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RANK CLOCKS AND PLANT COMMUNITY DYNAMICS
Author(s) -
Collins Scott L.,
Suding Katharine N.,
Cleland Elsa E.,
Batty Michael,
Pennings Steven C.,
Gross Katherine L.,
Grace James B.,
Gough Laura,
Fargione Joe E.,
Clark Christopher M.
Publication year - 2008
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1890/07-1646.1
Subject(s) - species richness , abundance (ecology) , ecology , shrubland , grassland , community structure , geography , rank (graph theory) , vegetation (pathology) , arid , biology , ecosystem , mathematics , medicine , pathology , combinatorics
Summarizing complex temporal dynamics in communities is difficult to achieve in a way that yields an intuitive picture of change. Rank clocks and rank abundance statistics provide a graphical and analytical framework for displaying and quantifying community dynamics. We used rank clocks, in which the rank order abundance for each species is plotted over time in temporal clockwise direction, to display temporal changes in species abundances and richness. We used mean rank shift and proportional species persistence to quantify changes in community structure in long‐term data sets from fertilized and control plots in a late successional old field, frequently and infrequently burned tallgrass prairie, and Chihuahuan desert grassland and shrubland communities. Rank clocks showed that relatively constant species richness masks considerable temporal dynamics in relative species abundances. In the old field, fertilized plots initially experienced high mean rank shifts that stabilized rapidly below that of unfertilized plots. Rank shifts were higher in infrequently burned vs. annually burned tallgrass prairie and in desert grassland compared to shrubland vegetation. Proportional persistence showed that arid grasslands were more dynamic than mesic grasslands. We conclude that rank clocks and rank abundance statistics provide important insights into community dynamics that are often hidden by traditional univariate approaches.