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Linking Keystone Species and Functional Groups: A New Operational Definition of the Keystone Species Concept Ecology and Society
Davic, Robert D; Ohio Environmental Protection Agency; r_davic@yahoo.com.
The concept of the "keystone species" is redefined to allow for the a priori prediction of these species within ecosystems. A keystone species is held to be a strongly interacting species whose top-down effect on species diversity and competition is large relative to its biomass dominance within a functional group. This operational definition links the community importance of keystone species to a specific ecosystem process, e.g., the regulation of species diversity, within functional groups at lower trophic levels that are structured by competition for a limited resource. The a priori prediction of keystone species has applied value for the conservation of natural areas.
Tipo: Peer-Reviewed Reports Palavras-chave: Keystone species; Competition; Dominance; Feeding guilds; Functional groups; Power laws; Species diversity; Biomass dominance.
Ano: 2003
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Speaking Stata: The protean quantile plot AgEcon
Cox, Nicholas J..
Quantile plots showing by default ordered values versus cumulative probabilities are both well known and also often neglected, considering their major advantages. Their flexibility and power is emphasized by using the qplot program to show several variants on the standard form, making full use of options for reverse, ranked, and transformed scales and for superimposing and juxtaposing quantile traces. Examples are drawn from the analysis of species abundance data in ecology. A revised version of qplot is formally released with this column. Distribution plots in which the axes are interchanged are also discussed briefly, in conjunction with a revised version of distplot, also released now.
Tipo: Journal Article Palavras-chave: Qplot; Distplot; Distributions; Quantile plots; Statistical graphics; Species abundance; Ecology; Whittaker plots; Broken stick; Lognormal; Power laws; Scaling laws; Research Methods/ Statistical Methods.
Ano: 2005 URL: http://purl.umn.edu/117532
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