How drought events during the last century have impacted biomass carbon in Amazonian rainforests

During the last two decades, inventory data show that droughts have reduced biomass carbon sink of the Amazon forest by causing mortality to exceed growth. However, process-based models have struggled to include drought-induced responses of growth and mortality and have not been evaluated against plot data. A process-based model, ORCHIDEE-CAN-NHA, including forest demography with tree cohorts, plant hydraulic architecture and drought-induced tree mortality, was applied over Amazonia rainforests forced by gridded climate fields and rising CO₂ from 1901 to 2019. The model reproduced the decelerating signal of net carbon sink and drought sensitivity of aboveground biomass (AGB) growth and mortality observed at forest plots across selected Amazon intact forests for 2005 and 2010. We predicted a larger mortality rate and a more negative sensitivity of the net carbon sink during the 2015/16 El Niño compared with the former droughts. 2015/16 was indeed the most severe drought since 1901 regarding both AGB loss and area experiencing a severe carbon loss. We found that even if climate change did increase mortality, elevated CO₂ contributed to balance the biomass mortality, since CO₂-induced stomatal closure reduces transpiration, thus, offsets increased transpiration from CO₂-induced higher foliage area.     

Comparison of forest above‐ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation‐based estimates

Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above‐ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365 ± 66 Pg C, the spread corresponding to the standard deviation between models, compared to 275 Pg C with an uncertainty of ~13.5% from GlobBiomass. Model‐data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000 km. The 95th percentile of AGB density (AGB₉₅) in tropics can be considered as the potential maximum of AGB density which can be reached for a given annual precipitation. GlobBiomass data show local deficits of AGB density compared to the AGB₉₅, particularly in transitional and/or wet regions in tropics. We hypothesize that local human disturbances cause more AGB density deficits from GlobBiomass than from DGVMs, which rarely represent human disturbances. We then analyse empirical relationships between AGB density deficits and forest cover changes, population density, burned areas and livestock density. Regression analysis indicated that more than 40% of the spatial variance of AGB density deficits in South America and Africa can be explained; in Southeast Asia, these factors explain only ~25%. This result suggests TRENDY v6 DGVMs tend to underestimate biomass loss from diverse and widespread anthropogenic disturbances, and as a result overestimate turnover time in AGB.     

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.     

Cryptic phenology in plants: Case studies,implications, and recommendations

Plant phenology—the timing of cyclic or recurrent biological events in plants—offers insight into the ecology, evolution, and seasonality of plant‐mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season‐initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are “cryptic”—that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.     

Aquatic carbon fluxes dampen the overall variation of net ecosystem productivity in the Amazon basin: An analysis of the interannual variability in the boundless carbon cycle

The river–floodplain network plays an important role in the carbon (C) cycle of the Amazon basin, as it transports and processes a significant fraction of the C fixed by terrestrial vegetation, most of which evades as CO₂ from rivers and floodplains back to the atmosphere. There is empirical evidence that exceptionally dry or wet years have an impact on the net C balance in the Amazon. While seasonal and interannual variations in hydrology have a direct impact on the amounts of C transferred through the river–floodplain system, it is not known how far the variation of these fluxes affects the overall Amazon C balance. Here, we introduce a new wetland forcing file for the ORCHILEAK model, which improves the representation of floodplain dynamics and allows us to closely reproduce data‐driven estimates of net C exports through the river–floodplain network. Based on this new wetland forcing and two climate forcing datasets, we show that across the Amazon, the percentage of net primary productivity lost to the river–floodplain system is highly variable at the interannual timescale, and wet years fuel aquatic CO₂ evasion. However, at the same time overall net ecosystem productivity (NEP) and C sequestration are highest during wet years, partly due to reduced decomposition rates in water‐logged floodplain soils. It is years with the lowest discharge and floodplain inundation, often associated with El Nino events, that have the lowest NEP and the highest total (terrestrial plus aquatic) CO₂ emissions back to atmosphere. Furthermore, we find that aquatic C fluxes display greater variation than terrestrial C fluxes, and that this variation significantly dampens the interannual variability in NEP of the Amazon basin. These results call for a more integrative view of the C fluxes through the vegetation‐soil‐river‐floodplain continuum, which directly places aquatic C fluxes into the overall C budget of the Amazon basin.     

Adaptable mesocosm facility to study oil spill impacts on corals

Although numerous studies have been carried out on the impacts of oil spills on coral physiology, most have relied on laboratory assays. This scarcity is partly explained by the difficulty of reproducing realistic conditions in a laboratory setting or of performing experiments with toxic compounds in the field. Mesocosm systems provide the opportunity to carry out such studies with safe handling of contaminants while reproducing natural conditions required by living organisms. The mesocosm design is crucial and can lead to the development of innovative technologies to mitigate environmental impacts. Therefore, this study aimed to develop a mesocosm system for studies simulating oil spills with several key advantages, including true replication and the use of gravity to control flow‐through that reduces reliance on pumps that can clog thereby decreasing errors and costs. This adaptable system can be configured to (a) have continuous flow‐through; (b) operate as an open or closed system; (c) be fed by gravity; (d) have separate mesocosm sections that can be used for individual and simultaneous experiments; and (e) simulate the migration of oil from ocean oil spills to the nearby reefs. The mesocosm performance was assessed with two experiments using the hydrocoral Millepora alcicornis and different configurations to simulate two magnitudes of oil spills. With few exceptions, physical and chemical parameters remained stable within replicates and within treatments throughout the experiments. Physical and chemical parameters that expressed change during the experiment were still within the range of natural conditions observed in Brazilian marine environments. The photosynthetic potential (F_v/F_m) of the algae associated with M. alcicornis decreased in response to an 1% crude‐oil contamination, suggesting a successful delivery of the toxic contaminant to the targeted replicates. This mesocosm is customizable and adjustable for several types of experiments and proved to be effective for studies of oil spills.     

Detecting single molecules launched from liquid surfaces in a time-of-flight mass spectrometer using ultraviolet and infrared lasers

The launch of molecules from liquid surfaces in a time of flight mass spectrometer has been investigated using different sample preparation techniques, and by exposing the liquid samples to two different laser wavelengths, 337 nm from a N2 ultraviolet laser and 10.6 microm from a CO2 infrared laser. The molecules were detected with cryodetectors measuring the energy of the individual molecules. We present insulin and lysozyme results from samples introduced into the vacuum through a micromachined silicon injector, and from samples consisting of a glycerol droplet deposited directly on the sample holder at the high voltage stage of the ion optics.     

Identification of two novel ACTH-responsive genes encoding manganese-dependent superoxide dismutase (SOD2) and the zinc finger protein TIS11b [tetradecanoyl phorbol acetate (TPA)-inducible sequence 11b

ACTH is the major trophic factor regulating and maintaining adrenocortical function, affecting such diverse processes as steroidogenesis, cell proliferation, cell migration, and cell survival. We used differential display RT-PCR to identify genes that are rapidly induced by ACTH in the bovine adrenal cortex. Of 42 PCR products differentially amplified from primary cultures of bovine adrenocortical cells treated with 10 nM ACTH, six identified mRNAs that were confirmed by Northern blot analysis to be induced by ACTH. Four of these amplicons encoded noninformative repetitive sequences. Of the other two sequenced amplicons, one encoded a partial sequence for mitochondrial manganese-dependent superoxide dismutase (SOD2), an enzyme that is likely to protect adrenocortical cells from the cytotoxic effects of radical oxygen species generated during steroid biosynthesis. The second was identified as TIS11b (phorbol-12-myristate-13-acetate-inducible sequence 11b)/ERF-1/cMG, a member of the CCCH double-zinc finger protein family. SOD2 induction by ACTH was independent of extracellular steroid concentration or oxidative stress. SOD2 and TIS11b mRNA expressions were rapidly induced by ACTH, reaching a maximal level after 8 h and 3 h of treatment, respectively. These ACTH effects were mimicked by forskolin but appeared independent of cortisol secretion. Upon ACTH treatment, induction of TIS11b expression closely followed the previously characterized peak of vascular endothelial growth factor (VEGF) expression. Transfection of a TIS11b expression plasmid into 3T3 fibroblasts induced a decrease in the expression of a reporter gene placed upstream of the VEGF 3'-untranslated region, indicating that TIS11b may be an important regulator of VEGF expression through interaction with its 3'-untranslated region.     

Viral discovery and sequence recovery using DNA microarrays

Because of the constant threat posed by emerging infectious diseases and the limitations of existing approaches used to identify new pathogens, there is a great demand for new technological methods for viral discovery. We describe herein a DNA microarray-based platform for novel virus identification and characterization. Central to this approach was a DNA microarray designed to detect a wide range of known viruses as well as novel members of existing viral families; this microarray contained the most highly conserved 70mer sequences from every fully sequenced reference viral genome in GenBank. During an outbreak of severe acute respiratory syndrome (SARS) in March 2003, hybridization to this microarray revealed the presence of a previously uncharacterized coronavirus in a viral isolate cultivated from a SARS patient. To further characterize this new virus, approximately 1 kb of the unknown virus genome was cloned by physically recovering viral sequences hybridized to individual array elements. Sequencing of these fragments confirmed that the virus was indeed a new member of the coronavirus family. This combination of array hybridization followed by direct viral sequence recovery should prove to be a general strategy for the rapid identification and characterization of novel viruses and emerging infectious disease.