用光合-蒸散耦合模型模拟冬小麦CO2通量的日变化

根据SPAC理论建立了一个冬小麦光合和蒸散的耦合模型.冬小麦CO2通量包括冠层光合、呼吸和土壤呼吸.冠层光合采用了Farquhar光合作用生化模型,并通过冠层阻力的参数化将光合作用与蒸腾作用耦合起来.用涡度相关方法观测了CO2通量,对模型进行了验证,结果显示模型可以较好地模拟CO2通量日变化过程.对模型的敏感性分析发现日间CO2通量最敏感的参数是初始量子效率.其次,CO2通量对光响应曲线凸度、CO2补偿点、凋萎点和叶面积指数的变化也有着较强的敏感性;夜间CO2通量敏感的参数是最适温度下Rubisco催化能力和暗呼吸参数.     

用光合-蒸散耦合模型模拟冬小麦CO2通量的日变化

根据 SPAC理论建立了一个冬小麦光合和蒸散的耦合模型。冬小麦 CO2 通量包括冠层光合、呼吸和土壤呼吸。冠层光合采用了 Farquhar光合作用生化模型 ,并通过冠层阻力的参数化将光合作用与蒸腾作用耦合起来。用涡度相关方法观测了 CO2通量 ,对模型进行了验证 ,结果显示模型可以较好地模拟 CO2 通量日变化过程。对模型的敏感性分析发现日间 CO2 通量最敏感的参数是初始量子效率。其次 ,CO2 通量对光响应曲线凸度、CO2 补偿点、凋萎点和叶面积指数的变化也有着较强的敏感性 ;夜间 CO2 通量敏感的参数是最适温度下 Rubisco催化能力和暗呼吸参数     

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.     

用光合-蒸散耦合模型模拟冬小麦水热通量的日变化

从SPAC理论出发,建立了一个冬小麦光合和蒸散的耦合模型．感热通量和潜热通量采用Shuttleworth-Wallace的双层模型计算,并通过冠层阻力的参数化,将光合作用与蒸腾作用耦合起来．用涡度相关方法,观测了感热通量和潜热通量,对模型进行了验证．结果表明,模拟值与观测值比较一致,模型可以很好地模拟感热通量和潜热通量的日变化过程．对模型的敏感性分析发现,冬小麦蒸腾比较敏感的参数有凋萎点、气孔导度参数、叶对红外辐射的反射率和光响应曲线凸度;土壤蒸发只对土壤阻力参数的敏感性较强．本模型对水热通量与环境因子作用过程的理论研究和指导农田的灌溉制度等有一定的意义．     

The importance of assessing parameter sensitivity when using biophysical models: a case study using plethodontid salamanders

Landscapes are continually changing due to numerous assaults, including habitat alteration, anthropogenic disturbances, and climate change. Understanding how species will respond to these changes is of critical importance for conservation and management. Mechanistic models, such as biophysical models (BPMs), are an increasingly popular tool to predict how local population dynamics or species’ distributions may be altered in response to environmental and climate changes. By mechanistically modeling relationships between environmental conditions, physiology and behavior, it is possible to make accurate predictions about how species may respond. However, BPMs are often difficult to implement due to lack of appropriate, species-specific data that is biologically realistic or relevant. In this study, we present a BPM for the salamander Plethodon jordani and assess how adding more biological realism has potential to alter model predictions about annual energy budgets. Additionally, we conducted local and global sensitivity analyses to evaluate the importance of accurately specifying model parameter values and functional relationships. We found that the addition of biological realism resulted in greater model complexity as well as substantially different estimates of energy balance. Correct parameterization of biophysical models is also critical, as small changes in parameter values can result in disproportionately large changes in downstream model estimates. Our model highlights the overall importance of using ecologically relevant and specific data for input parameters, as well as careful assessment of parameter sensitivity. We encourage researchers to be aware of the data they are using to parameterize BPMs, and urge the collection of system-specific data that is relevant in spatial and temporal scale. We also recommend greater and more transparent use of sensitivity analyses to provide a better understanding of the model, as well as greater confidence in model predictions.     

BIOME 6000: reconstructing global mid-Holocene vegetation patterns from palaeoecological records

Global change research needs data sets describing past states of the Earth system. Vegetation distributions for specified 'time slices' (with known forcings, such as changes in insolation patterns due to the Earth's orbital variations, changes in the extent of ice-sheets, and changes in atmospheric trace-gas composition) should provide a benchmark for coupled climate-biosphere models. Pollen and macrofossil records from dated sediments give spatially extensive coverage of data on vegetation distribution changes. Applications of such data have been delayed by the lack of a global synthesis. The BIOME 6000 project of IGBP aims at a synthesis for 6000 years bp. Success depends on community-wide participation for data compilation and quality assurance, and on a robust methodology for assigning palaeorecords to biomes. In the method summarized here, taxa are assigned to one or more plant functional types (PFTs) and biomes reconstructed using PFT-based definitions. By involving regional experts in PFT assignments, one can combine data from different floras without compromising global consistency in biome assignments. This article introduces a series of articles that substantially extend the BIOME 6000 data set. The list of PFTs and the reconstruction procedure itself are evolving. Some compromises (for example, restricted taxon lists in some regions) limit the precision of biome assignments and will become obsolete as primary data are put into community data bases. This trend will facilitate biome mapping for other time slices. Co-evolution of climate-biosphere modelling and palaeodata synthesis and analysis will continue.     

A global distribution of biodiversity inferred from climatic constraints: results from a process‐based modelling study

We investigated the connection between plant species diversity and climate by using a process‐based, generic plant model. Different ‘species' were simulated by different values for certain growth‐related model parameters. Subsequently, a wide range of values were tested in the framework of a ‘Monte Carlo' simulation for success; that is, the capability of each plant with these parameter combinations to reproduce itself during its lifetime. The range of successful parameter combinations approximated species diversity. This method was applied to a global grid, using daily atmospheric forcing from a climate model simulation. The computed distribution of plant ‘species' diversity compares very well with the observed, global‐scale distribution of species diversity, reproducing the majority of ‘hot spot' areas of biodiversity. A sensitivity analysis revealed that the predicted pattern is very robust against changes of fixed model parameters. Analysis of the climatic forcing and of two additional sensitivity simulations demonstrated that the crucial factor leading to this distribution of diversity is the early stage of a plant's life when water availability is highly coupled to the variability in precipitation because in this stage root‐zone storage of water is small. We used cluster analysis in order to extract common sets of species parameters, mean plant properties and biogeographic regions (biomes) from the model output. The successful ‘species' cannot be grouped into typical parameter combinations, which define the plant's functioning. However, the mean simulated plant properties, such as lifetime and growth, can be grouped into a few characteristic plant ‘prototypes', ranging from short‐lived, fast growing plants, similar to grasses, to long‐lived, slow growing plants, similar to trees. The classification of regions with respect to similar combinations of successful ‘species' yields a distribution of biomes similar to the observed distribution. Each biome has typical levels of climatic constraints, expressed for instance by the number of ‘rainy days' and ‘warm days'. The less the number of days favourable for growth, the greater the level of constraints and the less the ‘species' diversity. These results suggest that climate as a fundamental constraint can explain much of the global scale, observed distribution of plant species diversity.     

基于Budyko假设的潮河流域气候和植被变化对实际蒸散发的影响研究

由于参数较少且具有明确的物理学意义,基于水热平衡理论的Budyko假设常用于定量分析以及评价气候变化和植被变化对实际蒸散发的影响,对研究流域水量平衡和能量分配具有重要意义。依据位于我国北方密云水库上游的潮河流域1961—2015年的水文气象数据,选取了4种基于Budyko假设的模型来研究潮河流域水热耦合平衡关系,确定了该流域最适用模型以及模型参数最优值,并且采用情景设置法分析了流域实际蒸散发对气候以及植被变化的响应。结果表明:(1)与经典Budyko模型相比,采用流域下垫面参数修正的Budyko模型计算实际蒸散发的精度更高。其中,傅抱璞模型精度最高,决定系数、相对误差、纳什效率系数和均方根误差分别为0.85、4.30%、0.82和27.66 mm;(2)对傅抱璞模型下垫面参数ω进行优化,确定适用于潮河流域的模型参数取值为2.54,优化后的傅抱璞模型能够更好地反映流域实际蒸散发的变化特征;(3)情景模拟表明,气候变化和植被变化的共同作用导致潮河流域实际蒸散发的上升。其中,气候变化是引起流域蒸散发变化的主要驱动因素。     

The use and misuse of V c,max in Earth System Models

Earth System Models (ESMs) aim to project global change. Central to this aim is the need to accurately model global carbon fluxes. Photosynthetic carbon dioxide assimilation by the terrestrial biosphere is the largest of these fluxes, and in many ESMs is represented by the Farquhar, von Caemmerer and Berry (FvCB) model of photosynthesis. The maximum rate of carboxylation by the enzyme Rubisco, commonly termed V _c,max, is a key parameter in the FvCB model. This study investigated the derivation of the values of V _c,max used to represent different plant functional types (PFTs) in ESMs. Four methods for estimating V _c,max were identified; (1) an empirical or (2) mechanistic relationship was used to relate V _c,max to leaf N content, (3) V _c,max was estimated using an approach based on the optimization of photosynthesis and respiration or (4) calibration of a user-defined V _c,max to obtain a target model output. Despite representing the same PFTs, the land model components of ESMs were parameterized with a wide range of values for V _c,max (?46 to +77 % of the PFT mean). In many cases, parameterization was based on limited data sets and poorly defined coefficients that were used to adjust model parameters and set PFT-specific values for V _c,max. Examination of the models that linked leaf N mechanistically to V _c,max identified potential changes to fixed parameters that collectively would decrease V _c,max by 31 % in C₃ plants and 11 % in C₄ plants. Plant trait data bases are now available that offer an excellent opportunity for models to update PFT-specific parameters used to estimate V _c,max. However, data for parameterizing some PFTs, particularly those in the Tropics and the Arctic are either highly variable or largely absent.     

A biome classification of China based on plant functional types and the BIOME3 model

A biome classification for China was established based on plant functional types (PFTs) using the BIOME3 model to include 16 biomes. In the eastern part of China, the PFTs of trees determine mostly the physiognomy of landscape. Biomes range from boreal deciduous coniferous forest/woodland, boreal mixed forest/woodland, temperate mixed forest, temperate broad-leaved deciduous forest, warm-temperate broad-leaved evergreen/mixed forest, warm-temperate/cool-temperate evergreen coniferous forest, xeric woodland/scrub, to tropical seasonal and rain forest, and tropical deciduous forest from north to south. In the northern and western part of China, grass is the dominant PFT. From northeast to west and southwest the biomes range from moist savannas, tall grassland, short grassland, dry savannas, arid shrubland/steppe, desert, to alpine tundra/ice/polar desert. Comparisons between the classification introduced here and the four classifications which were established over the past two decades, i.e. the vegetation classification, the vegetation division, the physical ecoregion, and the initial biome classification have showed that the different aims of biome classifications have resulted in different biome schemes each with its own unique characteristics and disadvantages for global change study. The new biome classification relies not only on climatic variables, but also on soil factor, vegetation functional variables, ecophysiological parameters and competition among the PFTs. It is a comprehensive classification that using multivariables better expresses the vegetation distribution and can be compared with world biome classifications. It can be easily used in the response study of Chinese biomes to global change, regionally and globally.     

Climate and CO2 controls on global vegetation distribution at the last glacial maximum: analysis based on palaeovegetation data, biome modelling and palaeoclimate simulations

The global vegetation response to climate and atmospheric CO₂ changes between the last glacial maximum and recent times is examined using an equilibrium vegetation model (BIOME4), driven by output from 17 climate simulations from the Palaeoclimate Modelling Intercomparison Project. Features common to all of the simulations include expansion of treeless vegetation in high northern latitudes; southward displacement and fragmentation of boreal and temperate forests; and expansion of drought‐tolerant biomes in the tropics. These features are broadly consistent with pollen‐based reconstructions of vegetation distribution at the last glacial maximum. Glacial vegetation in high latitudes reflects cold and dry conditions due to the low CO₂ concentration and the presence of large continental ice sheets. The extent of drought‐tolerant vegetation in tropical and subtropical latitudes reflects a generally drier low‐latitude climate. Comparisons of the observations with BIOME4 simulations, with and without consideration of the direct physiological effect of CO₂ concentration on C₃ photosynthesis, suggest an important additional role of low CO₂ concentration in restricting the extent of forests, especially in the tropics. Global forest cover was overestimated by all models when climate change alone was used to drive BIOME4, and estimated more accurately when physiological effects of CO₂ concentration were included. This result suggests that both CO₂ effects and climate effects were important in determining glacial‐interglacial changes in vegetation. More realistic simulations of glacial vegetation and climate will need to take into account the feedback effects of these structural and physiological changes on the climate.     

Simulated distribution of vegetation types in response to climate change on the Tibetan Plateau

Abstract. Questions: What is the relationship between alpine vegetation patterns and climate? And how do alpine vegetation patterns respond to climate changes? Location: Tibetan Plateau, southwestern China. The total area is 2500000 km² with an average altitude over 4000 m. Methods: The geographic distribution of vegetation types on the Tibetan Plateau was simulated based on climatology using a small set of plant functional types (PFTs) embedded in the biogeochemistry‐biography model BIOME4. The paleoclimate for the early Holocene was used to explore the possibility of simulating past vegetation patterns. Changes in vegetation patterns were simulated assuming continuous exponential increase in atmospheric CO concentration, based on a transient ocean‐atmosphere simulation including sulfate aerosol effects during the 21st century. Results: Forest, shrub steppe, alpine steppe and alpine meadow extended while no desert vegetation developed under the warmer and humid climate of the early Holocene. In the future climate scenario, the simulated tree line is farther north in most sectors than at present. There are also major northward shifts of alpine meadows and a reduction in shrub‐dominated montane steppe. The boundary between montane desert and alpine desert will be farther to the south than today. The area of alpine desert would decrease, that of montane desert would increase. Conclusions: The outline of changes in vegetation distribution was captured with the simulation. Increased CO2 concentration would potentially lead to big changes in alpine ecosystems.     

植物种分布的模拟研究进展

从植物种水平研究植被与气候的关系一直是生态学的热点之一。该文综述了植物种与气候关系的早期研究历史和国内外近期研究进展,尤其是20世纪80年代以来,随着全球变化研究的不断发展和深入,植物种地理分布与气候因子关系研究的最新发展,汇总了最近20年来国际上模拟预测植物种潜在地理分布的模型,比较了不同模型的优缺点。统计模型主要包括以生物气候分室模型或气候分室模型为代表的相关模型、以广义线性模型和广义加性模型为代表的回归模型、以分类和回归树分析及人工神经网络为代表的基于规则的模型、以及生态位模型、气候响应面模型等。机理模型主要介绍了基于BIOME1生物地理模型和FORSKA林窗模型的STASH模型、基于过程的物候模型PHENOFIT,以及一种基于水分平衡、温度和植物物候现象的模型。总结不同模型模拟预测的不同地区植物种未来分布的格局,并介绍中国植物种潜在分布区及未来变化的模拟预测工作,从而为更加准确地模拟预测植物种在未来全球变化情景下的变化趋势提供背景知识。     

用于全球变化研究的中国植物功能型划分

 植物功能型(Plant functional types, PFTs)作为沟通植物的结构和功能与生态系统属性的桥梁,随着全球变化与植被的关系研究的深入而受到广泛重视。植物功能型的划分依赖于研究的背景、尺度和要解决的问题。为了区域尺度全球变化研究的需要,该文提出了一个基于植物关键特征的植物功能型划分方法。该方法首先选择了6项植物特征,包括3项冠层特征：木本-草本、常绿-落叶和针叶-阔叶,以及3项生理特征：光合途径(C3 / C4)、植物的水分需求和热量需求,作为划分植物功能型的关键特征;然后,先根据植物冠层特征划分得到5个基本类型,再根据水分和热量条件进行详细划分,得到29种备选类型;需要时,再根据研究目的从这29种备选类型中选择所需类型。根据这个方法,在充分考虑了我国季风气候条件下特有的水热配置和高海拔环境对植物的形态和功能特征影响的基础上,从备选类型中选择了一套适合中国气候和植被特征的植物功能型体系。这套体系包括18类植物功能型,其中含7类‘树’功能型、6类‘灌木’功能型和5类‘草’功能型,另根据需要设置2类‘裸地’功能型。并且根据植物的生理生态特征和中国植被的地理分布确定了用于限制植物功能型分布的气候因子,这些气候因子包括绝对最低温度、最暖月平均温度、有效积温、年最热月平均温和最冷月平均温之差、湿润指数、年均降水量。应用表明,这套植物功能型可用于模拟我国植被在当前气候条件下的分布。该研究为发展适于我国的植被模型和区域气候模型、评估全球变化对我国植被的影响及植被变化对气候的反馈作用提供依据与参数。     

Separating foliar physiology from morphology reveals the relative roles of vertically structured transpiration factors within red maple crowns and limitations of larger scale models

A spatially explicit mechanistic model, MAESTRA, was used to separate key parameters affecting transpiration to provide insights into the most influential parameters for accurate predictions of within-crown and within-canopy transpiration. Once validated among Acer rubrum L. genotypes, model responses to different parameterization scenarios were scaled up to stand transpiration (expressed per unit leaf area) to assess how transpiration might be affected by the spatial distribution of foliage properties. For example, when physiological differences were accounted for, differences in leaf width among A. rubrum L. genotypes resulted in a 25% difference in transpiration. An in silico within-canopy sensitivity analysis was conducted over the range of genotype parameter variation observed and under different climate forcing conditions. The analysis revealed that seven of 16 leaf traits had a ≥5% impact on transpiration predictions. Under sparse foliage conditions, comparisons of the present findings with previous studies were in agreement that parameters such as the maximum Rubisco-limited rate of photosynthesis can explain ～20% of the variability in predicted transpiration. However, the spatial analysis shows how such parameters can decrease or change in importance below the uppermost canopy layer. Alternatively, model sensitivity to leaf width and minimum stomatal conductance was continuous along a vertical canopy depth profile. Foremost, transpiration sensitivity to an observed range of morphological and physiological parameters is examined and the spatial sensitivity of transpiration model predictions to vertical variations in microclimate and foliage density is identified to reduce the uncertainty of current transpiration predictions.     

Implementing plant hydraulic architecture within the LPJ Dynamic Global Vegetation Model

Aim To implement plant hydraulic architecture within the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ–DGVM), and to test the model against a set of observational data. If the model can reproduce major patterns in vegetation and ecosystem processes, we consider this to be an important linkage between plant physiology and larger‐scale ecosystem dynamics. Location The location is global, geographically distributed. Methods A literature review was carried out to derive model formulations and parameter values for representing the hydraulic characteristics of major global plant functional types (PFTs) in a DGVM. After implementing the corresponding formulations within the LPJ–DGVM, present‐day model output was compared to observational data. Results The model reproduced observed broad‐scale patterns in potential natural vegetation, but it failed to distinguish accurately between different types of grassland and savanna vegetation, possibly related to inadequate model representations of water fluxes in the soil and wildfire effects. Compared to a version of the model using an empirical formulation for calculating plant water supply without considering plant hydraulic architecture, the new formulation improved simulated patterns of vegetation in particular for dry shrublands. Global‐scale simulation results for runoff and actual evapotranspiration (AET) corresponded well to available data. The model also successfully reproduced the magnitude and seasonal cycle of AET for most EUROFLUX forests, while modelled variation in NPP across a large number of sites spanning several biomes showed a strong correlation with estimates from field measurements. Main conclusions The model was generally confirmed by comparison to observational data. The novel model representation of water flow within plants makes it possible to resolve mechanistically the effects of hydraulic differences between plant functional groups on vegetation structure, water cycling, and competition. This may be an advantage when predicting ecosystem responses to nonextant climates, in particular in areas dominated by dry shrubland vegetation.     

Exploring the spatial distribution of light interception and photosynthesis of canopies by means of a functional-structural plant model

Background and Aims

At present most process-based models and the majority of three-dimensional models include simplifications of plant architecture that can compromise the accuracy of light interception simulations and, accordingly, canopy photosynthesis. The aim of this paper is to analyse canopy heterogeneity of an explicitly described tomato canopy in relation to temporal dynamics of horizontal and vertical light distribution and photosynthesis under direct- and diffuse-light conditions.

Methods

Detailed measurements of canopy architecture, light interception and leaf photosynthesis were carried out on a tomato crop. These data were used for the development and calibration of a functional–structural tomato model. The model consisted of an architectural static virtual plant coupled with a nested radiosity model for light calculations and a leaf photosynthesis module. Different scenarios of horizontal and vertical distribution of light interception, incident light and photosynthesis were investigated under diffuse and direct light conditions.

Key Results

Simulated light interception showed a good correspondence to the measured values. Explicitly described leaf angles resulted in higher light interception in the middle of the plant canopy compared with fixed and ellipsoidal leaf-angle distribution models, although the total light interception remained the same. The fraction of light intercepted at a north–south orientation of rows differed from east–west orientation by 10 % on winter and 23 % on summer days. The horizontal distribution of photosynthesis differed significantly between the top, middle and lower canopy layer. Taking into account the vertical variation of leaf photosynthetic parameters in the canopy, led to approx. 8 % increase on simulated canopy photosynthesis.

Conclusions

Leaf angles of heterogeneous canopies should be explicitly described as they have a big impact both on light distribution and photosynthesis. Especially, the vertical variation of photosynthesis in canopy is such that the experimental approach of photosynthesis measurements for model parameterization should be revised.     

New coupled model used inversely for reconstructing past terrestrial carbon storage from pollen data: validation of model using modern data

The knowledge of potential impacts of climate change on terrestrial vegetation is crucial to understand long-term global carbon cycle development. Discrepancy in data has long existed between past carbon storage reconstructions since the Last Glacial Maximum by way of pollen, carbon isotopes, and general circulation model (GCM) analysis. This may be due to the fact that these methods do not synthetically take into account significant differences in climate distribution between modern and past conditions, as well as the effects of atmospheric CO₂ concentrations on vegetation. In this study, a new method to estimate past biospheric carbon stocks is reported, utilizing a new integrated ecosystem model (PCM) built on a physiological process vegetation model (BIOME4) coupled with a process-based biospheric carbon model (DEMETER). The PCM was constrained to fit pollen data to obtain realistic estimates. It was estimated that the probability distribution of climatic parameters, as simulated by BIOME4 in an inverse process, was compatible with pollen data while DEMETER successfully simulated carbon storage values with corresponding outputs of BIOME4. The carbon model was validated with present-day observations of vegetation biomes and soil carbon, and the inversion scheme was tested against 1491 surface pollen spectra sample sites procured in Africa and Eurasia. Results show that this method can successfully simulate biomes and related climates at most selected pollen sites, providing a coefficient of determination ( R ) of 0.83–0.97 between the observed and reconstructed climates, while also showing a consensus with an R -value of 0.90–0.96 between the simulated biome average terrestrial carbon variables and the available observations. The results demonstrate the reliability and feasibility of the climate reconstruction method and its potential efficiency in reconstructing past terrestrial carbon storage.     

What Can We Learn from Global Sensitivity Analysis of Biochemical Systems?

Most biological models of intermediate size, and probably all large models, need to cope with the fact that many of their parameter values are unknown. In addition, it may not be possible to identify these values unambiguously on the basis of experimental data. This poses the question how reliable predictions made using such models are. Sensitivity analysis is commonly used to measure the impact of each model parameter on its variables. However, the results of such analyses can be dependent on an exact set of parameter values due to nonlinearity. To mitigate this problem, global sensitivity analysis techniques are used to calculate parameter sensitivities in a wider parameter space. We applied global sensitivity analysis to a selection of five signalling and metabolic models, several of which incorporate experimentally well-determined parameters. Assuming these models represent physiological reality, we explored how the results could change under increasing amounts of parameter uncertainty. Our results show that parameter sensitivities calculated with the physiological parameter values are not necessarily the most frequently observed under random sampling, even in a small interval around the physiological values. Often multimodal distributions were observed. Unsurprisingly, the range of possible sensitivity coefficient values increased with the level of parameter uncertainty, though the amount of parameter uncertainty at which the pattern of control was able to change differed among the models analysed. We suggest that this level of uncertainty can be used as a global measure of model robustness. Finally a comparison of different global sensitivity analysis techniques shows that, if high-throughput computing resources are available, then random sampling may actually be the most suitable technique.     

植物光合作用模型参数的温度依存性研究进展

综述了植物光合作用与温度响应模型研究的进展,围绕光合作用生化模型的4个主要参数：胞间CO₂浓度、RuBP最大碳同化速率(V_{c max})的活化能、RuBP最大再生速率(J_max)的活化能和J_max/V_{c max},讨论了影响光合作用 温度响应曲线的内在机理.随着生长温度的升高,所有物种的V_{c max}活化能均呈增加趋势,而其他参数的变化因物种不同而存在明显差异,说明V_{c max}的活化能可能是决定光合作用温度依存性的首要参数.最后分析了研究中存在的问题并提出研究展望,认为应整合叶片与群落水平的光合作用模型,从叶面积、太阳辐射、冠层结构、冠层小气候和光合能力等方面研究植物群落对全球变化的响应机理.这对于人们理解和准确估算植物生长、群落碳收支和生态系统初级生产力具有重要意义.