The biodiversity and climate change virtual laboratory:How ecology and big data can be utilised in the fight against vector-borne diseases

Hallgren, Willow, Beaumont, Linda, Bowness, Andrew, Chambers, Lynda, Graham, Erin, Holewa, Hamish, Laffan, Shawn, Mackey, Brendan, Nix, Henry, Price, Jeff, Vanderwal, Jeremy, Warren, Rachel ORCID: and Weis, Gerhard (2015) The biodiversity and climate change virtual laboratory:How ecology and big data can be utilised in the fight against vector-borne diseases. In: Proceedings - 21st International Congress on Modelling and Simulation, MODSIM 2015. Proceedings - 21st International Congress on Modelling and Simulation, MODSIM 2015 . Modelling and Simulation Society of Australia and New Zealand Inc., AUS, pp. 1448-1454. ISBN 9780987214355

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Advances in computing power and infrastructure, increases in the number and size of ecological and environmental datasets, and the number and type of data collection methods, are revolutionizing the field of Ecology. To integrate these advances, virtual laboratories offer a unique tool to facilitate, expedite, and accelerate research into the impacts of climate change on biodiversity. We introduce the uniquely cloud-based Biodiversity and Climate Change Virtual Laboratory (BCCVL), which provides access to numerous species distribution modelling tools; a large and growing collection of biological, climate, and other environmental datasets, as well as a variety of experiment types to conduct research into the impact of climate change on biodiversity. Users can upload and share datasets, potentially increasing collaboration and cross-fertilisation of ideas and innovation among the user community. Feedback confirms that the BCCVL's goals of lowering the technical requirements for species distribution modelling, and reducing time spent on such research, are being met. We present a case study that illustrates the utility of the BCCVL as a research tool that can be applied to the problem of vector borne diseases and the likelihood of climate change altering their future distribution across Australia. This case study presents the preliminary results of an ensemble modelling experiment which employs multiple (15) different species distribution modelling algorithms to model the distribution of one of the main mosquito vectors of the most common vector borne disease in Australia: Ross River Virus (RRV). We use the BCCVL to do future projection of these models with future climates based on two extreme emissions scenarios, for multiple years. Our results show a large range in both the modelled current distribution, and projected future distribution, of the mosquito species studied. Most models (that were built using different algorithms) show somewhat similar current distributions of the species however there are three models that are obvious outliers. The projected models show a similar range in the distribution of the species, with some models indicating a fewer areas (and also areas with a lower probability of occurrence in specific areas) where the species is likely to be found under a climate change scenario. However, a majority of models show an expanded distribution, with some areas that have a greater probability of the occurrence of this species; this will provide a more robust indication of future distribution for policy makers and planners, than if just one or a few models had been employed. The economic and human health impact of vector borne diseases underline the importance of scientifically sound projections of the future spread of common disease vectors such as mosquitos under various climate change scenarios. This is because such information is essential for policy-makers to identify vulnerable communities and to better manage outbreaks and potential epidemics of such diseases. The BCCVL has provided the means to effectively and robustly bracket multiple sources of uncertainty in the future spread of RRV: this study focuses on two of these - the future distribution of a primary mosquito vector of the disease under two extreme scenarios of climate change. Research is underway to expand our analysis to take into account more sources of uncertainty: more vector and amplifying host species, emissions scenarios, and future climate projections from a range of different global climate models.

Item Type: Book Section
Additional Information: Funding Information: Funding for the BCCVL comes from the Australian National eResearch Tools and Resources Project (NeCTAR). A complete list of participating institutions, development team members, and governance and advisory committee members can be found at No co-authors have any conflicts of interests to declare. Publisher Copyright: © 2020 Proceedings - 21st International Congress on Modelling and Simulation, MODSIM 2015. All rights reserved.
Uncontrolled Keywords: biodiversity,climate change,species distribution modelling,virtual laboratory,information systems and management,modelling and simulation,sdg 3 - good health and well-being,sdg 13 - climate action ,/dk/atira/pure/subjectarea/asjc/1800/1802
Faculty \ School: Faculty of Science > School of Environmental Sciences
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Depositing User: LivePure Connector
Date Deposited: 26 May 2022 09:31
Last Modified: 18 Dec 2022 07:30

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