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The establishment of invading organisms in natural ecosystems is one of the most serious environmental issues. In Brazil, the invasive species Limnoperna fortunei (Dunker, 1857), the golden mussel, is a mollusk capable of causing major changes in water systems, generating social and economic impacts, given its biofouling capacity. Limnoperna fortunei can easily block pipes and heat exchangers in the water systems of hydroelectric power plants due to its ability to strongly adhere to the substrate using its byssus -a bundle of filaments secreted by these animals. Therefore, the early detection of this invader is essential for management actions to be immediate, in order to control population growth rate at the beginning of the invasive process, preventing this environment from serving as a source for new infestations. The implantation of a method that integrates the active monitoring of prioritized areas, laboratory techniques, including molecular biology methods, and the sharing of hydrographic data between basin managers and users for early detection of the presence of species in Brazilian waters appears as an efficient option to prevent and control invasions.

Background Limnoperna fortunei is a freshwater bivalve mollusc originally from southern Asia that invaded South America in the 1990’s. Due to its highly efficient water pumping and filtering, and its capacity to form strong adhesions to a variety of substrates by byssus thread, this invasive species has been able to adapt to several environments across South America, causing significant ecological and economic damages. By gaining a deeper understanding of the biological and ecological aspects of L. fortunei we will be able to establish more effective strategies to manage its invasion. The gills of the mollusc are key structures responsible for several biological functions, including respiration and feeding. In this work, we characterized the ultrastructure of L. fortunei gills and its ciliary epithelium using light microscopy, transmission and scanning electron microscopies. This is the first report of the morphology of the epithelial cells and cilia of the gill of L. fortunei visualized in high resolution. Results The analysis showed highly organized and abundant ciliary structures (lateral cilia, laterofrontal cirri and frontal cilia) on the entire length of the branchial epithelium. Mitochondria, smooth endoplasmic reticulum and glycogen granules were abundantly found in the epithelial cells of the gills, demonstrating the energy-demanding function of these structures. Neutral mucopolysaccharides (low viscosity mucus) were observed on the frontal surface of the gill filaments and acid mucopolysaccharides (high viscosity mucus) were observed to be spread out, mainly on the lateral tract. Spherical vesicles, possibly containing mucus, could also be observed in these cells. These findings demonstrate the importance of the mucociliary processes in particle capture and selection. Conclusions Our data suggest that the mechanism used by this mollusc for particle capture and selection could contribute to a better understanding of key aspects of invasion and also in the establishment of more efficient and economically viable strategies of population control.

This work details the design and simulation results of a bioinspired minimalist algorithm based on C. elegans, using autonomous agents to forage for attractant energy sources. The robotic agents are energy-constrained and depend on the energy they forage to recharge their batteries, which is significant as the foraging task is one of the canonical testbeds for cooperative robotics.The algorithm consists of 6 input parameters which were simulated and optimised in 9 unbounded environments of varying difficulty levels, containing attractant sources which robots would then have to forage from to maintain energy levels and survive the entirety of the simulation.The robots running the algorithm were then optimised using Evolutionary Algorithms and the best solutions in all 9 environments were categorized with the use of clustering techniques. The clustering results highlighted the different strategies which emerged. Ultimately across the 9 environments, 6 different strategies have been identified. The results demonstrate the applicability of the proposed algorithm to localise attractant sources and harvest energy in different scenarios using the same core algorithm.