A method for computer-based determination of the motility characteristics of fish spermatozoa using ImageJ software and a number of original Excel macros that help optimizing the processing...

This is the first systematic review evaluating Black Sea plankton as biosensor organisms for Biological Early Warning Systems (BEWS)—real-time monitoring approaches that detect sublethal behavioral or physiological responses to pollutants before irreversible ecosystem damage occurs. The systematic literature review was guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach, ensuring methodological transparency and applicability. A total of 140 publications from databases (Web of Science Core Collection, Scopus, PubMed, and Google Scholar databases) were included in the final analysis. We assess nine native planktonic taxa as candidates for automated video-based water quality monitoring, using a multi-criteria framework encompassing biological sensitivity, technical detectability, and practical feasibility. Three species emerge as the most suitable candidates: Aurelia aurita as a universal indicator (sensitive to copper, surfactants, petroleum, and microplastics; its large size enables standard video detection); Acartia tonsa for trace contamination (reproductive toxicity at metal concentrations 4–33× below regulatory standards); and Mnemiopsis leidyi for metal-specific discrimination (bioluminescent responses: 650% Zn, 430% Cu, and 350% Hg at 0.001 mg/L). Analysis of 140 publications reveals critical gaps: 33% of species lack toxicological data, 95% of studies test single toxicants despite natural mixture exposure, and microplastic methodology varies 1000-fold in particle size. Threshold analysis suggests planktonic sublethal stress at “safe” concentrations under current standards, suggesting inadequate protection of marine food webs. A complementary monitoring approach integrating these species with computer vision algorithms offers autonomous early-warning capability for Black Sea environmental management.

The impact of different level of turbulence on developing eggs and prelarvae of the Black Sea turbot Scophthalmus maeoticus obtained from the pair of wild spawners at the end of natural spawning season has been studied. The incubation was carried out at three different levels of turbulence generated by diffused aeration: without aeration (calm water), low aeration (34 ml per minute) and high aeration (75 ml per minute). In calm water hatching rate (HR) of turbot eggs was 20 % and the hatched prelarvae had the longest standard length (SL = 3.13 mm) and the biggest volume of the yolk sac (VYS = 0.37 mm³) as compared to those of low aeration (HR = 18 %; SL = 3.10 mm; VYS = 0.32 mm³) and high aeration conditions (HR = 14 %; SL = 3.10 mm; VYS = 0.29 mm³). One day after hatching the percentage of survived prelarvae from the number of hatched was the highest – 86 % at high aeration, compared with that of low aeration (72 %) and calm water (61 %). Application of turbulence leads to elimination of “poor” quality eggs during incubation, presumably enables hatching of more viable larvae and can reduce mortality of more advanced larvae.

Pleurobrachia Fleming, 1822 is the native taxon of ctenophores in the Black Sea. Until now, its species affiliation has not been resolved: the Black Sea Pleurobrachia is referred to either P. pileus (O.F. Müller, 1776) or P. rhodopis (Chun, 1879). In the taxonomic reviews, both species are recognised as occurring in the Black Sea. The experts on zooplankton identified the Black Sea Pleurobrachia based solely on its definitive body size, but size cannot be a sufficient diagnostic feature. To clarify the species identity of the Black Sea Pleurobrachia, we carried out a phylogenetic analysis using the nucleotide sequences of the mitochondrial COI and the nuclear 18S rRNA gene fragments. As a result, we have established that the Black Sea Pleurobrachia is P. pileus and its identification as P. rhodopis in the global taxonomic databases is erroneous.

Marine pollution monitoring increasingly relies on Biological Early Warning Systems (BEWSs), which use living organisms as continuous, integrative sentinels of water quality. The moon jellyfish Aurelia aurita is a sensitive but under-exploited candidate for this role. We present a computer-vision BEWS pipeline that is unsupervised at inference time and operates without labelled pollution-response data, converting side-view aquarium video of single A. aurita medusae into a binary pollution alarm. Per-frame YOLO bounding-box detections are reduced to a continuous bell-area signal and a centroid trajectory, from which eleven pulsation, kinematic, and detection-quality features are extracted on 60 s sliding windows. A per-animal baseline is fitted on a clean-water baseline (recommended ≥15 min), and a two-layer detector—fast outlier detection on the mean absolute z-score with a k-of-N rule, plus one-sided CUSUM (cumulative sum) accumulation—flags any sustained deviation. Validation on six adult medusae exposed to diesel-WAF detected all six animals (95% CI 54–100%) and produced no false alarms in 203 clean-window opportunities (exact 95% upper bound 1.8%; rule-of-three estimate ≈1.5%). First-alarm latencies ranged from 1.0 to 23.7 min, and the observed responses were described as three descriptive patterns in this pilot dataset: sharp step-change, slow drift, and mixed. The deployed anomaly scoring step contains no neural-network weights, runs in under 300 lines of Python, and is designed for field-portable use in settings where a stationary side-view camera can be positioned alongside an aquarium, although field validation remains required. Per-animal anomaly detection accommodates the strong inter-individual variability of the diesel-WAF response that limits supervised clean-versus-polluted classification at this sample size.