Bayesian arrival model for Atlantic salmon smolt counts powered by environmental covariates and expert knowledge

Annual run size and timing of Atlantic salmon (Salmo salar) smolt migration was estimated using Bayesian model framework and data from 6 years of a video monitoring survey. The model has a modular structure. It separates subprocesses of departing, traveling, and observing, of which the first two tog...

Full description

Saved in:
Bibliographic Details
Published in:Canadian journal of fisheries and aquatic sciences 2020-03, Vol.77 (3), p.462-474
Main Authors: Pulkkinen, Henni, Orell, Panu, Erkinaro, Jaakko, Mäntyniemi, Samu
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian theory
Case studies
Environmental conditions
Estimates
Fish counters
Fish migration
Fishes
Marine
Migrations
Modelling
Modular structures
Monitoring
Performance prediction
Salmo salar
Salmon
Salmon fisheries
Smolts
Surveys
Videotape recordings
Water temperature
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Annual run size and timing of Atlantic salmon (Salmo salar) smolt migration was estimated using Bayesian model framework and data from 6 years of a video monitoring survey. The model has a modular structure. It separates subprocesses of departing, traveling, and observing, of which the first two together define the arrival distribution. The subprocesses utilize biological background and expert knowledge about the migratory behavior of smolts and about the probability to observe them from the video footage under varying environmental conditions. Daily mean temperature and discharge were used as environmental covariates. The model framework does not require assuming a simple distributional shape for the arrival dynamics and thus also allows for multimodal arrival distributions. Results indicate that 20%–43% of smolts passed the Utsjoki monitoring site unobserved during the years of study. Predictive studies were made to estimate daily run size in cases with missing counts either at the beginning or in the middle of the run, indicating good predictive performance.
ISSN:0706-652X
1205-7533
DOI:10.1139/cjfas-2018-0352