Rapid age estimation of longnose skate (Raja rhina) vertebrae using near-infrared spectroscopy
Morgan B. Arrington
A C , Thomas E. Helser B * , Irina M. Benson B * , Timothy E. Essington A , Mary Elizabeth Matta B * and André E. Punt A
+ Author Affiliations
- Author Affiliations
A School of Aquatic and Fishery Sciences, University of Washington, 1122 Boat Street, Seattle, WA 98195, USA.
B Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 7600 Sand Point Way NE, Seattle, WA 98115, USA.
C Corresponding author. Email: marringt@uw.edu
Marine and Freshwater Research 73(1) 71-80 https://doi.org/10.1071/MF21054
Submitted: 13 February 2021 Accepted: 30 July 2021 Published: 23 September 2021
Abstract
There is a paucity of age data for chondrichthyan fishes owing, in large part, to limitations in traditional age estimation methods. Fourier transform near-infrared (FT-NIR) spectroscopy has shown promise as an alternative, more efficient method for acquiring age data from chondrichthyans. However, studies are limited to sharks in the southern hemisphere. We explored FT-NIR spectroscopy to predict age for a batoid species in the northern hemisphere. The longnose skate (Raja rhina) is one of a small number of batoids for which annual band periodicity in vertebral centra has been validated, allowing for traditional age estimation and making it an ideal candidate for this study. We fit a multivariate partial least-square predictive model between FT-NIR spectra collected from vertebral centra and traditional age estimates, and tested model predictive skill by using external validation. Using FT-NIR spectroscopy, we were able to predict age for longnose skates between the ages of 1 and 14 years with precision and bias near equal to those of traditional methods in less than a quarter of the time. These results support potential for FT-NIR spectroscopy to increase the amount of age data available for assessments used to inform the conservation and management of this sensitive group of species.
Keywords: elasmobranchs, fisheries, marine, modelling.
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