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.

The present study examined the genetic diversity and spatial distribution of turbot (Scophthalmus maximus), an economically important species on the Bulgarian Black Sea coast. Maximum entropy (MaxEnt) modeling software Version 3.4.4. was utilized to develop a habitat suitability model for S. maximus in the Bulgarian Black Sea region. Data collected via demersal and pelagic surveys and genetic sampling from 2017 to 2021 were utilized to link species occurrence localities with selected abiotic factors. Our findings showed that the species’ habitat preferences are strongly influenced by temperature and dissolved oxygen, and projections based on simplified climatic scenarios indicated potential distribution shifts and a substantial reduction in reproduction habitats in the northern region. The assessment of genetic diversity was based on mtDNA COIII sequencing; MtDNA revealed a low level of polymorphism in all analyzed populations. The extensive fishing pressure may have increased the likelihood of genetic and population bottlenecks and a consequent decline in genetic diversity in the Shabla, Nesebar, and Tsarevo populations. The Tajima’s D values for the latter indicated that turbot underwent a bottleneck followed by rapid population expansion. Our findings are essential for the conservation and effective management of S. maximus stocks in the region.

Phytoplankton monitoring is essential for assessing marine ecosystem health, as phytoplankton are sensitive indicators of ecosystem disturbances. Imaging flow cytometry (IFC) has emerged as a powerful tool for high-throughput phytoplankton analysis, providing taxon-specific insights into community composition and dynamics. The integration of imaging technology with machine learning has enabled the automated classification of plankton images. Geographical variation in phytoplankton community composition necessitates the development of region-specific annotated datasets for accurate automated recognition. This paper presents an initial version of the regional Black Sea phytoplankton image dataset, comprising 8,077 taxonomically annotated images (JPEG format;Fig. 1), together with their associated morphological, optical scattering, fluorescence, and pulse-shape attributes (e.g., length, size, structure, shape, pigments). The dataset was built using Easyclus© software (https://www.easyclus.com/) from samples collected between June 2022 and June 2023 in Varna Bay, Black Sea, and analyzed using a CytoSense flow cytometer (CytoBuoy b.v., Woerden, The Netherlands). It encompasses 162 categories representing 160 taxa, including 49 identified to genus level and 111 to species level. Taxonomic names at the genus and species levels were verified against the World Register of Marine Species (WoRMS Editorial Board 2024). The number of images per category varies considerably, ranging from 1 to 1127, reflecting the naturally imbalanced representation of taxa in the collected samples and resulting image library (Fig. 2). The dataset was subsequently tested as a reference library for the automated classification of phytoplankton in 72 Black Sea samples using Easyclus© software, and the results were compared with those obtained using conventional light microscopy (LM). Automated classification of the test samples identified between 61 and 122 categories per sample. The proportion of particles successfully assigned to one of the predefined categories varied considerably among samples, from 7.3% to 92.6% of total particle counts, although the average classification rate was high (78.1%). The overall taxonomic coverage achieved by IFC was lower than that obtained by LM: 304 taxa were identified by LM, compared with 152 taxa by IFC. The taxonomic overlap between methods within individual samples ranged from 18% to 39%, with a mean of 29.3%. The observed discrepancy may be attributed to several factors, including the natural heterogeneity of field samples (Cermeño et al. 2013, Rodríguez-Ramos et al. 2013), identification difficulties associated with sample fixation (Mukherjee et al. 2014), sedimentation effects within the sample tubing (Peperzak et al. 2018), and gaps in the reference image dataset. In addition, the strong imbalance in the number of images across categories may affect classification performance by biasing results toward better-represented categories (Johnson and Khoshgoftaar 2019). Despite these limitations, CytoSense demonstrated greater sample-level taxon detection and substantially higher time efficiency, with an analysis time of approximately 20 min per sample compared with 8–10 h per sample for LM. Although further development of the Black Sea Phytoplankton Image Dataset is needed to increase both taxonomic coverage and the number of representative images per category, the results demonstrate the strong potential of this regional annotated image library for imaging flow cytometry-based phytoplankton monitoring in the Black Sea.