Data from: Bee phenology is predicted by climatic variation and functional traits
Stemkovski, Michael et al. (2020), Data from: Bee phenology is predicted by climatic variation and functional traits, Dryad, Dataset, https://doi.org/10.5061/dryad.t76hdr7zc
We gathered data at 18 sites around the Rocky Mountain Biological Laboratory (RMBL) in the Elk Mountains of western Colorado, USA from 2009 to 2017. Sites were located along an elevation transect (2456-3438 meters above sea-level) in montane and sub-alpine habitats dominated by a diverse mixture of perennial flowering species. We sampled bees in habitat types that were representative of dominant vegetation types: wet meadows dominated by Veratrum tenuipetalum, those dominated by Salix spp., rocky dry meadows, and Artemisia spp. steppe. We conducted biweekly bee abundance surveys at each site using pan traps (following LeBuhn et al. 2003). We set out 10 each of white, fluorescent yellow, and fluorescent blue pan traps along two approx. perpendicular 45-meter transects at intervals of 3 meters, an array that passively attracts bees by mimicking a display of flowers. We deployed pan traps between at approx. 0800 and 1700 (the period of maximum bee activity) only on warm, calm, sunny days and removed traps when these conditions changed drastically. Further details of the bee sampling are provided by Gezon et al. (2015).
The catches per day and sampling method are given, and the data processing code is included.
Several taxonomic groups and sites were excluded for the analysis. See the manuscript for details on which species and sites were used. We encourage other researchers to use these data for further studies, and also to contact Michael Stemkovski (firstname.lastname@example.org) or Rebecca Irwin (email@example.com) for insight on the ideosynchrosies of the dataset. We wish to enable others to do work using these data. The long-term bee phenology monitoring project is an ongoing effort that is headed by Rebecca Irwin, so we encourage users to get the most up-to-date data at https://osf.io/kmxyn/. The repository on Open Science Framework includes more recent years of data since the analysis of the present dataset.
National Science Foundation, Award: ABI-1759965
National Science Foundation, Award: NSF EF-1802605
U.S. Forest Service, Award: 18-CS-11046000-041
National Science Foundation, Award: 1745048
National Science Foundation, Award: DEB-0922080
National Science Foundation, Award: DEB-1354104