An alternative hypothesis, defined as the assumed clinically meaningful treatment effect at the primary endpoint, is determined when a randomized controlled trial (RCT) is designed. Nevertheless, most researchers conducting RCTs for superiority very often disregard evaluating the alternative hypothesis. Since most clinical trials are designed to demonstrate the statistical significance of the primary endpoint, in trials that fail to show this, aspects of the results that differ from what researchers initially intended are often inappropriately emphasized. The evaluation of treatment effects from clinical trials should be based on the primary endpoint. Therefore, we propose a quantitative statistical graph, referred to as the “ABC plot,” representing the alternative hypothesis, the Bayes factor, and the confidence interval, which enhances the visual evaluation of the treatment effect based on the result of the primary endpoint. The ABC plot was applied to three actual RCTs intended to show superiority. A discussion based only on statistical significance is insufficient for interpreting the primary endpoint. The ABC plot enables researchers to evaluate the alternative hypothesis on the estimation of the treatment effect.

Biodiversity big data plays an essential role in better understanding of biodiversity pattern in space and time and its underpinning macroecological mechanisms. Biodiversity as a concept is inductively quantified by the measurable multivariate data relative to taxonomic, functional and phylogenetic/genetic aspects. Therefore, conservation is also argued by using particular biodiversity metrics, context dependently, e.g., spatial conservation prioritization, design of protected areas network.Individual descriptive information accumulated in biogeography, ecology, physiology, molecular biology, taxonomy, and paleontology are aggregated through the spatial coordinates of biological distributions. Such biodiversity big data enables to visualize geography of 1) the richness of nature, 2) the value of nature, and 3) the uncertainty of nature, based on statistical models including maximum likelihood, machine learning, deep learning techniques. This special issue focuses on statistical and mathematical methods in terms of the quantitative visualization of biodiversity concepts. We hope that this special issue serves as an opportunity to involve researchers from different fields interested in biodiversity information and to develop into new research projects related to Nature Positive by 2030 that aims at halting and reversing the loss of biodiversity and ecosystem service.

Fisheries science is one of the disciplines of studying marine and riverine organisms, but it is different from usual biology in that it focuses on how to utilize organisms sustainably as renewable resources. In terms of sustainable use of fishery resources, it is important to make necessary decisions as soon as possible. However, large uncertainty involved with the fisheries data usually leads to inconclusive argument, resulting in inadequate decision making and the risk of collapsing the resource without doing possible measures. In the latter half of the 20th century, an emphasis on fisheries management science moved away from optimality to robustness. In order to achieve robustness against various uncertainties, biometric statistical methods utilizing the power of modern high-speed computers were actively incorporated into the assessment and risk management of fishery resources. This paper introduces how biometric statistical methods have been applied in recent years in fisheries science, especially in the context of recent development of the MSY-based fisheries management in Japan.