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Plant functional traits determine vegetation responses to environmental variation, but variation in trait values is large, even within a single site. Likewise, uncertainty in how these traits map to Earth system feedbacks is large. We use a vegetation demographic model (VDM), the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to explore parameter sensitivity of model predictions, and comparison to observations, at a tropical forest site: Barro Colorado Island in Panama. We define a single 12-dimensional distribution of plant trait variation, derived primarily from observations in Panama, and define plant functional types (PFTs) as random draws from this distribution. We compare several model ensembles, where individual ensemble members vary only in the plant traits that define PFTs, and separate ensembles differ from each other based on either model structural assumptions or non-trait, ecosystem-level parameters, which include (a) the number of competing PFTs present in any simulation and (b) parameters that govern disturbance and height-based light competition. While singlePFT simulations are roughly consistent with observations of productivity at Barro Colorado Island, increasing the number of competing PFTs strongly shifts model predictions towards higher productivity and biomass forests. Different ecosystem variables show greater sensitivity than others to the number of competing PFTs, with the predictions that are most dominated by large trees, such as biomass, being the most sensitive. Changing disturbance and height-sorting parameters, i.e., the rules of competitive trait filtering, shifts regimes of dominance or coexistence between early-and late-successional PFTs in the model. Increases to the extent or severity of disturbance, or to the degree of determinism in height-based light competition, all act to shift the community towards early-successional PFTs. In turn, these shifts in competitive outcomes alter predictions of ecosystem states and fluxes, with more early-successional-dominated forests having lower biomass. It is thus crucial to differentiate between plant traits, which are under competitive pressure in VDMs, from those model parameters that are not and to better understand the relationships between these two types of model parameters to quantify sources of uncertainty in VDMs.

Metadata describe the ancillary information needed for data preservation and independent interpretation, comparison across heterogeneous datasets, and quality assessment and quality control (QA/QC). Environmental observations are vastly diverse in type and structure, can be taken across a wide range of spatiotemporal scales in a variety of measurement settings and approaches, and saved in multiple formats. Thus, well-organized, consistent metadata are required to produce usable data products from diverse environmental observations collected across field sites. However, existing metadata reporting protocols do not support the complex data synthesis and model-data integration needs of interdisciplinary earth system research. We developed a metadata reporting framework (FRAMES) to enable management and synthesis of observational data that are essential in advancing a predictive understanding of earth systems. FRAMES utilizes best practices for data and metadata organization enabling consistent data reporting and compatibility with a variety of standardized data protocols. We used an iterative scientist-centered design process to develop FRAMES, resulting in a data reporting format that incorporates existing field practices to maximize data-entry efficiency. Thus, FRAMES has a modular organization that streamlines metadata reporting and can be expanded to incorporate additional data types. With FRAMES's multi-scale measurement position hierarchy, data can be reported at observed spatial resolutions and then easily aggregated and linked across measurement types to support model-data integration. FRAMES is in early use by both data originators (persons generating data) and consumers (persons using data and metadata). In this paper, we describe FRAMES, identify lessons learned, and discuss areas of future development.

Photosynthetic capacity, determined by light harvesting and carboxylation reactions, is a key plant trait that determines the rate of photosynthesis; however, in Earth System Models (ESMs) at a reference temperature, it is either a fixed value for a given plant functional type or derived from a linear function of leaf nitrogen content. In this study, we conducted a comprehensive analysis that considered correlations of environmental factors with photosynthetic capacity as determined by maximum carboxylation (V(cm)) rate scaled to 25 degrees C (i.e., V(c),25; μmol CO2 x m(-2)x s(-1)) and maximum electron transport rate (J(max)) scaled to 25 degrees C (i.e., J25; μmol electron x m(-2) x s(-1)) at the global scale. Our results showed that the percentage of variation in observed V(c),25 and J25 explained jointly by the environmental factors (i.e., day length, radiation, temperature, and humidity) were 2-2.5 times and 6-9 times of that explained by area-based leaf nitrogen content, respectively. Environmental factors influenced photosynthetic capacity mainly through photosynthetic nitrogen use efficiency, rather than through leaf nitrogen content. The combination of leaf nitrogen content and environmental factors was able to explain -56% and -66% of the variation in V(c),25 and J25 at the global scale, respectively. Our analyses suggest that model projections of plant photosynthetic capacity and hence land-atmosphere exchange under changing climatic conditions could be substantially improved if environmental factors are incorporated into algorithms used to parameterize photosynthetic capacity in ESMs.

Abstract Recent modeling studies of future vegetation change suggest the potential for large-scale forest die-off in the tropics. Taken together with observational evidence of increasing tree mortality in numerous ecosystem types, there is clearly a need for projections of vegetation change. To that end, the authors have performed an ensemble of climate–vegetation experiments with the National Science Foundation–DOE Community Atmosphere Model (CAM) coupled to the Community Land Model (CAM–CLM-CN) with its dynamic vegetation model enabled (CAM–CLM-CNDV). To overcome the limitations of using a single model, the authors employ the sea surface temperature (SST) warming patterns simulated by eight different models from the Coupled Model Intercomparison Program phase 3 (CMIP3) as boundary conditions. Since the SST warming pattern in part dictates how precipitation may change in the future, in this way a range of future vegetation–climate trajectories can be produced. On an annual average basis, this study’s CAM–CLM-CN simulations do not produce as large a spread in projected precipitation as the original CMIP3 archive. These differences are due to the tendency of CAM–CLM-CN to increase tropical precipitation under a global warming scenario, although this response is modulated by the SST warming patterns imposed. However, the CAM–CLM-CN simulations reproduce the enhanced dry season in the tropics simulated by CMIP3. These simulations show longer fire seasons and increases in fractional area burned. In one ensemble member, extreme droughts over tropical South America lead to fires that remove vegetation cover in the eastern Amazon, suggesting that large-scale die-offs are an unlikely but still possible event.

Drought is an environmental stress that impacts plant productivity. Projections show both an increase in intense rain events and a reduction in the number of rain days, conditions that leads to increased risk of drought. Consequently, the identification of molecular biomarkers suitable for evaluating the impact of water deprivation conditions on forest plant seedlings is of significant value for monitoring purposes and forest management. In this study, we evaluated a biochemical methodology for the assessment of drought stress coupled with variable soil temperature in European beech (Fagus sylvatica L.) seedlings by analyzing a set of metabolites and enzymes involved in free radical scavenging and cell wall synthesis. The results indicate that the specific activities and isoform profile of superoxide dismutases and glutathione peroxidases together with the variation of phenolic compounds enable discrimination between seedlings with different degrees of photosynthetic activity. This approach represents a promising platform for the assessment of drought stress in forest trees and could serve for enhancing selection and breeding practices, allowing for plants that are more tolerant of abiotic stress. Key words: beech, drought, superoxide dismutase, oxidative stress, glutathione peroxidase. La secheresse est un stress environnemental qui a des repercussions sur la productivity des plantes. On prevoit une augmentation des episodes de pluie intense et une reduction du nombre de jours de pluie, des conditions qui entrainent un risque accrue de secheresse. Par consequent, l'identification de biomarqueurs moleculaires capables d'evaluer les conditions associees a la privation d'eau chez les semis des plantes forestieres a une valeur importante a des fins de suivi et d'amenagement forestier. Dans cette etude, nous avons evalue une methodologie biochimique pour mesurer le stress hydrique couple a une temperature variable du sol chez des semis de hetre commun (Fagus sylvatica L.) en analysant un ensemble de metabolites et d'enzymes impliques dans l'elimination des radicaux libres et la synthese de la paroi cellulaire. Les resultats indiquent que l'activite specifique et le profil isoforme des superoxyde dismutases et des glutathion peroxydases, ensemble avec la variation des composes phenoliques, permettent de distinguer les semis selon leur degre d'activite photosynthetique. Cette approche constitue un cadre prometteur pour evaluer les stress hydriques chez les arbres forestiers et pourrait servir a ameliorer les pratiques de selection et d'amelioration genetique en permettant d'obtenir des plants plus tolerants aux stress abiotiques. [Traduit par la Redaction] Mots-cles: hetre, secheresse, superoxyde dismutase, stress oxydatif, glutathion peroxydase., 1. Introduction Plants are exposed to different environmental stress factors that affect their growth and development. Drought is considered the single most critical environmental stress, which decreases plant productivity more [...]

Plant functional traits determine vegetation responses to environmental variation, but variation in trait values is large, even within a single site. Likewise, uncertainty in how these traits map to Earth system feedbacks is large. We use a vegetation demographic model (VDM), the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to explore parameter sensitivity of model predictions, and comparison to observations, at a tropical forest site: Barro Colorado Island in Panama. We define a single 12-dimensional distribution of plant trait variation, derived primarily from observations in Panama, and define plant functional types (PFTs) as random draws from this distribution. We compare several model ensembles, where individual ensemble members vary only in the plant traits that define PFTs, and separate ensembles differ from each other based on either model structural assumptions or non-trait, ecosystem-level parameters, which include: (a) the number of competing PFTs present in any simulation, and (b) parameters that govern disturbance and height-based light competition. While single-PFT simulations are roughy consistent with observations of productivity at BCI, increasing the number of competing PFTs strongly shifts model predictions towards higher productivity and biomass forests. Different ecosystem variables show greater sensitivity than others to the number of competing PFTs, with the predictions that are most dominated by large trees, such as biomass, being the most sensitive. Changing disturbance and height-sorting parameters, i.e. the rules of competitive trait filtering, shifts regimes of dominance or coexistence between early and late successional PFTs in the model. Increases to the extent or severity of disturbance, or to the degree of determinism in height-based light competition, all act to shift the community towards early-successional PFTs. In turn, these shifts in competitive outcomes alter predictions of ecosystem states and fluxes, with more early-successional dominated forests having lower biomass. It is thus crucial to differentiate between plant traits, which are under competitive pressure in VDMs, from those model parameters that are not, and to better understand the relationships between these two types of model parameters, to quantify sources of uncertainty in VDMs. [ABSTRACT FROM AUTHOR]

Accelerated climate change and associated forest disturbances in the Southwestern USA are anticipated to have substantial impacts on regional water resources. Few studies have quantified the impact of both climate change and land cover disturbances on water balances at the basin scale, and none at the regional scale. In this work, we evaluate the impacts of forest disturbances and climate change for a headwater basin to the Colorado River, the San Juan River watershed, using a robustly-calibrated (Nash Sutcliff 0.80) hydrologic model run with updated formulations that improve estimates of evapotranspiration for semi-arid regions. Our results show that future disturbances will have a substantial impact on streamflow with implications for water resource management. Our findings are in contradiction with conventional thinking that forest disturbances reduce ET and increase streamflow. In this study, annual average regional streamflow under the coupled climate-disturbances scenarios is at least 6-11 % lower than those scenarios accounting for climate change alone, and for forested zones of the San Juan River basin streamflow is 15-21 % lower. The monthly signals of altered streamflow point to an emergent streamflow pattern related to changes in forests of the disturbed systems. Exacerbated reductions of mean and low flows under disturbance scenarios indicate a high risk of lower water availability for forested headwater systems to the Colorado River basin. These findings also indicate that explicit representation of land cover disturbances is required in modelling efforts that consider the impact of climate change on water resources. [ABSTRACT FROM AUTHOR]

Hydraulic systems of plants have evolved in the context of carbon allocation and fitness trade-offs of maximizing carbon gain and water transport in the face of short and long-term fluctuations in environmental conditions. The resulting diversity of traits include a continuum of isohydry-anisohydry or high to low relative stomatal closure during drought, shedding of canopy foliage or disconnecting roots from soil to survive drought, and adjusting root areas to efficiently manage canopy water costs associated with photosynthesis. These traits are examined within TREES, an integrated model that explicitly couples photosynthesis and carbon allocation to soil-plant hydraulics and canopy processes. Key advances of the model are its ability to account for differences in soil and xylem cavitation, transience of hydraulic impairment associated with delayed or no refilling of xylem, and carbon allocation to plant structures based on photosynthetic uptake of carbon and hydraulic limitations to water transport. The model was used to examine hydraulic traits of cooccurring isohydric (piñon pine) and anisohydric (one-seed juniper) trees from a field-based experimental drought. Model predictions of both transpiration and leaf water potential were improved when there was no refilling of xylem over simulations where xylem was able refill in response to soil water recharge. Model experiments with alternative root-to-leaf area ratios ( RR/L) showed the RR/L that supports maximum cumulative water use is not beneficial for supporting maximum carbon gain during extended drought, illustrating how a process model reveals trade-offs in plant traits. [ABSTRACT FROM AUTHOR]

Climate-induced tree mortality is an increasing concern for forest managers around the world. We used a coupled hydrologic and ecosystem carbon cycling model to assess temperature and precipitation impacts on productivity and survival of ponderosa pine (Pinus ponderosa). Model predictions were evaluated using observations of productivity and survival for three ponderosa pine stands located across an 800 m elevation gradient in the southern Rocky Mountains, USA, during a 10-year period that ended in a severe drought and extensive tree mortality at the lowest elevation site. We demonstrate the utility of a relatively simple representation of declines in non-structural carbohydrate (NSC) as an approach for estimating patterns of ponderosa pine vulnerability to drought and the likelihood of survival along an elevation gradient. We assess the sensitivity of simulated net primary production, NSC storage dynamics, and mortality to site climate and soil characteristics as well as uncertainty in the allocation of carbon to the NSC pool. For a fairly wide set of assumptions, the model estimates captured elevational gradients and temporal patterns in growth and biomass. Model results that best predict mortality risk also yield productivity, leaf area, and biomass estimates that are qualitatively consistent with observations across the sites. Using this constrained set of parameters, we found that productivity and likelihood of survival were equally dependent on elevation-driven variation in temperature and precipitation. Our results demonstrate the potential for a coupled hydrology-ecosystem carbon cycling model that includes a simple model of NSC dynamics to predict drought-related mortality. Given that increases in temperature and in the frequency and severity of drought are predicted for a broad range of ponderosa pine and other western North America conifer forest habitats, the model potentially has broad utility for assessing ecosystem vulnerabilities. [ABSTRACT FROM AUTHOR]

Regional drought and flooding from extreme climatic event are increasing in frequency and severity, with significant adverse ecosocial impacts. Detecting and monitoring drought at regional to global scales remains challenging, despite the availability of various drought indices and widespread availability of potentially synergistic global satellite observational records. The authors have developed a method to generate a near-real-time remotely sensed drought severity index (DSI) to monitor and detect drought glob- ally at 1-km spatial resolution and regular 8-day, monthly, and annual frequencies. The new DSI integrates and exploits information from current operational satellite-based terrestrial evapo-transpiration (ET) and vegetation greenness index [normalized difference vegetation index (NDVI)] products, which are sensitive to vegetation water stress. Specifically, this approach determines the annual DSI departure from its normal (2000-11) using the remotely sensed ratio of ET to potential ET (PET) and NDVI. The DSI results were derived globally and captured documented major regional droughts over the last decade, including severe events in Europe (2003), the Amazon (2005 and 2010), and Russia (2010). The DSI corresponded favorably (correlation coefficient r = 0.43) with the precipitation-based Palmer drought severity index (PDSI), while both indices captured similar wetting and drying patterns. The DSI was also correlated with satellite-based vegetation net primary production (NPP) records, indicating that the combined use of these products may be useful for assessing water supply and ecosystem interactions, including drought impacts on crop yields and forest productivity. The remotely sensed global terrestrial DSI enhances capabilities for near-real-time drought monitoring to assist decision makers in regional drought assessment and mitigation efforts, and without many of the constraints of more traditional drought monitoring methods. [ABSTRACT FROM AUTHOR]

In 2012, an extreme summer drought induced species-specific die-back in woody species in Northeastern Italy. Quercus pubescens and Ostrya carpinifolia were heavily impacted, while Prunus mahaleb was largely unaffected. By comparing seasonal changes in isotopic composition of xylem sap, rainfall and deep soil samples, we show that P. mahaleb has a deeper root system than the other two species. This morphological trait allowed P  mahaleb to maintain higher water potential (Ψ), gas exchange rates and non-structural carbohydrates content (NSC) throughout the summer, when compared with the other species. More favourable water and carbon states allowed relatively stable maintenance of stem hydraulic conductivity (k) throughout the growing season. In contrast, in Quercus pubescens and Ostrya carpinifolia, decreasing Ψ and NSC were associated with significant hydraulic failure, with spring-to-summer k loss averaging 60%. Our data support the hypothesis that drought-induced tree decline is a complex phenomenon that cannot be modelled on the basis of single predictors of tree status like hydraulic efficiency, vulnerability and carbohydrate content. Our data highlight the role of rooting depth in seasonal progression of water status, gas exchange and NSC, with possible consequences for energy-demanding mechanisms involved in the maintenance of vascular integrity., (© 2015 John Wiley & Sons Ltd.)