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Coastal lagoons are important sites for nitrogen (N) removal via sediment burial and denitrification. Blooms of heterocystous cyanobacteria may diminish N retention as dinitrogen (N2) fixation offsets atmospheric losses via denitrification. We measured N2 fixation in the Curonian Lagoon, Europe's largest coastal lagoon, to better understand the factors controlling N2 fixation in the context of seasonal changes in phytoplankton community composition and external N inputs. Temporal patterns in N2 fixation were primarily determined by the abundance of heterocystous cyanobacteria, mainly Aphanizomenon flos-aquae, which became abundant after the decline in riverine nitrate inputs associated with snowmelt. Heterocystous cyanobacteria dominated the summer phytoplankton community resulting in strong correlations between chlorophyll a (Chl a) and N2 fixation. We used regression models relating N2 fixation to Chl a, along with remote-sensing-based estimates of Chl a to derive lagoon-scale estimates of N2 fixation. N2 fixation by pelagic cyanobacteria was found to be a significant component of the lagoon's N budget based on comparisons to previously derived fluxes associated with riverine inputs, sediment–water exchange, and losses via denitrification. To our knowledge, this is the first study to derive ecosystem-scale estimates of N2 fixation by combining remote sensing of Chl a with empirical models relating N2 fixation rates to Chl a.

Coastal lagoons are important sites for nitrogen (N) removal via sediment burial and denitrification. Blooms of heterocystous cyanobacteria may diminish N retention as dinitrogen (N2) fixation offsets atmospheric losses via denitrification. We measured N2 fixation in the Curonian Lagoon, Europe's largest coastal lagoon, to better understand the factors controlling N2 fixation in the context of seasonal changes in phytoplankton community composition and external N inputs. Temporal patterns in N2 fixation were primarily determined by the abundance of heterocystous cyanobacteria, mainly Aphanizomenon flos-aquae, which became abundant after the decline in riverine nitrate inputs associated with snowmelt. Heterocystous cyanobacteria dominated the summer phytoplankton community resulting in strong correlations between chlorophyll a (Chl a) and N2 fixation. We used regression models relating N2 fixation to Chl a, along with remote-sensing-based estimates of Chl a to derive lagoon-scale estimates of N2 fixation. N2 fixation by pelagic cyanobacteria was found to be a significant component of the lagoon's N budget based on comparisons to previously derived fluxes associated with riverine inputs, sediment–water exchange, and losses via denitrification. To our knowledge, this is the first study to derive ecosystem-scale estimates of N2 fixation by combining remote sensing of Chl a with empirical models relating N2 fixation rates to Chl a.

Coastal lagoons are important sites for nitrogen (N) removal via sediment burial and denitrification. Blooms of heterocystous cyanobacteria may diminish N retention as dinitrogen (N2) fixation offsets atmospheric losses via denitrification. We measured N2 fixation in the Curonian Lagoon, Europe's largest coastal lagoon, to better understand the factors controlling N2 fixation in the context of seasonal changes in phytoplankton community composition and external N inputs. Temporal patterns in N2 fixation were primarily determined by the abundance of heterocystous cyanobacteria, mainly Aphanizomenon flos-aquae, which became abundant after the decline in riverine nitrate inputs associated with snowmelt. Heterocystous cyanobacteria dominated the summer phytoplankton community resulting in strong correlations between chlorophyll a (Chl a) and N2 fixation. We used regression models relating N2 fixation to Chl a, along with remote-sensing-based estimates of Chl a to derive lagoon-scale estimates of N2 fixation. N2 fixation by pelagic cyanobacteria was found to be a significant component of the lagoon's N budget based on comparisons to previously derived fluxes associated with riverine inputs, sediment–water exchange, and losses via denitrification. To our knowledge, this is the first study to derive ecosystem-scale estimates of N2 fixation by combining remote sensing of Chl a with empirical models relating N2 fixation rates to Chl a.

Coastal lagoons are important sites for nitrogen (N) removal via sediment burial and denitrification. Blooms of heterocystous cyanobacteria may diminish N retention as dinitrogen (N2) fixation offsets atmospheric losses via denitrification. We measured N2 fixation in the Curonian Lagoon, Europe's largest coastal lagoon, to better understand the factors controlling N2 fixation in the context of seasonal changes in phytoplankton community composition and external N inputs. Temporal patterns in N2 fixation were primarily determined by the abundance of heterocystous cyanobacteria, mainly Aphanizomenon flos-aquae, which became abundant after the decline in riverine nitrate inputs associated with snowmelt. Heterocystous cyanobacteria dominated the summer phytoplankton community resulting in strong correlations between chlorophyll a (Chl a) and N2 fixation. We used regression models relating N2 fixation to Chl a, along with remote-sensing-based estimates of Chl a to derive lagoon-scale estimates of N2 fixation. N2 fixation by pelagic cyanobacteria was found to be a significant component of the lagoon's N budget based on comparisons to previously derived fluxes associated with riverine inputs, sediment–water exchange, and losses via denitrification. To our knowledge, this is the first study to derive ecosystem-scale estimates of N2 fixation by combining remote sensing of Chl a with empirical models relating N2 fixation rates to Chl a.

Coastal lagoons are important sites for nitrogen (N) removal via sediment burial and denitrification. Blooms of heterocystous cyanobacteria may diminish N retention as dinitrogen (N2) fixation offsets atmospheric losses via denitrification. We measured N2 fixation in the Curonian Lagoon, Europe's largest coastal lagoon, to better understand the factors controlling N2 fixation in the context of seasonal changes in phytoplankton community composition and external N inputs. Temporal patterns in N2 fixation were primarily determined by the abundance of heterocystous cyanobacteria, mainly Aphanizomenon flos-aquae, which became abundant after the decline in riverine nitrate inputs associated with snowmelt. Heterocystous cyanobacteria dominated the summer phytoplankton community resulting in strong correlations between chlorophyll a (Chl a) and N2 fixation. We used regression models relating N2 fixation to Chl a, along with remote-sensing-based estimates of Chl a to derive lagoon-scale estimates of N2 fixation. N2 fixation by pelagic cyanobacteria was found to be a significant component of the lagoon's N budget based on comparisons to previously derived fluxes associated with riverine inputs, sediment–water exchange, and losses via denitrification. To our knowledge, this is the first study to derive ecosystem-scale estimates of N2 fixation by combining remote sensing of Chl a with empirical models relating N2 fixation rates to Chl a.

Coastal lagoons are important sites for nitrogen (N) removal via sediment burial and denitrification. Blooms of heterocystous cyanobacteria may diminish N retention as dinitrogen (N2) fixation offsets atmospheric losses via denitrification. We measured N2 fixation in the Curonian Lagoon, Europe's largest coastal lagoon, to better understand the factors controlling N2 fixation in the context of seasonal changes in phytoplankton community composition and external N inputs. Temporal patterns in N2 fixation were primarily determined by the abundance of heterocystous cyanobacteria, mainly Aphanizomenon flos-aquae, which became abundant after the decline in riverine nitrate inputs associated with snowmelt. Heterocystous cyanobacteria dominated the summer phytoplankton community resulting in strong correlations between chlorophyll a (Chl a) and N2 fixation. We used regression models relating N2 fixation to Chl a, along with remote-sensing-based estimates of Chl a to derive lagoon-scale estimates of N2 fixation. N2 fixation by pelagic cyanobacteria was found to be a significant component of the lagoon's N budget based on comparisons to previously derived fluxes associated with riverine inputs, sediment–water exchange, and losses via denitrification. To our knowledge, this is the first study to derive ecosystem-scale estimates of N2 fixation by combining remote sensing of Chl a with empirical models relating N2 fixation rates to Chl a.

Suspended particulate matter (SPM) causes most of the scattering in natural waters and thus has a strong influence on the underwater light field, and consequently on the whole ecosystem. Turbidity is related to the concentration of SPM which usually is measured gravimetrically, a rather time-consuming method. Measuring turbidity is quick and easy, and therefore also more cost-effective. When derived from remote sensing data the method becomes even more cost-effective because of the good spatial resolution of satellite data and the synoptic capability of the method. Turbidity is also listed in the European Union's Marine Strategy Framework Directive as a supporting monitoring parameter, especially in the coastal zone. In this study, we aim to provide a new Baltic Sea algorithm to retrieve SPM concentration from in situ turbidity and investigate how this can be applied to satellite data. An in situ dataset was collected in Swedish coastal waters to develop a new SPM model. The model was then tested against independent datasets from both Swedish and Lithuanian coastal waters. Despite the optical variability in the datasets, SPM and turbidity were strongly correlated (r = 0.97). The developed model predicts SPM reliably from in situ turbidity (R-2 = 0.93) with a mean normalized bias (MNB) of 2.4% for the Swedish and 14.0% for the Lithuanian datasets, and a relative error (RMS) of 25.3% and 37.3%, respectively. In the validation dataset, turbidity ranged from 0.3 to 49.8 FNU (Formazin Nephelometric Unit) and correspondingly, SPM concentration ranged from 0.3 to 34.0 g m(-3) which covers the ranges typical for Baltic Sea waters. Next, the medium-resolution imaging spectrometer (MERIS) standard SPM product MERIS Ground Segment (MEGS) was tested on all available match-up data (n = 67). The correlation between SPM retrieved from MERIS and in situ SPM was strong for the Swedish dataset with r = 0.74 (RMS = 47.4 and MNB = 11.3%; n = 32) and very strong for the Lithuanian datas

Suspended particulate matter (SPM) causes most of the scattering in natural waters and thus has a strong influence on the underwater light field, and consequently on the whole ecosystem. Turbidity is related to the concentration of SPM which usually is measured gravimetrically, a rather time-consuming method. Measuring turbidity is quick and easy, and therefore also more cost-effective. When derived from remote sensing data the method becomes even more cost-effective because of the good spatial resolution of satellite data and the synoptic capability of the method. Turbidity is also listed in the European Union's Marine Strategy Framework Directive as a supporting monitoring parameter, especially in the coastal zone. In this study, we aim to provide a new Baltic Sea algorithm to retrieve SPM concentration from in situ turbidity and investigate how this can be applied to satellite data. An in situ dataset was collected in Swedish coastal waters to develop a new SPM model. The model was then tested against independent datasets from both Swedish and Lithuanian coastal waters. Despite the optical variability in the datasets, SPM and turbidity were strongly correlated (r = 0.97). The developed model predicts SPM reliably from in situ turbidity (R-2 = 0.93) with a mean normalized bias (MNB) of 2.4% for the Swedish and 14.0% for the Lithuanian datasets, and a relative error (RMS) of 25.3% and 37.3%, respectively. In the validation dataset, turbidity ranged from 0.3 to 49.8 FNU (Formazin Nephelometric Unit) and correspondingly, SPM concentration ranged from 0.3 to 34.0 g m(-3) which covers the ranges typical for Baltic Sea waters. Next, the medium-resolution imaging spectrometer (MERIS) standard SPM product MERIS Ground Segment (MEGS) was tested on all available match-up data (n = 67). The correlation between SPM retrieved from MERIS and in situ SPM was strong for the Swedish dataset with r = 0.74 (RMS = 47.4 and MNB = 11.3%; n = 32) and very strong for the Lithuanian datas

Suspended particulate matter (SPM) causes most of the scattering in natural waters and thus has a strong influence on the underwater light field, and consequently on the whole ecosystem. Turbidity is related to the concentration of SPM which usually is measured gravimetrically, a rather time-consuming method. Measuring turbidity is quick and easy, and therefore also more cost-effective. When derived from remote sensing data the method becomes even more cost-effective because of the good spatial resolution of satellite data and the synoptic capability of the method. Turbidity is also listed in the European Union's Marine Strategy Framework Directive as a supporting monitoring parameter, especially in the coastal zone. In this study, we aim to provide a new Baltic Sea algorithm to retrieve SPM concentration from in situ turbidity and investigate how this can be applied to satellite data. An in situ dataset was collected in Swedish coastal waters to develop a new SPM model. The model was then tested against independent datasets from both Swedish and Lithuanian coastal waters. Despite the optical variability in the datasets, SPM and turbidity were strongly correlated (r = 0.97). The developed model predicts SPM reliably from in situ turbidity (R-2 = 0.93) with a mean normalized bias (MNB) of 2.4% for the Swedish and 14.0% for the Lithuanian datasets, and a relative error (RMS) of 25.3% and 37.3%, respectively. In the validation dataset, turbidity ranged from 0.3 to 49.8 FNU (Formazin Nephelometric Unit) and correspondingly, SPM concentration ranged from 0.3 to 34.0 g m(-3) which covers the ranges typical for Baltic Sea waters. Next, the medium-resolution imaging spectrometer (MERIS) standard SPM product MERIS Ground Segment (MEGS) was tested on all available match-up data (n = 67). The correlation between SPM retrieved from MERIS and in situ SPM was strong for the Swedish dataset with r = 0.74 (RMS = 47.4 and MNB = 11.3%; n = 32) and very strong for the Lithuanian datas

Suspended particulate matter (SPM) causes most of the scattering in natural waters and thus has a strong influence on the underwater light field, and consequently on the whole ecosystem. Turbidity is related to the concentration of SPM which usually is measured gravimetrically, a rather time-consuming method. Measuring turbidity is quick and easy, and therefore also more cost-effective. When derived from remote sensing data the method becomes even more cost-effective because of the good spatial resolution of satellite data and the synoptic capability of the method. Turbidity is also listed in the European Union's Marine Strategy Framework Directive as a supporting monitoring parameter, especially in the coastal zone. In this study, we aim to provide a new Baltic Sea algorithm to retrieve SPM concentration from in situ turbidity and investigate how this can be applied to satellite data. An in situ dataset was collected in Swedish coastal waters to develop a new SPM model. The model was then tested against independent datasets from both Swedish and Lithuanian coastal waters. Despite the optical variability in the datasets, SPM and turbidity were strongly correlated (r = 0.97). The developed model predicts SPM reliably from in situ turbidity (R-2 = 0.93) with a mean normalized bias (MNB) of 2.4% for the Swedish and 14.0% for the Lithuanian datasets, and a relative error (RMS) of 25.3% and 37.3%, respectively. In the validation dataset, turbidity ranged from 0.3 to 49.8 FNU (Formazin Nephelometric Unit) and correspondingly, SPM concentration ranged from 0.3 to 34.0 g m(-3) which covers the ranges typical for Baltic Sea waters. Next, the medium-resolution imaging spectrometer (MERIS) standard SPM product MERIS Ground Segment (MEGS) was tested on all available match-up data (n = 67). The correlation between SPM retrieved from MERIS and in situ SPM was strong for the Swedish dataset with r = 0.74 (RMS = 47.4 and MNB = 11.3%; n = 32) and very strong for the Lithuanian datas

Suspended particulate matter (SPM) causes most of the scattering in natural waters and thus has a strong influence on the underwater light field, and consequently on the whole ecosystem. Turbidity is related to the concentration of SPM which usually is measured gravimetrically, a rather time-consuming method. Measuring turbidity is quick and easy, and therefore also more cost-effective. When derived from remote sensing data the method becomes even more cost-effective because of the good spatial resolution of satellite data and the synoptic capability of the method. Turbidity is also listed in the European Union's Marine Strategy Framework Directive as a supporting monitoring parameter, especially in the coastal zone. In this study, we aim to provide a new Baltic Sea algorithm to retrieve SPM concentration from in situ turbidity and investigate how this can be applied to satellite data. An in situ dataset was collected in Swedish coastal waters to develop a new SPM model. The model was then tested against independent datasets from both Swedish and Lithuanian coastal waters. Despite the optical variability in the datasets, SPM and turbidity were strongly correlated (r = 0.97). The developed model predicts SPM reliably from in situ turbidity (R-2 = 0.93) with a mean normalized bias (MNB) of 2.4% for the Swedish and 14.0% for the Lithuanian datasets, and a relative error (RMS) of 25.3% and 37.3%, respectively. In the validation dataset, turbidity ranged from 0.3 to 49.8 FNU (Formazin Nephelometric Unit) and correspondingly, SPM concentration ranged from 0.3 to 34.0 g m(-3) which covers the ranges typical for Baltic Sea waters. Next, the medium-resolution imaging spectrometer (MERIS) standard SPM product MERIS Ground Segment (MEGS) was tested on all available match-up data (n = 67). The correlation between SPM retrieved from MERIS and in situ SPM was strong for the Swedish dataset with r = 0.74 (RMS = 47.4 and MNB = 11.3%; n = 32) and very strong for the Lithuanian datas

Suspended particulate matter (SPM) causes most of the scattering in natural waters and thus has a strong influence on the underwater light field, and consequently on the whole ecosystem. Turbidity is related to the concentration of SPM which usually is measured gravimetrically, a rather time-consuming method. Measuring turbidity is quick and easy, and therefore also more cost-effective. When derived from remote sensing data the method becomes even more cost-effective because of the good spatial resolution of satellite data and the synoptic capability of the method. Turbidity is also listed in the European Union's Marine Strategy Framework Directive as a supporting monitoring parameter, especially in the coastal zone. In this study, we aim to provide a new Baltic Sea algorithm to retrieve SPM concentration from in situ turbidity and investigate how this can be applied to satellite data. An in situ dataset was collected in Swedish coastal waters to develop a new SPM model. The model was then tested against independent datasets from both Swedish and Lithuanian coastal waters. Despite the optical variability in the datasets, SPM and turbidity were strongly correlated (r = 0.97). The developed model predicts SPM reliably from in situ turbidity (R-2 = 0.93) with a mean normalized bias (MNB) of 2.4% for the Swedish and 14.0% for the Lithuanian datasets, and a relative error (RMS) of 25.3% and 37.3%, respectively. In the validation dataset, turbidity ranged from 0.3 to 49.8 FNU (Formazin Nephelometric Unit) and correspondingly, SPM concentration ranged from 0.3 to 34.0 g m(-3) which covers the ranges typical for Baltic Sea waters. Next, the medium-resolution imaging spectrometer (MERIS) standard SPM product MERIS Ground Segment (MEGS) was tested on all available match-up data (n = 67). The correlation between SPM retrieved from MERIS and in situ SPM was strong for the Swedish dataset with r = 0.74 (RMS = 47.4 and MNB = 11.3%; n = 32) and very strong for the Lithuanian datas

Marine chemical ecology comprises the study of the production and interaction of bioactive molecules affecting organism behavior and function. Here we focus on bioactive compounds and interactions associated with phytoplankton, particularly bloom-forming diatoms, prymnesiophytes and dinoflagellates. Planktonic bioactive metabolites are structurally and functionally diverse and some may have multiple simultaneous functions including roles in chemical defense (antipredator, allelopathic and antibacterial compounds), and/or cell-to-cell signaling (e.g., polyunsaturated aldehydes (PUAs) of diatoms). Among inducible chemical defenses in response to grazing, there is high species-specific variability in the effects on grazers, ranging from severe physical incapacitation and/or death to no apparent physiological response, depending on predator susceptibility and detoxification capability. Most bioactive compounds are present in very low concentrations, in both the producing organism and the surrounding aqueous medium. Furthermore, bioactivity may be subject to synergistic interactions with other natural and anthropogenic environmental toxicants. Most, if not all phycotoxins are classic secondary metabolites, but many other bioactive metabolites are simple molecules derived from primary metabolism (e.g., PUAs in diatoms, dimethylsulfoniopropionate (DMSP) in prymnesiophytes). Producing cells do not seem to suffer physiological impact due to their synthesis. Functional genome sequence data and gene expression analysis will provide insights into regulatory and metabolic pathways in producer organisms, as well as identification of mechanisms of action in target organisms. Understanding chemical ecological responses to environmental triggers and chemically-mediated species interactions will help define crucial chemical and molecular processes that help maintain biodiversity and ecosystem functionality., ALGBACT

There is an increasing understanding and requirement to take into account the effects of invasive alien species (IAS) in environmental quality assessments. While IAS are listed amongst the most important factors threatening marine biodiversity, information on their impacts remains unquantified, especially for phytoplankton species. This study attempts to assess the impacts of invasive alien phytoplankton in the Baltic Sea during 1980-2008. A bioinvasion impact assessment method (BPL - biopollution level index) was applied to phytoplankton monitoring data collected from eleven sub-regions of the Baltic Sea. BPL takes into account abundance and distribution range of an alien species and the magnitude of the impact on native communities, habitats and ecosystem functioning. Of the 12 alien/cryptogenic phytoplankton species recorded in the Baltic Sea only one (the dinoflagellate Prorocentrum minimum) was categorized as an IAS, causing a recognizable environmental effect., authorCount :9