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:To reveal the characteristics of climate change and the controlling factors for vegetation dynamics in the Ordos, Inner Mongolia, China, 34 years (1982-2015) of regional climate variables and vegetation dynamics were investigated. The results show that: Annual mean air temperature (TMP) significantly increased with a linear slope of 0.473°C/10yr. Annual precipitation (PRE) had a non-significant positive trend nearly 5 times lower than the trend of potential evapotranspiration (PET). The average Normalized Difference Vegetation Index (NDVI) computed for the region was found to show a significant positive trend (6.131×10-4/yr). However, all climate variables displayed non-significant correlations with NDVI at annual scale. The reduction of desert and the increase of grassland over the past decades were accountable for the increased NDVI. Principal components analysis revealed that the regional climate change can be characterized as changes in temperature, humidity and the availability of radiant energy. Based on principal components regression coefficients, NDVI was mostly sensitive to humidity component, followed by growing season warmth (WMI). Spatially, 93.1% of the pixels displayed positive trend and 61.8% of the pixels displayed significant change over the past decades. Both principal regression analysis and partial correlation analysis revealed that NDVI in eastern part of Ordos was sensitive to TMP, whereas, NDVI in southern and western areas of Ordos displayed the high sensitivity to combined effects of PRE and cloud coverage (CLD). Partial correlation analyses also revealed that TMX was a surrogate for aridity, TMN was a representative of humidity, and temperature variations below the threshold of 5°C (CDI) were less important than WMI. We conclude that regional climate change can be characterized by warming and increased aridity. The significant positive trend of regional NDVI and the non-significant correlations between NDVI and climate variables at annual scale suggests the hidden role of the human activities.

Project description:Background Leprosy is an infectious disease caused by Mycobacterium leprae and remains a source of preventable disability if left undetected. Case detection delay is an important epidemiological indicator for progress in interrupting transmission and preventing disability in a community. However, no standard method exists to effectively analyse and interpret this type of data. In this study, we aim to evaluate the characteristics of leprosy case detection delay data and select an appropriate model for the variability of detection delays based on the best fitting distribution type. Methods Two sets of leprosy case detection delay data were evaluated: a cohort of 181 patients from the post exposure prophylaxis for leprosy (PEP4LEP) study in high endemic districts of Ethiopia, Mozambique, and Tanzania; and self-reported delays from 87 individuals in 8 low endemic countries collected as part of a systematic literature review. Bayesian models were fit to each dataset to assess which probability distribution (log-normal, gamma or Weibull) best describes variation in observed case detection delays using leave-one-out cross-validation, and to estimate the effects of individual factors. Results For both datasets, detection delays were best described with a log-normal distribution combined with covariates age, sex and leprosy subtype [expected log predictive density (ELPD) for the joint model: −1123.9]. Patients with multibacillary (MB) leprosy experienced longer delays compared to paucibacillary (PB) leprosy, with a relative difference of 1.57 [95% Bayesian credible interval (BCI): 1.14–2.15]. Those in the PEP4LEP cohort had 1.51 (95% BCI: 1.08–2.13) times longer case detection delay compared to the self-reported patient delays in the systematic review. Conclusions The log-normal model presented here could be used to compare leprosy case detection delay datasets, including PEP4LEP where the primary outcome measure is reduction in case detection delay. We recommend the application of this modelling approach to test different probability distributions and covariate effects in studies with similar outcomes in the field of leprosy and other skin-NTDs. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1186/s40249-023-01065-4.

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.

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