Literature DB >> 24660663

Analysis of regional scale risk of whirling disease in populations of Colorado and Rio Grande cutthroat trout using a Bayesian belief network model.

Kimberley Kolb Ayre1, Colleen A Caldwell, Jonah Stinson, Wayne G Landis.   

Abstract

Introduction and spread of the parasite Myxobolus cerebralis, the causative agent of whirling disease, has contributed to the collapse of wild trout populations throughout the intermountain west. Of concern is the risk the disease may have on conservation and recovery of native cutthroat trout. We employed a Bayesian belief network to assess probability of whirling disease in Colorado River and Rio Grande cutthroat trout (Oncorhynchus clarkii pleuriticus and Oncorhynchus clarkii virginalis, respectively) within their current ranges in the southwest United States. Available habitat (as defined by gradient and elevation) for intermediate oligochaete worm host, Tubifex tubifex, exerted the greatest influence on the likelihood of infection, yet prevalence of stream barriers also affected the risk outcome. Management areas that had the highest likelihood of infected Colorado River cutthroat trout were in the eastern portion of their range, although the probability of infection was highest for populations in the southern, San Juan subbasin. Rio Grande cutthroat trout had a relatively low likelihood of infection, with populations in the southernmost Pecos management area predicted to be at greatest risk. The Bayesian risk assessment model predicted the likelihood of whirling disease infection from its principal transmission vector, fish movement, and suggested that barriers may be effective in reducing risk of exposure to native trout populations. Data gaps, especially with regard to location of spawning, highlighted the importance in developing monitoring plans that support future risk assessments and adaptive management for subspecies of cutthroat trout.
© 2014 Society for Risk Analysis.

Entities:  

Keywords:  Bayesian belief network; ecological risk; emerging diseases

Mesh:

Year:  2014        PMID: 24660663     DOI: 10.1111/risa.12189

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  2 in total

1.  Representing causal knowledge in environmental policy interventions: Advantages and opportunities for qualitative influence diagram applications.

Authors:  John F Carriger; Brian E Dyson; William H Benson
Journal:  Integr Environ Assess Manag       Date:  2018-02-22       Impact factor: 2.992

2.  Predicting arboviral disease emergence using Bayesian networks: a case study of dengue virus in Western Australia.

Authors:  S H Ho; P Speldewinde; A Cook
Journal:  Epidemiol Infect       Date:  2016-09-13       Impact factor: 4.434

  2 in total

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