LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Aldrin, Magne; Huseby, Ragnar Bang; Stien, Audun; Grøntvedt, Randi Nygaard; Viljugrein, Hildegunn; Jansen, Peder Andreas (2017)
Languages: English
Types: Article
Subjects: Statistics - Applications, Aquaculture, Quantitative Biology - Populations and Evolution, :Matematikk og Naturvitenskap: 400::Zoologiske og botaniske fag: 480 [VDP], Quantitative Biology - Quantitative Methods, Population model, Sea lice counts, :Klinisk veterinærmedisinske fag: 950 [VDP], :Clinical veterinary sciences: 950 [VDP], Stochastic model
Salmon farming has become a prosperous international industry over the last decades. Along with growth in the production farmed salmon, however, an increasing threat by pathogens has emerged. Of special concern is the propagation and spread of the salmon louse, Lepeophtheirus salmonis. To gain insight into this parasite’s population dynamics in large scale salmon farming system, we present a fully mechanistic stage-structured population model for the salmon louse, also allowing for complexities involved in the hierarchical structure of full scale salmon farming. The model estimates parameters controlling a wide range of processes, including temperature dependent demographic rates, fish size and abundance effects on louse transmission rates, effect sizes of various salmon louse control measures, and distance based between farm transmission rates. Model parameters were estimated from data including 32 salmon farms, except the last production months for five farms, which were used to evaluate model predictions. We used a Bayesian estimation approach, combining the prior distributions and the data likelihood into a joint posterior distribution for all model parameters. The model generated expected values that fitted the observed infection levels of the chalimus, adult female and other mobile stages of salmon lice, reasonably well. Predictions for the periods not used for fitting the model were also consistent with the observational data. We argue that the present model for the population dynamics of the salmon louse in aquaculture farm systems may contribute to resolve the complexity of processes that drive this host-parasite relationship, and hence may improve strategies to control the parasite in this production system. Population model Aquaculture Stochastic model Sea lice counts

Share - Bookmark

Cite this article