Nearshore ecosystem predictors of a bacterial infection in juvenile Chinook salmon

Rhodes, Linda D.; Rice, Casimir A.; Greene, Correigh M.; Teel, David J.; Nance, Shelly L.; Moran, Paul; Durkin, Colleen A.; Gezhegne, Surafel B.

doi:10.3354/meps09160

Cited by 19 publications

(33 citation statements)

References 57 publications

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“…The causative agent, Renibacterium salmoninarum, occurs in salmonid populations worldwide and is endemic to the Salish Sea. Infection prevalence in wild or feral stocks of salmon ranges up to 40%, strongly supporting the designation of R. salmoninarum as an endemic pathogen for anadromous salmonids in the Salish Sea (Banner et al 1986;Rhodes et al 2011). Contrary to a widely held assumption, BKD can occur at moderate to high infection intensities among free-ranging salmonids in the Salish Sea and greater Pacific Northwest (Banner et al 1986).…”

Section: Bacterial Kidney Diseasementioning

confidence: 93%

Infectious Diseases of Fishes in the Salish Sea

et al. 2013

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Enfermedades infecciosas en los peces del mar de SalishRESUMEN: Tal como sucede en las regiones marinas de otras partes del mundo, los peces del mar de Salish hospedan cantidad de patógenos, incluyendo nematodos, tremátodos, protozoarios, protistas, bacterias, virus y crustáceos. En este trabajo se hace una revisión de algunas de las enfermedades infecciosas mejor documentadas que muy posiblemente contribuyen a la pérdida significativa de peces en el mar de Salish y se discuten los factores ambientales y ecológicos que pueden influenciar los impactos de la enfermedad a nivel poblacional. La demostración de estas enfermedades y sus consecuencias en recursos críti-cos y amenazados, justifica expandir la vigilancia sobre patógenos e incorporar un pronóstico de enfermedades y herramientas de mitigación en los esfuerzos de restauración de ecosistemas.ABSTRACT: As in marine regions throughout other areas of the world, fishes in the Salish Sea serve as hosts for many pathogens, including nematodes, trematodes, protozoans, protists, bacteria, viruses, and crustaceans. Here, we review some of the better-documented infectious diseases that likely contribute to significant losses among free-ranging fishes in the Salish Sea and discuss the environmental and ecological factors that may affect the population-level impacts of disease. Demonstration of these diseases and their impacts to critical and endangered resources provides justification to expand pathogen surveillance efforts and to incorporate disease forecasting and mitigation tools into ecosystem restoration efforts.

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Section: Bacterial Kidney Diseasementioning

confidence: 93%

Infectious Diseases of Fishes in the Salish Sea

et al. 2013

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“…Our study had two elements, first was a test of concept, an analysis of simulated data to inform and direct the re-analysis of two published data sets in the second element of our study. The reanalyzed data sets included fecundity in Yukon River adult Chinook salmon ( Oncorhynchus tshawytscha ) [13], and bacterial kidney disease (BKD) infection prevalence in Puget Sound juvenile Chinook salmon [9]. Originally, a third method was used but was abandoned due to poor performance.…”

Section: Methodsmentioning

confidence: 99%

“…To parameterize our model, we used the BKD data set [9] and a portion of the GAPS-Chinook microsatellite baseline [4], but we controlled the level of BKD prevalence among reporting groups. We also manipulated the reporting groups slightly to present a more demanding analytical problem relative to [9]. To do this, we split the Hood Canal and South Sound fall-run reporting group of [9] into two genetically similar reporting groups.…”

Section: Methodsmentioning

confidence: 99%

“…Most applications of individual assignment (IA) relate to study of genetic structure, connectivity and estimation of population genetic metrics and parameters [5]. However, more novel ecological applications include investigation of body burden of ecotoxic compounds [6], parasite loads [7], [8], ELISA or qFAT values [9], and habitat use [10]. These methods have even found use in killer whale ( Orcinus orca ) trophic studies of predation on specific Chinook salmon populations [11].…”

Section: Introductionmentioning

confidence: 99%

“…However, some fraction of individuals may have missing data for some loci, low assignment probabilities, or both, and there is always some assignment error, even among highly distinct populations. In these cases, investigators sometimes omit individual fish with these ‘problems’ by, for example, removing fish typed for fewer than some number of loci, or whose highest assignment probability to reporting group is below 0.8 [9], [12] or some other predetermined value. The logic is that individuals with uncertain origin – for whatever reason – will reduce estimation accuracy and precision.…”

Section: Introductionmentioning

confidence: 99%

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Use of Genetic Data to Infer Population-Specific Ecological and Phenotypic Traits from Mixed Aggregations

2014

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Many applications in ecological genetics involve sampling individuals from a mixture of multiple biological populations and subsequently associating those individuals with the populations from which they arose. Analytical methods that assign individuals to their putative population of origin have utility in both basic and applied research, providing information about population-specific life history and habitat use, ecotoxins, pathogen and parasite loads, and many other non-genetic ecological, or phenotypic traits. Although the question is initially directed at the origin of individuals, in most cases the ultimate desire is to investigate the distribution of some trait among populations. Current practice is to assign individuals to a population of origin and study properties of the trait among individuals within population strata as if they constituted independent samples. It seemed that approach might bias population-specific trait inference. In this study we made trait inferences directly through modeling, bypassing individual assignment. We extended a Bayesian model for population mixture analysis to incorporate parameters for the phenotypic trait and compared its performance to that of individual assignment with a minimum probability threshold for assignment. The Bayesian mixture model outperformed individual assignment under some trait inference conditions. However, by discarding individuals whose origins are most uncertain, the individual assignment method provided a less complex analytical technique whose performance may be adequate for some common trait inference problems. Our results provide specific guidance for method selection under various genetic relationships among populations with different trait distributions.

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