MultiSeaSpace · Developing a unified spatial modelling strategy that accounts for interactions between species at different marine trophic levels, and different types of survey data.
„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“
- Период
- 2019-11-01 → 2022-09-07
- Финансиране от ЕС
- 224 934 €
- Участници
- 1
- Схема
- MSCA-IF
Линиите свързват координатора с партньорите.
Накратко на български
Морските екосистеми се анализират чрез единен софтуер, който обединява данни за различни видове и техните местообитания. Това помага за по-доброто управление на природните ресурси и поддържането им в здравословно състояние.
Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.
Резултати накратко
Developing a unified spatial modelling strategy that accounts for interactions between species at different marine trophic levels, and different types of survey data.
The conservation of marine ecosystems for a productive human exploitation is a challenging task. A base principle of the ecosystem based management approach is to manage resources by keeping them as healthy as possible. To do so, we require statistical modelling techniques that maximize the the amount of inferable information from the available data. Unfortunately, collecting data on species in marine ecosystems is particularly challenging as the marine environment is largely inaccessible and individuals are mostly invisible to researchers. Surveys can typically only collect information on a limited number of aspects of individuals’ distribution in space, mainly dependant on the behaviour of a specific species within space and other practical limitations. As a result, a number of different sampling methods have been used, producing different data structures that require different statistical modelling approaches. Different statistical methods are often applied using different software or packages. This project was aimed at unifying different spatial distribution modelling approaches in a single software that allowed to: (a) Model habitat preferences of a range of different species within the same modelling framework, independent of the specific data collection approach; (b) jointly modelling data from different surveys on the same species; (c) jointly modelling data from the same/different surveys on several species. Specifically, we used the software package inlabru (www.inlabru.org/inlabru), which is based on integrated nested Laplace approximation (INLA) and the associated software R-INLA.
Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз
Цел на проекта
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).
Оригинален текст от CORDIS (на английски).
Участници
- THE UNIVERSITY COURT OF THE UNIVERSITY OF ST ANDREWS · ST ANDREWSКоординаторОбединеното кралство
Връзки
Данни: CORDIS, © Европейски съюз