INTRODUCTION
Marine ecosystems are dynamic, and conservation of key species that inhabit these ecosystems requires long-term monitoring of populations across a range of temporal and geographic scales Methods for long-term monitoring of coastal species, including harbor seals, are often invasive, costly, and time-consuming (Cunningham 2009), underscoring the need for new techniques for systematic data collection and analysis. The automation of these processes can be an effective technique for monitoring population dynamics, as automation increases reproducibility while decreasing cost and labor (.
Harbor seals are an ideal species for long-term monitoring as they are highly mobile animals that inhabit a large geographic range and are ecologically and economically important as top predators (Aarts et al., 2019). In addition, harbor seals can be observed non-invasively as they congregate at “haul-out” sites—essential areas where seals come out of the water to rest on rocky islets, allowing them to thermoregulate and avoid predation—which make them easily visible to researchers from afar (. As top predators, seal populations effect ecosystem dynamics, with healthy populations likely decreasing competition among species such as flounder, sole and dab, and, in turn, influencing the balance of both ecologically and economically critical fish populations . Increases in seal populations along the Atlantic coast have also increased the numbers of sharks that inhabit coastal waters, potentially affecting tourism revenue in addition to local ecosystems .
Harbor seals are important indicators of ecosystem health because they are susceptible to climate change and, given their extensive overlap with human activities both in and out of the water, are particularly vulnerable to increased anthropogenic activity (Allen et al., 1984). Over the last century, the Atlantic coast populations of harbor seals in northeastern North America have a history of heavy exploitation. Following the Marine Mammal Protection Act of 1972, populations of harbor seals off the Northeast coast of the U.S, successfully rebounded to healthy population numbers, but the steep decline in abundance prior to any legislation is evidence of the potential vulnerability of the population to acute or chronic ecological challenges.
As key regulators and indicators of ecosystem health, monitoring harbor seal population levels and movement patterns is essential. Tagging methods have been widely used in the past, however these GPS-monitoring devices are expensive, ranging from $1000 to $3000 for one device (GPS and VHF Tracking Collars Used for Wildlife Monitoring , 2017). In addition, the attachment of external devices may interfere with behaviors such as swimming speed, oxygen consumption, and metabolic rate, potentially corrupting the data collected or harming or disturbing the individual . Aerial and visual observation methods limit interference with seal behavior, but both techniques are time consuming and expensive . Photo based identification techniques also have the advantage of being non-invasive, but manual interpretation of photographs is time-intensive and often limited to small-scale projects. For seals, manual matching based on fur colors is difficult due to changing coat colors as seals mature and during annual molting. However, some promising progress has been made using analysis of pelage markings, i.e. spots on the seal’s coat that can be reliably used as diagnostic tools (Cunningham, 2009).
Here, we propose the use of automated facial recognition technology as a system for identification of seal individuals for ecological and population studies. We used deep learning methods and convolutional neural networks to develop SealNet, a redesign of the PrimNet software (Deb et al., 2018) developed for primates. CNN-based facial recognition software achieves identification accuracies of 93.8% with lemurs , 92.5% with chimpanzees , and 97.27% with pandas . Another software, BearID, recently achieved close to 100% face chipping accuracy (number of faces recognized in an unprocessed photo) despite an overall pipeline identification accuracy of 82.4% SealNet contributes a new software package to automate the process of seal identification for use by researchers in the field.
In this paper, we outline the creation of a graphical user interface (GUI), that allows the user to automatically identify, align and chip seal faces to facilitate the processing of raw data. Then, using a deep convolutional neural network (CNN) suitable for small datasets (e.g., 100 seals with five photos per seal), we developed a seal face recognition software. We trained and tested this software on a wild population of Atlantic harbor seals in Casco Bay, Maine, U.S.A. We compare the performance of SealNet with its predecessor PrimNet and show that SealNet outperforms this software in the prediction of harbor seal identities. In a time of rapid ecosystem changes, SealNet represents a new tool non-invasive tracking of seals for use in ecological and behavioral studies.