Efectos de muestras de tallas erróneas sobre los valores estimados del crecimiento individual y la condición de los stocks

Despite its importance in fisheries studies, there is insufficient understanding on the effect of sampling error or bias on individual growth and other stock indicators. We show the influence of sample length distributions on parameter estimates, illustrating with an example. For the brown swimming crab, we simulated length samples in five configurations and estimated parameters of von Bertalanffy (k, L∞L∞ , t0), asymptotic weight ( W∞W∞ ), weight-length relationship (a, b), growth performance (ϕ’) and condition factor (Kn). Parameter estimates were compared with baseline values using relative bias, standard error and root mean square error. The results show that the accuracy and bias of parameter estimates depend on the lengths sampled. For example, the bias and accuracy of L∞L∞ and W∞W∞ vary inversely with sampled length, whereas combining length segments yields smaller biases of k and t0 than those of L∞L∞ and W∞W∞ . In general, the accuracy of parameter estimates does not always depend on sampling the entire length range, and errors are not the same for all parameters. These results are useful to guide sampling when resources are scarce. We discuss potential reasons for incomplete length sample structure and offer recommendations to obtain best estimates for parameters of interest.A pesar de su importancia en los estudios de pesquerías, aún no se comprende lo suficiente el efecto del error o del sesgo del muestreo en los parámetros de crecimiento individual y otros indicadores poblacionales. Utilizando un ejemplo, aquí se muestra la influencia de las distribuciones muestrales de longitud en las estimaciones de parámetros poblacionales. Para la jaiba café, simulamos muestreo de longitud en cinco configuraciones y estimamos parámetros de von Bertalanffy (k, L∞L∞ , t0), peso asintótico ( W∞W∞ ), relación peso-longitud (a, b), eficiencia de crecimiento (ϕ’), y factor de condición (Kn). Las estimaciones de los parámetros se compararon con valores de referencia utilizando el sesgo relativo, el error estándar y el error cuadrático medio. Los resultados muestran cómo la precisión y el sesgo de las estimaciones de parámetros dependen de las longitudes muestreadas. Por ejemplo, el sesgo y la precisión de L∞L∞ y W∞W∞ , varían inversamente con la longitud muestreada, mientras que la combinación de segmentos de longitud produce sesgos de k y t0 más pequeños que los de L∞L∞ y W∞W∞ . En general, la precisión de las estimaciones de los parámetros no siempre depende del muestreo de todo el rango de tallas disponible, y los errores no son los mismos para todos los parámetros. Estos resultados son útiles para guiar el muestreo cuando los recursos son escasos. Discutimos las posibles razones de la estructura de la muestra de longitud incompleta y ofrecemos recomendaciones para obtener las mejores estimaciones para los parámetros de interés

Assessment of a Takagi–Sugeno-Kang fuzzy model assembly for examination of polyphasic loglinear allometry

Background The traditional allometric analysis relies on log- transformation to contemplate linear regression in geometrical space then retransforming to get Huxley’s model of simple allometry. Views assert this induces bias endorsing multi-parameter complex allometry forms and nonlinear regression in arithmetical scales. Defenders of traditional approach deem it necessary since generally organismal growth is essentially multiplicative. Then keeping allometry as originally envisioned by Huxley requires a paradigm of polyphasic loglinear allometry. A Takagi-Sugeno-Kang fuzzy model assembles a mixture of weighted sub models. This allows direct identification of break points for transition between phases. Then, this paradigm is seamlessly appropriate for efficient allometric examination of polyphasic loglinear allometry patterns. Here, we explore its suitability. Methods Present fuzzy model embraces firing strength weights from Gaussian membership functions and linear consequents. Weights are identified by subtractive clustering and consequents through recursive least squares or maximum likelihood. Intersection of firing strength factors set criterion to estimate breakpoints. A multi-parameter complex allometry model follows by adapting firing strengths by composite membership functions and linear consequents in arithmetical space. Results Takagi-Sugeno-Kang surrogates adapted complexity depending on analyzed data set. Retransformation results conveyed reproducibility strength of similar proxies identified in arithmetical space. Breakpoints were straightforwardly identified. Retransformed form implies complex allometry as a generalization of Huxley’s power model involving covariate depending parameters. Huxley reported a breakpoint in the log–log plot of chela mass vs. body mass of fiddler crabs (Uca pugnax), attributed to a sudden change in relative growth of the chela approximately when crabs reach sexual maturity. G.C. Packard implied this breakpoint as putative. However, according to present fuzzy methods existence of a break point in Huxley’s data could be validated. Conclusions Offered scheme bears reliable analysis of zero intercept allometries based on geometrical space protocols. Endorsed affine structure accommodates either polyphasic or simple allometry if whatever turns required. Interpretation of break points characterizing heterogeneity is intuitive. Analysis can be achieved in an interactive way. This could not have been obtained by relying on customary approaches. Besides, identification of break points in arithmetical scale is straightforward. Present Takagi-Sugeno-Kang arrangement offers a way to overcome the controversy between a school considering a log-transformation necessary and their critics claiming that consistent results can be only obtained through complex allometry models fitted by direct nonlinear regression in the original scales