Fischbestände der Ostsee, ihre Entwicklung seit 1970 und Schlussfolgerungen für ihre fischereiliche Nutzung - Teil 3: Sprotte

The sprat of the Baltic Sea is not as short-lived as inother Seas probably because fish predator species arerestricted mainly on cod and salmon. Sea bird popula-tions are much smaller and marine mammals are rare inthe Baltic Sea. The sprat stock biomass is fluctuatingstrongly. The fluctuation is mainly influenced by thestock recruitment and is also dependent on the strengthof the cod stock. After a strong decrease during the1980ies sprat catches increased again from 1992 onwardsand reached a peak with over half a million tonnes in 1997. At about the same time the character of the BalticSea sprat fishery changed from catches mainly for hu-man consumption to catches mainly for industrial pur-poses initiated by the fishery of Sweden. The recentrecord high catches of sprat have been possible only dueto the low level of the cod stock of the main Baltic SeaBasins over some years. A sprat fishery on such a highcatch level might cause conflicts with a recovering codstock in future.

Secchi depth maps for the Baltic Sea, 2003-2012, with links to GeoTIFs

Secchi depth is a measure of water transparency. In the Baltic Sea region, Secchi depth maps are used to assess eutrophication and as input for habitat models. Due to their spatial and temporal coverage, satellite data would be the most suitable data source for such maps. But the Baltic Sea's optical properties are so different from the open ocean that globally calibrated standard models suffer from large errors. Regional predictive models that take the Baltic Sea's special optical properties into account are thus needed. This paper tests how accurately generalized linear models (GLMs) and generalized additive models (GAMs) with MODIS/Aqua and auxiliary data as inputs can predict Secchi depth at a regional scale. It uses cross-validation to test the prediction accuracy of hundreds of GAMs and GLMs with up to 5 input variables. A GAM with 3 input variables (chlorophyll a, remote sensing reflectance at 678 nm, and long-term mean salinity) made the most accurate predictions. Tested against field observations not used for model selection and calibration, the best model's mean absolute error (MAE) for daily predictions was 1.07 m (22%), more than 50% lower than for other publicly available Baltic Sea Secchi depth maps. The MAE for predicting monthly averages was 0.86 m (15%). Thus, the proposed model selection process was able to find a regional model with good prediction accuracy. It could be useful to find predictive models for environmental variables other than Secchi depth, using data from other satellite sensors, and for other regions where non-standard remote sensing models are needed for prediction and mapping. Annual and monthly mean Secchi depth maps for 2003-2012 come with this paper as Supplementary materials.