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

Abstract

AbstractObjectiveState‐space models are a flexible modeling approach and are often fit to ecological time series data exhibiting temporal autocorrelation. Traditionally, angler effort data collected using on‐site creel surveys are analyzed using design‐based methods. With some exceptions, state‐space models are rarely used to model creel survey data that are also generally a time series of temporally autocorrelated counts.MethodsIn this study, we demonstrated how to fit state‐space models to a time series of angler counts in 11 sections of three trout fisheries in Idaho. The basic model was extended to make inference about processes that may regulate angling dynamics, such as “population growth rate” of angling effort and recreational carrying capacity.ResultEstimated angling effort varied from 21,179 h in the lowermost section of the Henrys Fork Snake River to 199,457 h in the uppermost section of the South Fork Snake River. The finite population growth rate of angling effort was 1.82 when transitioning from a weekday to a weekend, suggesting that angling effort was 1.82 times greater, on average, on Saturdays than on Fridays, and the population growth rate was 0.38 (i.e., 0.38 times smaller) when transitioning from a Sunday to a Monday. Estimated carrying capacity among fishery sections varied from 129 daily hours of angling effort on the Big Lost River to 693 h on the middle section of the South Fork Snake River. Carrying capacity was 1.88 times higher on fishery sections that had ≥0.5 access points/km than on sections with <0.5 access points/km.ConclusionThe state‐space models used in this study can be modified or extended to fit a variety of data types or can be used to evaluate additional hypotheses regarding the angling process.