Analysis of Regional Scale Risk of Whirling Disease in Populations of Colorado and Rio Grande Cutthroat Trout Using a Bayesian Belief Network Model

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.

Integration of Chlorpyrifos Acetylcholinesterase Inhibition, Water Temperature, and Dissolved Oxygen Concentration into a Regional Scale Multiple Stressor Risk Assessment Estimating Risk to Chinook Salmon

“…Bayesian networks have been applied to environmental management and risk assessment (Marcot et al ; Pollino et al ; Uusitalo ; Barton et al ) to guide research and monitoring to support decision making and resource management (Nyberg et al ; Carriger and Newman ). Since 2012, the utility of the integrated BN‐RRM has been applied in numerous contexts, including contaminated sites (Hines and Landis ; Landis, Ayre et al ), emergent disease (Ayre et al ), nonindigenous species (Herring et al ), and forestry management (Ayre and Landis ). A series of papers estimating risk due to Hg contamination and other factors in the South River in Virginia, USA demonstrated the applicability of the BN‐RRM to estimate risk to organisms and water quality (Landis, Ayre et al ), human health and well‐being (Harris et al ), and the evaluation of management alternatives and adaptive management (Johns et al ; Landis, Markiewicz et al ).…”

A regional-scale ecological risk framework for environmental flow evaluations

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“…These relative risk projections to the multiple socio-ecological endpoints considered are based on flow alterations associated with devel- opment scenarios, in the context of the exacerbating of nonflow variable determinants on regional scales. The cumulative risk of all ecological and social endpoints for each RR, for each temporal period, per scenario, were evaluated using Monte Carlo simulations (5000 trials, Oracle Crystal Ball software, Oregon) (Ayre et al, 2014). The outcomes included relative risk projections, displayed as relative profiles to single endpoints from multiple RRs, and multiple social and ecological endpoints or all endpoints per RR in the study for comparisons and evaluation.…”

“…The RRM has been applied to evaluate a range of natural and anthropogenic stressors including water pollution, diseases, alien species and a range of altered environmental states (Walker et al, 2001;Moraes et al, 2002;Hayes and Landis, 2004;Colnar and Landis, 2007;Anderson and Landis, 2012;Ayre and Landis, 2012;Bartolo et al, 2012;O'Brien et al, 2012;Hines and Landis, 2014;Ayre et al, 2014). This tool can be used to carry out holistic, probabilistic assessments of the risk to the availability and conditions of ecosystem service and ecological endpoints, and facilitate socio-ecological trade-offs.…”

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

“…[9][10][11]). The probability distribution of each node is either assigned based on a prior distribution model (for input nodes), or calculated using Bayes' Theorem from prior probabilities ('child' nodes) [12,13].…”