Large-scale integration of data for distribution models (and beyond)

One of the most important knowledge gaps for nature management is the species diversity in an area. In order to know the spatial distribution of richness of different species groups, observations of species are needed. This is challenging, because for many species groups it is resource-intensive to systematically map the occurrence of species over large areas. Instead, citizen science data has become more important, but as it is mostly collected opportunistically , there is a high geographical bias in the collection. If this is not taken into account, one gets the impression that the most species-rich areas are where there are the most people. This bias can be estimated and corrected if citizen science data is correctly combined with survey data, e.g. from national monitoring programs.

Integrated species distribution models (iSDM) are relatively new statistical tools that combine opportunistically collected data with systematically collected data with information about collection intensity. These models have great potential to benefit from the strengths of different types of data, while at the same time being able to take into account some of the challenges, but the models have not been used to a large extent in large-scale modeling of species diversity. Large-scale data presents both opportunities and problems. It allows us to leverage information across species, but the size of the data makes it infeasible to model every species together.

Here we will present our approach to this large-scale modelling, using iSDMs across species to produce fine-scale maps of communities over Norway, along with their associated uncertainties and biases. The model is based on a state space, using ideas from point processes, which has proved flexible for different data types and for which there are efficient and flexible fitting methods. We will outline the model and the methods we have used to overcome the problems of large data and the requirement for fine spatial scales. We will also outline how this is being extended to include time and topologies such as river networks.