Using state‐space models to estimate recreational angling effort and infer processes that regulate angler dynamics.

This article focuses on applying state-space models to analyze angler effort data collected from on-site creel surveys in 11 sections of three trout fisheries in Idaho. Traditionally analyzed using design-based methods, angler effort data are temporally autocorrelated counts, which state-space models can explicitly accommodate by separating the unobserved angling population dynamics (state process) from observation error. The study demonstrated fitting both density-independent and density-dependent state-space models, estimating parameters such as daily angling effort, population growth rates related to day type transitions (e.g., weekday to weekend), and recreational carrying capacity (K). Results indicated that carrying capacity was nearly twice as high in fishery sections with greater access (≥0.5 access points/km) compared to those with lower access, while catch rates had little effect on carrying capacity. The authors suggest that state-space models offer a flexible and potentially more precise alternative to traditional methods for estimating angling effort and understanding factors regulating angling dynamics, with implications for fisheries management and survey design.