DOI: 10.5061/dryad.ht76hdrtn
title: Data for: Predicting the contribution of single trait evolution to rescuing a plant population from demographic impacts of climate change

description: Evolutionary adaptation can allow a population to persist in the face of a
 new environmental challenge. With many populations now threatened by
 environmental change, it is important to understand whether this process
 of evolutionary rescue is feasible under natural conditions, yet work on
 this topic has been largely theoretical. We used unique long-term data to
 parameterize deterministic and stochastic models of the contribution of
 one trait to evolutionary rescue using field estimates for the subalpine
 plant Ipomopsis aggregata and hybrids with its close relative I.
 tenuituba. In the absence of evolution or plasticity, the two studied
 populations are projected to go locally extinct due to earlier snowmelt
 under climate change, which imposes drought conditions. Phenotypic
 selection on specific leaf area (SLA) was estimated in 12 years and
 multiple populations. Those data on selection and its environmental
 sensitivity to annual snowmelt timing in the spring were combined with
 previous data on heritability of the trait, phenotypic plasticity of the
 trait, and the impact of snowmelt timing on mean absolute fitness.
 Selection favored low values of SLA (thicker leaves). The evolutionary
 response to selection on that single trait was insufficient to allow
 evolutionary rescue by itself, but in combination with phenotypic
 plasticity it promoted evolutionary rescue in one of the two populations.
 The number of years until population size would stop declining and begin
 to rise again was heavily dependent upon stochastic environmental changes
 in snowmelt timing around the trend line. Our study illustrates how field
 estimates of quantitative genetic parameters can be used to predict the
 likelihood of evolutionary rescue. Although a complete set of parameter
 estimates are generally unavailable, it may also be possible to predict
 the general likelihood of evolutionary rescue based on published ranges
 for phenotypic selection and heritability and the extent to which early
 snowmelt impacts fitness.
description: The study sites consisted of three “Poverty Gulch” sites in
 Gunnsion National Forest and one site “Vera Falls” at the Rocky Mountain
 Biological Laboratory, all in Gunnison County, CO, USA. Focal plants
 included two sets of plants. One set (data from 2009-2019) consisted of
 plants in common gardens at three sites: an <em>I. aggregata
 </em>site (hereafter “agg”), an <em>I. tenuituba
 </em>site (hereafter “ten”) and a site at the center of the natural
 hybrid zone (hereafter “hyb”). The second set consisted of plants growing
 in situ at two of the same Poverty Gulch sites (“agg” and “hyb”), and
 an <em>I. aggregata </em>site at Vera Falls (hereafter “VF”;
 data from 2017-2023).  The common gardens were started
 from seed in 2007 and 2008. Measurements of SLA in these gardens began
 when plants were 2 years old, either 2009 or 2010 depending upon the
 garden, as they are only small seedlings during their first summer after
 seed maturation. By 2018, all but 15 of the 4512 plants originally planted
 had died, with or without blooming, and we stopped following these
 gardens. Starting in 2017, in situ vegetative plants at the <em>I.
 aggregata </em>site and the hybrid site whose longest leaf exceeded
 25 mm were marked with metal tags to facilitate
 identification.   In each year of the study, one leaf
 from each vegetative plant was collected in the field and transported on
 ice to the RMBL, 8 km distant. There each leaf was scanned with a flatbed
 scanner and analyzed using ImageJ to measure leaf area. The leaf was dried
 at 70 deg C for 2 hours and then weighed to obtain dry mass and calculate
 SLA as area/dry mass. For plants in the common gardens, SLA was measured
 on 982 leaves from 383 plants in 2009 – 2014. For in situ plants, SLA was
 measured on one leaf from each of 877 plants in 2017 – 2022. Fitness was
 estimated as the binary variable of survival to flowering. Plants that
 were still alive in 2019 in the common gardens or in 2023 at the end of
 the study were assumed to survive to flowering. These
 data were used to estimate selection differentials on SLA in each of 12
 years. We then combined this information with previous information on
 heritability and the effect of snowmelt date in the spring on mean
 absolute fitness, measured as the finite rate of population increase, from
 a previous demographic study. This information was used to parameterize
 models of evolutionary rescue that we developed. We developed two models
 that differed in how snowmelt timing changed: a Step-change model and a
 Gradual environmental change model and analyzed both deterministic and
 stochastic versions. All analysis and modeling was done in R ver
 4.2.2.  
description: # Data for: Predicting the contribution of single trait evolution to
 rescuing a plant population from demographic impacts of climate change
 Dataset DOI: [10.5061/dryad.ht76hdrtn](10.5061/dryad.ht76hdrtn) ##
 Description of the data and file structure File
 "mastervegtraitsSLA2023.csv" contains data on specific leaf area
 for Ipomopsis plants in the field. Files
 "masterdemography_insitu_2023.csv" and
 "masterdemography_commongarden.csv" provide the corresponding
 information on survival to flowering. File "snowmelt.csv"
 provides dates of snowmelt in the spring. File
 "selection_vs_snowmelt.csv" provides intermediate results on
 selection intensities from analysis with the first parts of the code
 "Campbell-EvolutionLettersMay2025.Rmd". File
 "IPMresults.csv" provides estimates of the finite rate of
 increase (lambda) predicted from the publication by Campbell
 [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.