Accurate monitoring of sea lamprey populations is critical to enabling the deployment of moretargeted and effective control measures to minimize the impact associated with this species. This dissertation demonstrates the development of an automated sea lamprey detection system and the effects of algal-based biofouling on its voltage response. The system is built around a sensor composed of two exposed carbon-based planar in- terdigitated electrodes (IDE) functioning as an underwater biological contact sensor. A microcontroller-based DC measurement system for the detection of lamprey attachment underwater is presented; measuring voltage instead of impedance reduces cost and signal processing complexity, making the device more attractive for field deployment. The system is calibrated to a baseline output voltage, and deviations from this baseline occur when ob- jects touch the IDE. Validation was done through testing on live adult sea lampreys using video recordings to correlate lamprey attachments to the sensor response. Three response types were identified corresponding to different attachments: sustained, short, and sliding- sustained (video demonstrations are included in this work). The response to sustained and sliding-sustained attachments showed a characteristic exponential decay, whereas the response due to short attachments was indistinguishable from measurement noise. Sea lam- prey size was found to have a weak linear correlation with both response parameters, positive for the voltage drop and negative for the time constant of the voltage drop. A representative circuit for the lamprey-sensor interaction is proposed and simulated using element values calculated from the response parameters. The response of the model shows agreement with experimental data. Characterization of the IDE sensor’s response to sea lamprey attachment allowed for the development of detection algorithms, to automate the process of detecting sea lamprey from the sensor output. Inherent limitations on the computing power of the microcontroller unit used to measure the sensor motivated the exploration of low-complexity models for the task: single-layer artificial neural networks, logistic regression, Gaussian Na ̈ıve-Bayes, decision trees, random forest, and Scalable, Efficient, and Fast classifieR (SEFR). Threshold models tuned using a multi-objective optimization formulation were also considered. Mod- els were trained/tuned with a data set generated through live animal testing and presented accuracies between 80-86%. The models were deployed on an Arduino microcontroller plat- form and compared in classification accuracy, detection performance, time complexity, and memory size using real-time detection testing. Classification accuracies between 65-75% were observed during validation. Models demonstrated capture rates of 63-85% for sea lamprey attachments and average detection delays of 9-36 seconds. A video demonstration of a real- time validation test is also presented. Sensor robustness, sensitivity, and detection speed were identified as areas of improvement for the system, so electrodes were updated to more durable, conductive carbon-based 3D-printed electrodes, and an updated measurement sys- tem combining a resistive bridge topology and an instrumentation amplifier was developed to allow for scaling the sensor response. The effects of biofilm accumulation on the baseline sensor response were also studied to simulate field conditions. Sensors were exposed to optimal biofilm growth conditions in a photobioreactor running a culture of Chlorella sorokiniana MSU, a robust algae species native to the Great Lakes region. Two responses to biofilm formation on the sensors were observed: (1) a consistent rise in voltage following the increasing form of the exponential decay function, and (2) a similar rise in baseline but followed by a period of approximately exponential decay. These responses were generally related to changes in sensor resistance, with (1) corresponding to a decrease and (2) to an increase. A study relating these changes in sensor resistance to biomass accumulation was performed, but did not produce a conclusive relationship. The sensors’ ability to pick up biological contact after fouling was observed to be inconsistently affected. The implications of these results for sea lamprey detection and potential ways to address them are discussed. Overall, this research presents the first step toward an electronic sea lamprey monitoring system that can provide a detailed view of sea lamprey activity, enhancing control and conservation efforts across its entire range.
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