The increasing importance of atmospheric demand for ecosystem water and carbon fluxes

Soil moisture supply and atmospheric demand for water independently limit—and profoundly affect—vegetation productivity and water use during periods of hydrologic stress1, 2, 3, 4. Disentangling the impact of these two drivers on ecosystem carbon and water cycling is difficult because they are often correlated, and experimental tools for manipulating atmospheric demand in the field are lacking. Consequently, the role of atmospheric demand is often not adequately factored into experiments or represented in models5, 6, 7. Here we show that atmospheric demand limits surface conductance and evapotranspiration to a greater extent than soil moisture in many biomes, including mesic forests that are of particular importance to the terrestrial carbon sink8, 9. Further, using projections from ten general circulation models, we show that climate change will increase the importance of atmospheric constraints to carbon and water fluxes in all ecosystems. Consequently, atmospheric demand will become increasingly important for vegetation function, accounting for >70% of growing season limitation to surface conductance in mesic temperate forests. Our results suggest that failure to consider the limiting role of atmospheric demand in experimental designs, simulation models and land management strategies will lead to incorrect projections of ecosystem responses to future climate conditions

C4 photosynthesis boosts growth by altering physiology, allocation and size.

C4 photosynthesis is a complex set of leaf anatomical and biochemical adaptations that have evolved more than 60 times to boost carbon uptake compared with the ancestral C3 photosynthetic type(1-3). Although C4 photosynthesis has the potential to drive faster growth rates(4,5), experiments directly comparing C3 and C4 plants have not shown consistent effects(1,6,7). This is problematic because differential growth is a crucial element of ecological theory(8,9) explaining C4 savannah responses to global change(10,11), and research to increase C3 crop productivity by introducing C4 photosynthesis(12). Here, we resolve this long-standing issue by comparing growth across 382 grass species, accounting for ecological diversity and evolutionary history. C4 photosynthesis causes a 19-88% daily growth enhancement. Unexpectedly, during the critical seedling establishment stage, this enhancement is driven largely by a high ratio of leaf area to mass, rather than fast growth per unit leaf area. C4 leaves have less dense tissues, allowing more leaves to be produced for the same carbon cost. Consequently, C4 plants invest more in roots than C3 species. Our data demonstrate a general suite of functional trait divergences between C3 and C4 species, which simultaneously drive faster growth and greater investment in water and nutrient acquisition, with important ecological and agronomic implications

Comparing two approaches for parsimonious vegetation modelling in semiarid regions using satellite data

[EN] Large portions of Earth's terrestrial surface are arid or semiarid. As in these regions, the hydrological cycle and the vegetation dynamics are tightly interconnected, a coupled modelling of these two systems is needed to fully reproduce the ecosystem behaviour. In this paper, the performance of two parsimonious dynamic vegetation models, suitable for the inclusion in operational ecohydrological models and based on well-established but different approaches, is compared in a semiarid Aleppo Pine region. The first model [water use efficiency (WUE) model] links growth to transpiration through WUE; the second model [light use efficiency (LUE) model] simulates biomass increase in relation to absorbed photosynthetically active radiation and LUE. Furthermore, an analysis of the information contained in MODIS products is presented to indicate the best vegetation indices to be used as observational verification for the models. Enhanced Vegetation Index is reported in literature to be highly correlated with leaf area index, so it is compared with modelled LAI(mod) (rWUE model = 0.45; rLUE model = 0.57). In contrast, Normalized Difference Vegetation Index appears highly linked to soil moisture, through the control exerted by this variable on chlorophyll production, and is therefore used to analyze LAI*(mod), models' output corrected by plant water stress (rWUE model = 0.62; rLUE model = 0.59). Moderate-resolution imaging spectroradiometer Leaf Area Index and evapotranspiration are found to be unrealistic in the studied area. The performance of both models in this semiarid region is found to be reasonable. However, the LUE model presents the advantages of a better performance, the possibility to be used in a wider range of climates and to have been extensively tested in literature. (C) Copyright 2014 John Wiley & Sons, Ltd.The research leading to these results has received funding from the Spanish Ministry of Economy and Competitiveness through the research projects FLOOD-MED (ref. CGL2008-06474-C02-02), SCARCE-CONSOLIDER (ref. CSD2009-00065) and ECO-TETIS (ref. CGL2011-28776-C02-01), and from the European Community's Seventh Framework Programme (FP7 2007-2013) under grant agreement no. 238366. The MODIS data were obtained through the online Data Pool at the NASA Land Processes Distributed Active Archive Centre (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Centre, Sioux Falls, South Dakota (https://lpdaac.usgs.gov/get_data). The meteorological data were provided by the Spanish National Weather Agency (AEMET). The authors thank Antonio Del Campo Garcia and Maria Gonzalez Sanchis at the Universitat Politecnica de Valencia for their support and valuable comments.Pasquato, M.; Medici, C.; Friend, A.; Francés, F. (2015). Comparing two approaches for parsimonious vegetation modelling in semiarid regions using satellite data. Ecohydrology. 8(6):1024-1036. https://doi.org/10.1002/eco.1559S102410368

Parsimony vs predictive and functional performance of three stomatal optimization principles in a big-leaf framework

Stomatal optimization models can improve estimates of water and carbon fluxes with relatively low complexity, yet there is no consensus on which formulations are most appropriate for ecosystem-scale applications. We implemented three existing analytical equations for stomatal conductance, based on different water penalty functions, in a big-leaf comparison framework, and determined which optimization principles were most consistent with flux tower observations from different biomes. We used information theory to dissect controls of soil water supply and atmospheric demand on evapotranspiration in wet to dry conditions and to quantify missing or inadequate information in model variants. We ranked stomatal optimization principles based on parameter uncertainty, parsimony, predictive accuracy, and functional accuracy of the interactions between soil moisture, vapor pressure deficit, and evapotranspiration. Performance was high for all model variants. Water penalty functions with explicit representation of plant hydraulics did not substantially improve predictive or functional accuracy of ecosystem-scale evapotranspiration estimates, and parameterizations were more uncertain, despite having physiological underpinnings at the plant level. Stomatal optimization based on water use efficiency thus provided more information about ecosystem-scale evapotranspiration compared to those based on xylem vulnerability and proved more useful in improving ecosystem-scale models with less complexity

Enhanced thylakoid photoprotection can increase yield and canopy radiation use efficiency in rice

High sunlight can raise plant growth rates but can potentially cause cellular damage. The likelihood of deleterious effects is lowered by a sophisticated set of photoprotective mechanisms, one of the most important being the controlled dissipation of energy from chlorophyll within photosystem II (PSII) measured as non-photochemical quenching (NPQ). Although ubiquitous, the role of NPQ in plant productivity remains uncertain because it momentarily reduces the quantum efficiency of photosynthesis. Here we used plants overexpressing the gene encoding a central regulator of NPQ, the protein PsbS, within a major crop species (rice) to assess the effect of photoprotection at the whole canopy scale. We accounted for canopy light interception, to our knowledge for the first time in this context. We show that in comparison to wild-type plants, psbS overexpressors increased canopy radiation use efficiency and grain yield in fluctuating light, demonstrating that photoprotective mechanisms should be altered to improve rice crop productivity

Predicting Maximum Tree Heights and Other Traits from Allometric Scaling and Resource Limitations

Terrestrial vegetation plays a central role in regulating the carbon and water cycles, and adjusting planetary albedo. As such, a clear understanding and accurate characterization of vegetation dynamics is critical to understanding and modeling the broader climate system. Maximum tree height is an important feature of forest vegetation because it is directly related to the overall scale of many ecological and environmental quantities and is an important indicator for understanding several properties of plant communities, including total standing biomass and resource use. We present a model that predicts local maximal tree height across the entire continental United States, in good agreement with data. The model combines scaling laws, which encode the average, base-line behavior of many tree characteristics, with energy budgets constrained by local resource limitations, such as precipitation, temperature and solar radiation. In addition to predicting maximum tree height in an environment, our framework can be extended to predict how other tree traits, such as stomatal density, depend on these resource constraints. Furthermore, it offers predictions for the relationship between height and whole canopy albedo, which is important for understanding the Earth's radiative budget, a critical component of the climate system. Because our model focuses on dominant features, which are represented by a small set of mechanisms, it can be easily integrated into more complicated ecological or climate models.National Science Foundation (U.S.) (Research Experience for Undergraduates stipend)Gordon and Betty Moore FoundationNational Science Foundation (U.S.) (Graduate Research Fellowship Program)Massachusetts Institute of Technology. Presidential FellowshipEugene V. and Clare Thaw Charitable TrustEngineering and Physical Sciences Research CouncilNational Science Foundation (U.S.) (PHY0202180)Colorado College (Venture Grant Program

Nitrogen balance and root behavior in four pigeonpea-based intercropping systems

A medium-duration pigeonpea (Cajanus cajan L. Millsp.) is usually grown as intercrop. A wide range of crop combination in pigeonpea-based intercropping systems is found in India and eastern Africa (Ofroi and Stern, 1987; Rao and Willey, 1980; Venkateswarlu and Subramanian, 1990). Although much information is available on the production efficiency and monetary advantage of intercropping, very little is known about the nitrogen (N) economy and root behavior. The study was carried out to examine how the nitrogen balance sheet and root development of pigeonpea could be altered by companion crops