Читать книгу Practical Field Ecology - C. Philip Wheater - Страница 67

Ecological data sets can be very complex and difficult to visualise. For example, a data set might include many variables collected as measurements (including counts), as ranks (e.g. scores of abundance), or in a binary form (e.g. presence or absence data). Chapter 5 introduces a number of techniques for visualising complex data sets to enable the use of a range of different types of data. Variables with large numbers of observations of zero (as can occur when surveying relatively rare species), cases where data are heavily skewed, or situations where variables are measured on scales of greatly differing magnitude, may require data transformation before using these techniques (p. 285).

As an example, we might collect information about woodlands on the basis of their size, age, distance to the nearest neighbouring woodland, etc. Since some of these variables will be related to each other, we might wish to find out the underlying pattern of interrelationships within the data and hence identify a number of unrelated factors that can be used instead of our large number of variables. This is a data reduction exercise, reducing the number of variables we have measured into a smaller number of unrelated factors that take into account the interrelationships between the variables.

Alternatively, we might wish to look at the range of species found in each of several woodlands and see which woodlands have similar species types. This is a similarity or clustering analysis and, depending on the technique used to calculate the similarities, data are normally recorded as a matrix (of species by woodlands) that contains either measurements (e.g. counts), ranks (e.g. ranked abundance), or binary data (e.g. species presence or absence). A similar technique to clustering enables us to visualise patterns in either the individuals (in this example, the woodlands) and/or the variables (here, the types of species). This is known as ordination and there are a number of different methods available depending on the algorithm (i.e. statistical formula). Such methods can utilise data comprising measurements, ranks, or binary information.