RDA P6 Climate Change Data Challenge Application: Göteborg University, Department of Marine Sciences

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26 August 2015 2203 reads
Göteborg University, Department of Marine Sciences represented by Matthias Obst
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In response to pressing societal challenges such as climate change, ecological research rapidly develops into a big-data science with a particular focus on the prediction of ecosystem-wide effects of climate change. The complexity and scope of such analyses however is vast and typically requires the integration of large and disparate data sets from totally different domains such as Biology, Environmental and Social sciences. In addition, such investigations typically depend on long chains of data transformation, formatting and analysis, which cannot be achieved by individual researchers alone. In the field of ecology such demanding in silico experimentation is today only accomplished by conventional practice, i.e. through well-funded research projects. We see a major challenge in providing appropriate mechanisms that allow individual and ad hoc integration of disparate datasets in the absence of specific funding. Such mechanisms, if provided through an infrastructure will allow a faster response to effects of climate change by enabling scientists to analyse complex information in response to an emerging demand and swiftly pass the generated knowledge to environmental managers. The Biodiversity Virtual e-Laboratory, BioVeL (www.biovel.eu) is a data infrastructure that responds to this challenge. It provides a seamlessly connected environment for integrating heterogeneous datasets to perform large scale modelling tasks that predict ecological impacts of climate change. The platform is a pilot implementation of the European ESFRI LifeWatch infrastructure (www.lifewatch.eu). It uses workflow technology based on the Taverna software suite (http://www.taverna.org.uk/) to format and feed heterogeneous data into analytical environments. The platform is fully compliant with RDA technologies and practices.

We currently have a several use cases that use the BioVeL infrastructure for cross-disciplinary analysis of ecological impacts from climate change in marine and terrestrial ecosystems. Here we will present one example where we integrate a dataset from the RDA Catalogue with other relevant information sources to carry out scalable and semi-automated analytical cycles that predict the spread of mosquito-borne diseases in response to the changing climate across the European continent.

We will use the CORINE land cover dataset provided by the European Environment Agency (Dataset Catalogue Code: RDA_ClimateChallenge_eeacorine_22) to generate appropriate data layers with information on land-types and hydrology in the European land-cover map. These characteristics are essential determinants for the distribution of mosquitos and will be made available as hierarchically structured raster layers in various resolutions. A workflow for ecological niche modelling (http://purl.ox.ac.uk/workflow/myexp-3355.20) will import these data into BioVeL’s modelling environment (http://portal.biovel.eu/), where it can be combined with other relevant datasets containing climate information such as temperature, precipitation, etc (Hijmans et al. 2005 Int J Climat 25: 1965-1978.), soil properties such as soil type, pH, etc (FAO, 2012 Harmonized World Soil Database), and biological information including species distribution, ecology, and taxonomy of the mosquito species (Andersson et al. personal data). The workflow will be used to generate predictive distribution models for a large number of disease-transmitting mosquito species from these different datasets across a wide range of algorithms and parameter settings. A fully documented pilot analysis will be published as a Research Object (www.researchobject.org) in the Zenodo online repository (https://zenodo.org).

Which are the datasets integrated in your solution demonstration? Select from the dropdown list.
Code: RDA_ClimateChallenge_eeacorine_22

Mosquito-borne infections are among the most important diseases globally and in Europe (Gubler 2002, Arch Med Res: 330–342). Diseases like West Nile Fever, Chikungunya, Dengue, Usutu and Sindbis have appeared in Europe for the first time or re-emerged during the last decades. Globalization and climate change give opportunity for pathogens and vectors to colonize new areas. The need for mosquito surveillance on a European level has been highlighted in reports from the European Centers for Disease Prevention and Control (ECDC 2012 & 2014, Guidelines for the surveillance of invasive mosquitoes, technical report). The cost of managing outbreaks increases every year (e.g. a 2007 vector borne Bluetongue outbreak was estimated at approximately € 6.3 million in Sweden alone). 

We expect that researchers, authorities, and consultant companies will use our platform in the future to predict the abundance and outbreaks of invasive species as well as pathogens such as mosquito-borne diseases based on landscape features, climate, biology as well as socio-economic data (e.g. traffic, land use). This will substantially increase the efficiency of surveillance programs. Most importantly it will lower the costs of disease management programs, because outbreaks can be detected in their early phase.

Many tropical countries will be able to use the technology and know how developed here, i.e. especially countries where climate change starts to show dramatic effects on the distribution of mosquito borne-diseases. In this way the platform will contribute to sustainable disease management in Europe and on a global level.