Juvenile Chinook Salmon Weight Prediction Using Image‐Based Morphometrics

Abstract

AbstractWe developed an empirical weight prediction model for juvenile Chinook Salmon Oncorhynchus tshawytscha ranging from 33–113 mm FL and 0.35–14.86 g using morphometrics analysis on images that were collected from live fish in the field. This method relies on consumer‐grade hardware and free software and addresses the issue of a lack of valuable weight and condition data for life history and survival modeling. A blind test revealed that the method was relatively precise, with a mean absolute error of 0.075 g and a mean absolute percentage of error of 2.86%, and unbiased, with a mean error of −0.001 g. Given the inherent variability in field wet‐weight measurements that are estimated, with an average standard deviation of 0.030 g and CV of 1.02% from a blind repeated‐measurements experiment, the observed level of predictive error suggests that the method is an adequate substitute for measuring field weight when conditions (i.e., boat sampling) preclude conventional scale taring and accurate weight measurement. Additionally, this method is useful in an automated data‐quality‐control workflow to identify erroneous length and/or weight measurements. The image landmark placement closely follows previous work on juvenile Chinook Salmon and can be incorporated with existing methodologies such as stock analysis and phenotypic differentiation to increase the available biometric information.