Aberrant IL-17 levels in rodent models of Autism Spectrum Disorder:a systematic review

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder characterised by stereotyped behaviours, specific interests, and impaired communication skills. Elevated levels of pro-inflammatory cytokines, such as interleukin-17A (IL-17A or IL-17), have been implicated as part of immune alterations that may contribute to this outcome. In this context, rodent models have helped elucidate the role of T-cell activation and IL-17 secretion in the pathogenesis of ASD. Regarding the preclinical findings, the data available is contradictory in offspring but not in the pregnant dams, pointing to IL-17 as one of the main drivers of altered behaviour in some models ASD, whilst there are no alterations described in IL-17 levels in others. To address this gap in the literature, a systematic review of altered IL-17 levels in rodent models of ASD was conducted. In total, 28 studies that explored IL-17 levels were included and observed that this cytokine was generally increased among the different models of ASD. The data compiled in this review can help the choice of animal models to study the role of cytokines in the development of ASD, seeking a parallel with immune alterations observed in individuals with this condition. Systematic Review Registration: PROSPERO, identifier CRD42022306558

Nocturnal activity of antiguan lizards under artificial light

Widespread human development has led to the proliferation of artificial light at night, an increasingly recognized but poorly understood component of anthropogenic global change. Animals specialized to diurnal activity are presented opportunities to use this night-light niche, but the ecological consequences are largely unknown. While published records make note of nocturnal activity in a diversity of diurnal taxa, few case studies have gone beyond isolated observations to quantify patterns of nocturnal activity, document animal behavior, and describe new species interactions. From 13 June to 15 July 2017, we conducted hourly nocturnal surveys to assess how two species of diurnal Anolis lizards (Leach’s Anole, Anolis leachii, and Watt’s Anole, A. wattsi) use artificial light on Long Island, Antigua. Our data show that both anole species foraged in artificially illuminated habitats and were more active prior to sunrise compared to the early night. Mark-resight data for a focal species, A. leachii, suggest that patterns of nocturnal activity were not significantly different between individuals. Finally, our behavioral observations for the two anoles and a third lizard species, the nocturnal Thick-tailed Gecko (Thecadactylus rapicauda), reveal a lack of agonistic interactions. Our study reveals an altered temporal niche for two diurnal Antiguan lizards and adds to a growing body of evidence documenting the broad influences of anthropogenic change on biodiversity

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Gould E, Fraser H, Parker T, et al. Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology. 2023.Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different (mostly social science) fields, and has found substantial variability among results, despite analysts having the same data and research question. We implemented an analogous study in ecology and evolutionary biology, fields in which there have been no empirical exploration of the variation in effect sizes or model predictions generated by the analytical decisions of different researchers. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment), and the project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future