It is now widely recognized that a multitude of concurrent biological, chemical and physical stressors caused by human activities are posing significant threats to global marine ecosystems and their components (IPCC, 2022). In polar regions, research has shown that changes to the development and reproduction of many marine organisms, including zooplankton, are particularly vulnerable to warming and ocean acidification (Johnston et al., 2022). Some zooplanktonic groups, such as gastropod molluscs known as thecosome (shelled) pteropods, are regarded as early responders to climate change (Bednaršek et al., 2016), as they produce fragile, aragonite shells that are highly susceptible to dissolution linked to high CO₂ partial pressures (pCO₂) due to increasing ocean acidification (Riebesell et al., 2000; Orr et al., 2005; Kroeker et al., 2013). Whilst recent studies have shown a relatively higher capacity to withstand such effects than previously assumed (Peck et al., 2018), it is the early developmental stages of thecosome pteropods that are at greatest risk to changing ocean chemistry (Gardner et al., 2018). These risks will undoubtedly present wider consequences throughout marine ecosystems as, like many zooplankton taxa, thecosome pteropods provide a key energetic link between basal and higher trophic levels as well as an important contributor to the global export of carbon and carbonate to the deep sea through the fluxing of fast-sinking fecal pellets and shells post-mortem (Manno et al., 2010; Manno et al., 2018).

Shell dissolution in thecosome pteropods has often been studied using the common species Limacina helicina from the Northern Hemisphere (Comeau et al., 2010; Lischka et al., 2011; Comeau et al., 2012b; Bednaršek et al., 2014), and its Southern Ocean congener species, Limacina helicina antarctica (Manno et al., 2007; Seibel et al., 2012; Johnson and Hofmann, 2017; Gardner et al., 2018). Whilst one study by Bednaršek et al. (2012) revealed in situ shell dissolution of juvenile L. h. antarctica from the Scotia Sea, situated in the Atlantic sector of the Southern Ocean, much of the effects of climate change on early life development of thecosome pteropods have been observed through laboratory-based manipulation experiments. Examined under predicted levels of ocean acidification and warming, incubated thecosome pteropods have shown a range of adverse responses, including degradation, reduction in and/or lack of shell development (Lischka et al., 2011; Comeau et al., 2012a; Gardner et al., 2018), increased larval mortality (Lischka et al., 2011; Thabet et al., 2015; Gardner et al., 2018), and a decrease in the proportion of eggs developing to advanced embryogenetic stages (Manno et al., 2016). These responses are bound to have wider ecological and long-term ramifications related to population stability and recruitment.

Thecosome pteropods are holoplanktonic with unique life history strategies. Most species begin life as males until they reach a particular size (e.g., shell diameter of ~4 mm for L. helicina) then subsequently develop female organs and mature into females whilst their male organs are resorbed (Lalli and Wells, 1978; Lalli and Gilmer, 1989), which characterizes them as protandrous hermaphrodites. Females spawn tens of thousands of transparent eggs during their lifespan which are embedded into ribbons within gel matrix egg masses. Embryogenetic development occurs within these clutches, and hatching generally occurs at the trochophore larval stage (Lalli and Gilmer, 1989; Thabet et al., 2015; Wakabayashi, 2017). Optimal clutch size theory posits that mature females will spawn variable numbers of eggs to maximize the offspring fitness as it relates to resource availability, intraspecific competition, and mortality (Godfray et al., 1991). Different forms of parental care exist in marine gastropods, but for many species, females control the number of eggs contained within egg masses in an effort to manage their fecundity under changing conditions (Spight and Emlen, 1976; Perron, 1981).

Challenges related to estimating fecundity in thecosome pteropods can be attributed to the high number of microscopic eggs embedded within each egg mass. Manually counting them can be time consuming and using abundance of mature-aged adults is a relatively inaccurate alternative given the range of egg masses released by each pteropod adult. Manually counting thecosome eggs has previously involved dissecting the egg ribbons from the egg mass, which may introduce stress, particularly if eggs are being placed in live culture for subsequent observational studies (Manno et al., 2016). One study by Lalli and Wells (1978) used a conversion factor of 35 eggs mm^-2 for L. helicina egg masses collected from Eastern Canada which is derived from estimating the number of eggs per area of egg mass measured, however this average value was based on complete measurements taken from only five egg masses. These challenges may be minimized with the use of image analyses platforms.

Autonomous image analysis techniques have previously been tested in plankton research involving the counting and measuring of round objects in aqueous solution, including the use of images and on-board, large-volume samples (Gorsky et al., 1989; Colas et al., 2018). Several studies have employed software platforms to automatically enumerate microscopic eggs of invertebrates from images with high degrees of success (Collin, 2010; Rosati et al., 2015; da Silva Júnior et al., 2018). The purpose of this study is to develop and validate a workflow that uses a combination of image segmentation and a supervised machine learning algorithmic approach to perform semi-automatic detection of thecosome pteropod eggs embedded within egg masses. This study aims to efficiently and accurately enumerate thecosome pteropod eggs embedded in their egg masses using the workflow developed in this study, and statistically compare this method to manual egg enumeration, which involves dissection under stereomicroscope. Reliably predicting the number of eggs within thecosome pteropod egg masses through non-destructive data imaging techniques can be beneficial to monitoring the health of marine ecosystems particularly prone to rapid chemical change.

Shelled pteropods perform essential ecological roles in polar regions and serve as sentinels of climate change, though much work is yet to be done to better understand how these changes affect their early life development. This study constructed a framework to perform automatic, albeit supervised, enumeration of microscopic eggs from thecosome pteropod egg masses using image analysis and machine learning algorithms. Prior to this study, egg counting from thecosome pteropod egg masses had been performed manually under stereomicroscope either through counting eggs along ribbons dissected from each clutch or through estimating the number of eggs over a known area (mm²) then extrapolating this value over the entire length of the egg mass. While the former is more accurate, this method is far more invasive and destructive, whereas the latter method does not account for high variability in egg density present along the length of the egg masses. The purpose of this study was to determine if a digital protocol for egg counting could be as accurate and efficient as the more invasive manual counting method and results here reveal no statistically significant difference between methods. There was a strong correlation found between semi-automatic and manual counting methods.

There are very few published data focused on thecosome pteropod egg numbers and morphology, and how these attributes change over time and under future predicted climate scenarios, though spatio-temporal studies on manually enumerated eggs have been conducted in other marine gastropod species for decades (Berry, 1987; Mandal et al., 2010). However, very few studies have examined gastropod egg number variation along a gradient of environmental factors (Przeslawski, 2014). An accurate egg enumeration workflow has the potential to answer questions pertaining to early life responses of shelled pteropods to climate change, and the application of machine learning within these studies allows for the automation and simplified analyses of large-sized datasets. While only a few studies have counted thecosome eggs for various research purposes, at the time of this study, no other studies have closely analyzed different enumeration techniques for pteropod eggs nor developed an image processing technique incorporating supervised or unsupervised machine learning algorithms. This is the first study to develop and propose a framework to analyze thecosome pteropod eggs digitally using open-source image analysis software and machine learning algorithms.

Digital egg enumeration has advantages over manual counting. Namely, it does not impose damage and potential stress to the individual eggs, thus allowing the eggs and egg masses to be maintained in live cultures for further ontogenetic studies. Images can be captured while the live egg masses are placed in petri dishes or well slides under stereomicroscope, and subsequently available to use for ontogenetic experiments. This can facilitate more research into understanding uncertainties related to early life development of species sensitive to ocean acidification and ocean sea surface temperature change.

There is capacity for improving detection accuracy and speed of operation on the workflow presented here. Firstly, the discovery of a stain that would easily differentiate eggs from non-egg materials would be a fruitful next step. This would require a deeper understanding of the constituents that make up organic and inorganic materials within and adjacent to the egg masses and result in more accurate segmentation by the TWS plugin. Secondly, there are additional Fiji/ImageJ-based plugins and tools that have shown promising results in pre- and postprocessing cell enumeration, including cell staining followed by the in-built Fiji/ImageJ Color Deconvolution plugin for color segmentation (Ruifrok and Johnston, 2001). This research included a single observer, an assessment of variability in results between multiple observers would be recommended to test user-induced bias and standardize steps, beginning with settings (e.g., magnification) and equipment (e.g., microscope and camera make and model) related to the acquisition of digital images. And finally, through the development of new macros with customizable parameters (based on egg roundness, area, diameter, etc.) that would enable batch processing of multiple image files, rendering the process more automatic. The workflow described here can serve as a baseline for future development with new functionality.

In conclusion, the semi-automatic machine learning approach to analyzing pteropod egg mass images developed here is a promising user-friendly, non-destructive, and highly practical methodology for enumerating eggs within their gel matrices. This study outlines a simple, stepwise workflow necessary to accomplish accurate pixel-based segmentation of pteropod egg mass images using the image analysis software, Fiji/ImageJ, and the in-build TWS plugin. The effectiveness of this workflow was shown through a comparative analysis with manual counting requiring dissection of egg ribbons embedded within the egg mass gel matrix under a stereomicroscope that revealed high correlation.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The author confirms all responsibility for study conception and design, sample and data collection, analyses and interpretation of results, and manuscript preparation.

This study was financially supported through a grant provided by the Holsworth Wildlife Research Endowment (grant #109804) of the Ecological Society of Australia, as well as funding from the Australian Government as part of the Antarctic Science Collaboration Initiative program (grant # ASCI000002). The Australian Antarctic Program Partnership is led by the University of Tasmania, and includes the Australian Antarctic Division, CSIRO Oceans and Atmosphere, Geoscience Australia, the Bureau of Meteorology, the Tasmanian State Government and Australia’s Integrated Marine Observing System.

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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