The success of precision medicine relies upon collecting data from many individuals at the population level. Although advancing technologies have made such large-scale studies increasingly feasible in some disciplines such as genomics, the standard workflows currently implemented in untargeted metabolomics were developed for small sample numbers and are limited by the processing of liquid chromatography/mass spectrometry data. Here we present an untargeted metabolomics workflow that is designed to support large-scale projects with thousands of biospecimens. Our strategy is to first evaluate a reference sample created by pooling aliquots of biospecimens from the cohort. The reference sample captures the chemical complexity of the biological matrix in a small number of analytical runs, which can subsequently be processed with conventional software such as XCMS. Although this generates thousands of so-called features, most do not correspond to unique compounds from the samples and can be filtered with established informatics tools. The features remaining represent a comprehensive set of biologically relevant reference chemicals that can then be extracted from the entire cohort’s raw data on the basis of m/z values and retention times by using Skyline. To demonstrate applicability to large cohorts, we evaluated >2000 human plasma samples with our workflow. We focused our analysis on 360 identified compounds, but we also profiled >3000 unknowns from the plasma samples. As part of our workflow, we tested 14 different computational approaches for batch correction and found that a random forest-based approach outperformed the others. The corrected data revealed distinct profiles that were associated with the geographic location of participants.

The spectrum of light that an animal sees—from ultraviolet to far red light—is governed by the number and wavelength sensitivity of a family of retinal proteins called opsins. It has been hypothesized that the spectrum of light available in an environment influences the range of colours that a species has evolved to see. However, invertebrates and vertebrates use phylogenetically distinct opsins in their retinae, and it remains unclear whether these distinct opsins influence what animals see, or how they adapt to their light environments. Systematically using published visual sensitivity data from across animal phyla, we found that terrestrial animals are more sensitive to shorter and longer wavelengths of light than aquatic animals and that invertebrates are more sensitive to shorter wavelengths of light than vertebrates. Using phylogenetically controlled analyses, we found that closed and open canopy habitat species have different spectral sensitivities when comparing across the Metazoa and excluding habitat generalists, while deepwater animals are no more sensitive to shorter wavelengths of light than shallow-water animals. Our results suggest that animals do adapt to their light environment; however, the invertebrate–vertebrate evolutionary divergence may limit the degree to which animals can perform visual tuning.

The spectrum of light that an animal sees – from ultraviolet to far red light – is governed by the number and wavelength sensitivity of a family of retinal proteins called opsins. It has been hypothesized that the spectrum of light available in an environment influences the range of colors that a species has evolved to see. However, invertebrates and vertebrates use phylogenetically distinct opsins in their retinae, and it remains unclear whether these distinct opsins influence what animals see, or how they adapt to their light environments. Systematically utilizing published visual sensitivity data from across animal phyla, we found that terrestrial animals are more sensitive to shorter and longer wavelengths of light than aquatic animals, and that invertebrates are more sensitive to shorter wavelengths of light than vertebrates. Controlling for phylogeny removes the effects of habitat and lineage on visual sensitivity. Closed and open habitat terrestrial species have similar spectral sensitivities when comparing across the Metazoa, and deep water animals are more sensitive to shorter wavelengths of light than shallow water animals. Our results suggest that animals do adapt to their light environment, however the invertebrate-vertebrate evolutionary divergence has limited the degree to which animals can perform visual tuning.

Community science, which engages students and the public in data collection and scientific inquiry, is often integrated into conservation and long-term monitoring efforts. However, it has the potential to also introduce the public to, and be useful for, sensory ecology and other fields of study. Here we describe a community science project that exposes participants to animal behavior and sensory ecology using the rich butterfly community of Northwest Arkansas, USA. Butterflies use visual signals to communicate and to attract mates. Brighter colors can produce stronger signals for mate attraction but can also unintentionally attract negative attention from predators. Environmental conditions such as weather can affect visual signaling as well, by influencing the wavelengths of light available and subsequent signal detection. However, we do not know whether the signals butterflies present correlate broadly with how they behave. In this study, we collaborated with hundreds of students and community members at the University of Arkansas (UARK) and the Botanical Gardens of the Ozarks (BGO) for over 3.5 years to examine relationships among wing pattern, weather, time of day, behavior, and flower choice. We found that both weather and wing color influenced general butterfly behavior. Butterflies were seen feeding more on cloudy days than on sunny or partly cloudy days. Brown butterflies fed or sat more often, while white butterflies flew more often relative to other butterfly colors. We also found that there was an interaction between the effects of weather and wing color on butterfly behavior. Furthermore, butterfly color predicted the choice of flower colors that butterflies visited, though this effect was influenced by observer group (UARK student or BGO participant). These results suggest that flower choice may be associated with butterfly wing pattern, and that different environmental conditions may influence butterfly behavior in wing-pattern-specific ways. They also illustrate one way that public involvement in behavioral studies can facilitate the identification of coarse-scale, community-wide behavioral patterns, and lay the groundwork for future studies of sensory niches.