Marine artificial light at night:An empirical and technical guide

The increasing illumination of our world by artificial light at night (ALAN) has created a new field of global change research with impacts now being demonstrated across taxa, biological ranks and spatial scales. Following advances in terrestrial ecology, marine ALAN has become a rapidly growing research area attracting scientists from across all biomes. Technological limitations, complexities of researching many coastal and marine ecosystems and the interdisciplinary nature of ALAN research present numerous challenges. Drawing on expertise from optical oceanographers, modellers, community ecologists, experimental and molecular biologists, we share practical advice and solutions that have proven useful for marine ALAN research. Discussing lessons learnt early on can help in the effective and efficient development of a field. The guide follows a sensory ecology approach to marine light pollution and consolidates physics, ecology and biology. First, we introduce marine lightscapes highlighting how these differ from terrestrial ones and provide an overview of biological adaptations to them. Second, we discuss study design and technology to best quantify ALAN exposure of and impacts on marine and coastal organisms including molecular tools and approaches to scale-up marine ALAN research. We conclude that the growing field of marine ALAN research presents opportunities not only for improving our understanding of this globally widespread stressor, but also for advancing fundamental marine photobiology, chronobiology and night-time ecology. Interdisciplinary research will be essential to gain insights into natural marine lightscapes shaping the ecology and evolution coastal and marine ecosystems

11 pressing research questions on how light pollution affects biodiversity

Artificial light at night (ALAN) is closely associated with modern societies and is rapidly increasing worldwide. A dynamically growing body of literature shows that ALAN poses a serious threat to all levels of biodiversity—from genes to ecosystems. Many “unknowns” remain to be addressed however, before we fully understand the impact of ALAN on biodiversity and can design effective mitigation measures. Here, we distilled the findings of a workshop on the effects of ALAN on biodiversity at the first World Biodiversity Forum in Davos attended by several major research groups in the field from across the globe. We argue that 11 pressing research questions have to be answered to find ways to reduce the impact of ALAN on biodiversity. The questions address fundamental knowledge gaps, ranging from basic challenges on how to standardize light measurements, through the multi-level impacts on biodiversity, to opportunities and challenges for more sustainable use

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