Estimating population size and survival of adult northern pike (Esox lucius) in Lower Green Bay

Abstract

Complex life history behaviours like spawning migrations can complicate population assessments. However, predictable aggregation of populations at certain times of year (e.g. at spawning grounds) presents an effective way to sample species that are otherwise spatially widespread. From 2014 to 2019, a mark‐recapture study on Green Bay northern pike (Esox lucius) entering a spawning wetland was used to estimate apparent survival and population size using Bayesian inference and the Cormack–Jolly–Seber model. Population estimates and apparent survival varied from 446 to 1949 and 0.22 (males in 2017) to 0.63 (males in 2015), respectively, among years. Apparent survival also increased with maximum mean daily water temperature. Estimated capture probability was consistently low across years (<30%) despite a sampling method with high capture efficiency of fish present at the wetland, suggesting Green Bay northern pike may be exhibiting low spawning site fidelity or other life history behaviours, like skipped spawning, which reduce their capture probability at a single wetland over time. Further, spawning site selection and timing may respond to water temperatures. Surveying multiple sites could further elucidate important life history traits while refining estimates of population size and structure in large systems.

State-space models to describe survival of an endemic species in the Little Tennessee River basin

“…These complex state-space models have been applied to a variety of taxa including the federally endangered Great Lakes Piping Plover (Charadrius melodus) (Saunders et al, 2018); marine protected Pantropical Spotted Dolphin (Stenella attenuate) (Hoyle and Maunder, 2004); and the Tricolored Blackbird (Agelaius tricolor), a species of conservation concern (Robinson et al, 2018). Integrated population models often combine sub-models that describe survival (Stuart et al, 2022), mortality (Doll et al, 2021), and movement patterns (Patterson et al, 2008). Studies of animal movement have taken advantage of this structure to model individual movement (Patterson et al, 2008) and population movement (Jonsen, 2016).…”