Yanzheng Yang

From plant functional types to plant functional traits: A new paradigm in modelling global vegetation dynamics

Dynamic global vegetation models (DGVMs) typically track the material and energy cycles in ecosystems with finite plant functional types (PFTs). Increasingly, the community ecology and modelling studies recognize that current PFT scheme is not sufficient for simulating ecological processes. Recent advances in the study of plant functional traits (FTs) in community ecology provide a novel and feasible approach for the improvement of PFT-based DGVMs. This paper reviews the development of current DGVMs over recent decades. After characterizing the advantages and disadvantages of the PFT-based scheme, it summarizes trait-based theories and discusses the possibility of incorporating FTs into DGVMs. More importantly, this paper summarizes three strategies for constructing next-generation DGVMs with FTs. Finally, the method’s limitations, current challenges and future research directions for FT theory are discussed for FT theory. We strongly recommend the inclusion of several FTs, namely specific leaf area (SLA), leaf nitrogen content (LNC), carbon isotope composition of leaves (Leaf δ13C), the ratio between leaf-internal and ambient mole fractions of CO2 (Leaf Ci/Ca), seed mass and plant height. These are identified as the most important in constructing DGVMs based on FTs, which are also recognized as important ecological strategies for plants. The integration of FTs into dynamic vegetation models is a critical step towards improving the results of DGVM simulations; communication and cooperation among ecologists and modellers is equally important for the development of the next generation of DGVMs.

Relationship between Air Pollutants and Economic Development of the Provincial Capital Cities in China during the Past Decade

With the economic development of China, air pollutants are also growing rapidly in recent decades, especially in big cities of the country. To understand the relationship between economic condition and air pollutants in big cities, we analysed the socioeconomic indictorssuch as Gross Regional Product per capita (GRP per capita), the concentration of air pollutants (PM10, SO2, NO2) and the air pollution index (API) from 2003 to 2012 in 31 provincial capitals of mainland China. The three main industries had a quadratic correlation with NO2, but a negative relationship with PM10 and SO2. The concentration of air pollutants per ten thousand yuan decreased with the multiplying of GRP in the provinical cities. The concentration of air pollutants and API in the provincial capital cities showed a declining trend or inverted-U trend with the rise of GRP per capita, which provided a strong evidence for the Environmental Kuznets Curve (EKC), that the environmental quality first declines, then improves, with the income growth. The results of this research improved our understanding of the alteration of atmospheric quality with the increase of social economy and demonstrated the feasibility of sustainable development for China.

A global meta-analysis of changes in soil carbon, nitrogen, phosphorus and sulfur, and stoichiometric shifts after forestation

Background and aimsPlanted forests, established on non-forest lands, play an important role in enhancing terrestrial carbon (C) sequestration. Understanding the changes in soil C, nutrients and stoichiometry in planted forests is important for forest management.MethodsWe conducted a global meta-analysis of changes in C, nitrogen (N), phosphorus (P) and sulfur (S) and their stoichiometry in mineral soils of planted forest across broad climatic zones from 139 papers.ResultsSoil C and N are slightly decreased after forestation on grassland, moderately increased after forestation on cropland, and substantially increased after forestation on barren land. Forestation does not affect total soil P, but the available P is significantly depleted after the forestation of grassland and cropland with N-fixers. Changes in soil nutrients (N, P and S) and shifts in stoichiometry (ratios of C:N, C:P and N:P) are significantly related to soil C dynamics (p < 0.05). Soil C sequestration is the lowest in the boreal zone, and greater under plantation with N-fixing species than under non-fixing species.ConclusionChanges in soil C and nutrients after forestation mainly differ to prior land use. Compared with forestation of grassland, forestation of barren land is a more effective approach to enhancing C sequestration.

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

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.

Qinghai–Tibetan Plateau peatland sustainable utilization under anthropogenic disturbances and climate change

Abstract Often referred to as the “Third Pole,” Chinas Qinghai–Tibetan Plateau developed large amounts of peatland owing to its unique alpine environment. As a renewable resource, peat helps to regulate the climate as well as performing other important functions. However, in recent years, intensifying climate change and anthropogenic disturbances have resulted in peatland degradation and consequently made sustainable development of peatland more difficult. This review summarizes peatland ecological and economic functions, including carbon sequestration, biodiversity conservation, energy supplies, and ecotourism. It identifies climate change and anthropogenic disturbances as the two key factors attributing to peatland degradation and ecosystem carbon loss. Current problems in environmental degradation and future challenges in peatland management under the effects of global warming are also discussed and highlighted.

Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites

Model simulation and in situ observations are often used to research water and carbon cycles in terrestrial ecosystems, but each of these methods has its own advantages and limitations. Combining these two methods could improve the accuracy of quantifying the dynamics of the water and carbon fluxes of an ecosystem. Data assimilation is an effective means of integrating modeling with in situ observation. In this study, the ensemble Kalman filter (EnKF) and the unscented Kalman filter (UKF) algorithms were used to assimilate remotely sensed leaf area index (LAI) data with the Biome-BGC model to simulate water and carbon fluxes at the Harvard Forest Environmental Monitoring Site (EMS) and the Dinghushan site. After MODIS LAI data from 2000–2004 were assimilated into the improved Biome-BGC model using the EnKF algorithm at the Harvard Forest site, the R2 between the simulated and observed results for NEE and evapotranspiration increased by 7.8% and 4.7%, respectively. In addition, the sum of the absolute error (SAE) and the root mean square error (RMSE) of NEE decreased by an average of 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration decreased by 24.5% and 25.5%, respectively. MODIS LAI data of 2003 were assimilated into the Biome-BGC model for the Dinghushan site, and the R2 values between the simulated and observed results for NEE and evapotranspiration were increased by 6.7% and 17.3%, respectively. In addition, the SAE values of NEE and ET were decreased by 11.3% and 30.7%, respectively, and the RMSE values of NEE and ET decreased by 10.1% and 30.9%, respectively. These results demonstrate that the accuracy of carbon and water flux simulations can be effectively improved when remotely sensed LAI data are properly integrated with ecosystem models through a data assimilation approach.

Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades

Both anthropogenic activities and climate change can affect the biogeochemical processes of natural wetland methanogenesis. Quantifying possible impacts of changing climate and wetland area on wetland methane (CH4) emissions in China is important for improving our knowledge on CH4 budgets locally and globally. However, their respective and combined effects are uncertain. We incorporated changes in wetland area derived from remote sensing into a dynamic CH4 model to quantify the human and climate change induced contributions to natural wetland CH4 emissions in China over the past three decades. Here we found that human-induced wetland loss contributed 34.3% to the CH4 emissions reduction (0.92 TgCH4), and climate change contributed 20.4% to the CH4 emissions increase (0.31 TgCH4), suggesting that decreasing CH4 emissions due to human-induced wetland reductions has offset the increasing climate-driven CH4 emissions. With climate change only, temperature was a dominant controlling factor for wetland CH4 emissions in the northeast (high latitude) and Qinghai-Tibet Plateau (high altitude) regions, whereas precipitation had a considerable influence in relative arid north China. The inevitable uncertainties caused by the asynchronous for different regions or periods due to inter-annual or seasonal variations among remote sensing images should be considered in the wetland CH4 emissions estimation.

The Spatial and Temporal Distribution of Dissolved Organic Carbon Exported from Three Chinese Rivers to the China Sea.

The lateral transport of dissolved organic carbon (DOC) plays an important role in linking the carbon cycles of terrestrial and aquatic ecosystems. Neglecting the lateral flow of dissolved organic carbon can lead to an underestimation of the organic carbon budget of terrestrial ecosystems. It is thus necessary to integrate DOC concentrations and flux into carbon cycle models, particularly with regard to the development of models that are intended to directly link terrestrial and ocean carbon cycles. However, to achieve this goal, more accurate information is needed to better understand and predict DOC dynamics. In this study, we compiled an inclusive database of available data collected from the Yangtze River, Yellow River and Pearl River in China. The database is collected based on online literature survey and analysed by statistic method. Overall, our results revealed a positive correlation between DOC flux and discharge in all three rivers, whereas the DOC concentration was more strongly correlated with the regional net primary productivity (NPP). We estimated the total DOC flux exported by the three rivers into the China Sea to be approximately 2.73 Tg yr-1. Specifically, the annual flux of DOC from the Yangtze River, Yellow River and Pearl River was estimated to be 1.85 Tg yr-1, 0.06 Tg yr-1 and 0.82 Tg yr-1, respectively, and the average annual DOC concentrations were estimated to be 2.24 ± 0.53 mg L-1, 2.70 ± 0.38 mg L-1 and 1.51 ± 0.09 mg L-1, respectively. Seasonal variations in DOC concentrations are greatly influenced by the interaction between temperature and precipitation. NPP is significantly and positively related to the DOC concentration in the Yangtze River and the Pearl River. In addition, differences in climate and the productivity of the vegetation may influence both the flux and concentrations of DOC transported by the rivers and thus potentially affect estuarine geochemistry.

Quantifying leaf trait covariation and its controls across climates and biomes

Plant functional ecology requires the quantification of trait variation and its controls. Field measurements on 483 species at 48 sites across China were used to analyse variation in leaf traits, and assess their predictability. Principal components analysis (PCA) was used to characterize trait variation, redundancy analysis (RDA) to reveal climate effects, and RDA with variance partitioning to estimate separate and overlapping effects of site, climate, life-form and family membership. Four orthogonal dimensions of total trait variation were identified: leaf area (LA), internal-to-ambient CO2 ratio (χ), leaf economics spectrum traits (specific leaf area (SLA) versus leaf dry matter content (LDMC) and nitrogen per area (Narea )), and photosynthetic capacities (Vcmax , Jmax at 25°C). LA and χ covaried with moisture index. Site, climate, life form and family together explained 70% of trait variance. Families accounted for 17%, and climate and families together 29%. LDMC and SLA showed the largest family effects. Independent life-form effects were small. Climate influences trait variation in part by selection for different life forms and families. Trait values derived from climate data via RDA showed substantial predictive power for trait values in the available global data sets. Systematic trait data collection across all climates and biomes is still necessary.

Spatial patterns of leaf δ13C and its relationship with plant functional groups and environmental factors in China

The leaf carbon isotope ratio (δ13C) is a useful parameter for predicting a plants water use efficiency, as an indicator for plant classification, and even in the reconstruction of paleoclimatic environments. In this study, we investigated the spatial pattern of leaf δ13C values and its relationship with plant functional groups and environmental factors throughout China. The high leaf δ13C in the database appeared in central and western China, and the averaged leaf δ13C was -27.15‰, with a range from -21.05‰ to -31.5‰. The order of the averaged δ13C for plant life forms from most positive to most negative was subshrubs > herbs = shrubs > trees > subtrees. Leaf δ13C is also influenced by some environmental factors, such as mean annual precipitation (MAP), relative humidity (RH), mean annual temperature (MAT), solar hours (SH), and altitude, although the overall influences are still relatively weak, in particular the influence of MAT and altitude. And we further found that plant functional types are dominant factors that regulate the magnitude of leaf δ13C for an individual site, whereas environmental conditions are key to understanding spatial patterns of leaf δ13C when we consider China as a whole. Ultimately, we conducted a multiple regression model of leaf δ13C with environmental factors and mapped the spatial distribution of leaf δ13C in China by using this model. However, this PLS model overestimated leaf δ13C for most life forms, especially for deciduous trees, evergreen shrubs and subtrees, and thus need more improvement in the future.