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dc.contributor.authorAvolio, Meghan L.
dc.contributor.authorCarroll, Ian T.
dc.contributor.authorCollins, Scott L.
dc.contributor.authorHouseman, Gregory R.
dc.contributor.authorHallett, Lauren M.
dc.contributor.authorIsbell, Forest I.
dc.contributor.authorKoerner, Sally E.
dc.contributor.authorKomatsu, Kimberly J.
dc.contributor.authorSmith, Melinda D.
dc.contributor.authorWilcox, Kevin R.
dc.date.accessioned2019-11-18T21:49:51Z
dc.date.available2019-11-18T21:49:51Z
dc.date.issued2019-10-17
dc.identifier.citationAvolio, Meghan L.; Carroll, Ian T.; Collins, Scott L.; Houseman, Gregory R.; Hallett, Lauren M.; Isbell, Forest I.; Koerner, Sally E.; Komatsu, Kimberly J.; Smith, Melinda D.; Wilcox, Kevin R. 2019. A comprehensive approach to analyzing community dynamics using rank abundance curves. Ecosphere, vol. 10:no. 10:art. no. e02881en_US
dc.identifier.issn2150-8925
dc.identifier.urihttps://doi.org/10.1002/ecs2.2881
dc.identifier.urihttp://hdl.handle.net/10057/16822
dc.description© 2019 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en_US
dc.description.abstractUnivariate and multivariate methods are commonly used to explore the spatial and temporal dynamics of ecological communities, but each has limitations, including oversimplification or abstraction of communities. Rank abundance curves (RACs) potentially integrate these existing methodologies by detailing species-level community changes. Here, we had three goals: first, to simplify analysis of community dynamics by developing a coordinated set of R functions, and second, to demystify the relationships among univariate, multivariate, and RACs measures, and examine how each is influenced by the community parameters as well as data collection methods. We developed new functions for studying temporal changes and spatial differences in RACs in an update to the R package library(“codyn”), alongside other new functions to calculate univariate and multivariate measures of community dynamics. We also developed a new approach to studying changes in the shape of RAC curves. The R package update presented here increases the accessibility of univariate and multivariate measures of community change over time and difference over space. Next, we use simulated and real data to assess the RAC and multivariate measures that are output from our new functions, studying (1) if they are influenced by species richness and evenness, temporal turnover, and spatial variability and (2) how the measures are related to each other. Lastly, we explore the use of the measures with an example from a long-term nutrient addition experiment. We find that the RAC and multivariate measures are not sensitive to species richness and evenness and that all the measures detail unique aspects of temporal change or spatial differences. We also find that species reordering is the strongest correlate of a multivariate measure of compositional change and explains most community change observed in long-term nutrient addition experiment. Overall, we show that species reordering is potentially an understudied determinant of community changes over time or differences between treatments. The functions developed here should enhance the use of RACs to further explore the dynamics of ecological communities.en_US
dc.description.sponsorshipSESYNC is funded from the National Science Foundation DBI-1052875. In addition, partial support for this work was provided by NSF DBI 1262458, 1262377, and 1262463.en_US
dc.language.isoen_USen_US
dc.publisherWiley-Blackwellen_US
dc.relation.ispartofseriesEcosphere;v.10:no.10
dc.subjectCodynen_US
dc.subjectCommunity compositionen_US
dc.subjectLong-term dataen_US
dc.subjectMultivariate analysisen_US
dc.subjectR packageen_US
dc.subjectRichnessen_US
dc.subjectSpatial variabilityen_US
dc.subjectTemporal variabilityen_US
dc.titleA comprehensive approach to analyzing community dynamics using rank abundance curvesen_US
dc.typeArticleen_US
dc.rights.holder© 2019 The Authorsen_US


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