Facing the threat of rapidly worsening water quality, there is an urgent need to develop novel approaches of monitoring its global supplies and early detection of environmental fluctuations. Global warming, urban growth and other factors have threatened not only the freshwater supply but also the well-being of many species inhabiting it. Traditionally, laboratory-based studies can be both time and money consuming and so, the development of a real-time, continuous monitoring method has proven necessary. The use of autonomous, self-actualizing entities became an efficient way of monitoring the environment. The Microbial Fuel Cells (MFC) will be investigated as an alternative energy source to allow for these entities to self-actualize. This concept has been improved with the use of various lifeforms in the role of biosensors in a structure called ”biohybrid” which we aim to develop further within the framework of project Robocoenosis relying on animal-robot interaction. We introduce a novel concept of a fully autonomous biohybrid agent with various lifeforms in the role of biosensors. Herein, we identify most promising organisms in the context of underwater robotics, among others Dreissena polymorpha, Anodonta cygnaea, Daphnia sp. and various algae. Special focus is placed on the ”ecosystem hacking” based on their interaction with the electronic parts. This project uses Austrian lakes of various trophic levels (Millstättersee, Hallstättersee and Neusiedlersee) as case studies and as a ”proof of concept”.

In the wake of climate change and water quality crisis, it is crucial to find novel ways to extensively monitor the environment and to detect ecological changes early. Biomonitoring has been found to be an effective way of observing the aggregate effect of environmental fluctuations. In this paper, we outline the development of biohybrids which will autonomously observe simple organisms (microorganisms, algae, mussels etc.) and draw conclusions about the state of the water body. These biohybrids will be used for continuous environmental monitoring and to detect sudden (anthropologically or ecologically catastrophic) events at an early stage. Our biohybrids are being developed within the framework of project Robocoenosis, where the operational area planned are Austrian lakes. Additionally, we discuss the possible use of various species found in these waters and strategies for biomonitoring. We present early prototypes of devices that are being developed for monitoring of organisms.

Biohybrids combine artificial robotic elements with living organisms. These novel technologies allow for obtaining useful data on the environment by implementing organisms as "living sensors". Natural water resources are under serious ecological threat and there is always a need for new, more efficient methods for aquatic monitoring. Project Robocoenosis introduces the use of biohybrid entities as low-cost and longterm environmental monitoring devices. This will be done by combining lifeforms with technical parts which will be powered with the use of MFCs. This concept will allow for a more well-rounded data collection and provide an insight into the water body with minimal human impact.

Many aquatic habitats have become vulnerable to rapid and long-term changes induced by industrialism, air pollution, tourism, fishing activities etc. These factors created an urgent need for extensive water monitoring and conservation. By observing the behaviour of lifeforms, we can monitor the state of the environment. Here, we present the methodology, calibration approaches and preliminary results of designing a biohybrid entity for aquatic monitoring. Biohybrid robots combine mechanical and electronic elements with living organisms or tissues. This biohybrid consists of several modules, each hosting or attracting different species and communities. We focus on animals such as Daphnia sp., zebra mussel Dreissena polymorpha and various representatives of the plankton community. The first results showed that 1) both Daphnia and D. polymorpha show no clear signs of confinement-induced stress, 2) the designed structures are examples of suitable tools for hosting the organisms, observing their behaviour and collecting and storing data and 3) their behaviour can be calibrated under laboratory conditions to be able to extrapolate the field data into environmental data.

Environmental monitoring should be minimally disruptive to the ecosystems that it is embedded in. Therefore, the project Robocoenosis suggests using biohybrids that blend into ecosystems and use life forms as sensors. However, such a biohybrid has limitations regarding memory—as well as power—capacities, and can only sample a limited number of organisms. We model the biohybrid and study the degree of accuracy that can be achieved by using a limited sample. Importantly, we consider potential misclassification errors (false positives and false negatives) that lower accuracy. We suggest the method of using two algorithms and pooling their estimations as a possible way of increasing the accuracy of the biohybrid. We show in simulation that a biohybrid could improve the accuracy of its diagnosis by doing so. The model suggests that for the estimation of the population rate of spinning Daphnia, two suboptimal algorithms for spinning detection outperform one qualitatively better algorithm. Further, the method of combining two estimations reduces the number of false negatives reported by the biohybrid, which we consider important in the context of detecting environmental catastrophes. Our method could improve environmental modeling in and outside of projects such as Robocoenosis and may find use in other fields.