A hardware-accelerated particle filter for the geolocation of demersal fishes

Chang Liu, Geoffrey W. Cowles, Douglas R. Zemeckis, Gavin Fay, Arnault Le Bris, Steven X. Cadrin

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Geolocation is increasingly employed to reconstruct the movements of demersal fishes using data retrieved from electronic archival tags. However, geolocation methods commonly suffer from limitations such as low horizontal resolution of locations, flawed land boundary treatment, and extensive computation time. We addressed these issues using a state-space approach based on the particle filter (PF), and developed a geolocation package with graphics processing unit (GPU) acceleration. Our method focused on application to demersal fish and utilizes comparison of the tag-recorded depth and temperature to the same variables from an unstructured grid regional oceanographic model. A rigorous boundary treatment scheme was implemented to handle regions with complex coastline geometry. Validation exercises using stationary mooring tags and double-electronic-tagged (archival and acoustic tags) Atlantic cod in the Gulf of Maine resulted in <10 km median errors of the estimated tracks. Sensitivity analyses suggest that using 200,000 particles was adequate to stabilize the location track estimation. Acceleration of the particle filter using GPUs resulted in faster processing than the single threaded CPU (central processing unit) implementation, enabling rapid geolocations using consumer grade computer hardware. The geolocation output of each tagged fish includes the most probable track and the associated spatial probability distribution. The resulting PF geolocation package enables high resolution and accelerated geolocation analyses to be performed on affordable consumer-grade computer hardware, resolving the time intensiveness problem of the PF that may have prevented its adoptions in marine animal geolocation. Expanded application of geolocation will yield more reliable migration information to support management. Geolocation results from archival tagging will contribute to our understanding of the spatial ecology of marine species.

Original languageEnglish (US)
Pages (from-to)160-171
Number of pages12
JournalFisheries Research
Volume213
DOIs
StatePublished - May 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Aquatic Science

Keywords

  • Archival tagging
  • Data storage tag
  • Demersal fish
  • Fish migration
  • Geolocation
  • Graphics processing unit
  • Particle filter

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