Dataset Information

Predicting the effect of climate change on a range-restricted lizard in southeastern Australia.

ABSTRACT: Climate change is ranked as one of the most severe threats to global biodiversity. This global phenomenon is particularly true for reptiles whose biology and ecology are closely linked to climate. In this study, we used over 1,300 independent occurrence points and different climate change emission scenarios to evaluate the potential risk of changing climatic conditions on the current and future potential distribution of a rock-dwelling lizard; the velvet gecko. Furthermore, we investigated if the current extent of protected area networks in Australia captures the full range distribution of this species currently and in the future. Our results show that climate change projections for the year 2075 have the potential to alter the distribution of the velvet gecko in southeastern Australia. Specifically, climate change may favor the range expansion of this species to encompass more suitable habitats. The trend of range expansion was qualitatively similar across the different climate change scenarios used. Additionally, we observed that the current network of protected areas in southeast Australia does not fully account for the full range distribution of this species currently and in the future. Ongoing climate change may profoundly affect the potential range distribution of the velvet gecko population. Therefore, the restricted habitat of the velvet geckos should be the focus of intensive pre-emptive management efforts. This management prioritization should be extended to encompass the increases in suitable habitats observed in this study in order to maximize the microhabitats available for the survival of this species.

SUBMITTER: Bezeng BS

PROVIDER: S-EPMC5905374 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Predicting the effect of climate change on a range-restricted lizard in southeastern Australia.

Bezeng Bezeng S BS Tesfamichael Solomon G SG Dayananda Buddhi B

Current zoology 20170327 2

Climate change is ranked as one of the most severe threats to global biodiversity. This global phenomenon is particularly true for reptiles whose biology and ecology are closely linked to climate. In this study, we used over 1,300 independent occurrence points and different climate change emission scenarios to evaluate the potential risk of changing climatic conditions on the current and future potential distribution of a rock-dwelling lizard; the velvet gecko. Furthermore, we investigated if th ...[more]

PMID: 30402056

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Project description:BackgroundClimate change is expected to exacerbate diarrhoea outbreaks across the developing world, most notably in Sub-Saharan countries such as South Africa. In South Africa, diseases related to diarrhoea outbreak is a leading cause of morbidity and mortality. In this study, we modelled the impacts of climate change on diarrhoea with various machine learning (ML) methods to predict daily outbreak of diarrhoea cases in nine South African provinces.MethodsWe applied two deep Learning DL techniques, Convolutional Neural Networks (CNNs) and Long-Short term Memory Networks (LSTMs); and a Support Vector Machine (SVM) to predict daily diarrhoea cases over the different South African provinces by incorporating climate information. Generative Adversarial Networks (GANs) was used to generate synthetic data which was used to augment the available data-set. Furthermore, Relevance Estimation and Value Calibration (REVAC) was used to tune the parameters of the ML methods to optimize the accuracy of their predictions. Sensitivity analysis was also performed to investigate the contribution of the different climate factors to the diarrhoea prediction method.ResultsOur results showed that all three ML methods were appropriate for predicting daily diarrhoea cases with respect to the selected climate variables in each South African province. However, the level of accuracy for each method varied across different experiments, with the deep learning methods outperforming the SVM method. Among the deep learning techniques, the CNN method performed best when only real-world data-set was used, while the LSTM method outperformed the other methods when the real-world data-set was augmented with synthetic data. Across the provinces, the accuracy of all three ML methods improved by at least 30 percent when data augmentation was implemented. In addition, REVAC improved the accuracy of the CNN method by about 2.5% in each province. Our parameter sensitivity analysis revealed that the most influential climate variables to be considered when predicting outbreak of diarrhoea in South Africa were precipitation, humidity, evaporation and temperature conditions.ConclusionsOverall, experiments indicated that the prediction capacity of our DL methods (Convolutional Neural Networks) was found to be superior (with statistical significance) in terms of prediction accuracy across most provinces. This study's results have important implications for the development of automated early warning systems for diarrhoea (and related disease) outbreaks across the globe.

Dataset Information

Predicting the effect of climate change on a range-restricted lizard in southeastern Australia.

Publications

Predicting the effect of climate change on a range-restricted lizard in southeastern Australia.

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