Document details for 'Nonparametric regression applied to sea urchin growth'

Authors Martínez-Silva, I., Sestelo, M., Bidegain, G., Lorenzo-Arribas, A. and Roca-Pardinas, J.
Publication details In "Sea urchins: habitat, embryonic development and importance in the environment". Nova Science Publishers.
Publisher details Nova Science Publishers
Keywords sea urchin, nonparametric regression, bootstrap, quantile, testing
Abstract An adequate nonparametric regression model is able to record specific patterns in the data that cannot be detected by a parametric model. In addition, quantile regression can provide a more complete description of functional changes than an exclusive focus on the least square regression. This chapter assesses the adequacy of a variety of nonparametric models to analyze the growth patterns of sea urchins by means of the length-weight relationship. For this purpose, data from fishery landings of green sea urchin Strongylocentrotus droebachiensis are used to analyze this relationship for lengths within the legal catch-size range (52.4 mm - 76 mm) at two depths (i.e. shallow waters, 4.6 m, and deep waters, 7.6 m). Overall, this gives insight into the study of the minimum capture size. We apply a Kernel nonparametric regression model to determine both its suitability and applicability as an alternative to the classic allometric model, for the estimation of a minimum catch size directed to obtain the maximum yield in weight from the fishery. The results demonstrate the suitability of the Kernel nonparametric regression model as an alternative approach to the classic allometric model to analyze the length-weight relationship in sea urchins and to estimate a minimum capture size, particularly for deeper waters sea urchins. Additionally, a boosting-based quantile regression technique is successfully applied which detects variability in sea urchin growth patterns throughout the length distribution and between depths. Differences in food availability and wave exposure between depths may explain these results.
ISBN 978-1-63321-550-4
Last updated 2015-05-01

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