Masqué, Pere; Lavery, P. S.; Mueller, U.; Kendrick, G. A.; Rozaimi, M.; Esteban, A.; Fourqurean, J. W.; Marbà, N.; Mateo, M. A.; Murray, K.; Rule, M. J.; Duarte, Carlos M. Citation Arias-Ortiz A, Serrano O, Masqué P, Lavery PS, Mueller U, et al. (2018) A marine heatwave drives massive losses from the world's largest seagrass carbon stocks. Nature Climate Change 8: 338-344. Available: http://dx.Seagrass ecosystems contain globally significant organic carbon (C) stocks. However, climate change and increasing frequency of extreme events threaten their preservation. Shark Bay, Western Australia, has the largest C stock reported for a seagrass ecosystem, containing up to 1.3% of the total C stored within the top metre of seagrass sediments worldwide. On the basis of field studies and satellite imagery, we estimate that 36% of Shark Bay's seagrass meadows were damaged following a marine heatwave in 2010/2011. Assuming that 10 to 50% of the seagrass sediment C stock was exposed to oxic conditions after disturbance, between 2 and 9 Tg CO 2 could have been released to the atmosphere during the following three years, increasing emissions from land-use change in Australia by 4-21% per annum. With heatwaves predicted to increase with further climate warming, conservation of seagrass ecosystems is essential to avoid adverse feedbacks on the climate system.

The impact of environmental change on transmission patterns of waterborne enteric diseases is a major public health concern. This study concerns the burden and spatial nature of enteric fever, attributable to Salmonella Typhi infection in the Central Division, Republic of Fiji at a sub-catchment scale over 30-months (2013-2015). Quantitative spatial analysis suggested relationships between environmental conditions of sub-catchments and incidence and recurrence of typhoid fever. Average incidence per inhabited sub-catchment for the Central Division was high at 205.9/100,000, with cases recurring in each calendar year in 26% of sub-catchments. Although the numbers of cases were highest within dense, urban coastal sub-catchments, the incidence was highest in low-density mountainous rural areas. Significant environmental determinants at this scale suggest increased risk of exposure where sediment yields increase following runoff. The study suggests that populations living on large systems that broaden into meandering mid-reaches and floodplains with alluvial deposition are at a greater risk compared to small populations living near small, erosional, high-energy headwaters and small streams unconnected to large hydrological networks. This study suggests that anthropogenic alteration of land cover and hydrology (particularly via fragmentation of riparian forest and connectivity between road and river networks) facilitates increased transmission of typhoid fever and that environmental transmission of typhoid fever is important in Fiji.

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Analysis of high‐resolution data offers greater opportunity to understand the nature of data variability, behaviours, trends and to detect small changes. Climate studies often require complete time series data which, in the presence of missing data, means imputation must be undertaken. Research on the imputation of high‐resolution temporal climate time series data is still at an early phase. In this study, multiple approaches to the imputation of missing values were evaluated, including a structural time series model with Kalman smoothing, an autoregressive integrated moving average (ARIMA) model with Kalman smoothing and multiple linear regression. The methods were applied to complete subsets of data from 12 month time series of hourly temperature, humidity and wind speed data from four locations along the coast of Western Australia. Assuming that observations were missing at random, artificial gaps of missing observations were studied using a five‐fold cross‐validation methodology with the proportion of missing data set to 10%. The techniques were compared using the pooled mean absolute error, root mean square error and symmetric mean absolute percentage error. The multiple linear regression model was generally the best model based on the pooled performance indicators, followed by the ARIMA with Kalman smoothing. However, the low error values obtained from each of the approaches suggested that the models competed closely and imputed highly plausible values. To some extent, the performance of the models varied among locations. It can be concluded that the modelling approaches studied have demonstrated suitability in imputing missing data in hourly temperature, humidity and wind speed data and are therefore recommended for application in other fields where high‐resolution data with missing values are common.

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