The Biodiversity and Climate Change Virtual Laboratory: how Ecology and Big Data can be utilised in the fight against vector-borne diseases
2015
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 cloudbased
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.
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