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African Americans and other ethnic minorities are severely underrepresented in both graduate education and among the professoriate in ecology and evolutionary biology (EEB). In the present research, we take a social psychological approach to studying inclusion by examining interrelationships among challenges to inclusion, the sense of belonging, and interest in pursuing graduate education in EEB. We conducted a survey of African American (N = 360), Latino/a/Hispanic (N = 313), White (N = 709), and Asian/Asian American (N = 524) college undergraduates majoring in science, technology, engineering, and math fields and used the results to test several interrelated hypotheses derived from our theoretical model. Compared to Whites, ethnic minorities were more likely to experience challenges to inclusion in EEB (e.g., less exposure to ecology, fewer same-race role models, discomfort in outdoor environments). Challenges to inclusion were associated with a decreased sense of belonging in EEB educational contexts. Finally, experiencing a low sense of belonging in EEB educational contexts was associated with lower interest in pursuing graduate education in EEB. Sense of belonging in EEB was especially low among African Americans relative to Whites. We discuss the implications of the study results for educational interventions.

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. Index Terms--Outlier detection, novelty detection, anomaly detection, statistical depth function, spatial depth, kernel method, unsupervised learning.

Species classification is an important task which is the foundation of industrial, commercial, ecological and scientific applications involving the study of species distributions, dynamics and evolution.While conventional approaches for this task use off‐the‐shelf machine learning (ML) methods such as existing Convolutional Neural Network (ConvNet) architectures, there is an opportunity to inform the ConvNet architecture using our knowledge of biological hierarchies among taxonomic classes.In this work, we propose a new approach for species classification termed hierarchy‐guided neural network (HGNN), which infuses hierarchical taxonomic information into the neural network's training to guide the structure and relationships among the extracted features. We perform extensive experiments on an illustrative use‐case of classifying fish species to demonstrate that HGNN outperforms conventional ConvNet models in terms of classification accuracy, especially under scarce training data conditions.We also observe that HGNN shows better resilience to adversarial occlusions, when some of the most informative patch regions of the image are intentionally blocked and their effect on classification accuracy is studied. [ABSTRACT FROM AUTHOR]