Trace elements in the livers of cod (Gadus morhua L.) from the Baltic Sea: levels and temporal trends

2012 ◽  
Vol 185 (1) ◽  
pp. 687-694 ◽  
Author(s):  
Lucyna Polak-Juszczak
Keyword(s):  
Trace Elements ◽  
Baltic Sea ◽  
Gadus Morhua ◽  
Temporal Trends ◽  
The Baltic Sea ◽  
Sea Levels ◽  
The Baltic

Radioactivity in the Baltic Sea: inventories and temporal trends of 137Cs and 90Sr in water and sediments

2009 ◽  
Vol 282 (2) ◽  
pp. 419-425 ◽  
Author(s):  
Tarja Katri Ikäheimonen ◽  
Iisa Outola ◽  
Vesa-Pekka Vartti ◽  
Pekka Kotilainen
Keyword(s):  
Baltic Sea ◽  
Temporal Trends ◽  
The Baltic Sea ◽  
The Baltic

scholarly journals Deoxygenation impacts on Baltic Sea cod: Dramatic declines in ecosystem services of an iconic keystone predator

AMBIO ◽  
2021 ◽  
Author(s):  
Alessandro Orio ◽  
Yvette Heimbrand ◽  
Karin Limburg
Keyword(s):  
Ecosystem Services ◽  
Baltic Sea ◽  
Gadus Morhua ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Maximum Length ◽  
The Baltic Sea ◽  
Hypoxic Zone ◽  
The Baltic ◽  
Baltic Cod

AbstractThe intensified expansion of the Baltic Sea’s hypoxic zone has been proposed as one reason for the current poor status of cod (Gadus morhua) in the Baltic Sea, with repercussions throughout the food web and on ecosystem services. We examined the links between increased hypoxic areas and the decline in maximum length of Baltic cod, a demographic proxy for services generation. We analysed the effect of different predictors on maximum length of Baltic cod during 1978–2014 using a generalized additive model. The extent of minimally suitable areas for cod (oxygen concentration ≥ 1 ml l−1) is the most important predictor of decreased cod maximum length. We also show, with simulations, the potential for Baltic cod to increase its maximum length if hypoxic areal extent is reduced to levels comparable to the beginning of the 1990s. We discuss our findings in relation to ecosystem services affected by the decrease of cod maximum length.

scholarly journals An ecosystem model of food web and fisheries interactions in the Baltic Sea

2003 ◽  
Vol 60 (5) ◽  
pp. 939-950 ◽  
Author(s):  
Chris J Harvey ◽  
Sean P Cox ◽  
Timothy E Essington ◽  
Sture Hansson ◽  
James F Kitchell
Keyword(s):  
Baltic Sea ◽  
Food Web ◽  
Species Interactions ◽  
Gadus Morhua ◽  
Population Analysis ◽  
Clupea Harengus ◽  
Trophic Relationships ◽  
The Baltic Sea ◽  
Management Actions ◽  
The Baltic

Abstract Because fisheries operate within a complex array of species interactions, scientists increasingly recommend multispecies approaches to fisheries management. We created a food web model for the Baltic Sea proper, using the Ecopath with Ecosim software, to evaluate interactions between fisheries and the food web from 1974 to 2000. The model was based largely on values generated by multispecies virtual population analysis (MSVPA). Ecosim outputs closely reproduced MSVPA biomass estimates and catch data for sprat (Sprattus sprattus), herring (Clupea harengus), and cod (Gadus morhua), but only after making adjustments to cod recruitment, to vulnerability to predation of specific species, and to foraging times. Among the necessary adjustments were divergent trophic relationships between cod and clupeids: cod exhibited top-down control on sprat biomass, but had little influence on herring. Fishing, the chief source of mortality for cod and herring, and cod reproduction, as driven by oceanographic conditions as well as unexplained variability, were also key structuring forces. The model generated many hypotheses about relationships between key biota in the Baltic Sea food web and may ultimately provide a basis for estimating community responses to management actions.

Environmental genotoxicity and cytotoxicity levels in herring (Clupea harengus), flounder (Platichthys flesus) and cod (Gadus morhua) inhabiting the Gdansk Basin of the Baltic Sea

2018 ◽  
Vol 133 ◽  
pp. 65-76 ◽  
Author(s):  
Roberta Valskienė ◽  
Janina Baršienė ◽  
Laura Butrimavičienė ◽  
Wlodzimierz Grygiel ◽  
Virmantas Stunžėnas ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Gadus Morhua ◽  
Clupea Harengus ◽  
The Baltic Sea ◽  
Platichthys Flesus ◽  
The Baltic ◽  
Environmental Genotoxicity

Statistical Downscaling of daily extreme Sea Level with Random Forest: Examples from South-East Asia and the Baltic Sea

2020 ◽  
Author(s):  
Svenja Bierstedt ◽  
Eduardo Zorita ◽  
Birgit Hünicke
Keyword(s):  
Random Forest ◽  
Baltic Sea ◽  
East Asia ◽  
Sea Level ◽  
Large Scale ◽  
South East Asia ◽  
The Baltic Sea ◽  
Sea Levels ◽  
Extreme Sea Levels ◽  
The Baltic

<p>The coastlines of the Baltic Sea and Indonesia are both relatively complex, so that the estimation of extreme sea levels caused by the atmospheric forcing becomes complex with conventional methods. Here, we explore whether Machine Learning methods can provide a model surrogate to compute more rapidly daily extremes in sea level from large-scale atmosphere-ocean fields. We investigate the connections between the atmospheric and ocean drivers of local extreme sea level in South East Asia and along the Baltic Sea based on statistical analysis by Random Forest Models, driven by large-scale meteorological predictors and daily extreme sea level measured by tide-gauge records over the last few decades.</p><p>First results show that in some Indonesian areas extremes are driven by large-scale climate fields; in other areas they are incoherently driven by local processes. An area where random forest predicted extremes show good correspondence to observed extremes is found to be the Malaysian coastline. For the Indonesian coasts, the Random Forest Algorithm was unable to predict extreme sea levels in line with observations. Along the Baltic Sea, in contrast, the Random Forest model is able to produce reasonable estimations of extreme sea levels based on the large-scale atmospheric fields. An analysis of the interrelations of extreme sea levels in the South Asia regions suggests that either the data quality may be compromised in some regions or that other forcing factors, distinct from the large-scale atmospheric fields, may also be involved.</p>

Simulating extreme sea levels at the Baltic Sea coast from synthetic cyclones

2020 ◽  
Author(s):  
Jani Särkkä ◽  
Jani Räihä ◽  
Matti Kämäräinen ◽  
Kirsti Jylhä
Keyword(s):  
Baltic Sea ◽  
Sea Level ◽  
Water Volume ◽  
The Baltic Sea ◽  
Data Set ◽  
Sea Levels ◽  
Extreme Sea Levels ◽  
Level Model ◽  
The Baltic ◽  
Sea Coast

<p>Coastal areas are under rapid changes. Management to face flooding hazards in changing climate is of great significance due to the major impact of flooding events in densely populated coastal regions, where also important and vulnerable infrastructure is located. The sea level of the Baltic Sea is affected by internal fluctuations caused by wind, air pressure and seiche oscillations, and by variations of the water volume due to the water exchange between the Baltic Sea and the North Sea through the Danish Straits. The highest sea level extremes are caused by cyclones moving over the region. The most vulnerable locations are at the ends of the bays. St. Petersburg, located at the eastern end of the Gulf of Finland, has experienced major sea floods in 1777, 1824 and 1924.</p><p>In order to study the effects of the depths and tracks of cyclones on the extreme sea levels, we have developed a method to generate cyclones for numerical sea level studies. A cyclone is modelled as a two-dimensional Gaussian function with adjustable horizontal size and depth. The cyclone moves through the Baltic Sea region with given direction and velocity. The output of this method is the gridded data set of mean sea level pressure and wind components which are used as an input for the sea level model. The internal variations of the Baltic Sea are calculated with a numerical barotropic sea level model, and the water volume variations are evaluated using a statistical sea level model based on wind speeds near the Danish Straits. The sea level model simulations allow us to study extremely rare but physically plausible sea level events that have not occurred during the observation period at the Baltic Sea coast. The simulation results are used to investigate extreme sea levels that could occur at selected sites at the Finnish coastline.</p>

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