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

International audienc

Historical and future contributions of inland waters to the Congo Basin carbon balance

International audienceAs the second largest area of contiguous tropical rainforest and second largest river basin in the world, the Congo Basin has a significant role to play in the global carbon (C) cycle. For the present day, it has been shown that a significant proportion of global terrestrial net primary productivity (NPP) is transferred laterally to the land-ocean aquatic continuum (LOAC) as dissolved CO 2 , dissolved organic carbon (DOC), and particulate organic carbon (POC). Whilst the importance of LOAC fluxes in the Congo Basin has been demonstrated for the present day, it is not known to what extent these fluxes have been perturbed historically, how they are likely to change under future climate change and land use scenarios, and in turn what impact these changes might have on the overall C cycle of the basin. Here we apply the ORCHILEAK model to the Congo Basin and estimate that 4 % of terrestrial NPP (NPP = 5800 ± 166 Tg C yr −1) is currently exported from soils and vegetation to inland waters. Further, our results suggest that aquatic C fluxes may have undergone considerable perturbation since 1861 to the present day, with aquatic CO 2 evasion and C export to the coast increasing by 26 % (186±41 to 235 ± 54 Tg C yr −1) and 25 % (12 ± 3 to 15 ± 4 Tg C yr −1), respectively, largely because of rising atmospheric CO 2 concentrations. Moreover, under climate scenario RCP6.0 we predict that this perturbation could continue; over the full simulation period (1861-2099), we estimate that aquatic CO 2 evasion and C export to the coast could increase by 79 % and 67 %, respectively. Finally, we show that the proportion of terrestrial NPP lost to the LOAC could increase from approximately 3 % to 5 % from 1861-2099 as a result of increasing atmospheric CO 2 concentrations and climate change. However, our future projections of the Congo Basin C fluxes in particular need to be interpreted with some caution due to model limitations. We discuss these limitations, including the wider challenges associated with applying the current generation of land surface models which ignore nutrient dynamics to make future projections of the tropical C cycle, along with potential next steps

How inert, perturbing, or interacting are cryogenic matrices? A combined spectroscopic (infrared, electronic, and x-ray absorption) and DFT investigation of matrix-isolated Iron, Cobalt, Nickel, and Zinc Dibromides

The interactions of FeBr2, CoBr2, NiBr2 and ZnBr2 with Ne, Ar, Kr, Xe, CH4 and N2 matrices have been investigated using IR, electronic absorption and X-ray absorption spectroscopies, as well as DFT calculations. ZnBr2 is linear in all the matrices. NiBr2 is linear in all but N2 matrices where it is severely bent. For FeBr2 and CoBr2 there is a more gradual change, with evidence of non-linearity in Xe and CH4 matrices as well as N2. In the N2 matrices the presence of νNN modes blue shifted from the “free” N2 values indicates the presence of physisorbed species, and the magnitude of the blue-shift correlates with the shift in the ν3 mode of the metal dibromide. In the case of NiCl2 and NiBr2 chemisorbed species are formed after photolysis, but only if deposition takes place below 10 K. There was no evidence for chemisorbed species for NiF2 and FeBr2 and in the case of CoBr2 the evidence was not strong

ArborZ: Photometric Redshifts Using Boosted Decision Trees

Precision photometric redshifts will be essential for extracting cosmological parameters from the next generation of wide-area imaging surveys. In this paper we introduce a photometric redshift algorithm, ArborZ, based on the machine-learning technique of Boosted Decision Trees. We study the algorithm using galaxies from the Sloan Digital Sky Survey and from mock catalogs intended to simulate both the SDSS and the upcoming Dark Energy Survey. We show that it improves upon the performance of existing algorithms. Moreover, the method naturally leads to the reconstruction of a full probability density function (PDF) for the photometric redshift of each galaxy, not merely a single "best estimate" and error, and also provides a photo-z quality figure-of-merit for each galaxy that can be used to reject outliers. We show that the stacked PDFs yield a more accurate reconstruction of the redshift distribution N(z). We discuss limitations of the current algorithm and ideas for future work.Comment: 10 pages, 13 figures, submitted to Ap

Environmental drivers of aquatic macrophyte communities in southern tropical African rivers:Zambia as a case study

The first-ever extensive macrophyte survey of Zambian rivers and associated floodplain waterbodies, conducted during 2006–2012, collected 271 samples from 228 sites, mainly located in five freshwater ecoregions of the world primarily represented in Zambia. The results supported the hypothesis that variation in macrophyte community structure (measured as species composition and diversity) in southern tropical African river systems, using Zambia as a case study area, is driven primarily by geographical variation in water physico-chemical conditions. In total, 335 macrophyte taxa were recorded, and a chronological cumulative species records curve for the dataset showed no sign of asymptoting: clearly many additional macrophyte species remain to be found in Zambian rivers. Emergent macrophytes were predominant (236 taxa), together with 26 floating and 73 submerged taxa. Several species were rare in a regional or international context, including two IUCN Red Data List species: Aponogeton rehmanii and Nymphaea divaricata. Ordination and classification analysis of the data found little evidence for temporal change in vegetation, at repeatedly-sampled sites, but strong evidence for the existence of seven groups of samples from geographically-varied study sites. These supported differing sets of vegetation (with eight species assemblages present in the sample-groups) and showed substantial inter-group differences in both macrophyte alpha-diversity, and geographically-varying physico-chemical parameters. The evidence suggested that the main environmental drivers of macrophyte community composition and diversity were altitude, stream order, shade, pH, alkalinity, NO3-N, and underwater light availability, while PO4-P showed slightly lower, but still significant variation between sample-groups

The presence of peat and variation in tree species composition are under different hydrological controls in Amazonian wetland forests

This research was funded by the Gordon and Betty Moore Foundation, through grant #5349 ‘Monitoring protected areas in Peru to increase forest resilience to climate change’, and NERC standard grant ‘Carbon Storage in Amazonian Peatlands: Distribution and Dynamics’(NE/R000751/1).The peat-forming wetland forests of Amazonia are characterised by high below-carbon stocks and supply fruit, fibres and timber to local communities. Predicting the future of these ecosystem services requires understanding how hydrological conditions are related to tree species composition and the presence, or absence, of peat. Here, we use continuous measurements of water table depth over 2.5 years and manual measurements of pore-water pH and electrical conductivity to understand the ecohydrological controls of these variables across the large peatland complex in northern Peruvian Amazonia. Measurements were taken in permanent forest plots in four palm swamps, four seasonally flooded forests and four peatland pole forests. All trees ≥10 cm diameter were also measured and identified in the plots to assess floristic composition. Peat occurs in eight of these twelve sites; three seasonally flooded forests and one palm swamp are not associated with peat. Variation in tree species composition among forest types was linked to high flood levels (maximum flooding height) and pH: seasonally flooded forests experience high flood levels (up to 3.66 m from the ground surface) and have high pH values (6?7), palm swamps have intermediate flood levels (up to 1.34 m) and peatland pole forests experience shallow flooding (up to 0.28 m) and have low pH (4). In contrast, the presence of peat was linked to variation in maximum water table depth (ie the depth to which the water table drops below the ground surface). Surface peat is found in all forest types where maximum water table depth does not fall >0.55 m below the ground surface at any time. Peat formation and variation in tree species composition therefore have different ecohydrological controls. Predicted increases in the frequency and strength of flooding events may alter patterns of tree species composition, whereas increases in drought severity and declines in minimum river levels may pose a greater risk to the belowground carbon stores of these peatland ecosystems.Publisher PDFPeer reviewe

Eight-Dimensional Mid-Infrared/Optical Bayesian Quasar Selection

We explore the multidimensional, multiwavelength selection of quasars from mid-IR (MIR) plus optical data, specifically from Spitzer-IRAC and the Sloan Digital Sky Survey (SDSS). We apply modern statistical techniques to combined Spitzer MIR and SDSS optical data, allowing up to 8-D color selection of quasars. Using a Bayesian selection method, we catalog 5546 quasar candidates to an 8.0 um depth of 56 uJy over an area of ~24 sq. deg; ~70% of these candidates are not identified by applying the same Bayesian algorithm to 4-color SDSS optical data alone. Our selection recovers 97.7% of known type 1 quasars in this area and greatly improves the effectiveness of identifying 3.5<z<5 quasars. Even using only the two shortest wavelength IRAC bandpasses, it is possible to use our Bayesian techniques to select quasars with 97% completeness and as little as 10% contamination. This sample has a photometric redshift accuracy of 93.6% (Delta Z +/-0.3), remaining roughly constant when the two reddest MIR bands are excluded. While our methods are designed to find type 1 (unobscured) quasars, as many as 1200 of the objects are type 2 (obscured) quasar candidates. Coupling deep optical imaging data with deep mid-IR data could enable selection of quasars in significant numbers past the peak of the quasar luminosity function (QLF) to at least z~4. Such a sample would constrain the shape of the QLF and enable quasar clustering studies over the largest range of redshift and luminosity to date, yielding significant gains in our understanding of quasars and the evolution of galaxies.Comment: 49 pages, 14 figures, 7 tables. AJ, accepte

The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression

Available online 5 March 2020.Through the Human Connectome Project (HCP) our understanding of the functional connectome of the healthy brain has been dramatically accelerated. Given the pressing public health need, we must increase our understanding of how connectome dysfunctions give rise to disordered mental states. Mental disorders arising from high levels of negative emotion or from the loss of positive emotional experience affect over 400 million people globally. Such states of disordered emotion cut across multiple diagnostic categories of mood and anxiety disorders and are compounded by accompanying disruptions in cognitive function. Not surprisingly, these forms of psychopathology are the leading cause of disability worldwide. The Research Domain Criteria (RDoC) initiative spearheaded by NIMH offers a framework for characterizing the relations among connectome dysfunctions, anchored in neural circuits and phenotypic profiles of behavior and self-reported symptoms. Here, we report on our Connectomes Related to Human Disease protocol for integrating an RDoC framework with HCP protocols to characterize connectome dysfunctions in disordered emotional states, and present quality control data from a representative sample of participants. We focus on three RDoC domains and constructs most relevant to depression and anxiety: 1) loss and acute threat within the Negative Valence System (NVS) domain; 2) reward valuation and responsiveness within the Positive Valence System (PVS) domain; and 3) working memory and cognitive control within the Cognitive System (CS) domain. For 29 healthy controls, we present preliminary imaging data: functional magnetic resonance imaging collected in the resting state and in tasks matching our constructs of interest (“Emotion”, “Gambling” and “Continuous Performance” tasks), as well as diffusion-weighted imaging. All functional scans demonstrated good signal-to-noise ratio. Established neural networks were robustly identified in the resting state condition by independent component analysis. Processing of negative emotional faces significantly activated the bilateral dorsolateral prefrontal and occipital cortices, fusiform gyrus and amygdalae. Reward elicited a response in the bilateral dorsolateral prefrontal, parietal and occipital cortices, and in the striatum. Working memory was associated with activation in the dorsolateral prefrontal, parietal, motor, temporal and insular cortices, in the striatum and cerebellum. Diffusion tractography showed consistent profiles of fractional anisotropy along known white matter tracts. We also show that results are comparable to those in a matched sample from the HCP Healthy Young Adult data release. These preliminary data provide the foundation for acquisition of 250 subjects who are experiencing disordered emotional states. When complete, these data will be used to develop a neurobiological model that maps connectome dysfunctions to specific behaviors and symptoms.This work was supported by the National Institutes of Health [grant number U01MH109985 under PAR-14-281]