Module 5

Investigating Coastal and Marine
Environments through Science

Readings

Reading 1

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.

Reading 2

Generalisations and the Evidence

The good thing about Resources 2A, 2B, 2C and 2D is that they can be used as a method of observation. This is an important part of "Working Scientifically". The end point, however, of Resources 2A, 2B, and 2C are that the generalisations made cannot be supported or rejected . Only in Resource 2D was the generalisation supported with data. Yet the problem with Resource 2D was that it seemed to collect data before asking a question or predicting what the outcome may be. Most data collection takes place because there has been an outcome predicted.

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:

• Given a set of conditions an event is likely to occur.

• Given a set of conditions no event will occur.

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.

Reading 3

Barnacles in Mangrove Forests: An Example

Imagine that you are entering a mangrove forest and are interested in the barnacles which live on bark of the trunk of the tree. For example, we may observe that there are more barnacles on the bark of the trees in the seaward than in the landward zone of a mangrove forest. We may also observe that in the landward zone, there is more light and there are fewer grazing snails than in the seaward zone. The landward zone is also further from the sea during low tide. A number of different generalisations could explain the pattern we have observed.

There are more barnacles on the trees in the seaward than the landward zone because

1. There are fewer barnacles in the landward zone
2. It is hotter there
3. There is more light there
4. There are more grazing snails there

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 the list:

5. There are more barnacles in the seaward than in the landward zone because more larvae are in the water column in the seaward zone than in the landward zone. How can we support some generalisations and not others? We have a number of generalisations for the patterns we have observed of barnacles in mangrove forests? So one by one they need to be subjected to a test.
   

Test 1

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 2-4

Test 5

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.

Reading 4

A Sample Field Trip for Secondary Students

A. At the field trip

1. Identification of plants and animals:
   Walk through the mangrove forest and identify some common plants and animals from the saltmarsh to the seagrass. Think about the patterns the organisms are distributed in and the factors which might be affecting them (temperature, wave action, predation etc). Also, think about how you might measure the density of one of these animals or plants and the factors affecting them.
   
   

2. Meet in small groups in the saltmarsh.
   • Describe the pattern of the organisms which you have observed.
   • What factors might be affecting it? (You now have a generalisation)
   • Write down your hypothesis from the generalisation I predict that if
   • Write down your null hypothesis from the generalisation There will be no difference
   • Discuss with your group and the teacher your generalisation and how you might test it.
     
     

3. Do the test. Record your data in a table, similar to the one below.

B. After the field trip, in the classroom

1. Graph your data and the factors which you measured.
2. Describe the density of your organism from the saltmarsh to the seagrass.

3. Describe the characteristic(s) of the environment that may influence the density of your organism.

4. Was the distribution of the organism you measured the same throughout the zones? Was your null hypothesis supported or rejected?

5. If the null hypothesis was rejected does this support your generalisation?

6. If the null hypothesis was accepted does this reject your generalisation?