In central Argentina there is intense conflict between productive livestock activities and wild carnivores, particularly the puma Puma concolor. Livestock guarding dogs are one of the most effective non-lethal tools for reducing predation on livestock, and their use has increased globally, and in this area recently. Using 5 years (2018–2022) of camera-trap data, we analysed the daily activity patterns of pumas and a guarding dog, identified the major factors affecting puma habitat use, and examined puma–dog interactions in a human-dominated landscape in central Argentina. In a total sampling effort of 23,738 trap-days, we recorded 212 events of pumas and 166 of the dog. Pumas had a nocturnal activity pattern, whereas the guarding dog was mostly diurnal, with activity peaks in the early morning and afternoon. The intensity of habitat use by pumas increased with the proportion of scrubland cover and the distance from points where human activity was the greatest but was not affected by the presence of the dog. We found that the interactions between pumas and the guarding dog were infrequent and limited in space, and some evidence that pumas may avoid the presence of the dog. This exploration of puma–livestock guarding dog interactions contributes to knowledge of how guarding dogs work as a non-lethal measure to mitigate human–carnivore conflicts and what their effects are on wildlife in a modified rural landscape where ranching is the main economic activity.

Double-zero-event studies (DZS) pose a challenge for accurately estimating the overall treatment effect in meta-analysis (MA). Current approaches, such as continuity correction or omission of DZS, are commonly employed, yet these ad hoc methods can yield biased conclusions. Although the standard bivariate generalized linear mixed model (BGLMM) can accommodate DZS, it fails to address the potential systemic differences between DZS and other studies. In this article, we propose a zero-inflated bivariate generalized linear mixed model (ZIBGLMM) to tackle this issue. This two-component finite mixture model includes zero inflation for a subpopulation with negligible or extremely low risk. We develop both frequentist and Bayesian versions of ZIBGLMM and examine its performance in estimating risk ratios against the BGLMM and conventional two-stage MA that excludes DZS. Through extensive simulation studies and real-world MA case studies, we demonstrate that ZIBGLMM outperforms the BGLMM and conventional two-stage MA that excludes DZS in estimating the true effect size with substantially less bias and comparable coverage probability.

When word learning is supported by instruction in experimental studies for adolescents, word knowledge outcomes tend to be collected from complex data structure, such as multiple aspects of word knowledge, multilevel reader data, multilevel item data, longitudinal design, and multiple groups. This study illustrates how generalized linear mixed models can be used to measure and explain word learning for data having such complexity. Results from this application provide deeper understanding of word knowledge than could be attained from simpler models and show that word knowledge is multidimensional and depends on word characteristics and instructional contexts.

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.

The presence of unobserved node-specific heterogeneity in exponential random graph models (ERGM) is a general concern, both with respect to model validity as well as estimation instability. We, therefore, include node-specific random effects in the ERGM that account for unobserved heterogeneity in the network. This leads to a mixed model with parametric as well as random coefficients, labelled as mixed ERGM. Estimation is carried out by iterating between approximate pseudolikelihood estimation for the random effects and maximum likelihood estimation for the remaining parameters in the model. This approach provides a stable algorithm, which allows to fit nodal heterogeneity effects even for large scale networks. We also propose model selection based on the Akaike Information Criterion to check for node-specific heterogeneity.

While logistic regression models are easily accessible to researchers, when applied to network data there are unrealistic assumptions made about the dependence structure of the data. For temporal networks measured in discrete time, recent work has made good advances (Almquist & Butts, 2014), but there is still the assumption that the dyads are conditionally independent given the edge histories. This assumption can be quite strong and is sometimes difficult to justify. If time steps are rather large, one would typically expect not only the existence of temporal dependencies among the dyads across observed time points but also the existence of simultaneous dependencies affecting how the dyads of the network co-evolve. We propose a general observation-driven model for dynamic networks that overcomes this problem by modeling both the mean and the covariance structures as functions of the edge histories using a flexible autoregressive approach. This approach can be shown to fit into a generalized linear mixed model framework. We propose a visualization method that provides evidence concerning the existence of simultaneous dependence. We describe a simulation study to determine the method's performance in the presence and absence of simultaneous dependence, and we analyze both a proximity network from conference attendees and a world trade network. We also use this last data set to illustrate how simultaneous dependencies become more prominent as the time intervals become coarser.

Statistical analysis is increasingly used in seed germination/viability studies across different disciplines. The objective of this opinion piece is to assess current trends in statistical analysis of such data, and draw attention of readers to the limitations of the usual inferential statistics in controlling error rates. The assessments are based on a survey of 429 papers published in 139 peer-reviewed journals in the past 11 years. My intention is to identify areas of concern across a wide range of studies. Accordingly, the areas of greatest concern found in the analysis of percentage seed germination and viability data were: (1) pseudoreplication and/or use of a few replicates; (2) ignoring assumptions of ANOVA and non-parametric tests (NPARTs); (3) uncritical data transformation; (4) arbitrary choice of multiple comparison tests; and (5) lack of emphasis on effect sizes. Given the prevalence of these problems, in my opinion we would be building a body of knowledge on a shaky ground. The discussions that follow will: (1) describe situations where germination data violate assumptions of ANOVA and NPARTs; (2) highlight the implications of the various problems to Type I and Type II error rates; and (3) indicate remedial measures based on the recent statistical literature.