Quantifying the influence of salinity and temperature on the population dynamics of a marine ectoparasite

Groner, Maya L.; McEwan, Gregor; Rees, Erin E.; Gettinby, G.; Revie, Crawford W.

doi:10.1139/cjfas-2015-0444

Cited by 44 publications

(30 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since most marine finfish aquaculture occurs within the coastal zone, salinity and temperature regimens can be dynamic (Groner et al, 2016). Salinity near salmon farms is influenced by fresh water inflow from rivers and precipitation, coastal and oceanic water exchange, mixing of the water column (due to wind and tides), estuarine circulation, and inlet bathymetry.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Remotely-Sensed Sea Surface Temperature and Salinity Products With in Situ Measurements From British Columbia, Canada

et al. 2018

Self Cite

View full text Add to dashboard Cite

Sea surface temperature (SST) and salinity (SSS) are essential variables at the ocean and atmosphere interface when considering risk factors for disease in farmed and wild fish stocks. Ecological research has witnessed a recent trend in use of digital and satellite technologies, including remote-sensing tools. We explored spatial coverage of remotely-sensed SST and SSS data and compared them with in situ measurements of water temperatures and salinity, which led to suggested adjustments to the remotely-sensed data for its use in aquaculture research. The in situ data were from farms and wild surveillance sites in coastal British Columbia, Canada, from 2003 to 2016. Concurrent SST and SSS values were extracted from remotely-sensed products and compared with 20,513 and 20,038 in situ records for water temperature and salinity, respectively, from 232 different sites. Among nine SST products evaluated, the UKMO OSTIA SST (UK Meteorological Office) had the highest retrieval, and highest concordance correlation coefficient (0.86), highest index of agreement (0.93), fewest missing values, and smallest mean and SD values for bias, when compared to in situ measurements. A mixed linear regression model with UKMO OSTIA SST as the predictor for in situ measurements estimated an adjustment coefficient of 0.89 • C for UKMO OSTIA SST. None of the three SSS products evaluated provided appropriate corresponding values for in situ sites, suggesting that spatial coverage for the study area is currently lacking. This study demonstrates that, among SST products, UKMO OSTIA SST is currently best suited for aquaculture studies in coastal BC. The near real-time availability of these data with the estimated adjustment would allow their use in forecast models, surveillance of pathogens, and the creation of risk maps.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparison of Remotely-Sensed Sea Surface Temperature and Salinity Products With in Situ Measurements From British Columbia, Canada

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Changing salinity may even control certain disease outbreaks. A relative reduction in salinity may control infections as such as Dermo disease in oysters (Burge et al 2014) and sea lice on salmon (Groner et al 2016), whereas a relative increase in salinity has been reported to control certain infections such as V. vulnificus in hybrid tilapia (Oreochromis sp.) (Chen et al 2006) and to decrease infection with the parasitic nematode Anguillicoloides (= Anguillicola) crassus in Atlantic eels (Anguilla spp.)…”

Section: Water Qualitymentioning

confidence: 99%

Climate change and aquaculture: considering biological response and resources

Reid¹,

Gurney-Smith²,

Dj³

et al. 2019

Aquacult. Environ. Interact.

103

View full text Add to dashboard Cite

The heavy reliance of most global aquaculture on the ambient environment suggests inherent vulnerability to climate change effects. This review explores the potential effects of climate change stressors on aquaculture biology and resources needed to support decision-making for vulnerability assessment, planned adaptation, and strategic research development. Climate change-mediated physiochemical outcomes important to aquaculture include extreme weather, precipitation and surge-based flooding, water stress, ocean acidification, sea-level rise, saltwater intrusion, and changes to temperature, salinity, and dissolved oxygen. Culture practices, environment, and region affect stressor exposure, and biological response between species or populations are not universal. Response to a climate change stressor will be a function of where changes occur relative to optimal ranges and tolerance limits of an organism's life stage and physiological processes; the average magnitude of the stressor over the production cycle; stressor rate of change; variation, frequency, duration, and magnitude of extremes; epigenetic expression, genetic strain, and variation within and between populations; health and nutrition; and simultaneous stressor occurrence. The effects of simultaneous stressors will frequently interact, but may not be fully additive or synergistic. Disease is a major aquaculture limiter, and climate change is expected to further affect plant and animal health through the host and/or infectious agents. Climate change may introduce further complexity to the aquaculture−wild fishery relationship, with over two-thirds of animal aquaculture production dependent on external feed inputs. Higher production costs could be an economic outcome of climate change for many aquaculture sectors. Some aquaculture practices may inadvertently reduce resiliency to climate change, such as a reduction of coastal vegetation, coastal ground-water pumping, and reduction of population variability in pursuit of consistent production traits. Information from the largest aquaculture producers such as China and the top 3 global culture species is still sparse in the literature. This potentially limits thorough understanding of climate change effects on some regional aquaculture sectors.

show abstract

“…When real production data has been used for estimation, these have been aggregated. For instance, Stien et al (2005) used only laboratory data published in previous papers, whereas Groner et al (2013) and Groner et al (2016) used values from Stien et al (2005) for some parameters and values from several other previous papers for other parameters. Furthermore, Revie et al (2005) and Gettinby et al (2011) used laboratory data from previous studies to estimate the majority of the parameters and real production data to estimate the remaining parameters, but the production data were aggregated over farms and to a monthly time scale.…”

Section: Modelling Backgroundmentioning

confidence: 99%

“…Alternatively, models have been developed for simulation purposes only (Groner et al, 2014) or focused on the population dynamics on single farms over a limited time period (Krkošek et al, 2010). Most of these approaches have relied heavily on estimates of demographic rates ob-tained in the laboratory (Stien et al, 2005;Revie et al, 2005;Gettinby et al, 2011;Groner et al, 2013;Rittenhouse et al, 2016;Groner et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A stage-structured Bayesian hierarchical model for salmon lice populations at individual salmon farms – Estimated from multiple farm data sets

Aldrin

Huseby

Stien

et al. 2017

Ecological Modelling

View full text Add to dashboard Cite

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. In order to gain insight into this parasites 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, effects 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 time 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 that drive this hostparasite relationship, and hence may improve strategies to control the parasite in this production system.

show abstract

Quantifying the influence of salinity and temperature on the population dynamics of a marine ectoparasite

Cited by 44 publications

References 38 publications

Comparison of Remotely-Sensed Sea Surface Temperature and Salinity Products With in Situ Measurements From British Columbia, Canada

Comparison of Remotely-Sensed Sea Surface Temperature and Salinity Products With in Situ Measurements From British Columbia, Canada

Climate change and aquaculture: considering biological response and resources

A stage-structured Bayesian hierarchical model for salmon lice populations at individual salmon farms – Estimated from multiple farm data sets

Contact Info

Product

Resources

About