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Diatoms represent an important class of aquatic phototrophs. They are not only one of the major contributors to global carbon fixation, but they also play a key role in the biogeochemical cycling of silica. Molecular identification methods based on conserved DNA sequences, such as internal transcribed spacer (ITS) have revolutionized our knowledge and understanding of conventional taxonomy. In this study, we aimed to compare the conventional identification methods with molecular identification methods. To do so, we isolated 4 diatom samples from the coast at Urla and characterized them using light microscopy (LM) and scanning electron microscopy (SEM) according to morphological features. Then we amplified ITS regions using a conventional polymerase chain reaction (PCR), sequenced the PCR products, and analyzed the sequences using bioinformatic tools. Bioinformatic analysis indicated that the isolated species had high sequence similarity to Pseudo-nitzschia delicatissima, Achnanthes taeniata, Amphora coffeaeformis, and Cylindrotheca closterium. We think that molecular identification methods enable rapid and more reliable identification of diatom species and are crucial for monitoring harmful algal blooms.

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ABSTRACT:Benthic habitat is defined as ecological environment where marine animals, plants and other organisms live in. Benthic habitat mapping is defined as plotting the distribution and extent of habitats to create a map with complete coverage of the seabed showing distinct boundaries separating adjacent habitats or the use of spatially continuous environmental data sets to represent and predict biological patterns on the seafloor. Seagrass is an essential endemic marine species that prevents coast erosion and regulates carbon dioxide absorption in both undersea and atmosphere. Fishing, mining, pollution and other human activities cause serious damage to seabed ecosystems and reduce benthic biodiversity. According to the latest studies, only 5-10% of the seafloor is mapped, therefore it is not possible to manage resources effectively, protect ecologically important areas. In this study, it is aimed to map seagrass cover using Landsat 8 OLI images in the northern part of Mediterranean coast of Turkey. After pre-processing (e.g. radiometric, atmospheric, water depth correction) of Landsat images, coverage maps are produced with supervised classification using in-situ data which are underwater photos and videos. Result maps and accuracy assessment are presented and discussed.

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