Developing a unified spatial modelling strategy that accounts for interactions between species at different marine trophic levels, and different types of survey data.
Healthy ecosystems are productive and resilient to climate change.  However, their conservation through ecosystem based management remains a challenging task. This is due to a lack of understanding of both the many complex interactions among all the components within the ecosystem and the impact of management action on their health. Hence, successful conservation relies on studies that use suitable data collection methods and appropriate statistical modelling approaches that reflect this complexity and help predict the impact of different management actions. 

Collecting data on species in marine ecosystems is particularly challenging as the marine environment is largely inaccessible and species are mostly invisible to researchers. Surveys can typically only collect information on some aspects of the distribution of individuals in space, mainly in dependence on the behaviour of a specific species within space and practical limitations. As a result, different sampling methods have been used, resulting in different data structures (e.g. point-process data, line transect data, telemetry data, fishery acoustic data, point-pattern data). Separate statistical modelling approaches along with different software packages have been developed for each of the different survey data structures.

MultiSeaSpace seeks to develop an integrated general spatial modelling strategy that allow us to integrate different sampling methods in a unified modelling framework that include trophic interactions. This unification provides a huge advantage since it: (a) allows us to operate within the same framework, avoiding the use of different software packages, facilitating comparison; (b) allows the pooling of information across different surveys, even if these resulted in different data structures; (c) avoids considering single species in isolation. To do so, we will use the recently developed software package inlabru, which is based on integrated nested Laplace aproximation (INLA).