Abundance of the introduced seastar, Asterias amurensis, and spatial variability in soft sediment assemblages in SE Tasmania: Clear correlations but complex interpretation

The northern Pacific seastar, Asterias amurensis, was first collected in southeast Tasmania in 1986. Mistaken for the endemic asteroid Uniophora granifera, its true identity was not realised until 1992. It is now a conspicuous predator in soft sediment habitats in this region, and is considered a major threat to native assemblages and commercial species. We examined the structure of soft sediment assemblages at different spatial scales in southeast Tasmania, and correlated spatial variation in community composition with seastar abundances. We found that the structure of soft sediment assemblages is highly variable at a range of spatial scales from metres to tens of kilometres. Clear differences in the composition of assemblages and abundances of major taxa were detected between areas with and without seastars and between areas with low and high seastar densities. However, the nature of these patterns suggests that they are more likely due to differences in sediment characteristics than due to impacts of the seastar. Thus, spatial differences in soft sediment assemblages might have been erroneously attributed to seastars without detailed information on important physical factors such as sediment characteristics. A second survey, using larger sampling units (1 m²) but across a more limited spatial extent, targeted bivalves and heart urchins that were identified as important prey of the seastar in observations of feeding and in experimental studies. Large-scale patterns of abundance and size structure were consistent with seastar effects anticipated from small-scale experimental and feeding studies for some, but not all, species. While the field survey ultimately provided evidence about the presence or absence of seastar impacts at large-scales, the identification of key ecological variables in experimental and feeding studies proved crucial to both the design and interpretation of patterns observed in the large-scale surveys. Overall, this work highlighted the necessity to consider multiple lines of evidence rather than relying on a single ‘inferential’ test, in the absence of pre-impact data.     

鲫鱼(Carassius auratus)在微冻保鲜过程中的质量变化

该文研究鲫鱼 ( Carassius auratus)在微冻 ( - 3℃ )保鲜过程中的质量变化规律。以感官评价、细菌总数、T- VBN值、p H值、ATPase活性、K值等作为质量指标。实验结果表明 ,微冻可以明显抑制细菌总数的增长 ,维持较低的 T- VBN和 K值。但是 ,微冻条件下鲫鱼的 ATPase活性下降速度较快     

关于海域面积精确计算的若干问题探讨

海域面积精确计算是实现海洋功能区划的基础和关键之一。本文通过对江苏省海洋功能区划工作中误差的分析 ,并针对其产生原因 ,在地图投影的选择和坐标的变换、扫描精度的控制、配准精度的控制、图象的数字化和图形编辑等方面 ,提出了相应的优化处理方法 ,提高了海域面积计算的精度 ,将其中误差控制在估计值范围内 ,从而在实际工作中有一定的应用价值。     

Industry participation in stock assessment: the New Zealand SNA1 snapper (Pagrus auratus) fishery

We describe industry funded contributions to the assessment of the SNA1 snapper (Pagrus auratus) fishery during the 1990s and discuss the advantages and disadvantages of industry funded research. We also provide background on the history of fisheries management in New Zealand, on the current assessment and management processes, and on the SNA1 snapper fishery. In the SNA1 fishery, the contributions of industry and the cooperation with Government scientists has resulted in high quality assessments. In our opinion, the advantages of industry funded research out weigh the disadvantages and suggest that industry funded assessments are highly desirable and should be an integral part of any management system.     

Preconditioned Optimizing Utility for Large-dimensional analyses (POpULar)

I present the derivation of the Preconditioned Optimizing Utility for Large-dimensional analyses (POpULar), which is developed for adopting a non-diagonal background error covariance matrix in nonlinear variational analyses (i.e., analyses employing a non-quadratic cost function). POpULar is based on the idea of a linear preconditioned conjugate gradient method widely adopted in ocean data assimilation systems. POpULar uses the background error covariance matrix as a preconditioner without any decomposition of the matrix. This preconditioning accelerates the convergence. Moreover, the inverse of the matrix is not required. POpULar therefore allows us easily to handle the correlations among deviations of control variables (i.e., the variables which will be analyzed) from their background in nonlinear problems. In order to demonstrate the usefulness of POpULar, we illustrate two effects which are often neglected in studies of ocean data assimilation before. One is the effect of correlations among the deviations of control variables in an adjoint analysis. The other is the nonlinear effect of sea surface dynamic height calculation required when sea surface height observation is employed in a three-dimensional ocean analysis. As the results, these effects are not so small to neglect.     

Risk assessment of TBT in the Japanese short-neck clam (Ruditapes philippinarum) of Tokyo Bay using a chemical fate model

A risk assessment of Tributyltin (TBT) in Tokyo Bay was conducted using the Margin of Exposure (MOE) method at the species level using the Japanese short-neck clam, Ruditapes philippinarum. The assessment endpoint was defined to protect R. philippinarum in Tokyo Bay from TBT (growth effects). A No Observed Effect Concentration (NOEC) for this species with respect to growth reduction induced by TBT was estimated from experimental results published in the scientific literature. Sources of TBT in this study were assumed to be commercial vessels in harbors and navigation routes. Concentrations of TBT in Tokyo Bay were estimated using a three-dimensional hydrodynamic model, an ecosystem model and a chemical fate model. MOEs for this species were estimated for the years 1990, 2000, and 2007. Estimated MOEs for R. philippinarum for 1990, 2000, and 2007 were approximately 1–3, 10, and 100, respectively, indicating a declining temporal trend in the probability of adverse growth effects.A simplified software package called RAMTB was developed by incorporating the chemical fate model and the databases of seasonal flow fields and distributions of organic substances (phytoplankton and detritus) in Tokyo Bay, simulated by the hydrodynamic and ecological model, respectively.