Nutrients and water availability constrain the seasonality of vegetation activity in a Mediterranean ecosystem

Anthropogenic nitrogen (N) deposition and resulting differences in ecosystem N and phosphorus (P) ratios are expected to impact photosynthetic capacity, that is, maximum gross primary productivity (GPP). However, the interplay between N and P availability with other critical resources on seasonal dynamics of ecosystem productivity remains largely unknown. In a Mediterranean tree–grass ecosystem, we established three landscape-level (24 ha) nutrient addition treatments: N addition (NT), N and P addition (NPT), and a control site (CT). We analyzed the response of ecosystem to altered nutrient stoichiometry using eddy covariance fluxes measurements, satellite observations, and digital repeat photography. A set of metrics, including phenological transition dates (PTDs; timing of green-up and dry-down), slopes during green-up and dry-down period, and seasonal amplitude, were extracted from time series of GPP and used to represent the seasonality of vegetation activity. The seasonal amplitude of GPP was higher for NT and NPT than CT, which was attributed to changes in structure and physiology induced by fertilization. PTDs were mainly driven by rainfall and exhibited no significant differences among treatments during the green-up period. Yet, both fertilized sites senesced earlier during the dry-down period (17–19 days), which was more pronounced in the NT due to larger evapotranspiration and water usage. Fertilization also resulted in a faster increase in GPP during the green-up period and a sharper decline in GPP during the dry-down period, with less prominent decline response in NPT. Overall, we demonstrated seasonality of vegetation activity was altered after fertilization and the importance of nutrient–water interaction in such water-limited ecosystems. With the projected warming-drying trend, the positive effects of N fertilization induced by N deposition on GPP may be counteracted by an earlier and faster dry-down in particular in areas where the N:P ratio increases, with potential impact on the carbon cycle of water-limited ecosystems.The authors acknowledge the Alexander von Humboldt Foundation for supporting this research with the Max-Planck Prize to Markus Reichstein. Yunpeng Luo and Mirco Migliavacca gratefully acknowledge financial support from the China Scholarship Council. Gerardo Moreno acknowledges financial support from the grant agreement IB16185 of the Regional Government of Extremadura

The effect of relative humidity on eddy covariance latent heat flux measurements and its implication for partitioning into transpiration and evaporation

While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evaporation or 'evapotranspiration’, ET), the measurement is susceptible to many uncertainties. One such issue is the potential underestimation of ET when relative humidity (RH) is high (>70%), due to low-pass filtering with some EC systems. Yet, this underestimation for different types of EC systems (e.g. open-path or closed-path sensors) has not been characterized for synthesis datasets such as the widely used FLUXNET2015 dataset. Here, we assess the RH-associated underestimation of latent heat fluxes (LE, or ET) from different EC systems for 163 sites in the FLUXNET2015 dataset. We found that the LE underestimation is most apparent during hours when RH is higher than 70%, predominantly observed at sites using closed-path EC systems, but the extent of the LE underestimation is highly site-specific. We then propose a machine learning based method to correct for this underestimation, and compare it to two energy balance closure based LE correction approaches (Bowen ratio correction, BRC, and attributing all errors to LE). Our correction increases LE by 189% for closed-path sites at high RH (>90%), while BRC increases LE by around 30% for all RH conditions. Additionally, we assess the influence of these corrections on ET-based transpiration (T) estimates using two different ET partitioning methods. Results show opposite responses (increasing vs. slightly decreasing T-to-ET ratios, T/ET) between the two methods when comparing T based on corrected and uncorrected LE. Overall, our results demonstrate the existence of a high RH bias in water fluxes in the FLUXNET2015 dataset and suggest that this bias is a pronounced source of uncertainty in ET measurements to be considered when estimating ecosystem T/ET and WUE.Peer reviewe

Evergreen broadleaf greenness and its relationship with leaf flushing, aging, and water fluxes

13 Pág. Departamento de Medio Ambiente y Agronomía​ (INIA)Remote sensing capabilities to monitor evergreen broadleaved vegetation are limited by the low temporal variability in the greenness signal. With canopy greenness computed from digital repeat photography (PhenoCam), we investigated how canopy greenness related to seasonal changes in leaf age and traits as well as variation of trees’ water fluxes (characterized by sap flow and canopy conductance). The results showed that sprouting leaves are mainly responsible for the rapid increase in canopy green chromatic coordinate (GCC) in spring. We found statistically significantly differences in leaf traits and spectral properties among leaves of different leaf ages. Specifically, mean GCC of young leaves was 0.385 ± 0.010 (mean ± SD), while for mature and old leaves was 0.369 ± 0.003, and 0.376 ± 0.004, respectively. Thus, the temporal dynamics of canopy GCC can be explained by changes in leaf spectral properties and leaf age. Sap flow and canopy conductance are both well explained by a combination of environmental drivers and greenness (96% and 87% of the variance explained, respectively). In particular, air temperature and vapor pressure deficit (VPD) explained most of sap flow and canopy conductance variance, respectively. Besides, GCC is an important explanatory variable for variation of canopy conductance may because GCC can represent the leaf ontogeny information. We conclude that PhenoCam GCC can be used to identify the leaf flushing for evergreen broadleaved trees, which carries important information about leaf ontogeny and traits. Thus, it can be helpful for better estimating canopy conductance which constraints water fluxes.The authors acknowledge the Alexander von Humboldt Foundation for supporting this research with the Max Planck Prize to Markus Reichstein. Yunpeng Luo and Mirco Migliavacca gratefully acknowledge the financial support from the China Scholarship Council. ADR acknowledges support for the PhenoCam network from the National Science Foundation ( DEB- 1702697 ). Javier Pacheco-Labrador and Mirco Migliavacca acknowledge the German Aerospace Center (DLR) project OBEF-Accross2 “The Potential of Earth Observations to Capture Patterns of Biodiversity” (Contract No. 50EE1912). The research also received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 721995 and Ministerio de Economíay Competitividad through FLUXPEC CGL2012-34383 and SynerTGE CGL2015-G9095-R (MINECO/FEDER, UE) projects.Peer reviewe

Global transpiration data from sap flow measurements : the SAPFLUXNET database

Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The "sapfluxnetr" R package - designed to access, visualize, and process SAPFLUXNET data - is available from CRAN.Peer reviewe

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

Viscoelastic Signals for Optimal Resuscitation in Trauma: Kaolin Thrombelastography Cutoffs for Diagnosing Hypofibrinogenemia (VISOR Study)

© 2019 International Anesthesia Research Society. BACKGROUND: Acute traumatic coagulopathy is common in trauma patients. Prompt diagnosis of hypofibrinogenemia allows for early treatment with cryoprecipitate or fibrinogen concentrate. At present, optimal cutoffs for diagnosing hypofibrinogenemia with kaolin thrombelastography (TEG) have not been established. We hypothesized that kaolin kaolin-TEG parameters, such as kinetic time (K-Time), α-Angle, and maximum amplitude (MA), would accurately diagnose hypofibrinogenemia (fibrinogen \u3c200 mg/dL) and severe hypofibrinogenemia (fibrinogen \u3c100 mg/dL). METHODS: Adult trauma patients (injury severity score \u3e15) presenting to our trauma center between October 2015 and October 2017 were identified retrospectively. All patients had a traditional plasma fibrinogen measurement and kaolin-TEG performed within 15 minutes of each other and within 1 hour of admission. Some patients had additional measurements after. Receiver operating characteristic (ROC) curve analysis was performed to evaluate whether K-Time, α-Angle, and MA could diagnose hypofibrinogenemia and severe hypofibrinogenemia. Area under the ROC curve (AUROC) was calculated for each TEG parameter with a bootstrapped 99% confidence interval (CI). Further, ROC analysis was used to estimate ideal cutoffs for diagnosing hypofibrinogenemia and severe hypofibrinogenemia by maximizing sensitivity and specificity. In addition, likelihood ratios were also calculated for different TEG variable cutoffs to diagnose hypofibrinogenemia and severe hypofibrinogenemia. RESULTS: Seven hundred twenty-Two pairs of TEGs and traditional plasma fibrinogen measurements were performed in 623 patients with 99 patients having additional pairs of tests after the first hour. MA (AUROC = 0.84) and K-Time (AUROC = 0.83) better diagnosed hypofibrinogenemia than α-Angle (AUROC = 0.8; P =.03 and P \u3c.001 for AUROC comparisons, respectively). AUROCs statistically improved for each parameter when severe hypofibrinogenemia was modeled as the outcome (P \u3c.001). No differences were found between parameters for diagnosing severe hypofibrinogenemia (P \u3e.05 for all comparisons). The estimated optimal cutoffs for diagnosing hypofibrinogenemia were 1.5 minutes for K-Time (95% CI, 1.4-1.6), 70.0° for α-Angle (95% CI, 69.8-71.0), and 60.9 mm for MA (95% CI, 59.2-61.8). The estimated optimal cutoffs for diagnosing severe hypofibrinogenemia were 2.4 minutes for K-Time (95% CI, 1.7-2.8), 60.6° for α-Angle (95% CI, 57.2-67.3), and 51.2 mm for MA (95% CI, 49.0-56.2). Currently recommended K-Time and α-Angle cutoffs from the American College of Surgeons had low sensitivity for diagnosing hypofibrinogenemia (3%-29%), but sensitivity improved to 74% when using optimal cutoffs. CONCLUSIONS: Kaolin-TEG parameters can accurately diagnose hypofibrinogenemia and severe hypofibrinogenemia in trauma patients. Currently recommended cutoffs for the treatment of hypofibrinogenemia are skewed toward high specificity and low sensitivity. Many patients are likely to be undertreated for hypofibrinogenemia using current national guidelines

Remote estimation of grassland gross primary production during extreme meteorological seasons

Different models driven by remotely sensed vegetation indexes (VIs) and incident photosynthetically active radiation (PAR) were developed to estimate gross primary production (GPP) in a subalpine grassland equipped with an eddy covariance flux tower. Hyperspectral reflectance was collected using an automatic system designed for high temporal frequency acquisitions for three consecutive years, including one (2011) characterized by a strong reduction of the carbon sequestration rate during the vegetative season. Models based on remotely sensed and meteorological data were used to estimate GPP, and a cross-validation approach was used to compare the predictive capabilities of different model formulations. Vegetation indexes designed to be more sensitive to chlorophyll content explained most of the variability in GPP in the ecosystem investigated, characterized by a strong seasonal dynamic. Model performances improved when including also PARpotential defined as the maximal value of incident PAR under clear sky conditions in model formulations. Best performing models are based entirely on remotely sensed data. This finding could contribute to the development of methods for quantifying the temporal variation of GPP also on a broader scale using current and future satellite sensors.JRC.H.4-Monitoring Agricultural Resource

NDVI derived from near-infrared-enabled digital cameras: Applicability across different plant functional types

Time series of vegetation indices (e.g. normalized difference vegetation index [NDVI]) and color indices (e.g. green chromatic coordinate [G CC ]) based on radiometric measurements are now available at different spatial and temporal scales ranging from weekly satellite observations to sub-hourly in situ measurements by means of near-surface remote sensing (e.g. spectral sensors or digital cameras). In situ measurements are essential for providing validation data for satellite-derived vegetation indices. In this study we used a recently developed method to calculate NDVI from near-infrared (NIR) enabled digital cameras (NDVI C ) at 17 sites (for a total of 74 year-sites) encompassing six plant functional types (PFT) from the PhenoCam network.The seasonality of NDVI C was comparable to both NDVI measured by ground spectral sensors and by the moderate resolution imaging spectroradiometer (MODIS). We calculated site- and PFT-specific scaling factors to correct NDVI C values and recommend the use of site-specific NDVI from MODIS in order to scale NDVI C . We also compared G CC extracted from red-green-blue images to NDVI C and found PFT-dependent systematic differences in their seasonalities. During senescence, NDVI C lags behind G CC in deciduous broad-leaf forests and grasslands, suggesting that G CC is more sensitive to changes in leaf color and NDVI C is more sensitive to changes in leaf area. In evergreen forests, NDVI C peaks later than G CC in spring, probably tracking the processes of shoot elongation and new needle formation. Both G CC and NDVI C can be used as validation tools for the MODIS Land Cover Dynamics Product (MCD12Q2) for deciduous broad-leaf spring phenology, whereas NDVI C is more comparable than G CC with autumn phenology derived from MODIS. For evergreen forests, we found a poor relationship between MCD12Q2 and camera-derived phenology, highlighting the need for more work to better characterize the seasonality of both canopy structure and leaf biochemistry in those ecosystems.Our results demonstrate that NDVI C is in excellent agreement with NDVI obtained from spectral measurements, and that NDVI C and G CC can complement each other in describing ecosystem phenology. Additionally, NDVI C allows the detection of structural changes in the canopy that cannot be detected by visible-wavelength imagery