Comparative analysis of morphological and molecular approaches integrated into the study of the dinoflagellate biodiversity within the recently deposited Black Sea sediments - benefits and drawbacks

DNA and RNA Stability of Marine Microalgae in Cold-Stored Sediments and Its Implications in Metabarcoding Analyses

The ever-increasing applications of metabarcoding analyses for environmental samples demand a well-designed assessment of the stability of DNA and RNA contained in cells that are deposited or buried in marine sediments. We thus conducted a qPCR quantification of the DNA and RNA in the vegetative cells of three microalgae entrapped in facsimile marine sediments and found that >90% of DNA and up to 99% of RNA for all microalgal species were degraded within 60 days at 4 °C. A further examination of the potential interference of the relic DNA of the vegetative cells with resting cyst detection in sediments was performed via a metabarcoding analysis in artificial marine sediments spiked with the vegetative cells of two Kareniaceae dinoflagellates and the resting cysts of another three dinoflagellates. The results demonstrated a dramatic decrease in the relative abundances of the two Kareniaceae dinoflagellates in 120 days, while those of the three resting cysts increased dramatically. Together, our results suggest that a positive detection of microalgae via metabarcoding analysis in DNA or RNA extracted from marine sediments strongly indicates the presence of intact or viable cysts or spores due to the rapid decay of relic DNA/RNA. This study provides a solid basis for the data interpretation of metabarcoding surveys, particularly in resting cyst detection.

A combined approach detected novel species diversity and distribution of dinoflagellate cysts in the Yellow Sea, China

Resting cysts of dinoflagellates seed harmful algal blooms (HABs) and their geographic expansion, which makes it fundamentally important to obtain comprehensive inventories of dinoflagellate resting cysts in HABs-prone regions. The Yellow Sea (YS) of China has observed numerous outbreaks of dinoflagellate HABs with some novel species recorded recently indicating an underestimated HABs-causing species diversity. We report our investigation of dinoflagellate cysts of YS via an approach combining metabarcoding sequencing and single-cyst morpho-molecular identification, which identified many novel cyst species and a significant controlling effect of the Yellow Sea Cold Water Mass on cyst composition. The metabarcoding and single cyst-based sequencing detected 11 cyst species never being unambiguously reported in China, 10 never reported as cyst producers, and 3 HABs-causing species never reported from YS. Our detections of many potentially toxic or HABs-causative, particularly novel, cysts and distribution pattern provide important insights into the risks and ecology of dinoflagellate HABs.

New Knowledge on Distribution and Abundance of Toxic Microalgal Species and Related Toxins in the Northwestern Black Sea

Numerous potentially toxic plankton species commonly occur in the Black Sea, and phycotoxins have been reported. However, the taxonomy, phycotoxin profiles, and distribution of harmful microalgae in the basin are still understudied. An integrated microscopic (light microscopy) and molecular (18S rRNA gene metabarcoding and qPCR) approach complemented with toxin analysis was applied at 41 stations in the northwestern part of the Black Sea for better taxonomic coverage and toxin profiling in natural populations. The combined dataset included 20 potentially toxic species, some of which (Dinophysis acuminata, Dinophysis acuta, Gonyaulax spinifera, and Karlodinium veneficum) were detected in over 95% of the stations. In parallel, pectenotoxins (PTX-2 as a major toxin) were registered in all samples, and yessotoxins were present at most of the sampling points. PTX-1 and PTX-13, as well as some YTX variants, were recorded for the first time in the basin. A positive correlation was found between the cell abundance of Dinophysis acuta and pectenotoxins, and between Lingulodinium polyedra and Protoceratium reticulatum and yessotoxins. Toxic microalgae and toxin variant abundance and spatial distribution was associated with environmental parameters. Despite the low levels of the identified phycotoxins and their low oral toxicity, chronic toxic exposure could represent an ecosystem and human health hazard.

Meta-analysis shows both congruence and complementarity of DNA and eDNA metabarcoding to traditional methods for biological community assessment

DNA metabarcoding is increasingly used for the assessment of aquatic communities, and numerous studies have investigated the consistency of this technique with traditional morpho‐taxonomic approaches. These individual studies have used DNA metabarcoding to assess diversity and community structure of aquatic organisms both in marine and freshwater systems globally over the last decade. However, a systematic analysis of the comparability and effectiveness of DNA‐based community assessment across all of these studies has hitherto been lacking. Here, we performed the first meta‐analysis of available studies comparing traditional methods and DNA metabarcoding to measure and assess biological diversity of key aquatic groups, including plankton, microphytobentos, macroinvertebrates, and fish. Across 215 data sets, we found that DNA metabarcoding provides richness estimates that are globally consistent to those obtained using traditional methods, both at local and regional scale. DNA metabarcoding also generates species inventories that are highly congruent with traditional methods for fish. Contrastingly, species inventories of plankton, microphytobenthos and macroinvertebrates obtained by DNA metabarcoding showed pronounced differences to traditional methods, missing some taxa but at the same time detecting otherwise overseen diversity. The method is generally sufficiently advanced to study the composition of fish communities and replace more invasive traditional methods. For smaller organisms, like macroinvertebrates, plankton and microphytobenthos, DNA metabarcoding may continue to give complementary rather than identical estimates compared to traditional approaches. Systematic and comparable data collection will increase the understanding of different aspects of this complementarity, and increase the effectiveness of the method and adequate interpretation of the results.

Morphology-based identification of Bemisia tabaci cryptic species puparia via embedded group-contrast convolution neural network analysis

The Bemisia tabaci species complex is a group of tropical–subtropical hemipterans, some species of which have achieved global distribution over the past 150 years. Several species are regarded currently as among the world’s most pernicious agricultural pests, causing a variety of damage types via direct feeding and plant-disease transmission. Long considered a single variable species, genetic, molecular and reproductive compatibility analyses have revealed that this “species” is actually a complex of between 24 and 48 morphologically cryptic species. However, determinations of which populations represent distinct species have been hampered by a failure to integrate genetic/molecular and morphological species–diagnoses. This, in turn, has limited the success of outbreak-control and eradication programs. Previous morphological investigations, based on traditional and geometric morphometric procedures, have had limited success in identifying genetic/molecular species from patterns of morphological variation in puparia. As an alternative, our investigation focused on exploring the use of a deep-learning convolution neural network (CNN) trained on puparial images and based on an embedded, group-contrast training protocol as a means of searching for consistent differences in puparial morphology. Fifteen molecular species were selected for analysis, all of which had been identified via DNA barcoding and confirmed using more extensive molecular characterizations and crossing experiments. Results demonstrate that all 15 species can be discriminated successfully based on differences in puparium morphology alone. This level of discrimination was achieved for laboratory populations reared on both hairy-leaved and glabrous-leaved host plants. Moreover, cross-tabulation tests confirmed the generality and stability of the CNN discriminant system trained on both ecophenotypic variants. The ability to identify B. tabaci species quickly and accurately from puparial images has the potential to address many long-standing problems in B. tabaci taxonomy and systematics as well as playing a vital role in ongoing pest-management efforts. [Aleyrodidae; entomology; Hemiptera; machine learning; morphometrics; pest control; systematics; taxonomy; whiteflies.]