PiSCES: Pi(scine) stream community estimation system

• Published in: Environmental modelling & software : with environment data news Vol. 127; p. 104703
• Main Authors: Cyterski, Mike, Barber, Craig, Galvin, Mike, Parmar, Rajbir, Johnston, John M., Smith, Deron, Ignatius, Amber, Prieto, Lourdes, Wolfe, Kurt
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.05.2020; Elsevier Science Ltd
• Subjects: Abundance; Body weight; Community size spectra; Community structure; Fish; Fish distributions; Freshwater fish; Geographical distribution; Geological surveys; Habitat preferences; Indigenous species; Introduced species; Relative abundance; Websites; United States > US; Community structure; Community size spectra; Fish distributions
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2020.104703

The Piscine Stream Community Estimation System (PiSCES) provides users with a hypothesized fish community for any stream reach in the conterminous United States using information obtained from Nature Serve, the US Geological Survey (USGS), StreamCat, and the Peterson Field Guide to Freshwater Fishes of North America for over 1000 native and non-native freshwater fish species. PiSCES can filter HUC8-based fish assemblages based on species-specific occurrence models; create a community abundance/biomass distribution by relating relative abundance to mean body weight of each species; and allow users to query its database to see ancillary characteristics of each species (e.g., habitat preferences and maximum size). Future efforts will aim to improve the accuracy of the species distribution database and refine/augment increase the occurrence models. The PiSCES tool is accessible at the EPA's Quantitative Environmental Domain (QED) website at https://qed.epacdx.net/pisces/ •Browser-based service used to display fish distributional information for continental US.•Database includes characteristics for 1018 species.•Estimation of a fish community for any stream segment in the continental US based on probabilistic occurrence models.•Community biomass/numbers spectrum estimation based on size-based thinning coefficients.•Potential use for decision-making and exploratory research.