Poisson point process models solve the "pseudo-absence problem" for presence-only data in ecology

Presence-only data, point locations where a species has been recorded as being present, are often used in modeling the distribution of a species as a function of a set of explanatory variables---whether to map species occurrence, to understand its association with the environment, or to predict its response to environmental change. Currently, ecologists most commonly analyze presence-only data by adding randomly chosen "pseudo-absences" to the data such that it can be analyzed using logistic regression, an approach which has weaknesses in model specification, in interpretation, and in implementation. To address these issues, we propose Poisson point process modeling of the intensity of presences. We also derive a link between the proposed approach and logistic regression---specifically, we show that as the number of pseudo-absences increases (in a regular or uniform random arrangement), logistic regression slope parameters and their standard errors converge to those of the corresponding Poisson point process model. We discuss the practical implications of these results. In particular, point process modeling offers a framework for choice of the number and location of pseudo-absences, both of which are currently chosen by ad hoc and sometimes ineffective methods in ecology, a point which we illustrate by example.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS331 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A general algorithm for covariance modeling of discrete data

We propose an algorithm that generalizes to discrete data any given covariance modeling algorithm originally intended for Gaussian responses, via a Gaussian copula approach. Covariance modeling is a powerful tool for extracting meaning from multivariate data, and fast algorithms for Gaussian data, such as factor analysis and Gaussian graphical models, are widely available. Our algorithm makes these tools generally available to analysts of discrete data and can combine any likelihood-based covariance modeling method for Gaussian data with any set of discrete marginal distributions. Previously, tools for discrete data were generally specific to one family of distributions or covariance modeling paradigm, or otherwise did not exist. Our algorithm is more flexible than alternate methods, takes advantage of existing fast algorithms for Gaussian data, and simulations suggest that it outperforms competing graphical modeling and factor analysis procedures for count and binomial data. We additionally show that in a Gaussian copula graphical model with discrete margins, conditional independence relationships in the latent Gaussian variables are inherited by the discrete observations. Our method is illustrated with a graphical model and factor analysis on an overdispersed ecological count dataset of species abundances

Obesity and the food environment: income and ethnicity differences among people with diabetes: the Diabetes Study of Northern California (DISTANCE).

ObjectiveIt is unknown whether any association between neighborhood food environment and obesity varies according to individual income and/or race/ethnicity. The objectives of this study were to test whether there was an association between food environments and obesity among adults with diabetes and whether this relationship differed according to individual income or race/ethnicity.Research design and methodsSubjects (n = 16,057) were participants in the Diabetes Study of Northern California survey. Kernel density estimation was used to create a food environment score for each individual's residence address that reflected the mix of healthful and unhealthful food vendors nearby. Logistic regression models estimated the association between the modeled food environment and obesity, controlling for confounders, and testing for interactions between food environment and race/ethnicity and income.ResultsThe authors found that more healthful food environments were associated with lower obesity in the highest income groups (incomes 301-600% and >600% of U.S. poverty line) among whites, Latinos, and Asians. The association was negative, but smaller and not statistically significant, among high-income blacks. On the contrary, a more healthful food environment was associated with higher obesity among participants in the lowest-income group (<100% poverty threshold), which was statistically significant for black participants in this income category.ConclusionsThese findings suggest that the availability of healthful food environments may have different health implications when financial resources are severely constrained

Characteristics of aquatic rescues undertaken by bystanders in Australia

An issue of growing importance within the field of drowning prevention is the undertaking of aquatic rescues by bystanders, who sometimes drown in the process. The main objectives of this study were to describe characteristics of bystanders making rescues in different Australian aquatic environments, identify the role of prior water safety training in conducting bystander rescues and provide insights into future public education strategies relating to bystander rescue scenarios. An online survey was disseminated via various social media platforms in 2017 and gathered a total of 243 complete responses. The majority of bystander rescues described took place in coastal waterways (76.5%; n = 186), particularly beaches (n = 67), followed by pools (17.3%; n = 42) and inland waterways (6.2%; n = 15). The majority of respondents were males (64.2%; n = 156) who rescued on average approximately twice as many people in their lifetime (6.5) than female respondents (3.6). Most rescues occurred more than 1 km from lifeguard/lifesaver services (67%; n = 163), but in the presence of others (94.2%; n = 229). The majority of bystander rescuers had water safety training (65.8%; n = 160), self-rated as strong swimmers (68.3%; n = 166), conducted the rescue without help from others (60%; n = 146), did not use a flotation device to assist (63%; n = 153), but were confident in their ability to make the rescue (76.5%; n = 186). However, most considered the situation to be very serious (58%; n = 141) and felt they had saved a life (70.1%; n = 172). With the exception of pools, most bystanders rescued strangers (76.1%; n = 185).While Australia clearly benefits from having a strong water safety culture, there is no clear consensus on the most appropriate actions bystanders should take when confronted with a potential aquatic rescue scenario. In particular, more research is needed to gather information regarding bystander rescues undertaken by those without prior water safety training

A Heuristic Image Search Algorithm for Active Shape Model Segmentation of the Caudate Nucleus and Hippocampus in Brain MR Images of Children with FASD

Magnetic Resonance Imaging provides a non-invasive means to study the neural correlates of Fetal Alcohol Spectrum Disorder (FASD) - the most common form of preventable mental retardation worldwide. One approach aims to detect brain abnormalities through an assessment of volume and shape of two sub-cortical structures, the caudate nucleus and hippocampus. We present a method for automatically segmenting these structures from high-resolution MR images captured as part of an ongoing study into the neural correlates of FASD. Our method incorporates an Active Shape Model, which is used to learn shape variation from manually segmented training data. A modified discrete Geometrically Deformable Model is used to generate point correspondence between training models. An ASM is then created from the landmark points. Experiments were conducted on the image search phase of ASM segmentation, in order to find the technique best suited to segmentation of the hippocampus and caudate nucleus. Various popular image search techniques were tested, including an edge detection method and a method based on grey profile Mahalanobis distance measurement. A novel heuristic image search method was also developed and tested. This heuristic method improves image segmentation by taking advantage of characteristics specific to the target data, such as a relatively homogeneous tissue colour in target structures. Results show that ASMs that use the heuristic image search technique produce the most accurate segmentations. An ASM constructed using this technique will enable researchers to quickly, reliably, and automatically segment test data for use in the FASD study

Untangling direct species associations from indirect mediator species effects with graphical models

Ecologists often investigate co‐occurrence patterns in multi‐species data in order to gain insight into the ecological causes of observed co‐occurrences. Apart from direct associations between the two species of interest, they may co‐occur because of indirect effects, where both species respond to another variable, whether environmental or biotic (e.g. a mediator species). A wide variety of methods are now available for modelling how environmental filtering drives species distributions. In contrast, methods for studying other causes of co‐occurence are much more limited. “Graphical” methods, which can be used to study how mediator species impact co‐occurrence patterns, have recently been proposed for use in ecology. However, available methods are limited to presence/absence data or methods assuming multivariate normality, which is problematic when analysing abundances. We propose Gaussian copula graphical models (GCGMs) for studying the effect of mediator species on co‐occurence patterns. GCGMs are a flexible type of graphical model which naturally accommodates all data types, for example binary (presence/absence), counts, as well as ordinal data and biomass, in a unified framework. Simulations demonstrate that GCGMs can be applied to a much broader range of data types than the methods currently used in ecology, and perform as well as or better than existing methods in many settings. We apply GCGMs to counts of hunting spiders, in order to visualise associations between species. We also analyse abundance data of New Zealand native forest cover (on an ordinal scale) to show how GCGMs can be used analyse large and complex datasets. In these data, we were able to reproduce known species relationships as well as generate new ecological hypotheses about species associations.F.K.C.H. is supported by an ANU cross‐disciplinary research grant. D.I.W. was supported by an Australian Research Council Future Fellowship (FT120100501). G.C.P. was supported by the Australia Postgraduate Award and ARC Discovery Project scheme (DP180103543). A.T.M. is supported by an Australia Research Council Discovery Grant (DP180100836). F.J.T. is supported from the Marsden Fast‐Start Fund and the Royal Society of New Zealand

Non-work-related services at the workplace : an exploratory study

In an era of diminishing public funds, the profession of social work is looking more and more toward the private sector as an arena for social work practice. Social work has had a long-standing interest in the impact of work and the workplace on the individual. This study was developed in response to the lack of documentation of non-work-related services in Oregon\u27s businesses and industries. The research team set out to discover what non-work-related services are available to employees at or through the workplace in the TriCounty area (Multnomah, Clackamas, and Washington Counties) of Oregon. This study was exploratory, similar to one done by Hans Spiegel and colleagues in 1974, through Hunter College in New York City