Data from: Wild bees as winners and losers: relative impacts of landscape composition, quality, and climate
Cite this dataset
Kammerer, Melanie et al. (2020). Data from: Wild bees as winners and losers: relative impacts of landscape composition, quality, and climate [Dataset]. Dryad. https://doi.org/10.5061/dryad.kwh70rz2s
Wild bees, like many other taxa, are threatened by land use and climate change, which in turn jeopardizes pollination of crops and wild plants. Understanding how land-use and climate factors interact is critical to predicting and managing pollinator populations and ensuring adequate pollination services, but most studies have evaluated either land-use or climate effects, not both. Further, bee species are incredibly variable, spanning an array of behavioral, physiological and life history traits that can increase or decrease resilience to land use or climate change. Thus, there are likely bee species that benefit, while others suffer, from changing climate and land use, but few studies have documented taxon-specific trends. To address these critical knowledge gaps, we analyzed a long-term dataset of wild bee occurrences from Maryland, Delaware, and Washington DC, USA, examining how different bee genera and functional groups respond to landscape composition, quality, and climate factors.
Despite a large body of literature documenting land-use effects on wild bees, in this study, climate factors emerged as the main drivers of wild-bee abundance and richness. For wild-bee communities in spring and summer/fall, temperature and precipitation were more important predictors than landscape composition, landscape quality, or topography. However, relationships varied substantially between wild-bee genera and functional groups.
In the Northeast USA, past trends and future predictions show a changing climate with warmer winters, more intense precipitation in winter and spring, and longer growing seasons with higher maximum temperatures. In almost all of our analyses, these conditions were associated with lower abundance of wild bees. Wild-bee richness results were more mixed, including neutral and positive relationships with predicted temperature and precipitation patterns. Thus, in this region and undoubtedly more broadly, changing climate poses a significant threat to wild-bee communities.
'bee_subcommunity_traits.csv': functional traits of most abundant, non-parasitics species of wild bees in USGS Bee Inventory and Monitoring Lab dataset (BIML). Functional traits include sociality, parasitism, intertegular span, foraging range, native status, and nest location. This is Table S3 in comma separated format for easier re-use.
'richness_cleaned_spring_siteyear.csv': number of species ('richness') of wild bees in the spring, adjusted to a common sample coverage ('SC_adj') using iNEXT package in R. These data also include climate, topography, landscape composition, and landscape quality predictors. These variables are described in Table 1.
'richness_cleaned_sumfall_siteyear.csv': number of species ('richness') of wild bees in the summer and fall, adjusted to a common sample coverage ('SC_adj') using iNEXT package in R.
'abund_cleaned_spring_siteyear.csv': abundance of wild bees in the spring, adjusted by sampling effort (see Kammerer et al 2020 Sci Data for raw USGS BIML datasets and description of sampling effort adjustment).
'abund_cleaned_sumfall_siteyear.csv': abundance of wild bees in the summer and fall, adjusted by sampling effort (see Kammerer et al 2020 Sci Data for raw USGS BIML datasets and description of sampling effort adjustment).
See 'Kammerer_GCB_2020_ReadMe.txt' for a key to all column names.
National Institute of Food and Agriculture, Award: PENW-2017-07007
Foundation for Food and Agriculture Research, Award: 549032
National Institute of Food and Agriculture, Award: PEN04606
National Institute of Food and Agriculture, Award: 1009362