Dataset Information

A novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China.

ABSTRACT: Increasing evidence indicates that current dynamic global vegetation models (DGVMs) have suffered from insufficient realism and are difficult to improve, particularly because they are built on plant functional type (PFT) schemes. Therefore, new approaches, such as plant trait-based methods, are urgently needed to replace PFT schemes when predicting the distribution of vegetation and investigating vegetation sensitivity. As an important direction towards constructing next-generation DGVMs based on plant functional traits, we propose a novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China. The results demonstrated that a Gaussian mixture model (GMM) trained with a LMA-Nmass-LAI data combination yielded an accuracy of 72.82% in simulating vegetation distribution, providing more detailed parameter information regarding community structures and ecosystem functions. The new approach also performed well in analyses of vegetation sensitivity to different climatic scenarios. Although the trait-climate relationship is not the only candidate useful for predicting vegetation distributions and analysing climatic sensitivity, it sheds new light on the development of next-generation trait-based DGVMs.

SUBMITTER: Yang Y

PROVIDER: S-EPMC4823651 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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A novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China.

Yang Yanzheng Y Zhu Qiuan Q Peng Changhui C Wang Han H Xue Wei W Lin Guanghui G Wen Zhongming Z Chang Jie J Wang Meng M Liu Guobin G Li Shiqing S

Scientific reports 20160407

Increasing evidence indicates that current dynamic global vegetation models (DGVMs) have suffered from insufficient realism and are difficult to improve, particularly because they are built on plant functional type (PFT) schemes. Therefore, new approaches, such as plant trait-based methods, are urgently needed to replace PFT schemes when predicting the distribution of vegetation and investigating vegetation sensitivity. As an important direction towards constructing next-generation DGVMs based o ...[more]

PMID: 27052108

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Project description:Vegetation is an essential component of the earth's surface system and its dynamics is a clear indicator of global climate change. However, the vegetation trends of most studies were based on time-unvarying methods, cannot accurately detect the long-term nonlinear characteristics of vegetation changes. Here, the ensemble empirical mode decomposition and the Breaks for Additive Seasonal and Trend algorithm were applied to reconstruct the the normalized difference vegetation index (NDVI) data and diagnose spatiotemporal evolution and abrupt changes of long-term vegetation trends in China during 1982-2018. Residual analysis was used to separate the influence of climate and human activities on NDVI variations, and the effect of specific human drivers on vegetation growth was obtained. The results suggest that based on the time-varying analysis, high vegetation browning was masked by overall vegetation greening. Vegetation growth in China experienced an abrupt change in the 1990s and 2000s, accounting for 50% and 33.6% of the whole China respectively. Of the area before the breakpoint, 45.4% showed a trend of vegetation decrease, which was concentrated mainly in east China, while 43% of the area after the breakpoint also showed vegetation degradation, mainly in northwest China. Climate was an important driving force for vegetation change in China. It played a positive role in south China, but had a negative effect in northwest China. The impact of human activities on vegetation growthchanged from an initial negative influence to a positive one. In terms of human activities, an inverted-U-shaped relation was detected between CO2 emissions and vegetation growth; that is, the fertilization effect of CO2 had a certain threshold. Once that threshold was exceeded, it would hinder vegetation growth. Population density had a slight constraint on vegetation growth, and the implementation of ecological restoration projects (e.g., the Grain for Green Program) can promote vegetation growth to a certain extent.

Project description:Climate and human-driven changes play an important role in regional droughts. Northwest Yunnan Province is a key region for biodiversity conservation in China, and it has experienced severe droughts since the beginning of this century; however, the extent of the contributions from climate and human-driven changes remains unclear. We calculated the ecosystem evapotranspiration (ET) and water yield (WY) of northwest Yunnan Province, China from 2001 to 2013 using meteorological and remote sensing observation data and a Surface Energy Balance System (SEBS) model. Multivariate regression analyses were used to differentiate the contribution of climate and vegetation coverage to ET. The results showed that the annual average vegetation coverage significantly increased over time with a mean of 0.69 in spite of the precipitation fluctuation. Afforestation/reforestation and other management efforts attributed to vegetation coverage increase in NW Yunnan. Both ET and WY considerably fluctuated with the climate factors, which ranged from 623.29 mm to 893.8 mm and -51.88 mm to 384.40 mm over the time period. Spatially, ET in the southeast of NW Yunnan (mainly in Lijiang) increased significantly, which was in line with the spatial trend of vegetation coverage. Multivariate linear regression analysis indicated that climatic factors accounted for 85.18% of the ET variation, while vegetation coverage explained 14.82%. On the other hand, precipitation accounted for 67.5% of the WY. We conclude that the continuous droughts in northwest Yunnan were primarily climatically driven; however, man-made land cover and vegetation changes also increased the vulnerability of local populations to drought. Because of the high proportion of the water yield consumed for subsistence and poor infrastructure for water management, local populations have been highly vulnerable to climate drought conditions. We suggest that conservation of native vegetation and development of water-conserving agricultural practices should be implemented as adaptive strategies to mitigate climate change.

Dataset Information

A novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China.

Publications

A novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China.

Similar Datasets

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