Early growth rate and feeding of a small group of laboratory-reared saury,Scomberesox saurus scombroides (Pisces: Scomberesocidae)

Sea-spawned saury eggs collected 20 km off the west coast of the Cape Peninsula were hatched in laboratory tanks and the larvae reared. The larvae were fed initially on nauplii of cultured copepods, Tisbe holothuriae Humes and Pseudodiaptomus hessei (Mrázek). Growth rate during the first 47 days post-hatch averaged 0,62 mm·day^?1 (at 18—19°C) and between the 47th and 120th day averaged 0,45 mm·day^?1 (at 14,8—15,5°C). Some observations on feeding behaviour are included.     

Spatial interpolation of marine environment data using P-MSN

ABSTRACT

When a marine study area is large, the environmental variables often present spatially stratified non-homogeneity, violating the spatial second-order stationary assumption. The stratified non-homogeneous surface can be divided into several stationary strata with different means or variances, but still with close relationships between neighboring strata. To give the best linear-unbiased estimator for those environmental variables, an interpolated version of the mean of the surface with stratified non-homogeneity (MSN) method called point mean of the surface with stratified non-homogeneity (P-MSN) was derived. P-MSN distinguishes the spatial mean and variogram in different strata and borrows information from neighboring strata to improve the interpolation precision near the strata boundary. This paper also introduces the implementation of this method, and its performance is demonstrated in two case studies, one using ocean color remote sensing data, and the other using marine environment monitoring data. The predictions of P-MSN were compared with ordinary kriging, stratified kriging, kriging with an external drift, and empirical Bayesian kriging, the most frequently used methods that can handle some extent of spatial non-homogeneity. The results illustrated that for spatially stratified non-homogeneous environmental variables, P-MSN outperforms other methods by simultaneously improving interpolation precision and avoiding artificially abrupt changes along the strata boundaries.     

秩亏GM模型参数估计的若干问题

本文依据秩亏ＧＭ模型中可估量的条件，提出了参数ｘ部分可估的概念，讨论了补偿系统误差的自由网平差问题中系统误差参数可估的条件。研究了可估量的ＢＬＵＥ以及不可估量的最佳线性最小偏差估计以及ｘ的线性估计之间的关系，从最佳线性最小偏差估计的角度分析了最小二乘最小范数解的性质。对线性约束下的参数估计，讨论了条件可估的ＢＬＵＥ。给出了ｘ可估需满足的边界条件的概念并研究了不同边界条件下参数估值的转换问题。     

Least-squares variance component estimation

Least-squares variance component estimation (LS-VCE) is a simple, flexible and attractive method for the estimation of unknown variance and covariance components. LS-VCE is simple because it is based on the well-known principle of LS; it is flexible because it works with a user-defined weight matrix; and it is attractive because it allows one to directly apply the existing body of knowledge of LS theory. In this contribution, we present the LS-VCE method for different scenarios and explore its various properties. The method is described for three classes of weight matrices: a general weight matrix, a weight matrix from the unit weight matrix class; and a weight matrix derived from the class of elliptically contoured distributions. We also compare the LS-VCE method with some of the existing VCE methods. Some of them are shown to be special cases of LS-VCE. We also show how the existing body of knowledge of LS theory can be used to one’s advantage for studying various aspects of VCE, such as the precision and estimability of VCE, the use of a-priori variance component information, and the problem of nonlinear VCE. Finally, we show how the mean and the variance of the fixed effect estimator of the linear model are affected by the results of LS-VCE. Various examples are given to illustrate the theory.