Incorporating Batch Mark–Recapture Data into an Integrated Population Model of Brown Trout. (27th July 2021) - Doll, Jason C.; Wood, Chris J.; Goodfred, David W.; Rash, Jacob M.
Abstract: Brown Trout Salmo trutta are a popular sport fish, and numerous populations that are unable to successfully reproduce are maintained with supplemental stockings in waters with habitat that can support individual survival (e.g., appropriate thermal refuge). Long‐term management of these populations requires understanding of their population dynamics to determine harvest restrictions and stocking rates. The objective of this research was to describe population dynamics of a tailrace Brown Trout fishery using an integrated population model (IPM) that incorporates monitoring data and low‐cost batch mark–recapture data. Further, we evaluated the relationship between water temperature and survival. We hypothesized that annual survival would be lower at high water temperature. Additionally, we used the IPM to project the Brown Trout population based on two different management scenarios (no minimum length limit with seven‐fish bag limit and 356‐mm‐TL minimum length limit with two‐fish bag limit). Management outcomes explored include total population size, age frequencies, and length indices. The results of this study suggested that recreational harvest and water temperature were the two main factors influencing the Brown Trout population. Water temperature was found to be a major factor in determining survival of Brown Trout. Simulations under various minimum length limits indicated the minimum length limit of 356 mm TL and a two‐fish bag limit will substantially increase the population size but with a reduction in length indices. Integrated population models have been applied historically to large systems with significant amounts of data. Estimates of survival and detection are important components of an IPM; to our knowledge, the IPM used here is the first to integrate batch mark–recapture data to estimate these key parameters. The data and modeling approach used here demonstrate the value of using novel statistical methods to make the most efficient use of low‐cost survey data.