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Adverse experiences in early life are associated with aging-related disease risk and mortality across many species. In humans, confounding factors, as well as the difficulty of directly measuring experiences and outcomes from birth till death, make it challenging to identify how early life adversity impacts aging and health. These challenges can be mitigated, in part, through the study of non-human animals, which are exposed to parallel forms of adversity and can age similarly to humans. Furthermore, studying the links between early life adversity and aging in natural populations of non-human animals provides an excellent opportunity to better understand the social and ecological pressures that shaped the evolution of early life sensitivities. Here, we highlight ongoing and future research directions that we believe will most effectively contribute to our understanding of the evolution of early life sensitivities and their repercussions.

 

Identifying biomarkers of age-related changes in immune system functioning that can be measured non-invasively is a significant step in progressing research on immunosenescence and inflammaging in free-ranging and wild animal populations. In the present study, we aimed to investigate the suitability of two urinary compounds, neopterin and suPAR, as biomarkers of age-related changes in immune activation and inflammation in a free-ranging rhesus macaque ( Macaca mulatta ) population. We also investigated age-associated variation in gene transcription from blood samples to understand the underlying proximate mechanisms that drive age-related changes in urinary neopterin or suPAR. Neopterin was significantly positively correlated with age, and had a moderate within-individual repeatability, indicating it is applicable as a biomarker of age-related changes. The age-related changes in urinary neopterin are not apparently driven by an age-related increase in the primary signaler of neopterin, IFN-y, but may be driven instead by an age-related increase in both CD14+ and CD14− monocytes. suPAR was not correlated with age, and had low repeatability within-individuals, indicating that it is likely better suited to measure acute inflammation rather than chronic age-related increases in inflammation (i.e., “inflammaging”). Neopterin and suPAR had a correlation of 25%, indicating that they likely often signal different processes, which if disentangled could provide a nuanced picture of immune-system function and inflammation when measured in tandem. 

When group-living animals develop individualized social relationships, they often regulate cooperation and conflict through a dominance hierarchy. Female common vampire bats have been an experimental system for studying cooperative relationships, yet surprisingly little is known about female conflict. Here, we recorded the outcomes of 1023 competitive interactions over food provided ad libitum in a captive colony of 33 vampire bats (24 adult females and their young). We found a weakly linear dominance hierarchy using three common metrics (Landau's h ’ measure of linearity, triangle transitivity and directional consistency). However, patterns of female dominance were less structured than in many other group-living mammals. Female social rank was not clearly predicted by body size, age, nor reproductive status, and competitive interactions were not correlated with kinship, grooming nor food sharing. We therefore found no evidence that females groomed or shared food up a hierarchy or that differences in rank explained asymmetries in grooming or food sharing. A possible explanation for such apparently egalitarian relationships among female vampire bats is the scale of competition. Female vampire bats that are frequent roostmates might not often directly compete for food in the wild. 

Brain structure is predicted by social network size but not other key social attributes in free-ranging monkeys. 

Monitoring genetic diversity in wild populations is a central goal of ecological and evolutionary genetics and is critical for conservation biology. However, genetic studies of nonmodel organisms generally lack access to species‐specific genotyping methods (e.g. array‐based genotyping) and must instead use sequencing‐based approaches. Although costs are decreasing, high‐coverage whole‐genome sequencing (WGS), which produces the highest confidence genotypes, remains expensive. More economical reduced representation sequencing approaches fail to capture much of the genome, which can hinder downstream inference. Low‐coverage WGS combined with imputation using a high‐confidence reference panel is a cost‐effective alternative, but the accuracy of genotyping using low‐coverage WGS and imputation in nonmodel populations is still largely uncharacterized. Here, we empirically tested the accuracy of low‐coverage sequencing (0.1–10×) and imputation in two natural populations, one with a large (n = 741) reference panel, rhesus macaques (Macaca mulatta), and one with a smaller (n = 68) reference panel, gelada monkeys (Theropithecus gelada). Using samples sequenced to coverage as low as 0.5×, we could impute genotypes at >95% of the sites in the reference panel with high accuracy (medianr² ≥ 0.92). We show that low‐coverage imputed genotypes can reliably calculate genetic relatedness and population structure. Based on these data, we also provide best practices and recommendations for researchers who wish to deploy this approach in other populations, with all code available on GitHub (https://github.com/mwatowich/LoCSI‐for‐non‐model‐species). Our results endorse accurate and effective genotype imputation from low‐coverage sequencing, enabling the cost‐effective generation of population‐scale genetic datasets necessary for tackling many pressing challenges of wildlife conservation.