Search

We studied the feeding of European perch Perca fluviatilis L. larvae in littoral and pelagic habitats of four different lakes â one Latvian (Auciema) and three Estonian (Akste, Kaiavere, and Prossa). Altogether, 162 perch larvae (81 from both habitats) were collected to estimate the diet composition of gathered larval specimens in spring (2019) using gut content analysis via epifluorescence microscopy. Attention was paid particularly to the question how does the larval perch food composition differ in pelagic and littoral habitats. We hypothesized that the consumption of zooplankton is higher and the larval condition is better in littoral habitats. We assessed the feeding on both protozoo- (ciliates) and metazooplankton and applied multiple indices (Hurlbertâs standardized niche breadth, Ivlevâs selectivity and relative importance index) to evaluate, respectively, the larval fish prey importance, feeding homogeneity and strategies. The results showed that larval length and weight were slightly higher and body condition was slightly better in the lakesâ littoral habitats. The feeding niche of perch larvae was narrower in the littoral, which can indicate more favourable feeding conditions in littoral than lake pelagic habitats. While the small cladocerans (Bosmina longirostrisMüller) were generally the preferred and important food objects, ciliates were avoided and consumed only when their share in the total zooplankton biomass was >40%. However, in shortage of cladocerans, ciliates could be vitally important food objects for perch larvae.

Phytoplankton primary production (PP) in lakes play an important role in the global carbon cycle. However, monitoring the PP in lakes with traditional complicated and costly in situ sampling methods are impossible due to the large number of lakes worldwide (estimated to be 117 million lakes). In this study, bio-optical modelling and remote sensing data (Sentinel-3 Ocean and Land Colour Instrument) was combined to investigate the spatial and temporal variation of PP in four Baltic lakes during 2018. The model used has three input parameters: concentration of chlorophyll-a, the diffuse attenuation coefficient, and incident downwelling irradiance. The largest of our studied lakes, Võrtsjärv (270 km2), had the highest total yearly estimated production (61 Gg C y−1) compared to the smaller lakes Lubans (18 Gg C y−1) and Razna (7 Gg C y−1). However, the most productive was the smallest studied, Lake Burtnieks (40.2 km2); although the total yearly production was 13 Gg C y−1, the daily average areal production was 910 mg C m−2 d−1 in 2018. Even if lake size plays a significant role in the total PP of the lake, the abundance of small and medium-sized lakes would sum up to a significant contribution of carbon fixation. Our method is applicable to larger regions to monitor the spatial and temporal variability of lake PP.

Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional in-situ methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel-2 and Ocean and Land Color Instrument (OLCI) on Sentinel-3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used in-situ data from 49 sampling points across four different lakes, collected during 2018. Level-2 optical water quality products, such as chlorophyll-a and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against in-situ data. Along with the water quality products, the optical water types were obtained, because in lakes one-method-to-all approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level-2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with R2 0.84−0.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing.