RAPID: Plant/pollinator networks in a rare, wet El Nino year

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Long-term records documenting the effects of climate variability on ecological interactions among species are rare, but this type of information is critical to making accurate predictions of ecological consequences of climate change. Lacking long term data, an alternative approach is to examine shorter-term change in species interactions in response to rapid climate shifts, and combine that information with a detailed understanding of long-term climatic trends. This project extends a detailed study of pollinator abundance and species diversity (primarily bumblebees) with plant flowering patterns in coastal, central California. This region had been experiencing an increasingly severe drought, and average temperatures that have been increasing for decades. The current warm, wet El Nino event presents a unique opportunity to assess the responses and resilience of this system of pollinators and plant species. A diverse and abundant assemblage of pollinator species is critical to the maintenance of native plant species in this region. This study will inform the management of nature reserves and will also provide important information on how pollinators, which are essential to US agriculture, respond to climate change. The project will help broaden participation in science through graduate student training, and through a workshop to engage land managers of relevant habitats.The work addresses three specific, testable hypotheses: (1) plant flowering and pollinator activity will be temporally disconnected in the wet El Nino year; (2) bumblebees respond immediately to the resource boom provided by heavy, extended flowering in the El Nino, but solitary bee responses will be delayed a year; and (3) pollinator networks will be more specialized in the wet El Nino year than during the drought, because bees will select more preferred plant species when flowers are abundant. The potential temporal mismatch is an important ecological problem in the context of changing climatic variability.