DOI: 10.25349/d91318
title: Data used in manuscript, "Context-dependent concordance between physiological divergence and phenotypic selection in sister taxa with contrasting phenology and mating systems"

description: PREMISE: The study of phenotypic divergence of, and selection on,
 functional traits in closely related taxa provides the opportunity to
 detect the role of natural selection in driving diversification. Where
 selection in field populations differs between taxa in a pattern that is
 consistent with the phenotypic difference between them, this provides
 evidence that natural selection reinforces the divergence. Few studies
 have sought evidence for such concordance for physiological traits.
 METHODS: Herbarium specimen records were used to detect phenological
 differences between sister taxa independent of the effects on flowering
 time of long-term variation in the climate across collection sites. In the
 field, physiological divergence in photosynthetic rate, transpiration
 rate, and instantaneous water use efficiency were recorded during
 vegetative growth and flowering in 13 field populations of two taxon pairs
 of Clarkia, each comprised of a self-pollinating and a outcrossing taxon.
 RESULTS: Historically, each selfing taxon flowered earlier than its
 outcrossing sister taxon, independent of the effects of local long-term
 climatic conditions. Sister taxa differed in all focal traits, but the
 degree and (in one case) the direction of divergence depended on life
 stage. In general, self-pollinating taxa exhibited higher gas exchange
 rates, consistent with their earlier maturation. In 6 of 18 comparisons,
 patterns of selection were concordant with the phenotypic divergence (or
 lack thereof) between sister taxa. CONCLUSIONS: Patterns of selection on
 physiological traits measured in heterogeneous conditions do not reliably
 reflect divergence between sister taxa, underscoring the need for
 replicated studies of the direction of selection within and among taxa.
description: <b><i>Study
 species</i>—</b><i>Clarkia unguiculata</i> Lindley
 and <i>C. xantiana ssp. xantiana</i> A.Gray are
 self-compatible winter annuals that flower in late spring, are endemic to
 California, and occupy oak and pine woodlands, grasslands, and roadside
 habitats, California. While <i>C. xantiana ssp. xantiana</i>
 is restricted to the southern Sierra Nevada and Transverse ranges to the
 south, the geographic range of <i>C.</i> <i>unguiculata
 </i>is much more widespread, occupying both the Sierra Nevada and
 the Coastal and Transverse Ranges (Lewis and Lewis, 1955). In the Lake
 Isabella region of the southern Sierra Nevada (Kern and Tulare counties),
 both taxa may occur alone or in sympatry with their predominantly selfing
 sister taxa (<i>C. exilis</i> and <i>C. xantiana
 </i>ssp. <i>parviflor</i><i>a</i>,
 respectively). Where sister taxa co-occur, reproductive isolation is
 reinforced by differences between sister taxa in flowering phenology and
 floral morphology, with selfers flowering earlier and showing greater
 levels of herkogamy and dichogamy than their bee-pollinated outcrossing
 counterparts (Eckhart and Geber, 1999; Dudley et al., 2007).
             In the southern Sierra Nevada, the sister taxa
 studied here differ with respect to habitat. Although<i>
 C.</i> <i>exilis </i>is sometimes encountered at the
 same sites as <i>C. unguiculata</i>, when these species are in
 sympatry, <i>C. unguiculata </i>occupies a broader range of
 habitats, growing in woodlands and encroaching into grasslands. By
 contrast, <i>C. exilis </i>has a more patchy distribution; it
 is generally restricted to relatively cool canyons, stream beds, and
 ravines, or is found near boulders or rocky outcrops that provide some
 shade and/or soil moisture (Vasek, 1958). The join distributions of
 <i>C.</i> <i>xantiana </i>ssp<i>. xantiana
 </i>and ssp. <i>parviflora </i>follow a west to east
 gradient, with <i>xantiana </i>occurring in populations to the
 west and <i>parviflora </i>occurring towards the east, nearly
 reaching the Mojave desert. Although there is a narrow central zone of
 contact between these subspecies (Eckhart et al., 2004; Geber and Eckhart,
 2005), sites occupied solely by ssp. <i>parviflora </i>receive
 less precipitation than those occupied solely by ssp.
 <i>xantiana.</i> <b><i>Historical climatic and phenological differences between sister taxa: herbarium-based study</i>—</b>Occurrence data and the GPS coordinates of all specimen records available for each of the focal taxa were downloaded from the Consortium of California Herbarium portal (https://cch2.org) on July 31, 2021 (Fig.1). The date of specimen collection was converted to day-of-year (DOY: 1-365; 366 for leap years). The GPS coordinates of each collection site were then used to extract from the climate database, ClimateNA v.7.01 (Wang et al., 2016) the mean values of 23 annual climatic variables estimated from 1951-1980, the 30-year period preceding the point at which climate warming is considered to have begun to accelerate (IPCC, 2013). The climate variables estimated are defined in Appendix S1 (see Supplemental Data with this article).  To estimate climatic parameters, ClimateNA uses interpolated values from the PRISM climate database (PRISM Climate Group, Oregon State University, http://prism.oregonstate.edu) to generate scale-free estimates of local climatic conditions at a resolution of 4km x 4km. The DOY of specimen collection does not provide a precise record of the flowering date of a specimen because an individual plant collected in flower may have been collected at any time after flowering has begun, and the duration of an individual’s flowering period may be several weeks or longer. Nevertheless, the date of specimen collection has been found to be a reliable proxy for flowering time (Davis et al., 2015), and has been used in many studies to detect the factors influencing flowering phenology (Davis et al., 2015; Matthews and Mazer, 2016; Willis et al., 2017; Ferris and Willis, 2018; Park and Mazer, 2018, 2019; Ellwood et al., 2019; Love et al., 2019; Park et al., 2019; Mazer et al., 2021; Park, Breckheimer, et al., 2021; Park, Ramirez‐Parada, et al., 2021; Pearson et al., 2021). Among the focal taxa of this study, images were available from CCH2 on 25 December 2021 for 26, 679, 10, and 33 specimens of <i>C. exilis</i>, <i>C. unguiculata</i>, <i>C. xantiana</i> ssp. <i>parviflora</i>, and <i>C. xantiana</i> ssp. <i>xantiana</i>, respectively. Each image was visually inspected to determine whether the individual plant(s) on each sheet bore one or more open flowers. Of these, 24 (92%), 659 (97%), 8 (80%), and 30 (91%) specimens bore one or more open flowers, indicating that the DOY of specimen collection for these taxa generally reflects the specimen’s flowering time at a given site. <b><i>Statistical analysis of herbarium-derived data and climate</i>—</b>To assess whether sister taxa differ with respect to mean flowering time independent of the effects of local climatic conditions on flowering phenology due to evolutionary and/or short-term plastic responses to climate, we controlled statistically for climatic variation among collection sites when testing for differences between taxa in mean flowering time. To characterize each collection site using a multivariate index of its climate, principal components (PCs) for the 24 annual climatic variables were estimated using the pooled data for each pair of sister taxa<i>. </i>In both pairs of taxa, the first two principal components (PC1 and PC2) for the 24 climatic parameters together account for &gt;75% of the variance in climate (Appendix S2). In the pooled data for <i>Clarkia unguiculata</i> and <i>C. exilis</i>, PC1 and PC2 account for 46% and 31% of the variance, respectively; in <i>Clarkia xantiana </i>ssp.<i> xantiana </i>and ssp<i>. parviflora</i>, PC1 and PC2 explain 62% and 21% of the variance, respectively (Appendix S2). In both taxon pairs, across their geographic distribution, the loadings of PC1 indicate that it represents an axis dominated by temperature, while PC2 represents an axis dominated by humidity (Appendix S3). For each data set, the first two PCs were used to determine whether sister taxa differ with respect to their flowering phenology (controlling for site-specific climatic conditions) and in the chronic climatic conditions to which they are exposed. The function <i>prcomp</i>() was used to estimate PCs, following centering and scaling of the data to unit variance; the function <i>fviz_pca_var</i>() in the package <i>factoextra</i> (v.1.0.7) was used to visualize the contributions of each variable in each PC. These and all subsequent analyses were conducted in RStudio (v. 1.4.1717) with R v. 4.1.1. <i>Phenological differences between sister taxa</i>—To determine whether sister taxa differ in their mean flowering date while controlling for the effects of geographic variation in local chronic climatic conditions (which influence DOY; see Results), we conducted four linear regressions on each sister pair. In all of these models, the day of year of specimen collection (DOY) was the response variable (Table 1).  In neither taxon pair were either of the two-way interactions (Taxon x PC1 or Taxon x PC2) statistically significant. Below, we present the results for the model with the lowest AIC value. Type III sums of squares were used to detect a significant difference between taxa in their mean date of collection, controlling for variation among collection sites with respect to the covariates. The <i>lm</i> function with the default setting for contrasts and the <i>lme4</i> package (v. 1.1-27.1) were used. <i>Climatic differences between sister taxa</i> —To determine whether sister taxa differ with respect to the chronic climatic conditions at their respective herbarium specimen collection sites, we used linear models to detect statistically significant differences between taxa with respect to the mean values of the first and the second PCs.  In these analyses, either PC1 or PC2 was the response variable, and taxon was the predictor variable. The <i>lm</i> function (using the default setting for contrasts: c[contr.treatment, contr.poly]) in the <i>lme4</i> package (v. 1.1-27.1) was used.   <b><i>Contemporary differences between sister taxa: phenotypic divergence in physiological traits—</i></b><i>Sampling of extant populations: sample sizes and life stages—</i>Naturally occurring field populations were surveyed in the region of Lake Isabella, California in the southern Sierra Nevada, where we sampled populations at elevations ranging from 457 to 1628 m. In spring 2010, 13 sites were identified that, at the onset of spring, contained a population of one or more of our focal taxa with a sufficient number of healthy juvenile plants that we judged we would be able to survey at least 90 individuals for their physiological performance during both vegetative and flowering stages (Fig.2; see Appendices S4 and S5 for a summary of each population’s GPS coordinates, elevation, sampling dates, and sample sizes). Each population was surveyed once during vegetative growth and once during flowering in spring 2010; a different group of individuals was sampled at each life stage. For each survey, 90 individuals were selected at random positions along one or more transects traversing the population, with the restriction that a surveyed plant had to bear at least one leaf long enough to sample with a LiCoR-6400 gas exchange analyzer (leaves that were too short could not cross the leaf chamber when it was closed). Sampled plants were typically at least 1m apart from one another. In total, we measured the physiological performance of  &gt;1800 individuals. We analyzed and compared these traits while controlling statistically for leaf position on the primary stem and for leaf temperature at the time of measurement, both of which can influence measures of physiological performance (Mazer et al., 2010).  <i>Physiological survey</i>—Gas exchange measurements were recorded on leaves produced by the primary branch on each plant. Measured leaves remained attached to the stem while using a portable infrared gas exchange analyzer (IRGA; LI-COR 6400, Lincoln, Nebraska, USA). We recorded photosynthetic rate (<i>A</i>; μmol CO<sub>2</sub> m<sup>-2</sup> s<sup>-1</sup>), transpiration rate (<i>E</i>; mol H<sub>2</sub>O m<sup>-2</sup> s<sup>-1</sup>), and the derived parameter, <i>A</i>/<i>E</i>, instantaneous water use efficiency (WUE<sub>i; </sub> μmol<sub>CO2</sub> mol<sub>H2O</sub><sup>-1 </sup>x 0.0001). In addition, the position of each measured leaf, recorded as the number of the node that bore it, was recorded; the node just above the cotyledons was referred to as node 1, with subsequently produced nodes counted in sequence. The temperature of the leaf (°C) at the time of measurement was also recorded. Node number and leaf temperature were included as covariates in our statistical models in order to control for their potential effects on physiological rates, as observed by Mazer et al. (2010). The following LI-COR settings were used: light source, 6400-40 fluorometer; stability, values of CO<sub>2</sub> and H<sub>2</sub>O remained stable for at least 15 s with a change in slope of &lt;1; stomatal ratio = 1; flow = 500 μmol s<sup>-1</sup>; PAR<sub>i</sub> (PAR in the leaf chamber) = 1500 mol m<sup>-2</sup> s<sup>-1</sup>. CO<sub>2</sub> concentration in the IRGA reference chamber was 400 ppm.   For leaves too narrow to fill the entire 2cm<sup>2</sup> circular area exposed within the LI-COR 6400’s leaf chamber, the actual leaf area exposed was calculated and then the LI-COR 6400’s Simulator Software was used to adjust those physiological parameters that were influenced by leaf area. To measure the leaf area exposed within the chamber, we used an ink pad to apply black ink to the foam gasket of the chamber; when the chamber was then closed upon the leaf, the gasket stamped with ink the outer boundary of the leaf’s area that was exposed within the chamber. Following gas exchange measurements, each leaf that failed to fill the chamber’s circular opening was removed, taped to a piece of paper with transparent tape, and subsequently scanned using a digital scanner. We then used the image analysis program ImageJ (http://rsb.info.nih.gov/ij/index.html) to measure the area of each leaf that had been exposed within the LI-COR chamber during gas exchange measurement.      Instantaneous gas exchange measurements and WUE<sub>i</sub> provide point estimates of the physiological performance of individual plants and are not equal to the same parameters measured over the entire lifespan of individuals (Zelitch, 1982; Serichol-Escobar et al., 2016).  In the current study, however, the large sample sizes obtained per taxon, the replication of each taxon within and among 2-4 populations, and the statistical control of variation in other plant attributes that are correlated with instantaneous gas exchange rates (e.g., leaf position on the stem, leaf temperature at the time of measurement, and the life cycle stage at which plants are measured) helped to account for the variance in physiological performance that can obscure differences between taxa. Numerous other recent studies have compared taxa, experimental treatments, or life history stages based on instantaneous gas exchange measurements (Mazer et al., 2010; Franks, 2011; Dudley et al., 2012, 2015; Gorai et al., 2015; Cheng et al., 2017; Gulias et al., 2018; Velikova et al., 2018; Wang et al., 2019; Scalon et al., 2021). In late June to mid-July 2010, all surviving plants were surveyed and, when flower production had ceased, the total number of full-sized or mature fruits borne by each plant was counted to provide an estimate of lifetime reproductive fitness. Many plants, however, suffered from frugivory by the larvae of <i>Hyles lineata</i> (the white-lined sphinx moth: Sphingidae) and, at some locations, by vertebrates, so total fruit production could not be accurately measured.  Consequently, as plants fully senesced and dried, we collected all above-ground parts (including fruits) from each plant, air-dried each skeleton in a paper bag, and returned them to the lab for weighing. All fruits retained by each plant were removed and counted, and the above-ground stem biomass (in mg, without any leaves, flowers, or ovaries) of each plant was recorded. To assess whether above-ground stem biomass is a reasonable proxy for lifetime reproductive success in these <i>Clarkia</i> taxa, we measured the relationship between above-ground stem biomass and total fruit production among individuals that were sampled during flowering and that suffered no frugivory. In all taxa, the correlation coefficient between these log-transformed variables was &gt; 0.60 (<i>C. unguiculata</i>, r = 0.61, n = 151, p &lt; 0.0001; <i>C. exilis</i>, r = 0. 90, n = 60, p &lt; 0.0001; <i>C. xantiana</i> ssp. <i>xantiana</i>, r = 0.76, n = 173, p &lt; 0. 0001; <i>C. xantiana</i> ssp. <i>parviflora</i>, r = 0.84, n= 81, p &lt; 0.0001). In the analyses of phenotypic selection described below, we used above-ground stem biomass as the response variable (i.e., the estimate of individual fitness). <i>Climate data</i> — To assess the influence of recent climatic conditions on the physiological rates of populations and taxa, monthly climatic data (cumulative precipitation; mean minimum, maximum and mean temperature; and vapor pressure deficit [VPD]) was extracted from the PRISM climate database (PRISM Climate Group, Oregon State University, http://prism.oregonstate.edu) for November 2007-June 2008 (corresponding to the growing season surveyed by Mazer et al., 2010) and for November 2009-June 2010 (the growing season of the current study). These data were used to determine whether differences between years in the magnitude or direction of physiological divergence between sister taxa was associated with year-specific climate.  In addition, these climate data were used to examine whether physiological differences between sister taxa observed during flowering in the current study were associated with the mean climatic conditions to which their sampled populations were exposed in the month when they were surveyed (Tables S3, S4). When comparing <i>C. exilis</i> and <i>C. unguiculata</i>, climatic conditions for the months of April and May 2010, respectively, were used; these corresponded most closely to the months when each species were surveyed. When comparing the subspecies of <i>C. xantiana</i>, climatic conditions for May 2010 were used for ssp. <i>xantiana</i>, except for the Democrat Springs and Saw Mill Road sites, for which conditions in June 2010 were used due to their mid-June survey dates (Appendix S6).  For <i>C. xantiana</i> ssp. <i>parviflora</i>, climatic conditions for May 2010 were used, except for the Long Valley population, for which June 2010 conditions were used.
description: This README.txt file was generated on 2022-02-01 by Susan Mazer
 and David Hunter <br> GENERAL
 INFORMATION 1. Title of Dataset:  2.
 Author Information     A. Principal Investigator
 Contact Information         Name: Susan J.
 Mazer         Institution: University of California,
 Santa Barbara         Address: Department of Ecology,
 Evolution & Marine Biology, UCSB, Santa Barbara, CA,
 93106         Email: sjmazer@ucsb.edu
     B. Associate or Co-investigator Contact Information
         Name: David J. Hunter       
  Institution: Westmont College, Santa Barbara, California, 93108
         Address: Department of Mathematics and Computer
 Science         Email: dhunter@westmont.edu
     C. Alternate Contact Information       
  Name: Alisa A. Hove         Institution: Warren Wilson
 College         Address: Biology Department, P.O. Box
 9000, Asheville, North Carolina, 28815         Email:
 ahove@warren-wilson.edu     C. Alternate Contact
 Information         Name: Leah Dudley
         Institution: East Central University
         Address: Department of Biological and Environmental
 Sciences, East Central University, Ada, Oklahoma, 74820
         Email: ldudley@ecok.edu 3. Date of
 data collection (single date, range, approximate date) &lt;suggested
 format YYYY-MM-DD&gt;:  Field data were collected
 from 2010-04-01 to 2010-06-15, with subsequent specimen processing (to
 measure above-ground stem biomass and to estimate fruit production)
 occurring approximately from 2010-07-01 to 2010-12-01
 4. Geographic location of data collection &lt;latitude,
 longitude, or city/region, State, Country, as
 appropriate&gt;:  Names and GPS coordinates of
 populations surveyed for physiological traits and total above-ground stem
 biomass in 2010. Locality Name                         
          Taxon               Elevation (m)    Latitude (N)    Longitude
 (W) Willow Springs                                 
 exilis                365                        35.670      
  -118.902 Stark Creek                                 
       exilis                457                        35.474      
  -118.726 Stark Creek                                 
       unguiculata      457                        35.474      
  -118.726 Live Oak                                     
       unguiculata      430                        35.479      
  -118.749 Democrat Springs Campground       
 unguiculata      590                        35.531      
  -118.663 Democrat Springs Campground        xantiana 
          590                        35.531        -118.663
 China Gardens                                   unguiculata     
 641                        35.533        -118.646 Borel
 Road                                        xantiana           707       
                  35.584        -118.522 Camp 3         
                                    xantiana            896               
         35.812         -118.453 Fay Ranch Road         
                      parviflora          927                       
 35.700         -118.304 Sawmill Road                   
               parviflora         1003                        35.678     
  -118.481 Sawmill Road                                 
 xantiana           1217                        35.675     
  -118.510 Long Valley                                 
    parviflora         1628                        35.815       
 -118.091 5. Information about funding sources that
 supported the collection of the data:  Funding for this
 research was awarded by the National Science Foundation (award
 IOS-0718227) to L.S.D. and S.J.M.  A.A.H. received funding from the Luce
 Foundation, the Ralph M. Parsons Foundation, and the California Native
 Plant Society.  SHARING/ACCESS INFORMATION
 1. Licenses/restrictions placed on the data: None
 2. Links to publications that cite or use the data:  Manuscript
 under review in the American Journal of Botany 3. Links
 to other publicly accessible locations of the data: N/A
 4. Links/relationships to ancillary data sets: N/A
 5. Was data derived from another source? No   
  A. If yes, list source(s):  6. Recommended citation
 for this dataset:  Mazer, S.J., D. J. Hunter, A. A.
 Hove, and L. S. Dudley. 2022. Data used in manuscript,
 "Context-dependent concordance between physiological divergence and
 phenotypic selection in sister taxa with contrasting phenology and mating
 systems" (in review in the American Journal of Botany"
 DATA & FILE OVERVIEW 1. File
 List:  <br> Three data sets are
 available. <br>   1)
 CUCE_herbarium_Normals_1951_1980_na.rm.csv:   These are the occurrence
 records and climatic parameters associated with the herbarium specimens of
 Clarkia unguiculata and Clarkia exilis analyzed in the current
 manuscript.<br>   2)
 CXXCXP_herbarium_Normals_1951_1980_na.rm.csv:   These are the occurrence
 records and climatic parameters associated with the herbarium specimens of
 Clarkia xantiana ssp. xantiana and Clarkia xantiana ssp. parviflora
 analyzed in the current manuscript.<br>   The R
 code used to analyze these data sets and to create the figures included in
 the manuscript is presented, along with the output, in the following html
 file (also uploaded into Dryad):
 2022-01-17-Analyses_of_Herbarium_Records-Dryad
 <br> 3) ClarkiaFieldData2010.csv: This data set contains
 all of the information for the field-collected data recorded in the wild
 populations of all four taxa (Clarkia unguiculata, C. exilis, C. xantiana
 ssp. xantiana and C. xantiana ssp. parviflora).  These data were used in
 the analyses of total and direct phenotypic selection on photosynthetic
 rate, transpiration rate, and instantaneous water use efficiency presented
 in the manuscript.  In addition, these data were used to detect
 physiological divergence between sister taxa.  The R code used to analyze
 this data set and to create the figures included in the manuscript is
 presented, along with the output, in the following html files (also
 uploaded into Dryad): <br>
 Gas_Exchange_Least_Squares_Means_Table.html
 Gas_Exchange_Confidence_Intervals.html
 Gas_Exchange_Summary.html
 Gas_Exchange_Means_Figure.html
 Total_Selection_Gradient_Models.html
 Total_Selection_Figure.html
 Direct_Selection_Gradient_Models.html
 Direct_Selection_Figure.html
 loadClarkiaData.R 2. Relationship between
 files, if important: The file loadClarkiaData.R is a
 helper script used by all of the other scripts.
 <br> 3. Additional related data collected that was not
 included in the current data package: N/A 4. Are there
 multiple versions of the dataset? No     A. If yes,
 name of file(s) that was updated:          i. Why was
 the file updated?          ii. When was the file
 updated?  METHODOLOGICAL INFORMATION
 1. Description of methods used for collection/generation of
 data: <br> Study species—Clarkia unguiculata Lindley and C. xantiana
 ssp. xantiana A.Gray are self-compatible winter annuals that flower in
 late spring, are endemic to California, and occupy oak and pine woodlands,
 grasslands, and roadside habitats, California. While C. xantiana ssp.
 xantiana is restricted to the southern Sierra Nevada and Transverse ranges
 to the south, the geographic range of C. unguiculata is much more
 widespread, occupying both the Sierra Nevada and the Coastal and
 Transverse Ranges (Lewis and Lewis, 1955). In the Lake Isabella region of
 the southern Sierra Nevada (Kern and Tulare counties), both taxa may occur
 alone or in sympatry with their predominantly selfing sister taxa (C.
 exilis and C. xantiana ssp. parviflora, respectively). Where sister taxa
 co-occur, reproductive isolation is reinforced by differences between
 sister taxa in flowering phenology and floral morphology, with selfers
 flowering earlier and showing greater levels of herkogamy and dichogamy
 than their bee-pollinated outcrossing counterparts (Eckhart and Geber,
 1999; Dudley et al., 2007).  In the southern Sierra
 Nevada, the sister taxa studied here differ with respect to habitat.
 Although C. exilis is sometimes encountered at the same sites as C.
 unguiculata, when these species are in sympatry, C. unguiculata occupies a
 broader range of habitats, growing in woodlands and encroaching into
 grasslands. By contrast, C. exilis has a more patchy distribution; it is
 generally restricted to relatively cool canyons, stream beds, and ravines,
 or is found near boulders or rocky outcrops that provide some shade and/or
 soil moisture (Vasek, 1958). The join distributions of C. xantiana ssp.
 xantiana and ssp. parviflora follow a west to east gradient, with xantiana
 occurring in populations to the west and parviflora occurring towards the
 east, nearly reaching the Mojave desert. Although there is a narrow
 central zone of contact between these subspecies (Eckhart et al., 2004;
 Geber and Eckhart, 2005), sites occupied solely by ssp. parviflora receive
 less precipitation than those occupied solely by ssp. xantiana.
 Historical climatic and phenological differences between sister
 taxa: herbarium-based study—Occurrence data and the GPS coordinates of all
 specimen records available for each of the focal taxa were downloaded from
 the Consortium of California Herbarium portal (https://cch2.org) on July
 31, 2021 (Fig.1). The date of specimen collection was converted to
 day-of-year (DOY: 1-365; 366 for leap years). The GPS coordinates of each
 collection site were then used to extract from the climate database,
 ClimateNA v.7.01 (Wang et al., 2016) the mean values of 23 annual climatic
 variables estimated from 1951-1980, the 30-year period preceding the point
 at which climate warming is considered to have begun to accelerate (IPCC,
 2013). The climate variables estimated are defined in Appendix S1 (see
 Supplemental Data with this article).  To estimate climatic parameters,
 ClimateNA uses interpolated values from the PRISM climate database (PRISM
 Climate Group, Oregon State University, http://prism.oregonstate.edu) to
 generate scale-free estimates of local climatic conditions at a resolution
 of 4km x 4km. The DOY of specimen collection does not
 provide a precise record of the flowering date of a specimen because an
 individual plant collected in flower may have been collected at any time
 after flowering has begun, and the duration of an individual’s flowering
 period may be several weeks or longer. Nevertheless, the date of specimen
 collection has been found to be a reliable proxy for flowering time (Davis
 et al., 2015), and has been used in many studies to detect the factors
 influencing flowering phenology (Davis et al., 2015; Matthews and Mazer,
 2016; Willis et al., 2017; Ferris and Willis, 2018; Park and Mazer, 2018,
 2019; Ellwood et al., 2019; Love et al., 2019; Park et al., 2019; Mazer et
 al., 2021; Park, Breckheimer, et al., 2021; Park, Ramirez‐Parada, et al.,
 2021; Pearson et al., 2021). Among the focal taxa of this study, images
 were available from CCH2 on 25 December 2021 for 26, 679, 10, and 33
 specimens of C. exilis, C. unguiculata, C. xantiana ssp. parviflora, and
 C. xantiana ssp. xantiana, respectively. Each image was visually inspected
 to determine whether the individual plant(s) on each sheet bore one or
 more open flowers. Of these, 24 (92%), 659 (97%), 8 (80%), and 30 (91%)
 specimens bore one or more open flowers, indicating that the DOY of
 specimen collection for these taxa generally reflects the specimen’s
 flowering time at a given site. Statistical analysis of
 herbarium-derived data and climate—To assess whether sister taxa differ
 with respect to mean flowering time independent of the effects of local
 climatic conditions on flowering phenology due to evolutionary and/or
 short-term plastic responses to climate, we controlled statistically for
 climatic variation among collection sites when testing for differences
 between taxa in mean flowering time. To characterize each collection site
 using a multivariate index of its climate, principal components (PCs) for
 the 24 annual climatic variables were estimated using the pooled data for
 each pair of sister taxa. In both pairs of taxa, the first two principal
 components (PC1 and PC2) for the 24 climatic parameters together account
 for &gt;75% of the variance in climate (Appendix S2). In the pooled
 data for Clarkia unguiculata and C. exilis, PC1 and PC2 account for 46%
 and 31% of the variance, respectively; in Clarkia xantiana ssp. xantiana
 and ssp. parviflora, PC1 and PC2 explain 62% and 21% of the variance,
 respectively (Appendix S2). In both taxon pairs, across their geographic
 distribution, the loadings of PC1 indicate that it represents an axis
 dominated by temperature, while PC2 represents an axis dominated by
 humidity (Appendix S3). For each data set, the first
 two PCs were used to determine whether sister taxa differ with respect to
 their flowering phenology (controlling for site-specific climatic
 conditions) and in the chronic climatic conditions to which they are
 exposed. The function prcomp() was used to estimate PCs, following
 centering and scaling of the data to unit variance; the function
 fviz_pca_var() in the package factoextra (v.1.0.7) was used to visualize
 the contributions of each variable in each PC. These and all subsequent
 analyses were conducted in RStudio (v. 1.4.1717) with R v.
 4.1.1. Phenological differences between sister taxa—To
 determine whether sister taxa differ in their mean flowering date while
 controlling for the effects of geographic variation in local chronic
 climatic conditions (which influence DOY; see Results), we conducted four
 linear regressions on each sister pair. In all of these models, the day of
 year of specimen collection (DOY) was the response variable (Table 1).  In
 neither taxon pair were either of the two-way interactions (Taxon x PC1 or
 Taxon x PC2) statistically significant. Below, we present the results for
 the model with the lowest AIC value. Type III sums of squares were used to
 detect a significant difference between taxa in their mean date of
 collection, controlling for variation among collection sites with respect
 to the covariates. The lm function with the default setting for contrasts
 and the lme4 package (v. 1.1-27.1) were used. Climatic
 differences between sister taxa —To determine whether sister taxa differ
 with respect to the chronic climatic conditions at their respective
 herbarium specimen collection sites, we used linear models to detect
 statistically significant differences between taxa with respect to the
 mean values of the first and the second PCs.  In these analyses, either
 PC1 or PC2 was the response variable, and taxon was the predictor
 variable. The lm function (using the default setting for contrasts:
 c[contr.treatment, contr.poly]) in the lme4 package (v. 1.1-27.1) was
 used. <strong>Contemporary differences between
 sister taxa: phenotypic divergence in physiological
 traits</strong>—Sampling of extant populations: sample sizes and
 life stages—Naturally occurring field populations were surveyed in the
 region of Lake Isabella, California in the southern Sierra Nevada, where
 we sampled populations at elevations ranging from 457 to 1628 m. In spring
 2010, 13 sites were identified that, at the onset of spring, contained a
 population of one or more of our focal taxa with a sufficient number of
 healthy juvenile plants that we judged we would be able to survey at least
 90 individuals for their physiological performance during both vegetative
 and flowering stages (Fig.2; see Appendices S4 and S5 for a summary of
 each population’s GPS coordinates, elevation, sampling dates, and sample
 sizes). Each population was surveyed once during vegetative growth and
 once during flowering in spring 2010; a different group of individuals was
 sampled at each life stage. For each survey, 90 individuals were selected
 at random positions along one or more transects traversing the population,
 with the restriction that a surveyed plant had to bear at least one leaf
 long enough to sample with a LiCoR-6400 gas exchange analyzer (leaves that
 were too short could not cross the leaf chamber when it was closed).
 Sampled plants were typically at least 1m apart from one another. In
 total, we measured the physiological performance of  &gt;1800
 individuals. We analyzed and compared these traits while controlling
 statistically for leaf position on the primary stem and for leaf
 temperature at the time of measurement, both of which can influence
 measures of physiological performance (Mazer et al., 2010). 
 <strong>Physiological survey</strong>—Gas exchange
 measurements were recorded on leaves produced by the primary branch on
 each plant. Measured leaves remained attached to the stem while using a
 portable infrared gas exchange analyzer (IRGA; LI-COR 6400, Lincoln,
 Nebraska, USA). We recorded photosynthetic rate (A; μmol CO2 m-2 s-1),
 transpiration rate (E; mol H2O m-2 s-1), and the derived parameter, A/E,
 instantaneous water use efficiency (WUEi;  μmolCO2 molH2O-1 x 0.0001). In
 addition, the position of each measured leaf, recorded as the number of
 the node that bore it, was recorded; the node just above the cotyledons
 was referred to as node 1, with subsequently produced nodes counted in
 sequence. The temperature of the leaf (°C) at the time of measurement was
 also recorded. Node number and leaf temperature were included as
 covariates in our statistical models in order to control for their
 potential effects on physiological rates, as observed by Mazer et al.
 (2010). The following LI-COR settings were used: light source, 6400-40
 fluorometer; stability, values of CO2 and H2O remained stable for at least
 15 s with a change in slope of &lt;1; stomatal ratio = 1; flow = 500
 μmol s-1; PARi (PAR in the leaf chamber) = 1500 mol m-2 s-1. CO2
 concentration in the IRGA reference chamber was 400 ppm.  
 For leaves too narrow to fill the entire 2cm2 circular area
 exposed within the LI-COR 6400’s leaf chamber, the actual leaf area
 exposed was calculated and then the LI-COR 6400’s Simulator Software was
 used to adjust those physiological parameters that were influenced by leaf
 area. To measure the leaf area exposed within the chamber, we used an ink
 pad to apply black ink to the foam gasket of the chamber; when the chamber
 was then closed upon the leaf, the gasket stamped with ink the outer
 boundary of the leaf’s area that was exposed within the chamber. Following
 gas exchange measurements, each leaf that failed to fill the chamber’s
 circular opening was removed, taped to a piece of paper with transparent
 tape, and subsequently scanned using a digital scanner. We then used the
 image analysis program ImageJ (http://rsb.info.nih.gov/ij/index.html) to
 measure the area of each leaf that had been exposed within the LI-COR
 chamber during gas exchange measurement.     
 Instantaneous gas exchange measurements and WUEi provide point
 estimates of the physiological performance of individual plants and are
 not equal to the same parameters measured over the entire lifespan of
 individuals (Zelitch, 1982; Serichol-Escobar et al., 2016).  In the
 current study, however, the large sample sizes obtained per taxon, the
 replication of each taxon within and among 2-4 populations, and the
 statistical control of variation in other plant attributes that are
 correlated with instantaneous gas exchange rates (e.g., leaf position on
 the stem, leaf temperature at the time of measurement, and the life cycle
 stage at which plants are measured) helped to account for the variance in
 physiological performance that can obscure differences between taxa.
 Numerous other recent studies have compared taxa, experimental treatments,
 or life history stages based on instantaneous gas exchange measurements
 (Mazer et al., 2010; Franks, 2011; Dudley et al., 2012, 2015; Gorai et
 al., 2015; Cheng et al., 2017; Gulias et al., 2018; Velikova et al., 2018;
 Wang et al., 2019; Scalon et al., 2021). In late June
 to mid-July 2010, all surviving plants were surveyed and, when flower
 production had ceased, the total number of full-sized or mature fruits
 borne by each plant was counted to provide an estimate of lifetime
 reproductive fitness. Many plants, however, suffered from frugivory by the
 larvae of Hyles lineata (the white-lined sphinx moth: Sphingidae) and, at
 some locations, by vertebrates, so total fruit production could not be
 accurately measured.  Consequently, as plants fully senesced and dried, we
 collected all above-ground parts (including fruits) from each plant,
 air-dried each skeleton in a paper bag, and returned them to the lab for
 weighing. All fruits retained by each plant were removed and counted, and
 the above-ground stem biomass (in mg, without any leaves, flowers, or
 ovaries) of each plant was recorded. To assess whether above-ground stem
 biomass is a reasonable proxy for lifetime reproductive success in these
 Clarkia taxa, we measured the relationship between above-ground stem
 biomass and total fruit production among individuals that were sampled
 during flowering and that suffered no frugivory. In all taxa, the
 correlation coefficient between these log-transformed variables was
 &gt; 0.60 (C. unguiculata, r = 0.61, n = 151, p &lt; 0.0001; C.
 exilis, r = 0. 90, n = 60, p &lt; 0.0001; C. xantiana ssp. xantiana, r
 = 0.76, n = 173, p &lt; 0. 0001; C. xantiana ssp. parviflora, r =
 0.84, n= 81, p &lt; 0.0001). In the analyses of phenotypic selection
 described below, we used above-ground stem biomass as the response
 variable (i.e., the estimate of individual fitness).
 Climate data — To assess the influence of recent climatic
 conditions on the physiological rates of populations and taxa, monthly
 climatic data (cumulative precipitation; mean minimum, maximum and mean
 temperature; and vapor pressure deficit [VPD]) was extracted from the
 PRISM climate database (PRISM Climate Group, Oregon State University,
 http://prism.oregonstate.edu) for November 2007-June 2008 (corresponding
 to the growing season surveyed by Mazer et al., 2010) and for November
 2009-June 2010 (the growing season of the current study). These data were
 used to determine whether differences between years in the magnitude or
 direction of physiological divergence between sister taxa was associated
 with year-specific climate.  In addition, these climate data were used to
 examine whether physiological differences between sister taxa observed
 during flowering in the current study were associated with the mean
 climatic conditions to which their sampled populations were exposed in the
 month when they were surveyed (Tables S3, S4). When comparing C. exilis
 and C. unguiculata, climatic conditions for the months of April and May
 2010, respectively, were used; these corresponded most closely to the
 months when each species were surveyed. When comparing the subspecies of
 C. xantiana, climatic conditions for May 2010 were used for ssp. xantiana,
 except for the Democrat Springs and Saw Mill Road sites, for which
 conditions in June 2010 were used due to their mid-June survey dates
 (Appendix S6).  For C. xantiana ssp. parviflora, climatic conditions for
 May 2010 were used, except for the Long Valley population, for which June
 2010 conditions were used. <br> 2. Methods for
 processing the data:  <br> The submitted data
 reflect the data recorded in the field.   <br> 3.
 Instrument- or software-specific information needed to interpret the
 data:  <br> R Packages and libraries used to
 analyze the data are included in the description of the Methods provided
 above. <br> 4. Standards and calibration
 information, if appropriate:  N/A <br> 5.
 Environmental/experimental conditions: N/A <br>
 6. Describe any quality-assurance procedures performed on the
 data:  See R code for cases in which data were filtered
 to exclude clear outliers. <br> 7. People
 involved with sample collection, processing, analysis and/or
 submission:  <br> SJM and LSD were awarded
 funding from the National Science Foundation to conduct the work described
 here; SJM, LSD, and AAH designed and conducted the field work; all authors
 contributed to planning the statistical analyses; DJH and SJM conducted
 the analyses; all authors discussed and reached consensus on the
 interpretation of the results; SJM wrote the manuscript, with editorial
 contributions from all co-authors. S. K. Emms
 participated in the sampling design and recording of field data.  We are
 grateful to H. Guo for field assistance and to UCSB undergraduates K. Law,
 M. Echt, and S. Ricceri for assistance with measuring lifetime fruit
 production. <br> DATA-SPECIFIC INFORMATION
 FOR:  <br>
 CUCE_herbarium_Normals_1951_1980_na.rm.csv   
 <br> 1. Number of variables: 30 2.
 Number of cases/rows: 778 3. Variable List: Note that
 the climate variables in this data set refer to 30-year annual means from
 1951-1980 Specimen ID: reference number of no
 particular use <br> Taxon = subspecific epithet:
 "unguiculata" refers to Clarkia unguiculata, and
 "exilis" refers to Clarkia exilis DOY = Day
 of Year of specimen collection<br> Year = Year of specimen
 collection<br> Latitude, in decimal degrees<br> Longitude, in
 decimal degrees<br> Mean_Ann_Temp = MAT<br>
 Mean_Warmest_Month_Temp = MWMT, Mean daily temperature of the warmest
 month, in degrees Centigrade<br> Mean_Coldest_Month_Temp = MCMT,
 Mean daily temperature of the coldest month, in degrees
 Centigrade<br> MWMT-MCMT = the difference between the mean
 temperature of the warmest month and the mean temperature of the coldest
 month, in degrees Centigrade<br> Mean_Ann_PPT_mm = MAP, or Mean
 Annual Precipitation<br> Mean_Summer_PPT_mm = May to September
 precipitation, in millimeters<br> Ann_Heat_Moisture_Index = Annual
 Heat Moisture Index, or AHM<br> Summer_Heat_Moisture_Index =
 SHM<br> Deg_Days_Below_O = DD &lt; 0 degrees
 Centigrade<br> Deg_Days_Above_5 = DD &gt; 5 degrees
 Centigrade<br> Deg_Days_Below_18 = DD &lt; 18 degrees
 Centigrade<br> Deg_Days_Above_18 = DD &gt; 18 degrees
 Centigrade<br> NFFD = number of frost free days in a given
 year<br> bFFP = the day of the year (from 1-365) on which the
 frost-free period begins<br> eFFP = the day of the year (from 1-365)
 on which the frost-free period end<br> FFP = the length of the
 frost-free period (typically eFFP - bFFP)<br> PPT_as_Snow_mm =
 precipitation as snow (mm).  For individual years, it covers the period
 between August in the previous year and July in the current
 year.<br> EMT = extreme minimum temperature over 30 years (degrees
 Centigrade)<br> EXT = extreme maximum temperature over 30 years
 (degrees Centigrade)<br> Eref = Hargreaves reference evaporation
 (mm)<br> Hargreaves_CMD_mm =  Hargreaves climatic moisture deficit
 (mm)<br> Rel_Humidity = mean annual relative humidity (%) <br>
 CMI = Hogg’s climate moisture index (mm)<br> DD1040 = Degree-days
 above 10°C and below 40°C <br>
 CXXCXP_herbarium_Normals_1951_1980_na.rm.csv  
 <br> 1. Number of variables: 30 2.
 Number of cases/rows: 65 3. Variable List: the climate
 variables in this data set refer to 30-year annual means from
 1951-1980 Specimen ID: reference number of no
 particular use <br> Taxon = subspecific epithet:
 "xantiana" refers to Clarkia xantiana ssp. xantiana, and
 "parviflora" refers to Clarkia xantiana ssp.
 parviflora DOY = Day of Year of specimen
 collection<br> Year<br> Latitude, in decimal degrees<br>
 Longitude, in decimal degrees<br> Mean_Ann_Temp = MAT<br>
 Mean_Warmest_Month_Temp = MWMT, Mean daily temperature of the warmest
 month, in degrees Centigrade<br> Mean_Coldest_Month_Temp = MCMT,
 Mean daily temperature of the coldest month, in degrees
 Centigrade<br> MWMT-MCMT = the difference between the mean
 temperature of the warmest month and the mean temperature of the coldest
 month, in degrees Centigrade<br> Mean_Ann_PPT_mm = MAP, or Mean
 Annual Precipitation<br> Mean_Summer_PPT_mm = May to September
 precipitation, in millimeters<br> Ann_Heat_Moisture_Index = Annual
 Heat Moisture Index, or AHM<br> Summer_Heat_Moisture_Index =
 SHM<br> Deg_Days_Below_O = DD &lt; 0 degrees
 Centigrade<br> Deg_Days_Above_5 = DD &gt; 5 degrees
 Centigrade<br> Deg_Days_Below_18 = DD &lt; 18 degrees
 Centigrade<br> Deg_Days_Above_18 = DD &gt; 18 degrees
 Centigrade<br> NFFD = number of frost free days in a given
 year<br> bFFP = the day of the year (from 1-365) on which the
 frost-free period begins<br> eFFP = the day of the year (from 1-365)
 on which the frost-free period end<br> FFP = the length of the
 frost-free period (typically eFFP - bFFP)<br> PPT_as_Snow_mm =
 precipitation as snow (mm).  For individual years, it covers the period
 between August in the previous year and July in the current
 year.<br> EMT = extreme minimum temperature over 30 years (degrees
 Centigrade)<br> EXT = extreme maximum temperature over 30 years
 (degrees Centigrade)<br> Eref = Hargreaves reference evaporation
 (mm)<br> Hargreaves_CMD_mm =  Hargreaves climatic moisture deficit
 (mm)<br> Rel_Humidity = mean annual relative humidity (%) <br>
 CMI = Hogg’s climate moisture index (mm)<br> DD1040 = Degree-days
 above 10°C and below 40°C <br> 4. Missing data
 codes: N/A 5. Specialized formats or other
 abbreviations used: N/A <br>
 ******************** ClarkiaFieldData2010.csv
 <br> 1. Number of variables: 11
 <br> 2. Number of cases/rows: 2350
 <br> 3. Variable List:   Taxon = Which
 of the four focal taxa a given record (row) represents <br>
 Population = The name of the field population in which the focal
 individual was observed<br> Reproductive_status = The life history
 stage of the population on the date of observation: Vegetative vs.
 Reproductive (flowering stage)<br> Biomass = Above ground stem
 biomass (in grams), measured at senescence, excluding leaves and
 fruits<br> DayGE_Node = Node number of the leaf on which gas
 exchange traits were measured<br> Photo_DayGE = Photosynthetic rate,
 umol/m2/s   <br> Trans_DayGE = Transpiration rate,
 mol/m2/s<br> WUE_DayGE = Instantaneous water use efficiency, [(umol
 CO2/m2/sec)/(mol H2O/m2/sec)]*1E4              <br> Tleaf_DayGE =
 Temperature of the leaf on which gas exchange rates were measured, at the
 time of measurement        <br> Cond_DayGE = Stomatal conductance,
 mol/m2/s          <br> Shoot_height_cm = Height of the plant in
 centimeters, recorded in each population 0-2 days before gas exchange
 traits were measured, with one exception.  The Willow Springs population
 of Clarkia exilis was measured for gas exchange traits during flowering on
 April 26, 2010, but the height of these plants was measured on April 14.  
        <br> 4. Missing data codes: 
 NA 5. Specialized formats or other
 abbreviations used:  N/A <br>