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AbstractHow bees shift the timing of their seasonal activity (phenology) to track favorable conditions influences the degree to which bee foraging and flowering plant reproduction overlap. While bee phenology is known to shift due to interannual climatic variation and experimental temperature manipulation, the underlying causes of these shifts are poorly understood. Most studies of bee phenology have been phenomenological and have only examined shifts of point estimates, such as first appearance or peak timing. Such cross-sectional measures are convenient for analysis, but foraging activity is distributed across time, and pollination interactions are better described by overlap in phenological abundance curves. Here, we make simultaneous inferences about interannual shifts in bee phenology, emergence and senescence rates, population size, and the effect of floral abundance on observed bee abundance. We do this with a model of transition rates between life stages implemented in a hierarchical Bayesian framework and parameterized with fine-scale abundance time series of the sweat bee <i>Halictus rubicundus</i> at the Rocky Mountain Biological Laboratory in Colorado. We find that <i>H. rubicundus</i>'s emergence cueing was highly sensitive to the timing of snowmelt but that emergence rate, senescence rate, and population size did not differ greatly across years. The present approach can be used to glean information about vital rates from other datasets on bee and flower phenology, improving our understanding of pollination interactions.
Knowledge graph centered on Bee phenological distributions predicted by inferr with 15 nodes and 60 connections. Top connected: Interannual bumble bee abundance is driven by indi, Effects of climate change on phenologies and distr, Phenology as a process rather than an event: from , Bee phenology is predicted by climatic variation a, Lasioglossum.
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