A vision for incorporating human mobility in the study of human-wildlife interactions

As human activities increasingly shape land- and seascapes, understanding human-wildlife interactions is imperative for preserving biodiversity. Habitats are impacted not only by static modifications, such as roads, buildings and other infrastructure, but also by the dynamic movement of people and their vehicles occurring over shorter time scales. While there is increasing realization that both components of human activity significantly affect wildlife, capturing more dynamic processes in ecological studies has proved challenging. Here, we propose a novel conceptual framework for developing a ‘Dynamic Human Footprint’ that explicitly incorporates human mobility, providing a key link between anthropogenic stressors and ecological impacts across spatiotemporal scales. Specifically, the Dynamic Human Footprint integrates a range of metrics to fully acknowledge the time-varying nature of human activities and to enable scale-appropriate assessments of their impacts on wildlife behavior, demography, and distributions. We review existing terrestrial and marine human mobility data products and provide a roadmap for how these could be integrated and extended to enable more comprehensive analyses of human impacts on biodiversity in the Anthropocene

Influence of socioeconomic factors on pregnancy outcome in women with structural heart disease

OBJECTIVE: Cardiac disease is the leading cause of indirect maternal mortality. The aim of this study was to analyse to what extent socioeconomic factors influence the outcome of pregnancy in women with heart disease.  METHODS: The Registry of Pregnancy and Cardiac disease is a global prospective registry. For this analysis, countries that enrolled ≥10 patients were included. A combined cardiac endpoint included maternal cardiac death, arrhythmia requiring treatment, heart failure, thromboembolic event, aortic dissection, endocarditis, acute coronary syndrome, hospitalisation for cardiac reason or intervention. Associations between patient characteristics, country characteristics (income inequality expressed as Gini coefficient, health expenditure, schooling, gross domestic product, birth rate and hospital beds) and cardiac endpoints were checked in a three-level model (patient-centre-country).  RESULTS: A total of 30 countries enrolled 2924 patients from 89 centres. At least one endpoint occurred in 645 women (22.1%). Maternal age, New York Heart Association classification and modified WHO risk classification were associated with the combined endpoint and explained 37% of variance in outcome. Gini coefficient and country-specific birth rate explained an additional 4%. There were large differences between the individual countries, but the need for multilevel modelling to account for these differences disappeared after adjustment for patient characteristics, Gini and country-specific birth rate.  CONCLUSION: While there are definite interregional differences in pregnancy outcome in women with cardiac disease, these differences seem to be mainly driven by individual patient characteristics. Adjustment for country characteristics refined the results to a limited extent, but maternal condition seems to be the main determinant of outcome

Biological Earth observation with animal sensors

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmen-tal change

Endogeneity and Marketing Strategy Research: An Overview

Endogeneity in empirical marketing research is an increasingly discussed topic in academic research. Mentions of endogeneity and related procedures to correct for it have risen 5x across the field’s top journals in the past 20 years, but represent an overall small portion of extant research. Yet there is often substantial difficulty in reconciling issues of endogeneity with many of the substantive questions of interest to marketing strategy for both theoretical and/or practical reasons. This paper provides an overview of main causes of endogeneity, approaches to addressing it, and guidance to marketing strategy researchers to balance these issues as the field continues to move towards more methodological sophistication, potentially at the expense of managerial tractability

The Evolution of Internal Market Structure

We present a dynamic factor-analytic choice model to capture evolution of brand positions in latent attribute space. Our dynamic model allows researchers to investigate brand positioning in new categories or mature categories affected by structural change such as entry. We argue that even for mature categories not affected by structural change, the assumption of stable attributes may be untenable. We allow for evolution in attributes by modeling individual-level time-specific attributes as arising from dynamic means. The dynamic attribute means are modeled as a Bayesian dynamic linear model (DLM). The DLM is nested within a factor-analytic choice model. Our approach makes efficient use of the data by leveraging estimates from previous and future periods to estimate current period attributes. We demonstrate the robustness of our model with data that simulate a variety of dynamic scenarios, including stationary behavior. We show that misspecified attribute dynamics induce temporal heteroskedasticty and correlation between the preference weights and the error term. Applying the model to a panel data set on household purchases in the malt beverage category, we find considerable evidence for dynamics in the latent brand attributes. From a managerial perspective, we find advertising expenditures help explain variation in the dynamic attribute means.choice modeling, Bayesian estimation, dynamic models, factor-analytic models

A new method to aid copy testing of paid search text advertisements

The authors propose a new approach to evaluate the perceptions and performance of a large set of paid search ads. This approach consists of two parts. First, primary data on hundreds of ads are collected through paired comparisons of their relative ability to generate awareness, interest, desire, action, and click performance. The authors use the Elo algorithm, a statistical model calibrated on paired comparisons, to score the full set of ads on relative perceptions and click performance. The estimated scores validate the theoretical link between perceptions and performance. Second, the authors predict the perceptions and performance of new ads relative to the existing set using textual content metrics. The predictive model allows for direct effects and interactions of the text metrics, resulting in a “large p, small n” problem. They address this problem with a novel Bayesian implementation of the VANISH model, a penalized regression approach that allows for differential treatment of main and interaction effects, in a system of equations. The authors demonstrate that this approach ably forecasts relative ad performance by leveraging perceptions inferred from content alone