Document Detail


Modeling fish health to inform research and management: Renibacterium salmoninarum dynamics in Lake Michigan.
MedLine Citation:
PMID:  19425436     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Little is known about the interaction between fish pathogens and managed freshwater fish populations. We develop a model of chinook salmon (Oncorhynchus tschawytscha)-Renibacterium salmoninarum (Rs) dynamics based on free-swimming Lake Michigan fish by synthesizing population and epidemiological theory. Using the model, we expose critical uncertainties about the system, identify opportunities for efficient and insightful data collection, and pose testable hypotheses. Our simulation results suggest that hatcheries potentially play an important role in Lake Michigan Rs dynamics, and understanding vertical transmission will be critical for quantifying this role. Our results also show that disease-mediated responses to chinook salmon density need to be considered when evaluating management actions. Related to this, a better understanding of the stock-recruitment relationship and natural mortality rates for wild-spawned fish and the impact of hatchery stocking on recruitment is required. Finally, to further develop models capable of assisting fishery management, fish health surveys ought to be integrated with stock assessment. This is the first time a host-pathogen modeling framework has been applied to managed, freshwater ecosystems, and we suggest that such an approach should be used more frequently to inform other emerging and chronic fish health issues.
Authors:
Eli P Fenichel; Jean I Tsao; Michael L Jones
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Publication Detail:
Type:  Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Ecological applications : a publication of the Ecological Society of America     Volume:  19     ISSN:  1051-0761     ISO Abbreviation:  Ecol Appl     Publication Date:  2009 Apr 
Date Detail:
Created Date:  2009-05-11     Completed Date:  2009-07-01     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9889808     Medline TA:  Ecol Appl     Country:  United States    
Other Details:
Languages:  eng     Pagination:  747-60     Citation Subset:  IM    
Affiliation:
Michigan State University, Department of Fisheries and Wildlife, East Lansing, Michigan 48824, USA. eli.fenichel@asu.edu
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MeSH Terms
Descriptor/Qualifier:
Actinomycetales Infections / epidemiology,  microbiology,  veterinary*
Animals
Computer Simulation
Fish Diseases / epidemiology,  microbiology*,  mortality
Fisheries / methods
Fresh Water / microbiology*
Great Lakes Region
Micrococcaceae / physiology*
Models, Biological*
Population Density
Population Dynamics
Prevalence
Salmon / microbiology*,  physiology
Time Factors

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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