Productivity and biodiversity are two fundamental properties of ecosystems. However, there has been a long-running debate over their interrelationship and the mechanisms behind their relationship for decades. In this paper, the control experiments, meta-analysis and multi-site observation studies over the past decades for testing relationships between productivity and biodiversity and the mechanisms of their relationships are reviewed. It is found that the forms of productivity-biodiversity relationships mainly are: unimodal relationship, positive relationship, negative relationship, no relationship and U-shaped relationship. Besides, there is no general consensus about underlying mechanisms of their relationships. Meanwhile, both the relationships and the underlying mechanisms are strongly dependent on scales. These results illustrate that the productivity-biodiversity relationships are not a simple or direct causal relationship, but are driven by interconnected and multivariate ecological processes. Therefore, we should give up a fixation on bivariate patterns and adopt the approach of multivariate drivers to the problem of the biodiversity pattern in order to further strengthen our understanding of complex ecosystem processes and provide more meaningful guidance for conservation management.

Surface runoff change is affected by climate change and human activities. Quantitative assessment the impacts of climate change and human activities on surface runoff changes is significant for water resources management. This paper analyzes the mechanism that climate change and human activities affect the change of surface runoff based on the process of hydrological cycle and compares the methods that separate the impacts of climate change and human activities. Then, the paper analyzes the differences of the contribution rates of climate change and human activities on surface runoff of some watersheds on globe. At present, integrating multiple mutation test methods is beneficial for improving the accuracy in identifying the abrupt change point of surface runoff. Eliminating the interference factors (such as the selection of meteorological and hydrological data, the parameter setting of model method and inherent uncertainty of methods) is of great significance to improve the consistency of the results of different quantitative methods. The key of future research is to find ways that could better couple the physical hydrological model methods and mathematical empirical methods to separate the impact of climate change and human activities on the change of surface runoff.