On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model

Phenology, the timing of recurring life cycle events, controls numerous land surface feedbacks to the climate system through the regulation of exchanges of carbon, water and energy between the biosphere and atmosphere. <br><br> Terrestrial biosphere models, however, are known to have systematic errors in the simulation of spring phenology, which potentially could propagate to uncertainty in modeled responses to future climate change. Here, we used the Harvard Forest phenology record to investigate and characterize sources of uncertainty in predicting phenology, and the subsequent impacts on model forecasts of carbon and water cycling. Using a model-data fusion approach, we combined information from 20 yr of phenological observations of 11 North American woody species, with 12 leaf bud-burst models that varied in complexity. <br><br> Akaike's Information Criterion indicated support for spring warming models with photoperiod limitations and, to a lesser extent, models that included chilling requirements. <br><br> We assessed three different sources of uncertainty in phenological forecasts: parameter uncertainty, model uncertainty, and driver uncertainty. The latter was characterized running the models to 2099 using 2 different IPCC climate scenarios (A1fi vs. B1, i.e. high CO<sub>2</sub> emissions vs. low CO<sub>2</sub> emissions scenario). Parameter uncertainty was the smallest (average 95% Confidence Interval – CI: 2.4 days century<sup>−1</sup> for scenario B1 and 4.5 days century<sup>−1</sup> for A1fi), whereas driver uncertainty was the largest (up to 8.4 days century<sup>−1</sup> in the simulated trends). The uncertainty related to model structure is also large and the predicted bud-burst trends as well as the shape of the smoothed projections varied among models (±7.7 days century<sup>−1</sup> for A1fi, ±3.6 days century<sup>−1</sup> for B1). The forecast sensitivity of bud-burst to temperature (i.e. days bud-burst advanced per degree of warming) varied between 2.2 days °C<sup>−1</sup> and 5.2 days °C<sup>−1</sup> depending on model structure. <br><br> We quantified the impact of uncertainties in bud-burst forecasts on simulated photosynthetic CO<sub>2</sub> uptake and evapotranspiration (ET) using a process-based terrestrial biosphere model. Uncertainty in phenology model structure led to uncertainty in the description of forest seasonality, which accumulated to uncertainty in annual model estimates of gross primary productivity (GPP) and ET of 9.6% and 2.9%, respectively. A sensitivity analysis shows that a variation of ±10 days in bud-burst dates led to a variation of ±5.0% for annual GPP and about ±2.0% for ET. <br><br> For phenology models, differences among future climate scenarios (i.e. driver) represent the largest source of uncertainty, followed by uncertainties related to model structure, and finally, related to model parameterization. The uncertainties we have quantified will affect the description of the seasonality of ecosystem processes and in particular the simulation of carbon uptake by forest ecosystems, with a larger impact of uncertainties related to phenology model structure, followed by uncertainties related to phenological model parameterization

Study on NGF and VEGF during the Equine Perinatal Period—Part 2: Foals Affected by Neonatal Encephalopathy

Simple Summary Based on human medicine, Neonatal Encephalopathy is the term used by equine clinicians for newborn foals which develop a variety of non-infectious neurological signs in the immediate postpartum period. It has become the preferred term because it does not imply a specific underlying etiology or pathophysiology, as hypoxia and ischemia may not be recognized in all cases. Understanding the underlying pathophysiology is important in formulating a rational approach to diagnosis. Our aim is to clinically characterize a population of foals spontaneously affected by Neonatal Encephalopathy and to evaluate the levels of trophic factors, such as nerve growth factor and vascular epithelial growth factor, and thyroid hormones obtained at birth/admission from a population of affected foals and in the first 72 h of life/hospitalization, as well as the expression of trophic factors in the placenta of mares that delivered foals affected by Neonatal Encephalopathy. The less pronounced decrease of the two trophic factors compared to healthy foals, their close relationship with thyroid hormones over time, and the dysregulation of trophic factor expression in placental tissues, could be key regulators in the mechanisms of equine Neonatal Encephalopathy. Neonatal Encephalopathy (NE) may be caused by hypoxic ischemic insults or inflammatory insults and modified by innate protective or excitatory mechanisms. Understanding the underlying pathophysiology is important in formulating a rational approach to diagnosis. The preliminary aim was to clinically characterize a population of foals spontaneously affected by NE. The study aimed to: (i) evaluate nerve growth factor (NGF) and vascular endothelial growth factor (VEGF) levels in plasma samples obtained in the affected population at parturition from the mare's jugular vein, umbilical cord vein and foal's jugular vein, as well as in amniotic fluid; (ii) evaluate the NGF and VEGF content in the plasma of foals affected by NE during the first 72 h of life/hospitalization; (iii) evaluate NGF and VEGF levels at birth/admission in relation to selected mare's and foal's clinical parameters; (iv) evaluate the relationship between the two trophic factors and thyroid hormone levels (TT3 and TT4) in the first 72 h of life/hospitalization; and (v) assess the mRNA expression of NGF, VEGF and brain-derived neurotrophic factor (BDNF), and their cell surface receptors, in the placenta of mares that delivered foals affected by NE. Thirteen affected foals born from mares hospitalized for peripartum monitoring (group NE) and twenty affected foals hospitalized after birth (group exNE) were included in the study. Dosage of NGF and VEGF levels was performed using commercial ELISA kits, whereas NGF, VEGF, and BDNF placental gene expression was performed using a semi-quantitative real-time PCR. In group NE, NGF levels decreased significantly from T0 to T24 (p = 0.0447) and VEGF levels decreased significantly from T0 to T72 (p = 0.0234), whereas in group exNE, only NGF levels decreased significantly from T0 to T24 (p = 0.0304). Compared to healthy foals, a significant reduction of TT3 levels was observed in both NE (T24, p = 0.0066; T72 p = 0.0003) and exNE (T0, p = 0.0082; T24, p < 0.0001; T72, p < 0.0001) groups, whereas a significant reduction of TT4 levels was observed only in exNE group (T0, p = 0.0003; T24, p = 0.0010; T72, p = 0.0110). In group NE, NGF levels were positively correlated with both TT3 (p = 0.0475; r = 0.3424) and TT4 levels (p = 0.0063; r = 0.4589).In the placenta, a reduced expression of NGF in the allantois (p = 0.0033) and a reduced expression of BDNF in the amnion (p = 0.0498) were observed. The less pronounced decrease of the two trophic factors compared to healthy foals, their relationship with thyroid hormones over time, and the reduced expression of NGF and BDNF in placental tissues of mares that delivered affected foals, could be key regulators in the mechanisms of equine NE

Study on NGF and VEGF during the Equine Perinatal Period—Part 1: Healthy Foals Born from Normal Pregnancy and Parturition

The importance of trophic factors, such as nerve growth factor (NGF), vascular endothelial growth factor (VEGF), and brain-derived neurotrophic factor (BDNF) during the perinatal period, is now emerging. Through their functional activities of neurogenesis and angiogenesis, they play a key role in the final maturation of the nervous and vascular systems. The present study aims to: (i) evaluate the NGF and VEGF levels obtained at parturition from the mare, foal and umbilical cord vein plasma, as well as in amniotic fluid; (ii) evaluate NGF and VEGF content in the plasma of healthy foals during the first 72 h of life (T0, T24 and T72); (iii) evaluate NGF and VEGF levels at parturition in relation to the selected mares’ and foals’ clinical parameters; (iv) evaluate the relationship between the two trophic factors and the thyroid hormone levels (TT3 and TT4) in the first 72 h of life; (v) assess mRNA expression of NGF, VEGF and BDNF and their cell surface receptors in the placenta. Fourteen Standardbred healthy foals born from mares with normal pregnancies and parturitions were included in the study. The dosage of NGF and VEGF levels was performed using commercial ELISA kits, whereas NGF, VEGF and BDNF placental gene expression was performed using semi-quantitative real-time PCR. In foal plasma, both NGF and VEGF levels decreased significantly over time, from T0 to T24 (p = 0.0066 for NGF; p < 0.0001 for VEGF) and from T0 to T72 (p = 0.0179 for NGF; p = 0.0016 for VEGF). In foal serum, TT3 levels increased significantly over time from T0 to T24 (p = 0.0058) and from T0 to T72 (p = 0.0013), whereas TT4 levels decreased significantly over time from T0 to T24 (p = 0.0201) and from T0 to T72 (p < 0.0001). A positive correlation was found in the levels of NGF and VEGF in foal plasma at each time point (p = 0.0115; r = 0.2862). A positive correlation was found between NGF levels in the foal plasma at T0 and lactate (p = 0.0359; r = 0.5634) as well as between VEGF levels in the foal plasma at T0 and creatine kinase (p = 0.0459; r = 0.5407). VEGF was expressed in all fetal membranes, whereas NGF and its receptors were not expressed in the amnion. The close relationship between the two trophic factors in foal plasma over time and their fine expression in placental tissues appear to be key regulators of fetal development and adaptation to extra-uterine life

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

Gianluca Tramontana was supported by the GEOCARBON EU FP7 project (GA 283080). Dario Papale, Martin Jung and Markus Reichstein acknowledge funding from the EU FP7 project GEOCARBON (grant agreement no. 283080) and the EU H2020 BACI project (grant agreement no. 640176). Gustau Camps-Valls wants to acknowledge the support by an ERC Consolidator Grant with grant agreement 647423 (SEDAL). Kazuhito Ichii was supported by Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan and the JAXA Global Change Observation Mission (GCOM) project (no. 115). Christopher R. Schwalm was supported by National Aeronautics and Space Administration (NASA) grants nos. NNX12AP74G, NNX10AG01A, and NNX11AO08A. M. Altaf Arain thanks the support of Natural Sciences and Engineering Research Council (NSREC) of Canada. Penelope Serrano Ortiz was partially supported by the GEISpain project (CGL2014-52838-C2-1-R) funded by the Spanish Ministry of Economy and Competitiveness and the European Union ERDF funds. Sebastian Wolf acknowledges support from a Marie Curie International Outgoing Fellowship (European Commission, grant 300083). The FLUXCOM initiative is coordinated by Martin Jung, Max Planck Institute for Biogeochemistry (Jena, Germany). This work used eddy-covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, FluxnetCanada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy-covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, the Max Planck Institute for Biogeochemistry, the National Science Foundation, the University of Tuscia and the US Department of Energy, and the databasing and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, the University of California - Berkeley, and the University of Virginia.Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R2  0.6), gross primary production (R2> 0.7), latent heat (R2 > 0.7), sensible heat (R2 > 0.7), and net radiation (R2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.European Union (EU) GA 283080 283080 640176European Research Council (ERC) 647423Ministry of the Environment, Japan 2-1401JAXA Global Change Observation Mission (GCOM) project 115National Aeronautics & Space Administration (NASA) NNX12AP74G NNX10AG01A NNX11AO08ANatural Sciences and Engineering Research Council of CanadaGEISpain project - Spanish Ministry of Economy and Competitiveness CGL2014-52838-C2-1-REuropean Commission Joint Research Centre 300083United States Department of Energy (DOE) DE-FG02-04ER63917 DE-FG02-04ER63911FAO-GTOS-TCOiLEAPSMax Planck Institute for BiogeochemistryNational Science Foundation (NSF)University of Tusci

Estimations of isoprenoid emission capacity from enclosure studies: measurements, data processing, quality and standardized measurement protocols

The capacity for volatile isoprenoid production under standardized environmental conditions at a certain time (ES, the emission factor) is a key characteristic in constructing isoprenoid emission inventories. However, there is large variation in published ES estimates for any given species partly driven by dynamic modifications in ES due to acclimation and stress responses. Here we review additional sources of variation in ES estimates that are due to measurement and analytical techniques and calculation and averaging procedures, and demonstrate that estimations of ES critically depend on applied experimental protocols and on data processing and reporting. A great variety of experimental setups has been used in the past, contributing to study-to-study variations in ES estimates. We suggest that past experimental data should be distributed into broad quality classes depending on whether the data can or cannot be considered quantitative based on rigorous experimental standards. Apart from analytical issues, the accuracy of ES values is strongly driven by extrapolation and integration errors introduced during data processing. Additional sources of error, especially in meta-database construction, can further arise from inconsistent use of units and expression bases of ES. We propose a standardized experimental protocol for BVOC estimations and highlight basic meta-information that we strongly recommend to report with any ES measurement. We conclude that standardization of experimental and calculation protocols and critical examination of past reports is essential for development of accurate emission factor databases.JRC.H.7-Climate Risk Managemen

METHODS AND CHALLENGES IN TIMESERIES ANALYSIS OF VEGETATION IN THE GEOSPATIAL DOMAIN

The increasing availability of remotely sensed data have offered unprecedented possibilities for monitoring and analysis of environmental variables, including boosting recent studies in the field of ecosystem resilience relying on indicators derived from timeseries analysis, such as the temporal autocorrelation of vegetation indices. A forest ecosystem with decreased resilience will be more susceptible to external drivers and their change and could shift into an alternative system configuration by crossing a tipping point. Nevertheless, remote sensing data quantifying vegetation and forests properties inherently carry information related to the climate as well, which has to be accounted for before performing any modelling exercise. In this paper, we aim to present the general workflow and the challenges encountered in processing and analysing the historical, high-frequency and high-resolution timeseries of vegetation and climatic data. The final aim is training a machine learning model (Random Forest) in order to model and explore the performance and importance of a set of climatic and environmental metrics in predicting an indicator of the resilience of forests. In this case, the resilience of forests is quantified through the temporal autocorrelation (TAC) of the kernel NDVI (kNDVI). Climatic and environmental predictors include 2-meter air temperature, total precipitation, vapour pressure deficit, surface solar radiation, forest cover and soil organic carbon content. Results show a good performance of the Random Forest model and the ranking in the importance of the predicting variables captured in terms of background climate and climate variability. This application allows to separate and identify the main drivers of the temporal autocorrelation of kNDVI

Design and in vitro study of a dual drug-loaded delivery system produced by electrospinning for the treatment of acute injuries of the central nervous system

Vascular and traumatic injuries of the central nervous system are recognized as global health priorities. A polypharmacology approach that is able to simultaneously target several injury factors by the combination of agents having synergistic effects appears to be promising. Herein, we designed a polymeric delivery system loaded with two drugs, ibuprofen (Ibu) and thyroid hormone triiodothyronine (T3) to in vitro release the suitable amount of the anti-inflammation and the remyelination drug. As a production method, electrospinning technology was used. First, Ibuloaded micro (diameter circa 0.95–1.20 µm) and nano (diameter circa 0.70 µm) fibers were produced using poly(L-lactide) PLLA and PLGA with different lactide/glycolide ratios (50:50, 75:25, and 85:15) to select the most suitable polymer and fiber diameter. Based on the in vitro release results and in-house knowledge, PLLA nanofibers (mean diameter = 580 ± 120 nm) loaded with both Ibu and T3 were then successfully produced by a co-axial electrospinning technique. The in vitro release studies demonstrated that the final Ibu/T3 PLLA system extended the release of both drugs for 14 days, providing the target sustained release. Finally, studies in cell cultures (RAW macrophages and neural stem cell-derived oligodendrocyte precursor cells—OPCs) demonstrated the anti-inflammatory and promyelinating efficacy of the dual drug-loaded delivery platform

On the choice of the driving temperature for eddy-covariance carbon dioxide flux partitioning

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