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Understanding drivers of success for alien species can inform on potential future invasions. Recent conceptual advances highlight that species may achieve invasiveness via performance along at least three distinct dimensions: 1) local abundance, 2) geographic range size, and 3) habitat breadth in naturalized distributions. Associations among these dimensions and the factors that determine success in each have yet to be assessed at large geographic scales. Here, we combine data from over one million vegetation plots covering the extent of Europe and its habitat diversity with databases on species’ distributions, traits, and historical origins to provide a comprehensive assessment of invasiveness dimensions for the European alien seed plant flora. Invasiveness dimensions are linked in alien distributions, leading to a continuum from overall poor invaders to super invaders—abundant, widespread aliens that invade diverse habitats. This pattern echoes relationships among analogous dimensions measured for native European species. Success along invasiveness dimensions was associated with details of alien species’ introduction histories: earlier introduction dates were positively associated with all three dimensions, and consistent with theory-based expectations, species originating from other continents, particularly acquisitive growth strategists, were among the most successful invaders in Europe. Despite general correlations among invasiveness dimensions, we identified habitats and traits associated with atypical patterns of success in only one or two dimensions—for example, the role of disturbed habitats in facilitating widespread specialists. We conclude that considering invasiveness within a multidimensional framework can provide insights into invasion processes while also informing general understanding of the dynamics of species distributions.

Human plasma α 1 -acid glycoprotein (AGP) from cancer patients and healthy volunteers was purified by sequential application of ion-exchange columns, and N-linked glycans enzymatically released from AGP were labeled and applied to a mass spectrometer. Additionally, a novel software system for use in combination with a mass spectrometer to determine N-linked glycans in AGP was developed. A database with 607 glycans including 453 different glycan structures that were theoretically predicted to be present in AGP was prepared for designing the software called AGPAS. This AGPAS was applied to determine relative abundance of each glycan in the AGP molecules based on mass spectra. It was found that the relative abundance of fucosylated glycans in tri- and tetra-antennary structures (FUCAGP) was significantly higher in cancer patients as compared with the healthy group ( P < 0.001 ). Furthermore, extremely elevated levels of FUCAGP were found specifically in patients with a poor prognosis but not in patients with a good prognosis. In conclusion, the present software system allowed rapid determination of the primary structures of AGP glycans. The fucosylated glycans as novel tumor markers have clinical relevance in the diagnosis and assessment of cancer progression as well as patient prognosis.

The use of molecular markers has revolutionized the pace and precision of plant genetic analysis which in turn facilitated the implementation of molecular breeding of crops. The last three decades have seen tremendous advances in the evolution of marker systems and the respective detection platforms. Markers based on single nucleotide polymorphisms (SNPs) have rapidly gained the center stage of molecular genetics during the recent years due to their abundance in the genomes and their amenability for high-throughput detection formats and platforms. Computational approaches dominate SNP discovery methods due to the ever-increasing sequence information in public databases; however, complex genomes pose special challenges in the identification of informative SNPs warranting alternative strategies in those crops. Many genotyping platforms and chemistries have become available making the use of SNPs even more attractive and efficient. This paper provides a review of historical and current efforts in the development, validation, and application of SNP markers in QTL/gene discovery and plant breeding by discussing key experimental strategies and cases exemplifying their impact.

The contributions of species to ecosystem functions or services depend not only on their presence but also on their local abundance. Progress in predictive spatial modelling has largely focused on species occurrence rather than abundance. As such, limited guidance exists on the most reliable methods to explain and predict spatial variation in abundance. We analysed the performance of 68 abundance‐based species distribution models fitted to 800 000 standardised abundance records for more than 800 terrestrial bird and reef fish species. We found a large amount of variation in the performance of abundance‐based models. While many models performed poorly, a subset of models consistently reconstructed range‐wide abundance patterns. The best predictions were obtained using random forests for frequently encountered and abundant species and for predictions within the same environmental domain as model calibration. Extending predictions of species abundance outside of the environmental conditions used in model training generated poor predictions. Thus, interpolation of abundances between observations can help improve understanding of spatial abundance patterns, but our results indicate extrapolated predictions of abundance under changing climate have a much greater uncertainty. Our synthesis provides a road map for modelling abundance patterns, a key property of species distributions that underpins theoretical and applied questions in ecology and conservation.

Elements with weak and blended spectral features in stellar spectra are challenging to measure and require specialized analysis methods to precisely measure their chemical abundances. In this work, we have created a catalog of approximately 120,000 giants with high signal-to-noise Apache Point Observatory Galactic Evolution Experiment (APOGEE) Data Release 17 (DR17) spectra, for which we explore weak and blended species to measure Na, P, S, V, Cu, Ce, and Nd abundances and 12C/13C isotopic ratios. We employ an updated version of the Brussels Automatic Code for Characterizing High-accuracy Spectra (BACCHUS) code to derive these abundances using the stellar parameters measured by APOGEE’s DR17 Stellar Parameters and Chemical Abundances Pipeline, quality flagging to identify suspect spectral lines, and a prescription for upper limits. Combined, these allow us to provide our BACCHUS Analysis of Weak Lines in APOGEE Spectra catalog of precise chemical abundances for these weak and blended species, which agrees well with the literature and improves upon APOGEE abundances for these elements, some of which are unable to be measured with APOGEE’s current, grid-based approach without computationally expensive expansions. This new catalog can be used alongside APOGEE and provides measurements for many scientific applications ranging from nuclear physics to Galactic chemical evolution and Milky Way population studies. To illustrate this we show some examples of uses for this catalog, such as showing that we observe stars with enhanced s-process abundances or that we can use the 12C/13C ratios to explore extra mixing along the red giant branch.

Quantifying and assessing changes in biological diversity are central aspects of many ecological studies, yet accurate methods of estimating biological diversity from sampling data have been elusive. Hill numbers, or the effective number of species, are increasingly used to characterize the taxonomic, phylogenetic, or functional diversity of an assemblage. However, empirical estimates of Hill numbers, including species richness, tend to be an increasing function of sampling effort and, thus, tend to increase with sample completeness. Integrated curves based on sampling theory that smoothly link rarefaction (interpolation) and prediction (extrapolation) standardize samples on the basis of sample size or sample completeness and facilitate the comparison of biodiversity data. Here we extended previous rarefaction and extrapolation models for species richness (Hill number q D , where q = 0) to measures of taxon diversity incorporating relative abundance (i.e., for any Hill number q D , q > 0) and present a unified approach for both individual-based (abundance) data and sample-based (incidence) data. Using this unified sampling framework, we derive both theoretical formulas and analytic estimators for seamless rarefaction and extrapolation based on Hill numbers. Detailed examples are provided for the first three Hill numbers: q = 0 (species richness), q = 1 (the exponential of Shannon's entropy index), and q = 2 (the inverse of Simpson's concentration index). We developed a bootstrap method for constructing confidence intervals around Hill numbers, facilitating the comparison of multiple assemblages of both rarefied and extrapolated samples. The proposed estimators are accurate for both rarefaction and short-range extrapolation. For long-range extrapolation, the performance of the estimators depends on both the value of q and on the extrapolation range. We tested our methods on simulated data generated from species abundance models and on data from large species inventories. We also illustrate the formulas and estimators using empirical data sets from biodiversity surveys of temperate forest spiders and tropical ants.

We present a spatially resolved H ii region study of the gas-phase metallicity, ionization parameter, and interstellar medium (ISM) pressure maps of six local star-forming and face-on spiral galaxies from the TYPHOON program. Self-consistent metallicity, ionization parameter, and pressure maps are calculated simultaneously through an iterative process to provide useful measures of the local chemical abundance and its relation to localized ISM properties. We constrain the presence of azimuthal variations in metallicity by measuring the residual metallicity offset Δ(O/H) after subtracting the linear fits to the radial metallicity profiles. We, however, find weak evidence of azimuthal variations in most of the galaxies, with small (mean 0.03 dex) scatter. The galaxies instead reveal that H ii regions with enhanced and reduced abundances are found distributed throughout the disk. While the spiral pattern plays a role in organizing the ISM, it alone does not establish the relatively uniform azimuthal variations we observe. Differences in the metal abundances are more likely driven by the strong correlations with the local physical conditions. We find a strong and positive correlation between the ionization parameter and the local abundances as measured by the relative metallicity offset Δ(O/H), indicating a tight relationship between local physical conditions and their localized enrichment of the ISM. Additionally, we demonstrate the impact of unresolved observations on the measured ISM properties by rebinning the data cubes to simulate low-resolution (1 kpc) observations, typical of large IFU surveys. We find that the ionization parameter and ISM pressure diagnostics are impacted by the loss of resolution such that their measured values are larger relative to the measured values on sub-H ii region scales.

Large-scale surveys open the possibility to investigate Galactic evolution both chemically and kinematically; however, reliable stellar ages remain a major challenge. Detailed chemical information provided by high-resolution spectroscopic surveys of the stars in clusters can be used as a means to calibrate recently developed chemical tools for age-dating field stars. Using data from the Open Cluster Abundances and Mapping survey, based on the Sloan Digital Sky Survey/Apache Point Observatory Galactic Evolution Experiment 2 survey, we derive a new empirical relationship between open cluster stellar ages and the carbon-to-nitrogen ([C/N]) abundance ratios for evolved stars, primarily those on the red giant branch. With this calibration, [C/N] can be used as a chemical clock for evolved field stars to investigate the formation and evolution of different parts of our Galaxy. We explore how mixing effects at different stellar evolutionary phases, like the red clump, affect the derived calibration. We have established the [C/N]–age calibration for APOGEE Data Release 17 (DR17) giant star abundances to be log[Age(yr)]DR17=10.14(±0.08)+2.23(±0.19)[C/N] , usable for 8.62≤log(Age[yr])≤9.82 , derived from a uniform sample of 49 clusters observed as part of APOGEE DR17 applicable primarily to metal-rich, thin- and thick-disk giant stars. This measured [C/N]–age APOGEE DR17 calibration is also shown to be consistent with asteroseismic ages derived from Kepler photometry.

Background Plant health and growth are negatively affected by pathogen invasion; however, plants can dynamically modulate their rhizosphere microbiome and adapt to such biotic stresses. Although plant-recruited protective microbes can be assembled into synthetic communities for application in the control of plant disease, rhizosphere microbial communities commonly contain some taxa at low abundance. The roles of low-abundance microbes in synthetic communities remain unclear; it is also unclear whether all the microbes enriched by plants can enhance host adaptation to the environment. Here, we assembled a synthetic community with a disease resistance function based on differential analysis of root-associated bacterial community composition. We further simplified the synthetic community and investigated the roles of low-abundance bacteria in the control of Astragalus mongholicus root rot disease by a simple synthetic community. Results Fusarium oxysporum infection reduced bacterial Shannon diversity and significantly affected the bacterial community composition in the rhizosphere and roots of Astragalus mongholicus . Under fungal pathogen challenge, Astragalus mongholicus recruited some beneficial bacteria such as Stenotrophomonas , Achromobacter , Pseudomonas , and Flavobacterium to the rhizosphere and roots. We constructed a disease-resistant bacterial community containing 10 high- and three low-abundance bacteria enriched in diseased roots. After the joint selection of plants and pathogens, the complex synthetic community was further simplified into a four-species community composed of three high-abundance bacteria ( Stenotrophomonas sp., Rhizobium sp., Ochrobactrum sp.) and one low-abundance bacterium ( Advenella sp.). Notably, a simple community containing these four strains and a thirteen-species community had similar effects on the control root rot disease. Furthermore, the simple community protected plants via a synergistic effect of highly abundant bacteria inhibiting fungal pathogen growth and less abundant bacteria activating plant-induced systemic resistance. Conclusions Our findings suggest that bacteria with low abundance play an important role in synthetic communities and that only a few bacterial taxa enriched in diseased roots are associated with disease resistance. Therefore, the construction and simplification of synthetic communities found in the present study could be a strategy employed by plants to adapt to environmental stress. -VujMZjCpFzTumqS8Rf9nH Video abstract

Determination of fundamental parameters of stars impacts all fields of astrophysics, from galaxy evolution to constraining the internal structure of exoplanets. This paper presents a detailed spectroscopic analysis of Barnard’s star (otherwise known as Gl 699) that compares an exceptionally high-quality (an average signal-to-noise ratio of ∼1000 in the entire domain), high-resolution near-infrared (NIR) spectrum taken with Canada-France-Hawaii Telescope/SPIRou to PHOENIX-ACES stellar atmosphere models. The observed spectrum shows thousands of lines not identified in the models with a similarly large number of lines present in the model but not in the observed data. We also identify several other caveats, such as continuum mismatch, unresolved contamination, and spectral lines significantly shifted from their expected wavelengths; all of these can be a source of bias for the determination of abundance. Out of >104 observed lines in the NIR that could be used for chemical spectroscopy, we identify a short list of a few hundred lines that are reliable. We present a novel method for determining the effective temperature (T eff) and overall metallicity of slowly rotating M dwarfs that uses several groups of lines as opposed to bulk spectral fitting methods. With this method, we infer T eff = 3231 ± 21 K for Barnard's star, consistent with the value of 3238 ± 11 K inferred from the interferometric method. We also provide measurements of the abundance of 15 different elements for Barnard's star, including the abundances of four elements (K, O, Y, Th) never reported before for this star. This work emphasizes the need to improve current atmosphere models to fully exploit the NIR domain for chemical spectroscopy analysis.