Modeling of juvenile steelhead growth and movement in the Pine River, Michigan

Limited research has been conducted to quantify the capacity of Great Lakes tributaries for steelhead production. The goals of my dissertation research were to (1) develop modeling techniques for estimating steelhead growth and local movement behavior and (2) identify the relationships between habitat condition and both steelhead growth and movement behavior in the Pine River watershed. Chapter 1 presents a new approach for modeling growth that addresses the limitation of missing recapture observations on individual growth estimation. Age-1 steelhead were individually tagged and recaptured by electrofishing. Individual growth was modeled using daily water temperature and observed fish sizes as inputs and by determining the proportion of maximum consumption ( P) parameter for the bioenergetics equation. Results demonstrate that individual growth can be accurately modeled using water temperature and a temporally-specific P identical for all individuals. Additionally, a modification to the model that allows for estimating variability of P among fish within a stream reach is presented. The magnitude of steelhead growth and consumption variation at multiple spatial-temporal scales within the Pine River watershed are reported in Chapter 2. Growth and consumption was greatest in the East Branch relative to the rest of the watershed, greater during spring than summer--autumn, and greater during 2002 than 2001. Spatial-temporal differences in consumption rather than temperature were more responsible for the substantial intra-annual and inter-annual growth variation that occurred both within and among study reaches. Consumption was positively correlated with total phosphorus. Chapter 3 presents a modeling approach to quantitatively estimate the propensity of steelhead movement within streams and identify the relative importance of environmental conditions, habitat condition, and fish density on local movement behavior. Mark-recapture field experiments were conducted and daily probability of movement, P( move), inferred by performing log-likelihood-based parameter optimizations that minimized the differences in observed and predicted spatial distributions generated by four individual-based simulation models. Steelhead exhibited an unambiguously high daily probability of movement (mean P( move) > 0.67 for both density-independent models and >0.44 for both density-dependent models when steelhead density was zero). The habitat-dependent, density-independent model provided the most parsimonious fit to the observed data. Water temperature was positively correlated with P( move).