Golebiowska, J. | bioRxiv Science

bioRxiv2026

Exploring Warming Effects on lower food-web dynamics in the plankton of the River Elbe Estuarine Ecosystem in summer: Insights from a Mesocosm Experiment

Understanding how warming alters estuarine plankton communities is essential for predicting future changes in biodiversity and ecosystem functioning. We conducted a four-week indoor mesocosm experiment using natural summer plankton from the Elbe River to examine the effects of warming (+2 {degrees}C and +4 {degrees}C) on abiotic conditions and responses of the plankton community. In this study, oxygen concentrations, primary producer biomass (chlorophyll a, microphytoplankton) and microzooplankton abundances declined sharply during the first 10 days across all treatments while mesozooplankton abundances increased. This suggests a strong top-down control by mesozooplankton on lower trophic levels across all temperature treatments. Primary producers biomass and oxygen concentrations recovered after an initial decline, however to lower levels compared to the onset of the experiment while micro- and mesozooplankton remained low during the second half of the experiment. Nutrient dynamics indicated progressive remineralization, with increasing ammonium, NOx, and silicate concentrations, while phosphate concentrations remained low throughout the experiment. Complementary DNA and RNA metabarcoding revealed similar community turnover over time in all treatments and temperature effects became only pronounced at the end of the experiment. Overall, warming effects were subtle relative to the strong internal trophic dynamics likely caused by the artificial mesocosm setup. Our findings of changes in plankton community dynamics indicate that biotic interactions, changes in trophic diversity and other environmental factors, i.e. oxygen concentrations are likely the drivers of this estuarine system rather than warming alone.

ecology↗

bioRxiv2024

Deep Learning Algorithms Reveal Increased Social Activity of Rats at the Start of Evening Twilight

1.The rapid decrease of light intensity is a potent and natural stimulus of rats activity. The nature of this activity, in that, the character of the social behavior and the composition of concomitant ultrasonic vocalizations (USVs) is unknown. This study, using deep learning algorithms, sought to assess the social life of rats pairs kept in the semi-natural conditions at the two twilight periods. Over six days, animals were video and audio recorded during the morning and the evening sessions lasting for 20-minutes each. The videos were used to train and use the DeepLabCut neural network examining animals movement in space and time. Numerical data generated by DeepLabCut were subjected to the Simple Behavioral Analysis (SimBA) toolkit, to build models of 11 distinct social and non-social behaviors. DeepSqueak toolkit was used to examine USVs. Deep learning algorithms revealed lights-off induced increases of fighting, mounting, crawling, and rearing behaviors, as well as of 22-kHz alarm calls and 50-kHz flat and short, but not frequency modulated calls. In contrast, lights-on stimulus increased the general activity, as well as adjacent lying (huddling), anogenital sniffing and rearing behaviors. The animals adapted to the housing conditions showing decreased ultrasonic calls, as well as of grooming and rearing behaviors, but not fighting. Present study shows lights-off induced increase of aggressive behavior but fail to demonstrate an increase in the positive affect defined by the hedonic USVs. We further confirm and extend the utility of deep learning algorithms in analyzing rat social behavior and ultrasonic vocalizations. Highlights- Rats display a natural increase in activity induced by lights-off stimulus - Deep learning algorithms allow for rapid characterization of social behavior and ultrasonic calls - The darkness increased aggressive behavior, 22-kHz alarm calls and 50-kHz flat and short, but not frequency modulated calls

animal behavior and cognition↗