Improving the Accuracy of a Biohybrid for Environmental Monitoring

Abstract

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.