Effects of chlorophyll fluorescence on environment and gross primary productivity of moso bamboo during the leaf-expansion stage.

Chlorophyll fluorescence is the long-wave light released by the residual energy absorbed by vegetation after photosynthesis and dissipation, which can directly and non-destructively reflect the photosynthetic state of plants from the perspective of the mechanism of photosynthetic process. Moso bamboo has a substantial carbon sequestration ability, and leaf-expansion stage is an important phenological period for carbon sequestration. Gross primary production (GPP) is a key parameter reflecting vegetation carbon sequestration process. However, the ability of chlorophyll fluorescence in moso bamboo to explain GPP changes is unclear. The research area of this study is located in the bamboo forest near the flux station of Anji County, Zhejiang Province, where an observation tower is built to monitor the carbon flux and meteorological change of bamboo forest. The chlorophyll fluorescence physiological parameters (Fp) and fluorescence yield (Fy) indices were measured and calculated for the leaves of newborn moso bamboo (I Du bamboo) and the old leaves of 4- to 5-year-old moso bamboo (Ⅲ Du bamboo) during the leaf-expansion stage. The chlorophyll fluorescence in response to the environment and its effect on carbon flux were analyzed. The results showed that: Fv/Fm, Y(II) and α of Ⅰ Du bamboo gradually increased, while Ⅲ Du bamboo gradually decreased, and FYint and FY687/FY738 of Ⅰ Du bamboo were higher than those of Ⅲ Du bamboo; moso bamboo was sensitive to changes in air temperature(Ta), relative humidity(RH), water vapor pressure(E), soil temperature(ST) and soil water content (SWC), the Fy indices of the upper, middle and lower layers were significantly correlated with Ta, E and ST; single or multiple vegetation indices were able to estimate the fluorescence yield indices well (all with R2 greater than 0.77); chlorophyll fluorescence (Fp and Fy indices) of Ⅰ Du bamboo and Ⅲ Du bamboo could explain 74.4% and 72.7% of the GPP variation, respectively; chlorophyll fluorescence and normalized differential vegetation index of the canopy (NDVIc) could estimate GPP well using random forest (Ⅰ Du bamboo: r = 0.929, RMSE = 0.069 g C·m-2; Ⅲ Du bamboo: r = 0.899, RMSE = 0.134 g C·m-2). The results of this study show that chlorophyll fluorescence can provide a basis for judging the response of moso bamboo to environmental changes and can well explain GPP. This study has important scientific significance for evaluating the potential mechanisms of growth, stress feedback and photosynthetic carbon sequestration of bamboo.

Long-term trend in vegetation gross primary production, phenology and their relationships inferred from the FLUXNET data.

Climate-induced changes in plant phenology and physiology plays an important role in control of carbon exchange between terrestrial ecosystems and the atmosphere. Based on dataset during 1997-2014 from 41 flux tower sites (440 site-years) across the northern hemisphere, relationships between long-term trends in start of growing season (SOS), end of growing season (EOS), length of growing season (LOS), maximal gross primary production (GPPmax), and seasonal and annual gross primary production (GPP) were analyzed. Statistical Models of Integrated Phenology and Physiology (SMIPP) were built for predicting the long-term trends in annual GPP. Results showed that SOS advanced and EOS delayed for forest sites, while both SOS and EOS for grassland (GRA) sites delayed. Long-term trends in SOS and EOS of evergreen needle-leaf forests (ENF) sites were greater than those of deciduous broadleaf forests (DBF) sites. Seasonal and annual GPP for forest sites increased, among which long-term trend in annual GPP of ENF sites was the largest. Spring GPP of GRA sites decreased, but annual GPP increased. Strong relationships between long-term trends in phenological and physiological indicators and seasonal GPP were found. Long-term trend in GPPmax had the highest relationship with long-term trend in annual GPP for forest sites, but long-term trend in SOS was the most related to long-term trend in annual GPP for GRA sites. Increases in spring and autumn GPP due to a one-day advance in SOS and delay in EOS for DBF sites were greater than ENF sites. Delay in EOS resulted in more carbon sequestration than advance in SOS for forest sites, while advance in SOS significantly increased spring GPP for GRA sites. The SMIPP model driven by long-term trends in LOS and GPPmax had stronger explanatory power for predicting long-term trend in annual GPP than the SMIPP model driven by long-term trends in SOS, EOS, and GPPmax. Long-term trend in annual GPP was accurately predicted by using the SMIPP model, while long-term trend in annual GPP for GRA sites was more difficult to be captured than the forest sites. Drought and disturbance effects on phenology and physiology were major factors for model uncertainty. This study is helpful to understand changes in phenology and carbon uptake and their differences among different vegetation types and provides a potential way for predicting annual rate of change in carbon uptake through vegetation photosynthesis at a global scale.

Spatiotemporal evolution and impacts of climate change on bamboo distribution in China.

Understanding the impact and restriction of climate change on potential distribution of bamboo forest is crucial for sustainable management of bamboo forest and bamboo-based economic development. In this study, climatic variables and maximum entropy model were used to simulate the potential distribution of bamboo forest in China under the future climate scenarios. Seven climatic variables, such as Spring precipitation, Summer precipitation, Autumn precipitation, average annual relative humidity, Autumn average temperature, average annual temperature range and annual total radiation, were selected as input variables of maximum entropy model based on the relative importance of those climate variables for predicting bamboo forest presence. The suitable ranges of the seven climatic variables for potential distribution of bamboo forest were 337-794 mm, 496-705 mm, 213-929 mm, 74.3%-83.4%, 16.6-23.8 °C, 2.3-10.1 °C and 3.2 × 104-4.3 × 104 W m-2, respectively. Under RCP4.5 and RCP8.5 climate scenarios, the suitable area of bamboo forest growth first increased and then decreased, and showed range contractions towards the interior and expansions towards southwest in China. The results of the present study can serve as a useful reference to dynamic monitoring of the spatial distribution and sustainable utilization of bamboo forest in the future under climate change.

Response of carbon uptake to abiotic and biotic drivers in an intensively managed Lei bamboo forest.

Lei bamboo (Phyllostachys praecox) is widely distributed in southeastern China. We used eddy covariance to analyze carbon sequestration capacity of a Lei bamboo forest (2011-2013) and to identify the seasonal biotic and abiotic determinants of carbon fluxes. A machine learning algorithm called random forest (RF) was used to identify factors that affected carbon fluxes. The RF model predicted well the gross ecosystem productivity (GEP), ecosystem respiration (RE) and net ecosystem exchange (NEE), and displayed variations in the drivers between different seasons. Mean annual NEE, RE, and GEP were -105.2 ±â€¯23.1, 1264.5 ±â€¯45.2, and 1369.6 ±â€¯52.5 g C m-2, respectively. Climate warming increased RE more than GEP when water inputs were not limiting. Summer drought played little role in suppressing GEP, but low soil moisture contents suppressed RE and increased the carbon sink during drought in the summer. The most important drivers of NEE were soil temperature in spring, summer, and winter, and photosynthetically active radiation in autumn. Air and soil temperature were important drivers of GEP in all seasons.

Optimizing selective cutting strategies for maximum carbon stocks and yield of Moso bamboo forest using BIOME-BGC model.

The selective cutting method currently used in Moso bamboo forests has resulted in a reduction of stand productivity and carbon sequestration capacity. Given the time and labor expense involved in addressing this problem manually, simulation using an ecosystem model is the most suitable approach. The BIOME-BGC model was improved to suit managed Moso bamboo forests, which was adapted to include age structure, specific ecological processes and management measures of Moso bamboo forest. A field selective cutting experiment was done in nine plots with three cutting intensities (high-intensity, moderate-intensity and low-intensity) during 2010-2013, and biomass of these plots was measured for model validation. Then four selective cutting scenarios were simulated by the improved BIOME-BGC model to optimize the selective cutting timings, intervals, retained ages and intensities. The improved model matched the observed aboveground carbon density and yield of different plots, with a range of relative error from 9.83% to 15.74%. The results of different selective cutting scenarios suggested that the optimal selective cutting measure should be cutting 30% culms of age 6, 80% culms of age 7, and all culms thereafter (above age 8) in winter every other year. The vegetation carbon density and harvested carbon density of this selective cutting method can increase by 74.63% and 21.5%, respectively, compared with the current selective cutting measure. The optimized selective cutting measure developed in this study can significantly promote carbon density, yield, and carbon sink capacity in Moso bamboo forests.

Development of the BIOME-BGC model for the simulation of managed Moso bamboo forest ecosystems.

Numerical models are the most appropriate instrument for the analysis of the carbon balance of terrestrial ecosystems and their interactions with changing environmental conditions. The process-based model BIOME-BGC is widely used in simulation of carbon balance within vegetation, litter and soil of unmanaged ecosystems. For Moso bamboo forests, however, simulations with BIOME-BGC are inaccurate in terms of the growing season and the carbon allocation, due to the oversimplified representation of phenology. Our aim was to improve the applicability of BIOME-BGC for managed Moso bamboo forest ecosystem by implementing several new modules, including phenology, carbon allocation, and management. Instead of the simple phenology and carbon allocation representations in the original version, a periodic Moso bamboo phenology and carbon allocation module was implemented, which can handle the processes of Moso bamboo shooting and high growth during "on-year" and "off-year". Four management modules (digging bamboo shoots, selective cutting, obtruncation, fertilization) were integrated in order to quantify the functioning of managed ecosystems. The improved model was calibrated and validated using eddy covariance measurement data collected at a managed Moso bamboo forest site (Anji) during 2011-2013 years. As a result of these developments and calibrations, the performance of the model was substantially improved. Regarding the measured and modeled fluxes (gross primary production, total ecosystem respiration, net ecosystem exchange), relative errors were decreased by 42.23%, 103.02% and 18.67%, respectively.

Spatiotemporal patterns of net primary productivity of subtropical forests in China and its response to drought.

Net primary productivity (NPP) is an important indicator used to evaluate the carbon sequestration capacity of forest ecosystems. Subtropical forest ecosystems play an indispensable role in maintaining the global carbon balance, while frequently occurring drought events in recent years have seriously damaged their productivity. However, the spatiotemporal patterns of NPP, as well as its response to drought, remain uncertain. In this study, the multiscale drought characteristics in subtropical China during 1981-2015 were analyzed based on the standardized precipitation evapotranspiration index. Then, simulated and analyzed the spatiotemporal NPP of subtropical forests by the boreal ecosystem productivity simulator model. Finally, the response of NPP to drought was analyzed based on multiple statistical indices. The results show that most regions in subtropical China experienced mild and moderate drought during 1981-2015. In particular, the extent of drought severity has shown a noticeable increasing trend after 2000. The forest NPP ranged from 622.64 to 1323.82 gC·m-2·a-1, with an overall increase rate of 16.15 gC·m-2·a-1; in particular, the contribution of the western forest NPP became increasingly important. Drought stress has limited the growth of subtropical forest NPP in China, with summer and wet season time scales of drought having the greatest impact on forest NPP anomalies, followed by autumn time scales. The limitation is mostly because the drought duration continually increased, leading to differences in the impact of drought on forest NPP before and after 2000, with declines of 59.55 % and 82.45 %, respectively, mainly concentrated in southwestern regions, such as Yunnan, Guangxi, and Sichuan provinces. This study quantitatively analyzed the impact of drought on subtropical forest NPP, and provides scientific basis for subtropical forest response and adaptation to climate change.

Full phenology cycle carbon flux dynamics and driving mechanism of Moso bamboo forest.

Introduction: Moso bamboo forests, widely distributed in subtropical regions, are increasingly valued for their strong carbon sequestration capacity. However, the carbon flux variations and the driving mechanisms of Moso bamboo forest ecosystems of each phenology period have not been adequately explained. Methods: Hence, this study utilizes comprehensive observational data from a Moso bamboo forest eddy covariance observation for the full phenological cycle (2011-2015), fitting a light response equation to elucidate the evolving dynamics of carbon fluxes and photosynthetic characteristics throughout the entire phenological cycle, and employing correlation and path analysis to reveal the response mechanisms of carbon fluxes to both biotic and abiotic factors. Results: The results showed that, First, the net ecosystem exchange (NEE) of Moso bamboo forest exhibits significant variations across six phenological periods, with LSOFF demonstrating the highest NEE at -23.85 ± 12.61 gC·m-2·5day-1, followed by LSON at -19.04 ± 11.77 gC·m-2·5day-1 and FGON at -17.30 ± 9.58 gC·m-2·5day-1, while NFOFF have the lowest value with 3.37 ± 8.24 gC·m-2·5day-1. Second, the maximum net photosynthetic rate (Pmax) and apparent quantum efficiency (α) fluctuated from 0.42 ± 0.20 (FGON) to 0.75 ± 0.24 mg·m-2·s-1 (NFOFF) and from 2.3 ± 1.3 (NFOFF) to 3.3 ± 1.8 µg·µmol-1 (LSOFF), respectively. Third, based on the path analysis, soil temperature was the most important driving factor of photosynthetic rate and NEE variation, with path coefficient 0.81 and 0.55, respectively, followed by leaf area index (LAI), air temperature, and vapor pressure difference, and precipitation. Finally, interannually, increased LAI demonstrated the potential to enhance the carbon sequestration capability of Moso bamboo forests, particularly in off-years, with the highest correlation coefficient with NEE (-0.59) among the six factors. Discussion: The results provide a scientific basis for carbon sink assessment of Moso bamboo forests and provide a reference for developing Moso bamboo forest management strategies.

Spatiotemporal dynamic of subtropical forest carbon storage and its resistance and resilience to drought in China.

Subtropical forests are rich in vegetation and have high photosynthetic capacity. China is an important area for the distribution of subtropical forests, evergreen broadleaf forests (EBFs) and evergreen needleleaf forests (ENFs) are two typical vegetation types in subtropical China. Forest carbon storage is an important indicator for measuring the basic characteristics of forest ecosystems and is of great significance for maintaining the global carbon balance. Drought can affect forest activity and may even lead to forest death and the stability characteristics of different forest ecosystems varied after drought events. Therefore, this study used meteorological data to simulate the standardized precipitation evapotranspiration index (SPEI) and the Biome-BGC model to simulate two types of forest carbon storage to quantify the resistance and resilience of EBF and ENF to drought in the subtropical region of China. The results show that: 1) from 1952 to 2019, the interannual drought in subtropical China showed an increasing trend, with five extreme droughts recorded, of which 2011 was the most severe one; 2) the simulated average carbon storage of the EBF and ENF during 1985-2019 were 130.58 t·hm-2 and 78.49 t·hm-2, respectively. The regions with higher carbon storage of EBF were mainly concentrated in central and southeastern subtropics, where those of ENF mainly distributed in the western subtropic; 3) The median of resistance of EBF was three times higher than that of ENF, indicating the EBF have stronger resistance to extreme drought than ENF. Moreover, the resilience of two typical forest to 2011 extreme drought and the continuous drought events during 2009 - 2011 were similar. The results provided a scientific basis for the response of subtropical forests to drought, and indicating that improve stand quality or expand the plantation of EBF may enhance the resistance to drought in subtropical China, which provided certain reference for forest protection and management under the increasing frequency of drought events in the future.

Simulated net ecosystem productivity of subtropical forests and its response to climate change in Zhejiang Province, China.

Net ecosystem productivity (NEP) is an important index that indicates the carbon sequestration capacity of forest ecosystems. However, the effect of climate change on the spatiotemporal variability in NEP is still unclear. Using the Integrated Terrestrial Ecosystem Carbon-budget (InTEC) model, this study takes the typical subtropical forests in the Zhejiang Province, China as an example, simulated the spatiotemporal patterns of forest NEP from 1979 to 2079 based on historically observed climate data (1979-2015) and data from three representative concentration pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5) provided by the Coupled Model Intercomparison Project 5 (CMIP5). We analyzed the responses of NEP at different forest age classes to the variation in meteorological factors. The NEP of Zhejiang's forests decreased from 1979 to 1985 and then increased from 1985 to 2015, with an annual increase rate of 9.66 g C·m-2·yr-1 and a cumulative NEP of 364.99 Tg·C. Forest NEP decreased from 2016 to 2079; however, the cumulative NEP continued to increase. The simulated cumulative NEP under the RCP2.6, RCP4.5, and RCP8.5 scenarios was 750 Tg·C, 866 Tg·C, and 958 Tg·C, respectively, at the end of 2079. Partial correlation analysis between forest NEP at different age stages and meteorological factors showed that temperature is the key climatic factor that affects the carbon sequestration capacity of juvenile forests (1979-1999), while precipitation is the key climatic factor that affects middle-aged forests (2000-2015) and mature forests (2016-2079). Adopting appropriate management strategies for forests, such as selective cutting of different ages, is critical for the subtropical forests to adapt to climate change and maintain their high carbon sink capacity.

Combined MODIS land surface temperature and greenness data for modeling vegetation phenology, physiology, and gross primary production in terrestrial ecosystems.

Vegetation phenology such as the start (SOS) and end (EOS) of the growing season, physiology (represented by seasonal maximum capacity of carbon uptake, GPPmax), and gross primary production (GPP) are sensitive indicators for monitoring ecosystem response to environmental change. However, uncertainty and disagreement between models limit the use phenology metrics and GPP derived from remote sensing data. Statistical models for estimating phenology and physiology were constructed based on key predictor variables derived from enhanced vegetation index (EVI) and land surface temperature (LST) data. Then, a statistical model that integrated remote sensing-based phenology and physiology (RS-SMIPP) data was constructed to estimate seasonal and annual GPP. These models were calibrated and validated with GPP observations from 512 site-years of FLUXNET data covering four plant functional types (PFTs) in the northern hemisphere: deciduous broadleaf forest, evergreen needle-leaf forest, mixed forest, and grassland. Our results showed that phenology and physiology were accurately estimated with relative root mean squared error (RMSEr) <20%, and the errors varied among the PFTs. Spring EVI was an important factor in explaining variation of GPPmax. The RS-SMIPP model outperformed the MOD17 algorithm in accurately estimating seasonal and annual GPP and reduced RMSEr from 25.34%-43.44% to 9.53%-26.19% for annual GPP of the different PFTs. These findings demonstrate that remote sensing-based phenological and physiological indicators could be used to explain the variations of seasonal and annual GPP, and provide an efficient way for improving GPP estimations at a global scale.

Assimilating spatiotemporal MODIS LAI data with a particle filter algorithm for improving carbon cycle simulations for bamboo forest ecosystems.

Bamboo forests are an important part of the forest ecosystem, which has strong carbon sequestration potential and plays an important role in the global carbon cycle. As a key parameter for simulating the carbon cycle using forest ecosystem models, the quality of leaf area index (LAI) data has a direct influence on the accuracy of modelling results. Here, we used the particle filter (PF) algorithm and PROSAIL model to assimilate MODIS LAI products, which were then used to drive a boreal ecosystem productivity simulator model to simulate the bamboo forest carbon cycle. The results showed that the relationship between the assimilated and observed LAI values was very significant, with an R2 of 0.95 and an RMSE of 0.28, greatly improving the precision of MODIS LAI products. The R2 values for the gross primary productivity (GPP), net ecosystem exchange (NEE), and total ecosystem respiration (TER) simulated by the assimilated LAI values and observed carbon fluxes were 0.65, 0.45 and 0.70, respectively, and the RMSE values were 1.10 g C m-2 day-1, 1.00 g C m-2 day-1 and 0.35 g C m-2 day-1, respectively. Compared with the results of the carbon cycle simulated by non-assimilated LAI, the R2 values of the GPP, NEE and TER values that were simulated by assimilated LAI increased by 27.5%, 45.2% and 6.1%, and the RMSE values decreased by 29.9%, 23.7% and 22.2%, respectively. Therefore, coupling the PF and PROSAIL models can greatly improve the simulation precision for the large-scale bamboo forest carbon cycle. This study laid the foundation for simulating the carbon cycle over a large-scale bamboo forest based on low-resolution data in the future.

Soil respiration of a Moso bamboo forest significantly affected by gross ecosystem productivity and leaf area index in an extreme drought event.

Moso bamboo has large potential to alleviate global warming through carbon sequestration. Since soil respiration (R s ) is a major source of CO2 emissions, we analyzed the dynamics of soil respiration (R s ) and its relation to environmental factors in a Moso bamboo (Phllostachys heterocycla cv. pubescens) forest to identify the relative importance of biotic and abiotic drivers of respiration. Annual average R s was 44.07 t CO2 ha-1 a-1. R s correlated significantly with soil temperature (P < 0.01), which explained 69.7% of the variation in R s at a diurnal scale. Soil moisture was correlated significantly with R s on a daily scale except not during winter, indicating it affected R s . A model including both soil temperature and soil moisture explained 93.6% of seasonal variations in R s . The relationship between R s and soil temperature during a day showed a clear hysteresis. R s was significantly and positively (P < 0.01) related to gross ecosystem productivity and leaf area index, demonstrating the significance of biotic factors as crucial drivers of R s .