Dataset Information

Modeling the winter-to-summer transition of prokaryotic and viral abundance in the Arctic Ocean.

ABSTRACT: One of the challenges in oceanography is to understand the influence of environmental factors on the abundances of prokaryotes and viruses. Generally, conventional statistical methods resolve trends well, but more complex relationships are difficult to explore. In such cases, Artificial Neural Networks (ANNs) offer an alternative way for data analysis. Here, we developed ANN-based models of prokaryotic and viral abundances in the Arctic Ocean. The models were used to identify the best predictors for prokaryotic and viral abundances including cytometrically-distinguishable populations of prokaryotes (high and low nucleic acid cells) and viruses (high- and low-fluorescent viruses) among salinity, temperature, depth, day length, and the concentration of Chlorophyll-a. The best performing ANNs to model the abundances of high and low nucleic acid cells used temperature and Chl-a as input parameters, while the abundances of high- and low-fluorescent viruses used depth, Chl-a, and day length as input parameters. Decreasing viral abundance with increasing depth and decreasing system productivity was captured well by the ANNs. Despite identifying the same predictors for the two populations of prokaryotes and viruses, respectively, the structure of the best performing ANNs differed between high and low nucleic acid cells and between high- and low-fluorescent viruses. Also, the two prokaryotic and viral groups responded differently to changes in the predictor parameters; hence, the cytometric distinction between these populations is ecologically relevant. The models imply that temperature is the main factor explaining most of the variation in the abundances of high nucleic acid cells and total prokaryotes and that the mechanisms governing the reaction to changes in the environment are distinctly different among the prokaryotic and viral populations.

SUBMITTER: Winter C

PROVIDER: S-EPMC3527615 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Modeling the winter-to-summer transition of prokaryotic and viral abundance in the Arctic Ocean.

Winter Christian C Payet Jérôme P JP Suttle Curtis A CA

PloS one 20121220 12

One of the challenges in oceanography is to understand the influence of environmental factors on the abundances of prokaryotes and viruses. Generally, conventional statistical methods resolve trends well, but more complex relationships are difficult to explore. In such cases, Artificial Neural Networks (ANNs) offer an alternative way for data analysis. Here, we developed ANN-based models of prokaryotic and viral abundances in the Arctic Ocean. The models were used to identify the best predictors ...[more]

PMID: 23285186

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Project description:Zooplankton plays a pivotal role in linking primary production to higher level consumers in the food webs of marine ecosystems. The distribution of zooplankton is affected by general water conditions, monsoons, currents, and spatial and temporal factors. In the Arctic Ocean, the sea surface is naturally covered with ice. Under ice, water masses interplay to create complex environments that facilitate the transport and distribution of zooplankton, thus altering community structures at geospatial and vertical scales. The present study investigated the species composition and copepod community structures by using geospatial and multiple depth scales, and using multivariate analyses to evaluate the relation of sampling stations and layers. During July-August 2010, zooplankton samples were collected and the temperature and salinity of seawater measured from three stations in the Canada Basin and two stations in the Makarov Basin of the Arctic Ocean (maximum distance of approximately 1400 km). A total of 55 copepod species (including 25 species that were solely identified to the generic level) and 7 taxa of copepodites, altogether belonging to 28 genera, 11 families, and 2 orders were identified, and significant differences were detected in copepod community structures among sampling strata and at geospatial scales. Numerically, Calanus hyperboreus, Calanus copepodite, Calanoida copepodite, Calanus glacialis, and Metridia longa were the most dominant species and taxa. At the local scale, copepod compositions responded differently at each of the sampling stations. At the geospatial scale, the distance between stations MS03 and ICE explained variations in the pattern of dominant species and of copepod community richness. Our study demonstrated varied spatial distribution which indicates that (1) the abundance of copepods at 0-200 m was significantly higher than at other strata, (2) vertical strata affected the distribution of copepod communities, and (3) the interplay of North Pacific and Atlantic waters shaping the copepod assemblage structure at geospatial scales in the Arctic Ocean. The results of our research provide base data for Arctic zooplankton biodiversity and biogeographic distribution.

Dataset Information

Modeling the winter-to-summer transition of prokaryotic and viral abundance in the Arctic Ocean.

Publications

Modeling the winter-to-summer transition of prokaryotic and viral abundance in the Arctic Ocean.

Similar Datasets

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