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Quantifying effectiveness and best practices for bumblebee identification from photographs

January 10, 2024

Understanding pollinator networks requires species level data on pollinators. New photographic approaches to identification provide avenues to data collection that reduce impacts on declining bumblebee species, but limited research has addressed their accuracy. Using blind identification of 1418 photographed bees, of which 561 had paired specimens, we assessed identification and agreement across 20 bumblebee species netted in Montana, North Dakota, and South Dakota by people with minimal training. An expert identified 92.4% of bees from photographs, whereas 98.2% of bees were identified from specimens. Photograph identifiability decreased for bees that were wet or matted; bees without clear pictures of the abdomen, side of thorax, or top of thorax; bees photographed with a tablet, and for species with more color morphs. Across paired specimens, the identification matched for 95.1% of bees. When combined with a second opinion of specimens without matching identifications, data suggested a similar misidentification rate (2.7% for photographs and 2.5% specimens). We suggest approaches to maximize accuracy, including development of rulesets for collection of a subset of specimens based on difficulty of identification and to address cryptic variation, and focused training on identification that highlights detection of species of concern and species frequently confused in a study area.

Publication Year 2024
Title Quantifying effectiveness and best practices for bumblebee identification from photographs
DOI 10.1038/s41598-023-41548-w
Authors Anne Colgan, Richard G. Hatfield, Amy Dolan, Wendy Velman, Rebecca Newton, Tabitha Graves
Publication Type Article
Publication Subtype Journal Article
Series Title Scientific Reports
Index ID 70251009
Record Source USGS Publications Warehouse
USGS Organization Northern Rocky Mountain Science Center