Abstract
Direct measures of body mass of marine mammals are logistically complicated to obtain even for pinnipeds. An alternative method for mass estimation based on 3D imaging technology and automated processing algorithms, was tested in southern elephant seals ( Mirounga leonina ). Two models of artificial neural networks (ANN)—nonlinear neural network and self‐organizing maps—were trained to compute the volume and to estimate the mass of the digital models, previously obtained by scanning individuals with an infrared light sensor. Body mass estimates were as accurate (mean % error = 4.4) as estimates in previous photogrammetry studies in southern elephant seals and the mass predictive ability of the trained ANN was higher (99% of the variance explained) than other predictive models using photogrammetry in pinniped studies. While this method has proven to produce accurate body mass estimates, it also overcame some of the constraints of other indirect techniques, avoiding animal disturbance caused by physical restraint or chemical immobilization, minimizing risks, and capitalizing on the time working in the field. The results of these estimations were promising, which shows that the proposed methodology can provide adequate results with lower logistic and computational requirements.