Читать книгу Practical Field Ecology - C. Philip Wheater - Страница 65

We may wish to collect data to set up a model that enables us to predict the outcome in a hypothetical situation, one of the simplest of which is known as a linear regression model. Thus, if we are interested in looking at a possible relationship between woodland size and the number of birds and knew that this was likely to produce a significant linear relationship, then we may wish to use this fact to calculate the expected number of birds found in any woodland. This could be used theoretically or in conservation management to check that we have the sort of bird biodiversity that we expect from other data. Here, it is important to note that any such prediction should only be made if the woodland area in which we are interested lies between the minimum and maximum value of the data set we used to establish the model. We first need to establish which variable is the dependent and which is the independent variable: that is, which is likely to be affected (the dependent or response variable – plotted on the y axis of a scatterplot) by the other (independent variable – plotted on the x axis of a scatterplot). Here, obviously, the number of birds (the dependent variable) is more likely to be dependent on the size of the woodland (the independent variable) than vice versa. We can think of this as woodland size driving the size of the bird count. We can extend the technique to cover the case where there are a number of independent variables (e.g. woodland area, habitat diversity, area of associated green space, distance to nearest waterbody) that might influence or drive bird numbers.