Max Planck Institute For Biogeochemistry Research Articles

Thirty-meter map of young forest age in China

Abstract. Young forest age mapping at a fine spatial resolution is important for increasing the accuracy of estimating land–atmosphere carbon fluxes and guiding forest management practices. In recent decades, China has actively conducted afforestation and forest protection projects, thereby laying the foundation for the realization of carbon neutrality. However, very few studies have been conducted which map the ages of young forests for the whole of China at a fine spatial resolution. In this research, a continuous change detection and classification (CCDC)-based method suitable for large-scale forest age mapping is proposed and used to estimate young forest ages across China in 2020 at a spatial resolution of 30 m. First, a 10 m spatial-resolution land cover dataset (WorldCover2020) from the European Space Agency (ESA) was used to determine the forest cover areas in 2020. Then, the CCDC algorithm was used to identify stand-replacing disturbances to determine the stand age based on 436 967 Landsat tiles across China from 1990 to 2020. A validation sample set composed of multiple land use and land cover (LULC) products was used to calculate the overall accuracy (OA) of the 2020 young forest age (1–31-year) map of China, and the OA was 90.28 %. The reliability and applicability of the proposed CCDC-based forest age mapping method were validated by comparing the forest age map with Hansen's forest change dataset, Max Planck Institute for Biogeochemistry (MPI-BGC) 1 km global forest age datasets, and field measurements. The CCDC-based method has strong application potential in real-time mapping of the age of young forests at the global scale. The produced forest age map provides a basic dataset for research on the forest carbon cycle and forest ecosystem services as well as important guidance for government departments, such as the National Forestry and Grassland Administration and the National Development and Reform Commission in China. Data presented in this study is available at https://doi.org/10.6084/m9.figshare.21627023.v7 (Xiao, 2022).

One common enemy, a pandemic, uniting interdisciplinary teams

One common enemy, a pandemic, uniting interdisciplinary teams

Simultaneous measurement of <i>δ</i><sup>13</sup>C, <i>δ</i><sup>18</sup>O and <i>δ</i><sup>17</sup>O of atmospheric CO<sub>2</sub> – performance assessment of a dual-laser absorption spectrometer

Abstract. Using laser absorption spectrometry for the measurement of stable isotopes of atmospheric CO2 instead of the traditional isotope ratio mass spectrometry method decreases sample preparation time significantly, and uncertainties in the measurement accuracy due to CO2 extraction and isobaric interferences are avoided. In this study we present the measurement performance of a new dual-laser instrument developed for the simultaneous measurement of the δ13C, δ18O and δ17O of atmospheric CO2 in discrete air samples, referred to as the Stable Isotopes of CO2 Absorption Spectrometer (SICAS). We compare two different calibration methods: the ratio method, based on the measured isotope ratio and a CO2 mole fraction dependency correction, and the isotopologue method, based on measured isotopologue abundances. Calibration with the ratio method and isotopologue method is based on three different assigned whole-air references calibrated on the VPDB (Vienna Pee Dee Belemnite) and the WMO 2007 (World Meteorological Organization) scale for their stable isotope compositions and their CO2 mole fractions, respectively. An additional quality control tank is included in both methods to follow long-term instrument performance. Measurements of the quality control tank show that the measurement precision and accuracy of both calibration methods is of similar quality for δ13C and δ18O measurements. During one specific measurement period the precision and accuracy of the quality control tank reach WMO compatibility requirements, being 0.01 ‰ for δ13C and 0.05 ‰ for δ18O. Uncertainty contributions of the scale uncertainties of the reference gases add another 0.03 ‰ and 0.05 ‰ to the combined uncertainty of the sample measurements. Hence, reaching WMO compatibility for sample measurements on the SICAS requires reduction of the scale uncertainty of the reference gases used for calibration. An intercomparison of flask samples over a wide range of CO2 mole fractions has been conducted with the Max Planck Institute for Biogeochemistry, resulting in a mean residual of 0.01 ‰ and −0.01 ‰ and a standard deviation of 0.05 ‰ and 0.07 ‰ for the δ13C measurements calibrated using the ratio method and the isotopologue method, respectively. The δ18O could not be compared due to depletion of the δ18O signal in our sample flasks because of storage times being too long. Finally, we evaluate the potential of our Δ17O measurements as a tracer for gross primary production by vegetation through photosynthesis. Here, a measurement precision of <0.01 ‰ would be a prerequisite for capturing seasonal variations in the Δ17O signal. Lowest standard errors for the δ17O and Δ17O of the ratio method and the isotopologue method are 0.02 ‰ and 0.02 ‰ and 0.01 ‰ and 0.02 ‰, respectively. The accuracy results show consequently results that are too enriched for both the δ17O and Δ17O measurements for both methods. This is probably due to the fact that two of our reference gases were not measured directly but were determined indirectly. The ratio method shows residuals ranging from 0.06 ‰ to 0.08 ‰ and from 0.06 ‰ to 0.1 ‰ for the δ17O and Δ17O results, respectively. The isotopologue method shows residuals ranging from 0.04 ‰ to 0.1 ‰ and from 0.05 ‰ to 0.13 ‰ for the δ17O and Δ17O results, respectively. Direct determination of the δ17O of all reference gases would improve the accuracy of the δ17O and thereby of the Δ17O measurements.

Sun-induced fluorescence and near-infrared reflectance of vegetation track the seasonal dynamics of gross primary production over Africa

Abstract. The carbon cycle of tropical terrestrial vegetation plays a vital role in the storage and exchange of atmospheric CO2. But large uncertainties surround the impacts of land-use change emissions, climate warming, the frequency of droughts, and CO2 fertilization. This culminates in poorly quantified carbon stocks and carbon fluxes even for the major ecosystems of Africa (savannas and tropical evergreen forests). Contributors to this uncertainty are the sparsity of (micro-)meteorological observations across Africa's vast land area, a lack of sufficient ground-based observation networks and validation data for CO2, and incomplete representation of important processes in numerical models. In this study, we therefore turn to two remotely sensed vegetation products that have been shown to correlate highly with gross primary production (GPP): sun-induced fluorescence (SIF) and near-infrared reflectance of vegetation (NIRv). The former is available from an updated product that we recently published (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval – SIFTER v2), which specifically improves retrievals in tropical environments. A comparison against flux tower observations of daytime-partitioned net ecosystem exchange from six major biomes in Africa shows that SIF and NIRv reproduce the seasonal patterns of GPP well, resulting in correlation coefficients of >0.9 (N=12 months, four sites) over savannas in the Northern and Southern hemispheres. These coefficients are slightly higher than for the widely used Max Planck Institute for Biogeochemistry (MPI-BGC) GPP products and enhanced vegetation index (EVI). Similarly to SIF signals in the neighboring Amazon, peak productivity occurs in the wet season coinciding with peak soil moisture and is followed by an initial decline during the early dry season, which reverses when light availability peaks. This suggests similar leaf dynamics are at play. Spatially, SIF and NIRv show a strong linear relation (R>0.9; N≥250 pixels) with multi-year MPI-BGC GPP even within single biomes. Both MPI-BGC GPP and the EVI show saturation relative to peak NIRv and SIF signals during high-productivity months, which suggests that GPP in the most productive regions of Africa might be larger than suggested.

Ecosystem Carbon Fluxes Analyzed Using Eddy Covariance Technique

Eddy covariance (EC) is an important measurement technique used in physical geography and atmospheric sciences to measure the exchange of carbon dioxide between an ecosystem and the atmosphere at a specific location. However, EC produces a net exchange of carbon dioxide yet research questions require an understanding of component fluxes, carbon dioxide uptake by plants through photosynthesis and carbon dioxide emissions due to plant and soil respiration. There are two major methods to partition EC measurements into these component fluxes: night-time and day-time partitioning methods. In the night-time method, nighttime measurements are used to estimate daytime respiration and calculate photosynthesis as a residual and in the daytime method, a light response curve is created to estimate daytime respiration and photosynthesis. This study investigates the benefits and drawbacks of these partitioning methods on two carbon dioxide exchange datasets from ecosystems in Canada. 
 
 The research sites were a) Mer Bleue, a peatland bog near Ottawa, Ontario and b) Cape Bounty, a high arctic tundra in Nunavut. By using a combination of the REddy-Proc software package, developed by the Max Planck Institute for Biogeochemistry, along with additional Matlab processing, the differences in photosynthesis and respiration due to partitioning methods are presented and discussed.

Predicting carbon and water vapor fluxes using machine learning and novel feature ranking algorithms

Gap-filling eddy covariance flux data using quantitative approaches has increased over the past decade. Numerous methods have been proposed previously, including look-up table approaches, parametric methods, process-based models, and machine learning. Particularly, the REddyProc package from the Max Planck Institute for Biogeochemistry and ONEFlux package from AmeriFlux have been widely used in many studies. However, there is no consensus regarding the optimal model and feature selection method that could be used for predicting different flux targets (Net Ecosystem Exchange, NEE; or Evapotranspiration –ET), due to the limited systematic comparative research based on the identical site-data. Here, we compared NEE and ET gap-filling/prediction performance of the least-square-based linear model, artificial neural network, random forest (RF), and support vector machine (SVM) using data obtained from four major row-crop and forage agroecosystems located in the subtropical or the climate-transition zones in the US. Additionally, we tested the impacts of different training-testing data partitioning settings, including a 10-fold time-series sequential (10FTS), a 10-fold cross validation (CV) routine with single data point (10FCV), daily (10FCVD), weekly (10FCVW) and monthly (10FCVM) gap length, and a 7/14-day flanking window (FW) approach; and implemented a novel Sliced Inverse Regression-based Recursive Feature Elimination algorithm (SIRRFE). We benchmarked the model performance against REddyProc and ONEFlux-produced results. Our results indicated that accurate NEE and ET prediction models could be systematically constructed using SVM/RF and only a few top informative features. The gap-filling performance of ONEFlux is generally satisfactory (R2 = 0.39–0.71), but results from REddyProc could be very limited or even unreliable in many cases (R2 = 0.01–0.67). Overall, SIRRFE-refined SVM models yielded excellent results for predicting NEE (R2 = 0.46–0.92) and ET (R2 = 0.74–0.91). Finally, the performance of various models was greatly affected by the types of ecosystem, predicting targets, and training algorithms; but was insensitive towards training-testing partitioning. Our research provided more insights into constructing novel gap-filling models and understanding the underlying drivers affecting boundary layer carbon/water fluxes on an ecosystem level.

The relationship between solar-induced fluorescence and gross primary productivity under different growth conditions: global analysis using satellite and biogeochemical model data

ABSTRACT Solar-induced fluorescence (SIF) has become a milestone for estimating terrestrial gross primary productivity (GPP) at the global scale, and therefore it is important to understand how the relationship between SIF and GPP varies with environmental stresses and canopy structures. Our study investigated the impact of environmental conditions on the relationship between SIF and GPP at the global scale based on Global Ozone Monitoring Instrument 2 (GOME-2) SIF and Max Planck Institute for Biogeochemistry (MPI-BGC) GPP. GOME-2 SIF had strong correlations with gridded GPP data at instantaneous (R 2 = 0.618, p < 0.001) and daily timescales (R 2 = 0.621, p < 0.001) across the globe in eleven major biomes. Strong linear relationships were also consistently found for nine individual biomes at the daily timescale (R 2 = 0.512–0.839, p < 0.001) but were not found for evergreen broadleaf forests (EBF) (R 2 = 0.142, p < 0.001) and closed shrublands (R 2 = 0.395, p < 0.001). Generally, high coefficients of determination (R 2 > 0.8) were mainly distributed in southern Europe, southern and central Africa, northeastern and central China, North America, India and southern Russia, and the slope had a similar spatial distribution as the coefficients of determination. The relationship between SIF and GPP varied with air temperature (T Air), vapour pressure deficit (VPD) and leaf area index (LAI). The slope of the SIF-GPP relationship had a maximal value when the air temperature ranged between approximately 10°C and 20°C, when the linear relationships between SIF and GPP were also the strongest. Our analysis based on the Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model also indicated that the SIF-GPP relationship was complex and affected by T Air, VPD and LAI. Our study can improve our understanding of the functional relationship between SIF and GPP and the information contained in remotely sensed SIF.

Deterministic and Probabilistic Weather Forecasting: An Analysis towards Big Data Samples Using the Google Search Random Surfer

The concept of big data has become one of the most important topics in the field of information science and engineering. In this paper, we offer modeling of data and its stability and forecasting by considering anti-symmetric traceless and symmetric models for atmospheric pressure variations. The data sample has been collected every 10 minutes for several years during 2009-2016 at the Weather Station, Max Planck Institute for Biogeochemistry, Jena, Germany. Subsequently, we extend the proposed model with a probabilistic transformation matrix by considering the Google search random surfer matrix with a small damping factor . Following the Principal Component Analysis (PCA), our study plays a vital role in big data samples and their stability analysis. A comparative discussion is provided for the above transformation matrix and its probabilistic counterpart. Finally, predictions are made towards feature selection, PCA and data compression sensing in the light of big data.

Recommendations for gap-filling eddy covariance latent heat flux measurements using marginal distribution sampling

Accurate eddy covariance (EC) measurements require that the atmospheric and orographic conditions meet certain criteria. It is common that up to 60% of the original data must be rejected. In particular, a high percentage of data is often removed during nocturnal periods. Currently, the most widely used method for filling gaps in EC datasets is the tool developed at the Max Planck Institute for Biogeochemistry [as reported by Falge et al. (2001), Reichstein et al. (2005), and Wutzler et al. (2018)]. This tool has been primarily developed and tested for the gap-filling of CO2 fluxes. In this study, we provide the first detailed testing of this gap-filling tool on LE fluxes and explore alternative settings in the gap-filling procedure using different meteorological drivers. The tests were conducted using five EC data sets. Random artificial gaps of four different gap-length scenarios were used to compare the settings. Error propagation for both the default and alternative settings was computed for various time aggregations. In general, we confirm a good performance of the standard gap-filling tool with a bias error of − 0.09 and − 0.21 W m−2 for nocturnal growing and non-growing season cases, respectively, while daytime average bias error was 0.01 W m−2. Alternative settings produced similar results to the default settings for diurnal cases; however, the alternative settings substantially (81%) improved the performance of night-time gap-filling. At sites where night-time LE fluxes are significant, we recommend using net radiation instead of global radiation and relative air humidity instead of vapour pressure deficit to drive the LE gap-filling.

Eddy Covariance vs. Biometric Based Estimates of Net Primary Productivity of Pedunculate Oak (Quercus robur L.) Forest in Croatia during Ten Years

We analysed 10 years (2008–2017) of continuous eddy covariance (EC) CO2 flux measurements of net ecosystem exchange (NEE) in a young pedunculate oak forest in Croatia. Measured NEE was gap-filled and partitioned into gross primary productivity (GPP) and ecosystem reparation (RECO) using the online tool by Max Planck Institute for Biogeochemistry in Jena, Germany. Annual NEE, GPP, and RECO were correlated with main environmental drivers. Net primary productivity was estimated from EC (NPPEC), as a sum of −NEE and Rh obtained using a constant Rh:RECO ratio, and from independent periodic biometric measurements (NPPBM). For comparing the NPP at the seasonal level, we propose a simple model that aimed at accounting for late-summer and autumn carbon storage in the non-structural carbohydrate pool. Over the study period, Jastrebarsko forest acted as a carbon sink, with an average (±std. dev.) annual NEE of −319 (±94) gC m−2 year−1, GPP of 1594 (±109) gC m−2 year−1, and RECO of 1275 (±94) gC m−2 year−1. Annual NEE showed high inter-annual variability and poor correlation with annual average global radiation, air temperature, and total precipitation, but significant (R2 = 0.501, p = 0.02) correlation with the change in soil water content between May and September. Comparison of annual NPPEC and NPPBM showed a good overall agreement (R2 = 0.463, p = 0.03), although in all years NPPBM was lower than NPPEC, with averages of 680 (±88) gC m−2 year−1 and 819 (±89) gC m−2 year−1, respectively. Lower values of NPPBM indicate that fine roots and grasses contributions to NPP, which were not measured in the study period, could have an important contribution to the overall ecosystem NPP. At a seasonal level, two NPP estimates showed differences in their dynamic, but the application of the proposed model greatly improved the agreement in the second part of the growing season. Further research is needed on the respiration partitioning and mechanisms of carbon allocation.

Rugged optical mirrors for Fourier transform spectrometers operated in harsh environments

Abstract. The Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC) operate a number of Fourier transform spectrometers (FTSs) that measure trace gases in the atmosphere by observing solar spectra. To guide the sunlight into the FTS, a solar tracker has to be placed outside. This device needs high-quality optical mirrors with good reflectance in the near and mid-infrared.More and more FTS stations are operated in remote locations with harsh environments. Optical mirrors are usually made for laboratory conditions and might not last very long there. At the TCCON site on Ascension Island which is operated by the Max Planck Institute for Biogeochemistry (MPI-BGC), several mirrors from different optical manufacturers were destroyed within weeks.To continue operation, the MPI-BGC had to develop rugged mirrors that could sustain the harsh conditions for months or even years. While commercially available mirrors are typically made from a substrate covered with a thin reflective coating, these rugged mirrors were made from stainless steel with no additional coating. Except for their lower reflectance (which can easily be compensated for), their optical properties are comparable to existing mirrors. However, their rugged design makes them mostly immune to corrosion and scratching. Unlike most coated mirrors, they can also be cleaned easily.

Atmospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;, δ(O&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;/N&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;) and δ&amp;lt;sup&amp;gt;13&amp;lt;/sup&amp;gt;CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; measurements at Jungfraujoch, Switzerland: results from a flask sampling intercomparison program

Abstract. We present results from an intercomparison program of CO2, δ(O2/N2) and δ13CO2 measurements from atmospheric flask samples. Flask samples are collected on a bi-weekly basis at the High Altitude Research Station Jungfraujoch in Switzerland for three European laboratories: the University of Bern, Switzerland, the University of Groningen, the Netherlands and the Max Planck Institute for Biogeochemistry in Jena, Germany. Almost 4 years of measurements of CO2, δ(O2/N2) and δ13CO2 are compared in this paper to assess the measurement compatibility of the three laboratories. While the average difference for the CO2 measurements between the laboratories in Bern and Jena meets the required compatibility goal as defined by the World Meteorological Organization, the standard deviation of the average differences between all laboratories is not within the required goal. However, the obtained annual trend and seasonalities are the same within their estimated uncertainties. For δ(O2/N2) significant differences are observed between the three laboratories. The comparison for δ13CO2 yields the least compatible results and the required goals are not met between the three laboratories. Our study shows the importance of regular intercomparison exercises to identify potential biases between laboratories and the need to improve the quality of atmospheric measurements.

Dispatches

Dispatches

Integrating empirical–modeling approaches to improve understanding of terrestrial ecology processes

Recent decades have seen tremendous increases in the quantity of empirical ecological data collected by individual investigators, as well as through research networks such as FLUXNET (Baldocchi et al., 2001). At the same time, advances in computer technology have facilitated the development and implementation of large and complex land surface and ecological process models. Separately, each of these information streams provides useful, but imperfect information about ecosystems. To develop the best scientific understanding of ecological processes, and most accurately predict how ecosystems may cope with global change, integration of empirical and modeling approaches is necessary. However, true integration - in which models inform empirical research, which in turn informs models (Fig. 1) - is not yet common in ecological research (Luo et al., 2011). The goal of this workshop, sponsored by the Department of Energy, Office of Science, Biological and Environmental Research (BER) program, was to bring together members of the empirical and modeling communities to exchange ideas and discuss scientific practices for increasing empirical - model integration, and to explore infrastructure and/or virtual network needs for institutionalizing empirical - model integration (Yiqi Luo, University of Oklahoma, Norman, OK, USA). The workshop included presentations and small group discussions that coveredmore » topics ranging from model-assisted experimental design to data driven modeling (e.g. benchmarking and data assimilation) to infrastructure needs for empirical - model integration. Ultimately, three central questions emerged. How can models be used to inform experiments and observations? How can experimental and observational results be used to inform models? What are effective strategies to promote empirical - model integration?« less

Dispatches

Dispatches

Global intercomparison of 12 land surface heat flux estimates

A global intercomparison of 12 monthly mean land surface heat flux products for the period 1993-1995 is presented. The intercomparison includes some of the first emerging global satellite-based products (developed at Paris Observatory, Max Planck Institute for Biogeochemistry, University of California Berkeley, University of Maryland, and Princeton University) and examples of fluxes produced by reanalyses (ERA-Interim, MERRA, NCEP-DOE) and off-line land surface models (GSWP-2, GLDAS CLM/ Mosaic/Noah). An intercomparison of the global latent heat flux (Q(sub le)) annual means shows a spread of approx 20 W/sq m (all-product global average of approx 45 W/sq m). A similar spread is observed for the sensible (Q(sub h)) and net radiative (R(sub n)) fluxes. In general, the products correlate well with each other, helped by the large seasonal variability and common forcing data for some of the products. Expected spatial distributions related to the major climatic regimes and geographical features are reproduced by all products. Nevertheless, large Q(sub le)and Q(sub h) absolute differences are also observed. The fluxes were spatially averaged for 10 vegetation classes. The larger Q(sub le) differences were observed for the rain forest but, when normalized by mean fluxes, the differences were comparable to other classes. In general, the correlations between Q(sub le) and R(sub n) were higher for the satellite-based products compared with the reanalyses and off-line models. The fluxes were also averaged for 10 selected basins. The seasonality was generally well captured by all products, but large differences in the flux partitioning were observed for some products and basins.

Soil respiration across scales: towards an integration of patterns and processes

New Phytologist Forum Editorial The New Phytologist Tansley Medal New Phytologist was founded by Sir Arthur Tansley and was first published in 1902. The long and independent history of the journal has allowed a positive inflow of revenue that is managed by the New Phytologist Trust and which is ploughed back into the scientific community to foster plant science (for a brief history of New Phytologist see Lewis & Ingram, 2002). The Trust currently finances two scientific meetings a year (e.g. Bruns & Kennedy, 2009; Harvey & Strauss, 2009) in addition to providing support for other meetings and activities (see http://www.newphytologist. org). A new innovation from the Trust is an annual com- petition for the New Phytologist Tansley Medal, specifically aimed at plant scientists in the early stages of their career. The plant scientists are required to submit their best research, either as a research article or as a small review, in a global competition for the award of a £2000 prize, in addi- tion to publication of the work and an editorial comment in New Phytologist. Details of the practicalities can be found on the New Phytologist Trust website at http://www. newphytologist.org/tansleymedal.htm. The New Phytologist Tansley Medal competition is a two- round process. In the first round, candidates submit an extended abstract of their work, together with their curricu- lum vitae and a reference from a scientist who has agreed to support the application. The number of applications at this stage is large and they are sifted by two Editors to produce a short list of candidates (seven candidates for the 2009 medal) who progress to the second round. Those who make it to this stage are requested to submit a complete manu- script, which is then peer-reviewed in the usual way. In addition, the Referees and Editors are asked for their assess- ments of the suitability of the manuscript for the Tansley Medal. Two Editors then make the final choice of the medal award. This year we are delighted to award the first Tansley Medal to Steven Spoel from the Institute of Molecular Plant Sciences at the University of Edinburgh, UK, for his paper on Post-translational protein modification as a tool for transcription reprogramming (this issue, pp. 333–339). As the title suggests, the manuscript deals with the control of transcriptional reprogramming. This process contributes to, for example, the co-ordinated changes in gene expression The Authors (2010) Journal compilation New Phytologist Trust (2010) that occur during development or during adaptation to environmental stress. Spoel and his co-authors focus on the contribution that protein modification makes to the control of transcriptional reprogramming. In a lucid and highly accessible account they discuss how the activation and repression of genes can be achieved by phosphorylation, ubiquitinylation, S-nitrosylation and disulphide-bonding of transcriptional activators, co-activators and repressor pro- teins. The Minireview provides examples of how these processes operate and their significance in relatively well- understood systems such as yeast. However, the main focus is on the plant immune response and especially on how modification of the immune co-activator protein NPR1 can control its localization and abundance in plant cells. The result is a highly authoritative review of an emerging topic of fundamental importance to our understanding of plant cell biology. New Phytologist also publishes papers of candidates in the final short list, following successful review. Congratulations are due to these candidates who have provided manuscripts on a wide range of topics in plant sciences and which can be read in this issue. • Ward Capoen, Sesbania rostrata: a case study of natural variation in legume nodulation (pp. 340–345) • Colleen Iversen, Digging deeper: fine-root responses to rising atmospheric CO 2 concentration in forested ecosystems (pp. 346–357) • Katherine McCulloh, Moving water well: comparing hydraulic efficiency in twigs and trunks of coniferous, ring- porous, and diffuse-porous saplings from temperate and tropi- cal forests (pp. 439–450) • Rebecca Mosher, Maternal control of Pol IV-dependent siRNAs in Arabidopsis endosperm (pp. 358–364) • Nicholas Rouhier, Plant glutaredoxins: pivotal players in redox biology and iron–sulfur center assembly (pp. 365–372) Thank you to the many applicants who submitted extended abstracts and also to those Editors and Referees who contributed in full to this first enterprise for the New Phytologist Tansley Medal. F. Ian Woodward Editor-in-Chief, New Phytologist (email f.i.woodward@sheffield.ac.uk) Alistair M. Hetherington Tansley review Editor, New Phytologist (email alistair.hetherington@bristol.ac.uk) New Phytologist (2010) 186: 263–264 263 www.newphytologist.com