Predicting lake dissolved organic carbon at a global scale.

The pool of dissolved organic carbon (DOC), is one of the main regulators of the ecology and biogeochemistry of inland water ecosystems, and an important loss term in the carbon budgets of land ecosystems. We used a novel machine learning technique and global databases to test if and how different environmental factors contribute to the variability of in situ DOC concentrations in lakes. In order to estimate DOC in lakes globally we predicted DOC in each lake with a surface area larger than 0.1 km2. Catchment properties and meteorological and hydrological features explained most of the variability of the lake DOC concentration, whereas lake morphometry played only a marginal role. The predicted average of the global DOC concentration in lake water was 3.88 mg L-1. The global predicted pool of DOC in lake water was 729 Tg from which 421 Tg was the share of the Caspian Sea. The results provide global-scale evidence for ecological, climate and carbon cycle models of lake ecosystems and related future prognoses.

Predicting the cover and richness of intertidal macroalgae in remote areas: a case study in the Antarctic Peninsula.

Antarctica is an iconic region for scientific explorations as it is remote and a critical component of the global climate system. Recent climate change causes a dramatic retreat of ice in Antarctica with associated impacts to its coastal ecosystem. These anthropogenic impacts have a potential to increase habitat availability for Antarctic intertidal assemblages. Assessing the extent and ecological consequences of these changes requires us to develop accurate biotic baselines and quantitative predictive tools. In this study, we demonstrated that satellite-based remote sensing, when used jointly with in situ ground-truthing and machine learning algorithms, provides a powerful tool to predict the cover and richness of intertidal macroalgae. The salient finding was that the Sentinel-based remote sensing described a significant proportion of variability in the cover and richness of Antarctic macroalgae. The highest performing models were for macroalgal richness and the cover of green algae as opposed to the model of brown and red algal cover. When expanding the geographical range of the ground-truthing, even involving only a few sample points, it becomes possible to potentially map other Antarctic intertidal macroalgal habitats and monitor their dynamics. This is a significant milestone as logistical constraints are an integral part of the Antarctic expeditions. The method has also a potential in other remote coastal areas where extensive in situ mapping is not feasible.

Dissolved organic carbon and its potential predictors in eutrophic lakes.

Understanding of the true role of lakes in the global carbon cycle requires reliable estimates of dissolved organic carbon (DOC) and there is a strong need to develop remote sensing methods for mapping lake carbon content at larger regional and global scales. Part of DOC is optically inactive. Therefore, lake DOC content cannot be mapped directly. The objectives of the current study were to estimate the relationships of DOC and other water and environmental variables in order to find the best proxy for remote sensing mapping of lake DOC. The Boosted Regression Trees approach was used to clarify in which relative proportions different water and environmental variables determine DOC. In a studied large and shallow eutrophic lake the concentrations of DOC and coloured dissolved organic matter (CDOM) were rather high while the seasonal and interannual variability of DOC concentrations was small. The relationships between DOC and other water and environmental variables varied seasonally and interannually and it was challenging to find proxies for describing seasonal cycle of DOC. Chlorophyll a (Chl a), total suspended matter and Secchi depth were correlated with DOC and therefore are possible proxies for remote sensing of seasonal changes of DOC in ice free period, while for long term interannual changes transparency-related variables are relevant as DOC proxies. CDOM did not appear to be a good predictor of the seasonality of DOC concentration in Lake Võrtsjärv since the CDOM-DOC coupling varied seasonally. However, combining the data from Võrtsjärv with the published data from six other eutrophic lakes in the world showed that CDOM was the most powerful predictor of DOC and can be used in remote sensing of DOC concentrations in eutrophic lakes.

Role of a productive lake in carbon sequestration within a calcareous catchment.

For a long time, lakes were considered unimportant in the global carbon (C) cycle because of their small total area compared to the ocean. Over the last two decades, a number of studies have highlighted the important role of lakes in both sequestering atmospheric C and modifying the C flux from the catchment by degassing CO2 and methane and burying calcite and organic matter in the sediment. Based on a full C mass balance, high frequency measurements of lake metabolism and stable isotope analysis of a large shallow eutrophic lake in Estonia, we assess the role alkaline lakes play in augmenting the strength of terrestrial carbonate weathering as a temporary CO2 sink. We show that a large part of organic C buried in the sediments in this type of lakes originates from the catchment although a direct uptake from the atmosphere during periods of intensive phytoplankton growth in eutrophic conditions contributes to the carbon sink.

Assessing impacts of invasive phytoplankton: the Baltic Sea case.

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.