Using phenotypic manipulations to study multivariate selection of floral trait associations

被引:73
作者
Campbell, Diane R. [1 ]
机构
[1] Univ Calif Irvine, Dept Ecol & Evolutionary Biol, Irvine, CA 92697 USA
关键词
Colour; correlational selection; experiment; floral integration; multivariate selection; phenotypic manipulation; pollination syndrome; pollinator visitation; POLLINATOR-MEDIATED SELECTION; NATURAL-SELECTION; FLOWER COLOR; ADAPTIVE SIGNIFICANCE; NECTAR PRODUCTION; POLLEN REMOVAL; HUMMINGBIRD; EVOLUTION; BEHAVIOR; INTEGRATION;
D O I
10.1093/aob/mcp032
中图分类号
Q94 [植物学];
学科分类号
071001 ;
摘要
Background A basic theme in the study of plant-pollinator interactions is that pollinators select not just for single floral traits, but for associations of traits. Responses of pollinators to sets of traits are inherent in the idea of pollinator syndromes. In its most extreme form, selection on a suite of traits can take the form of correlational selection, in which a response to one trait depends on the value of another, thereby favouring floral integration. Despite the importance of selection for combinations of traits in the evolution of flowers, evidence is relatively sparse and relies mostly on observational approaches. Scope Here, methods for measuring selection on multivariate suites of floral traits are presented, and the studies to date are reviewed. It is argued that phenotypic manipulations present a powerful, but rarely used, approach to teasing apart the separate and combined effects of particular traits. The approach is illustrated with data from studies of alpine plants in Colorado and New Zealand, and recommendations are made about several features of the design of such experiments. Conclusions Phenotypic manipulations of two or more traits in combination provide a direct way of testing for selection of floral trait associations. Such experiments will be particularly valuable if rooted in hypotheses about differences between types of pollinators and tied to a proposed evolutionary history.
引用
收藏
页码:1557 / 1566
页数:10
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