Scalable population estimates using spatial-stream-network (SSN) models, fish density surveys, and national geospatial database frameworks for streams

Abstract

Population size estimates for stream fishes are important for conservation and management, but sampling costs limit the extent of most estimates to small portions of river networks that encompass 100s–10 000s of linear kilometres. However, the advent of large fish density data sets, spatial-stream-network (SSN) models that benefit from nonindependence among samples, and national geospatial database frameworks for streams provide the components to create a broadly scalable approach to population estimation. We demonstrate such an approach with density surveys for trout species from 108 sites in a 735 km river network. Universal kriging was used to predict a continuous map of densities among survey locations, and block kriging (BK) was used to summarize discrete map areas and make population estimates at stream, river, and network scales. The SSN models also accommodate covariates, which facilitates hypothesis testing and provides insights about factors affecting patterns of abundance. The SSN–BK population estimator can be applied using free software and geospatial resources to develop valuable information at low cost from many existing fisheries data sets.