Assessing Ecological Vulnerability under Climate Change and Anthropogenic Influence in the Yangtze River Estuarine Island-Chongming Island, China

Understanding and assessing ecological vulnerability for estuarine islands are important for maintaining estuarine island ecosystem services and its sustainable development. However, due to its complex fresh water–sea–land interaction mechanism and multiple stressors from both climate change and anthropogenic influence, a comprehensive evaluation of ecological vulnerability for estuarine islands has been limited. Therefore, taking the typical estuary island of Chongming Island as an example, we developed a comprehensive evaluation system of ecological vulnerability for an estuarine island ecosystem based on the pressure-state-response (PSR) conceptual model, and explored the spatial and temporal distribution of ecological vulnerability in 2005 and 2015. The results indicated that the main pressures of Chongming Island from saltwater intrusion intensity and land use intensity were mainly distributed in northern coastal areas and eastern areas of wetland; the ecological vulnerability index (EV) of Chongming Island showed a slight decrease from 2005 to 2015; and three categories of towns based on ecological vulnerability assessment for an eco-island planning and environmental management were identified. Our study provides an effective evaluation system of ecological vulnerability for estuarine islands, which could be helpful for planners and decision makers in improving eco-island planning and environmental management.

Microclimates can be accurately predicted across ecologically important remote ecosystems

ABSTRACTMicroclimate information is often crucial for understanding ecological patterns and processes, including under climate change, but is typically absent from ecological and biogeographic studies owing to difficulties in obtaining microclimate data. Recent advances in microclimate modelling, however, suggest that microclimate conditions can now be predicted anywhere at any time using hybrid physically- and empirically-based models. Here, for the first time, we test the utility of this approach across a remote, inaccessible, and climate change threatened polar island ecosystem at ecologically relevant scales. Microclimate predictions were generated at a 100 × 100 m grain (at a height of 4 cm) across the island, with models parameterised using either meteorological observations from the island’s weather station (AWS) or climate reanalysis data (CRA). AWS models had low error rates and were highly correlated with observed seasonal and daily temperatures (root mean squared error of predicted seasonal average Tmean ≤ 0.6 °C; Pearson’s correlation coefficient (r) for the daily Tmean ≥ 0.86). By comparison, CRA models had a slight warm bias in all seasons and a smaller diurnal range in the late summer period than in situ observations. Despite these differences, the modelled relationship between the percentage cover of the threatened endemic cushion plant Azorella macquariensis and microclimate varied little with the source of microclimate data (r = 0.97), suggesting that both model parameterisations capture similar patterns of spatial variation in microclimate conditions across the island ecosystem. Here, we have shown that the accurate prediction of microclimate conditions at ecologically relevant spatial and temporal scales is now possible using hybrid physically- and empirically-based models across even the most remote and climatically extreme environments. These advances will help add the microclimate dimension to ecological and biogeographic studies, which could be critical for delivering climate change-resilient conservation planning in climate-change exposed ecosystems.