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Related Experiment Video

Updated: Dec 17, 2025

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
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A Bayesian nested patch occupancy model to estimate steelhead movement and abundance.

Lynn Waterhouse1,2, Jody White3, Kevin See4

  • 1Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive #0202, La Jolla, California, 92093-0202, USA.

Ecological Applications : a Publication of the Ecological Society of America
|June 26, 2020
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Summary

We developed a Bayesian framework to track fish movements in river networks using passive integrated transponder (PIT) tag data. This approach improves population estimates for conservation and management of riverine species.

Keywords:
BayesianPIT tagabundancedetection probabilityescapementhierarchical modelnested patch occupancyriver networksalmonidsteelhead

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Area of Science:

  • Ecology
  • Conservation Biology
  • Fisheries Science

Background:

  • Anthropogenic impacts on rivers raise concerns about aquatic species' population status.
  • Passive integrated transponder (PIT) tag studies with in-stream detectors are used to monitor species movements in river networks.
  • Quantifying animal movements using imperfect detection data in river networks is challenging.

Purpose of the Study:

  • To develop a flexible Bayesian analytic framework for modeling imperfectly detected movements of tagged individuals in river networks.
  • To provide probabilistic estimates of upstream migration routes and convert them into abundance estimates.
  • To evaluate model performance and inform future monitoring and management decisions.

Main Methods:

  • A flexible Bayesian framework was developed to model imperfectly detected movements in a nested PIT tag array river network.
  • The model estimates probabilistic upstream migration routes based on nested state variables.
  • A simulation framework was used to evaluate model performance based on tagging rates and array detection probabilities.

Main Results:

  • The framework provides probabilistic estimates of individual movement routes within river networks.
  • Movement estimates can be converted into abundance estimates when local abundance is known.
  • Model performance was evaluated using steelhead (Oncorhynchus mykiss) data from the Upper Columbia River basin.

Conclusions:

  • The proposed Bayesian framework offers a robust method for analyzing PIT tag data in river networks.
  • The approach can improve the accuracy of abundance estimates for species of concern.
  • Simulation results inform decisions on optimal PIT tag array configurations and tagging strategies for effective riverine species monitoring and management.