Observed Growth Rates Research Articles

Supermassive black holes are growing slowly by z∼5

ABSTRACT We investigate the black hole mass function at z ∼ 5 using XQz5, our recent sample of the most luminous quasars between the redshifts 4.5 < z < 5.3. We include 72 quasars with black hole masses estimated from velocity-broadened emission-line measurements and single-epoch virial prescriptions in the footprint of a highly complete parent survey. The sample mean Eddington ratio and standard deviation is log λ ≈ −0.20 ± 0.24. The completeness-corrected mass function is modelled as a double power law, and we constrain its evolution across redshift assuming accretion-dominated mass growth. We estimate the evolution of the mass function from z = 5–4, presenting joint constraints on accretion properties through a measured dimensionless e-folding parameter, kef ≡ 〈λ〉U(1 − ϵ)/ϵ = 1.79 ± 0.06, where 〈λ〉 is the mean Eddington ratio, U is the duty cycle, and ϵ is the radiative efficiency. If these supermassive black holes were to form from seeds smaller than $10^8\, {\rm M}_{\odot }$, the growth rate must have been considerably faster at z ≫ 5 than observed from z = 5–4. A growth rate exceeding 3 × the observed rate would reduce the initial heavy seed mass to $10^{5-6}\, {\rm M}_{\odot }$, aligning with supermassive star and/or direct collapse seed masses. Stellar mass ($10^2\, {\rm M}_{\odot }$) black hole seeds would require ≳4.5 × the observed growth rate at z ≫ 5 to reproduce the measured active black hole mass function. A possible pathway to produce the most extreme quasars is radiatively inefficient accretion flow, suggesting black holes with low angular momentum or photon trapping in supercritically accreting thick discs.

The Significant Role of New Particle Composition and Morphology on the HNO3-Driven Growth of Particles down to Sub-10 nm.

New particle formation and growth greatly influence air quality and the global climate. Recent CERN Cosmics Leaving OUtdoor Droplets (CLOUD) chamber experiments proposed that in cold urban atmospheres with highly supersaturated HNO3 and NH3, newly formed sub-10 nm nanoparticles can grow rapidly (up to 1000 nm h-1). Here, we present direct observational evidence that in winter Beijing with persistent highly supersaturated HNO3 and NH3, nitrate contributed less than ∼14% of the 8-40 nm nanoparticle composition, and overall growth rates were only ∼0.8-5 nm h-1. To explain the observed growth rates and particulate nitrate fraction, the effective mass accommodation coefficient of HNO3 (αHNO3) on the nanoparticles in urban Beijing needs to be 2-4 orders of magnitude lower than those in the CLOUD chamber. We propose that the inefficient uptake of HNO3 on nanoparticles is mainly due to the much higher particulate organic fraction and lower relative humidity in urban Beijing. To quantitatively reproduce the observed growth, we show that an inhomogeneous "inorganic core-organic shell" nanoparticle morphology might exist for nanoparticles in Beijing. This study emphasized that growth for nanoparticles down to sub-10 nm was largely influenced by their composition, which was previously ignored and should be considered in future studies on nanoparticle growth.

Food quality shapes gradual phenotypic plasticity in ectotherms facing temperature variability.

Organisms exhibit reversible physiological adjustments as a response to rapidly changing environments. Yet such plasticity of the phenotype is gradual and may lag behind environmental fluctuations, thereby affecting long-term average performance of the organisms. By supplying energy and essential compounds for optimal tissue building, food determines the range of possible phenotypic changes and potentially the rate at which they occur. Here, we assess how differences in the dietary supply of essential lipids modulate the phenotypic plasticity of an ectotherm facing thermal fluctuations. We use three phytoplankton strains to create a gradient of polyunsaturated fatty acid and sterol supply for Daphnia magna under constant and fluctuating temperatures. We used three different fluctuation periodicities to unravel the temporal dynamics of gradual plasticity and its long-term consequences for D. magna performance measured as juvenile somatic growth rate. In agreement with gradual plasticity theory, we show that in D. magna, fluctuation periodicity determines the differential between observed growth rates and those expected from constant conditions. Most importantly, we show that diet modulates both the size and the direction of the growth rate differential. Overall, we demonstrate that the nutritional context is essential for predicting ectotherm consumers' performance in fluctuating thermal environments.

Modelling individual tree maximum basal area growth rates of five tall eucalypt species growing in even-aged forests

Inventory plot data were available providing over 87,000 individual tree basal area growth rates from even-aged native forests of three ash eucalypts (Eucalyptus regnans, E. obliqua, and E. delegatensis), from temperate regions, and two other species from more sub-tropical climes (E. grandis and E. pilularis). Models were developed relating maximum observed growth rates for these species in relation to tree size when, presumably, trees were under ideal environmental conditions and without competition from neighbours for site growth resources. These maximum growth rates increased with increasing tree size to a maximum of their own and then declined as tree size (hence age) increased further. The tree sizes, at which these maximum growth rates reached their maxima, were much greater for the ash eucalypts than for the other two species. It is hypothesised that the ash eucalypts may have evolved physiological characteristics that make them more efficient in compensating for the well-known physiological constraints imposed on growth rates as trees grow to great heights and ages.

Simultaneous observation on the post-sunset occurrence of travelling ionospheric disturbance alongside ionospheric irregularities over the East Africa low latitude region

Using a data set from the network of more than 30 GNSS receivers within the East Africa sector; a study on the transient wave-like total electron content (TEC) modulation alongside the post-sunset irregularities has been presented. The study period is during the years 2019 to 2021 and the year 2014. The TEC modulation was detected from the quasi-periodic variation of detrended TEC and occurs nearly on a day-to-day basis at post-sunset hours just like the post-sunset ionospheric irregularities. The wavelike TEC modulation is likely due to gravity waves generated at the lower atmosphere that propagates into the F region and is quantified as travelling ionospheric disturbance (TIDs). The dominant quasi-periods of these TIDs are between 10 and 50 min. The TIDS, just like the irregularities, were confined in the latitudinal range of 0–9.4oS magnetic latitudes within the East Africa sector. A statistical analysis of simultaneous observations of the post-sunset TIDs shows that the day-to-day variations are identical to that of the ionospheric irregularity occurrence rate. The TIDs exhibit a relatively strong wave oscillation with multiple wavelengths preceding/accompanying the occurrence of irregularities. The simultaneous observation of TIDs and irregularities shows that the wavelike seeding perturbation associated with gravity waves from the lower atmosphere combined with strong seeding forces (unknown source) could facilitate the observed growth rate of the Rayleigh Taylor Instability mechanism that is associated with the formation of ionospheric irregularities. Additional experiments and tropospheric observation may be required to ascertain the source of gravity waves that are linked to TEC perturbation during nighttime hours.

Proteome efficiency of metabolic pathways in Escherichia coli increases along the nutrient flow.

Understanding the allocation of the cellular proteome to different cellular processes is central to unraveling the organizing principles of bacterial physiology. Proteome allocation to protein translation itself is maximally efficient, i.e., it represents the minimal allocation of dry mass able to sustain the observed protein production rate. In contrast, recent studies on bacteria have demonstrated that the concentrations of many proteins exceed the minimal level required to support the observed growth rate, indicating some heterogeneity across pathways in their proteome efficiency. Here, we systematically analyze the proteome efficiency of metabolic pathways, which together account for more than half of the Escherichia coli proteome during exponential growth. Comparing the predicted minimal and the observed proteome allocation to different metabolic pathways across growth conditions, we find that the protein abundance in the most costly biosynthesis pathways-those for amino acid biosynthesis and cofactor biosynthesis-is regulated for near-optimal efficiency. Overall, proteome efficiency increases along the carbon flow through the metabolic network; proteins involved in pathways of nutrient uptake and central metabolism tend to be highly over-abundant, while proteins involved in anabolic pathways and in protein translation are much closer to the expected minimal abundance across conditions. Our work thus provides a bird's-eye view of metabolic pathway efficiency, demonstrating systematic deviations from optimal cellular efficiency at the network level. IMPORTANCE Protein translation is the most expensive cellular process in fast-growing bacteria, and efficient proteome usage should thus be under strong natural selection. However, recent studies show that a considerable part of the proteome is unneeded for instantaneous cell growth in Escherichia coli. We still lack a systematic understanding of how this excess proteome is distributed across different pathways as a function of the growth conditions. We estimated the minimal required proteome across growth conditions in E. coli and compared the predictions with experimental data. We found that the proteome allocated to the most expensive internal pathways, including translation and the synthesis of amino acids and cofactors, is near the minimally required levels. In contrast, transporters and central carbon metabolism show much higher proteome levels than the predicted minimal abundance. Our analyses show that the proteome fraction unneeded for instantaneous cell growth decreases along the nutrient flow in E. coli.

Local flux coordination and global gene expression regulation in metabolic modeling

Genome-scale metabolic networks (GSMs) are fundamental systems biology representations of a cell’s entire set of stoichiometrically balanced reactions. However, such static GSMs do not incorporate the functional organization of metabolic genes and their dynamic regulation (e.g., operons and regulons). Specifically, there are numerous topologically coupled local reactions through which fluxes are coordinated; the global growth state often dynamically regulates many gene expression of metabolic reactions via global transcription factor regulators. Here, we develop a GSM reconstruction method, Decrem, by integrating locally coupled reactions and global transcriptional regulation of metabolism by cell state. Decrem produces predictions of flux and growth rates, which are highly correlated with those experimentally measured in both wild-type and mutants of three model microorganisms Escherichia coli, Saccharomyces cerevisiae, and Bacillus subtilis under various conditions. More importantly, Decrem can also explain the observed growth rates by capturing the experimentally measured flux changes between wild-types and mutants. Overall, by identifying and incorporating locally organized and regulated functional modules into GSMs, Decrem achieves accurate predictions of phenotypes and has broad applications in bioengineering, synthetic biology, and microbial pathology.

Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding.

When bacterial species with the same resource preferences share the same growth environment, it is commonly believed that direct competition will arise. A large variety of competition and more general 'interaction' models have been formulated, but what is currently lacking are models that link monoculture growth kinetics and community growth under inclusion of emerging biological interactions, such as metabolite cross-feeding. In order to understand and mathematically describe the nature of potential cross-feeding interactions, we design experiments where two bacterial species Pseudomonas putida and Pseudomonas veronii grow in liquid medium either in mono- or as co-culture in a resource-limited environment. We measure population growth under single substrate competition or with double species-specific substrates (substrate 'indifference'), and starting from varying cell ratios of either species. Using experimental data as input, we first consider a mean-field model of resource-based competition, which captures well the empirically observed growth rates for monocultures, but fails to correctly predict growth rates in co-culture mixtures, in particular for skewed starting species ratios. Based on this, we extend the model by cross-feeding interactions where the consumption of substrate by one consumer produces metabolites that in turn are resources for the other consumer, thus leading to positive feedback in the species system. Two different cross-feeding options were considered, which either lead to constant metabolite cross-feeding, or to a regulated form, where metabolite utilization is activated with rates according to either a threshold or a Hill function, dependent on metabolite concentration. Both mathematical proof and experimental data indicate regulated cross-feeding to be the preferred model to constant metabolite utilization, with best co-culture growth predictions in case of high Hill coefficients, close to binary (on/off) activation states. This suggests that species use the appearing metabolite concentrations only when they are becoming high enough; possibly as a consequence of their lower energetic content than the primary substrate. Metabolite sharing was particularly relevant at unbalanced starting cell ratios, causing the minority partner to proliferate more than expected from the competitive substrate because of metabolite release from the majority partner. This effect thus likely quells immediate substrate competition and may be important in natural communities with typical very skewed relative taxa abundances and slower-growing taxa. In conclusion, the regulated bacterial interaction network correctly describes species substrate growth reactions in mixtures with few kinetic parameters that can be obtained from monoculture growth experiments.

Resource allocation accounts for the large variability of rate-yield phenotypes across bacterial strains.

Different strains of a microorganism growing in the same environment display a wide variety of growth rates and growth yields. We developed a coarse-grained model to test the hypothesis that different resource allocation strategies, corresponding to different compositions of the proteome, can account for the observed rate-yield variability. The model predictions were verified by means of a database of hundreds of published rate-yield and uptake-secretion phenotypes of Escherichia coli strains grown in standard laboratory conditions. We found a very good quantitative agreement between the range of predicted and observed growth rates, growth yields, and glucose uptake and acetate secretion rates. These results support the hypothesis that resource allocation is a major explanatory factor of the observed variability of growth rates and growth yields across different bacterial strains. An interesting prediction of our model, supported by the experimental data, is that high growth rates are not necessarily accompanied by low growth yields. The resource allocation strategies enabling high-rate, high-yield growth of E. coli lead to a higher saturation of enzymes and ribosomes, and thus to a more efficient utilization of proteomic resources. Our model thus contributes to a fundamental understanding of the quantitative relationship between rate and yield in E. coli and other microorganisms. It may also be useful for the rapid screening of strains in metabolic engineering and synthetic biology.

Impact of instrumentation reliability on mainstream suspended Partial denitrification anammox (PdNA).

This study successfully revealed the importance of probe reliability and sensitivity with ion sensitive electrode (ISE) probes on achieving high partial denitrification (PdN) efficiency; and decreasing carbon overdosing events that cause the decline of microbial populations and performance of partial denitrification-anammox (PdNA). In a mainstream integrated hybrid granule-floc system, an average PdN efficiency of 76% was achieved with acetate as the carbon source. Thauera was identified as the dominant PdN species; Its presence in the system was analogous to instrumentation reliability and PdN selection and wasn't a consequence of bioaugmentation. Up to 27-121 mg TIN/L/d, an equivalent of 18 - 48% of the overall TIN removed, was achieved through the PdNA pathway. Candidatus Brocadia was the main anoxic ammonium oxidizing bacteria (AnAOB) species that was seeded from sidestream, and enriched and retained in the mainstream system with observed growth rates of 0.04 - 0.13 d-1 . Moreover, there was no direct negative impact of methanol's use for post-polishing on AnAOB activity and growth.

Chromosomal Position of Ribosomal Protein Genes Affects Long-Term Evolution of Vibrio cholerae.

It is unclear how gene order within the chromosome influences genome evolution. Bacteria cluster transcription and translation genes close to the replication origin (oriC). In Vibrio cholerae, relocation of s10-spc-α locus (S10), the major locus of ribosomal protein genes, to ectopic genomic positions shows that its relative distance to the oriC correlates to a reduction in growth rate, fitness, and infectivity. To test the long-term impact of this trait, we evolved 12 populations of V. cholerae strains bearing S10 at an oriC-proximal or an oriC-distal location for 1,000 generations. During the first 250 generations, positive selection was the main force driving mutation. After 1,000 generations, we observed more nonadaptative mutations and hypermutator genotypes. Populations fixed inactivating mutations at many genes linked to virulence: flagellum, chemotaxis, biofilm, and quorum sensing. Throughout the experiment, all populations increased their growth rates. However, those bearing S10 close to oriC remained the fittest, indicating that suppressor mutations cannot compensate for the genomic position of the main ribosomal protein locus. Selection and sequencing of the fastest-growing clones allowed us to characterize mutations inactivating, among other sites, flagellum master regulators. Reintroduction of these mutations into the wild-type context led to a ≈10% growth improvement. In conclusion, the genomic location of ribosomal protein genes conditions the evolutionary trajectory of V. cholerae. While genomic content is highly plastic in prokaryotes, gene order is an underestimated factor that conditions cellular physiology and evolution. A lack of suppression enables artificial gene relocation as a tool for genetic circuit reprogramming. IMPORTANCE The bacterial chromosome harbors several entangled processes such as replication, transcription, DNA repair, and segregation. Replication begins bidirectionally at the replication origin (oriC) until the terminal region (ter) organizing the genome along the ori-ter axis gene order along this axis could link genome structure to cell physiology. Fast-growing bacteria cluster translation genes near oriC. In Vibrio cholerae, moving them away was feasible but at the cost of losing fitness and infectivity. Here, we evolved strains harboring ribosomal genes close or far from oriC. Growth rate differences persisted after 1,000 generations. No mutation was able to compensate for the growth defect, showing that ribosomal gene location conditions their evolutionary trajectory. Despite the high plasticity of bacterial genomes, evolution has sculpted gene order to optimize the ecological strategy of the microorganism. We observed growth rate improvement throughout the evolution experiment that occurred at expense of energetically costly processes such the flagellum biosynthesis and virulence-related functions. From the biotechnological point of view, manipulation of gene order enables altering bacterial growth with no escape events.

Leptogorgia sarmentosa (Anthozoa: Octocorallia) in NE Sardinia (Mediterranean Sea): distribution and growth patterns

Distribution and occurrence data of the sea fan Leptogorgia sarmentosa (Esper, 1791) in the Mediterranean Sea were updated thanks to new photographic surveys, highlighting the presence of the species off NE Sardinia and, in particular, in the Tavolara-Punta Coda Cavallo Marine Protected Area (TPCCMPA). The estimated average distance among colonies strongly suggests that they are not able to sexually reproduce and that they probably can be considered a sterile pseudo-population supported by inputs of larvae from the Italian mainland coast towards Sardinia. The putative conveyor belt for larval input to this area is a small permanent gyre (the North-Tyrrhenian Gyre) counterclockwise moving from the Italian to the Sardinian coasts. In TPCCMPA, the growth of one colony settled at 30 m depth was investigated across 7 years (2016–2022). The morphometric parameters indicated a linear development of the colony, in terms of branch development and 3D complexity estimated through the fractal dimension. The observed growth rates were not regular but were reached through a highly variable positive and negative oscillation. A significant correlation was obtained between growth rate and average Autumn and Winter water temperature, suggesting that somatic growth occurs in these periods. In the context of long-lasting elevated seawater temperatures and local damage linked to fishing activities, L. sarmentosa was confirmed to be a resilient species compared with other erect seascape components, like other gorgonians and sponges.

Detecting Inference Attacks Involving Raw Sensor Data: A Case Study.

With the advent of sensors, more and more services are developed in order to provide customers with insights about their health and their appliances' energy consumption at home. To do so, these services use new mining algorithms that create new inference channels. However, the collected sensor data can be diverted to infer personal data that customers do not consent to share. This indirect access to data that are not collected corresponds to inference attacks involving raw sensor data (IASD). Towards these new kinds of attacks, existing inference detection systems do not suit the representation requirements of these inference channels and of user knowledge. In this paper, we propose RICE-M (Raw sensor data based Inference ChannEl Model) that meets these inference channel representations. Based on RICE-M, we proposed RICE-Sy an extensible system able to detect IASDs, and evaluated its performance taking as a case study the MHEALTH dataset. As expected, detecting IASD is proven to be quadratic due to huge sensor data managed and a quickly growing amount of user knowledge. To overcome this drawback, we propose first a set of conceptual optimizations that reduces the detection complexity. Although becoming linear, as online detection time remains greater than a fixed acceptable query response limit, we propose two approaches to estimate the potential of RICE-Sy. The first one is based on partitioning strategies which aim at partitioning the knowledge of users. We observe that by considering the quantity of knowledge gained by a user as a partitioning criterion, the median detection time of RICE-Sy is reduced by 63%. The second approach is H-RICE-SY, a hybrid detection architecture built on RICE-Sy which limits the detection at query-time to users that have a high probability to be malicious. We show the limits of processing all malicious users at query-time, without impacting the query answer time. We observe that for a ratio of 30% users considered as malicious, the median online detection time stays under the acceptable time of 80 ms, for up to a total volume of 1.2 million user knowledge entities. Based on the observed growth rates, we have estimated that for 5% of user knowledge issued by malicious users, a maximum volume of approximately 8.6 million user's information can be processed online in an acceptable time.

Multicharged ion processing for targeted nanostructure formation

We report on nano-patterning with multiply charged argon ions that facilitates the self-assembly of epitaxial Cu3Si nanostructures. In particular, we show that the impact sites formed from the dissipation of the incident ion potential energy for Arq+ (q=1,4,8) modulate the growth density and growth rate for silicide nanostructures. The observed nanostructure densities were found to vary as q∼0.9 for strain-driven, shape transition-type growth, and the observed growth rates far exceeded those obtained under thermal conditions. Relating the growth density to an underlying sputter yield for SiO2, we find a dependence on the ion potential energy relatively similar to that observed by others for Iq+ ions incident on a thermally grown oxide.

Climate‐driven thermal opportunities and risks for leaf miners in aspen canopies

AbstractIn tree canopies, incoming solar radiation interacts with leaves and branches to generate temperature differences within and among leaves, presenting thermal opportunities and risks for leaf‐dwelling ectotherms. Although leaf biophysics and insect thermal ecology are well understood, few studies have examined them together in single systems. We examined temperature variability in aspen canopies, Populus tremuloides, and its consequences for a common herbivore, the leaf‐mining caterpillar Phyllocnistis populiella. We shaded leaves in the field and measured effects on leaf temperature and larval growth and survival. We also estimated larval thermal performance curves for feeding and growth and measured upper lethal temperatures. Sunlit leaves directly facing the incoming rays reached the highest temperatures, typically 3–8°C above ambient air temperature. Irradiance‐driven increases in temperature, however, were transient enough that they did not alter observed growth rates of leaf miners. Incubator and ramping experiments suggested that larval performance peaks between 25 and 32°C and declines to zero between 35 and 40°C, depending on the duration of temperature exposure. Upper lethal temperatures during 1‐h heat shocks were 42–43°C. When larvae were active in early spring, temperatures generally were low enough to depress rates of feeding and growth below their maxima, and only rarely did estimated mine temperatures rise beyond optimal temperatures. Observed leaf or mine temperatures never approached larval upper lethal temperatures. At this site during our experiments, larvae thus appeared to have a significant thermal safety margin; the more pressing problem was inadequate heat. Detailed information on mine temperatures and larval performance curves, however, allowed us to leverage long‐term data sets on air temperature to estimate potential future shifts in performance and longer‐term risks to larvae from lethally high temperatures. This analysis suggests that, in the past 20 years, larval performance has often been limited by cold and that the risk of heat stress has been low. Future warming will raise mean rates of feeding and growth but also the risk of exposure to injuriously or lethally high temperatures.

Growth Kinetics of Single Polymer Particles in Solution via Active-Feedback 3D Tracking.

The ability to directly observe chemical reactions at the single-molecule and single-particle level has enabled the discovery of behaviors otherwise obscured by ensemble averaging in bulk measurements. However powerful, a common restriction of these studies to date has been the absolute requirement to surface tether or otherwise immobilize the chemical reagent/reaction of interest. This constraint arose from a fundamental limitation of conventional microscopy techniques, which could not track molecules or particles rapidly diffusing in three dimensions, as occurs in solution. However, many chemical processes occur entirely in the solution phase, leaving single-particle/-molecule analysis of this critical area of science beyond the scope of available technology. Here, we report the first kinetics studies of freely diffusing and actively growing single polymer-particles at the single-particle level freely diffusing in solution. Active-feedback single-particle tracking was used to capture three-dimensional (3D) trajectories and real-time volumetric images of freely diffusing polymer particles (D ≈ 10-12 m2/s) and extract the growth rates of individual particles in the solution phase. The observed growth rates show that the average growth rate is a poor representation of the true underlying variability in polymer-particle growth behavior. These data revealed statistically significant populations of faster- and slower-growing particles at different depths in the sample, showing emergent heterogeneity while particles are still freely diffusing in solution. These results go against the prevailing premise that chemical processes in freely diffusing solution will exhibit uniform kinetics. We anticipate that these studies will launch new directions of solution-phase, nonensemble-averaged measurements of chemical processes.

Electromagnetic Weibel instability in spatial anisotropic electron–ion plasmas

The Weibel instability due to temperature anisotropy of electrons and ions in a plasma in the presence of cold and warm ions is reported. Numerical calculations of the normalized growth rate are carried out when the frequency of electromagnetic waves is greater than or less than the thermal velocity of electrons for typical existing plasma parameters. The normalized growth rate increases with an increasing normalized wave number, and after attaining maxima, it decreases due to thermal effects. Therefore, a parabolic plot is obtained for the growth rate. The threshold values of the growth rate depend on the anisotropy parameters. On increasing the value of the temperature anisotropy ratio of either plasma component, the observed growth rate increases. There is a considerable and contrasting effect of the presence of cold and warm ions on the growth rate of the Weibel instability in the plasma. The addition of cold ions stabilizes the instability and reduces the maximum growth rate values, while the addition of warm ions to the plasma increases the instability with a considerable decrease in the domain of instability. Our theoretical investigations of the effect of temperature anisotropy on the growth rate of the Weibel instability are in good agreement with the existing experimental results.

How much does reducing inequality matter for global poverty?

The goals of ending extreme poverty by 2030 and working towards a more equal distribution of incomes are part of the United Nations’ Sustainable Development Goals. Using data from 166 countries comprising 97.5% of the world’s population, we simulate scenarios for global poverty from 2019 to 2030 under various assumptions about growth and inequality. We use different assumptions about growth incidence curves to model changes in inequality, and rely on a machine-learning algorithm called model-based recursive partitioning to model how growth in GDP is passed through to growth as observed in household surveys. When holding within-country inequality unchanged and letting GDP per capita grow according to World Bank forecasts and historically observed growth rates, our simulations suggest that the number of extreme poor (living on less than $1.90/day) will remain above 600 million in 2030, resulting in a global extreme poverty rate of 7.4%. If the Gini index in each country decreases by 1% per year, the global poverty rate could reduce to around 6.3% in 2030, equivalent to 89 million fewer people living in extreme poverty. Reducing each country’s Gini index by 1% per year has a larger impact on global poverty than increasing each country’s annual growth 1 percentage point above forecasts. We also study the impact of COVID-19 on poverty and find that the pandemic may have driven around 60 million people into extreme poverty in 2020. If the pandemic increased the Gini index by 2% in all countries, then more than 90 million may have been driven into extreme poverty in 2020.

Modeling Atlantic sea scallop (Placopecten magellanicus) scope for growth on the Northeast U.S. Shelf

AbstractAtlantic sea scallops support one of the most valuable fisheries in the eastern United States. The scallop population is susceptible to climate‐related environmental stress. Assessing and projecting climate impacts rely on the fundamental understanding of scallop ecophysiology, including the influences of temperature and food supply on its energy balance and growth potential. In this study, we developed a scope for growth (SFG) model driven by high‐resolution hydrodynamic and biological models to assess the spatial and seasonal variability of scallop energy dynamics. The overall SFG on the Northeast U.S. Shelf is higher in May–June and lower in January–February, with substantial spatial heterogeneity. In the Mid‐Atlantic Bight (MAB), negative SFG occurs from July to October due to strong thermal stress. Particulate organic matter in detrital form is an important food source for scallops, with higher/lower contribution in the cold/warm seasons, respectively. Warming and food deficiency induce a noticeable contraction of suitable scallop habitats in the MAB, while their impacts on Georges Bank are insignificant. Known seasonal spawning patterns and observed growth rates in these regions match the patterns of SFG generated by the model. The sensitivity of SFG to the variations in food and temperature increases with scallop size. Large scallops are more likely to experience low or negative SFGs than smaller ones, implying that the habitats shrink as scallops grow older/bigger. This study provides key information about scallop growth potential and biogeography from the perspective of energy balance, thus helping the development of adaptive fisheries management strategies.

Hydroclimate proxies for eastern Australia using stable isotopes in grey mangroves (Avicennia marina)

The development of high-resolution terrestrial palaeoclimate records in Australia is hindered by the scarcity of tree species suitable for conventional dendrochronology. However, novel analytical techniques have made it possible to obtain climate information from tree species that do not reliably form annual growth rings. In this paper we assess the potential of stable carbon and oxygen isotopes in the xylem wood of grey mangroves (Avicennia marina (Forssk.) Vierh.) as hydroclimate proxies for eastern Australia. Bomb-pulse radiocarbon dating and simple age models were used to estimate the age of the growth layers in radial sequence in stems from four grey mangrove trees in two adjacent estuaries in New South Wales, Australia. Stable isotope data measured from the xylem wood of the four stems were composited to yield mean δ18O and δ13C series for the 1962–2016 period. Significant negative Spearman correlations were found between δ18O and rainfall, sea level, instrumental Palmer Drought Severity Index (scPDSI) and the El Niño Southern Oscillation (ENSO), while δ13C was positively correlated with temperature, vapour pressure and evapotranspiration. The results demonstrate that stable oxygen isotopes in grey mangroves have the potential to yield valuable information about pre-instrumental hydroclimate. Grey mangroves can survive with intact centres for an estimate of >250 years based on observed growth rates, are widespread along northern Australian and tropical coastlines and could provide important information regarding pre-instrumental climate in regions currently lacking high-resolution (i.e., near annual) centennial scale climate proxy records.