433 research outputs found
Results of SIMPER analysis conducted using the R package Vegan.
No full textResults of SIMPER analysis conducted using the R package Vegan.</p
Data from: PER-SIMPER - a new tool for inferring community assembly processes from taxon occurrences
No full textAim: Understanding how ecosystem functioning and evolution shape taxonomic as- semblages is a lively debate basically involving two major opposite views: the niche- and dispersal-assembly hypotheses. Here, we introduce a new method allowing for the identification of the first-order process of assembly underlying a set of taxonomic assemblages.
Methods: Building on Clarke's SIMPER (for "similarity percentage") analysis of a taxon/ locality occurrence data set, we develop a permutation-based algorithm named PER- SIMPER, allowing for the identification of the first-order process—either niche- or dispersal-assembly—that drives species distribution within two or more groups of assemblages. We demonstrate the reliability and robustness of the method through cellular automaton-like simulations generating niche-assembled and/or dispersal-as- sembled species occurrence data sets. Sensitivity analysis further allows evaluation of its accuracy and robustness to sampling effort, including reduced numbers of sampled localities and/or species.
Main conclusions: Niche- and/or dispersal-assembled communities generate very dif- ferent SIMPER profiles, which, in turn, allow for the accurate and consistent identifica- tion of the first-order process of assembly operating within two or more groups of species assemblages through a threefold randomization procedure named PER-SIMPER. The PER-SIMPER method appears robust to varying sampling efforts that may affect the number of sampled localities and/or species, especially when one of the two processes of assembly dominates the other. The PER-SIMPER analysis can be achieved on any empirical occurrence data set using a dedicated R function available as Supporting Information.R PerSIMPER functionBased on a presence/absence matrix, this function allows the identification of the first-order process of assembly underlying a set of taxonomic assemblages. Its use should therefore be limited to the comparison of significantly distinct taxonomic sets. But at the same time connected enough to allow the potential dispersal of species between these different sets. The PER-SIMPER method distinguishes the main ecological assembly process (between species dispersal capacity and niche richness) at the origin of the observed taxonomic differences between two (or more) compared sets of assemblages. The PER-SIMPER method is associated with the calculation of the E index (the logarithm of the sum of squared deviations between empirical and simulated SIMPER profiles) to assist in distinguishing the result of PER-SIMPER analyses.PERSIMPER.RHelp notes PER-SIMPERHelp notes of the PER-SIMPER R function. R function included too.PerSIMPERgroupsPerSIMPERmatri
Data from: PER-SIMPER - a new tool for inferring community assembly processes from taxon occurrences
No full textAim: Understanding how ecosystem functioning and evolution shape taxonomic as- semblages is a lively debate basically involving two major opposite views: the niche- and dispersal-assembly hypotheses. Here, we introduce a new method allowing for the identification of the first-order process of assembly underlying a set of taxonomic assemblages.
Methods: Building on Clarke’s SIMPER (for “similarity percentage”) analysis of a taxon/ locality occurrence data set, we develop a permutation-based algorithm named PER- SIMPER, allowing for the identification of the first-order process—either niche- or dispersal-assembly—that drives species distribution within two or more groups of assemblages. We demonstrate the reliability and robustness of the method through cellular automaton-like simulations generating niche-assembled and/or dispersal-as- sembled species occurrence data sets. Sensitivity analysis further allows evaluation of its accuracy and robustness to sampling effort, including reduced numbers of sampled localities and/or species.
Main conclusions: Niche- and/or dispersal-assembled communities generate very dif- ferent SIMPER profiles, which, in turn, allow for the accurate and consistent identifica- tion of the first-order process of assembly operating within two or more groups of species assemblages through a threefold randomization procedure named PER-SIMPER. The PER-SIMPER method appears robust to varying sampling efforts that may affect the number of sampled localities and/or species, especially when one of the two processes of assembly dominates the other. The PER-SIMPER analysis can be achieved on any empirical occurrence data set using a dedicated R function available as Supporting Information
Data from: PER-SIMPER - a new tool for inferring community assembly processes from taxon occurrences
No full textAim: Understanding how ecosystem functioning and evolution shape taxonomic as- semblages is a lively debate basically involving two major opposite views: the niche- and dispersal-assembly hypotheses. Here, we introduce a new method allowing for the identification of the first-order process of assembly underlying a set of taxonomic assemblages.
Methods: Building on Clarke’s SIMPER (for “similarity percentage”) analysis of a taxon/ locality occurrence data set, we develop a permutation-based algorithm named PER- SIMPER, allowing for the identification of the first-order process—either niche- or dispersal-assembly—that drives species distribution within two or more groups of assemblages. We demonstrate the reliability and robustness of the method through cellular automaton-like simulations generating niche-assembled and/or dispersal-as- sembled species occurrence data sets. Sensitivity analysis further allows evaluation of its accuracy and robustness to sampling effort, including reduced numbers of sampled localities and/or species.
Main conclusions: Niche- and/or dispersal-assembled communities generate very dif- ferent SIMPER profiles, which, in turn, allow for the accurate and consistent identifica- tion of the first-order process of assembly operating within two or more groups of species assemblages through a threefold randomization procedure named PER-SIMPER. The PER-SIMPER method appears robust to varying sampling efforts that may affect the number of sampled localities and/or species, especially when one of the two processes of assembly dominates the other. The PER-SIMPER analysis can be achieved on any empirical occurrence data set using a dedicated R function available as Supporting Information
Data from: PER-SIMPER - a new tool for inferring community assembly processes from taxon occurrences
No full textAim: Understanding how ecosystem functioning and evolution shape taxonomic as- semblages is a lively debate basically involving two major opposite views: the niche- and dispersal-assembly hypotheses. Here, we introduce a new method allowing for the identification of the first-order process of assembly underlying a set of taxonomic assemblages.
Methods: Building on Clarke’s SIMPER (for “similarity percentage”) analysis of a taxon/ locality occurrence data set, we develop a permutation-based algorithm named PER- SIMPER, allowing for the identification of the first-order process—either niche- or dispersal-assembly—that drives species distribution within two or more groups of assemblages. We demonstrate the reliability and robustness of the method through cellular automaton-like simulations generating niche-assembled and/or dispersal-as- sembled species occurrence data sets. Sensitivity analysis further allows evaluation of its accuracy and robustness to sampling effort, including reduced numbers of sampled localities and/or species.
Main conclusions: Niche- and/or dispersal-assembled communities generate very dif- ferent SIMPER profiles, which, in turn, allow for the accurate and consistent identifica- tion of the first-order process of assembly operating within two or more groups of species assemblages through a threefold randomization procedure named PER-SIMPER. The PER-SIMPER method appears robust to varying sampling efforts that may affect the number of sampled localities and/or species, especially when one of the two processes of assembly dominates the other. The PER-SIMPER analysis can be achieved on any empirical occurrence data set using a dedicated R function available as Supporting Information
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