Biodiversity has become a terminology familiar to virtually every citizen in modern societies.It is said that ecology studies the economy of nature,and economy studies the ecology of humans;then measuring biodiversity...Biodiversity has become a terminology familiar to virtually every citizen in modern societies.It is said that ecology studies the economy of nature,and economy studies the ecology of humans;then measuring biodiversity should be similar with measuring national wealth.Indeed,there have been many parallels between ecology and economics,actually beyond analogies.For example,arguably the second most widely used biodiversity metric,Simpson(1949)’s diversity index,is a function of familiar Gini-index in economics.One of the biggest challenges has been the high“diversity”of diversity indexes due to their excessive“speciation”-there are so many indexes,similar to each country’s sovereign currency-leaving confused diversity practitioners in dilemma.In 1973,Hill introduced the concept of“numbers equivalent”,which is based on Renyi entropy and originated in economics,but possibly due to his abstruse interpretation of the concept,his message was not widely received by ecologists until nearly four decades later.What Hill suggested was similar to link the US dollar to gold at the rate of$35 per ounce under the Bretton Woods system.The Hill numbers now are considered most appropriate biodiversity metrics system,unifying Shannon,Simpson and other diversity indexes.Here,we approach to another paradigmatic shift-measuring biodiversity on ecological networks-demonstrated with animal gastrointestinal microbiomes representing four major invertebrate classes and all six vertebrate classes.The network diversity can reveal the diversity of species interactions,which is a necessary step for understanding the spatial and temporal structures and dynamics of biodiversity across environmental gradients.展开更多
The gut microbiota of migratory waterbirds is affected by various complex factors,including cross-species transmission,which increases the risk of pathogen spreading among sympatric animals and poses a potential publi...The gut microbiota of migratory waterbirds is affected by various complex factors,including cross-species transmission,which increases the risk of pathogen spreading among sympatric animals and poses a potential public health risk to humans.In this study,we investigated the microbial communities of wintering Bean Geese(Anser fabalis),Domestic Ducks(A.platyrhynchos domesticus),humans,and soil using high-throughput sequencing of the 16S rRNA gene region in Shengjin Lake,China.In total,6,046,677 clean reads were obtained,representing 41,119 operational taxonomic units(OTUs)across the four groups.The dominant microbial phyla were the Proteobacteria,Firmicutes,Bacteroidota,and Actinobacteriota.The Sorensen similarity index and alpha and beta diversity results showed that the gut microbial communities of Bean Geese and Domestic Ducks were more similar to those of the other pairs.Network analysis revealed that Faecalibacterium prausnitzii,Pseudomonas fragi,and Bradyrhizobium elkanii were hubs of the three major modules.Fourteen common microbiomes were iden-tified in Bean Geese,Domestic Ducks,humans,and soil in Shengjin Lake.A total of 96 potential pathogens were identified among the four groups,with 20 specific potentially pathogenic microbiomes found in the gut of Bean Geese.Some of these pathogens are responsible for significant financial losses in the poultry industry and pose risks to human health.Klebsiella pneumoniae,Morganella morganii,Escherichia coli,and Ralstonia insidiosa are potential core pathogens found in the four groups at Shengjin Lake that can cause diseases in humans and an-imals and facilitate cross-species transmission through various media.Therefore,humans are at risk of con-tracting these pathogens from migratory birds because of their frequent contact with domestic poultry.However,further studies are required to explore the potential pathogenic species and transmission pathways among sympatric wintering Bean Geese,Domestic Ducks,humans,and soil.展开更多
The species accumulation curve, or collector＇s curve, of a population gives the expected number of observed species or distinct classes as a function of sampling effort. Species accumulation curves allow researchers ...The species accumulation curve, or collector＇s curve, of a population gives the expected number of observed species or distinct classes as a function of sampling effort. Species accumulation curves allow researchers to assess and compare diversity across populations or to evaluate the benefits of additional sampling. Traditional applications have focused on ecological populations but emerging large-scale applications, for example in DNA sequencing, are orders of magnitude larger and present new challenges. We developed a method to estimate accumulation curves for predicting the complexity of DNA sequencing libraries. This method uses rational function approximations to a classical non- parametric empirical Bayes estimator due to Good and Toulmin [Biometrika, 1956, 43, 45~63]. Here we demonstrate how the same approach can be highly effective in other large-scale applications involving biological data sets. These include estimating microbial species richness, immune repertoire size, and R-mer diversity for genome assembly applications. We show how the method can be modified to address populations containing an effectively infinite number of species where saturation cannot practically be attained. We also introduce a flexible suite of tools implemented as an R package that make these methods broadly accessible.展开更多
基金supported by the National Natural Science Foundation of China(31970116,72274192)。
文摘Biodiversity has become a terminology familiar to virtually every citizen in modern societies.It is said that ecology studies the economy of nature,and economy studies the ecology of humans;then measuring biodiversity should be similar with measuring national wealth.Indeed,there have been many parallels between ecology and economics,actually beyond analogies.For example,arguably the second most widely used biodiversity metric,Simpson(1949)’s diversity index,is a function of familiar Gini-index in economics.One of the biggest challenges has been the high“diversity”of diversity indexes due to their excessive“speciation”-there are so many indexes,similar to each country’s sovereign currency-leaving confused diversity practitioners in dilemma.In 1973,Hill introduced the concept of“numbers equivalent”,which is based on Renyi entropy and originated in economics,but possibly due to his abstruse interpretation of the concept,his message was not widely received by ecologists until nearly four decades later.What Hill suggested was similar to link the US dollar to gold at the rate of$35 per ounce under the Bretton Woods system.The Hill numbers now are considered most appropriate biodiversity metrics system,unifying Shannon,Simpson and other diversity indexes.Here,we approach to another paradigmatic shift-measuring biodiversity on ecological networks-demonstrated with animal gastrointestinal microbiomes representing four major invertebrate classes and all six vertebrate classes.The network diversity can reveal the diversity of species interactions,which is a necessary step for understanding the spatial and temporal structures and dynamics of biodiversity across environmental gradients.
基金supported by the Natural Science Foundation for the Higher Education Institutions of Anhui Province of China(grant no.KJ 2021A0246).
文摘The gut microbiota of migratory waterbirds is affected by various complex factors,including cross-species transmission,which increases the risk of pathogen spreading among sympatric animals and poses a potential public health risk to humans.In this study,we investigated the microbial communities of wintering Bean Geese(Anser fabalis),Domestic Ducks(A.platyrhynchos domesticus),humans,and soil using high-throughput sequencing of the 16S rRNA gene region in Shengjin Lake,China.In total,6,046,677 clean reads were obtained,representing 41,119 operational taxonomic units(OTUs)across the four groups.The dominant microbial phyla were the Proteobacteria,Firmicutes,Bacteroidota,and Actinobacteriota.The Sorensen similarity index and alpha and beta diversity results showed that the gut microbial communities of Bean Geese and Domestic Ducks were more similar to those of the other pairs.Network analysis revealed that Faecalibacterium prausnitzii,Pseudomonas fragi,and Bradyrhizobium elkanii were hubs of the three major modules.Fourteen common microbiomes were iden-tified in Bean Geese,Domestic Ducks,humans,and soil in Shengjin Lake.A total of 96 potential pathogens were identified among the four groups,with 20 specific potentially pathogenic microbiomes found in the gut of Bean Geese.Some of these pathogens are responsible for significant financial losses in the poultry industry and pose risks to human health.Klebsiella pneumoniae,Morganella morganii,Escherichia coli,and Ralstonia insidiosa are potential core pathogens found in the four groups at Shengjin Lake that can cause diseases in humans and an-imals and facilitate cross-species transmission through various media.Therefore,humans are at risk of con-tracting these pathogens from migratory birds because of their frequent contact with domestic poultry.However,further studies are required to explore the potential pathogenic species and transmission pathways among sympatric wintering Bean Geese,Domestic Ducks,humans,and soil.
文摘The species accumulation curve, or collector＇s curve, of a population gives the expected number of observed species or distinct classes as a function of sampling effort. Species accumulation curves allow researchers to assess and compare diversity across populations or to evaluate the benefits of additional sampling. Traditional applications have focused on ecological populations but emerging large-scale applications, for example in DNA sequencing, are orders of magnitude larger and present new challenges. We developed a method to estimate accumulation curves for predicting the complexity of DNA sequencing libraries. This method uses rational function approximations to a classical non- parametric empirical Bayes estimator due to Good and Toulmin [Biometrika, 1956, 43, 45~63]. Here we demonstrate how the same approach can be highly effective in other large-scale applications involving biological data sets. These include estimating microbial species richness, immune repertoire size, and R-mer diversity for genome assembly applications. We show how the method can be modified to address populations containing an effectively infinite number of species where saturation cannot practically be attained. We also introduce a flexible suite of tools implemented as an R package that make these methods broadly accessible.
