Explore the vast range of titles available.

Measuring species-specific competitive interactions is key to understanding plant communities. Repeat censused large forest dynamics plots offer an ideal setting to measure these interactions by estimating the species-specific competitive effect on neighboring tree growth. Estimating these interaction values can be difficult, however, because the number of them grows with the square of the number of species. Furthermore, confidence in the estimates can be overestimated if any spatial structure of model errors is not considered. Here we measured these interactions in a forest dynamics plot in a transitional oak-hickory forest. We analytically fit Bayesian linear regression models of annual tree radial growth as a function of that tree's species, its size, and its neighboring trees. We then compared these models to test whether the identity of a tree's neighbors matters and if so at what level: based on trait grouping, based on phylogenetic family, or based on species. We used a spatial cross-validation scheme to better estimate model errors while avoiding potentially over-fitting our models. Since our model is analytically solvable we can rapidly evaluate it, which allows our proposed cross-validation scheme to be computationally feasible. We found that the identity of the focal and competitor trees mattered for competitive interactions, but surprisingly, identity mattered at the family rather than species-level.

We provide a computational exercise suitable for early introduction in an undergraduate statistics or data science course that allows students to \"play the whole game\" of data science: performing both data collection and data analysis. While many teaching resources exist for data analysis, such resources are not as abundant for data collection given the inherent difficulty of the task. Our proposed exercise centers around student use of Google Calendar to collect data with the goal of answering the question \"How do I spend my time?\" On the one hand, the exercise involves answering a question with near universal appeal, but on the other hand, the data collection mechanism is not beyond the reach of a typical undergraduate student. A further benefit of the exercise is that it provides an opportunity for discussions on ethical questions and considerations that data providers and data analysts face in today's age of large-scale internet-based data collection.

Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking. We present the forestecology package providing methods to (a) specify neighborhood competition models, (b) evaluate the effect of competitor species identity using permutation tests, and (cs) measure model performance using spatial cross‐validation. Following Allen and Kim (PLoS One, 15, 2020, e0229930), we implement a Bayesian linear regression neighborhood competition model. We demonstrate the package's functionality using data from the Smithsonian Conservation Biology Institute's large forest dynamics plot, part of the ForestGEO global network of research sites. Given ForestGEO’s data collection protocols and data formatting standards, the package was designed with cross‐site compatibility in mind. We highlight the importance of spatial cross‐validation when interpreting model results. The package features (a) tidyverse‐like structure whereby verb‐named functions can be modularly “piped” in sequence, (b) functions with standardized inputs/outputs of simple features sf package class, and (c) an S3 object‐oriented implementation of the Bayesian linear regression model. These three facts allow for clear articulation of all the steps in the sequence of analysis and easy wrangling and visualization of the geospatial data. Furthermore, while the package only has Bayesian linear regression implemented, the package was designed with extensibility to other methods in mind. Schematic of spatial cross‐validation. Using the k = 1 fold (bottom‐left) as the test set, k = 2 through 4 as the training set, along with a “fold buffer” extending outward from the test set to maintain spatial independence between it and the training set.

We present a programmatic approach to incorporating ethics into an undergraduate major in statistical and data sciences. We discuss departmental-level initiatives designed to meet the National Academy of Sciences recommendation for integrating ethics into the curriculum from top-to-bottom as our majors progress from our introductory courses to our senior capstone course, as well as from side-to-side through co-curricular programming. We also provide six examples of data science ethics modules used in five different courses at our liberal arts college, each focusing on a different ethical consideration. The modules are designed to be portable such that they can be flexibly incorporated into existing courses at different levels of instruction with minimal disruption to syllabi. We connect our efforts to a growing body of literature on the teaching of data science ethics, present assessments of our effectiveness, and conclude with next steps and final thoughts.

As the climate changes, warmer spring temperatures are causing earlier leaf-out 1 – 3 and commencement of CO 2 uptake 1 , 3 in temperate deciduous forests, resulting in a tendency towards increased growing season length 3 and annual CO 2 uptake 1 , 3 – 7 . However, less is known about how spring temperatures affect tree stem growth 8 , 9 , which sequesters carbon in wood that has a long residence time in the ecosystem 10 , 11 . Here we show that warmer spring temperatures shifted stem diameter growth of deciduous trees earlier but had no consistent effect on peak growing season length, maximum growth rates, or annual growth, using dendrometer band measurements from 440 trees across two forests. The latter finding was confirmed on the centennial scale by 207 tree-ring chronologies from 108 forests across eastern North America, where annual ring width was far more sensitive to temperatures during the peak growing season than in the spring. These findings imply that any extra CO 2 uptake in years with warmer spring temperatures 4 , 5 does not significantly contribute to increased sequestration in long-lived woody stem biomass. Rather, contradicting projections from global carbon cycle models 1 , 12 , our empirical results imply that warming spring temperatures are unlikely to increase woody productivity enough to strengthen the long-term CO 2 sink of temperate deciduous forests. Warmer spring temperatures affect the timing of stem diameter growth of temperate deciduous trees but have little effect on annual growth.

Cluster detection is an important public health endeavor, and in this article, we describe and apply a recently developed Bayesian method. Commonly used approaches are based on so-called scan statistics and suffer from a number of difficulties, which include how to choose a level of significance and how to deal with the possibility of multiple clusters. The basis of our model is to partition the study region into a set of areas that are either “null” or “non-null,” the latter corresponding to clusters (excess risk) or anticlusters (reduced risk). We demonstrate the Bayesian method and compare with a popular existing approach, using data on breast, brain, lung, prostate, and colorectal cancer, in the Puget Sound region of Washington State.

We present a data set consisting of user profile data for 59,946 San Francisco OkCupid users (a free online dating website) from a period in the 2010s. The data set includes typical user information, lifestyle variables, and text responses to 10 essays questions. We present four example analyses suitable for use in undergraduate introductory probability and statistics and data science courses that use R. The statistical and data science concepts covered include basic data visualization, exploratory data analysis, multivariate relationships, text analysis, and logistic regression for prediction.

As judicial caseloads have risen, arbitration and other forms of alternative dispute resolutions have become popular, especially as methods for the resolution of employer-employee disputes. In an attempt to lessen litigation expenses and speed up the resolution of disputes, employers and employees have moved away from litigation and toward predispute mandatory arbitration agreements. However, despite the wide-ranging use of binding commercial arbitration, federal courts traditionally refused to enforce agreements mandating the arbitration of statutory employment discrimination claims. An article discusses the important distinctions between labor arbitration through collective bargaining agreements and individual employment arbitration. It concludes that collective interest problems within the labor arbitration process render the current system unsuitable for final and binding resolution of statutory discrimination disputes. A 2-track labor arbitration mechanism under which individual union members would have greater freedom to control statutory discrimination claims is proposed.

\"O where are the sympathies of Christians for the slave and where are their exertions for their liberation? . . . It seems as if the church were asleep.\" David Ingraham, 1839 In 2015, the historian Chris Momany helped discover a manuscript that had been forgotten in a storage closet at Adrian College in Michigan. He identified it as the journal of a nineteenth-century Christian abolitionist and missionary, David Ingraham. As Momany and a fellow historian Doug Strong pored over the diary, they realized that studying this document could open new conversations for twenty-first-century Christians to address the reality of racism today. They invited a multiracial team of fourteen scholars to join in, thus launching the Dialogue on Race and Faith Project. Awakening to Justice presents the groundbreaking work of these scholars. In addition to reflecting on Ingraham's journal, chapters also explore the life and writings of two of Ingraham's Black colleagues, James Bradley and Nancy Prince. Appendixes feature writings by all three abolitionists so readers can engage the primary sources directly. Through considering connections between the revivalist, holiness, and abolitionist movements; the experiences of enslaved and freed people; abolitionists' spiritual practices; various tactics used by abolitionists; and other themes, the authors offer insight and hope for Christians concerned about racial justice. They highlight how Christians associated with Charles Finney's style of revivalism formed intentional, countercultural communities such as Oberlin College to be exemplars of interracial cooperation and equality. Christians have all too often compromised with racism throughout history, but that's not the whole story. Hearing the prophetic witness of revivalist social justice efforts in the nineteenth century can provide a fresh approach to today's conversations about race and faith in the church.

We provide a computational exercise suitable for early introduction in an undergraduate statistics or data science course that allows students to 'play the whole game' of data science: performing both data collection and data analysis. While many teaching resources exist for data analysis, such resources are not as abundant for data collection given the inherent difficulty of the task. Our proposed exercise centers around student use of Google Calendar to collect data with the goal of answering the question 'How do I spend my time?' On the one hand, the exercise involves answering a question with near universal appeal, but on the other hand, the data collection mechanism is not beyond the reach of a typical undergraduate student. A further benefit of the exercise is that it provides an opportunity for discussions on ethical questions and considerations that data providers and data analysts face in today's age of large-scale internet-based data collection.