|As described in the main text, there are
two main ways of summarising distributions and abundances
of animals and plants, or animal or human behaviour, so
that they can be digested and understood. These are:|
For example, the abundances of different animals (or plants) can be summarised into a table as shown below. This takes the information from a number of different data sheets and puts it all onto the same sheet so that it is easier to comprehend. In this example, limpets have been counted at three levels on the shore - lowshore, midshore and highshore levels. To accurately represent each level, two sites were sampled in each level. To represent each site, the numbers of limpets in each of six quadrats were counted in each site.
A summary table
Numbers of yellow and red spotted limpets per quadrat
|If you are
interested in whether densities of limpets vary from site to site, the
quadrats were simply the means of estimating densities within each site.
It is therefore normal to calculate an average density of limpets per
site, i.e. the average number of the six quadrats in each site.
The standard deviation (SD) gives you an idea of how variable the
replicate quadrats are. A large SD tells you that there is a lot of
variability among replicate quadrats and a small SD tells you that the
replicate quadrats are quite similar. Most small calculators can
calculate a standard deviation.
Tables, even summary tables, are often also largely indigestible. Many people therefore prefer to present the average values in a graph and not present the data for each quadrat separately. Three types of graphs showing the same results as the table above are shown in Figure 8. A point graph shows the average values, with the variability among the different quadrats indicated by the standard deviation shown above and below the average value. The same data can also be shown in a line graph or column graph.
Figure 8. Different ways to graph the same information
These graphs show the same information and the choice of graph is entirely personal. These data indicate that this limpet is most common in Sites 3 and 4, less common in Sites 1 and 2 and least common in Sites 5 and 6. Even though the average values calculated from the samples differs between Site 3 (33.4 limpets per quadrat) and Site 4 (29.8 limpets per quadrat), the true densities are unlikely to differ between these two areas. This is indicated by the large standard deviations around each average. In order to tell whether these average densities are, in fact, different, you would need to compare the data statistically. It is important to note that simply because two average values are not identical, it does not mean they are statistically different. This is because, you didn't count all of the limpets in each area. You only sampled them - i.e. estimated how many were there. The real number in any area will be slightly different from that estimated from the samples. Two areas may have exactly the same number of animals or plants present, but it is extremely unlikely that samples from each area would be exactly the same. In the same way, your samples may be identical, but that does not mean that the true numbers of animals in each area are the same.
Sometimes you may be interested in showing the numbers of more than one species at the same time. The information should be summarized in one graph, because this is easier to understand than a number of different graphs. Figure 9 shows the average percentage cover of seaweeds and average numbers of barnacles and periwinkles per quadrat at three heights on some hypothetical seashore. This the same information as that shown in Figure 8 but with the different species shown on the same graph. They suggest that leafy seaweeds are most common low on the shore, barnacles are most common at midshore levels and periwinkles are equally common at all heights. Note that you can plot numbers and percentage cover on the same graph. In this case, the graphs have the same scale on the vertical axis, but it does not matter if the two sets of data have different ranges.
There are many different ways of showing the same sort of information, as shown in Figure 10. In Figure l0a, the data are presented as a column graph, as in Figs 8 and 9. Figure l0b is a so-called bar graph, which is actually a column graph with the columns placed on top of each other (note that when this is done they total 100% because together they account for all of the cover that you have measured). Figure lOc shows the same data in a pie chart The circumference of the circle is equivalent to 100% and is divided up into the different categories accordingly.
Figure 10. Different ways to show percentage cover in three different areas
techniques can be used to display your data for many different types of
investigations. Figure 11 shows how a column graph is used to show how
many softdrink cans were found in 20 m X 20 m quadrats in three
different bushland sites in summer and winter (top), how many ravens are
found in gum trees and other trees in two different school grounds
(middle) and the average time three sparrows spent on different types of
activity during a 30 minute period (bottom). Fig. 12 shows how vegetation maps
can be combined with counts of animals and plants to give you a picture
of what different habitats there are and where particular species might
be found. For example, if you can show the locations of nests of blue-tailed whipple birds through an area composed of grassland, shrubland
and forest. |
Figure 11. |
HOW TO INTERPRET THE PATTERNS YOU FIND
As described in the main text, there is only one realistic method to answer questions about why animals and plants are found in certain places. That is by planning and doing experiments designed specifically to answer that question. Anything else is guesswork and although some guesses may be more realistic than others - that is really all that they are.
Although this is not the place to go into much detail about the need for field experiments to interpret patterns of abundance of animals and plants in nature, a brief outline about the way these are designed follows for your information. This will give you some idea about how " science" and, therefore, "ecology" is often done.
The example is that there are two types of plants, one native and one exotic, that are not found in the same places. You may be concerned that the exotic species is displacing the native species in some areas. Remember, this is simply a theoretical example to illustrate the process of an ecological study. It is not a statement about the spread of bitou bush.
First, we think have observed something that interests or concerns us.
The first thing to do is to find out whether this is true. It is perfectly possible that we only think that there are more bitou bushes in some areas and more Hakea in others. Where there is a lot of bitou bushes, the Hakea may be small and hidden which is why we don't see them. If this were true, we should be concerned about the size of the plants differing from place to place, not the numbers. The first part of any study is collect accurate observations of interesting patterns, so that they can be confirmed. This is what these Guidelines have been describing. - how to collect good, accurate observations of patterns of animals and plants in nature. This is not all of ecology, but is the essential first step in ecology. Without good, accurate observations there is nothing to explain.
We then validate the observations using appropriate sampling and statistical tests to confirm that what we think that we have seen is true and not a figment of our enthusiastic imaginations. (This may seem trivial, but it is amazing how many scientific papers describe investigations of patterns that have not been shown to be there in the first place).
Now we know that there is something worth investigating We then think up as many different explanations for the patterns we have seen that we can. These explanations are often called models or theories. You do not have to be a "scientist" to think of a model. Any theory is as good as any other - even though some may sound fancier and "more scientific" than others. It is interesting to do this for any information you have collected in your study. With a little imagination, a lot of interesting alternative theories can be thought of. Any model (theory) is as good as any other as long as it explains the pattern that you have seen.
You will see that all of these models can explain the patterns. You will probably be able to think up some more. There are many, many possible explanations for bitou bush and Hakea being more abundant in different places. The role of ecology is to be able to distinguish between these alternative models to see which one is most likely to explain the patterns found. If we are concerned about the spread of bitou bush and decline of native Hakea in the study area, we need to be able to work out how these plants interact before beginning any remedial action to try to change the situation. If bitou bush prevents the growth and/or survival of Hakea (Models 1 and 2), then remedial action will involve the removal of bitou bush with the prediction that Hakea will then grow in these areas. If bitou bush and Hakea grow in different areas because they have different requirements (Model 4), then removal of bitou bush will not change the numbers of Hakea. Similarly, if bitou bush only grow in areas after Hakea have already been removed (Model 3), then we need to find out what else is affecting the Hakea because removal of bitou bush will not cause an increase in Hakea. If Model 5 were correct and insects are destroying the Hakea, remedial action may involve spraying for insects rather than removal of bitou bush. You can see how important it is to distinguish between these different models because the effort we put into trying to increase the cover of Hakea and control the spread of bitou bush will differ according to which of these explanation is correct. If we make the wrong choice, time and money will be wasted. Therefore, we want to do everything you can to try to make the correct choice.
To do this, we make a specific prediction from each model. Each model, if it is true, should allow us to predict something that we haven't yet seen. This prediction is called an hypothesis. It can be any prediction - as long as it is predicted from the model and is something that has not been seen or done yet.
Hypotheses can be developed from all of the models. Note that they must predict something that has not yet happened. They generally involve you doing something in the field and saying what will happen in response to what you do. You cannot simply compare survival or growth of Hakea between places which already have lots of bitou bush and those that already have very little or no bitou bush. You already know that these places are different (your original observations) and so any difference in the survival of Hakea from place to place cannot simply be attributed to the amount of bitou bush in each place. That is guessing. You need to predict what will happen if you change conditions in the field in specific ways.
This leads to the final step in the process - the field experiment. This is the test of the hypothesis. This test is a complex procedure with different experimental and control treatments and many different levels of replication. It is not easy and needs specialized training in experimental design. Otherwise tests are flawed and frequently not worth the paper they are written on. Well-designed field experiments (and, occasionally, laboratory experiments) of carefully thought-out hypotheses of models that do explain the observations are the only realistic way to investigate ecological processes.
When different hypotheses have been tested with such experiments, you are in a position to throw away some models (or theories) because their predictions were not supported by the experiments. They therefore cannot explain the patterns you saw. Therefore, if Hakea seeds do not germinate in patches of bush which were sprayed to remove insects, then ideas about insects eating seeds and thereby killing Hakea in patches of bushland are not likely to be useful. More than one model may still seem to explain the patterns you found. You need to repeat the process above -making the models and hypotheses more specific - so that you can carry on trying to eliminate those that are wrong. Note that you will never know that a particular model is correct, even if it does explain your observations. Someone else (induding you) might think of another idea that could explain what you saw. Your current idea will then have to be evaluated against the new one. This process should never end - there are always new explanations for patterns of species in nature.
To repeat the comments made earlier, it is important to realise that decisions about management of the environment need to be based on accurate, reliable information which is collected at a number of different scales in many different ways. The design of such a sampling programme is not easy and requires specialist training and knowledge. More importantly, the interpretation of patterns of distribution and abundance (i.e. the explanations or models as described above) can only be done after statistical analysis of properly designed field experiments to test the hypotheses (predictions) from each explanation. It takes training as a quantitative field ecologist to achieve this.
Community groups may assist by collecting accurate data about particular species, problems or habitats in which they are interested. These Guidelines cannot tell community groups how to sample the environment in order to make decisions about managing it. This requires expert advice. These Guidelines will, however, allow you to learn more about local habitats - what lives in them, how people use them, how similar they are to other places, etc. and to share this knowledge With others. This increased awareness will also allow you to discuss your environmental concerns with more confidence and more knowledge.
Those who are particularly interested in the study of ecology and how it can be used to unravel the complexities of interactions which occur in the natural environment, might like to read the following papers:
AJ. Underwood, 1990. Experiments in ecology and management: their logics, functions and interpretations. Australian Journal of Ecology, Vol.15, pp.365-389.
AJ. Underwood, 1991. The logic of ecological experiments: a case history from studies of the distribution of macro-algae on rocky intertidal shores. Journal of the Marine Biological Association of the United Kingdom, Vol.71, pp.841-866.
These articles give examples of this form of logic and how it can be used to examine ecological interactions. These journals will be held in many University libraries.
© Centre for Research on Ecological Impacts of Coastal Cities