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Aim: To assess the extent to which climate change might cause changes in potential natural vegetation (PNV) across Europe. Location: Europe. Method: We parameterized a generalized dynamic vegetation model (LPJGUESS) for the most common European tree species, and, for the first time, modelled large-scale vegetation dynamics using a process-based model explicitly representing tree species, age cohorts, gap dynamics and biogeochemical cycles in a single framework. For projections, the model was driven with climate scenario data from two atmosphere-ocean general circulation models (AOGCMs), downscaled to 10 x 10' spatial resolution (c. 18.5 x 12 km at 50° N). Results: At a general level, modelled present-day PNV corresponded better with an expert reconstruction of the PNV than most earlier plant functional type (PFT)-based simulations, but at a finer scale the model and the expert map showed substantial discrepancies in some areas. Simulations until 2085 showed considerable successional shifts in vegetation types in most areas: 31-42% of the total area of Europe was projected to be covered by a different vegetation type by the year 2085. In the long term, equilibrium changes are substantially larger: simulations with one climate scenario suggest that 76-80% of the European land surface could exist within another PNV if climate was stabilized by the end of the century and vegetation had unlimited time to achieve equilibrium with the new climate. 'Hotspots' of change include arctic and alpine ecosystems, where trees replace tundra in the model, and the transition zone between temperate broad-leaved and boreal conifer forest. In southern Europe, the model projected widespread shifts from forest to shrublands as a result of drought. Main conclusions: The model presents a considerable advance in modelling dynamic changes in natural vegetation across Europe. Climate change might cause substantial changes in PNV across Europe, which should be considered in the management of reserves and forestry.

Increased tree mortality during and after drought has become a research focus in recent years. This focus has been driven by: the realisation that drought-related tree mortality is more widespread than previously thought; the predicted increase in the frequency of climate extremes this century; and the recognition that current vegetation models do not predict drought-related tree mortality and forest dieback well despite the large potential effects of these processes on species composition and biogeochemical cycling. To date, the emphasis has been on understanding the causal mechanisms of drought-related tree mortality, and on mechanistic models of plant function and vegetation dynamics, but a consensus on those mechanisms has yet to emerge. In order to generate new hypotheses and to help advance the modelling of vegetation dynamics in the face of incomplete mechanistic understanding, we suggest that general patterns should be distilled from the diverse and as-yet inconclusive results of existing studies, and more use should be made of optimisation and probabilistic modelling approaches that have been successfully applied elsewhere in plant ecology. The outcome should inform new empirical studies of tree mortality, help improve its prediction and reduce model complexity.

We discuss the usefulness of the concept of Potential Natural Vegetation (PNV), which describes the expected state of mature vegetation in the absence of human intervention. We argue that it is impossible to model PNV because of (i) the methodological problems associated to its definition and (ii) the issues related to the ecosystems dynamics.We conclude that the approach to characterizing PNV is unrealistic and provides scenarios with limited predictive power. In places with a long-term human history, interpretations of PNV need to be very cautious, and explicit acknowledgement made of the limitations inherent in available data.

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

• Vegetation nutrient limitation is essential for understanding ecosystem responses to global change. In particular, leaf nitrogen (N) is known to be plastic under changed nutrient limitation. However, models can often not capture these observed changes, leading to erroneous predictions of whole-ecosystem stocks and fluxes. • We hypothesise that an optimality approach can improve representation of leaf N content compared to existing empirical approaches. Unlike previous optimality-based approaches, which adjust foliar N concentrations based on canopy carbon export, we use a maximisation criterion based on whole-plant growth, and allow for a lagged response of foliar N to this maximisation criterion to account for the limited plasticity of this plant trait. We test these model variants at a range of Free-Air CO₂ Enrichment and N fertilisation experimental sites. • We show that a model based solely on canopy carbon export fails to reproduce observed patterns and predicts decreasing leaf N content with increased N availability. However, an optimal model which maximises total plant growth can correctly reproduce the observed patterns. • The optimality model we present here is a whole-plant approach which reproduces biologically realistic changes in leaf N and can thereby improve ecosystem-level predictions under transient conditions.

Numerous devastating air pollution events from wildfire smoke occurred in this century in the western USA, leading to severe environmental consequences. This study projects future fire emissions in this region under climate change with a focus on comparing the relative contributions from future changes in burned area, fuel loading and fuel moisture. The three properties were projected using an empirical fire model, a dynamical global vegetation model and meteorological conditions respectively. The regional climate change scenarios for the western USA were obtained by dynamical downscaling of global climate projections. The results show overall increasing wildfires and fuel loading and decreasing fuel moisture. As a result, fire emissions are projected to increase by ~50% from 2001–2010 to 2050–2059. The changes in wildfires and fuel loading contribute nearly 75% and 25% of the total fire emission increase, respectively, but the contribution from fuel moisture change is minimal. The findings suggest that the air pollution events caused by wildfire smoke could become much more serious in the western USA by the middle of this century, and that it would be essential to take the future changes in fuel conditions into account to improve the accuracy of fire emission projections.

This article is a Commentary on Venturas et al., 230: 1896–1910.

There is a lack in representation of biosphere–atmosphere interactions in current climate models. To fill this gap, one may introduce vegetation dynamics in surface transfer schemes or couple global climate models (GCMs) with vegetation dynamics models. As these vegetation dynamics models were not designed to be included in GCMs, how are the latest generation dynamic global vegetation models (DGVMs) suitable for use in global climate studies? This paper reviews the latest developments in DGVM modelling as well as the development of DGVM–GCM coupling in the framework of global climate studies. Limitations of DGVM and coupling are shown and the challenges of these methods are highlighted. During the last decade, DGVMs underwent major changes in the representation of physical and biogeochemical mechanisms such as photosynthesis and respiration processes as well as in the representation of regional properties of vegetation. However, several limitations such as carbon and nitrogen cycles, competition, land-use and land-use changes, and disturbances have been identified. In addition, recent advances in model coupling techniques allow the simulation of the vegetation–atmosphere interactions in GCMs with the help of DGVMs. Though DGVMs represent a good alternative to investigate vegetation–atmosphere interactions at a large scale, some weaknesses in evaluation methodology and model design need to be further investigated to improve the results.

Aim It remains poorly understood why the position of the forest–savanna biome boundary, in a domain defined by precipitation and temperature, differs in South America, Africa and Australia. Process based Dynamic Global Vegetation Models (DGVMs) are a valuable tool to investigate the determinants of vegetation distributions; however, many DGVMs fail to predict the spatial distribution or indeed presence of the South American savanna biome. Evidence suggests that fire plays a significant role in mediating forest–savanna biome boundaries; however, fire alone appears to be insufficient to predict these boundaries in South America. We hypothesize that interactions between precipitation, constraints on tree rooting depth and fire affect the probability of savanna occurrence and the position of the savanna–forest boundary. Location Tropical forest and savanna sites in Brazil and Venezuela north of 23°S. Methods We tested our hypotheses using a novel DGVM, aDGVM2, which allows plant trait spectra, constrained by trade-offs between traits, to evolve in response to abiotic and biotic conditions. Plant hydraulics is represented by the cohesion–tension theory, this allowed us to explore how soil and plant hydraulics control biome distributions and plant traits. The resulting community trait distributions are emergent properties of model dynamics. Results We showed that across much of South America the biome state is not determined by climate alone. Interactions between plant rooting depth, fire and precipitation affected the probability of observing a given biome state and the emergent traits of plant communities. Simulations where plant rooting depth varied in space provided the best match to satellite derived biomass estimates and generated biome distributions that reproduced contemporary biome maps well. Main conclusions Our findings support the contention that areas where multiple vegetation states are possible are widespread and highlight the importance of considering the influence of fire and constraints on plant rooting depth for predicting biome boundaries.