An analytical pipeline to support robust research on the ecology, evolution, and function of floral volatiles

Abstract

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.

Gain efficiency with streamlined and automated data processing: Examples from high-throughput monoclonal antibody production

“…In the long run, we expect that this approach will be beneficial for ASCC, as it can help to: i) standardize and automate relevant data storage; ii) facilitate data-centric conclusions to gain novel insights; iii) make informed decisions for potential troubleshooting; iv) use data to optimize processes by systematic analysis through artificial intelligence or "design of experiment" approaches. As pipelines for continuous data processing and analysis are now essential in domains such as multi-omics data integration (33,34), HT/HC screening (35), data-driven modeling (36), as well as long-term environmental monitoring (37), evolutionary biology (38)(39)(40) or plant phenotyping (41), among others, we believe that design recommendations proposed here can find their target audience and be a source of inspiration to other researchers in developing their own data processing modules.…”

The Development of Floral Scent Research: A Comprehensive Bibliometric Analysis (1987–2022)

“…Indeed, FSs represent a pivotal functional trait shaping interactions between flowers and insects in ecosystems [3]. These pollination systems can exhibit specialization [4,5] or generality [6,7], and the release of FSs is intricately regulated by a host of biotic or abiotic factors [8], ranging from drought stress [9] to temperature [10], profoundly influencing a flower's allure to pollinators. Notably, the ramifications of habitat loss affect plant-pollinator dynamics, with direct consequences for plant reproduction [11] and thus relate to the functional areas of ecosystems [12].…”