This study undertook the first investigation of malacosporean infections in Neotropical fish. We used polymerase chain reaction detection with a primer set generally targeting known malacosporeans to assay for infection in the kidney of 146 fish in 21 species belonging to 12 families collected from two areas in the Amazon Basin. Infections were found in 13 fish variously belonging to seven species in six families and included the first identification of a malacosporean infection in cartilaginous fish (a freshwater stingray). Based on ssrDNA, all infections represented a single Buddenbrockia species (Buddenbrockia sp. E) that demonstrates an exceptionally broad range of fish species infected, and countered our expectations of high Neotropical malacosporean diversity. Infections were characterized at varying and often high prevalences in fish species but sample sizes were small. Ascertaining whether highly divergent malacosporeans have not been detected by current primers, and more comprehensive sampling may reveal whether malacosporeans are truly as species poor in the Amazon Basin as present data suggest. Our results prompt speculations about evolutionary scenarios including introduction via marine incursions and patterns of host use over time.

In testimony before US Congress on March 11, 2020, members of the House Oversight and Reform Committee were informed that estimated mortality for the novel coronavirus was 10-times higher than for seasonal influenza. Additional evidence, however, suggests the validity of this estimation could benefit from vetting for biases and miscalculations. The main objective of this article is to critically appraise the coronavirus mortality estimation presented to Congress. Informational texts from the World Health Organization and the Centers for Disease Control and Prevention are compared with coronavirus mortality calculations in Congressional testimony. Results of this critical appraisal reveal information bias and selection bias in coronavirus mortality overestimation, most likely caused by misclassifying an influenza infection fatality rate as a case fatality rate. Public health lessons learned for future infectious disease pandemics include: safeguarding against research biases that may underestimate or overestimate an associated risk of disease and mortality; reassessing the ethics of fear-based public health campaigns; and providing full public disclosure of adverse effects from severe mitigation measures to contain viral transmission.

Researchers interested in describing or understanding agroecological systems have many reasons to consider on-farm research. Yet, despite the inherent realism and pedagogical value of on-farm studies, recruiting cooperators can be difficult and this difficulty can result in so-called “convenience samples” containing a potentially large and unknown bias. There is often no formal justification for claiming that on-farm research results can be extrapolated to farms beyond those participating in the study. In some sufficiently well-understood research areas, models may be able to correct for potential bias; however, no theoretical argument is as persuasive as a direct comparison between a randomized and a convenience sample. In a 30-cooperator on-farm study investigating weed community dynamics across the state of Wisconsin, we distributed a written survey probing farmer weed management behaviors and attitudes. The survey contained 59 questions that overlapped a large, randomized survey of farmer corn pest management behavior. We compared 187 respondents from the larger survey with the 18 respondents from our on-farm study. For dichotomous response questions, we found no difference in response rate for 80% of the questions (α = 0.2, β > 0.5). Differences between the two groups were logically connected to the selection criteria used to recruit cooperators in the on-farm study. Similarly, comparisons of nondichotomous response questions did not differ for 80% of the questions (α = 0.05, β > 0.9). Exploratory multivariate analyses failed to reveal differences that might have been hidden from the marginal analyses. We argue that our findings support the notion that the convenience samples often associated with on-farm research may be representative of the more general class of farms, despite lack of bias protection provided by truly randomized designs.