An official website of the United States government
Abstract PremiseFloral scent is a complex trait that mediates many plant–insect interactions, but our understanding of how floral scent variation evolves, either independently or in concert with other traits, remains limited. Assessing variation in floral scent at multiple levels of biological organization and comparing patterns of variation in scent to variation in other floral traits can contribute to our understanding of how scent variation evolves in nature. MethodsWe used a greenhouse common garden experiment to investigate variation in floral scent at three scales—within plants, among plants, and among populations—and to determine whether scent, alone or in combination with morphology and rewards, contributes to population differentiation inOenothera cespitosasubsp.marginata. Its range spans most of the biomes in the western United States, such that variation in both the abiotic and biotic environment could contribute to trait variation. ResultsMultiple analytical approaches demonstrated substantial variation among and within populations in compound‐specific and total floral scent measures. Overall, populations were differentiated in morphology and reward traits and in scent. Across populations, coupled patterns of variation in linalool, leucine‐derived compounds, and hypanthium length are consistent with a long‐tongued moth pollination syndrome. ConclusionsThe considerable variation in floral scent detected within populations suggests that, similar to other floral traits, variation in floral scent may have a heritable genetic component. Differences in patterns of population differentiation in floral scent and in morphology and rewards indicate that these traits may be shaped by different selective pressures.
more »
« less
An analytical pipeline to support robust research on the ecology, evolution, and function of floral volatiles
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.
more »
« less
- PAR ID:
- 10379278
- Date Published:
- Journal Name:
- Frontiers in Ecology and Evolution
- Volume:
- 10
- ISSN:
- 2296-701X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
A current frontier of character displacement research is to determine if displacement occurs via multiple phenotypic pathways and varies across communities with different species compositions. Here, we conducted the first test for context-dependent character displacement in multi-modal floral signals by analyzing variation in floral scent in a system that exhibits character displacement in flower size, and that has multiple types of sympatric communities. In a greenhouse common garden experiment, we measured quantitative variation in volatile emission rates of the progeny of two species of Clarkia from replicated parental communities that contain one, two, or four Clarkia species. The first two axes of a constrained correspondence analysis, which explained 24 percent of the total variation in floral scent, separated the species and community types, respectively. Of the 23 compounds that were significantly correlated with these axes, nine showed patterns consistent with character displacement. Two compounds produced primarily by C. unguiculata and two compounds produced primarily by C. cylindrica were emitted in higher amounts in sympatry. Character displacement in some volatiles varied across sympatric parental communities and occurred in parallel with displacement in flower size, demonstrating that this evolutionary process can be context-dependent and may occur through multiple pathways.more » « less
-
Abstract In flowering plants that produce concealed rewards, pollinator foraging preferences may select for floral advertisement traits that are correlated with rewards. To date, studies have not focused on the potential for honest signals to vary across populations, which could occur due to differences in pollinator communities or plant mating system.We tested for variation in honest signals across and within populations and mating systems inArabis alpina, a broadly distributed arctic‐alpine perennial herb that is visited by a variable community of insects. In a greenhouse common garden, we tested for correlations between corolla area, floral scent and nectar volume in 29 populations. In 12 field populations, we examined variation in pollen limitation and corolla area.Across and within populations and mating systems, larger flowers generally produced more nectar. Total scent emission was not correlated with nectar production, but two compounds—phenylacetaldehyde and benzyl alcohol—may be honest signals in some populations. Corolla area was correlated with pollen limitation only across populations.Our results suggest that honest signals may be similar across populations but may not result from contemporary direct selection on floral advertisements. Read the freePlain Language Summaryfor this article on the Journal blog.more » « less
-
Abstract Large language models (LLMs) are capable of successfully performing many language processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify and explain social phenomena like persuasiveness and political ideology, then LLMs could augment the computational social science (CSS) pipeline in important ways. This work provides a road map for using LLMs as CSS tools. Towards this end, we contribute a set of prompting best practices and an extensive evaluation pipeline to measure the zero-shot performance of 13 language models on 25 representative English CSS benchmarks. On taxonomic labeling tasks (classification), LLMs fail to outperform the best fine-tuned models but still achieve fair levels of agreement with humans. On free-form coding tasks (generation), LLMs produce explanations that often exceed the quality of crowdworkers’ gold references. We conclude that the performance of today’s LLMs can augment the CSS research pipeline in two ways: (1) serving as zero-shot data annotators on human annotation teams, and (2) bootstrapping challenging creative generation tasks (e.g., explaining the underlying attributes of a text). In summary, LLMs are posed to meaningfully participate in social science analysis in partnership with humans.more » « less
-
Compilation and retrieval of reliable data on biological interactions is one of the critical bottlenecks affecting efficiency and statistical power in testing ecological theories. TaxonWorks, a web-based workbench, can facilitate such research by enabling the digitization of complex biological interactions involving multiple species, individuals, and trophic levels. These data can be further organized into spatial and temporal axes, and annotated at the level of individual or grouped interactions (e.g. singularly citing the combined elements of a tritrophic interaction). The simple, customizable nature of tools ultimately reduces the time-consuming steps of data gathering, cleaning, and formatting of datasets for subsequent exploration and analysis while also improving the asserted semantics. An example use case is provided with a dataset of associations among plants, pathogens and insect vectors. The curated data are accessed through the JSON serving TaxonWorks API (Application Programming Interface) by an R package. Analysis and visualization of the network graphs persisted in TaxonWorks is demonstrated using core R functionality and the igraph package (Csardi and Nepusz 2006). TaxonWorks is open-source, collaboratively built software available at http://taxonworks.org.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fevo.2022.1006416
