Login / Signup

Documenting fishes in an inland sea with citizen scientist diver surveys: using taxonomic expertise to inform the observation potential of fish species.

Elizabeth A AshleyChristy V Pattengill-SemmensJames W OrrJanna D NicholsJoseph K Gaydos
Published in: Environmental monitoring and assessment (2022)
Long-term monitoring enables scientists and managers to track changes in the temporal and spatial distributions of fishes. Given the anthropogenic stressors affecting marine ecosystem health, there is a critical need for robust, comprehensive fish monitoring programs. Citizen science can serve as a meaningful, cost-effective strategy to survey fish communities. We compared data from 13,000 surveys collected over 21 years (1998-2019) by Reef Environmental Education Foundation (REEF) volunteer divers to a published compilation of Salish Sea ichthyofauna collected using an assortment of methods. Volunteer divers observed 138 of 261 recognized species in the Salish Sea, expanded the range of 18 species into additional Salish Sea sub-basins, and identified one species novel to the Salish Sea (Gibbonsia metzi - Striped Kelpfish). To identify Salish Sea fish species that are most suitable to be monitored by underwater visual census and to evaluate confidence in in situ identification, we developed a categorization system based on the likelihood of recreational divers and snorkelers encountering a given species, and on whether identification required a specimen in hand or could be classified to species visually (with or without a high-quality photograph). REEF divers encountered 62% (138 of 223) of the visually detectable species occurring in the region and 85% (102 of 120) of species most likely to be observed by recreational divers. Our findings show that citizen scientists provide valuable monitoring data for over half of the 261 marine and anadromous fish species known to occupy the Salish Sea, many of which are not routinely monitored otherwise.
Keyphrases
  • healthcare
  • public health
  • genetic diversity
  • systematic review
  • machine learning
  • social media
  • big data
  • deep learning