How to be an attractive male: floral dimorphism and attractiveness to pollinators in a dioecious plant

BACKGROUND: Sexual selection theory predicts that males are limited in their reproductive success by access to mates, whereas females are more limited by resources. In animal-pollinated plants, attraction of pollinators and successful pollination is crucial for reproductive success. In dioecious plant species, males should thus be selected to increase their attractiveness to pollinators by investing more than females in floral traits that enhance pollinator visitation. We tested the prediction of higher attractiveness of male flowers in the dioecious, moth-pollinated herb Silene latifolia, by investigating floral signals (floral display and fragrance) and conducting behavioral experiments with the pollinator-moth, Hadena bicruris. RESULTS: As found in previous studies, male plants produced more but smaller flowers. Male flowers, however, emitted significantly larger amounts of scent than female flowers, especially of the pollinator-attracting compounds. In behavioral tests we showed that naïve pollinator-moths preferred male over female flowers, but this preference was only significant for male moths. CONCLUSION: Our data suggest the evolution of dimorphic floral signals is shaped by sexual selection and pollinator preferences, causing sexual conflict in both plants and pollinators

Postpollination changes in floral odor in Silene latifolia: Adaptive mechanisms for seed-predator avoidance?

ISSN:0098-0331ISSN:1573-156

Computational models of reinforcement learning: the role of dopamine as a reward signal

Reinforcement learning is ubiquitous. Unlike other forms of learning, it involves the processing of fast yet content-poor feedback information to correct assumptions about the nature of a task or of a set of stimuli. This feedback information is often delivered as generic rewards or punishments, and has little to do with the stimulus features to be learned. How can such low-content feedback lead to such an efficient learning paradigm? Through a review of existing neuro-computational models of reinforcement learning, we suggest that the efficiency of this type of learning resides in the dynamic and synergistic cooperation of brain systems that use different levels of computations. The implementation of reward signals at the synaptic, cellular, network and system levels give the organism the necessary robustness, adaptability and processing speed required for evolutionary and behavioral success