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Dryad

Data from: Flying by night: Comparing nocturnal pollinator networks over time in the Colorado Rocky Mountains

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Nov 20, 2024 version files 61.57 KB

Abstract

Because pollen-transport networks tend to vary widely over short periods of time but remain consistent over longer periods of time, it is important to account for study length when characterizing pollen-transport network structure. The study of nocturnal pollen-transport networks independently from diurnal pollinator networks has also been emphasized, as nocturnal pollen-transport networks are an often overlooked ecosystem component. 

Here, we systematically compare the properties of moth pollen-transport pollinator networks in Colorado, USA, over the course of a season, and test for a link between specialization and pollinator morphology. We sampled moths for pollen over nine weeks in June through August 2021. We then constructed pollen-transport networks, and used mixed models to test for differences in network indices between different parts of the 2021 season; we also tested for a relationship between moth proboscis length, moth presence across seasons, and the number of flower species pollinated.

As expected, we found high rates of species turnover and plant-pollinator interactions over the summer, and we found that longer proboscis lengths – as well as length of presence over the summer – were predictive of higher generalization within individual species. However, variation in the overall structure of pollen-transport networks was relatively low over the course of the season, with linkage density and robustness being the only metrics that changed.

We suggest that even with high rates of species turnover, nocturnal moth pollen-transport networks may maintain their overall structure over a season, with variations in some metrics possibly resulting from variations in the availability of floral resources. Our study highlights the necessity of continuing to research moth pollen-transport networks over different temporal scales, as aggregating pollination data over time can obfuscate patterns in temporal variation in network structure.