Geology topics

Ian González-Afanador

Publications and source records attributed to Ian González-Afanador.

2 recordsLinked to original sources

USGS2025

Electric field-induced detachment and vibration analysis for Sea Lamprey

Objective: Sea lamprey Petromyzon marinus , a known ecological threat to native fish populations in the Great Lakes, use their oral suction disc to navigate challenging environments when moving upstream to spawn. This study investigated the physiological response of adult sea lamprey to detach under pulsed direct current electric fields. Methods: Adult sea lampreys in an attaching position were exposed to pulsed direct current electric fields at four different field strengths (0.7, 1.1, 1.3, and 1.9 V/cm). Behavioral responses, including detachment and vibration, were observed and analyzed. Fast Fourier Transform analysis was applied to vibration data to quantify the frequency of vibration exhibited by non-detached sea lamprey. Statistical correlations between detachment rates and vibration responses to sea lamprey sex, weight, and length were evaluated to determine influencing factors. Results: The highest detachment rate (91%) was observed at 1.1 V/cm, with the detachment rate above 70% for other field strengths. Fast Fourier Transform analysis revealed that the vibration frequencies closely matched the signal while stronger fields elicited a more pronounced vibration amplitude without significantly increasing detachment. Non-detached sea lampreys exhibited “twitching” behavior synchronized with the electric field frequency. Sex, weight, and length had minimal influence on detachment rates and vibration responses. Conclusion: This study provided insights into the behavioral responses of sea lamprey under electric fields, highlighting the potential for optimizing electric deterrents for selective control. While not all sea lampreys detach during electroshocking, understanding their behavior can be crucial in maximizing detachment rates and contributing to improved management strategies for this invasive species.

Transactions of the American Fisheries Society↗

USGS2023

Automated soft pressure sensor array-based sea lamprey detection using machine learning

Sea lamprey, a destructive invasive species in the Great Lakes in North America, is among very few fishes that rely on oral suction during migration and spawning. Recently, soft pressure sensors have been proposed to detect the attachment of sea lamprey as part of the monitoring and control effort. However, human decision is still required for the recognition of patterns in the measured signals. In this article, a novel automated soft pressure sensor array-based sea lamprey detection framework is proposed using object detection convolutional neural networks. First, the resistance measurements of the pressure sensor array are converted to mappings of relative change in resistance. These mappings typically show two different types of patterns under lamprey attachment: a high-pressure circular pattern corresponding to the mouth rim compressed against the sensor (“compression” pattern), and a low-pressure blob corresponding to the partial vacuum region of the sucking mouth (“suction” pattern). Three types of object detection algorithms, single-shot detector (SSD), RetinaNet, and YOLOv5s, are applied to the dataset of measurements collected in the presence of sea lamprey attachment, and the comparison of their performance shows that YOLOv5s model achieves the highest mean average precision (mAP) and the fastest inference speed. Furthermore, to improve the accuracy of the prediction model and reduce the false positive (FP) rate due to the sensor’s memory effect, a filter branch with different detection thresholds for the compression and suction patterns, respectively, is added to the original machine-learning algorithm. The trained model is validated and used to automatically detect sea lamprey attachments and locate the suction area on the sensor in real time.

IEEE Sensors Journal↗