Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model

The Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ) combines process‐based, large‐scale representations of terrestrial vegetation dynamics and land‐atmosphere carbon and water exchanges in a modular framework. Features include feedback through canopy conductance between photosynthesis and transpiration and interactive coupling between these ‘fast’ processes and other ecosystem processes including resource competition, tissue turnover, population dynamics, soil organic matter and litter dynamics and fire disturbance. Ten plants functional types (PFTs) are differentiated by physiological, morphological, phenological, bioclimatic and fire‐response attributes. Resource competition and differential responses to fire between PFTs influence their relative fractional cover from year to year. Photosynthesis, evapotranspiration and soil water dynamics are modelled on a daily time step, while vegetation structure and PFT population densities are updated annually. Simulations have been made over the industrial period both for specific sites where field measurements were available for model evaluation, and globally on a 0.5°° × 0.5°° grid. Modelled vegetation patterns are consistent with observations, including remotely sensed vegetation structure and phenology. Seasonal cycles of net ecosystem exchange and soil moisture compare well with local measurements. Global carbon exchange fields used as input to an atmospheric tracer transport model (TM2) provided a good fit to observed seasonal cycles of CO₂ concentration at all latitudes. Simulated inter‐annual variability of the global terrestrial carbon balance is in phase with and comparable in amplitude to observed variability in the growth rate of atmospheric CO₂. Global terrestrial carbon and water cycle parameters (pool sizes and fluxes) lie within their accepted ranges. The model is being used to study past, present and future terrestrial ecosystem dynamics, biochemical and biophysical interactions between ecosystems and the atmosphere, and as a component of coupled Earth system models.     

Extending the range of applicability of the semi‐empirical ecosystem flux model PRELES for varying forest types and climate

Applications of ecosystem flux models on large geographical scales are often limited by model complexity and data availability. Here we calibrated and evaluated a semi‐empirical ecosystem flux model, PREdict Light‐use efficiency, Evapotranspiration and Soil water (PRELES), for various forest types and climate conditions, based on eddy covariance data from 55 sites. A Bayesian approach was adopted for model calibration and uncertainty quantification. We applied the site‐specific calibrations and multisite calibrations to nine plant functional types (PFTs) to obtain the site‐specific and PFT‐specific parameter vectors for PRELES. A systematically designed cross‐validation was implemented to evaluate calibration strategies and the risks in extrapolation. The combination of plant physiological traits and climate patterns generated significant variation in vegetation responses and model parameters across but not within PFTs, implying that applying the model without PFT‐specific parameters is risky. But within PFT, the multisite calibrations performed as accurately as the site‐specific calibrations in predicting gross primary production (GPP) and evapotranspiration (ET). Moreover, the variations among sites within one PFT could be effectively simulated by simply adjusting the parameter of potential light‐use efficiency (LUE), implying significant convergence of simulated vegetation processes within PFT. The hierarchical modelling of PRELES provides a compromise between satellite‐driven LUE and physiologically oriented approaches for extrapolating the geographical variation of ecosystem productivity. Although measurement errors of eddy covariance and remotely sensed data propagated a substantial proportion of uncertainty or potential biases, the results illustrated that PRELES could reliably capture daily variations of GPP and ET for contrasting forest types on large geographical scales if PFT‐specific parameterizations were applied.     

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。     

BioMove – an integrated platform simulating the dynamic response of species to environmental change

BioMove simulates plant species' geographic range shifts in response to climate, habitat structure and disturbance, at annual time steps. This spatially explicit approach integrates species' bioclimatic suitability and population‐level demographic rates with simulation of landscape‐level processes (dispersal, disturbance, species' response to dynamic dominant vegetation structure). Species population dynamics are simulated through matrix modelling that includes scaling demographic rates by climatic suitability. Dispersal functions simulate population spread. User‐specified plant functional types (PFTs) provide vegetation structure that determines resource competition and disturbance. PFTs respond annually through dispersal, inter‐PFT competition and demographic shifts. BioMove provides a rich framework for dynamic range simulations.     

An improved method for searching plant functional types by numerical analysis

Abstract. The use of plant functional types (PFTs) to describe patterns and processes in plant communities has become essential to study and predict consequences of global change on vegetation and ecosystem processes. A PFT is a group of plants that, irrespective of phylogeny, are similar in a given set of traits and similar in their association to certain variables, which may be factors to which the plants are responding or effects of the plants in the ecosystem. To define PFTs relevant traits must be selected and an appropriate method must be used to classify plants into types. We critically review methods used for the analysis of PFT‐based data and describe a new recursive algorithm to numerically search for traits and find optimal PFTs. The algorithm uses three data matrices: describing populations by traits, communities by these populations and community sites by environmental factors or effects. It defines PFTs polythetically by cluster analysis, revealing plant types whose performance in communities is maximally associated to the specified environmental variables. We test the method with data from natural grassland communities of southern Brazil, which were experimentally subjected to combinations of grazing levels and N‐fertilizer. The new method is found to be better than similar analytical procedures previously described. Redundancy among traits is discussed and a procedure for comparing alternative solutions is presented based on the similarity in terms of PFT responses between different trait subsets. The concept of PFT response group is illustrated by example.     

Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model

Functional diversity is critical for ecosystem dynamics, stability and productivity. However, dynamic global vegetation models (DGVMs) which are increasingly used to simulate ecosystem functions under global change, condense functional diversity to plant functional types (PFTs) with constant parameters. Here, we develop an individual‐ and trait‐based version of the DGVM LPJmL (Lund‐Potsdam‐Jena managed Land) called LPJmL‐ flexible individual traits (LPJmL‐FIT) with flexible individual traits) which we apply to generate plant trait maps for the Amazon basin. LPJmL‐FIT incorporates empirical ranges of five traits of tropical trees extracted from the TRY global plant trait database, namely specific leaf area (SLA), leaf longevity (LL), leaf nitrogen content (N_area), the maximum carboxylation rate of Rubisco per leaf area (), and wood density (WD). To scale the individual growth performance of trees, the leaf traits are linked by trade‐offs based on the leaf economics spectrum, whereas wood density is linked to tree mortality. No preselection of growth strategies is taking place, because individuals with unique trait combinations are uniformly distributed at tree establishment. We validate the modeled trait distributions by empirical trait data and the modeled biomass by a remote sensing product along a climatic gradient. Including trait variability and trade‐offs successfully predicts natural trait distributions and achieves a more realistic representation of functional diversity at the local to regional scale. As sites of high climatic variability, the fringes of the Amazon promote trait divergence and the coexistence of multiple tree growth strategies, while lower plant trait diversity is found in the species‐rich center of the region with relatively low climatic variability. LPJmL‐FIT enables to test hypotheses on the effects of functional biodiversity on ecosystem functioning and to apply the DGVM to current challenges in ecosystem management from local to global scales, that is, deforestation and climate change effects.     

Sensitivity of plant functional types to climate change: classification tree analysis of a simulation model

Question: The majority of studies investigating the impact of climate change on local plant communities ignores changes in regional processes, such as immigration from the regional seed pool. Here we explore: (i) the potential impact of climate change on composition of the regional seed pool, (ii) the influence of changes in climate and in the regional seed pool on local community structure, and (iii) the combinations of life history traits, i.e. plant functional types (PFTs), that are most affected by environmental changes. Location: Fire‐prone, Mediterranean‐type shrublands in southwestern Australia. Methods: Spatially explicit simulation experiments were conducted at the population level under different rainfall and fire regime scenarios to determine the effect of environmental change on the regional seed pool for 38 PFTs. The effects of environmental and seed immigration changes on local community dynamics were then derived from community‐level experiments. Classification tree analyses were used to investigate PFT‐specific vulnerabilities to climate change. Results: The classification tree analyses revealed that responses of PFTs to climate change are determined by specific trait characteristics. PFT‐specific seed production and community patterns responded in a complex manner to climate change. For example, an increase in annual rainfall caused an increase in numbers of dispersed seeds for some PFTs, but decreased PFT diversity in the community. Conversely, a simulated decrease in rainfall reduced the number of dispersed seeds and diversity of PFTs. Conclusions: PFT interactions and regional processes must be considered when assessing how local community structure will be affected by environmental change.     

Endemic species and ecosystem sensitivity to climate change in Namibia

We present a first assessment of the potential impacts of anthropogenic climate change on the endemic flora of Namibia, and on its vegetation structure and function, for a projected climate in ～2050 and ～2080. We used both niche‐based models (NBM) to evaluate the sensitivity of 159 endemic species to climate change (of an original 1020 plant species modeled) and a dynamic global vegetation model (DGVM) to assess the impacts of climate change on vegetation structure and ecosystem functioning. Endemic species modeled by NBM are moderately sensitive to projected climate change. Fewer than 5% are predicted to experience complete range loss by 2080, although more than 47% of the species are expected to be vulnerable (range reduction >30%) by 2080 if they are assumed unable to migrate. Disaggregation of results by life‐form showed distinct patterns. Endemic species of perennial herb, geophyte and tree life‐formsare predicted to be negatively impacted in Namibia, whereas annual herb and succulent endemic species remain relatively stable by 2050 and 2080. Endemic annual herb species are even predicted to extend their range north‐eastward into the tree and shrub savanna with migration, and tolerance of novel substrates. The current protected area network is predicted to meet its mandate by protecting most of the current endemicity in Namibia into the future. Vegetation simulated by DGVM is projected to experience a reduction in cover, net primary productivity and leaf area index throughout much of the country by 2050, with important implications for the faunal component of Namibia's ecosystems, and the agricultural sector. The plant functional type (PFT) composition of the major biomes may be substantially affected by climate change and rising atmospheric CO₂– currently widespread deciduous broad leaved trees and C₄ PFTs decline, with the C₄ PFT particularly negatively affected by rising atmospheric CO₂ impacts by ～2080 and deciduous broad leaved trees more likely directly impacted by drying and warming. The C₃ PFT may increase in prominence in the northwestern quadrant of the country by ～2080 as CO₂ concentrations increase. These results suggest that substantial changes in species diversity, vegetation structure and ecosystem functioning can be expected in Namibia with anticipated climate change, although endemic plant richness may persist in the topographically diverse central escarpment region.     

Ecophysiological and bioclimatic foundations for a global plant functional classification

Question: What plant properties might define plant functional types (PFTs) for the analysis of global vegetation responses to climate change, and what aspects of the physical environment might be expected to predict the distributions of PFTs? Methods: We review principles to explain the distribution of key plant traits as a function of bioclimatic variables. We focus on those whole‐plant and leaf traits that are commonly used to define biomes and PFTs in global maps and models. Results: Raunkiær's plant life forms (underlying most later classifications) describe different adaptive strategies for surviving low temperature or drought, while satisfying requirements for reproduction and growth. Simple conceptual models and published observations are used to quantify the adaptive significance of leaf size for temperature regulation, leaf consistency for maintaining transpiration under drought, and phenology for the optimization of annual carbon balance. A new compilation of experimental data supports the functional definition of tropical, warm‐temperate, temperate and boreal phanerophytes based on mechanisms for withstanding low temperature extremes. Chilling requirements are less well quantified, but are a necessary adjunct to cold tolerance. Functional traits generally confer both advantages and restrictions; the existence of trade‐offs contributes to the diversity of plants along bioclimatic gradients. Conclusions: Quantitative analysis of plant trait distributions against bioclimatic variables is becoming possible; this opens up new opportunities for PFT classification. A PFT classification based on bioclimatic responses will need to be enhanced by information on traits related to competition, successional dynamics and disturbance.     

Differences in xylem and leaf hydraulic traits explain differences in drought tolerance among mature Amazon rainforest trees

Considerable uncertainty surrounds the impacts of anthropogenic climate change on the composition and structure of Amazon forests. Building upon results from two large‐scale ecosystem drought experiments in the eastern Brazilian Amazon that observed increases in mortality rates among some tree species but not others, in this study we investigate the physiological traits underpinning these differential demographic responses. Xylem pressure at 50% conductivity (xylem‐P₅₀), leaf turgor loss point (TLP), cellular osmotic potential (π_o), and cellular bulk modulus of elasticity (ε), all traits mechanistically linked to drought tolerance, were measured on upper canopy branches and leaves of mature trees from selected species growing at the two drought experiment sites. Each species was placed a priori into one of four plant functional type (PFT) categories: drought‐tolerant versus drought‐intolerant based on observed mortality rates, and subdivided into early‐ versus late‐successional based on wood density. We tested the hypotheses that the measured traits would be significantly different between the four PFTs and that they would be spatially conserved across the two experimental sites. Xylem‐P₅₀, TLP, and π_o, but not ε, occurred at significantly higher water potentials for the drought‐intolerant PFT compared to the drought‐tolerant PFT; however, there were no significant differences between the early‐ and late‐successional PFTs. These results suggest that these three traits are important for determining drought tolerance, and are largely independent of wood density—a trait commonly associated with successional status. Differences in these physiological traits that occurred between the drought‐tolerant and drought‐intolerant PFTs were conserved between the two research sites, even though they had different soil types and dry‐season lengths. This more detailed understanding of how xylem and leaf hydraulic traits vary between co‐occuring drought‐tolerant and drought‐intolerant tropical tree species promises to facilitate a much‐needed improvement in the representation of plant hydraulics within terrestrial ecosystem and biosphere models, which will enhance our ability to make robust predictions of how future changes in climate will affect tropical forests.