One of the most basic but problematic issues in modern morphometrics is how many specimens one needs to achieve accuracy in samples. Indeed, this is one of the most regularly posed questions in introductory courses. There is no simple and certainly no absolute answer to this question. However, there are a number of techniques for exploring the effect of sampling, and our aim is to provide an example of how this might function in a simplified but informative way. Thus, using resampling methods and sensitivity analyses based on randomized subsamples, we assessed sampling error in horse teeth from several modern and fossil populations. Centroid size and shape of an upper premolar (PM2) were captured using Procrustes geometric morphometrics. Means and variances (using three different statistics for shape variance) were estimated, as well as their confidence intervals. Also, the largest population sample was randomly split into progressively smaller subsamples to assess how reducing sample size affects statistical parameters. Results indicate that mean centroid size is highly accurate; even when sample size is small, errors are generally considerably smaller than differences among populations. In contrast, mean shape estimation requires large samples of tens of specimens (ca. >20), although this requirement may be less stringent when variance in a population is very small (e.g. populations that underwent strong genetic bottlenecks). Variance in either centroid size or shape can be highly inaccurate in small samples, to the point that sampling error makes it as variable as differences among spatially and chronologically well-separated populations, including two which are highly distinctive as a consequence of strong artificial selection. Likely, centroid size and shape variance require no <15–20 specimens to achieve a reasonable degree of accuracy. Results from the simplified sensitivity analysis were largely congruent with the pattern suggested by bootstrapped confidence intervals, as well as with the observations of a previous study on African monkeys. The analyses we performed, especially the sensitivity assessment, are simple and do not require much time or computational effort; however, they do necessitate that at least one sample is large (50 or more specimens). If this type of analyses became more common in geometric morphometrics, it could provide an effective tool for the preliminarily exploration of the effect of sampling on results and therefore assist in assessing their robustness. Finally, as the use of sensitivity studies increases, the present case could form part of a set of examples that allow us to better understand and estimate what a desirable sample size might be, depending on the scientific question, type of data and taxonomic level under investigation

Reducing the burden of neglected tropical diseases (NTDs) is one of the key strategic targets advanced by the Sustainable Development Goals. Despite the unprecedented effort deployed for NTD elimination in the past decade, their control, mainly through drug administration, remains particularly challenging: persistent poverty and repeated exposure to pathogens embedded in the environment limit the efficacy of strategies focused exclusively on human treatment or medical care. Here, we present a simple modelling framework to illustrate the relative role of ecological and socio-economic drivers of environmentally transmitted parasites and pathogens. Through the analysis of system dynamics, we show that periodic drug treatments that lead to the elimination of directly transmitted diseases may fail to do so in the case of human pathogens with an environmental reservoir. Control of environmentally transmitted diseases can be more effective when human treatment is complemented with interventions targeting the environmental reservoir of the pathogen. We present mechanisms through which the environment can influence the dynamics of poverty via disease feedbacks. For illustration, we present the case studies of Buruli ulcer and schistosomiasis, two devastating waterborne NTDs for which control is particularly challenging.This article is part of the themed issue 'Conservation, biodiversity and infectious disease: scientific evidence and policy implications'.

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The cemetery of Le Morne in Mauritius dates from the 1830s and is thought to contain the remains of slaves, freed slaves or potentially free Madagascans, which in itself has economic and social implications and makes the cemetery all the more intriguing. During 2010, excavations recovered the remains of 11 individuals, of which six were children. Although a small sample, the burials of the non‐adults show several interesting features. Two neonates were buried contemporaneously and may have been twins, while a late term foetus in a grave with a young woman may represent an incident of coffin birth. This sample has the potential to cast light on the burial practices of the slave/ex‐slave community in Mauritius, about which little is currently known. Copyright © 2012 John Wiley & Sons, Ltd.

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Modelling of emerging vector borne diseases serves as an important complement to clinical studies of modern zoonoses. This article presents an archaeo‐historic epidemiological modelling study of Rift Valley fever (RVF), using data‐driven neural network technology. RVF affects both human and animal populations, can rapidly decimate herds causing catastrophic economic hardship, and is identified as a Category A biodefense pathogen by the US Center for Disease Control. Despite recent origins circa the early 1900s, little is known about the circumstances of its inception nor the relationships between factors that affect transmission. This evidence could be vital as the disease continues to expand from its epicentre in Kenya to other parts of Africa and the Arabian Peninsula. RVF is a relevant case for archaeological/palaeopathological investigations of disease as it intersects between numerous human, animal, spatial, temporal, and sociopolitical dimensions. By integrating landscape archaeology, historical evidence, and climatic data, with evidence of human behaviour gathered through ethnoarchaeological study, this article presents an applied framework for human–animal palaeopathology. This framework aligns with the One Health approach that observes disease to be intrinsically tied to ecological and societal factors. We provide a useable alternative way of thinking about disease modelling in the present and the past, ultimately seeking to support efforts to accurately predict future impacts. Tapping into longitudinal evidence from the last 50–300 years offers a powerful way to respond to the threat zoonoses will pose to human populations around the world as the climate warms.