Slide 38: How good are the predictions?
Some factors should always be borne in mind when modelling species environmental niches. It is important to have both testing and training data sets for evaluation of predictions (and if there are no independent datasets, the data should be randomly divided into these sets in a 70:30 ratio). Comparison across models and across variables within models is essential.
The number of variables used in a model should not be too great, because there is a chance that factors could then obscure the action of others. On the other hand, there should be sufficient variables to reasonably explain the species distribution.
The development and improvement is an iterative process, which involves repeating it a number of times over until the best combination of model and factors is achieved.
If possible, the delineating of the predictive ability of predictor variables is very useful, as it can give insight into the factors that are most important for species distribution.
Evaluating the model against historical data provides a useful means of testing model accuracy, if such records are available (especially in a highly-transformed landscape).
For further reading on the use of modelled data in conservation planning, it would be useful to find and read the following papers: (Hannah et al, Cabeza at al 2004, Loiselle et al 2003).