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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-P 50 ), 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-P 50 , 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., (© 2017 John Wiley & Sons Ltd.)

Amazon forests, which store ∼ 50% of tropical forest carbon and play a vital role in global water, energy, and carbon cycling, are predicted to experience both longer and more intense dry seasons by the end of the 21st century. However, the climate sensitivity of this ecosystem remains uncertain: several studies have predicted large-scale die-back of the Amazon, whereas several more recent studies predict that the biome will remain largely intact. Combining remote-sensing and ground-based observations with a size- and age-structured terrestrial ecosystem model, we explore the sensitivity and ecological resilience of these forests to changes in climate. We demonstrate that water stress operating at the scale of individual plants, combined with spatial variation in soil texture, explains observed patterns of variation in ecosystem biomass, composition, and dynamics across the region, and strongly influences the ecosystem's resilience to changes in dry season length. Specifically, our analysis suggests that in contrast to existing predictions of either stability or catastrophic biomass loss, the Amazon forest's response to a drying regional climate is likely to be an immediate, graded, heterogeneous transition from high-biomass moist forests to transitional dry forests and woody savannah-like states. Fire, logging, and other anthropogenic disturbances may, however, exacerbate these climate change-induced ecosystem transitions.

We assess the significance of high-frequency variability of environmental parameters (sunlight, precipitation, temperature) for the structure and function of terrestrial ecosystems under current and future climate. We examine the influence of hourly, daily, and monthly variance using the Ecosystem Demography model version 2 in conjunction with the long-term record of carbon fluxes measured at Harvard Forest. We find that fluctuations of sunlight and precipitation are strongly and nonlinearly coupled to ecosystem function, with effects that accumulate through annual and decadal timescales. Increasing variability in sunlight and precipitation leads to lower rates of carbon sequestration and favors broad-leaved deciduous trees over conifers. Temperature variability has only minor impacts by comparison. We also find that projected changes in sunlight and precipitation variability have important implications for carbon storage and ecosystem structure and composition. Based on Intergovernmental Panel on Climate Change model estimates for changes in high-frequency meteorological variability over the next 100 years, we expect that terrestrial ecosystems will be affected by changes in variability almost as much as by changes in mean climate. We conclude that terrestrial ecosystems are highly sensitive to high-frequency meteorological variability, and that accurate knowledge of the statistics of this variability is essential for realistic predictions of ecosystem structure and functioning.

There is large uncertainty whether Amazon forests will remain a carbon sink as atmospheric CO2 increases. Hence, we simulated an old-growth tropical forest using six versions of four terrestrial models differing in scale of vegetation structure and representation of biogeochemical (BGC) cycling, all driven with CO2 forcing from the preindustrial period to 2100. The models were benchmarked against tree inventory and eddy covariance data from a Brazilian site for present-day predictions. All models predicted positive vegetation growth that outpaced mortality, leading to continual increases in present-day biomass accumulation. Notably, the two vegetation demographic models (VDMs) (ED2 and ELM-FATES) always predicted positive stem diameter growth in all size classes. The field data, however, indicated that a quarter of canopy trees didn't grow over the 15-year period, and while high interannual variation existed, biomass change was near neutral. With a doubling of CO2, three of the four models predicted an appreciable biomass sink (0.77 to 1.24 Mg ha−1 year−1). ELMv1-ECA, the only model used here that includes phosphorus constraints, predicted the lowest biomass sink relative to initial biomass stocks (+21%), lower than the other BGC model, CLM5 (+48%). Models projections differed primarily through variations in nutrient constraints, then carbon allocation, initial biomass, and density-dependent mortality. The VDM's performance was similar or better than the BGC models run in carbon-only mode, suggesting that nutrient competition in VDMs will improve predictions. We demonstrate that VDMs are comparable to nondemographic (i.e., “big-leaf”) models but also include finer scale demography and competition that can be evaluated against field observations.

The impact of increases in drought frequency on the Amazon forest's composition, structure and functioning remain uncertain. We used a process- and individual-based ecosystem model (ED2) to quantify the forest's vulnerability to increased drought recurrence. We generated meteorologically realistic, drier-than-observed rainfall scenarios for two Amazon forest sites, Paracou (wetter) and Tapajós (drier), to evaluate the impacts of more frequent droughts on forest biomass, structure and composition. The wet site was insensitive to the tested scenarios, whereas at the dry site biomass declined when average rainfall reduction exceeded 15%, due to high mortality of large-sized evergreen trees. Biomass losses persisted when year-long drought recurrence was shorter than 2-7 yr, depending upon soil texture and leaf phenology. From the site-level scenario results, we developed regionally applicable metrics to quantify the Amazon forest's climatological proximity to rainfall regimes likely to cause biomass loss > 20% in 50 yr according to ED2 predictions. Nearly 25% (1.8 million km2 ) of the Amazon forests could experience frequent droughts and biomass loss if mean annual rainfall or interannual variability changed by 2σ. At least 10% of the high-emission climate projections (CMIP5/RCP8.5 models) predict critically dry regimes over 25% of the Amazon forest area by 2100.

Ungulates in alpine ecosystems are constrained by winter harshness through resource limitation and direct mortality from weather extremes. However, little empirical evidence has definitively established how current climate change and other anthropogenic modifications of resource availability affect ungulate winter distribution, especially at their range limits. Here, we used a combination of historical (1997–2002) and contemporary (2012–2015) Eurasian roe deer (Capreolus capreolus) relocation datasets that span changes in snowpack characteristics and two levels of supplemental feeding to compare and forecast probability of space use at the species' altitudinal range limit. Scarcer snow cover in the contemporary period interacted with the augmented feeding site distribution to increase the elevation of winter range limits, and we predict this trend will continue under climate change. Moreover, roe deer have shifted from historically using feeding sites primarily under deep snow conditions to contemporarily using them under a wider range of snow conditions as their availability has increased. Combined with scarcer snow cover during December, January, and April, this trend has reduced inter-annual variability in space use patterns in these months. These spatial responses to climate- and artificial resource-provisioning shifts evidence the importance of these changing factors in shaping large herbivore spatial distribution and, consequently, ecosystem dynamics. [ABSTRACT FROM AUTHOR]

The article presents a study which examined the effects of atmospheric carbon dioxide (CO2) increases to the Amazon forests as a carbon sink. Also cited are the use of terrestrial models to analyze the vegetation structure and biogeochemical cycles in the forests, the increase in biomass sink due to higher CO2, and the effect of positive vegetation growth on biomass accumulation.

Land surface models are excellent tools for studying how climate change and land use affect surface hydrology. However, in order to assess the impacts of Earth processes on river flows, simulated changes in runoff need to be routed through the landscape. In this technical note, we describe the integration of the Ecosystem Demography (ED2) model with a hydrological routing scheme. The purpose of the study was to create a tool capable of incorporating to hydrological predictions the terrestrial ecosystem responses to climate, carbon dioxide, and land-use change, as simulated with terrestrial biosphere models. The resulting ED2+R model calculates the lateral routing of surface and subsurface runoff resulting from the terrestrial biosphere models' vertical water balance in order to determine spatiotemporal patterns of river flows within the simulated region. We evaluated the ED2+R model in the Tapajós, a 476674km² river basin in the southeastern Amazon, Brazil. The results showed that the integration of ED2 with the lateral routing scheme results in an adequate representation (Nash-Sutcliffe efficiency up to 0.76, Kling-Gupta efficiency up to 0.86, Pearson's R up to 0.88, and volume ratio up to 1.06) of daily to decadal river flow dynamics in the Tapajós. These results are a consistent step forward with respect to the "no river representation" common among terrestrial biosphere models, such as the initial version of ED2. [ABSTRACT FROM AUTHOR]

ABSTRACT Developing high-quality long-term data sets at uniform space-time resolution is essential for improved climate studies. This article processes the outputs from two global and regional climate models, the Community Climate System Model ( CCSM3) and the Regional Climate Model driven by the Hadley Centre Coupled Model ( RegCM3). The results are bias-corrected time series of atmospheric variables corresponding to Intergovernmental Panel on Climate Change ( IPCC's) historical ( 20C3M) and future ( A2) climate scenarios over the Amazon Basin. We use a series of simple but effective interpolation approaches to produce hourly climate data sets at 1° by 1° grid cells. A quantile-based mapping approach is used to reduce the biases of temperature and precipitation in CCSM3 and RegCM3. Adjustments are also made on specific humidity and downwelling longwave radiation to avoid inconsistency between those variables and bias-corrected temperature values. We also interpolated an already bias-corrected Parallel Climate Model data set ( PCM1) from 3-hourly to the hourly resolution. The final climate data sets can be used as forcing of ecosystem and hydrologic models to study climate changes and impact assessments over the Amazon Basin. [ABSTRACT FROM AUTHOR]

There is considerable interest in understanding the fate of the Amazon over the coming century in the face of climate change, rising atmospheric CO2 levels, ongoing land transformation, and changing fire regimes within the region. In this analysis, we explore the fate of Amazonian ecosystems under the combined impact of these four environmental forcings using three terrestrial biosphere models ( ED2, IBIS, and JULES) forced by three bias-corrected IPCC AR4 climate projections ( PCM1, CCSM3, and Had CM3) under two land-use change scenarios. We assess the relative roles of climate change, CO2 fertilization, land-use change, and fire in driving the projected changes in Amazonian biomass and forest extent. Our results indicate that the impacts of climate change are primarily determined by the direction and severity of projected changes in regional precipitation: under the driest climate projection, climate change alone is predicted to reduce Amazonian forest cover by an average of 14%. However, the models predict that CO2 fertilization will enhance vegetation productivity and alleviate climate-induced increases in plant water stress, and, as a result, sustain high biomass forests, even under the driest climate scenario. Land-use change and climate-driven changes in fire frequency are predicted to cause additional aboveground biomass loss and reductions in forest extent. The relative impact of land use and fire dynamics compared to climate and CO2 impacts varies considerably, depending on both the climate and land-use scenario, and on the terrestrial biosphere model used, highlighting the importance of improved quantitative understanding of all four factors - climate change, CO2 fertilization effects, fire, and land use - to the fate of the Amazon over the coming century. [ABSTRACT FROM AUTHOR]

Considerable uncertainty surrounds the fate of Amazon rainforests in response to climate change., Here, carbon (C) flux predictions of five terrestrial biosphere models (Community Land Model version 3.5 (CLM3.5), Ecosystem Demography model version 2.1 (ED2), Integrated BIosphere Simulator version 2.6.4 (IBIS), Joint UK Land Environment Simulator version 2.1 (JULES) and Simple Biosphere model version 3 (SiB3)) and a hydrodynamic terrestrial ecosystem model (the Soil-Plant-Atmosphere (SPA) model) were evaluated against measurements from two large-scale Amazon drought experiments., Model predictions agreed with the observed C fluxes in the control plots of both experiments, but poorly replicated the responses to the drought treatments. Most notably, with the exception of ED2, the models predicted negligible reductions in aboveground biomass in response to the drought treatments, which was in contrast to an observed c. 20% reduction at both sites. For ED2, the timing of the decline in aboveground biomass was accurate, but the magnitude was too high for one site and too low for the other., Three key findings indicate critical areas for future research and model development. First, the models predicted declines in autotrophic respiration under prolonged drought in contrast to measured increases at one of the sites. Secondly, models lacking a phenological response to drought introduced bias in the sensitivity of canopy productivity and respiration to drought. Thirdly, the phenomenological water-stress functions used by the terrestrial biosphere models to represent the effects of soil moisture on stomatal conductance yielded unrealistic diurnal and seasonal responses to drought. [ABSTRACT FROM AUTHOR]

Boreal forests are under strong influences from climate change, and alterations in forest dynamics will have significant impacts on global climate-biosphere feedback as well as local to regional conservation and resource management. To understand the mechanisms of forest dynamics and to assess the fate of boreal forests, simulation studies should be based on plant ecophysiological responses onto environmental conditions. In central Canadian boreal forests, local geomorphology created by past glacial activities often generates a mosaic of very distinctive forest types. On sandy hilltop of a glacial till, due to limitations in moisture availability and short fire return intervals, drought-tolerant and fire-adapted jack pine usually becomes the dominant species. On mesic and nutrient-rich slopes, fast-growing and resource-demanding trembling aspen forms mixed forests with coniferous species. In bottomland, black spruce, slowly growing but tolerant species, is often the only species that can survive to the adult stage. These three very distinctive forest types often occur within a scale of 10 m. Simulation models of boreal forests should be able to reproduce this heterogeneity in forest structure and composition as an emergent property of plant ecophysiological responses to varying environmental properties. In this study, a process-based forest dynamics model, ecosystem demography model version 1.0, is used to mechanically reproduce the landscape heterogeneity due to edaphic variations. First, boreal tree species of northern Manitoba, Canada, are parameterized according to field observations, and, to explicitly capture interactions among tree saplings, allometric equations based on diameter at height of 0.15 m, instead of the conventional breast height of 1.37 m, is parameterized. Then, soil moisture regime and nutrient concentrations are statistically incorporated from a dataset. The resultant simulation successfully reproduces the distinctive forest dynamics influenced by the edaphic heterogeneity. The sequences of succession and the trajectories of forest development are generally consistent with the field observations. The differences in resource availability are the essential control on equilibrium values of total forest leaf area index. Next, to show the effect of anthropogenic atmospheric changes, changes in temperature and CO2 concentrations are studied by a set of factorial experiments. The magnitude of CO2 fertilization is largely affected by soil fertility. The temperature rise will increase the length of growing season, but can have a negative impact on forest growth by increasing aridity and autotrophic respiration. Overall, the boreal forest responses to climate change are complex due to the inherent edaphic variations and ecophysiological responses. [ABSTRACT FROM AUTHOR]

Atmospheric measurements and land-based inventories imply that terrestrial ecosystems in the northern hemisphere are taking up significant amounts of anthropogenic cabon dioxide (CO2) emissions; however, there is considerable disagreement about the causes of this uptake, and its expected future trajectory. In this paper, we use the ecosystem demography (ED) model to quantify the contributions of disturbance history, CO2 fertilization and climate variability to the past, current, and future terrestrial carbon fluxes in the Eastern United States. The simulations indicate that forest regrowth following agricultural abandonment accounts for uptake of 0.11 Pg C yr−1 in the 1980s and 0.15 Pg C yr−1 in the 1990s, and regrowth following forest harvesting accounts for an additional 0.1 Pg C yr−1 of uptake during both these decades. The addition of CO2 fertilization into the model simulations increases carbon uptake rates to 0.38 Pg C yr−1 in the 1980s and 0.47 Pg C yr−1 in the 1990s. Comparisons of predicted aboveground carbon uptake to regional-scale forest inventory measurements indicate that the model's predictions in the absence of CO2 fertilization are 14% lower than observed, while in the presence of CO2 fertilization, predicted uptake rates are 28% larger than observed. Comparable results are obtained from comparisons of predicted total Net Ecosystem Productivity to the carbon fluxes observed at the Harvard Forest flux tower site and in model simulations free-air CO2 enrichment (FACE) experiments. These results imply that disturbance history is the principal mechanism responsible for current carbon uptake in the Eastern United States, and that conventional biogeochemical formulations of plant growth overestimate the response of plants to rising CO2 levels. Model projections out to 2100 imply that the carbon uptake arising from forest regrowth will increasingly be dominated by forest regrowth following harvesting. Consequently, actual carbon storage declines to near zero by the end of the 21st century as the forest regrowth that has occurred since agricultural abandonment comes into equilibrium with the landscape's new disturbance regime. Incorporating interannual climate variability into the model simulations gives rise to large interannual variation in regional carbon fluxes, indicating that long-term measurements are necessary to detect the signature of processes that give rise to long-term uptake and storage. [ABSTRACT FROM AUTHOR]

In just 20 years, the field of biosphere–atmosphere interactions has gone from a nascent discipline to a central area of modern climate change research. The development of terrestrial biosphere models that predict the responses of ecosystems to climate and increasing CO2 levels has highlighted several mechanisms by which changes in ecosystem composition and function might alter regional and global climate. However, results from empirical studies suggest that ecosystem responses can differ markedly from the predictions of terrestrial biosphere models. As I discuss here, the challenge now is to connect terrestrial biosphere models to empirical ecosystem measurements. Only by systematically evaluating the predictions of terrestrial biosphere models against suites of ecosystem observations and experiments measurements will a true predictive science of the biosphere be achieved. [Copyright &y& Elsevier]

Understanding how changes in climate will affect terrestrial ecosystems is particularly important in tropical forest regions, which store large amounts of carbon and exert important feedbacks onto regional and global climates. By combining multiple types of observations with a state-of-the-art terrestrial ecosystem model, we demonstrate that the sensitivity of tropical forests to changes in climate is dependent on the length of the dry season and soil type, but also, importantly, on the dynamics of individual-level competition within plant canopies. These interactions result in ecosystems that are more sensitive to changes in climate than has been predicted by traditional models but that transition from one ecosystem type to another in a continuous, non–tipping-point manner., Organismic and Evolutionary Biology