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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.

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.

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

ABSTRACT A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data. After conducting the initial bioinformatic analysis of amplicon sequencing data, performing the subsequent statistics and data mining based on the operational taxonomic unit and taxonomic assignment tables is still complicated and time-consuming. To address this problem, we present an integrated R package-‘microeco’ as an analysis pipeline for treating microbial community and environmental data. This package was developed based on the R6 class system and combines a series of commonly used and advanced approaches in microbial community ecology research. The package includes classes for data preprocessing, taxa abundance plotting, venn diagram, alpha diversity analysis, beta diversity analysis, differential abundance test and indicator taxon analysis, environmental data analysis, null model analysis, network analysis and functional analysis. Each class is designed to provide a set of approaches that can be easily accessible to users. Compared with other R packages in the microbial ecology field, the microeco package is fast, flexible and modularized to use and provides powerful and convenient tools for researchers. The microeco package can be installed from CRAN (The Comprehensive R Archive Network) or github (https://github.com/ChiLiubio/microeco). An integrated and powerful R package-microeco was developed for researchers to perform data mining of amplicon sequencing in microbial community ecology.

Disentangling the drivers of diversity gradients can be challenging. The Measurement of Biodiversity (MoB) framework decomposes scale-dependent changes in species diversity into three components of community structure: species abundance distribution (SAD), total community abundance, and within-species spatial aggregation. Here we extend MoB from categorical treatment comparisons to quantify variation along continuous geographic or environmental gradients. Our approach requires sites along a gradient, each consisting of georeferenced plots of abundance-based species composition data. We demonstrate our method using a case study of ants sampled along an elevational gradient of 28 sites in a mixed deciduous forest of the Great Smoky Mountains National Park, USA. MoB analysis revealed that decreases in ant species richness along the elevational gradient were associated with decreasing evenness and total number of species, which counteracted the modest increase in richness associated with decreasing spatial aggregation along the gradient. Total community abundance had a negligible effect on richness at all but the finest spatial grains, SAD effects increased in importance with sampling effort, and the aggregation effect had the strongest effect at coarser spatial grains. These results do not support the more-individuals hypothesis, but they are consistent with a hypothesis of stronger environmental filtering at coarser spatial grains. Our extension of MoB has the potential to elucidate how components of community structure contribute to changes in diversity along environmental gradients and should be useful for a variety of assemblage-level data collected along gradients.

Occupancy–abundance (OA) relationships are a foundational ecological phenomenon and field of study, and occupancy models are increasingly used to track population trends and understand ecological interactions. However, these two fields of ecological inquiry remain largely isolated, despite growing appreciation of the importance of integration. For example, using occupancy models to infer trends in abundance is predicated on positive OA relationships. Many occupancy studies collect data that violate geographical closure assumptions due to the choice of sampling scales and application to mobile organisms, which may change how occupancy and abundance are related. Little research, however, has explored how different occupancy sampling designs affect OA relationships. We develop a conceptual framework for understanding how sampling scales affect the definition of occupancy for mobile organisms, which drives OA relationships. We explore how spatial and temporal sampling scales, and the choice of sampling unit (areal vs. point sampling), affect OA relationships. We develop predictions using simulations, and test them using empirical occupancy data from remote cameras on 11 medium-large mammals. Surprisingly, our simulations demonstrate that when using point sampling, OA relationships are unaffected by spatial sampling grain (i.e., cell size). In contrast, when using areal sampling (e.g., species atlas data), OA relationships are affected by spatial grain. Furthermore, OA relationships are also affected by temporal sampling scales, where the curvature of the OA relationship increases with temporal sampling duration. Our empirical results support these predictions, showing that at any given abundance, the spatial grain of point sampling does not affect occupancy estimates, but longer surveys do increase occupancy estimates. For rare species (low occupancy), estimates of occupancy will quickly increase with longer surveys, even while abundance remains constant. Our results also clearly demonstrate that occupancy for mobile species without geographical closure is not true occupancy. The independence of occupancy estimates from spatial sampling grain depends on the sampling unit. Point-sampling surveys can, however, provide unbiased estimates of occupancy for multiple species simultaneously, irrespective of home-range size. The use of occupancy for trend monitoring needs to explicitly articulate how the chosen sampling scales define occupancy and affect the occupancy–abundance relationship.

Species are not uniformly distributed across the landscape. For every species, there should be few favoured sites where abundance is high and many other sites of lower suitability where abundance is low. Consequently, local abundance could be thought of as a natural expression of species response to local conditions. The correlation between abundance and environmental suitability has been well documented, and a recent meta‐analysis has suggested that this relationship could be a generality. Despite the importance and potential implication of the abundance–suitability relationship, its predictive power for meaningful extrapolations has been surprisingly poorly explored. In this study, we showed how a highly predictable trend can be extracted from the abundance–suitability relationship, accurately predicting the variation in species abundance at a high spatial resolution. We produced high‐quality environmental suitability estimations for 50 endemic species in the Australian Wet Tropics. Environmental suitability derived from species distribution models was related to observed abundance estimated using data from 29 years of uninterrupted monitoring effort. We used the fitted relationship to accurately predict abundance at a fine scale across the species range. Our results showed that the abundance–suitability relationship was strong for endemic species in the Australian Wet Tropics. The predictive power of our models was high, explaining, on average, 55% of the deviance across taxa. Despite interspecific variation in the strength of the abundance–suitability relationship associated with potential intrinsic estimation biases, our approach provides a powerful tool for predicting abundance across the species range at a fine scale. The potential for robust abundance predictions from occurrence‐based species distribution models shown in this study are numerous, and it could have a significant impact in enhancing species conservation and management decisions.