A Promising Approach to Quantifying Pteropod Eggs Using Image Analysis and Machine Learning

Abstract

A newly developed protocol to semi-automate egg counting in Southern Ocean shelled (thecosome) pteropods using image analysis software and machine learning algorithms was developed and tested for accuracy. Preserved thecosome pteropod (Limacina helicina antarctica) egg masses collected from two austral summer research voyages in East Antarctica were digitally photographed to develop a streamlined approach to enumerate eggs within egg masses using Fiji/ImageJ and the associated machine learning plugin known as Trainable Weka Segmentation. Results from this semi-automated approach were then used to compare with manual egg counts from eggs dissected from egg masses under stereomicroscope. A statistically significant correlation was observed between manual and semi-automated approaches (R2 = 0.92, p < 0.05). There was no significant difference between manual and automated protocols when egg counts were divided by the egg mass areas (mm2) (t(29.6) = 1.98, p = 0.06). However, the average time to conduct semi-automated counts (M = 7.4, SD = 1.2) was significantly less than that for the manual enumeration technique (M = 35.9, SD = 5.7; t(30) = 2.042, p < 0.05). This new approach is promising and, unlike manual enumeration, could allow specimens to remain intact for use in live culturing experiments. Despite some limitations that are discussed, this user-friendly and simplistic protocol can provide the basis for further future development, including the addition of macro scripts to improve reproducibility and through the association with other imaging platforms to enhance interoperability. Furthermore, egg counting using this technique may lead to a relatively unexplored monitoring tool to better understand the responses of a species highly sensitive to multiple stressors connected to climate change.

Innovative and practical tools for monitoring and assessing biodiversity status and impacts of multiple human pressures in marine systems

“…In particular, imaging instruments combined with Machine Learning tools to automatically classify the collected images (e.g., Weldrick, 2022), have received a high level of interest, due to their ability to provide rapid and unbiased data that can be stored digitally. Thus, they offer the opportunity to help overcome many of the limitations that characterize traditional methods of collecting and analyzing zooplankton samples.…”