The response of zooplankton network indicators to winter water warming using shallow artificial reservoirs as model case study.

To predict the most likely scenarios, the consequences of the rise in water surface temperature have been studied using various methods. We tested the hypothesis that winter water warming significantly alters the importance and nature of the relationships in zooplankton communities in shallow reservoirs. These relationships were investigated using network graph analysis for three thermal variants: warm winters (WW), moderate winters (MW) and cold winters (CW). The CW network was the most cohesive and was controlled by eutrophic Rotifera and Copepoda, with a corresponding number of positive and negative interspecific relationships. An increase in water temperature in winter led to a decrease in the centrality of MW and WW networks, and an increase in the importance of species that communicated with the highest number of species in the subnetworks. The WW network was the least cohesive, controlled by psammophilous and phytophilous rotifers, and littoral cladocerans. Adult copepods were not identified in the network and the importance of antagonistic relationships decreased, indicating that the WW network structure was weak and unstable. This study can serve as a model for generalisations of zooplankton community response to the disappearance of long winter periods of low temperatures, as predicted in global climate change projections.

Predicting the effects of winter water warming in artificial lakes on zooplankton and its environment using combined machine learning models.

This work deals with the consequences of climate warming on aquatic ecosystems. The study determined the effects of increased water temperatures in artificial lakes during winter on predicting changes in the biomass of zooplankton taxa and their environment. We applied an innovative approach to investigate the effects of winter warming on zooplankton and physico-chemical factors. We used a modelling scheme combining hierarchical clustering, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) algorithms. Under the influence of increased water temperatures in winter, weight- and frequency-dominant Crustacea taxa such as Daphnia cucullata, Cyclops vicinus, Cryptocyclops bicolor, copepodites and nauplii, and the Rotifera: Polyarthra longiremis, Trichocerca pusilla, Keratella quadrata, Asplanchna priodonta and Synchaeta spp. tend to decrease their biomass. Under the same conditions, Rotifera: Lecane spp., Monommata maculata, Testudinella patina, Notholca squamula, Colurella colurus, Trichocerca intermedia and the protozoan species Centropyxis acuelata and Arcella discoides with lower size and abundance responded with an increase in biomass. Decreases in chlorophyll a, suspended solids and total nitrogen were predicted due to winter warming. Machine learning ensemble models used in innovative ways can contribute to the research utility of studies on the response of ecological units to environmental change.

Zooplankton network conditioned by turbidity gradient in small anthropogenic reservoirs.

Water turbidity can significantly influence interspecific interactions in aquatic ecosystems. We tested the hypothesis that the turbidity gradient significantly differentiates the dynamics, significance and type of relationships in the structure of zooplankton communities colonizing mine pit reservoirs. The interactions between zooplankton species were evaluated by network graph analysis for three water turbidity classes: high turbidity (HT), moderate turbidity (MT) and low turbidity (LT). The HT network was most cohesive, and it was controlled by taxa grazing on various food sources within one ecological niche (Polyarthra longiremis, Brachionus angularis, Cyclops vicinus, Codonella cratera) and the positive and negative relationships between them were balanced. The MT biocenotic network was composed of three sub-networks connected by nodes with high communication attributes (Polyarthra vulgaris, Bosmina longirostris, C. vicinus), and antagonistic interactions (predation and competition) were less important. The LT network was most heterogeneous, and Daphnia cuculllata exerted the strongest influence on the network's structure by forming numerous positive (coexistence with predators) and negative (interference competition with microphagous rotifers) interspecific relationships. The study provides new information about the ecology of aquatic ecosystems, that are disturbed by changes in water turbidity.

Effects of suspended micro- and nanoscale particles on zooplankton functional diversity of drainage system reservoirs at an open-pit mine.

Water from mining drainage is turbid because of suspensions. We tested the hypothesis that the chemical composition as well as shape and size of particles in suspensions of natural origin affect the density and functional diversity of zooplankton. The suspensions were analyzed with atomic force microscopy (AFM), energy dispersive X-ray spectroscopy (EDS), scanning electron microscopy (SEM), and optical microscopy. Elements found in the beidellite clays were also identified in the mineral structure of the particles. As the size of the microparticles decreased, the weight proportions of phosphorus, sulfur, and chlorine increased in the suspensions. These conditions facilitated the biomass growth of large and small microphages and raptorials. As the size of the nanoparticles decreased, the shares of silicon, aluminum, iron, and magnesium increased. These conditions inhibited raptorials the most. Ecosystem functionality was the highest with intermediate suspension parameters, which were at the lower range of the microphase and the upper range of the nanophase. The functional traits of zooplankton demonstrate their potential for use as sensitive indicators of disruptions in aquatic ecosystems that are linked with the presence of suspensions, and they facilitate gaining an understanding of the causes and scales of the impact of suspensions.