A description of a use of Pareto optimality in genetic programming is given and an analogy with Genetic Algorithm fitness niches is drawn. Techniques to either spread the population across many pareto optimal fitness values or to reduce the spread are described. It is speculated that a wide spread may not aid Genetic Programming. It is suggested that this might give useful insight into many GPs whose fitness is composed of several sub-objectives. The successful use of demic populations in GP leads to speculation that smaller evolutionary steps might aid GP in the long run. An example is given where Price's covariance theorem helped when designing a GP fitness function.