Building confidence: Developing image-based methods to incorporate fishery-collected data in the American lobster stock assessment

Abstract

The American lobster, Homarus americanus, supports the most valuable single-species fishery in North America; however to-date, a reliable and robust method to determine age does not exist, and thus some of the more traditional catch-at-age stock assessment methods cannot be used to determine status. In lieu of this, the Atlantic States Marine Fisheries Commission’s American Lobster Stock Assessment Model uses a probabilistic growth transition matrix to determine how lobsters of different sizes will grow and recruit into the fishery. Developing and updating the growth transition matrix to reflect current growth dynamics requires estimates of molt increment and interval. Tagging studies are an inexpensive method to collect this type of information, but relying on fishing industry reported recapture data and buy-in can be challenging, resulting in varied return rates and uncertainty in measurement accuracy. Here, we report on a subset of recaptured lobsters released in the Gulf of Maine and Georges Bank regions between 2015 and 2020, where in addition to recapture locations, harvesters were encouraged to submit images of recaptured lobsters alongside a standard lobster gauge as a scale to estimate carapace length. Some fishermen were able to provide both direct measurements and images. Images were analyzed in ImageJ to estimate individual lobster carapace length (CL); and each image was assigned a quality score. For all groups, image-derived lengths were correlated with measured observations, regardless of the overall image quality, with only a slight underestimation at the maximum end of CL. This image-based method for length estimation provides high-quality length predictions regardless of image quality and can significantly increase the likelihood of harvesters contributing data with broad potential to engage commercial fisherman in collaborative science. Using this method we were able to expand the dataset of length records from this project by 251 or 33 %, providing additional data at a rate similar to tagging an additional 6000 lobsters.