Martínez-Silva, I., Sestelo, M., Bidegain, G., Lorenzo-Arribas, A. and Roca-Pardinas, J.
In "Sea urchins: habitat, embryonic development and importance in the environment". Nova Science Publishers.
Nova Science Publishers
sea urchin, nonparametric regression, bootstrap, quantile, testing
||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.