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Bee monitoring by community scientists: comparing a collections-based program with iNaturalist.

Nash E TurleySarah E KaniaIsabella R PetittaElizabeth A OtrubaDavid J BiddingerThomas M ButzlerValerie V SeslerMargarita M López-Uribe
Published in: Annals of the Entomological Society of America (2024)
Bee monitoring, or widespread efforts to document bee community biodiversity, can involve data collection using lethal (specimen collections) or non-lethal methods (observations, photographs). Additionally, data can be collected by professional scientists or by volunteer participants from the general public. Collection-based methods presumably produce more reliable data with fewer biases against certain taxa, while photography-based approaches, such as data collected from public natural history platforms like iNaturalist, can involve more people and cover a broader geographic area. Few efforts have been made to quantify the pros and cons of these different approaches. We established a community science monitoring program to assess bee biodiversity across the state of Pennsylvania (USA) using specimen collections with nets, blue vane traps, and bowl traps. We recruited 26 participants, mostly Master Gardeners, from across the state to sample bees after receiving extensive training on bee monitoring topics and methods. The specimens they collected were identified to species, stored in museum collections, and the data added to public databases. Then, we compared the results from our collections to research-grade observations from iNaturalist during the same time period (2021 and 2022). At state and county levels, we found collections data documented over twice as much biodiversity and novel baseline natural history data (state and county records) than data from iNaturalist. iNaturalist data showed strong biases toward large-bodied and non-native species. This study demonstrates the value of highly trained community scientists for collections-based research that aims to document patterns of bee biodiversity over space and time.
Keyphrases
  • electronic health record
  • big data
  • healthcare
  • mental health
  • public health
  • machine learning
  • data analysis
  • artificial intelligence
  • deep learning
  • high intensity
  • genetic diversity