooted cuttings. Hence, individual Salix
‘‘cultivars’’ are defined as genetically identical
‘‘clones’’. The clone Katla was originally collected

37. in Yakutat, Alaska, in 1985 but selected for wider use
in the mid-1990s owing to its good performance in
wind-exposed areas along the south coast of Iceland.
Katla performed best among 118 willow clones over
five growing seasons in trial plots at Keflavik Airport
(Riege & Sigurgeirsson, manuscript in preparation
for Icelandic Agricultural Sciences).
One method in Iceland for large-scale afforesta-
tion uses a rotating three-armed star machine to
transplant seedlings into depressions excavated every
2 m. This treatment was simulated manually in
lupine stands by the use of hand picks, with
excavations 2 m apart, approx. 50 cm in diameter
and approx. 20 cm deep with all vegetation cleared
from the excavations. The depressions were intended
to help shelter the seedlings from wind and to reduce
competition. To test whether the depression treat-
ment improved tree establishment, seedlings were

38. also transplanted directly into the lupine cover.
Containerized seedlings (150 ml for willow, 100 ml
for others) were transplanted with a Finnish Potti-
putki planting tube. A teabag of RTI Silva-Pak slow-
release fertilizer was inserted next to each plant. This
fertilizer was found to be effective for tree seedling
establishment in trials in southern Iceland
(Óskarsson & Sigurgeirsson, 2004). Afforestation
on barren land without fertilization usually yields
high mortality and stagnant growth in Iceland
(Óskarsson & Sigurgeirsson, 2001).
During June 2002, seedlings were transplanted in
adjacent blocks within (1) lupine cover, (2) depres-
sions in lupine cover, (3) black plastic mulch in non-
lupine cover, and (4) non-lupine cover as control.
The number of seedlings of each species dedicated to
each treatment is given in Table I. A complete,
balanced factorial design for the treatments was not

39. practical within the logistical constraints for the
experiments. Since a main goal of the project was
to evaluate the long-term effect of lupine facilitation,
the number of control seedlings was limited, so that
most seedlings could be placed within lupine cover.
Other experiments initiated in the region in 1998
will provide long-term results for seedlings under
treatments in non-lupine cover. Seedlings were
planted 2 m apart in alternating groups of five to
ensure heterogeneous dispersion of types (not for
Figure 1. Location of Keflavik International Airport in Iceland.
Table I. Experimental design: number of plants of each species
per treatment at each site.
Picea Salix Betula
Control 10 10 10
Plastic mulch 20 10 10
Lupine 50 20 20
Depression�lupinea 50 20 20

40. Note:
a
no treatment at site FA.
386 D. A. Riege & A. Sigurgeirsson
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use as replications in analysis). FA was too rocky for
the depression treatment. Lupine plants at FA were

42. aligned in rows. There, seedlings were planted
approx. 20 cm south-west of the lupine plants along
the rows (leeward of the more damaging north-east
winds). Seedlings were placed 1 m apart in holes
punched in the midlines of two parallel strips of
photodegradable black plastic mulch that were 1.5 m
wide and 30 m long, with a 2 m gap separating the
strips. The plastic strips were covered with a thin
layer of gravel.
Seedlings were scored for survival and heights (at
highest living part) were measured during 11�14
June 2003, 20�22 May 2004, 22�27 June 2005 and
11�12 May 2008. Three hypotheses on survival and
seedling heights were tested. Survival and heights of
Site: FB F5
F2 F3 FA
Picea sitchensis
Salix hookeriana
Betula pubescens
D = lupine + depression P = black plastic mulch L = lupineC =

43. control
PC
L
D
0
25
50
75
100
%
s
u
rv
iv
a
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P
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L
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0

44. 50
100
150
200
h
e
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h
t
(c
m
) P
C
L
D
P
C
L
D
P
C
L

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P
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L
P
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L
D
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25
50
75
100
%
s
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L

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P
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L
D
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L
D
P
C
L
P
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L
D PC
L
D P
C
L
D
P
C

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0
25
50
75
100
h
e
ig
h
t
(c
m
)
P
C
L
2002 2005 2008
P
C

48. L
D
2002 2005 2008
P
C
LD
2002 2005 2008
P
C
L
D
2002 2005 2008
P
C
L
D
2002 2005 2008
P
C

49. L
D
P
C
L
D P
C
L
P
C
L
D
P
C
LD
0
25
50
75

50. h
e
ig
h
t
(c
m
)
P
C
L
D
P
C
L
D
P
C
L
P
C L
D
0

51. 25
50
75
100
%
s
u
rv
iv
a
l
P
C
L
D
Figure 2. Survival and mean height of Picea sitchensis, Salix
hookeriana and Betula pubescens seedlings by site and
treatment, 2002�2008.
(Standard error bars omitted for clarity; standard errors for 2008
given in Table IV.)
Afforestation facilitation by lupine 387
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seedlings in: (H1) lupine are greater than in control
plots, (H2) depressions in lupine are greater than
those planted directly into lupine, and (H3) black
plastic mulch are greater than in control plots. As
cover varied among the experimental sites, site
effects were also examined. Thus, a randomized
factorial design for hypotheses on seedling heights,

54. with each plant randomly assigned to treatment and
site, was used. The ANOVA model was:
Yijk �m�ai �bj �abij �oijk
where m is the grand mean, a is the treatment effect,
b is the site effect, ab is the interaction effect, and o is
the error. For analysis of survival, values for percen-
tage survival per treatment per site were arcsin
transformed, with the interaction effect removed
from the ANOVA model since there was no replica-
tion. For analysis of H1 and H3, 2005 data were
used, because lupine spread over the FB and F5
control plots thereafter; 2008 data were used for H2.
Data were analyzed with Statistix 9.0 software
(www.statistix.com). Results of H2 were considered
significant if pB0.05. Results for H1 and H3 were
considered significant if pB0.025, adjusted by a/2 as
the control data were in two comparisons (Dawson
& Trapp, 2001). Some analytical results include

55. treatments that have a relatively low number of
seedlings. However, the major findings of the study
were supported by p values50.005.
Results
Site cover
In 2002 the lupine plots at F5 and F2 had almost
total lupine cover (Table II). Grasses, primarily
seeded hairgrass, were abundant at these two sites,
with growth facilitated by the nitrogen-fixing lupine.
From 2002 to 2008, the grass cover within the lupine
stands continued to thicken. The stand at FB, where
lupine was transplanted rather than sown, had only
72% lupine cover in 2002 and still had small barren
patches (Table II). However, by 2005 lupine cover
had increased to almost 100% at FB, although grass
cover was less and of different composition than in
F5 and F2. Unseeded fescue species (Festuca vivi-
para and F. rubra) rather than seeded hairgrass were

56. dominant at FB. In 2002, lupine covered only about
half of its plot at F3, illustrating the slower spread
when faced with competitive grass and moss (Table
II). By 2008, lupine covered approx. 70% of F3.
Lupine spread has also been slower (2008 cover
�70%) in the FA moss�heath than at other sites.
Survival
Survival ranged from 10% for downy birch in
depressions in lupine at F2 to 100% for several
species�treatment�site combinations (Figure 2).
Neither significant treatment nor site effects on
survival of the three species were found by ANOVA
(results not shown), with the one exception of a site
effect in the depression in lupine treatment on Sitka
spruce ( p�0.02). However, the two-way ANOVA
without replication was unable to measure interac-
tion effects. Survival of Sitka spruce seedlings varied
considerably by site and treatment (Figure 2). The
1-year spruce seedlings suffered greater mortality

57. than 2-year seedlings at all sites, had very few
survivors in dense grass, and are not recommended
for planting into dense lupine (data not shown). The
1-year spruce seedlings were therefore not further
dealt with in the present analysis. Survival of Sitka
spruce seedlings remained high in black plastic
mulch at all sites. The highest mortality of spruce
seedlings occurred within lupine and depressions in
lupine at F5 and F2. Many spruce seedlings at these
sites were found beneath thick grass, suggesting that
competition from grass caused mortality. In contrast,
spruce mortality was very low at FB (100% survival
for seedlings in lupine). FB had almost total lupine
cover in 2008, like F5 and F2, but less grass cover
than those two sites. Grass cover (mostly fescue) had
increased since 2002 in lupine at FB, F3 and FA, but
Table II. Cover characteristics in lupine stands at afforestation
experimental sites during year of tree seedling transplants,
July�August 2002.

58. Percentage cover by site
FB F5 F2 F3 FA
Lupine 72 96 99 52 48
Grass
a
21 (F) 69 (D) 89 (D) 65 (F) 32 (F)
Moss 6 3 B1 33 34
Dwarf shrubs
b
12 0 0 11 43
Forbs 11 4 4 15 6
Bare 10 B1 0 B1 B1
Note: before restoration treatment in 1996�1999, FB, F5 and F2
were barren, F3 was grass�moss, and FA was dwarf
shrub�moss heath.
a
F�dominated by unseeded Festuca vivipara and F. rubra;
D�dominated by seeded Deschampsia beringensis and D.
caespitosa; bprimarily
Empetrum nigrum and Calluna vulgaris.
388 D. A. Riege & A. Sigurgeirsson
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www.statistix.com
was less dense than the hairgrass cover of F5 and F2.
Similar to Sitka spruce, downy birch seedlings
suffered high mortality when in competition with
thick grasses under lupine (Figure 2: F5, F2).
Hooker willow seedlings survived better than spruce

61. or birch in lupine cover (Figure 2). Many willow
seedlings emerged above the lupine plants by 2004�
2005 (lupine�60�80 cm tall). Competition from
the lupine and tall grasses was less detrimental to
the willows. Although in many cases survival of
plants in control plots exceeded that of treatment
plots after 6 years, most control plants exhibited very
poor growth in comparison to plants in lupine or
black plastic mulch.
Growth
Lupine effects. Overall, seedling heights of Sitka
spruce seedlings were significantly greater in lupine
than in control plots (Table III). However, there
were significant site and interaction effects, as spruce
heights were not greater in the two sites (F5, F2)
with denser grasses (Figure 2). Mean willow heights
were greater in lupine than in control plots at all
sites (Figure 2), although site and interaction effects
were still significant (Table III). Performance of

62. Hooker willow seedlings was poorest in the moss�
heath cover of site FA, but even at FA, survival
and growth of willow seedlings were boosted in
lupine cover (Figure 2). Heights of surviving birch
seedlings were taller in lupine than in control plots at
all sites (Figure 2), with no significant site or
interaction effects. However, the sample sizes of
birch heights were low at F5 and F2 as few seedlings
survived.
Depression in lupine effects. The excavation of depres-
sions within lupine significantly increased the
heights of Hooker willow and downy birch, but
not Sitka spruce (Table III). Depressions particu-
larly did not improve spruce heights in F5 and F2,
where grasses rapidly recolonized the excavations
(Table IV, Figure 2). The depression effect on
willow heights was not as strong as the black
plastic or lupine effects (Table III). Again, the low

63. sample size of birch in depressions at F5 and F2
Table III. Effects of site and treatment on height of seedlings
under three different hypotheses (H1�H3).
Picea Salix Betula
df F p df F p df F p
H1
Lupine (L) 1 24.52 B0.001 1 42.01 B0.001 1 10.42 0.002
Site (S) 4 3.91 0.005 4 10.17 B0.001 4 0.90 0.469
L�S 4 5.63 B0.001 4 3.17 0.017 4 1.11 0.358
Error 196 111 84
H2
Depression (D) 1 1.41 0.237 1 4.51 0.036 1 7.99 0.006
Site (S) 3 3.30 0.022 3 10.71 B0.001 3 0.85 0.470
D�S 3 0.84 0.475 3 2.98 0.034 3 2.63 0.058
Error 181 129 60
H3
Plastic (P) 1 27.91 B0.001 1 134.85 B0.001 1 8.45 0.005
Site (S) 4 10.34 B0.001 4 23.80 B0.001 4 9.43 B0.001
P�S 4 4.12 0.004 4 4.09 0.005 4 8.29 B0.001
Error 128 77 70

64. Note: H1�lupine�control (2005); H2�depressions in
lupine�lupine (2008); H3�black plastic mulch�control (2005).
Table IV. Height (cm) by treatment, species and site in May
2008.
Site Control Lupine
Depression�
lupine
Plastic
mulch
Picea
FB N/A 42911 42918 2597
F5 N/A 40910 42913 70930
F2 31911 29911 31911 47914
F3 2497 37914 45921 58915
FA 1598 51915 N/A 26911
Salix
FB N/A 167936 200946 93928
F5 N/A 131937 148942 183912
F2 96950 145945 133942 206930
F3 77942 122947 154933 195918

65. FA 1996 122945 N/A 128949
Betula
FB N/A 65935 105931 40913
F5 N/A 54920 89912 90925
F2 41918 64927 91927 83927
F3 38910 75926 74926 72926
FA 3098 95935 N/A 3699
Note: data are shown as mean9SE.
N/A: not applicable. Controls at sites FB and F5 overgrown by
lupine and no longer valid.
Afforestation facilitation by lupine 389
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tempers interpretation of the increase in birch
heights (Table IV, Figure 2).
Black plastic mulch effects. Seedling heights for all
species very significantly increased in black plastic
mulch versus controls, although accompanied by site
and interaction effects (Table III). Spruce heights
were greater in plastic mulch than in controls at all
sites (Figure 2). Spruce seedlings at three sites (F5,
F2, F3) made a burst of growth from 2004 to 2008
(Figure 2), such that average heights in plastic at
these sites came to exceed those in lupine and in

68. depressions within lupine (Table IV). However, at
FB and FA all three species mostly performed better
in lupine than in plastic mulch (Table IV, Figure 2).
This may reflect lower grass competition or less
fertile soil at FB and FA. Hooker willow seedlings
showed a substantial increase in height in black
plastic mulch at F5, F2 and F3, with many plants
�2 m tall. Downy birch seedlings survived well in
black plastic mulch, with the exception of the heath
site FA (Figure 2), but birch heights in plastic mulch
generally did not exceed those in the lupine treat-
ments.
Discussion
Facilitation and competition by lupine
Nootka lupine, which is used extensively for land
reclamation in Iceland, shows strong promise as a
nurse crop to facilitate establishment of trees (Ara-
dóttir, 2004; Óskarsson & Sigurgeirsson, 2004;

69. Mattson et al., 2007). In the experiments reported
here, lupine facilitated growth of Hooker willow,
Sitka spruce and downy birch in most cases. For
Hooker willow, seedling survival was high at all sites.
Most willow seedlings emerged above the lupine
plants. However, for Sitka spruce and downy birch,
survival of seedlings decreased in areas where dense
grass accompanied lupine, indicating a competitive
effect. In these more competitive sites, Sitka spruce
also did not show a height increase in lupine.
It is not uncommon for one plant species to show
both positive and negative effects on another species.
The net effect may be positive (facilitation) or
negative (competition), depending on densities, age
and abiotic factors. According to the stress-gradient
hypothesis, facilitative effects are stronger when
environmental stress is greater (Callaway & Walker,
1997; Michalet, 2005; Veblen, 2008). Competitive

70. intensity increases as nurse plants become denser.
The environment of south-west Iceland is physically
stressful with infertile soils, high winds and recurrent
frost heaving. This is reflected by the vegetation,
which is either semi-barren or ground-hugging.
Lupine cover ameliorates these conditions. The
results indicated that lupine generally facilitated
tree seedling establishment in this stressful environ-
ment. However, as lupine stands increase in density,
the physical environment becomes less stressful and
competition increases. Prevalence of competition
over facilitation can be seen in the results on spruce
and birch seedling performance at the two sites of
highest plant density (Figure 2: F5 and F2). In
southern Iceland Aradóttir (2004) noted a switch
from facilitation to competition with increasing
density of lupine. She found that downy birch
seedlings were facilitated by low-density lupine

71. cover, but survival of birch seedlings was inversely
correlated with further increase in lupine density.
However, Óskarsson and Sigurgeirsson (2004)
found high survival and better growth of Sitka spruce
and downy birch seedlings in denser lupine in a
gravelly outwash plain in southern Iceland.
These studies of lupine facilitation of tree seed-
lings in Iceland partially support the stress-gradient
model of Callaway and Walker (1997) that facilita-
tion will decrease and competition increase with
density of the nurse plants. However, an indirect
mechanism, and not nurse-plant density itself, may
be behind the possible switch from lupine facilitation
to lupine competition. The inhibitor of tree seedling
establishment appeared to be the increased density
of grasses that were stimulated by the fertility
provided by lupine. Dense grass cover has been
reported to inhibit establishment and growth of Sitka

72. spruce seedlings in North America (Coates et al.,
1993; Riege & del Moral, 2004). In the absence of
dense grass, dense lupine facilitated tree seedling
establishment at site FB and at the site of Óskarsson
and Sigurgeirsson (2004). Callaway and Walker
(1997) and Callaway and Pennings (2000) empha-
sized the importance of indirect interactions on the
balance of facilitation versus competition, but in
their examples plant species A usually indirectly
facilitates species B by inhibiting species C, which is
a competitor of species B. In the current case, the
indirect mechanism differs. Species A (lupine) may
indirectly inhibit seedlings of tree species B by
facilitating species C (hairgrass), which then out-
competes species B.
Facilitation by black plastic mulch
Black plastic mulch was an effective facilitator of tree
seedling establishment in this study, increasing

73. growth of Sitka spruce, Hooker willow and downy
birch (at most sites), while maintaining high survival
rates. The expectation that shade-tolerant Sitka
spruce would perform less well in the open black
390 D. A. Riege & A. Sigurgeirsson
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75. plastic than in lupine was not supported at the sites
with dense grass competition. Here, spruce seedlings
exhibited more growth in black plastic than under
other treatments (Figure 2). This may indicate that
the chief benefit of the black plastic is to decrease
competition, although the plastic cover also increases
soil temperature and moisture (Tarara, 2000; Green
et al., 2003). Black plastic was least effective at FA.
The thick moss cover at FA may have counteracted
the soil benefits by the ability of moss to insulate
temperature change and to absorb moisture.
Although many studies show that plastic mulch can
promote tree seedling establishment, other studies
show no benefit to mulching (see review in Green
et al., 2003). For example, Houle and Babeaux
(1994) found that plastic mulch had no effect on
growth or survival of four species of tree seedlings
(including Salix planifolia and Picea glauca) after 4

76. years in subarctic Quebec. Continued monitoring of
the present plastic-mulch facilitation experiment and
others in the region (Sigurgeirsson, 2000) will be
critical to evaluate its utility for afforestation in
Iceland. There is a dearth of literature on the long-
term success of plastic mulch in establishing forests.
Management implications
Both nurse crops of Nootka lupine and black plastic
mulch may be recommended as treatments that can
facilitate tree seedling establishment in stressful
environments similar to those of Iceland. However,
lupine in combination with dense grass cover may
competitively inhibit tree seedlings. It is recom-
mended that lupine be seeded without grass seeding
or fertilization that promotes grasses. Tree seedlings
should be planted in young, open lupine stands. If
seedlings are planted into older, dense lupine, they
may benefit from site preparation that reduces

77. competition, such as mowing (Aradóttir, 2004), the
plowing of trenches within the lupine (Óskarsson &
Sigurgeirsson, 2004) or the use of taller plant
material (Sigurdsson, 2005). The results present
caution, however, that excavated ground can be
rapidly recovered by neighboring vegetation in the
fertile lupine stands. In these experiments the extra
cost of excavation did not improve establishment of
Sitka spruce. On barren or moss�heath sites, tree
seedlings can be planted directly into lupine without
excavations, accompanied by packets of slow-release
fertilizer. Results at the moss�heath site indicated
that lupine facilitated growth of spruce, willow and
birch seedlings without a scarification treatment.
Contrary to the prediction, Hooker willow seed-
lings may be more successful than Sitka spruce or
downy birch in afforestation via lupine, owing to the
fast growth of willow to overtop the lupine. How-
ever, in sites without dense grass, it appears that the

78. facilitative effects on spruce seedlings will outweigh
the competitive effects to allow the seedlings to
emerge above the lupine. Although Hooker willow
showed better initial performance and should be a
major component of multispecies afforestation,
methods that promote Sitka spruce are desirable.
Spruce trees will provide greater height and longevity
to the future forest. Facilitation of tree seedling
establishment by black plastic mulch exceeded that
of lupine in this study in areas of thick grass, where
the plastic suppressed competition. A benefit in the
use of plastic mulch is that tree seedlings can be
planted immediately without a lag of 2�3 years
after lupine seeding. However, it is possible that
performance of the seedlings in plastic mulch will
decrease in the long term in comparison to lupine,
owing to the lack of nitrogen input that lupine
provides.

79. Two caveats to the findings are that some results
are based on low numbers of seedlings and that long-
term success will not be determined for several years.
The authors believe that these three species are
capable of long-term establishment in the region,
based on the presence of mature stands in a few,
relatively sheltered locales (Riege & Sigurgeirsson,
personal observation). Use of shelterbelts and plant-
ing in large blocks should enhance survival and
growth in the more open areas.
An afforestation technique is proposed for south-
west Iceland that combines the benefits of lupine and
black plastic mulch. In the first year, tree seedlings
are planted into black plastic along the edges of the
site, while the interior of the site is seeded with
lupine. The establishment of shelterbelts, particu-
larly on edges that face prevailing winds, will help to
protect the interior. Hooker willow will be the

80. primary species of the shelterbelt, but it will also
include a mix of Sitka spruce, downy birch and
shrubby willow species. The shelterbelt will be
modeled on the system of Robertson and Eysteins-
son (2002), where a mixture of deciduous and
evergreen trees and shrubs provides surface rough-
ness that is efficient in reducing winds and trapping
snow. After 2�3 years of lupine growth, a mixture of
appropriate varieties of Sitka spruce, willow and
birch will be transplanted into the site interior. This
combination of species mimics natural succession
where the pioneer willows and birches grow more
rapidly as early dominants to be eventually super-
seded by spruce (Reed & Harms, 1956; Slettjord,
1993). Over the years lupine will expand into the
shelterbelts in concert with the deterioration of the
plastic, where it will facilitate continuation of shel-
terbelt growth.
Afforestation facilitation by lupine 391

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Conclusions
Nootka lupine can be an effective nurse crop for tree
seedling establishment under harsh conditions in
northern maritime climates with low soil fertility.

83. Lupine may also promote growth of competitive
plants, particularly grasses, that can inhibit tree
seedlings. Tipping the balance from facilitation to
competition by a nurse plant promoting a competi-
tive species that inhibits the target species is a
mechanism that differs from common models of
the stress-gradient hypothesis. For best results, tree
seedlings should be planted into young, open lupine
stands with low amounts of competitive cover.
Planting seedlings into excavations within lupine
can improve early growth but, in sites of high
competition, grasses and lupine can rapidly spread
over the depressions and deter slower growing
species, such as Sitka spruce. Black plastic mulch
also shows potential as a facilitator of establishment
of tree seedlings in these regions. For creation of tree
plantations in these environmentally stressful areas,
it is recommended that a mix of species be planted

84. directly into young lupine stands that are bordered
by shelterbelts of seedlings, primarily willows,
planted into black plastic mulch.
Acknowledgements
We express thanks to David James of the Atlantic
Division, US Naval Facilities Engineering Com-
mand, for his vision in promoting vegetation restora-
tion and afforestation research on the Keflavik
International Airport. Funding, field and office
support was generously provided by the Icelandic
Forest Research Service, US Naval Facilities En-
gineering Command, the Environmental Division of
the former Keflavik NATO Base, and Sudurlandss-
kógar. We thank Mats Hannerz and two anonymous
reviewers for suggestions that improved the manu-
script.
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Afforestation facilitation by lupine 393
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95. Written Assignment 1: Trace the Scientific Method in a Primary
Research Scientific Article
Addresses course outcomes 1 - 4
-recognize and explain how the scientific method is used to
solve problems
· make observations and discriminate between scientific and
pseudoscientific explanations
· weigh evidence and make decisions based on strengths and
limitations of scientific knowledge and the scientific method
· use knowledge of biological principles and the scientific
method to ask relevant questions, develop hypotheses, design
and conduct experiments, interpret results, and draw
conclusions
Before attempting this assignment, you might want to revisit the
Scientific Method Tutorial in the Science Learning Center under
the Course Content area.
While living on the former US Naval Air Station Keflavik in
Iceland in 2002 (and teaching for (university)), I began ecology
experiments on growing trees in the barren landscape in
collaboration with Iceland Forest Research. Results of these
experiments were published in a paper by Riege &
Sigurgeirsson in the Scandinavian Journal of Forest Research in
2009. I believe this paper serves well for your general biology
assignment to trace and critique the scientific method in a
primary research article.
Please do not worry that I take offense at any criticism of the
article. When these manuscripts are first submitted to journals,
the reviewers rake the authors over the coals and usually require
major revisions before the journal will accept the article for
publication. So scientists have thick skin about heavy criticism
the good news is that the critique and revision makes the final
product much stronger.
Please read and study the original paper, assigned in Week 3,
then submit a 2 page review of the article that answers the
following questions:
For ONE of the experiments in Riege & Sigurgeirsson -

96. 1. What was the hypothesis of this experiment? (Reminder:
Hypothesis is a statement.) What question(s) was (were) the
investigator asking? (Hint: do not use the general stress
gradient hypothesis, but a specific hypothesis from one of the
experiments.)
2. Which is the control group? Why?
3. Which is the treatment group? Why?
4. Did the researchers follow the scientific method in their
experimental design? Explain.
5. Do you think that there may be any possible biases or other
problems in this experiment? Explain.
6. Based on the data, was the hypothesis supported, and what
can you conclude from this experiment?
Additional Requirements for Written Assignment 1 - The report
should be submitted to the Assignment Folder as an attached
MS Word document. Use size 12 font.