The Southeastern Fishes Council Technical Advisory Committee reviewed the diversity, distribution, and status of all native freshwater and diadromous fishes across 51 major drainage units of the southern United States. The southern United States supports more native fishes than any area of comparable size on the North American continent north of Mexico, but also has a high proportion of its fishes in need of conservation action. The review included 662 native freshwater and diadromous fishes and 24 marine fishes that are significant components of freshwater ecosystems. Of this total, 560 described, freshwater fish species are documented, and 49 undescribed species are included provisionally pending formal description. Described subspecies (86) are recognized within 43 species, 6 fishes have undescribed subspecies, and 9 others are recognized as complexes of undescribed taxa. Extinct, endangered, threatened, or vulnerable status is recognized for 28% (187 taxa) of southern freshwater and diadromou...

Statistical depth functions provide from the deepest point a center-outward ordering of multidimensional data. In this sense, depth functions can measure the extremeness or outlyingness of a data point with respect to a given data set. Hence, they can detect outliers observations that appear extreme relative to the rest of the observations. Of the various statistical depths, the spatial depth is especially appealing because of its computational efficiency and mathematical tractability. In this article, we propose a novel statistical depth, the kernelized spatial depth (KSD), which generalizes the spatial depth via positive definite kernels. By choosing a proper kernel, the KSD can capture the local structure of a data set while the spatial depth fails. We demonstrate this by the half-moon data and the ring-shaped data. Based on the KSD, we propose a novel outlier detection algorithm, by which an observation with a depth value less than a threshold is declared as an outlier. The proposed algorithm is simple in structure: the threshold is the only one parameter for a given kernel. It applies to a one-class learning setting, in which normal observations are given as the training data, as well as to a missing label scenario, where the training set consists of a mixture of normal observations and outliers with unknown labels. We give upper bounds on the false alarm probability of a depth-based detector. These upper bounds can be used to determine the threshold. We perform extensive experiments on synthetic data and data sets from real applications. The proposed outlier detector is compared with existing methods. The KSD outlier detector demonstrates a competitive performance.