Role of hydraulic traits in stomatal regulation of transpiration under different vapour pressure deficits across five Mediterranean tree crops

The differential stomatal regulation of transpiration among plant species in response to water deficit is not fully understood, although several hydraulic traits have been reported to influence it. This knowledge gap is partly due to a lack of direct and concomitant experimental data on transpiration, stomatal conductance, and hydraulic traits. We measured sap flux density (Js), stomatal conductance (gs), and different hydraulic traits in five crop species. Our aim was to contribute to establishing the causal relationship between water consumption and its regulation using a hydraulic trait-based approach. The results showed that the species-specific regulation of Js by gs was overall coordinated with the functional hydraulic traits analysed. Particularly relevant was the negative and significant relationship found between the Huber value (Hv) and its functional analogue ratio between maximum Js and gs (Jsmax/gsmax) which can be understood as a compensation to maintain the hydraulic supply to the leaves. The Hv was also significantly related to the slope of the relationship between gs and Js response to vapour pressure deficit and explained most of its variability, adding up to evidence recognizing Hv as a major trait in plant water relations. Thus, a hydraulic basis for regulation of tree water use should be considered.

Spatial variations and long-term trends of potential evaporation in Canada

Assessing the status and trend of potential evaporation (PE) is essential for investigating the climate change impact on the terrestrial water cycle. Despite recent advances, evaluating climate change impacts on PE using pan evaporation (E_pan) data in cold regions is hindered by the unavailability of E_pan measurements in cold seasons due to the freezing of water and sparse spatial distribution of sites. This study generated long-term PE datasets in Canada for 1979–2016 by integrating the dynamic evolutions of water–ice–snow processes into estimation in the Ecological Assimilation of Land and Climate Observations (EALCO) model. The datasets were compared with E_pan before the spatial variations and trends were analyzed. Results show that EALCO PE and E_pan measurements demonstrate similar seasonal variations and trends in warm seasons in most areas. Annual PE in Canada varied from 100 mm in the Northern Arctic to approximately 1000 mm in southern Canadian Prairies, southern Ontario, and East Coast, with about 600 mm for the entire landmass. Annual PE shows an increasing trend at a rate of 1.5–4 mm/year in the Northern Arctic, East, and West Canada. The increase is primarily associated with the elevated air temperature and downward longwave and shortwave radiation, with some regions contributed by augmented wind speed. The increase of annual PE is mainly attributed to the augmentation of PE in warm seasons.

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model-data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter-model variation is generally large and model agreement varies with timescales. In severely water-limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily-monthly) timescales and reduces on longer (seasonal-annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter-model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.

Soil Moisture–Vegetation–Carbon Flux Relationship under Agricultural Drought Condition using Optical Multispectral Sensor

Agricultural drought is triggered by a depletion of moisture content in the soil, which hinders photosynthesis and thus increases carbon dioxide (CO2) concentrations in the atmosphere. The aim of this study is to analyze the relationship between soil moisture (SM) and vegetation activity toward quantifying CO2 concentration in the atmosphere. To this end, the MODerate resolution imaging spectroradiometer (MODIS), an optical multispectral sensor, was used to evaluate two regions in South Korea for validation. Vegetation activity was analyzed through MOD13A1 vegetation indices products, and MODIS gross primary productivity (GPP) product was used to calculate the CO2 flux based on its relationship with respiration. In the case of SM, it was calculated through the method of applying apparent thermal inertia (ATI) in combination with land surface temperature and albedo. To validate the SM and CO2 flux, flux tower data was used which are the observed measurement values for the extreme drought period of 2014 and 2015 in South Korea. These two variables were analyzed for temporal variation on flux tower data as daily time scale, and the relationship with vegetation index (VI) was synthesized and analyzed on a monthly scale. The highest correlation between SM and VI (correlation coefficient (r) = 0.82) was observed at a time lag of one month, and that between VI and CO2 (r = 0.81) at half month. This regional study suggests a potential capability of MODIS-based SM, VI, and CO2 flux, which can be applied to an assessment of the global view of the agricultural drought by using available satellite remote sensing products.

Advancing global change biology through experimental manipulations: Where have we been and where might we go?

This commentary summarizes the publication history of Global Change Biology for works on experimental manipulations over the past 25 years and highlights a number of key publications. The retrospective summary is then followed by some thoughts on the future of experimental work as it relates to mechanistic understanding and methodological needs. Experiments for elevated CO2 atmospheres and anticipated warming scenarios which take us beyond historical analogs are suggested as future priorities. Disturbance is also highlighted as a key agent of global change. Because experiments are demanding of both personnel effort and limited fiscal resources, the allocation of experimental investments across Earth's biomes should be done in ecosystems of key importance. Uncertainty analysis and broad community consultation should be used to identify research questions and target biomes that will yield substantial gains in predictive confidence and societal relevance. A full range of methodological approaches covering small to large spatial scales will continue to be justified as a source of mechanistic understanding. Nevertheless, experiments operating at larger spatial scales encompassing organismal, edaphic, and environmental diversity of target ecosystems are favored, as they allow for the assessment of long-term biogeochemical feedbacks enabling a full range of questions to be addressed. Such studies must also include adequate investment in measurements of key interacting variables (e.g., water and nutrient availability and budgets) to enable mechanistic understanding of responses and to interpret context dependency. Integration of ecosystem-scale manipulations with focused process-based manipulations, networks, and large-scale observations will aid more complete understanding of ecosystem responses, context dependence, and the extrapolation of results. From the outset, these studies must be informed by and integrated with ecosystem models that provide quantitative predictions from their embedded mechanistic hypotheses. A true two-way interaction between experiments and models will simultaneously increase the rate and robustness of Global Change research.

A Forest Model Intercomparison Framework and Application at Two Temperate Forests Along the East Coast of the United States

State-of-the-art forest models are often complex, analytically intractable, and computationally expensive, due to the explicit representation of detailed biogeochemical and ecological processes. Different models often produce distinct results while predictions from the same model vary with parameter values. In this project, we developed a rigorous quantitative approach for conducting model intercomparisons and assessing model performance. We have applied our original methodology to compare two forest biogeochemistry models, the Perfect Plasticity Approximation with Simple Biogeochemistry (PPA-SiBGC) and Landscape Disturbance and Succession with Net Ecosystem Carbon and Nitrogen (LANDIS-II NECN). We simulated past-decade conditions at flux tower sites located within Harvard Forest, MA, USA (HF-EMS) and Jones Ecological Research Center, GA, USA (JERC-RD). We mined field data available from both sites to perform model parameterization, validation, and intercomparison. We assessed model performance using the following time-series metrics: Net ecosystem exchange, aboveground net primary production, aboveground biomass, C, and N, belowground biomass, C, and N, soil respiration, and species total biomass and relative abundance. We also assessed static observations of soil organic C and N, and concluded with an assessment of general model usability, performance, and transferability. Despite substantial differences in design, both models achieved good accuracy across the range of pool metrics. While LANDIS-II NECN showed better fidelity to interannual NEE fluxes, PPA-SiBGC indicated better overall performance for both sites across the 11 temporal and two static metrics tested (HF-EMS R 2 ¯ = 0.73 , + 0.07 , R M S E ¯ = 4.68 , − 9.96 ; JERC-RD R 2 ¯ = 0.73 , + 0.01 , R M S E ¯ = 2.18 , − 1.64 ). To facilitate further testing of forest models at the two sites, we provide pre-processed datasets and original software written in the R language of statistical computing. In addition to model intercomparisons, our approach may be employed to test modifications to forest models and their sensitivity to different parameterizations.

Vertical leaf mass per area gradient of mature sugar maple reflects both height-driven increases in vascular tissue and light-driven increases in palisade layer thickness

A key trait used in canopy and ecosystem function modeling, leaf mass per area (LMA), is influenced by changes in both leaf thickness and leaf density (LMA = Thickness × Density). In tall trees, LMA is understood to increase with height through two primary mechanisms: (i) increasing palisade layer thickness (and thus leaf thickness) in response to light and/or (ii) reduced cell expansion and intercellular air space in response to hydrostatic constraints, leading to increased leaf density. Our objective was to investigate within-canopy gradients in leaf anatomical traits in order to understand environmental factors that influence leaf morphology in a sugar maple (Acer saccharum Marshall) forest canopy. We teased apart the effects of light and height on anatomical traits by sampling at exposed and closed canopies that had different light conditions at similar heights. As expected, palisade layer thickness responded strongly to cumulative light exposure. Mesophyll porosity, however, was weakly and negatively correlated with light and height (i.e., hydrostatic gradients). Reduced mesophyll porosity was not likely caused by limitations on cell expansion; in fact, epidermal cell width increased with height. Palisade layer thickness was better related to LMA, leaf density and leaf thickness than was mesophyll porosity. Vein diameter and fraction of vascular tissue also increased with height and LMA, density and thickness, revealing that greater investment in vascular and support tissue may be a third mechanism for increased LMA with height. Overall, decreasing mesophyll porosity with height was likely due to palisade cells expanding into the available air space and also greater investments in vascular and support tissue, rather than a reduction of cell expansion due to hydrostatic constraints. Our results provide evidence that light influences both palisade layer thickness and mesophyll porosity and indicate that hydrostatic gradients influence leaf vascular and support tissues in mature Acer saccharum trees.

Simulating carbon capture by enhanced weathering with croplands: an overview of key processes highlighting areas of future model development

Enhanced weathering (EW) aims to amplify a natural sink for CO2 by incorporating powdered silicate rock with high reactive surface area into agricultural soils. The goal is to achieve rapid dissolution of minerals and release of alkalinity with accompanying dissolution of CO2 into soils and drainage waters. EW could counteract phosphorus limitation and greenhouse gas (GHG) emissions in tropical soils, and soil acidification, a common agricultural problem studied with numerical process models over several decades. Here, we review the processes leading to soil acidification in croplands and how the soil weathering CO2 sink is represented in models. Mathematical models capturing the dominant processes and human interventions governing cropland soil chemistry and GHG emissions neglect weathering, while most weathering models neglect agricultural processes. We discuss current approaches to modelling EW and highlight several classes of model having the potential to simulate EW in croplands. Finally, we argue for further integration of process knowledge in mathematical models to capture feedbacks affecting both longer-term CO2 consumption and crop growth and yields.

How vertical patterns in leaf traits shift seasonally and the implications for modeling canopy photosynthesis in a temperate deciduous forest

Leaf functional traits are used in modeling forest canopy photosynthesis (Ac) due to strong correlations between photosynthetic capacity, leaf mass per area (LMA) and leaf nitrogen per area (Narea). Vertical distributions of these traits may change over time in temperate deciduous forests as a result of acclimation to light, which may result in seasonal changes in Ac To assess both spatial and temporal variations in key traits, we measured vertical profiles of Narea and LMA from leaf expansion through leaf senescence in a sugar maple (Acer saccharum Marshall) forest. To investigate mechanisms behind coordinated changes in leaf morphology and function, we also measured vertical variation in leaf carbon isotope composition (δ(13)C), predawn turgor pressure, leaf water potential and osmotic potential. Finally, we assessed potential biases in Ac estimations by parameterizing models with and without vertical and seasonal Narea variations following leaf expansion. Our data are consistent with the hypothesis that hydrostatic constraints on leaf morphology drive the vertical increase in LMA with height early in the growing season; however, LMA in the upper canopy continued to increase over time during light acclimation, indicating that light is primarily driving gradients in LMA later in the growing season. Models with no seasonal variation in Narea overestimated Ac by up to 11% early in the growing season, while models with no vertical variation in Narea overestimated Ac by up to 60% throughout the season. According to the multilayer model, the upper 25% of leaf area contributed to over 50% of Ac, but when gradients of intercellular CO2, as estimated from δ(13)C, were accounted for, the upper 25% of leaf area contributed to 26% of total Ac Our results suggest that ignoring vertical variation of key traits can lead to considerable overestimation of Ac.

Are trees able to grow in periods of stem shrinkage?

Separating continuously measured stem radius (SR) fluctuations into growth-induced irreversible stem expansion (GRO) and tree water deficit-induced reversible stem shrinkage (TWD) requires a conceptualization of potential growth processes that may occur during periods of shrinking and expanding SR below a precedent maximum. Here, we investigated two physiological concepts: the linear growth (LG) concept, assuming linear growth, versus the zero growth (ZG) concept, assuming no growth during periods of stem shrinkage. We evaluated the physiological mechanisms underlying these two concepts and assessed their respective plausibilities using SR data obtained from 15 deciduous and evergreen trees. The application of the LG concept produced steady growth rates, whereas growth rates varied strongly under the ZG concept, more in accordance with mechanistic expectations. Further, growth increased for a maximum of 120 min after periods of stem shrinkage, indicating limited growth activity during those periods. However, this extra growth was found to be a small fraction of total growth only. Furthermore, TWD under the ZG concept was better explained by a hydraulic plant model than TWD under the LG concept. We conclude that periods of stem shrinkage allow for very little growth in the four tree species investigated. However, further studies should focus on obtaining independent growth data to ultimately validate these findings.

Plants in silico: why, why now and what?--an integrative platform for plant systems biology research

A paradigm shift is needed and timely in moving plant modelling from largely isolated efforts to a connected community endeavour that can take full advantage of advances in computer science and in mechanistic understanding of plant processes. Plants in silico (Psi) envisions a digital representation of layered dynamic modules, linking from gene networks and metabolic pathways through to cellular organization, tissue, organ and whole plant development, together with resource capture and use efficiency in dynamic competitive environments, ultimately allowing a mechanistically rich simulation of the plant or of a community of plants in silico. The concept is to integrate models or modules from different layers of organization spanning from genome to phenome to ecosystem in a modular framework allowing the use of modules of varying mechanistic detail representing the same biological process. Developments in high-performance computing, functional knowledge of plants, the internet and open-source version controlled software make achieving the concept realistic. Open source will enhance collaboration and move towards testing and consensus on quantitative theoretical frameworks. Importantly, Psi provides a quantitative knowledge framework where the implications of a discovery at one level, for example, single gene function or developmental response, can be examined at the whole plant or even crop and natural ecosystem levels.

Soil respiration simulation based on soil temperature and water content in artificial smooth brome grassland

Correctly quantifying the relationships between soil respiration and environmental factors and their sources of variability is essential to predict future carbon fluxes and climate feedback. Soil water conditions and soil temperature strongly affect soil respiration and the dynamics of soil organic matter. Despite this, simulation of soil respiration (Rs) based on soil temperature (Ts) and soil volumetric water content (θ) must still be improved, as demonstrated by its discrepant model performance among different seasons. With the objective of gaining a further understanding of the relationships of Rs with Ts and θ and providing an improved model to simulate Rs variations, we measured hourly Rs, Ts and θ using the chamber technique in artificial smooth brome grassland for analysis. We began by dividing the four seasons of a year according to the daily mean air temperature, followed by representing the seasonal variation of Rs, Ts and θ. We found that Rs correlated significantly with Ts in an exponential relationship and with θ in a parabolic relationship seasonally, where the determination coefficient of the Rs-θ relationship was significantly larger than that of the Rs-Ts relationship. We also discovered that the shape of the Rs-θ relationship was seasonally dependent because the optimal θ and the width of the peak Rs around the optimal θ were seasonally specific. Finally, by considering seasonality, the combinational simulation model explained more variation of soil respiration. Thus, seasonality should be considered for more reliable model simulations of soil respiration. These findings are relevant for more accurate predictions and modelling of soil respiration, particularly in temperate artificial grasslands with a continental monsoon climate, where the ‘Birch effect’ strengthens seasonality, and these findings further our understanding of changes in the rates of soil carbon losses as artificial grassland is established.

Needle age and season influence photosynthetic temperature response and total annual carbon uptake in mature Picea mariana trees

BACKGROUND AND AIMS

The carbon (C) balance of boreal terrestrial ecosystems is sensitive to increasing temperature, but the direction and thresholds of responses are uncertain. Annual C uptake in Picea and other evergreen boreal conifers is dependent on seasonal- and cohort-specific photosynthetic and respiratory temperature response functions, so this study examined the physiological significance of maintaining multiple foliar cohorts for Picea mariana trees within an ombrotrophic bog ecosystem in Minnesota, USA.

METHODS

Measurements were taken on multiple cohorts of needles for photosynthetic capacity, foliar respiration (Rd) and leaf biochemistry and morphology of mature trees from April to October over 4 years. The results were applied to a simple model of canopy photosynthesis in order to simulate annual C uptake by cohort age under ambient and elevated temperature scenarios.

KEY RESULTS

Temperature responses of key photosynthetic parameters [i.e. light-saturated rate of CO2 assimilation (Asat), rate of Rubisco carboxylation (Vcmax) and electron transport rate (Jmax)] were dependent on season and generally less responsive in the developing current-year (Y0) needles compared with 1-year-old (Y1) or 2-year-old (Y2) foliage. Temperature optimums ranged from 18·7 to 23·7, 31·3 to 38·3 and 28·7 to 36·7 °C for Asat, Vcmax and Jmax, respectively. Foliar cohorts differed in their morphology and photosynthetic capacity, which resulted in 64 % of modelled annual stand C uptake from Y1&2 cohorts (LAI 0·67 m(2 )m(-2)) and just 36 % from Y0 cohorts (LAI 0·52 m(2 )m(-2)). Under warmer climate change scenarios, the contribution of Y0 cohorts was even less; e.g. 31 % of annual C uptake for a modelled 9 °C rise in mean summer temperatures. Results suggest that net annual C uptake by P. mariana could increase under elevated temperature, and become more dependent on older foliar cohorts.

CONCLUSIONS

Collectively, this study illustrates the physiological and ecological significance of different foliar cohorts, and indicates the need for seasonal- and cohort-specific model parameterization when estimating C uptake capacity of boreal forest ecosystems under ambient or future temperature scenarios.

Root structural and functional dynamics in terrestrial biosphere models--evaluation and recommendations

There is wide breadth of root function within ecosystems that should be considered when modeling the terrestrial biosphere. Root structure and function are closely associated with control of plant water and nutrient uptake from the soil, plant carbon (C) assimilation, partitioning and release to the soils, and control of biogeochemical cycles through interactions within the rhizosphere. Root function is extremely dynamic and dependent on internal plant signals, root traits and morphology, and the physical, chemical and biotic soil environment. While plant roots have significant structural and functional plasticity to changing environmental conditions, their dynamics are noticeably absent from the land component of process-based Earth system models used to simulate global biogeochemical cycling. Their dynamic representation in large-scale models should improve model veracity. Here, we describe current root inclusion in models across scales, ranging from mechanistic processes of single roots to parameterized root processes operating at the landscape scale. With this foundation we discuss how existing and future root functional knowledge, new data compilation efforts, and novel modeling platforms can be leveraged to enhance root functionality in large-scale terrestrial biosphere models by improving parameterization within models, and introducing new components such as dynamic root distribution and root functional traits linked to resource extraction.

Estimation of vegetation photosynthetic capacity from space-based measurements of chlorophyll fluorescence for terrestrial biosphere models

Photosynthesis simulations by terrestrial biosphere models are usually based on the Farquhar's model, in which the maximum rate of carboxylation (Vcmax ) is a key control parameter of photosynthetic capacity. Even though Vcmax is known to vary substantially in space and time in response to environmental controls, it is typically parameterized in models with tabulated values associated to plant functional types. Remote sensing can be used to produce a spatially continuous and temporally resolved view on photosynthetic efficiency, but traditional vegetation observations based on spectral reflectance lack a direct link to plant photochemical processes. Alternatively, recent space-borne measurements of sun-induced chlorophyll fluorescence (SIF) can offer an observational constraint on photosynthesis simulations. Here, we show that top-of-canopy SIF measurements from space are sensitive to Vcmax at the ecosystem level, and present an approach to invert Vcmax from SIF data. We use the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model to derive empirical relationships between seasonal Vcmax and SIF which are used to solve the inverse problem. We evaluate our Vcmax estimation method at six agricultural flux tower sites in the midwestern US using spaced-based SIF retrievals. Our Vcmax estimates agree well with literature values for corn and soybean plants (average values of 37 and 101 μmol m(-2)  s(-1) , respectively) and show plausible seasonal patterns. The effect of the updated seasonally varying Vcmax parameterization on simulated gross primary productivity (GPP) is tested by comparing to simulations with fixed Vcmax values. Validation against flux tower observations demonstrate that simulations of GPP and light use efficiency improve significantly when our time-resolved Vcmax estimates from SIF are used, with R(2) for GPP comparisons increasing from 0.85 to 0.93, and for light use efficiency from 0.44 to 0.83. Our results support the use of space-based SIF data as a proxy for photosynthetic capacity and suggest the potential for global, time-resolved estimates of Vcmax .

CO2 enrichment alters diurnal stem radius fluctuations of 36-yr-old Larix decidua growing at the alpine tree line

To understand how trees at high elevations might use water differently in the future, we investigated the effects of CO2 enrichment and soil warming (separately and combined) on the water relations of Larix decidua growing at the tree line in the Swiss Alps. We assessed diurnal stem radius fluctuations using point dendrometers and applied a hydraulic plant model using microclimate and soil water potential data as inputs. Trees exposed to CO2 enrichment for 9 yr showed smaller diurnal stem radius contractions (by 46 ± 16%) and expansions (42 ± 16%) compared with trees exposed to ambient CO2 . Additionally, there was a delay in the timing of daily maximum (40 ± 12 min) and minimum (63 ± 14 min) radius values for trees growing under elevated CO2 . Parameters optimized with the hydraulic model suggested that CO2 -enriched trees had an increased flow resistance between the xylem and bark, representing a more buffered water supply system. Soil warming did not alter diurnal fluctuation dynamics or the CO2 response. Elevated CO2 altered the hydraulic water flow and storage system within L. decidua trees, which might have contributed to enhanced growth during 9 yr of CO2 enrichment and could ultimately influence the future competitive ability of this key tree-line species.

Light drives vertical gradients of leaf morphology in a sugar maple (Acer saccharum) forest

Leaf mass per area (LMA, g m(-2)) is an essential trait for modeling canopy function due to its strong association with photosynthesis, respiration and leaf nitrogen. Leaf mass per area, which is influenced by both leaf thickness and density (LMA = thickness × density), generally increases from the bottom to the top of tree canopies, yet the mechanisms behind this universal pattern are not yet resolved. For decades, the light environment was assumed to be the most influential driver of within-canopy variation in LMA, yet recent evidence has shown hydrostatic gradients to be more important in upper canopy positions, especially in tall evergreen trees in temperate and tropical forests. The aim of this study was to disentangle the importance of various environmental drivers on vertical LMA gradients in a mature sugar maple (Acer saccharum Marshall) forest. We compared LMA, leaf density and leaf thickness relationships with height, light and predawn leaf water potential (ΨPre) within a closed and an exposed canopy to assess leaf morphological traits at similar heights but different light conditions. Contrary to our expectations and recent findings in the literature, we found strong evidence that light was the primary driver of vertical gradients in leaf morphology. At similar heights (13-23 m), LMA was greater within the exposed canopy than the closed canopy, and light had a stronger influence over LMA compared with ΨPre. Light also had a stronger influence over both leaf thickness and density compared with ΨPre; however, the increase in LMA within both canopy types was primarily due to increasing leaf thickness with increasing light availability. This study provides strong evidence that canopy structure and crown exposure, in addition to height, should be considered as a parameter for determining vertical patterns in LMA and modeling canopy function.

Multimodel simulations of forest harvesting effects on long‐term productivity and CN cycling in aspen forests

The effects of forest management on soil carbon (C) and nitrogen (N) dynamics vary by harvest type and species. We simulated long-term effects of bole-only harvesting of aspen (Populus tremuloides) on stand productivity and interaction of CN cycles with a multiple model approach. Five models, Biome-BGC, CENTURY, FORECAST, LANDIS-II with Century-based soil dynamics, and PnET-CN, were run for 350 yr with seven harvesting events on nutrient-poor, sandy soils representing northwestern Wisconsin, United States. Twenty CN state and flux variables were summarized from the models' outputs and statistically analyzed using ordination and variance analysis methods. The multiple models' averages suggest that bole-only harvest would not significantly affect long-term site productivity of aspen, though declines in soil organic matter and soil N were significant. Along with direct N removal by harvesting, extensive leaching after harvesting before canopy closure was another major cause of N depletion. These five models were notably different in output values of the 20 variables examined, although there were some similarities for certain variables. PnET-CN produced unique results for every variable, and CENTURY showed fewer outliers and similar temporal patterns to the mean of all models. In general, we demonstrated that when there are no site-specific data for fine-scale calibration and evaluation of a single model, the multiple model approach may be a more robust approach for long-term simulations. In addition, multimodeling may also improve the calibration and evaluation of an individual model.

Traceable components of terrestrial carbon storage capacity in biogeochemical models

Biogeochemical models have been developed to account for more and more processes, making their complex structures difficult to be understood and evaluated. Here, we introduce a framework to decompose a complex land model into traceable components based on mutually independent properties of modeled biogeochemical processes. The framework traces modeled ecosystem carbon storage capacity (Xss ) to (i) a product of net primary productivity (NPP) and ecosystem residence time (τE ). The latter τE can be further traced to (ii) baseline carbon residence times (τ'E ), which are usually preset in a model according to vegetation characteristics and soil types, (iii) environmental scalars (ξ), including temperature and water scalars, and (iv) environmental forcings. We applied the framework to the Australian Community Atmosphere Biosphere Land Exchange (CABLE) model to help understand differences in modeled carbon processes among biomes and as influenced by nitrogen processes. With the climate forcings of 1990, modeled evergreen broadleaf forest had the highest NPP among the nine biomes and moderate residence times, leading to a relatively high carbon storage capacity (31.5 kg cm(-2) ). Deciduous needle leaf forest had the longest residence time (163.3 years) and low NPP, leading to moderate carbon storage (18.3 kg cm(-2) ). The longest τE in deciduous needle leaf forest was ascribed to its longest τ'E (43.6 years) and small ξ (0.14 on litter/soil carbon decay rates). Incorporation of nitrogen processes into the CABLE model decreased Xss in all biomes via reduced NPP (e.g., -12.1% in shrub land) or decreased τE or both. The decreases in τE resulted from nitrogen-induced changes in τ'E (e.g., -26.7% in C3 grassland) through carbon allocation among plant pools and transfers from plant to litter and soil pools. Our framework can be used to facilitate data model comparisons and model intercomparisons via tracking a few traceable components for all terrestrial carbon cycle models. Nevertheless, more research is needed to develop tools to decompose NPP and transient dynamics of the modeled carbon cycle into traceable components for structural analysis of land models.

DRAINMOD-FOREST: Integrated Modeling of Hydrology, Soil Carbon and Nitrogen Dynamics, and Plant Growth for Drained Forests

We present a hybrid and stand-level forest ecosystem model, DRAINMOD-FOREST, for simulating the hydrology, carbon (C) and nitrogen (N) dynamics, and tree growth for drained forest lands under common silvicultural practices. The model was developed by linking DRAINMOD, the hydrological model, and DRAINMOD-N II, the soil C and N dynamics model, to a forest growth model, which was adapted mainly from the 3-PG model. The forest growth model estimates net primary production, C allocation, and litterfall using physiology-based methods regulated by air temperature, water deficit, stand age, and soil N conditions. The performance of the newly developed DRAINMOD-FOREST model was evaluated using a long-term (21-yr) data set collected from an artificially drained loblolly pine ( L.) plantation in eastern North Carolina, USA. Results indicated that the DRAINMOD-FOREST accurately predicted annual, monthly, and daily drainage, as indicated by Nash-Sutcliffe coefficients of 0.93, 0.87, and 0.75, respectively. The model also predicted annual net primary productivity and dynamics of leaf area index reasonably well. Predicted temporal changes in the organic matter pool on the forest floor and in forest soil were reasonable compared to published literature. Both predicted annual and monthly nitrate export were in good agreement with field measurements, as indicated by Nash-Sutcliffe coefficients above 0.89 and 0.79 for annual and monthly predictions, respectively. This application of DRAINMOD-FOREST demonstrated its capability for predicting hydrology and C and N dynamics in drained forests under limited silvicultural practices.

Separating foliar physiology from morphology reveals the relative roles of vertically structured transpiration factors within red maple crowns and limitations of larger scale models

A spatially explicit mechanistic model, MAESTRA, was used to separate key parameters affecting transpiration to provide insights into the most influential parameters for accurate predictions of within-crown and within-canopy transpiration. Once validated among Acer rubrum L. genotypes, model responses to different parameterization scenarios were scaled up to stand transpiration (expressed per unit leaf area) to assess how transpiration might be affected by the spatial distribution of foliage properties. For example, when physiological differences were accounted for, differences in leaf width among A. rubrum L. genotypes resulted in a 25% difference in transpiration. An in silico within-canopy sensitivity analysis was conducted over the range of genotype parameter variation observed and under different climate forcing conditions. The analysis revealed that seven of 16 leaf traits had a ≥5% impact on transpiration predictions. Under sparse foliage conditions, comparisons of the present findings with previous studies were in agreement that parameters such as the maximum Rubisco-limited rate of photosynthesis can explain ∼20% of the variability in predicted transpiration. However, the spatial analysis shows how such parameters can decrease or change in importance below the uppermost canopy layer. Alternatively, model sensitivity to leaf width and minimum stomatal conductance was continuous along a vertical canopy depth profile. Foremost, transpiration sensitivity to an observed range of morphological and physiological parameters is examined and the spatial sensitivity of transpiration model predictions to vertical variations in microclimate and foliage density is identified to reduce the uncertainty of current transpiration predictions.

Ecological forecasting and data assimilation in a data-rich era

Several forces are converging to transform ecological research and increase its emphasis on quantitative forecasting. These forces include (1) dramatically increased volumes of data from observational and experimental networks, (2) increases in computational power, (3) advances in ecological models and related statistical and optimization methodologies, and most importantly, (4) societal needs to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-oriented models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today's models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting and estimates of uncertainty is data assimilation (DA), which uses data to inform initial conditions and model parameters, thereby constraining a model during simulation to yield results that approximate reality as closely as possible. This paper discusses the meaning and history of DA in ecological research and highlights its role in refining inference and generating forecasts. DA can advance ecological forecasting by (1) improving estimates of model parameters and state variables, (2) facilitating selection of alternative model structures, and (3) quantifying uncertainties arising from observations, models, and their interactions. However, DA may not improve forecasts when ecological processes are not well understood or never observed. Overall, we suggest that DA is a key technique for converting raw data into ecologically meaningful products, which is especially important in this era of dramatically increased availability of data from observational and experimental networks.

Digging deeper: fine-root responses to rising atmospheric CO concentration in forested ecosystems

Experimental evidence from a diverse set of forested ecosystems indicates that CO2 enrichment may lead to deeper rooting distributions. While the causes of greater root production at deeper soil depths under elevated CO2 concentration ([CO2]) require further investigation, altered rooting distributions are expected to affect important ecosystem processes. The depth at which fine roots are produced may influence root chemistry, physiological function, and mycorrhizal infection, leading to altered nitrogen (N) uptake rates and slower turnover. Also, soil processes such as microbial decomposition are slowed at depth in the soil, potentially affecting the rate at which root detritus becomes incorporated into soil organic matter. Deeper rooting distributions under elevated [CO2] provide exciting opportunities to use novel sensors and chemical analyses throughout the soil profile to track the effects of root proliferation on carbon (C) and N cycling. Models do not currently incorporate information on root turnover and C and N cycling at depth in the soil, and modification is necessary to accurately represent processes associated with altered rooting depth distributions. Progress in understanding and modeling the interface between deeper rooting distributions under elevated [CO2] and soil C and N cycling will be critical in projecting the sustainability of forest responses to rising atmospheric [CO2].

Simulating carbon dioxide exchange rates of deciduous tree species: evidence for a general pattern in biochemical changes and water stress response

BACKGROUND AND AIMS

Deciduous trees have a seasonal carbon dioxide exchange pattern that is attributed to changes in leaf biochemical properties. However, it is not known if the pattern in leaf biochemical properties - maximum Rubisco carboxylation (V(cmax)) and electron transport (J(max)) - differ between species. This study explored whether a general pattern of changes in V(cmax), J(max), and a standardized soil moisture response accounted for carbon dioxide exchange of deciduous trees throughout the growing season.

METHODS

The model MAESTRA was used to examine V(cmax) and J(max) of leaves of five deciduous trees, Acer rubrum 'Summer Red', Betula nigra, Quercus nuttallii, Quercus phellos and Paulownia elongata, and their response to soil moisture. MAESTRA was parameterized using data from in situ measurements on organs. Linking the changes in biochemical properties of leaves to the whole tree, MAESTRA integrated the general pattern in V(cmax) and J(max) from gas exchange parameters of leaves with a standardized soil moisture response to describe carbon dioxide exchange throughout the growing season. The model estimates were tested against measurements made on the five species under both irrigated and water-stressed conditions.

KEY RESULTS

Measurements and modelling demonstrate that the seasonal pattern of biochemical activity in leaves and soil moisture response can be parameterized with straightforward general relationships. Over the course of the season, differences in carbon exchange between measured and modelled values were within 6-12 % under well-watered conditions and 2-25 % under water stress conditions. Hence, a generalized seasonal pattern in the leaf-level physiological change of V(cmax) and J(max), and a standardized response to soil moisture was sufficient to parameterize carbon dioxide exchange for large-scale evaluations.

CONCLUSIONS

Simplification in parameterization of the seasonal pattern of leaf biochemical activity and soil moisture response of deciduous forest species is demonstrated. This allows reliable modelling of carbon exchange for deciduous trees, thus circumventing the need for extensive gas exchange experiments on different species.