Evolutionary Ecology

, Volume 29, Issue 3, pp 323–340

Passive partner choice through exploitation barriers

  • Miguel A. Rodríguez-Gironés
  • Shan Sun
  • Luis Santamaría
Original Paper

Abstract

Floral features that affect the efficiency with which pollinators can harvest their resources, or the profitability they obtain from them, affect the foraging decisions of pollinators. Foraging choices of pollinators, in turn, affect pollen flow: increases in flower constancy lead to more efficient pollen transport. It follows that exploitation barriers—flower traits that differentially affect net intake rates of potential visitors—will promote resource partitioning and enhance pollen export. In this paper we first generalise foraging models to show that exploitation barriers can lead to partial resource partitioning even when flowers are randomly distributed in space. Then we develop a model to study how the foraging rules of pollinators, pollen removal and pollen deposition, affect pollen flow. The model shows that resource partitioning, even incomplete, can substantially increase the efficiency of pollen flow. Finally, we use computer simulations to demonstrate that exploitation barriers promoting partial resource partitioning can evolve. Many of the flower traits associated with pollination syndromes have small but consistent effects on the efficiency with which different taxonomic groups exploit flowers, and can be considered exploitation barriers. Even if these barriers are not strong enough to promote strict specialisation, and may have little effect on the female component of fitness when pollinators are not a limiting resource, they are likely to be selected because they enhance the male component of fitness.

Keywords

Exploitation barriers Floral diversification Optimal foraging Resource partitioning Specialisation 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Miguel A. Rodríguez-Gironés
    • 1
  • Shan Sun
    • 2
  • Luis Santamaría
    • 3
  1. 1.Estación Experimental de Zonas Áridas, EEZA-CSICLa Cañada de San Urbano, AlmeríaSpain
  2. 2.State Key Laboratory of Grassland and Agro-Ecosystems, School of Life SciencesLanzhou UniversityLanzhouPeople’s Republic of China
  3. 3.Estación Biológica de Doñana (EBD-CSIC)Isla de la Cartuja, SevillaSpain

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