Rapid Lateglacial tree population dynamics and ecosystem changes in the eastern Baltic region

A growing body of evidence implies that the concept of 'treeless tundra' in eastern and northern Europe fails to explain the rapidity of Lateglacial and postglacial tree population dynamics of the region, yet the knowledge of the geographic locations and shifting of tree populations is fragmentary. Pollen, stomata and plant macrofossil stratigraphies from Lake Kurjanovas in the poorly studied eastern Baltic region provide improved knowledge of ranges of north‐eastern European trees during the Lateglacial and subsequent plant population responses to the abrupt climatic changes of the Lateglacial/Holocene transition. The results prove the Lateglacial presence of tree populations (Betula, Pinus and Picea) in the eastern Baltic region. Particularly relevant is the stomatal and plant macrofossil evidence showing the local presence of reproductive Picea populations during the Younger Dryas stadial at 12 900–11 700 cal. a BP, occurring along with Dryas octopetala and arctic herbs, indicating semi‐open vegetation. The spread of Pinus–Betula forest at ca. 14 400 cal. a BP, the rise of Picea at ca. 12 800 cal. a BP and the re‐establishment of Pinus–Betula forest at ca. 11 700 cal. a BP within a span of centuries further suggest strikingly rapid, climate‐driven ecosystem changes rather than gradual plant succession on a newly deglaciated land. Copyright © 2009 John Wiley & Sons, Ltd.     

Long-term changes in summer phytoplankton communities of the open northern Baltic Sea

Changes in the biomass and species composition of phytoplankton may reflect major shifts in environmental conditions. We investigated relationships between the late summer biomass of different phytoplankton taxa and environmental factors, and their long-term (1979–2003) trends in two areas of the Baltic Sea, the northern Baltic proper (NBP) and the Gulf of Finland (GF), with statistical analyses. An increasing trend was found in late summer temperature and chlorophyll a of the surface water layer (0–10 m) in both areas. There was also a significant decrease in summer salinity and an increase in winter dissolved inorganic nitrogen to phosphorus (DIN:DIP) ratio in the NBP, as well as increases in winter DIN concentrations and DIN:SiO₄ ratio in the GF. Simultaneously, the biomass of chrysophytes and chlorophytes increased in both areas. In the NBP, also the biomass of dinophytes increased and that of euglenophytes decreased, whereas in the GF, cyanobacteria increased and cryptophytes decreased. Redundancy analysis (RDA) indicated that summer temperature and winter DIN concentration were the most important factors with respect to changes in the phytoplankton community structure. Thus, the phytoplankton communities seem to reflect both hydrographic changes and the ongoing eutrophication process in the northern Baltic Sea.     

Palaeolimnology of Lake Piene-Kuuppalanlampi (Kurkijoki, Karelian Republic, Russia): isolation history, lake ecosystem development and long-term agricultural impact

The post-glacial history of Lake Pieni-Kuuppalanlampi, western Lake Ladoga region, was studied by means of stratigraphic pollen and diatom analyses. Diatoms were analysed to track the isolation history of the basin and the limnological effects of the early land-use phases indicated by pollen analysis. Chrysophycean stomatocysts and Isoëtes spores were also employed in the limnological reconstructions. Sediment dating was provided by six conventional radiocarbon dates.The lower part of the 370-cm long sediment sequence represents early Holocene, large lake conditions: the (freshwater) Yoldia and Ancylus stages of the Baltic basin, with a short-term lagoonal or isolation phase at the end of Yoldia. The basin was isolated due to Ancylus lake regression at 9785 cal B.P. For the small-lake sequence (0–250 cm) we used diatom inferences (WA-method) for hindcasting water chemistry. The post-isolation limnology of Pieni-Kuuppalanlampi reflects the development of vegetation on its small hill-top catchment. In its early development, the lake was mesotrophic, but became more acidic (pH about 6) and oligotrophic after the decline of temperate hardwood trees and the spread of spruce to the area after around 5000 B.P. The lake ecosystem appears to have reacted sensitively to agricultural land-use in the catchment from AD 400–800 onwards: inferred total phosphorus levels and pH both rise during these periods.     

Importance of climate,forest fires and human population size in the Holocene boreal forest composition change in northern Europe

The relative importance of climate, forest fires and human population size on long‐term boreal forest composition were statistically investigated at regional and local scales in Fennoscandia. We employ pollen data from lakes, reflecting regional vegetation, and small forest hollows, reflecting local vegetation, from Russia, Finland and Sweden to reconstruct the long‐term forest composition. As potential drivers of the Holocene forest dynamics we consider climate, generated from a climate model and oxygen isotope data, past forest fires generated from sedimentary charcoal data and human population size derived from radiocarbon dated archaeological findings. We apply the statistical method of variation partitioning to assess the relative importance of these environmental variables on long‐term boreal forest composition. The results show that climate is the main driver of the changes in Holocene boreal forest composition at the regional scale. However, at the local scale the role of climate is relatively small. In general, the importance of forest fires is low both at regional and local scales. The fact that both climate and forest fires explain relatively small proportions of variation in long‐term boreal vegetation in small forest hollow records demonstrates the complexity of factors affecting stand‐scale forest dynamics. The relative importance of human population size was low in both the prehistorical and the historical time periods. However, this is the first time that this type of data has been used to statistically assess the importance of human population size on boreal vegetation and the spatial representativeness of the data may cause bias to the analysis.     

Estimation of pH optima and tolerances of diatoms in lake sediments by the methods of weighted averaging,least squares and maximum likelihood,and their use for the prediction of lake acidity

Ecological optima and tolerances with respect to autumn pH were estimated for 63 diatom taxa in 47 Finnish lakes. The methods used were weighted averaging (WA), least squares (LS) and maximum likelihood (ML), the two latter methods assuming the Gaussian response model.WA produces optimum estimates which are necessarily within the observed lake pH range, whereas there is no such restriction in ML and LS. When the most extreme estimates of ML and LS were excluded, a reasonably close agreement among the results of different estimation methods was observed. When the species with unrealistic optima were excluded, the tolerance estimates were also rather similar, although the ML estimates were systematically greater.The parameter estimates were used to predict the autumn pH of 34 other lakes by weighted averaging. The ML and LS estimates including the extreme optima produced inferior predictions. A good prediction was obtained, however, when prediction with these estimates was additionally scaled with inverse squared tolerances, or when the extreme values were removed (censored). Tolerance downweighting was perhaps more efficient, and when it was used, no additional improvement was gained by censoring. The WA estimates produced good predictions without any manipulations, but these predictions tended to be biased towards the centroid of the observed range of pH values.At best, the average bias in prediction, as measured by mean difference between predicted and observed pH, was 0.082 pH units and the standard deviation of the differences, measuring the average random prediction error, was 0.256 pH units.     

Estimation of groundwater storage from seismic data using deep learning

Convolutional neural networks can provide a potential framework to characterize groundwater storage from seismic data. Estimation of key components, such as the amount of groundwater stored in an aquifer and delineate water table level, from active-source seismic data are performed in this study. The data to train, validate and test the neural networks are obtained by solving wave propagation in a coupled poroviscoelastic–elastic media. A discontinuous Galerkin method is applied to model wave propagation, whereas a deep convolutional neural network is used for the parameter estimation problem. In the numerical experiment, the primary unknowns estimated are the amount of stored groundwater and water table level, while the remaining parameters, assumed to be of less of interest, are marginalized in the convolutional neural network-based solution. Results, obtained through synthetic data, illustrate the potential of deep learning methods to extract additional aquifer information from seismic data, which otherwise would be impossible based on a set of reflection seismic sections or velocity tomograms.     

Variability in inorganic and organic nitrogen uptake associated with riverine nutrient input in the Gulf of Riga, Baltic Sea

Concentrations and rates of uptake of dissolved organic nitrogen (DON, free amino acids, and urea) and inorganic nitrogen (DIN, nitrate, and ammonium) were measured along two transects in the Gulf of Riga, a sub-basin of the Baltic Sea, during May and July 1996. Concentrations of total dissolved nitrogen (TDN) were 23±3 μg-at N 1⁻¹ in the northern region (mouth) and 41±5 μg-at N 1⁻¹ in the southern region (head) of the Gulf. Rates of nitrogen uptake, determined with¹⁵N-labeled substrates, reflected differences in TDN concentration between the regions. In May, uptake of DIN+DON measured 0.17 and 0.43 μg-at N 1⁻¹ h⁻¹ in the northern and southern parts of the Gulf, respectively. In July, DIN+DON uptake measured 0.38 and 0.68 μg-at N 1⁻¹ h⁻¹ in the north and south, respectively. Most of the variability in total nitrogen flux between the northern and southern regions was due to heterogeneity of DON utilization. Uptake of urea and dissolved free amino acid were up to 6 and 3 times greater in the south compared to the north. As evidenced by size-fractionation, plankton size structure appeared to play a role in the uptake of DON. The community in the southern part was largely composed of cells <5 μm, while up to 67% of the community in the northern part was composed of cells >5 μm. Our results indicate that DON was a major source of nitrogen to phytoplankton, particularly in the southern part of the Gulf.     

Spatial Modelling of Stream Water Quality Along an Urban–Rural Gradient

It is widely known that intensive land use generally decreases stream water quality, but the influence of watershed physiography is relatively poorly understood. Since management planning has to take into account the protection of water quality, the current status of stream water must be identified. The potential effects of land use and watershed physiography variables on water quality were studied in an extensive set of 83 watersheds in the Helsinki region, Finland, covering wide land‐use intensity gradient. The aims of this study were to test if the geographical information of watershed land‐use data can be used to model the stream water quality, and to examine whether the spatial water quality models are improved after including predictors of watershed physiography to the land‐use model. Water quality variables were related to watershed predictors by utilizing generalized additive models and linear mixed models, and the independent effect of the variables was investigated using a hierarchical partitioning approach. While land use turned out to be the most influential factor explaining water quality, all models improved significantly after incorporating the watershed characteristics, such as topography and soil. These results were consistent across three modelling techniques. This study, with its novel approach to examine the impacts of several watershed physiographic characteristics on urban stream water quality in northern Europe, demonstrates that spatial land use and watershed physiography data can be used as cost‐efficient predictors in stream water quality models.