A comparative phytosociological and habitat analysis of 84 pleustonic phytocoenoses in northern Poland was carried out in order to determine the scale of their floristic distinctiveness and to obtain an answer to the question whether and to what extent the floristic differentiation of pleustonic communities corresponds to the differentiation of their habitat conditions. Based on phytosociological studies, it was shown that the investigated phytocoenoses are not random groups of species, but well differentiated in terms of floristic composition and phytocoenotic structure by communities. It was shown that the studied phytocoenoses represent the following separate associations in phytosociological terms: Salvinio natantis-Spirodeletum polyrhizae, Lemno-Spirodeletum polyrhizae, Riccietum fluitantis and Ricciocarpetum natantis. Hydrochemical studies showed that the floristic distinctiveness of these communities is largely confirmed by the differentiation of properties of the water habitat. Salvinio natantis-Spirodeletum polyrhizae is characterised by the deepest waters (mainly 0.7–1.2 m), alkaline, of a low colour, rich in ions (Mg2+, Na+, Cl−) and therefore characterised by the highest electrolytic conductivity, poor in dissolved organic matter, NO3− and total iron. Lemno-Spirodeletum polyrhizae is characterised by medium deep waters (mainly 0.3–0.8 m), mostly alkaline, of a low colour, less rich in ions (Ca2+, Na+, Cl−) and relatively low in dissolved organic matter and NO3−. Riccietum fluitantis is characterised by shallow waters (mainly 0.2–0.4 m), most often slightly acidic, of a quite high colour, low in cations and Cl−, and relatively rich in total iron, NO3−, and dissolved organic matter. Ricciocarpetum natantis is characterised by the shallowest waters, (mainly < 0.3 m), most often acidic, of the highest colour, low in ions, and rich in total iron, dissolved silica, NO3− and dissolved organic matter. By demonstrating habitat distinctiveness, the studied pleustonic communities may have significance as indicators of various types of aquatic environment.

The adaptation of sponges to freshwater environments was a major event in the evolutionary history of this clade. The transition from a marine environment to freshwater ecosystems entailed a great number of adaptations to more unstable habitats, such as the ability to form resistance gemmules as a defense mechanism against environmental adversity. However, data on the parameters that modulate hatching and growth of these animals are scarce. In the present study, the growth response capacity of Ephydatia fluviatilis (Porifera: Spongillidae) has been evaluated in relation to both water alkalinity and light intensity. The results obtained revealed a positive association between the growth capacity of this freshwater sponge and high alkalinity values. On the other hand, exposure to light, regardless of its intensity, affected the development and distribution of the symbionts, which in turn, corresponds to a higher growth rate of the sponge. The obtained data suggest an explanation for the greater distribution of this species in alkaline environments. The results of this work also shed light on the importance of the symbiosis phenomenon in E. fluviatilis.

The online version contains supplementary material available at 10.1007/s10452-023-10008-y.

Acoustic cameras, or imaging sonars, are high-potential devices for many applications in aquatic ecology, notably for fisheries management and population monitoring. However, how to extract such data into high-value information without a time-consuming entire data set reading by an operator is still a challenge. Moreover, the analysis of acoustic imaging, due to its low signal-to-noise ratio, is a perfect training ground for experimenting with new approaches, especially concerning deep learning techniques. We present hereby a novel approach that takes advantage of both convolutional neural network (CNN) and classical computer vision (CV) techniques, able to detect fish passages in acoustic video streams. The pipeline pre-treats the acoustic images to localise the signals of interest and to improve the detection performances. The YOLOv3-based model was trained with fish data from multiple species recorded by the two most frequently used models of acoustic cameras, the DIDSON and ARIS, including species of high ecological interest, as Atlantic salmon or European eels. The pre-treatment of images greatly improves the model performance, increasing its F1-score from 0.52 to 0.69. The model we developed provides satisfying results detecting almost 80% of fish passages and minimising the false-positive rate. On a validation data set, 40 h of videos and around 1 800 fish passages, the efficiency increases with the fish sizes, notably reaching a recall higher than 95% for Atlantic salmon. Conversely, the model appears much less efficient for eel detections on ARIS videos than on DIDSON data (31% recall vs 75%).

Abstract Efficacious monitoring of fish stocks is critical for efficient management. Multibeam acoustic cameras, that use sound-reflectance to generate moving pictures, provide an important alternative to traditional video-based methods that are inoperable in turbid waters. However, acoustic cameras, like standard video monitoring methods, produce large volumes of imagery from which it is time consuming and costly to extract data manually. Deep learning, a form of machine learning, can be used to automate the processing and analysis of acoustic data. We used convolutional neural networks (CNNs) to detect and count fish in a publicly available dual-frequency identification sonar (DIDSON) dataset. We compared three types of detections, direct acoustic, acoustic shadows, and a combination of direct and shadows. The deep learning model was highly reliable at detecting fish to obtain abundance data using acoustic data. Model accuracy for counts-per-image was improved by the inclusion of shadows (F1 scores, a measure of the model accuracy: direct 0.79, shadow 0.88, combined 0.90). Model accuracy for MaxN per video was high for all three types of detections (F1 scores: direct 0.90, shadow 0.90, combined 0.91). Our results demonstrate that CNNs are a powerful tool for automating underwater acoustic data analysis. Given this promise, we suggest broadening the scope of testing to include a wider range of fish shapes, sizes, and abundances, with a view to automating species (or ‘morphospecies’) identification and counts.