A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Evaluating nonindigenous species management in a Bayesian networks derived relative risk framework for Padilla Bay, WA, USA
ABSTRACTMany coastal regions are encountering issues with the spread of nonindigenous species (NIS). In this study, we conducted a regional risk assessment using a Bayesian network relative risk model (BN‐RRM) to analyze multiple vectors of NIS introductions to Padilla Bay, Washington, a National Estuarine Research Reserve. We had 3 objectives in this study. The 1st objective was to determine whether the BN‐RRM could be used to calculate risk from NIS introductions for Padilla Bay. Our 2nd objective was to determine which regions and endpoints were at greatest risk from NIS introductions. Our 3rd objective was to incorporate a management option into the model and predict endpoint risk if it were to be implemented. Eradication can occur at different stages of NIS invasions, such as the elimination of these species before being introduced to the habitat or removal of the species after settlement. We incorporated the ballast water treatment management scenario into the model, observed the risk to the endpoints, and compared this risk with the initial risk estimates. The model results indicated that the southern portion of the bay was at greatest risk because of NIS. Changes in community composition, Dungeness crab, and eelgrass were the endpoints most at risk from NIS introductions. The currents node, which controls the exposure of NIS to the bay from the surrounding marine environment, was the parameter that had the greatest influence on risk. The ballast water management scenario displayed an approximate 1% reduction in risk in this Padilla Bay case study. The models we developed provide an adaptable template for decision makers interested in managing NIS in other coastal regions and large bodies of water.Integr Environ Assess Manag2015;X:000–000. ©2015 SETAC
Key PointsWe approached the issue of nonindigenous species (NIS) from a landscape‐scale ecological risk assessment, analyzing multiple vectors of introduction and their associated spectrum of NIS, and created a model that enables us to quantitatively calculate risk from NIS introductions to coastal endpoints.The most important variable in the estimation of risk was the Currents node, which described both an important vector for the rate introduction of propagules from transportation and as a connection to existing patches of NIS in the regionWe incorporated a ballast water treatment management scenario into the Bayesian network model, which predicted little change in risk from the initial risk calculations.The BN‐RRM approach described in this paper can be used as a template for other coastal communities interested in calculating and managing risk from NIS.
Evaluating nonindigenous species management in a Bayesian networks derived relative risk framework for Padilla Bay, WA, USA
ABSTRACTMany coastal regions are encountering issues with the spread of nonindigenous species (NIS). In this study, we conducted a regional risk assessment using a Bayesian network relative risk model (BN‐RRM) to analyze multiple vectors of NIS introductions to Padilla Bay, Washington, a National Estuarine Research Reserve. We had 3 objectives in this study. The 1st objective was to determine whether the BN‐RRM could be used to calculate risk from NIS introductions for Padilla Bay. Our 2nd objective was to determine which regions and endpoints were at greatest risk from NIS introductions. Our 3rd objective was to incorporate a management option into the model and predict endpoint risk if it were to be implemented. Eradication can occur at different stages of NIS invasions, such as the elimination of these species before being introduced to the habitat or removal of the species after settlement. We incorporated the ballast water treatment management scenario into the model, observed the risk to the endpoints, and compared this risk with the initial risk estimates. The model results indicated that the southern portion of the bay was at greatest risk because of NIS. Changes in community composition, Dungeness crab, and eelgrass were the endpoints most at risk from NIS introductions. The currents node, which controls the exposure of NIS to the bay from the surrounding marine environment, was the parameter that had the greatest influence on risk. The ballast water management scenario displayed an approximate 1% reduction in risk in this Padilla Bay case study. The models we developed provide an adaptable template for decision makers interested in managing NIS in other coastal regions and large bodies of water.Integr Environ Assess Manag2015;X:000–000. ©2015 SETAC
Key PointsWe approached the issue of nonindigenous species (NIS) from a landscape‐scale ecological risk assessment, analyzing multiple vectors of introduction and their associated spectrum of NIS, and created a model that enables us to quantitatively calculate risk from NIS introductions to coastal endpoints.The most important variable in the estimation of risk was the Currents node, which described both an important vector for the rate introduction of propagules from transportation and as a connection to existing patches of NIS in the regionWe incorporated a ballast water treatment management scenario into the Bayesian network model, which predicted little change in risk from the initial risk calculations.The BN‐RRM approach described in this paper can be used as a template for other coastal communities interested in calculating and managing risk from NIS.
Evaluating nonindigenous species management in a Bayesian networks derived relative risk framework for Padilla Bay, WA, USA
Integr Envir Assess & Manag
Herring, Carlie E (author) / Stinson, Jonah (author) / Landis, Wayne G (author)
Integrated Environmental Assessment and Management ; 11 ; 640-652
2015-10-01
Article (Journal)
Electronic Resource
English
| British Library Conference Proceedings | 1996
A relative-risk framework for evaluating transient pathogen intrusion in distribution systems
| Taylor & Francis Verlag | 2004