ABSTRACT
BACKGROUND: Studying habitat use and vertical movement patterns of individual fish over continuous time and space is innately challenging and has therefore largely remained elusive for a wide range of species. Amongst sharks, this applies particularly to smaller-bodied and less wide-ranging species such as the spurdog (Squalus acanthias Linnaeus, 1758), which, despite its importance for fisheries, has received limited attention in biologging and biotelemetry studies, particularly in the North-East Atlantic. METHODS: To investigate seasonal variations in fine-scale niche use and vertical movement patterns in female spurdog, we used archival data from 19 pregnant individuals that were satellite-tagged for up to 365 days in Norwegian fjords. We estimated the realised niche space with kernel densities and performed continuous wavelet analyses to identify dominant periods in vertical movement. Triaxial acceleration data were used to identify burst events and infer activity patterns. RESULTS: Pregnant females frequently utilised shallow depths down to 300 m at temperatures between 8 and 14 °C. Oscillatory vertical moments revealed persistent diel vertical migration (DVM) patterns, with descents at dawn and ascents at dusk. This strict normal DVM behaviour dominated in winter and spring and was associated with higher levels of activity bursts, while in summer and autumn sharks predominantly selected warm waters above the thermocline with only sporadic dive and bursts events. CONCLUSIONS: The prevalence of normal DVM behaviour in winter months linked with elevated likely foraging-related activity bursts suggests this movement behaviour to be foraging-driven. With lower number of fast starts exhibited in warm waters during the summer and autumn months, habitat use in this season might be rather driven by behavioural thermoregulation, yet other factors may also play a role. Individual and cohort-related variations indicate a complex interplay of movement behaviour and habitat use with the abiotic and biotic environment. Together with ongoing work investigating fine-scale horizontal movement as well as sex- and age-specific differences, this study provides vital information to direct the spatio-temporal distribution of a newly reopened fishery and contributes to an elevated understanding of the movement ecology of spurdog in the North-East Atlantic and beyond.
ABSTRACT
The age structure of a fish population has important implications for recruitment processes and population fluctuations, and is a key input to fisheries-assessment models. The current method of determining age structure relies on manually reading age from otoliths, and the process is labor intensive and dependent on specialist expertise. Recent advances in machine learning have provided methods that have been remarkably successful in a variety of settings, with potential to automate analysis that previously required manual curation. Machine learning models have previously been successfully applied to object recognition and similar image analysis tasks. Here we investigate whether deep learning models can also be used for estimating the age of otoliths from images. We adapt a pre-trained convolutional neural network designed for object recognition, to estimate the age of fish from otolith images. The model is trained and validated on a large collection of images of Greenland halibut otoliths. We show that the model works well, and that its precision is comparable to documented precision obtained by human experts. Automating this analysis may help to improve consistency, lower cost, and increase the extent of age estimation. Given that adequate data are available, this method could also be used to estimate age of other species using images of otoliths or fish scales.