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A Quantitative Theory of Reproductive Effort in Rhizomatous Perennial Plants
Author(s) -
Armstrong Robert A.
Publication year - 1982
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.2307/1936788
Subject(s) - biology , perennial plant , production (economics) , vegetative reproduction , growth rate , limiting , biomass (ecology) , work (physics) , rhizome , botany , ecology , mathematics , economics , microeconomics , physics , mechanical engineering , geometry , engineering , thermodynamics
I develop here several models of resource partitioning between seed production and vegetative growth in rhizomatous perennial plants. These models apply strictly to species whose ramets, because of developmental constraints, do not alter their apportionments of energy to seed production and vegetative growth as functions of age or position (central vs. peripheral) in a clone. The limiting prediction of these models is that the quantity ku 2 , where k is the rare of seed production per unit area occupied by a clone, and where u is the ultimate rate of radial spread of a clone, should be maximized. This prediction arises in both density—dependent and density—independent models, and is to a large extent independent of the mortality schedule of the plants involved. Both these features arise from the assumption that growth in these plants can ultimately be described by a power law. Generation of numerical predictions from the maximization of ku 2 requires the construction of ancillary theories describing the relationships among seed production, growth, and the energy devoted to each. The simplest assumption possible, that seed production and radial growth rate are each directly proportional to energy expended on them, yields the prediction that a plant should devote one—third of its postmaintenance energy to seed production and two—thirds to vegetative expansion. Nonlinear dependence of k and u on energy expenditures may generate different numerical predictions.

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