Australia’s Out-Dated Concern over Fishing Threatens Wise Marine Conservation and Ecologically Sustainable Seafood Supply

Seafood plays an important role in human nutrition and its increased consumption is actively recommended for sustenance and health benefits in both developing and developed countries. In parallel to this, the public receives confusing advice as to what seafood is sustainably produced and is frequently misled about the environmental impacts of fishing, especially in locations such as Australia where contemporary fishery management has a conservation and sustainability focus. It is recognised globally that Australia’s traditional fishery management driven by strict sustainability and biodiversity regulations, has achieved impressive results in managing both fish stocks and the effects of fishing on marine environments. Despite this, continued pressure from non-government organisations (NGOs) and a perpetuation of the misuse of management terms such as “overfished” is used to promote the misguided need for ever increasing fishing restrictions, most obviously in “protected areas”. This paper questions the motives of some NGOs and governments in Australia in pursuing additional restrictions on fishing which are mostly unnecessary and disproportionate to the sustainability requirements of other sources of food. This is done within the context of the global need for sustainable seafood supply and the need for effective marine conservation that addresses all threats to marine ecosystems in proportion to the magnitude of each threat.

Distributed Penalized Modal Regression for Massive Data

Nowadays,researchers are frequently confronted with challenges from massive data computing by a number of limitations of computer primary memory.Modal regression(MR)is a good alternative of the mean regression and likelihood based methods,because of its robustness and high efficiency.To this end,the authors extend MR to massive data analysis and propose a computationally and statistically efficient divide and conquer MR method(DC-MR).The major novelty of this method consists of splitting one entire dataset into several blocks,implementing the MR method on data in each block,and deriving final results through combining these regression results via a weighted average,which provides approximate estimates of regression results on the entire dataset.The proposed method significantly reduces the required amount of primary memory,and the resulting estimator is theoretically as efficient as the traditional MR on the entire data set.The authors also investigate a multiple hypothesis testing variable selection approach to select significant parametric components and prove the approach possessing the oracle property.In addition,the authors propose a practical modified modal expectation-maximization(MEM)algorithm for the proposed procedures.Numerical studies on simulated and real datasets are conducted to assess and showcase the practical and effective performance of our proposed methods.