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
Due to the tremendous diversity of microbial organisms in topsoil,the estimation of saturated richness in a belowground ecosystem is still challenging.Here,we intensively surveyed the 16S rRNA gene in four 1 m2 sampli...Due to the tremendous diversity of microbial organisms in topsoil,the estimation of saturated richness in a belowground ecosystem is still challenging.Here,we intensively surveyed the 16S rRNA gene in four 1 m2 sampling quadrats in a typical grassland,with 141 biological or technical replicates generating over 11 million sequences per quadrat.Through these massive data sets and using both non-asymptotic extrapolation and non-parametric asymptotic approaches,results revealed that roughly 15919±193,27193±1076 and 56985±2347 prokaryotic species inhabited in 1 m2 topsoil,classifying by DADA2,UPARSE(97%cutoff)and Deblur,respectively,and suggested a huge difference among these clustering tools.Nearly 500000 sequences were required to catch 50%species in 1 m2,while any estimator based on 500000 sequences would still lose about a third of total richness.Insufficient sequencing depth will greatly underestimate both observed and estimated richness.At least~911000,~3461000,and~1878000 sequences were needed for DADA2,UPARSE,and Deblur,respectively,to catch 80%species in 1 m2 topsoil,and the numbers of sequences would be nearly twice to three times on this basis to cover 90%richness.In contrast,α-diversity indexes characterized by higher order of Hill numbers,including Shannon entropy and inverse Simpson index,reached saturation with fewer than 100000 sequences,suggesting sequencing depth could be varied greatly when focusing on exploring differentα-diversity characteristics of a microbial community.Our findings were fundamental for microbial studies that provided benchmarks for the extending surveys in large scales of terrestrial ecosystems.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the National Natural Science Foundation of China(NSFC Grant No.U1906223)the National Key Research and Development Program(Grant No.2019YFC1905001)。
文摘Due to the tremendous diversity of microbial organisms in topsoil,the estimation of saturated richness in a belowground ecosystem is still challenging.Here,we intensively surveyed the 16S rRNA gene in four 1 m2 sampling quadrats in a typical grassland,with 141 biological or technical replicates generating over 11 million sequences per quadrat.Through these massive data sets and using both non-asymptotic extrapolation and non-parametric asymptotic approaches,results revealed that roughly 15919±193,27193±1076 and 56985±2347 prokaryotic species inhabited in 1 m2 topsoil,classifying by DADA2,UPARSE(97%cutoff)and Deblur,respectively,and suggested a huge difference among these clustering tools.Nearly 500000 sequences were required to catch 50%species in 1 m2,while any estimator based on 500000 sequences would still lose about a third of total richness.Insufficient sequencing depth will greatly underestimate both observed and estimated richness.At least~911000,~3461000,and~1878000 sequences were needed for DADA2,UPARSE,and Deblur,respectively,to catch 80%species in 1 m2 topsoil,and the numbers of sequences would be nearly twice to three times on this basis to cover 90%richness.In contrast,α-diversity indexes characterized by higher order of Hill numbers,including Shannon entropy and inverse Simpson index,reached saturation with fewer than 100000 sequences,suggesting sequencing depth could be varied greatly when focusing on exploring differentα-diversity characteristics of a microbial community.Our findings were fundamental for microbial studies that provided benchmarks for the extending surveys in large scales of terrestrial ecosystems.
