ABSTRACT
Interaction networks are widely used as tools to understand plant-pollinator communities, and to examine potential threats to plant diversity and food security if the ecosystem service provided by pollinating animals declines. However, most networks to date are based on recording visits to flowers, rather than recording clearly defined effective pollination events. Here we provide the first networks that explicitly incorporate measures of pollinator effectiveness (PE) from pollen deposition on stigmas per visit, and pollinator importance (PI) as the product of PE and visit frequency. These more informative networks, here produced for a low diversity heathland habitat, reveal that plant-pollinator interactions are more specialized than shown in most previous studies. At the studied site, the specialization index [Formula: see text] was lower for the visitation network than the PE network, which was in turn lower than [Formula: see text] for the PI network. Our study shows that collecting PE data is feasible for community-level studies in low diversity communities and that including information about PE can change the structure of interaction networks. This could have important consequences for our understanding of threats to pollination systems.
Subject(s)
Bees/physiology , Diptera/physiology , Ecosystem , Ericaceae/physiology , Flowers/physiology , Pollination , Ulex/physiology , Animals , Data Collection , England , Pollen/physiology , Species SpecificityABSTRACT
Recurrent fires impose a strong selection pressure in many ecosystems worldwide. In such ecosystems, plant flammability is of paramount importance because it enhances population persistence, particularly in non-resprouting species. Indeed, there is evidence of phenotypic divergence of flammability under different fire regimes. Our general hypothesis is that flammability-enhancing traits are adaptive; here, we test whether they have a genetic component. To test this hypothesis, we used the postfire obligate seeder Ulex parviflorus from sites historically exposed to different fire recurrence. We associated molecular variation in potentially adaptive loci detected with a genomic scan (using AFLP markers) with individual phenotypic variability in flammability across fire regimes. We found that at least 42% of the phenotypic variation in flammability was explained by the genetic divergence in a subset of AFLP loci. In spite of generalized gene flow, the genetic variability was structured by differences in fire recurrence. Our results provide the first field evidence supporting that traits enhancing plant flammability have a genetic component and thus can be responding to natural selection driven by fire. These results highlight the importance of flammability as an adaptive trait in fire-prone ecosystems.