Kuwait’s waters were first faced with a bloom of ichthyotoxic dinoflagellate Karenia and fish kill in 1999. Since then, there have been sporadic blooms and records of various kareniacean taxa, but accurate species identification has been challenging due to the high morphological similarity among morphotypes and the lack of molecular studies. Ten clonal kareniacean strains isolated from Kuwait’s coastal waters were characterized based on light and fluorescence microscopy and the LSU rDNA gene sequencing. Seven strains represented the distinctive morphology of Karenia papilionacea and showed LSU rDNA sequences with > 99 % similarity to the type material of this species. One Karenia strain represented the typical morphology of Karenia selliformis and the LSU rDNA sequence fell within a well-supported K. selliformis clade. Two other strains showed the typical morphology of Karlodinium ballantinum and phylogenetic analysis supported the morphological results. Consequently, a combination of morphological and molecular analyses confirmed the presence of K. papilionacea and K. selliformis in Kuwait’s waters, thereby resolving the previous regional taxonomic uncertainty regarding these species. The identification of K. ballantinum represents the first regional record. Investigating the morphology and phylogeny of commonly occurring Kareniaceae enhances the monitoring and risk assessment of harmful algal blooms in Kuwait’s marine environment.

Identification and extraction of material in the urban scenario are always challenging due to the presence of different heterogeneous materials. Airborne hyperspectral sensors with the advantage of both high spectral and spatial resolution can be used in such areas for the identification and mapping of urban materials. Manual extraction of the endmembers in highly dense heterogeneous urban regions is a very difficult task to achieve, but with automated extraction techniques, it yields promising results. To counter the mixed pixels problem in heterogeneous urban areas automated endmember extraction techniques like Endmember Average RMS (EAR), Minimum Average Spectral Angle (MASA), and Count Based Endmember Selection (CoB) was used for pure endmember identification in the densely populated urban area of Kolkata region using AVIRIS NG imagery. Spectral Unmixing Multiple Endmember Spectral Mixture Analysis (MESMA), Linear Spectral Unmixing (LSU), and Mixture Tuned Matched Filtering (MTMF) was performed on AVIRIS NG data using the extracted endmembers of different urban materials. The rule-based classification was performed on abundance images obtained from MESMA, MTMF, and LSU techniques for comparative analysis. Classification using MESMA abundance images outperformed in mapping mixed urban environments with an overall accuracy (OA) of 88.6 % followed by the rule-based classified output of MTMF and LSU with OA of 75.27% and 72.45% respectively.

An attempt was made to infer the phylogeny of Pseudo-nitzschia species by using the mitochondrial-encoded gene, cytochrome c oxidase subunit I (cox1), and comparing it with the nuclear-encoded large subunit ribosomal DNA (LSU rDNA). A pair of primers targeting Pseudo-nitzschia cox1 was designed in silico and used to infer the molecular phylogeny of Pseudo-nitzschia. The primer pair was tested using genomic DNAs isolated from six species of Pseudo-nitzschia from Malaysia. The phylogenetic inference of cox1 was then compared to the LSU rDNA phylogeny. Phylogenetic reconstructions of both data sets revealed monophyly of Pseudo-nitzschia species complexes. The range of genetic divergences among Pseudo-nitzschia species were higher in the cox1 data set (3.5–20.4 %) compared to the LSU rDNA data set (0.1–8.8 %). The present study suggests that high genetic divergence in cox1 of Pseudo-nitzschia species could be a useful genetic marker for DNA bar coding.

In order to quantify and monitor the spatio-temporal growth of urban areas, remote sensing data and GIS techniques are getting attention for its unparallel utility by the city planners as well as decision makers. The present study aims to analyse the rapid increase of built-up area in Dehradun city, the capital of Uttarakhand, India. The study uses multi-temporal Landsat images to monitor the pattern of urban growth of Dehradun. To classify coarse satellite data the traditional supervised and unsupervised techniques are not very useful because of mixed pixels in the classified data and poor accuracy. In this situation linear spectral unmixing (LSU) is a very useful classification technique because it classifies land use land cover classes by estimating the fractional values of each class. The study shows that built-up area of the city has grown about 20 per cent during 2000–2010 which is also identical with the population growth of Dehradun when compared with the Census of India data. The accuracy of Landsat based LSU model was assessed using high resolution LISS IV data and the RMSE was 0.07 and 0.11 respectively for vegetation and built-up fraction classes. It is seen that built-up area in the Kaulagarh, Kargi, Deep Nagar, Indirapuram and Kawli wards of Dehradun city have experienced more than 30% increase mainly in the South and South-West part of the city. The multi-temporal, multi-scale maps of the Dehradun city generated from remote satellite data will be very useful for planning both at micro- and macro-levels and further to identify the potential areas for urban expansion and future planning keeping in view protecting the urban green landscape of the Himalayan city of Dehradun.

Studies in soil meiofauna with traditional methods are highly laborious and identification exclusively by means of morphological traits usually underestimates species richness. Although the mitochondrial DNA Cytochrome c Oxidase subunit I gene (COI, CO1, cox1, coxI) has been proposed as a standard DNA barcode for animals, nuclear ribosomal DNA markers such as Large Subunit (LSU, 28S) and Internal Transcribed Spacer (ITS) have proven efficient to distinguish among species of animals, eukaryotic microbes and plants. The COI and LSU markers discriminate among closely related species of Collembola, but the efficiency of ITS2 has never been tested. We evaluated the relative performance of these three markers for DNA barcoding of Collembola. All three markers proved highly efficient in discriminating species, but LSU exhibited very low differences among certain closely related species. We conclude that ITS2 may serve as an alternative barcode to identify Collembola and has a potential to be used for metabarcoding analyses.

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