Project Contacts
Nick Bouwes, ELR

Development Team
Carl Saunders, ELR
Joe Wheaton, USU
Pete McHugh, ELR
Eric Wall, ELR
Chris Jordan, NOAA

Current Status
Production, published

ISEMP, Asotin

The ability to predict the effects of stream restoration or extrapolate knowledge gained to new situations is highly dependent on the degree to which a model represents actual mechanisms. Ecohydraulic fish habitat models that are mechanistically based have successfully predicted fish location and abundance. In ISEMP and CHaMP, we developed a monitoring protocol that allows for the use of net rate of energy intake (NREI; energy gains through capture and consumption of drifting invertebrates minus energy cost through swimming to maintain a foraging position) models, where temperature and food availability (drift) is used as supporting information to hydraulic models to estimate habitat quality and carrying capacity.

The NREI model uses a foraging model that incorporates depth, velocity and prey abundance (drifting invertebrates) to predict prey encounter rates, capture success, and consumption rates at locations throughout the modeled hydraulic environment of a reach. Bioenergetics models then estimate gross rate of energy input (GREI) from prey consumed and swimming costs (SC) at the focal velocity under a given temperature, with GREI-SC=NREI. To estimate carrying capacity, the highest NEI value on each modeled cross section is compared to a user-defined NEI threshold and locations meeting or exceeding the NEI threshold (e.g., NREI>0) receive a fish. A minimum distance between fish is set by the fish territory size. Placement proceeds downstream until the last location has been evaluated for fish placement, with carrying capacity equal to the sum of all fish in the reach

Findings and Uses

Data collected using the CHaMP protocol has allowed for application of a Net Rate of Energy Intake (NREI) ecohydraulic model to:

  • Predict potential improvements to carrying capacity as the result of restoration actions,
  • Help identify types of restoration actions and areas where such actions can be most cost-effective, and
  • Allow for eventual extrapolation to the network scale to help determine the most cost-effective restoration actions and where to apply them.

This approach can be upscaled to address population-level predictions, and was recently used in overall life-cycle assessment of steelhead population persistence following a large-scale restoration effort in the Middle Fork of the John Day River in Central Oregon (Wheaton et al. 2017).

(A) Depth and velocity estimated from a 2.5D hydraulic model (B) spatially explicit prediction of NREI based on foraging and swim cost models (C) predicted locations of fish based on NREI values greater than zero and territory size. From Wall et al. 2015.

Linear regression between observed and predicted steelhead densities that shows that NREI-predicted capacities are positively correlated with observed densities.