Investigating Coastal and Marine
Observing, Collecting Data and Generalising
The start of investigating any coastal and marine environment is by observing. There are many ways we can observe. For most observations we use our senses. We can visit the environment in which the organism lives and observe it. We can collect the organisms if they are too tiny to see with our eyes and observe them using a microscope. We could make up lots of questions from these observations. If we are interested in the investigations which have been done on an organism, we can make a search of a CD ROM data base and find References to articles on this organism. We can then go to these articles and read about where the organism lives. We can also talk to our friends and to scientists and ask them about the observations we have made.
Through this process of observation (reading, talking, drawing, writing) we may have discarded some of our questions (because they have answers) and now have other questions (which do not have answers). We might have questions about how an organism lives in a coastal and marine environment. We may have questions about how people use the coast or how they affect the coastal and marine environment. Whatever our question, before we come to an answer we will probably go through two more steps - generalising (our answer) and data collecting.
Already we probably have some general statements in mind which summarise
our observations and questions. So how will we give support to our generalisation?
What type of evidence is needed so we can convince other people of the
answer to the question we have?
One of the most common forms of evidence is to provide people with data.
But, just as there are different ways to observe, there are also different
ways to collect data. There are two main types of data we can collect,
qualitative and quantitative. Examples of qualitative data collection
include making more observations or looking at the same pattern in a number
of places. This type of evidence, however, has problems. The disadvantage
is that we are dependent on the person collecting the qualitative data
to be objective and have no bias. It is rare, if not impossible, to have
no biases when collecting data because we are humans and have an emotional
as well as a rational response to a problem. Thus qualitative data is
difficult for marine scientists to accept as evidence.
The second way to support a generalisation is to collect quantitative
data. The advantage of collecting quantitative data is that it provides
an objective view. The disadvantage is that our data are limited to that
moment in time and that place we measured. The collection of quantitative
data can also depend on the skills of the observer to set up a fair sampling
design. Sampling normally involves the use of a tool (eg. quadrat, percentage
cover) to save time. Thus, although the collection of quantitative data
has problems it is more likely to be believed and thought of as valid
by marine scientists.
So, we have made observations, asked questions and generalised an answer. We have then collected data. Did we ever predict what the answer would be before collecting data? Are there steps we can add to this flow chart? The answer to this is 'yes'. Indeed, we probably predicted the outcome before we collected the data. This step sometimes is not done consciously nor do we often state our prediction clearly. We also probably did the observations and question sections many times before making a generalisation.
Generalisations and the Evidence
The first steps in investigating coastal and marine environments are
to observe and make generalisations. We also can have more than one generalisation
to explain the same observations. These generalisations are just that;
they may be true or they may not. There needs to be some sequence which
will provide evidence for some generalisations and not others.
How to do this, thus becomes the question.
There are two ways we could proceed. First, we could just go out and
make more observations (qualitative). With this method, however, we may
be tempted to accept or reject our generalisations based on whether our
new observations are consistent with our generalisation. This is not regarded
as a satisfactory way to proceed because the test becomes the opinion
or guess of the individual. Also it is not possible to make all the observations
in all areas of an environment at all times. Every observation possible
would be necessary to prove a generalisation. We could, however, try and
subject our generalisation to a test. This test is often called an experiment.
Deciding which procedure to use as a valid test of a generalisation has
been a popular debate since the early 1980s with scientists who investigate
coastal and marine environments. It has also started to become an issue
in curriculum documents (Resources 3B
and 3C). Previous to the debate
in the scientific community, a large number of the studies done in coastal
and marine environments were qualitative, based on 'natural history',
and provided subjective generalisations for the patterns which were observed.
This method was problematic for marine scientists, because they believe
that the aim of science is to predict what will happen given a new set
of conditions. When you say that, given a new set of conditions something
is likely to occur, this is called a prediction or hypothesis. To do this
requires quantitative data.
A discussion of logic ensued in the scientific community and resulted
in the conclusion that generalisations are hard to prove, but easy to
disprove. If generalisations can withstand disproof, then we have
the evidence which is required to support them. The method which is agreed
on by modern science is to subject observations and generalisations to
the possibility of being disproven by quantitative data. Thus the null
hypothesis became the focus of the test or experiment.
Let's however think about whether it is possible to disprove the following predictions:
The second prediction is easy to disprove; the first much more difficult. The second prediction needs to be tested once to find whether there is a difference or not. A prediction which includes a statement that 'no difference' or 'no event' will occur is called a null hypothesis. The null hypothesis is tested by an experiment. If we reject a null hypothesis which says there will be no difference (because of the results of the experiment), then there must be a difference. The event predicted by the hypothesis must occur and the generalisation is supported. If the null hypothesis is accepted by the test and results of the experiment then the hypothesis is rejected and the generalisation must be wrong.
Barnacles in Mangrove Forests: An Example
There are more barnacles on the trees in the seaward than the landward
If we knew something about the current literature and life history of
these organisms (see Resource 2 D),
we might also include another generalisation. Barnacles have a stage in
their life known as a larvae and these swim around in the water column
eventually returning to the mangrove forest. This larval stage attaches
itself onto a tree trunk. Thus, another generalisation can be added to
Test 1Generalisation: There are more barnacles on the trees in the seaward than the landward zone (an observation) because there were fewer barnacles observed in the landward zone (the reason).
This is the first generalisation which requires testing on a field trip.
This is necessary because it may be that the observer is suffering from
a delusion and the observations do not represent reality.
Prediction/Hypothesis: If the seaward and landward zone
were sampled, there will be more barnacles in the seaward and fewer in
the landward zone.
Null Hypothesis: There will be no difference in
the number of barnacles in the seaward and landward zone.
The test or experiment: Sample the number of barnacles
in the seaward zone and landward zone in a number of places at a number
of times. The aim here is to make this test as fair as possible. This
was done in a mangrove forest in Sydney.
Results: The average number of barnacles in the seaward zone was 70.3 per 6.25 sq. cm., compared to 20.5 per 6.25 sq. cm. in the landward zone.
Thus the null hypothesis can be rejected and support given to the generalisation.
Tests 2-4Now we have support for our initial generalisation we can do the same things for each generalisation (ii).- (iv).
Test 5From Resource 2D, the generalisation (v) was that there are more barnacles in the seaward than in the landward zone (observation) because more larvae are in the water column in the seaward zone than in the landward zone (reason).
Hypothesis: If the water column was sampled, there will be more larvae in the seaward and fewer in the landward zone.
Null hypothesis: There will be no difference in the number of larvae in the seaward and landward zone.
Test/Experiment: This was done by sampling the water column in the two areas and counting the number of larvae.
Results: In September 1991 in the Seaward zone there were 80 larvae per cubic meter of water, but only seven larvae per cubic meter of water in the Landward zone.
Conclusion: Reject the null hypothesis and accept the generalisation.
A Sample Field Trip for Secondary Students
A. At the field trip
B. After the field trip, in the classroom