6. Conclusions

These five examples have demonstrated a number of important points.

1. First, the population modeller can construct very different models based on more-or-less the same biological assumptions. The above models all have negative density-dependence for reproduction and density-independent mortality, but differ in their degree of disaggregation and the age-dependency of population processes. The modeller should be aware of these choices, and ideally be able to justify a choice vis-a-vis the alternatives.
2. Second, mathematically-identical models (the three age-class models) can be implemented in different ways. These do not affect the results produced, but do affect the ease of working with the model and the ease of communicating the model to others. Again, the modeller should be aware of such choices.
3. Third, Simile is unusual — if not unique — in enabling these very different models to be constructed within a single, easy-to-use environment. This minimises the amount of effort the modellers have to put in to explore these alternative approaches, compared with having to use different packages, or implementing the different models in a programming language.

Finally, we can broaden our vision beyond the modelling of one population. Usually, a population model will be a component of some larger system: the food on which the population depends; other predator populations; various abiotic factors; possibly even human factors (for example, in farming systems or wildlife conservation). Any of the population models we have considered above could be integrated into a larger, ecosystem model. Moreover, it could be wrapped up in an outer submodel envelope, and the alternative versions could be swapped in and out of the ecosystem model on a plug-and-play basis: another strong argument for enabling different modelling approaches to be implemented within a single modelling environment.