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{"Abstract":["Evolutionary adaptation can allow a population to persist in the face of a\n new environmental challenge. With many populations now threatened by\n environmental change, it is important to understand whether this process\n of evolutionary rescue is feasible under natural conditions, yet work on\n this topic has been largely theoretical. We used unique long-term data to\n parameterize deterministic and stochastic models of the contribution of\n one trait to evolutionary rescue using field estimates for the subalpine\n plant Ipomopsis aggregata and hybrids with its close relative I.\n tenuituba. In the absence of evolution or plasticity, the two studied\n populations are projected to go locally extinct due to earlier snowmelt\n under climate change, which imposes drought conditions. Phenotypic\n selection on specific leaf area (SLA) was estimated in 12 years and\n multiple populations. Those data on selection and its environmental\n sensitivity to annual snowmelt timing in the spring were combined with\n previous data on heritability of the trait, phenotypic plasticity of the\n trait, and the impact of snowmelt timing on mean absolute fitness.\n Selection favored low values of SLA (thicker leaves). The evolutionary\n response to selection on that single trait was insufficient to allow\n evolutionary rescue by itself, but in combination with phenotypic\n plasticity it promoted evolutionary rescue in one of the two populations.\n The number of years until population size would stop declining and begin\n to rise again was heavily dependent upon stochastic environmental changes\n in snowmelt timing around the trend line. Our study illustrates how field\n estimates of quantitative genetic parameters can be used to predict the\n likelihood of evolutionary rescue. Although a complete set of parameter\n estimates are generally unavailable, it may also be possible to predict\n the general likelihood of evolutionary rescue based on published ranges\n for phenotypic selection and heritability and the extent to which early\n snowmelt impacts fitness."],"Methods":["The study sites consisted of three “Poverty Gulch” sites in\n Gunnsion National Forest and one site “Vera Falls” at the Rocky Mountain\n Biological Laboratory, all in Gunnison County, CO, USA. Focal plants\n included two sets of plants. One set (data from 2009-2019) consisted of\n plants in common gardens at three sites: an I. aggregata\n site (hereafter “agg”), an I. tenuituba\n site (hereafter “ten”) and a site at the center of the natural\n hybrid zone (hereafter “hyb”). The second set consisted of plants growing\n in situ at two of the same Poverty Gulch sites (“agg” and “hyb”), and\n an I. aggregata site at Vera Falls (hereafter “VF”;\n data from 2017-2023).  The common gardens were started\n from seed in 2007 and 2008. Measurements of SLA in these gardens began\n when plants were 2 years old, either 2009 or 2010 depending upon the\n garden, as they are only small seedlings during their first summer after\n seed maturation. By 2018, all but 15 of the 4512 plants originally planted\n had died, with or without blooming, and we stopped following these\n gardens. Starting in 2017, in situ vegetative plants at the I.\n aggregata site and the hybrid site whose longest leaf exceeded\n 25 mm were marked with metal tags to facilitate\n identification.   In each year of the study, one leaf\n from each vegetative plant was collected in the field and transported on\n ice to the RMBL, 8 km distant. There each leaf was scanned with a flatbed\n scanner and analyzed using ImageJ to measure leaf area. The leaf was dried\n at 70 deg C for 2 hours and then weighed to obtain dry mass and calculate\n SLA as area/dry mass. For plants in the common gardens, SLA was measured\n on 982 leaves from 383 plants in 2009 – 2014. For in situ plants, SLA was\n measured on one leaf from each of 877 plants in 2017 – 2022. Fitness was\n estimated as the binary variable of survival to flowering. Plants that\n were still alive in 2019 in the common gardens or in 2023 at the end of\n the study were assumed to survive to flowering. These\n data were used to estimate selection differentials on SLA in each of 12\n years. We then combined this information with previous information on\n heritability and the effect of snowmelt date in the spring on mean\n absolute fitness, measured as the finite rate of population increase, from\n a previous demographic study. This information was used to parameterize\n models of evolutionary rescue that we developed. We developed two models\n that differed in how snowmelt timing changed: a Step-change model and a\n Gradual environmental change model and analyzed both deterministic and\n stochastic versions. All analysis and modeling was done in R ver\n 4.2.2.  "],"TechnicalInfo":["# Data for: Predicting the contribution of single trait evolution to\n rescuing a plant population from demographic impacts of climate change\n Dataset DOI: [10.5061/dryad.ht76hdrtn](10.5061/dryad.ht76hdrtn) ##\n Description of the data and file structure File\n "mastervegtraitsSLA2023.csv" contains data on specific leaf area\n for Ipomopsis plants in the field. Files\n "masterdemography_insitu_2023.csv" and\n "masterdemography_commongarden.csv" provide the corresponding\n information on survival to flowering. File "snowmelt.csv"\n provides dates of snowmelt in the spring. File\n "selection_vs_snowmelt.csv" provides intermediate results on\n selection intensities from analysis with the first parts of the code\n "Campbell-EvolutionLettersMay2025.Rmd". File\n "IPMresults.csv" provides estimates of the finite rate of\n increase (lambda) predicted from the publication by Campbell\n [https://doi.org/10.1073/pnas.1820096116](https://doi.org/10.1073/pnas.1820096116) File "Campbell-EvolutionLettersMay2025.Rmd" provides the R code for statistical analysis and the deterministic and stochastic models of evolutionary rescue. All data analysis and modeling was done in R ver. 4.4.2 on a Windows machine. All necessary input data files are provided. The R code is annotated to indicate which portions produce analyses and figures in the manuscript. For the multipart figures 6-9 the code needs to be manually updated to produce each part of the figure before assembling them. In those cases, each part represents a model with a unique set of parameters. ### Files and variables #### File: Data\\_files\\_for\\_EVL\\_Campbell\\_2025.zip **Description:** All data files Blank cells are indicated by "." except in "selection_vs_snowmelt.csv" where they are indicated by "NA" **File:** mastervegtraitsSLA2023.csv * meltday = first day of bare ground at the Rocky Mountain Biological Lab (RMBL) in units of days starting with January 1 * year = year * site = site. agg = site with I. aggregata. hyb = site with natural hybrids. ten = site with I. tenuituba. VF = Vera Falls site containing I. aggregata. * idtag = metal tag used to identify plant * planttype = type of plant. AA = progeny of I. aggregata x I. aggregata. AT = progeny of I. aggregata x I. tenuituba. TA = progeny of I. tenuituba x I. aggregata. TT = progeny of I. tenuituba x I. tenuituba. F2 = progeny of F1 (either AT or TA) x F1. agg = natural I. aggregata. hyb = natural hybrid. * sla = specific leaf area in units of cm2/g * uniqueid = an id used to identify the plant uniquely across all years and sites **File:** masterdemography\\_insitu\\_2023.csv * site = site. agg = site with I. aggregata. hyb = site with hybrids. VF = Vera Falls site containing I. aggregata. * idtag = metal tag used to identify plant * yeartagged = year the plant was first tagged * flrlabelxx = label for plants flowering in year 20xx * stagexxxx = stage in year xxxx. 0 = dead. 1 = single vegetative rosette. 2 = single inflorescence. 3 = multiple vegetative rosette. 4 = multiple inflorescence. * lengthxx = length of longest leaf in year 20xx in mm * leavesxx = number of leaves in rosette(s) in year 20xx **File:** masterdemography_commongarden.csv * site = site. agg = site with I. aggregata. hyb = site with natural hybrids. ten = site with I. tenuituba. * IDTAG = metal tag used to identify plant * Planttype = type of plant. AA = progeny of I. aggregata x I. aggregata. AT = progeny of I. aggregata x I. tenuituba. TA = progeny of I. tenuituba x I. aggregata. TT = progeny of I. tenuituba x I. tenuituba. F2full = full-sib progeny of F1 (either AT or TA) x F1. F2non = non full-sib progeny of F1 x F1. * stagexx = stage of plant in year 20xx. 0 = dead. 1 = single vegetative rosette. 2 = single inflorescence. 3 = multiple vegetative rosette. 4 = multiple inflorescence. * lengthxx = length of longest leaf in year 20xx in mm. * leavesxx = number of leaves in rosette(s) in year 20xx. **File:** snowmelt.csv * Year = year * Snowmelt = day of first bare ground at the RMBL in units of day starting with January 1. Values prior to 1975 were estimated. **File:** selection*vs*snowmelt.csv * meltday = day of first bare ground at the RMBL in units of day starting with January 1. * year = year * Sbyyearwithsite = standardized selection differential on SLA in model that includes site. These values are reproduced with standard errors in Table 1. * bwithsite = regression coefficient for raw survival on raw SLA in model that includes site. * meansurv = mean survival * covwsla = raw selection differential on SLA * bwithsitehyb = regression coefficient for raw survival on SLA at site hyb * meansurvhyb = mean survival at site hyb * covwslahyb = raw selection differential on SLA at site hyb used in the Gradual environmental change model * covwslaagg = raw selection differential on SLA at site agg used in the Gradual environmental change model * meansurvagg = mean survival at site agg * melthyb = estimated date of bare ground at site hyb * meltagg = estimated date of bare ground at site agg **File:** IPMresults.csv * site = site. agg = site with I. aggregata. hyb = site with natural hybrids. * day = predicted day of snowmelt (all predictions are from Campbell, D. R. 2019. Early snowmelt projected to cause population decline in a subalpine plant. PNAS (USA) 116(26) 1290-12906.) Units are days starting with January 1. * lambda = predicted finite rate of increase **File:** Campbell-EvolutionLettersMay2025.Rmd Contains R code for data analysis and modeling. All analysis and modeling was done in R ver 4.2.2."]} 

Abstract In ecological speciation, incipient species diverge due to natural selection that is ecologically based. In flowering plants, different pollinators could mediate that selection (pollinator-mediated divergent selection) or other features of the environment that differ between habitats of 2 species could do so (environment-mediated divergent selection). Although these mechanisms are well understood, they have received little rigorous testing, as few studies of divergent selection across sites of closely related species include both floral traits that influence pollination and vegetative traits that influence survival. This study employed common gardens in sites of the 2 parental species and a hybrid site, each containing advanced generation hybrids along with the parental species, to test these forms of ecological speciation in plants of the genus Ipomopsis. A total of 3 vegetative traits (specific leaf area, leaf trichomes, and photosynthetic water-use efficiency) and 5 floral traits (corolla length and width, anther insertion, petal color, and nectar production) were analyzed for impacts on fitness components (survival to flowering and seeds per flower, respectively). These traits exhibited strong clines across the elevational gradient in the hybrid zone, with narrower clines in theory reflecting stronger selection or higher genetic variance. Plants with long corollas and inserted anthers had higher seeds per flower at the Ipomopsis tenuituba site, whereas selection favored the reverse condition at the Ipomopsis aggregata site, a signature of divergent selection. In contrast, no divergent selection due to variation in survival was detected on any vegetative trait. Selection within the hybrid zone most closely resembled selection within the I. aggregata site. Across traits, the strength of divergent selection was not significantly correlated with width of the cline, which was better predicted by evolvability (standardized genetic variance). These results support the role of pollinator-­mediated divergent selection in ecological speciation and illustrate the importance of genetic variance in determining divergence across hybrid zones. 

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. 

Summary Vegetative traits of plants can respond directly to changes in the environment, such as those occurring under climate change. That phenotypic plasticity could be adaptive, maladaptive, or neutral.We manipulated the timing of spring snowmelt and amount of summer precipitation in factorial combination and examined responses of specific leaf area (SLA), trichome density, leaf water content (LWC), photosynthetic rate, stomatal conductance and intrinsic water‐use efficiency (iWUE) in the subalpine herbIpomopsis aggregata. The experiment was repeated in three years differing in natural timing of snowmelt. To examine natural selection, we used survival, relative growth rate, and flowering as fitness indices.A 50% reduction in summer precipitation reduced stomatal conductance and increased iWUE, and doubled precipitation increased LWC. Combining natural and experimental variation, earlier snowmelt reduced soil moisture, photosynthetic rate and stomatal conductance, and increased trichome density and iWUE. Precipitation reduction reversed the mortality selection favoring high stomatal conductance under normal and doubled precipitation, and higher LWC improved growth.Earlier snowmelt is a strong signal of climate change and can change expression of leaf morphology and gas exchange traits, just as reduced precipitation can. Stomatal conductance and SLA showed adaptive plasticity under some conditions.