  The quantity and types of biodiversity data being collected have increased in
recent years. If we are to model and monitor biodiversity effectively, we need
to respect how different data sets were collected, and effectively integrate
these data types together. The framework that has emerged to do this is based
on a point process formulation, with individuals as points and their
distribution as a realisation of a random field. We describe this formulation
and how the process model for the actual distribution is linked to the data
that is collected through observation models. The observation models describe
the data collection process and its uncertainties and biases. We provide an
example of using these methods to model species of Norwegian freshwater fish,
which shows how integrated models can be adapted to the data we can collect. We
summarise the modelling issues that arise and the approaches that could be
taken to solve them.
