Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations

We performed a synthetic analysis of Harvard Forest net ecosystem exchange of CO₂ (NEE) time series and a simple ecosystem carbon flux model, the simplified Photosynthesis and Evapo‐Transpiration model (SIPNET). SIPNET runs at a half‐daily time step, and has two vegetation carbon pools, a single aggregated soil carbon pool, and a simple soil moisture sub‐model. We used a stochastic Bayesian parameter estimation technique that provided posterior distributions of the model parameters, conditioned on the observed fluxes and the model equations. In this analysis, we estimated the values of all quantities that govern model behavior, including both rate constants and initial conditions for carbon pools. The purpose of this analysis was not to calibrate the model to make predictions about future fluxes but rather to understand how much information about process controls can be derived directly from the NEE observations. A wavelet decomposition enabled us to assess model performance at multiple time scales from diurnal to decadal. The model parameters are most highly constrained by eddy flux data at daily to seasonal time scales, suggesting that this approach is not useful for calculating annual integrals. However, the ability of the model to fit both the diurnal and seasonal variability patterns in the data simultaneously, using the same parameter set, indicates the effectiveness of this parameter estimation method. Our results quantify the extent to which the eddy covariance data contain information about the ecosystem process parameters represented in the model, and suggest several next steps in model development and observations for improved synthesis of models with flux observations.     

Uncertainty analysis of forest carbon sink forecast with varying measurement errors: a data assimilation approach

Aims Accurate forecast of ecosystem states is critical for improving natural resource management and climate change mitigation. Assimilating observed data into models is an effective way to reduce uncertainties in ecological forecasting. However, influences of measurement errors on parameter estimation and forecasted state changes have not been carefully examined. This study analyzed the parameter identifiability of a process-based ecosystem carbon cycle model, the sensitivity of parameter estimates and model forecasts to the magnitudes of measurement errors and the information contributions of the assimilated data to model forecasts with a data assimilation approach.Methods We applied a Markov Chain Monte Carlo method to assimilate eight biometric data sets into the Terrestrial ECOsystem model. The data were the observations of foliage biomass, wood biomass, fine root biomass, microbial biomass, litter fall, litter, soil carbon and soil respiration, collected at the Duke Forest free-air CO₂ enrichment facilities from 1996 to 2005. Three levels of measurement errors were assigned to these data sets by halving and doubling their original standard deviations.Important findings Results showed that only less than half of the 30 parameters could be constrained, though the observations were extensive and the model was relatively simple. Higher measurement errors led to higher uncertainties in parameters estimates and forecasted carbon (C) pool sizes. The long-term predictions of the slow turnover pools were affected less by the measurement errors than those of fast turnover pools. Assimilated data contributed less information for the pools with long residence times in long-term forecasts. These results indicate the residence times of C pools played a key role in regulating propagation of errors from measurements to model forecasts in a data assimilation system. Improving the estimation of parameters of slow turnover C pools is the key to better forecast long-term ecosystem C dynamics.     

Model-data synthesis of diurnal and seasonal CO2 fluxes at Niwot Ridge, Colorado

Eddy covariance records hold great promise for understanding the processes controlling the net ecosystem exchange of CO₂ (NEE). However, NEE is the small difference between two large fluxes: photosynthesis and ecosystem respiration. Consequently, separating NEE into its component fluxes, and determining the process‐level controls over these fluxes, is a difficult problem. In this study, we used a model‐data synthesis approach with the Simplified PnET (SIPNET) flux model to extract process‐level information from 5 years of eddy covariance data at an evergreen forest in the Colorado Rocky Mountains. SIPNET runs at a twice‐daily time step, and has two vegetation carbon pools, a single aggregated soil carbon pool, and a soil moisture submodel that models both evaporation and transpiration. By optimizing the model parameters before evaluating model‐data mismatches, we were able to probe the model structure independent of any arbitrary parameter set. In doing so, we were able to learn about the primary controls over NEE in this ecosystem, and in particular the respiration component of NEE. We also used this parameter optimization, coupled with a formal model selection criterion, to investigate the effects of making hypothesis‐driven changes to the model structure. These experiments lent support to the hypotheses that (1) photosynthesis, and possibly foliar respiration, are down‐regulated when the soil is frozen and (2) the metabolic processes of soil microbes vary in the summer and winter, possibly because of the existence of distinct microbial communities at these two times. Finally, we found that including water vapor fluxes, in addition to carbon fluxes, in the parameter optimization did not yield significantly more information about the partitioning of NEE into gross photosynthesis and ecosystem respiration.     

Modeling Net Ecosystem Carbon Exchange of Alpine Grasslands with a Satellite-Driven Model

Estimate of net ecosystem carbon exchange (NEE) between the atmosphere and terrestrial ecosystems, the balance of gross primary productivity (GPP) and ecosystem respiration (Reco) has significant importance for studying the regional and global carbon cycles. Using models driven by satellite data and climatic data is a promising approach to estimate NEE at regional scales. For this purpose, we proposed a semi-empirical model to estimate NEE in this study. In our model, the component GPP was estimated with a light response curve of a rectangular hyperbola. The component Reco was estimated with an exponential function of soil temperature. To test the feasibility of applying our model at regional scales, the temporal variations in the model parameters derived from NEE observations in an alpine grassland ecosystem on Tibetan Plateau were investigated. The results indicated that all the inverted parameters exhibit apparent seasonality, which is in accordance with air temperature and canopy phenology. In addition, all the parameters have significant correlations with the remote sensed vegetation indexes or environment temperature. With parameters estimated with these correlations, the model illustrated fair accuracy both in the validation years and at another alpine grassland ecosystem on Tibetan Plateau. Our results also indicated that the model prediction was less accurate in drought years, implying that soil moisture is an important factor affecting the model performance. Incorporating soil water content into the model would be a critical step for the improvement of the model.     

NEE观测误差分布类型对陆地生态系统机理模型参数估计的影响——以长白山温带阔叶红松林为例

基于观测数据的陆地生态系统模型参数估计有助于提高模型的模拟和预测能力,降低模拟不确定性.在已有参数估计研究中,涡度相关技术测定的净生态系统碳交换量(NEE)数据的随机误差通常被假设为服从零均值的正态分布.然而近年来已有研究表明NEE数据的随机误差更服从双指数分布.为探讨NEE观测误差分布类型的不同选择对陆地生态系统机理模型参数估计以及碳通量模拟结果造成的差异,以长白山温带阔叶红松林为研究区域,采用马尔可夫链-蒙特卡罗方法,利用2003～2005年测定的NEE数据对陆地生态系统机理模型CEVSA2的敏感参数进行估计,对比分析了两种误差分布类型(正态分布和双指数分布)的参数估计结果以及碳通量模拟的差异.结果表明,基于正态观测误差模拟的总初级生产力和生态系统呼吸的年总量分别比基于双指数观测误差的模拟结果高61～86 g C m-2 a-1和107～116 g C m-2 a-1,导致前者模拟的NEE年总量较后者低29～47 g C m-2 a-1,特别在生长旺季期间有明显低估.在参数估计研究中,不能忽略观测误差的分布类型以及相应的目标函数的选择,它们的不合理设置可能对参数估计以及模拟结果产生较大影响.     

Inverse analysis of coupled carbon-nitrogen cycles against multiple datasets at ambient and elevated CO2

Aims Carbon (C) sequestration in terrestrial ecosystems is strongly regulated by nitrogen (N) processes. However, key parameters that determine the degree of N regulation on terrestrial C sequestration have not been well quantified.Methods Here, we used a Bayesian probabilistic inversion approach to estimate 14 target parameters related to ecosystem C and N interactions from 19 datasets obtained from Duke Forests under ambient and elevated carbon dioxide (CO₂).Important findings Our results indicated that 8 of the 14 target parameters, such as C:N ratios in most ecosystem compartments, plant N uptake and external N input, were well constrained by available datasets whereas the others, such as N allocation coefficients, N loss and the initial value of mineral N pool were poorly constrained. Our analysis showed that elevated CO₂ led to the increases in C:N ratios in foliage, fine roots and litter. Moreover, elevated CO₂ stimulated plant N uptake and increased ecosystem N capital in Duke Forests by 25.2 and 8.5%, respectively. In addition, elevated CO₂ resulted in the decrease of C exit rates (i.e. increases in C residence times) in foliage, woody biomass, structural litter and passive soil organic matter, but the increase of C exit rate in fine roots. Our results demonstrated that CO₂ enrichment substantially altered key parameters in determining terrestrial C and N interactions, which have profound implications for model improvement and predictions of future C sequestration in terrestrial ecosystems in response to global change.     

The relationship between leaf and ecosystem CO<Subscript>2</Subscript> exchanges in a maize field

The relationship between leaf photosynthetic rate (A) in a vegetation canopy and the net ecosystem CO₂ exchange (NEE) over an entire ecosystem is not well understood. The aim of the present study is to assess the coordinated changes in NEE derived with eddy covariance, A measured in leaf cuvette, and their associations in a rainfed maize field. The light response-curves were estimated for the carbon assimilation rate at both the leaf and ecosystem scales. NEE and A synchronically changed throughout the day and were greater around noon and persisted longer during rapid growth periods. The leaf A had a similar pattern of daytime changes in the top, middle, and bottom leaves. Only severe leaf ageing led to a significant decline in the maximum efficiency of photosystem II (PSII) photochemistry. The greater maximum NEE was associated with a higher ecosystem quantum yield. NEE was positively and significantly correlated with the leaf A averaged based on the vertical distribution of leaf area. The finding highlights the feasibility of assessing NEE by leaf CO₂ exchange because of most of experimental data obtained with leaf cuvette methods; and also implies that simultaneously enhancing leaf photosynthetic rate, electron transport rate, net carbon assimilation at whole ecosystem might play a critical role for the enhancement of crop productivity.     

Biophysical regulations of NEE light response in a steppe and a cropland in Inner Mongolia

Aims Ecosystem carbon models often require accurate net ecosystem exchange of CO₂ (NEE) light-response parameters, which can be derived from the Michaelis–Menten equation. These parameters include maximum net ecosystem exchange (NEE max), apparent quantum use efficiency (α) and daytime ecosystem respiration rate (R e). However, little is known about the effects of land conversion between steppe and cropland on these parameters, especially in semi-arid regions. To understand how these parameters vary in responses to biotic and abiotic factors under land conversions, seasonal variation of light-response parameters were evaluated for a steppe and a cropland of Inner Mongolia, China, during three consecutive years (2006–08) with different precipitation amounts.Methods NEE was measured over a steppe and a cropland in Duolun, Inner Mongolia, China, using the eddy covariance technique, and NEE light-response parameters (NEE max, α and R e) were derived using the Michaelis–Menten model. Biophysical regulations of these parameters were evaluated using a stepwise regression analysis.Important findings The maximum absolute values of NEE max occurred in the meteorological regimes of 15°C ≤ T a < 25°C, vapor pressure deficit (VPD) < 1 KPa and 0.21 m 3 m ? 3 ≤ volumetric soil water content at 10 cm (SWC) < 0.28 m 3 m ? 3 for both the steppe and the cropland ecosystems. The variations of α and R e showed no regular variation pattern in different T air, VPD and SWC regimes. Under the same regime of T air, VPD and SWC, the cropland had higher absolute values of NEE max than the steppe. Canopy conductance and leaf area index (LAI) were dominant drivers for variations in NEE light-response parameters of the steppe and the cropland. The seasonal variation of NEE light-response parameters followed the variation of LAI for two ecosystems. The peak values of all light-response parameters for the steppe and the cropland occurred from July to August. The values of NEE light-response parameters (NEE max, α and R e) were lower in the driest year (2007). Seasonally averaged NEE light-response parameters for the cropland surpassed those for the steppe. Land conversion from steppe to cropland enhanced NEE light-response parameters during the plant growing period. These results will have significant implications for improving the models on regional NEE variation under climate change and land-use change scenarios.     

Endogenous circadian regulation of carbon dioxide exchange in terrestrial ecosystems

It is often assumed that daytime patterns of ecosystem carbon assimilation are mostly driven by direct physiological responses to exogenous environmental cues. Under limited environmental variability, little variation in carbon assimilation should thus be expected unless endogenous plant controls on carbon assimilation, which regulate photosynthesis in time, are active. We evaluated this assumption with eddy flux data, and we selected periods when net ecosystem exchange (NEE) was decoupled from environmental variability in seven sites from highly contrasting biomes across a 74° latitudinal gradient over a total of 36 site‐years. Under relatively constant conditions of light, temperature, and other environmental factors, significant diurnal NEE oscillations were observed at six sites, where daily NEE variation was between 20% and 90% of that under variable environmental conditions. These results are consistent with fluctuations driven by the circadian clock and other endogenous processes. Our results open a promising avenue of research for a more complete understanding of ecosystem fluxes that integrates from cellular to ecosystem processes.     

生态系统光合与呼吸拆分的同位素理论、方法和应用进展

生态系统光合和呼吸是构成净生态系统CO₂交换量(NEE)的重要组分。涡度相关技术可直接观测生态系统NEE,并通过建立温度回归或光响应曲线等函数将NEE统计拆分为生态系统光合和呼吸,但是存在自相关和高估白天呼吸等问题。稳定同位素红外光谱技术的进步使高时间分辨率大气CO₂及其稳定碳同位素组成(δ¹³C)的连续观测成为可能,与涡度相关技术观测的NEE数据相结合,可实现昼夜和季节尺度生态系统光合和呼吸拆分。本文系统阐述了生态系统光合与呼吸的同位素通量拆分方法的基本理论与假设,阐述了同位素通量观测技术的发展及其应用进展,综述了同位素通量拆分理论解析生态系统光合与呼吸过程的新机制认识,最后总结并展望了同位素通量拆分理论的不确定性以及开展多种拆分方法综合比较的必要性。     

基于模型数据融合的千烟洲亚热带人工林碳水通量模拟

人工林生态系统是我国森林生态系统的重要组成部分,在全球碳平衡中的作用越来越受到重视.利用千烟洲亚热带人工针叶林通量观测站的碳水通量和气象观测数据,通过模型数据融合方法对碳水循环过程模型——SIPNET模型关键参数进行反演,模拟了2004-2009年千烟洲人工林生态系统的碳水通量.结果表明:仅用碳通量观测数据优化模型参数时,净生态系统碳交换量(NEE)模拟效果较好(R2=0.934),而生态系统蒸散(ET)模拟效果较差(R2=0.188);同时用碳水通量观测数据优化时,NEE模拟效果稍差(R2=0.929),但ET模拟效果显著提升(R2=0.824),说明利用碳水通量观测数据同时优化,SIPNET模型才能较好地模拟试验站点碳水通量.在此基础上,开展了人工林生态系统碳通量对降水变化响应的敏感性分析,发现降水量减少对光合作用的影响比对呼吸作用的影响更为强烈,且碳水通量同时参与优化时模型才能较好地模拟碳通量随降水减少而快速降低的趋势,表明如果不能同时利用碳水通量进行参数优化,模型无法正确揭示生态系统碳循环对降水变异的响应.     

基于模型数据融合的中国温带和亚热带典型森林生态系统碳通量模拟

生态系统碳循环过程对水分响应的研究已成为全球变化关注的焦点问题之一。基于长白山温带针阔混交林与千烟洲亚热带人工针叶林观测站2003—2009年生长季的碳通量(NEE)和气象观测数据,综合考虑水分对光合、呼吸作用的影响,构建不同的NEE模型,并应用模型数据融合方法优化模型参数、遴选最适模型,系统分析了水分因子对不同森林生态系统碳循环的影响。结果表明:(1)优化后的模型参数均能被NEE实测数据较好约束。长白山生长季的光合、呼吸参数值均高于千烟洲,未考虑空气饱和水汽压差(VPD)的模型高估了千烟洲温度敏感性参数(Q10)值、低估了千烟洲基础呼吸速率参数(BR)值;(2)仅考虑VPD对光合作用影响的模型是长白山生长季碳通量模拟的最优模型,但模拟精度提高不显著。不同模型间碳通量组分模拟结果差异较小;(3)考虑VPD和土壤含水量对光合、呼吸作用共同影响的模型是千烟洲生长季碳通量模拟的最优模型,并且显著提高了模拟精度。未考虑水分的模型在生长季高估了总生态系统生产力(GEP)总量2.0%(21.85 g C/m~2),同时更大幅度地高估了生态系统呼吸(RE)总量4.4%(38.02 g C/m~2),从而导致NEE总量低估于实测值7.8%(18.55 g C/m~2)。     

Influences of error distributions of net ecosystem exchange on parameter estimation of a process-based terrestrial model: A case of broad-leaved Korean pine mixed forest in Changbaishan, China

Model predictions can be improved by parameter estimation from measurements. It was assumed that measurement errors of net ecosystem exchange (NEE) of CO₂ follow a normal distribution. However, recent studies have shown that errors in eddy covariance measurements closely follow a double exponential distribution. In this paper, we compared effects of different distributions of measurement errors of NEE data on parameter estimation. NEE measurements in the Changbaishan forest were assimilated into a process-based terrestrial ecosystem model. We used the Markov chain Monte Carlo method to derive probability density functions of estimated parameters. Our results showed that modeled annual total gross primary production (GPP) and ecosystem respiration (Re) using the normal error distribution were higher than those using the double exponential distribution by 61–86 gC m^?2 a^?1 and 107–116 gC m^?2 a^?1, respectively. As a result, modeled annual sum of NEE using the normal error distribution was lower by 29–47 gC m^?2 a^?1 than that using the double exponential error distribution. Especially, modeled daily NEE based on the normal distribution underestimated the strong carbon sink in the Changbaishan forest in the growing season. We concluded that types of measurement error distributions and corresponding cost functions can substantially influence the estimation of parameters and carbon fluxes.     

Model parameterization to represent processes at unresolved scales and changing properties of evolving systems

Modeling has become an indispensable tool for scientific research. However, models generate great uncertainty when they are used to predict or forecast ecosystem responses to global change. This uncertainty is partly due to parameterization, which is an essential procedure for model specification via defining parameter values for a model. The classic doctrine of parameterization is that a parameter is constant. However, it is commonly known from modeling practice that a model that is well calibrated for its parameters at one site may not simulate well at another site unless its parameters are tuned again. This common practice implies that parameter values have to vary with sites. Indeed, parameter values that are estimated using a statistically rigorous approach, that is, data assimilation, vary with time, space, and treatments in global change experiments. This paper illustrates that varying parameters is to account for both processes at unresolved scales and changing properties of evolving systems. A model, no matter how complex it is, could not represent all the processes of one system at resolved scales. Interactions of processes at unresolved scales with those at resolved scales should be reflected in model parameters. Meanwhile, it is pervasively observed that properties of ecosystems change over time, space, and environmental conditions. Parameters, which represent properties of a system under study, should change as well. Tuning has been practiced for many decades to change parameter values. Yet this activity, unfortunately, did not contribute to our knowledge on model parameterization at all. Data assimilation makes it possible to rigorously estimate parameter values and, consequently, offers an approach to understand which, how, how much, and why parameters vary. To fully understand those issues, extensive research is required. Nonetheless, it is clear that changes in parameter values lead to different model predictions even if the model structure is the same.     

FLUXNET and modelling the global carbon cycle

Measurements of the net CO₂ flux between terrestrial ecosystems and the atmosphere using the eddy covariance technique have the potential to underpin our interpretation of regional CO₂ source–sink patterns, CO₂ flux responses to forcings, and predictions of the future terrestrial C balance. Information contained in FLUXNET eddy covariance data has multiple uses for the development and application of global carbon models, including evaluation/validation, calibration, process parameterization, and data assimilation. This paper reviews examples of these uses, compares global estimates of the dynamics of the global carbon cycle, and suggests ways of improving the utility of such data for global carbon modelling. Net ecosystem exchange of CO₂ (NEE) predicted by different terrestrial biosphere models compares favourably with FLUXNET observations at diurnal and seasonal timescales. However, complete model validation, particularly over the full annual cycle, requires information on the balance between assimilation and decomposition processes, information not readily available for most FLUXNET sites. Site history, when known, can greatly help constrain the model‐data comparison. Flux measurements made over four vegetation types were used to calibrate the land‐surface scheme of the Goddard Institute for Space Studies global climate model, significantly improving simulated climate and demonstrating the utility of diurnal FLUXNET data for climate modelling. Land‐surface temperatures in many regions cool due to higher canopy conductances and latent heat fluxes, and the spatial distribution of CO₂ uptake provides a significant additional constraint on the realism of simulated surface fluxes. FLUXNET data are used to calibrate a global production efficiency model (PEM). This model is forced by satellite‐measured absorbed radiation and suggests that global net primary production (NPP) increased 6.2% over 1982–1999. Good agreement is found between global trends in NPP estimated by the PEM and a dynamic global vegetation model (DGVM), and between the DGVM and estimates of global NEE derived from a global inversion of atmospheric CO₂ measurements. Combining the PEM, DGVM, and inversion results suggests that CO₂ fertilization is playing a major role in current increases in NPP, with lesser impacts from increasing N deposition and growing season length. Both the PEM and the inversion identify the Amazon basin as a key region for the current net terrestrial CO₂ uptake (i.e. 33% of global NEE), as well as its interannual variability. The inversion's global NEE estimate of −1.2 Pg [C] yr⁻¹ for 1982–1995 is compatible with the PEM‐ and DGVM‐predicted trends in NPP. There is, thus, a convergence in understanding derived from process‐based models, remote‐sensing‐based observations, and inversion of atmospheric data. Future advances in field measurement techniques, including eddy covariance (particularly concerning the problem of night‐time fluxes in dense canopies and of advection or flow distortion over complex terrain), will result in improved constraints on land‐atmosphere CO₂ fluxes and the rigorous attribution of mechanisms to the current terrestrial net CO₂ uptake and its spatial and temporal heterogeneity. Global ecosystem models play a fundamental role in linking information derived from FLUXNET measurements to atmospheric CO₂ variability. A number of recommendations concerning FLUXNET data are made, including a request for more comprehensive site data (particularly historical information), more measurements in undisturbed ecosystems, and the systematic provision of error estimates. The greatest value of current FLUXNET data for global carbon cycle modelling is in evaluating process representations, rather than in providing an unbiased estimate of net CO₂ exchange.     

基于数据同化的哈佛森林地区水、碳通量模拟

模型模拟和站点观测是陆地生态系统水、碳循环研究最主要的两种手段,但各有优势和不足,若二者相互结合,则能更准确地反映生态系统水、碳通量的动态变化.数据同化为模型与观测结合提供了一条有效的途径.本文采用哈佛森林环境监测站相关数据,利用集合卡曼滤波同化算法,将实测叶面积指数(LAI)和遥感LAI同化进入Biome BGC模型中,对该地区水、碳通量进行模拟.结果表明：与未同化模拟相比,将1998、1999和2006年实测LAI数据同化后,模型模拟碳通量(NEE)与通量观测NEE的决定系数(R²)平均提升8.4%;蒸散发(ET)的R²平均提升10.6%;NEE的绝对误差和(SAE)和均方根误差(RMSE)平均下降17.7%和21.2%,ET的SAE和RMSE平均下降26.8%和28.3%.将2000-2004年MODIS LAI 产品与模型同化后,NEE、ET模拟值与观测值间的R²分别提升7.8%和4.7%;NEE的SAE和 RMSE分别下降21.9%和26.3%,ET的SAE和 RMSE分别下降24.5%和25.5%.无论实测LAI还是遥感观测LAI,同化进入模型都能不同程度地提高水碳通量的模拟精度.     

Optimization and evaluation of vegetation photosynthesis and respiration model using the measurements collected from the forest site of subtropical coniferous-evergreen

 碳循环模型参数的确定和优化对生态系统净CO₂交换(NEE)的模型计算至关重要。该文利用2010–2012年ChinaFLUX千烟洲站点的通量观测资料, 对植被光合呼吸模型(VPRM)的参数进行了优化。通过比较两种不同的拟合方案, 发现利用传统光响应方程得到的参数不适用于VPRM, 而利用模型自身反演方案拟合得到的参数最大光量子效率(λ)达0.203, 大于C₃植物平均值, 但与其他相关研究结果吻合。采用VPRM模型反演方案优化得到的参数后, VPRM能较准确地模拟千烟洲站不同季节的NEE。其对全年半小时NEE模拟的平均误差为–0.86 μmol·m^–2·s^–1, 相关系数为0.72。模型可准确地模拟生长旺季NEE平均日变化, 但低估了非生长旺季白天吸收峰值约52%。通过个例分析发现, VPRM模型可以准确模拟晴天条件下NEE的时间变化, 但对阴雨天条件下NEE的模拟还存在较大的不确定性。该研究将有助于进一步改进CO₂通量及浓度的区域数值模拟。     

生态系统性状对宏生态研究的启示与挑战

功能性状在器官-物种-种群-群落-生态系统水平都具有其特定的适应或功能优化的意义,但目前对功能性状的测定和研究大都局限于器官或物种水平。然而,当前高速发展的宏生态新研究技术和方法(如遥感观测、通量观测、模型模拟)的研究对象都是在生态系统或区域尺度上,如何将传统功能性状与其相连结并服务于生态环境问题和全球变化问题是科学界的一大难题。为了解决传统性状与宏生态研究"尺度不统一"和"量纲不统一"的难题,研究人员最新发展了"生态系统性状(Ecosystem traits, ESTs)"概念体系,并从"理念-数据源-推导方法-应用"等多角度为后续研究提供了可借鉴案例。生态系统性状将传统性状研究从器官水平拓展到了群落和生态系统水平,以单位土地面积为基础构建了传统性状与宏生态研究(或地学研究)的桥梁,开启了性状研究从"器官到群落"、从"经典理论验证到宏观应用"的美好愿景,为多学科交叉提供了新思路。然而,它在方法学和数据源等方面还存在诸多问题与挑战;在此,我们呼吁相关专家从研究方法、概念体系和应用实践上赋予"生态系统性状"更强大的生命力,尤其从动物群落性状和微生物群落性状等角度。本文在深入解读先前生态性状概念体系、理论意义和潜在挑战的基础上,结合最新进展进行了补充,希望通过广泛讨论,完善生态系统性状概念体系,逐步形成"以性状为基础的生态系统生态学"新研究框架,切实推动宏生态研究和区域生态环境问题的解决。     

Climatic influences on net ecosystem CO<Subscript>2</Subscript> exchange during the transition from wintertime carbon source to springtime carbon sink in a high-elevation,subalpine forest

The transition between wintertime net carbon loss and springtime net carbon assimilation has an important role in controlling the annual rate of carbon uptake in coniferous forest ecosystems. We studied the contributions of springtime carbon assimilation to the total annual rate of carbon uptake and the processes involved in the winter-to-spring transition across a range of scales from ecosystem CO₂ fluxes to chloroplast photochemistry in a coniferous, subalpine forest. We observed numerous initiations and reversals in the recovery of photosynthetic CO₂ uptake during the initial phase of springtime recovery in response to the passage of alternating warm- and cold-weather systems. Full recovery of ecosystem carbon uptake, whereby the 24-h cumulative sum of NEE (NEE_daily) was consistently negative, did not occur until 3–4 weeks after the first signs of photosynthetic recovery. A key event that preceded full recovery was the occurrence of isothermality in the vertical profile of snow temperature across the snow pack; thus, providing consistent daytime percolation of melted snow water through the snow pack. Interannual variation in the cumulative annual NEE (NEE_annual) was mostly explained by variation in NEE during the snow-melt period (NEE_snow-melt), not variation in NEE during the snow-free part of the growing season (NEE_snow-free). NEE_snow-melt was highest in those years when the snow melt occurred later in the spring, leading us to conclude that in this ecosystem, years with earlier springs are characterized by lower rates of NEE_annual, a conclusion that contrasts with those from past studies in deciduous forest ecosystems. Using studies on isolated branches we showed that the recovery of photosynthesis occurred through a series of coordinated physiological and biochemical events. Increasing air temperatures initiated recovery through the upregulation of PSII electron transport caused in part by disengagement of thermal energy dissipation by the carotenoid, zeaxanthin. The availability of liquid water permitted a slightly slower recovery phase involving increased stomatal conductance. The most rate-limiting step in the recovery process was an increase in the capacity for the needles to use intercellular CO₂, presumably due to slow recovery of Rubisco activity. Interspecific differences were observed in the timing of photosynthetic recovery for the dominant tree species. The results of our study provide (1) a context for springtime CO₂ uptake within the broader perspective of the annual carbon budget in this subalpine forest, and (2) a mechanistic explanation across a range of scales for the coupling between springtime climate and the carbon cycle of high-elevation coniferous forest ecosystems.