4 resultados para SALMO-SALAR L.
em Université de Lausanne, Switzerland
Resumo:
Mountain regions worldwide are particularly sensitive to on-going climate change. Specifically in the Alps in Switzerland, the temperature has increased twice as fast than in the rest of the Northern hemisphere. Water temperature closely follows the annual air temperature cycle, severely impacting streams and freshwater ecosystems. In the last 20 years, brown trout (Salmo trutta L) catch has declined by approximately 40-50% in many rivers in Switzerland. Increasing water temperature has been suggested as one of the most likely cause of this decline. Temperature has a direct effect on trout population dynamics through developmental and disease control but can also indirectly impact dynamics via food-web interactions such as resource availability. We developed a spatially explicit modelling framework that allows spatial and temporal projections of trout biomass using the Aare river catchment as a model system, in order to assess the spatial and seasonal patterns of trout biomass variation. Given that biomass has a seasonal variation depending on trout life history stage, we developed seasonal biomass variation models for three periods of the year (Autumn-Winter, Spring and Summer). Because stream water temperature is a critical parameter for brown trout development, we first calibrated a model to predict water temperature as a function of air temperature to be able to further apply climate change scenarios. We then built a model of trout biomass variation by linking water temperature to trout biomass measurements collected by electro-fishing in 21 stations from 2009 to 2011. The different modelling components of our framework had overall a good predictive ability and we could show a seasonal effect of water temperature affecting trout biomass variation. Our statistical framework uses a minimum set of input variables that make it easily transferable to other study areas or fish species but could be improved by including effects of the biotic environment and the evolution of demographical parameters over time. However, our framework still remains informative to spatially highlight where potential changes of water temperature could affect trout biomass. (C) 2015 Elsevier B.V. All rights reserved.-
Resumo:
Of all Pacific salmonids, Chinook salmon Oncorhynchus tshawytscha display the greatest variability in return times to freshwater. The molecular mechanisms of these differential return times have not been well described. Current methods, such as long serial analysis of gene expression (LongSAGE) and microarrays, allow gene expression to be analyzed for thousands of genes simultaneously. To investigate whether differential gene expression is observed between fall- and spring-run Chinook salmon from California's Central Valley, LongSAGE libraries were constructed. Three libraries containing between 25,512 and 29,372 sequenced tags (21 base pairs/tag) were generated using messenger RNA from the brains of adult Chinook salmon returning in fall and spring and from one ocean-caught Chinook salmon. Tags were annotated to genes using complementary DNA libraries from Atlantic salmon Salmo salar and rainbow trout O. mykiss. Differentially expressed genes, as estimated by differences in the number of sequence tags, were found in all pairwise comparisons of libraries (freshwater versus saltwater = 40 genes; fall versus spring = 11 genes: and spawning versus nonspawning = 51 genes). The gene for ependymin, an extracellular glycoprotein involved in behavioral plasticity in fish, exhibited the most differential expression among the three groupings. Reverse transcription polymerase chain reaction analysis verified the differential expression of ependymin between the fall- and spring-run samples. These LongSAGE libraries, the first reported for Chinook salmon, provide a window of the transcriptional changes during Chinook salmon return migration to freshwater and spawning and increase the amount of expressed sequence data.
Resumo:
Populations of the marble trout (Salmo marmoratus) have declined critically due to introgression by brown trout (Salmo trutta) strains. In order to define strategies for long-term conservation, we examined the genetic structure of the 8 known pure populations using 15 microsatellite loci. The analyses reveal extraordinarily strong genetic differentiation among populations separated by < 15 km, and extremely low levels of intrapopulation genetic variability. As natural recolonization seems highly unlikely, appropriate management and conservation strategies should comprise the reintroduction of pure populations from mixed stocks (translocation) to avoid further loss of genetic diversity.
Resumo:
The taxonomic composition of egg-associated microbial communities can play a crucial role in the development of fish embryos. In response, hosts increasingly influence the composition of their associated microbial communities during embryogenesis, as concluded from recent field studies and laboratory experiments. However, little is known about the taxonomic composition and the diversity of egg-associated microbial communities within ecosystems; e.g., river networks. We sampled late embryonic stages of naturally spawned brown trout at nine locations within two different river networks and applied 16S rRNA pyrosequencing to describe their bacterial communities. We found no evidence for a significant isolation-by-distance effect on the composition of bacterial communities, and no association between neutral genetic divergence of fish host (based on 11 microsatellites) and phylogenetic distances of the composition of their associated bacterial communities. We characterized core bacterial communities on brown trout eggs and compared them to corresponding water samples with regard to bacterial composition and its presumptive function. Bacterial diversity was positively correlated with water temperature at the spawning locations. We discuss this finding in the context of the increased water temperatures that have been recorded during the last 25 years in the study area.