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Abstract Background and AimsFloral volatiles, visual traits and rewards mediate attraction and defence in plant–pollinator and plant–herbivore interactions, but these floral traits might be altered by global warming through direct effects of temperature or longer-term impacts on plant resources. We examined the effect of warming on floral and leaf volatile emissions, floral morphology, plant height, nectar production, and oviposition by seed predators. MethodsWe used open-top chambers that warmed plants in the field by +2–3 °C on average (+6–11 °C increase in daily maxima) for 2–4 weeks across 1–3 years at three sites in Colorado, USA. Volatiles were sampled from two closely related species of subalpine Ipomopsis with different pollinators: Ipomopsis aggregata ssp. aggregata, visited mainly by hummingbirds, and Ipomopsis tenuituba ssp. tenuituba, often visited by hawkmoths. Key ResultsAlthough warming had no detected effects on leaf volatiles, the daytime floral volatiles of both I. aggregata and I. tenuituba responded in subtle ways to warming, with impacts that depended on the species, site and year. In addition to the long-term effect of warming, temperature at the time of sampling independently affected the floral volatile emissions of I. aggregata during the day and I. tenuituba at night. Warming had little effect on floral morphology for either species and it had no effect on nectar concentration, maximum inflorescence height or flower redness in I. aggregata. However, warming increased nectar production in I. aggregata by 41 %, a response that would attract more hummingbird visits, and it reduced oviposition by fly seed predators by ≥72 %. ConclusionsOur results suggest that floral traits can show different levels of plasticity to temperature changes in subalpine environments, with potential effects on animal behaviours that help or hinder plant reproduction. They also illustrate the need for more long-term field warming studies, as shown by responses of floral volatiles in different ways to weeks of warming vs. temperature at the time of sampling. 

Research on floral volatiles has grown substantially in the last 20 years, which has generated insights into their diversity and prevalence. These studies have paved the way for new research that explores the evolutionary origins and ecological consequences of different types of variation in floral scent, including community-level, functional, and environmentally induced variation. However, to address these types of questions, novel approaches are needed that can handle large sample sizes, provide quality control measures, and make volatile research more transparent and accessible, particularly for scientists without prior experience in this field. Drawing upon a literature review and our own experiences, we present a set of best practices for next-generation research in floral scent. We outline methods for data collection (experimental designs, methods for conducting field collections, analytical chemistry, compound identification) and data analysis (statistical analysis, database integration) that will facilitate the generation and interpretation of quality data. For the intermediate step of data processing, we created the R package bouquet , which provides a data analysis pipeline. The package contains functions that enable users to convert chromatographic peak integrations to a filtered data table that can be used in subsequent statistical analyses. This package includes default settings for filtering out non-floral compounds, including background contamination, based on our best-practice guidelines, but functions and workflows can be easily customized as necessary. Next-generation research into the ecology and evolution of floral scent has the potential to generate broadly relevant insights into how complex traits evolve, their genomic architecture, and their consequences for ecological interactions. In order to fulfill this potential, the methodology of floral scent studies needs to become more transparent and reproducible. By outlining best practices throughout the lifecycle of a project, from experimental design to statistical analysis, and providing an R package that standardizes the data processing pipeline, we provide a resource for new and seasoned researchers in this field and in adjacent fields, where high-throughput and multi-dimensional datasets are common.