2019
DOI: 10.1002/ecs2.2881
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A comprehensive approach to analyzing community dynamics using rank abundance curves

Abstract: Univariate 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 univ… Show more

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Cited by 105 publications
(101 citation statements)
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“…We compared plant species richness in each 30 m × 30 m plot across deer exclosure treatments (fenced, not fenced) using a t ‐test, with burn regimes (annual, four‐year) as replicates to determine if plant species richness was inversely related to deer and fire presence. For each subplot, we calculated plant species richness and Evar, an evenness metric based on variance in plant species abundance that is independent of species richness (Smith and Wilson, 1996; Avolio et al, 2019). We assessed differences in richness and evenness at the subplot level using linear mixed‐effects regression models, with deer exclosure treatments (fenced, not fenced), burn regime (annual, four‐year), and their interaction as fixed effects, and plot nested within burn regime as a random effect.…”
Section: Methodsmentioning
confidence: 99%
“…We compared plant species richness in each 30 m × 30 m plot across deer exclosure treatments (fenced, not fenced) using a t ‐test, with burn regimes (annual, four‐year) as replicates to determine if plant species richness was inversely related to deer and fire presence. For each subplot, we calculated plant species richness and Evar, an evenness metric based on variance in plant species abundance that is independent of species richness (Smith and Wilson, 1996; Avolio et al, 2019). We assessed differences in richness and evenness at the subplot level using linear mixed‐effects regression models, with deer exclosure treatments (fenced, not fenced), burn regime (annual, four‐year), and their interaction as fixed effects, and plot nested within burn regime as a random effect.…”
Section: Methodsmentioning
confidence: 99%
“…Front yards were, on average, 108 m 2 smaller than back yards. We used the community_strucure() function in the codyn package (Hallett et al, 2019) to calculate species richness and evenness using Evar, a measure of evenness (Smith and Wilson, 1996;Avolio et al, 2019a).…”
Section: Data Analysesmentioning
confidence: 99%
“…We applied RAC_change() function (Avolio et al, 2019) in codyn package to calculate species gain and loss within each plot from 2017 to 2018. Species gains and losses were then compared across precipitation and clipping treatments using ANCOVA.…”
Section: Species Gains Losses and Turnovermentioning
confidence: 99%