Project description DEENESFRITPL New modelling strategy to pool marine data Ecosystems with a greater number of species have proven to be more resistant to extreme climatic events, including prolonged wet and dry periods. It is complicated to study species in the marine environment as different sampling methods are being used. The EU-funded MultiSeaSpace project will develop a new spatial modelling strategy to integrate these different methods into a single modelling framework. It will use a new software package to allow for easier comparison and the pooling of information across different surveys. Project outcomes will contribute to the understanding of how living marine resources respond to our changing climate. Show the project objective Hide the project objective Objective 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). Fields of science agricultural sciencesagriculture, forestry, and fisheriesfisheriesnatural sciencescomputer and information sciencessoftwarenatural sciencesbiological sciencesecologyecosystemsnatural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changes Keywords MultiSeaSpace Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2018 - Individual Fellowships Call for proposal H2020-MSCA-IF-2018 See other projects for this call Funding Scheme MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF) Coordinator THE UNIVERSITY COURT OF THE UNIVERSITY OF ST ANDREWS Net EU contribution € 224 933,76 Address NORTH STREET 66 COLLEGE GATE KY16 9AJ St Andrews United Kingdom See on map Region Scotland Eastern Scotland Clackmannanshire and Fife Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 224 933,76
