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Mosquito-Borne Diseases: Advances in Modelling Climate-Change Impacts

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TLDR
The current state-of-the-art in both mechanistic and correlative disease modelling, the data driving these models, the vectors and diseases covered, and climate models applied to assess future risk are reviewed.
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This article is published in Trends in Parasitology.The article was published on 2017-12-08. It has received 79 citations till now. The article focuses on the topics: Climate model & Climate change.

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Citations
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Biodiversity increases the resistance of ecosystem productivity to climate extremes

TL;DR: In this paper, the authors used data from 46 experiments that manipulated grassland plant diversity to test whether biodiversity provides resistance during and resilience after climate events, and found that biodiversity increased ecosystem resilience for a broad range of climate events.
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Global expansion and redistribution of Aedes-borne virus transmission risk with climate change.

TL;DR: While climate change will lead to increased net and new exposures to Aedes-borne viruses, the most extreme increases in Ae.
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Impact of Recent and Future Climate Change on Vector-Borne Diseases

TL;DR: This review highlights significant regional changes in vector and pathogen distribution reported in temperate, peri‐Arctic, Arctic, and tropical highland regions during recent decades, changes that have been anticipated by scientists worldwide.
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The effect of global change on mosquito-borne disease

TL;DR: It is shown, through a review of contemporary modelling studies, that no consensus on how future changes in climatic conditions will impact mosquito-borne diseases exists and research should not focus solely on the role of climate change but instead consider growing evidence for additional factors that modulate disease risk.
References
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Journal ArticleDOI

Maximum entropy modeling of species geographic distributions

TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.
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Species Distribution Models: Ecological Explanation and Prediction Across Space and Time

TL;DR: Species distribution models (SDMs) as mentioned in this paper are numerical tools that combine observations of species occurrence or abundance with environmental estimates, and are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time.
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Impacts of climate change on the future of biodiversity.

TL;DR: Overall, this review shows that current estimates of future biodiversity are very variable, depending on the method, taxonomic group, biodiversity loss metrics, spatial scales and time periods considered.
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