Dataset Information

Predicting coral dynamics through climate change.

ABSTRACT: Thermal-stress events are changing the composition of many coral reefs worldwide. Yet, determining the rates of coral recovery and their long-term responses to increasing sea-surface temperatures is challenging. To do so, we first estimated coral recovery rates following past disturbances on reefs in southern Japan and Western Australia. Recovery rates varied between regions, with the reefs in southern Japan showing more rapid recovery rates (intrinsic rate of increase, r?=?0.38 year-1) than reefs in Western Australia (r?=?0.17 year-1). Second, we input these recovery rates into a novel, nonlinear hybrid-stochastic-dynamical system to predict the responses of Indo-Pacific coral populations to complex inter-annual temperature cycles into the year 2100. The coral recovery rates were overlaid on background increases in global sea-surface temperatures, under three different climate-change scenarios. The models predicted rapid recovery at both localities with the infrequent and low-magnitude temperature anomalies expected under a conservative climate-change scenario, Representative Concentration Pathway (RCP) 4.5. With moderate increases in ocean temperatures (RCP 6.0) the coral populations showed a bimodal response, with model runs showing either recovery or collapse. Under a business-as-usual climate-change scenario (RCP 8.5), with frequent and intense temperature anomalies, coral recovery was unlikely.

SUBMITTER: Woesik RV

PROVIDER: S-EPMC6301976 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Predicting coral dynamics through climate change.

Woesik Robert van RV Köksal Semen S Ünal Arzu A Cacciapaglia Chris W CW Randall Carly J CJ

Scientific reports 20181220 1

Thermal-stress events are changing the composition of many coral reefs worldwide. Yet, determining the rates of coral recovery and their long-term responses to increasing sea-surface temperatures is challenging. To do so, we first estimated coral recovery rates following past disturbances on reefs in southern Japan and Western Australia. Recovery rates varied between regions, with the reefs in southern Japan showing more rapid recovery rates (intrinsic rate of increase, r = 0.38 year<sup>-1</sup ...[more]

PMID: 30573804

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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 coral dynamics through climate change.

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Predicting coral dynamics through climate change.

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