Applying Generalized Joint Attribute Modeling to Select Local Indicator Fish Species for River Management

To protect local ecosystem and biodiversity, it is critical to implement continuous and comprehensive monitoring programs that can effectively track changes in environmental conditions and species dynamics. However, the high costs associated with such programs (e.g., monitoring programs) pose significant challenges to their consistent and long‐term implementation. To reduce the costs and enhance the continuity of programs, selecting local indicator species that effectively reflect environmental conditions and predict species distributions can be a valuable approach. Here, we examined whether generalized joint attribute modeling (GJAM) has the potential to select local indicator species using data on water environmental factors and fish densities in two rivers in Kanagawa Prefecture, Japan. We applied GJAM to select local indicator species for water quality and species richness. Candidate local indicator species for water quality were selected based on the coefficient matrix of environmental factors derived from GJAM, and candidate local indicator species for high species richness were selected based on the correlation matrix of species densities derived from GJAM. To validate this approach, we calculated rank correlations between biochemical oxygen demand, total nitrogen, species richness, and the densities of these candidate species. Cottus pollux was selected as a candidate local indicator species for water quality, and Tribolodon hakonensis , Pseudogobio esocinus esocinus , Cobitis sp. BIWAE type C, and Rhinogobius spp., excluding R. flumineus , were selected as local indicator species for high species richness. This method will be effective for the simultaneous selection of local indicator species for water quality and species richness.