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Grand Challenges in Biodiversity Informatics.

Using Biodiversity Information Effectively.

Invasive species have increasingly become a major environmental concern in the past decades. Freshwater fish rank among the most extensively invasive species worldwide, driven by socio-economic and climate forces. The former is primarily caused by human demands for food aquaculture, ornamental, fishing, and fisheries. The latter is primarily due to climate change, which threatens the structure and function of aquatic ecosystems by altering thermal water regimes. This chapter reviews the spatiotemporal patterns of freshwater fish species in China. It introduces fundamental concepts in explaining the mechanism behind freshwater fish distributions, followed by a description of the driving forces determining the distributions of freshwater fish species in China. Furthermore, it presents some fundamental concepts about invasive freshwater fish and invasion pathways in the field of invasion ecology. Finally, the chapter reviews the spatiotemporal patterns of invasive freshwater fish in China and describes the impacts of climate change on freshwater fish invasion with several case studies. The primary aim of this chapter is to provide fundamental knowledge and scientific information on endemic and non-native freshwater fish species in China. It seeks to understand what determines their biogeochemical distribution patterns and how ongoing climatic change affects freshwater fish invasions in China. Accordingly, effective freshwater fish conservation policies can be formulated to prevent invasive freshwater fish and preserve the biodiversity in Chinese freshwater ecosystems.

In many situations we are faced with the need to estimate the number of classes in a population from observed count data: this arises not only in biology, where we are interested in the number of taxa such as species, but also in many other fields such as public health, criminal justice, software engineering, etc. This problem has a rich history in theoretical statistics, dating back at least to 1943, and many approaches have been proposed and studied. However, to date only one approach has been implemented in readily available software, namely a relatively simple nonparametric method which, while straightforward to program, is not flexible and can be prone to information loss. Here we present CatchAll, a new, platform-independent, user-friendly, computationally optimized software package which calculates a powerful and flexible suite of parametric models (based on current statistical research) in addition to all existing nonparametric procedures. We briefly describe the software and its mathematical underpinnings (which are treated in depth elsewhere), and we work through an applied example from microbial ecology in detail.