Postglacial expansion of the arctic keystone copepod calanus glacialis

Calanus glacialis, a major contributor to zooplankton biomass in the Arctic shelf seas, is a key link between primary production and higher trophic levels that may be sensitive to climate warming. The aim of this study was to explore genetic variation in contemporary populations of this species to infer possible changes during the Quaternary period, and to assess its population structure in both space and time. Calanus glacialis was sampled in the fjords of Spitsbergen (Hornsund and Kongsfjorden) in 2003, 2004, 2006, 2009 and 2012. The sequence of a mitochondrial marker, belonging to the ND5 gene, selected for the study was 1249 base pairs long and distinguished 75 unique haplotypes among 140 individuals that formed three main clades. There was no detectable pattern in the distribution of haplotypes by geographic distance or over time. Interestingly, a Bayesian skyline plot suggested that a 1000-fold increase in population size occurred approximately 10,000 years before present, suggesting a species expansion after the Last Glacial Maximum.GAME from the National Science Centre, the Polish Ministry of Science and Higher Education Iuventus Plus [IP2014 050573]; FCT-PT [CCMAR/Multi/04326/2013]; [2011/03/B/NZ8/02876

Diversity of hard-bottom fauna relative to environmental gradients in Kongsfjorden, Svalbard

A baseline study of hard-bottom zoobenthos in relation to environmental gradients in Kongsfjorden, a glacial fjord in Svalbard, is presented, based on collections from 1996 to 1998. The total species richness in 62 samples from 0 to 30 m depth along five transects was 403 species. Because 32 taxa could not be identified to species level and because 11 species are probably new to science, the total number of identified species was 360. Of these, 47 species are new for Svalbard waters. Bryozoa was the most diverse group. Biogeographic composition revealed features of both Arctic and sub-Arctic properties of the fauna. Species richness, frequency of species occurrence, mean abundance and biomass generally decreased towards the tidal glaciers in inner Kongsfjorden. Among eight environmental factors, depth was most important for explaining variance in the composition of the zoobenthos. The diversity was consistently low at shallow depths, whereas the non-linear patterns of species composition of deeper samples indicated a transitional zone between surface and deeper water masses at 15–20 m depth. Groups of “colonial” and “non-colonial” species differed in diversity, biogeographic composition and distribution by location and depth as well as in relation to other environmental factors. “Non-colonial” species made a greater contribution than “colonial” species to total species richness, total occurrence and biomass in samples, and were more influenced by the depth gradient. Biogeographic composition was sensitive to variation of zoobenthic characteristics over the studied depth range. A list of recorded species and a description of sampling sites are presented

Application of neural networks to the prediction of significant wave height at selected locations on the Baltic Sea

This paper describes the application of methodology based on the artificial neural network technique to make short-term wave forecasts. The neural network model is used to predict significant wave height at a selected location on the Baltic Sea based on wave and/or wind data at ten points scattered on the sea. High quality hindcast data were used in the process of developing the forecast methodology. The data originated from the WAM4 wave model. The results show that the neural network technique allowed significant wave height to be predicted accurately. The agreement obtained by a comparison with a testing data set was sufficiently good to confirm the effectiveness of this approach

Passive acoustic detection and observations of wind-wave breaking processes

This study is a preliminary work aiming to obtain statistics of wind waves on the base of the ambient sea noise parameters. In experiment conducted on the Baltic Sea, passive acoustic methods were exploited in order to detect and parameterize breaking evens. A four hydrophones array was used to record ambient sea noise for five days. Data collected during this experiment allowed us to track moving acoustic sources, to estimate their speed, duration and number in a unit of time. All these parameters were determined using generalized the cross correlation method. Some examples of obtained cross correlation time series with brief description of encountered difficulties in analysis are presented. Variation in number, duration and speed of breaking events in time are compared with averaged noise level and significant wave height. This last parameter, describing in a unified way the wave field, constitutes the essence of further investigations. Distributions of obtained values are shown and further improvement of a method is shortly introduced

Application of statistical methods for the prediction of extreme wave events

A statistically-based model is applied to forecast sea states for severe storms. The model is based on the application of a neural network and predicts extreme sea state parameters at specified locations. The results show that the neural network can be applied to forecast extreme sea state parameters. This requires a special treatment of the input data. The analysis shows that different types of input data and training data sets should be considered and the representativity of the training data set must be improved. Moreover, a sensitivity analysis should be conducted to remove excess information from the input data. The processing of data sets significantly reduces the number of parameters applied in the model and improves the prediction for most severe storms. The analysis indicates that this neural network model may be helpful in the selection of a measurement system for the forecasting of extreme sea state parameters. This is important because typical installations of wave buoys along the coast have a limited forecasting applicability range