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1. Data for: Predicting the contribution of single trait evolution to rescuing a plant population from demographic impacts of climate change

   https://doi.org/10.5061/dryad.ht76hdrtn  

   Campbell, Diane; Powers, John; Kipness, Justin; Laboratory, Rocky_Mountain Biological (January 2025, Dryad)

   {"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."]} 

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2. NEON Tree Species Predictions

   https://doi.org/10.5281/zenodo.10041906  

   Weinstein, Ben; Marconi, Sergio; Zare, Alina; Bohlman, Stephanie; Singh, Aditya; Graves, Sarah; Magee, Lukas; Johnson, Daniel; Record, Sydne; Rubio, Vanessa; et al (April 2024, Zenodo)

   Weinstein, Ben (Ed.)

   # Individual Tree Predictions for 100 million trees in the National Ecological Observatory Network Preprint: https://www.biorxiv.org/content/10.1101/2023.10.25.563626v1 ## Manuscript Abstract The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales allows an unprecedented view of forest ecosystems, forest restoration and responses to disturbance. To create detailed maps of tree species, airborne remote sensing can cover areas containing millions of trees at high spatial resolution. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual tree species using ground truthed data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees for 24 sites in the National Ecological Observatory Network. Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1km^2 shapefiles with individual tree species prediction, as well as crown location, crown area and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of six species per site, ranging from 3 to 15 species. All predictions were uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. These data can be used to study forest macro-ecology, functional ecology, and responses to anthropogenic change. ## Data Summary Each NEON site is a single zip archive with tree predictions for all available data. For site abbreviations see: https://www.neonscience.org/field-sites/explore-field-sites. For each site, there is a .zip and .csv. The .zip is a set 1km .shp tiles. The .csv is all trees in a single file. ## Prediction metadata *Geometry* A four pointed bounding box location in utm coordinates. *indiv_id* A unique crown identifier that combines the year, site and geoindex of the NEON airborne tile (e.g. 732000_4707000) is the utm coordinate of the top left of the tile.  *sci_name* The full latin name of predicted species aligned with NEON's taxonomic nomenclature.  *ens_score* The confidence score of the species prediction. This score is the output of the multi-temporal model for the ensemble hierarchical model.  *bleaf_taxa* Highest predicted category for the broadleaf submodel *bleaf_score* The confidence score for the broadleaf taxa submodel  *oak_taxa* Highest predicted category for the oak model  *dead_label* A two class alive/dead classification based on the RGB data. 0=Alive/1=Dead. *dead_score* The confidence score of the Alive/Dead prediction.  *site_id* The four letter code for the NEON site. See https://www.neonscience.org/field-sites/explore-field-sites for site locations. *conif_taxa* Highest predicted category for the conifer model *conif_score* The confidence score for the conifer taxa submodel *dom_taxa* Highest predicted category for the dominant taxa mode submodel *dom_score* The confidence score for the dominant taxa submodel ## Training data The crops.zip contains pre-cropped files. 369 band hyperspectral files are numpy arrays. RGB crops are .tif files. Naming format is __, for example. "NEON.PLA.D07.GRSM.00583_2022_RGB.tif" is RGB crop of the predicted crown of NEON data from Great Smoky Mountain National Park (GRSM), flown in 2022.Along with the crops are .csv files for various train-test split experiments for the manuscript. ### Crop metadata There are 30,042 individuals in the annotations.csv file. We keep all data, but we recommend a filtering step of atleast 20 records per species to reduce chance of taxonomic or data cleaning errors. This leaves 132 species. *score* This was the DeepForest crown score for the crop. *taxonID*For letter species code, see NEON plant taxonomy for scientific name: https://data.neonscience.org/taxonomic-lists *individual*unique individual identifier for a given field record and crown crop *siteID*The four letter code for the NEON site. See https://www.neonscience.org/field-sites/explore-field-sites for site locations. *plotID* NEON plot ID within the site. For more information on NEON sampling see: https://www.neonscience.org/data-samples/data-collection/observational-sampling/site-level-sampling-design *CHM_height* The LiDAR derived height for the field sampling point. *image_path* Relative pathname for the hyperspectral array, can be read by numpy.load -> format of 369 bands * Height * Weight *tile_year*  Flight year of the sensor data *RGB_image_path* Relative pathname for the RGB array, can be read by rasterio.open() # Code repository The predictions were made using the DeepTreeAttention repo: https://github.com/weecology/DeepTreeAttentionKey files include model definition for a [single year model](https://github.com/weecology/DeepTreeAttention/blob/main/src/models/Hang2020.py) and [Data preprocessing](https://github.com/weecology/DeepTreeAttention/blob/cae13f1e4271b5386e2379068f8239de3033ec40/src/utils.py#L59). 

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3. Data from: Ligand-to-metal charge transfer control of ⁵¹V NMR thermal sensitivity

   https://doi.org/10.5061/dryad.3ffbg79zn  

   Zadrozny, Joseph; Crans, Debbie (January 2026, Dryad)

   Abstract: Understanding thermal sensitivity of magnetic nuclei is an important step toward noninvasive thermometers in magnetic resonance imaging applications. This data set is associated with the exploration of a specific nucleus, 51V, and its associated temperature dependent spectroscopic properties in a series of four V(V) complexes: [VO(3-OEtHshed)(tbad)] (1, tbad = 5-(adamantan-1-yl)-3-(tert-butyl)benzene-1,2-diol, 3-OEtHshed = (E)-2-ethoxy-6-(((2-((2-hydroxyethyl)amino)ethyl)imino)methyl)phenol), [VO(3-OEtHshed)(cat)] (2, cat = catechol), [VO(3-OEtHshed)(2OHP)] (3, 2OHP = 2-(hydroxymethyl)phenol) and [VO2(3-OEtHshed)] (4). These data demonstrate an increasing thermal sensitivity for the 51V nuclear magnetic resonance signal when the supporting ligand enables a low-energy ligand-to-metal-charge-transfer transition. This is the first demonstration of any sort of design strategy to increase the thermal sensitivity of the 51V nucleus. Methods: Methods for analysis are described in detail in the manuscript and accompanying supplementary information available from the Royal Society of Chemistry at: http://dx.doi.org/10.1039/D6CC02350A Citation for manuscript: A. C. Bates, J. V. Grundy, J. R. Stapf, Ö. Üngör, D. C. Crans, J. M. Zadrozny; Ligand-to-Metal Charge Transfer Control of 51V NMR Thermal Sensitivity Chemical Communications, 2026, DOI: 10.1039/D6CC02350A TechnicalInfo: # Data from: Ligand-to-metal charge transfer control of ^51^V NMR thermal sensitivity Dataset DOI: [10.5061/dryad.3ffbg79zn](https://doi.org/10.5061/dryad.3ffbg79zn) ## Description of the data and file structure All contained data are either the raw data directly from the instruments used to acquire the data, or aggregated collections of the data in a format sufficient for plotting and analysis in external programs. The data that are attached are compressed, zip folders of the raw data and other data used to make figures for the manuscript. Unzip these folders to access the raw data. All contained data are either the raw data directly from the instruments used to acquire the data, or aggregated collections of the data in a format sufficient for plotting and analysis in external programs. Most of the data was processed using Excel and MestreNova, with OriginPro allowing for final production of manuscript figures. ### Files and variables ### File: UV-Vis_Spectra.zip This compressed folder has five different files in it. The UV-Vis summary Excel file "UV-Vis summary.xlsx" contains the final tabulated electronic absorption spectroscopy data for compounds 1-4 from the main manuscript. The data is saved as an Excel file. Data are organized in columns: wavelength (nm), absorbance (A), and molar absorptivity (epsilon) for each of the complexes, respectively. An additional column that converts the wavelength to wavenumbers is provided for convenience. The main manuscript contains the plot of epsilon versus wavenumber as Figure 3. See main manuscript for measurement conditions, used instrument, and other relevant experimental points. The other four files, labeled as "compound name 0.1mM.csv" are the raw instrumental outputs saved in comma-delimited files that can be opened via any text editor and Excel. These data were all collected at 0.1 mM concentration. These .csv files have all experimental data. Briefly, the block of text under "summary" contains instrumental summary information (spectrometer, model, software version, sample name); "parameter" has explicit instrumental collection conditions, e.g., the wavelength window, the slit width, the scanning speed; and below that are the actual data in two columns, one of wavelength, and one of absorbance. ### **File: Low-field_NMR.zip** This compressed file contains two folders that constitute the worked up ("processed") and raw ("unprocessed") low-field NMR spectra. In the unprocessed data folder, there are four subfolders labeled with the compound #s from the manuscript. Dragging one of these folders into MestreNova (see NMR program details below) will immediately open the results of the experiment. There are subfolders with instrumental parameters and a .jdx file, which is a standardized spectral formula (see NMR notes below). The processed data folder contains the worked-up data for each compound. They are saved as tab-delimited .csv files, each with two columns, ppm on the left and spectral intensity on the right. These can be opened in any graphing program. The folder also contains all of them collected into a single MNova file as well, which can be opened with MestreNova.  ### **File: RT_51V_NMR_spectra.zip** This compressed data set has the room-temperature ^51^V NMR spectra for complexes 1-4, which are depicted in Figure 3 in the main manuscript. This folder contains ten different files. There are four tab-delimited .csv files, one for each complex, which are the xy coordinates for the NMR spectra as depicted in Figure 3. These data are separated into two columns, left is chemical shift in ppm and the right one is the 51V NMR signal intensity. These data can be opened with any standard text edit/spreadsheet software. There are also five folders entitled, e.g., "Compound 1 25 dC". These folders contain the raw instrumental output from the NMR for each of the four compounds, all collected at 25 degrees Celsius. These folders can be dragged, in their entirety, into the program MestreNova (see notes below) and it will give an initial process of the corresponding NMR spectrum. The final file in this folder is "All compounds in MeCN at 25dC.mnova". This is the aggregate data set from the room temperature ^51^V NMR spectra, all collected in acetonitrile (MeCN) in a format that can be opened by MestreNova by simply dragging the file into the opened program window (see notes below about MestreNova and NMR processing).  ### File: 2D_NMRs.zip This compressed folder contains all of the 2-dimensional NMR spectra used to assign structure for complexes 1-4. In this folder are four compressed folders that contain relevant NMR spectra for the labeled compound. The folder for compound 1 contains the one-dimensional proton NMR spectra, labeled as 1H and 1H D2O spike for this complex. The files including COSY and NOESY in this folder correspond to the 2D spectra depicted in the manuscript SI file. The folder for compound 2 contains the one-dimensional 1H NMR spectrum (labeled as "1H") and the two-dimensional spectra are labeled with "COSY" and "NOESY". The folder for compound 3 contains the one-dimensional proton ("1H") and COSY and NOESY spectra. Compound 4 only contains the COSY and NOESY 1H spectra. For all folders, data is supplied as folders that can be opened with MestreNova (or other appropriate NMR software, see below) or the MestreNova files themselves. ### File: VT_NMR_data.zip The compressed folder with this data contains variable-temperature NMR spectra for all complexes and tabulated variable-temperature NMR linewidths discussed in the manuscript. All temperatures are in degrees C. There are four subfolders associated with this data set, which are labeled according to the complexes (1-4) which they correspond to. Within these subfolders, there are a set of subfolders labeled with the name of the complex, e.g., "VO(3OEtHSHED)(TBAD)" for complex 1 and then a number, ranging from 25 to 50. These folders are the solution phase NMR spectra for the complexes, and the number corresponds to the temperature of the experiment. Like with other NMR spectra collected here, these folders can be dragged into the MNova (or other) software for analysis. We have also collected the worked up variable temperature data for the compound into a single MNova file in each of these folders. Finally, there is an Excel spreadsheet that collects the temperature dependence of peak positions and their linewidths. This spreadsheet can be opened in Excel or other spreadsheet software. The data are organized in this spreadsheet by compound (1 to 4, from left to right). The solvent of measurement is listed under the name of the compound. The top rows of variable temperature data contain the chemical shift information. Below that, in the data sets marked, e.g., "Peak 4 (SW)" we have the signal linewidths in Hz and then the same values in ppm even further down. There is a "Dd/DT" row which is the thermal sensitivity of the chemical shift for the variable temperature data sets and then a "Resolution" row even further down which contains values of thermal sensitivity divided by the linewidth. ## Code/software Data that is recorded as .csv, .txt, or in other common spreadsheet extensions can be opened in Excel or other standard plotting software. The majority of our NMR data were analysed and processed using [MNova](https://mestrelab.com/main-product/mnova). MestreNova (or MNova) is a common NMR-processing software. Many universities have access to it by purchasing a license, and analysis of the data can be performed simply by dragging the FID files into the program. There are alternatives for analysis, e.g. [Topspin](https://www.bruker.com/en/products-and-solutions/mr/nmr-software/topspin.html). There are also free software packages available for analysis. MNova has [NMR Lite](https://mestrelab.com/download-nmr-lite), which is a cheaper version and can be used free for an extended period. The program [NUTS](https://www.aiinmr.com/NUTS-Program-Download) is also a free software package that is available from Anasazi Instruments. In some cases, NMR spectra are saved in the .jdx file type, which can be opened with many different spectral software, including the free [jspecview](https://jspecview.sourceforge.net/). See [here](https://opg.optica.org/as/abstract.cfm?uri=as-47-8-1093) for more details. ## Access information Other publicly accessible locations of the data: * Figures and some tabulated data are available in the SI of the manuscript at DOI: 10.1039/D6CC02350A Data was derived from the following sources: * N/A 

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4. Data from: Adaptational lag at high elevations depends on life stage in a California wildflower

   https://doi.org/10.5061/dryad.ncjsxktbf  

   Quarles-Chidyagwai, Brandie; Ashlock, Sarah; Schmitt, Johanna; Maloof, Julin; Gremer, Jennifer (June 2026, Dryad)

   Abstract: High elevation populations are expected to receive reduced snowpack, warmer temperatures, and more variable precipitation with climate change, potentially putting them at risk if rates of adaptation do not keep pace. Populations from climates more closely aligned with changing conditions at high elevations may prove better suited to current climate than the local populations. Thus, it is essential to assess 1) whether high elevation populations are locally adapted to current climate, or 2) whether fitness of lower elevation populations from warmer climates is higher than for local populations in high elevation conditions. We conducted a common garden study with Streptanthus tortuosus at a high elevation site. Plants from twenty-three populations from across the species range, which vary in climate and life history, were transplanted and subsequently measured for mortality and reproductive output across two growing seasons. We examined the effects of climatic distance from the site of origin on plant performance. We hypothesized that lower elevation populations would survive the first warm growth season well, but the seasonal constraint of cold temperatures and snowpack would reduce their survival into the second year, thus limiting their overall fitness. We observed evidence for adaptational lag for high elevation populations, including the local population, but only for some life stages. Lower elevation populations, with climates further from the garden, had higher survival through the first year and over winter, resulting in higher probabilities of reproducing and total fitness. However, survival to reproduction in year 2, and total reproductive output, driven largely by reproduction in the second year, were higher in high elevation populations, with climates closer to the garden. Thus, adaptational lag differed among life stages, and depended on life history (e.g., first year versus second year fitness). Our results highlight the importance of considering variation in life history and seasonal timing when evaluating climate adaptation, as well as vulnerability of high elevation populations to climate change. Further, these findings provide information for management of populations at risk, including strategic assisted gene flow that introduces beneficial alleles from warm-adapted populations but preserves some components of high elevation adaptation. TechnicalInfo: # Data from: Adaptational lag at high elevations depends on life stage in a California wildflower Dataset DOI: [10.5061/dryad.ncjsxktbf](https://doi.org/10.5061/dryad.ncjsxktbf) ## Description of the data and file structure This dataset contains all the data required to replicate analyses in Quarles-Chidyagwai et al. 2026 (Journal of Ecology). We used a high elevation common garden to assess whether high elevation populations were locally adapted to their current climate or whether low elevation populations would have higher fitness than local populations in high elevation conditions.  ### Files and variables #### File: Raw.Data.zip **Description:** This folder includes all data collected and used for analyses in this study. The contents are listed below. ##### Final_2023_2024_Pop_Loc_Info.csv: File used to match unique.IDs with population and maternal family information for 2024. Missing values are indicated with blank cells or "NA". * bed.block.order: used to organize the data sheets in the order of block. * bed.order: used to organize the data sheets by bed. * AB.CD.order: used to organize the data sheets in the order that censuses were conducted in.  * column.order: used to organize the data sheets by columns within beds.  * Pop.Type: identifies whether the plant is a "2023-survivor" or plant planted in 2023 that was still alive in 2024, a "2023-TM2-fruit" or location where TM2 plant reproduced in 2023 that we were watching for seedlings in, or a plant planted in 2024 identified as either a "Parent" population, "F1" or "F2" cross.  * status: identified spots that were available to plant in 2024 versus still had plants from 2023.  * block: which of the 13 blocks the plant was planted in.  * loc: the combination of the bed, bedrow, and bedcol the plant was planted in.  * bed: which of the 11 beds the plant was planted in. * bedrow: which row within beds the plant was planted in.  * bedcol: which column within beds the plant was planted in.  * pop: which population the plant belonged to.  * mf: which maternal family the plant belonged to.  * rep: which number replicate of pop-mf the plant was.  * unique.ID: the unique identifier of the plant used on all other data sheets.  ##### Pops_for_2023_WL2.csv: File with information about the populations used in the study. * parent.pop: one of the 23 populations.  * phylogroup: which phylogroup the population belonged to. Note that this information was not used in this study.  * elevation.group: which "low", "mid", "high" elevation category the population belonged to.  * seed year: which year the seeds used in the study were collected for each population.  ##### Strep_tort_locs.csv: File with information about 54 populations of *Streptanthus tortuosus*, including the 23 used in this study. Missing values are indicated by blank cells.  * Species epithet: full species name.  * Species Code: species code name.  * Site: population name.  * Site code: population code name. * Lat: population latitude. * Long: population longitude. * Elevation (m): population elevation in meters. ##### Updated_Flint_Climate_Oct2024.csv: File with Flint climate data downloaded for all 23 populations used in the study. The data ranges from October 1895 to August 2024. Each column corresponds to a site (see the "Clim_vars_all_sites_years_timeframes.Rmd" script for how the numbers correspond to sites). Each row represents a climate variable, year, and month. Variable names are listed in Table 1 of the manuscript.  ##### Updated_Flint_Sept-Dec2024.csv: File with Flint climate data for all 23 populations included in the study from September 2024 to December 2024.  ##### WL2_2022_2023_iButton_Data_Corrected.csv: File with ibutton soil temperature data for the garden in 2023.  * Bed: the bed the ibutton was buried in. * Date_Time: calendar date and time of each measurement.  * column to indicate the measurements are in Celsius.  * SoilTemp: the soil temperature measurements. ##### WL2_2023_Bed_C_Soil_Moisture_Corrected: File with soil moisture data for the garden in 2023.  * Date_Time: calendar date and time of each measurement.  * Port_1 through Port_5: soil volumetric water content measurements in cubic meters per cubic meter. ##### WL2_annual_census_20231027_corrected.csv: File with annual census data from 2023.  * date: the date the measurements were taken.  * block through rep: the same variables as explained for "Final_2023_2024_Pop_Loc_Info.csv." * pheno: the phenological stage of the plant. V=vegetative, B=budding, F=flowering, P=post flowering, X=dead.  * diam.mm: the stem diameter in mm.  * height.cm: height in cm.  * long.leaf.cm: length of the longest leaf in cm. * num.flw: the number of flowers. * num.fruit: the number of fruits. * long.fruit.cm: length of the longest fruit in cm.  * total.branch: the total number of basal branches. * repro.branch: the number of reproductive basal branches.  * herbiv.y.n: yes or no to wether there were signs of herbivory on the plant.  * survey.notes: any notes from the survey.  ##### WL2_Annual_Census_20241023_corrected.csv: File with annual census data from 2024.  * bed through unique.ID: the same variables as explained for "Final_2023_2024_Pop_Loc_Info.csv." * phen through total.branch: the same variables as explained for "WL2_annual_census_20231027_corrected.csv." * overhd.diam: the widest overhead diameter of the plant in cm.  * overhd.perp: the overhead diameter perpendicular to "overhd.diam." * survey.date: the date the measurements were taken.  * collected.date: the date fruits were collected.  * survey.notes: any notes from the survey.  ##### WL2_DNA_Collection_Size_survey_combined20230703_corrected.csv: File with the pre-transplant size measurements.  * Pop through rep: columns explained previously.  * DNA: 1 for tissue collected, 0 for tissue not collected.  * height (cm) through Notes explained previously.  ##### WL2_Extras_DNA_collection_size_survey_combined20230706_corrected.csv: File with additional pre-transplant size measurements. All variables previously described.  ##### WL2_mort_pheno_20231020_corrected.csv: File with mortality and phenology dates for 2023.  * block through rep and survey.notes described previously.  * bud.date: first bud date.  * flower.date: first flower date.  * fruit.date: first fruiting date. * last.flower.date: the date the plant was no longer producing flowers or buds.  * last.fruit.date: the date the plant was no longer producing flowers and all fruits had elongated.  * death.date: the date a plant was identified as dead.  ##### WL2_mort_pheno_20241023_corrected.csv : File with mortality and phenology dates for 2024. All variables described previously except the following: * last.FL.date: last flower date as described above.  * lastFR.date: last fruit date as described above.  * missing.date: the date was not found in the field with no obvious signs of death. This could be caused by gopher holes for example.  ##### WL2_status_check_20240603_corrected.csv: File with winter survival data. All variables described previously except different symbols were used to categorize the state of plants post-winter in the death.date column.  * A - alive, “happy” leaves. * B = “barely alive”, still has leaves but major damage. * C = no leaves, stem not brittle. * D = brittle, no leaves, definitely dead. #### File: Processed.Data.zip **Description:** This folder includes all data files output by the analysis scripts. the contents are described below.  ##### All_Clim_Dist.csv: File with the calculated climate distances between the 23 populations and the high elevation garden.  * parent.pop through Long: described previously.  * columns F through Q: Gower's climate distance for different time periods (recent versus historic), seasonal summaries (water year versus growth season), and years (2023, 2024, or the average of the two).  * columns R through AO: temperature and precipitation distance for different time periods, seasonal summaries, and years. * column AP: geographic distance between each population and the garden site based on the haversine formula.  * column AQ: the elevation distance between each population and the garden site in meters.  ##### All_Clim.csv: File with the summarized climate for each population in the study.  * parent.pop through Long: described previously.  * timeframe: historic versus recent 30 year periods. * year: the year the climate data has been averaged for.  * columns H through V: climate variables summarized for each year. Variable names are listed in Table 1 of the manuscript.  * Season: seasonal summary (water year versus growth season).  ##### Prediction data frames from the fitness ~ climate distance models: In each file listed below, ".preds" represent the predictions of each corresponding fitness metric. There are also columns for the raw climate and geographic distances and the scaled and centered ones. The file names are formatted as such "Fitness Metric_Distance Metric_Season and Time Period." "GD" is for Gower's climate distance, "GrwSsn" for growth season, "WY" for water year, "Recent" for the recent 30 years and "Hist" for the historic 30 years, "Geo" for geographic distance, "Sub" for temperature or precipitation distance as indicated at the end of the file name. These files are used to create the prediction lines in the manuscript's fitness figures.  * Establishment predictions: Est_GDPreds_GrwSsnHist.csv, Est_GDPreds_WYRecent.csv, Est_GeoDist_Preds.csv, Est_SubPreds_GrwSsnRecentTemp.csv, Est_SubPreds_WYRecentPPT.csv, Est_SubPreds_WYRecentTemp.csv * Probability of successfully reproducing predictions: ProbRep_GDPreds_WYRecent.csv, ProbRep_SubPreds.csv * Total fruit production of reproductive individuals predictions: RepOutput_GDPreds_GSRecent.csv, RepOutput_SubPreds_GSRecentPPT.csv, RepOutput_SubPreds_GSRecentTemp.csv, RepOutput_SubPreds_WtrYrHistPPT.csv * Survival to budding in year 2 predictions: RepSurv2_GDPreds_GrwSsnRecent.csv, RepSurv2_GDPreds_WYRecent.csv, RepSurv2_GeoDist_Preds.csv, RepSurv2_SubPreds_WYRecentTemp.csv * Total fitness (total fruit number including 0s) predictions: TotalFitness_GDPreds_WYRecent.csv * Winter survival predictions: WintSurv_GDPreds_WYRecent.csv * Year 1 survival predictions: Y1Surv_GDPreds_GrwSsnHist.csv, Y1Surv_GDPreds_WYRecent.csv, Y1Surv_SubPreds_WYRecentTemp.csv ##### Files with the processed fitness data: Only new variables not previously described in this document are explained below.  * WL2_Establishment.csv: 3 week survival calculated for each individual with 0 for no survival and 1 for survival is in column BB. * WL2_Fruits_Y1.csv: the number of flowers (y1_flowers), fruits (y1_fruits), and fruits plus flowers (FrFlN_y1) that each reproductive individual had in 2023. * WL2_Fruits_Y2.csv: the number of flowers (y2_flowers), fruits (y2_fruits), and fruits plus flowers (FrFlN_y2) that each reproductive individual had in 2024. * WL2_Mortality_2023.csv: all 2023 mortality and bud initiation dates. * WL2_ProbFit.csv: the probability of making a fruit (ProbFitness) with 0 for no fruits and 1 for one or more fruits; accounts for both year 1 and 2 reproduction. * WL2_Surv_to_Rep_Y2.csv: survival to budding in 2024 (SurvtoRep_y2) with 0 for no survival and 1 for survival. Only individuals that survived to year 2 are in this data set. * WL2_SurvtoRep_y1.csv: survival to budding in 2023 (SurvtoRep_Y1) with 0 for no survival and 1 for survival. This is conditional on successful establishment. * WL2_Total_Fitness.csv: the total number of fruits, accounting for year 1 and 2 reproduction. * WL2_TotalRepOutput.csv: the total number of fruits across year 1 and 2 of reproductive individuals (Total_Fecundity). * WL2_WinterSurv.csv: winter survival (WinterSurv) of plants that survived to the end of the first year. * WL2_Y1Surv.csv: survival to the end of year 1 (Y1Survival) given establishment. ## Code/software To open the files in the Raw.Data and Processed.Data folders, use any software that can open .csv files, including spreadsheet software, plain text editors, and more. To run the code in the Scripts folder of the GitHub repository linked to through Zenodo, use R version 4.5.3. To run the code, you will need packages: raster v. 3.6-32, tidyverse v. 2.0.0, ggrepel v. 0.9.8, corrplot 0.95, vegan v. 2.7-3, ggfortify v. 0.4.19, viridis v. 0.6.5, elevatr v. 0.99.1, terra v. 1.9-11, sf v. 1.1-0, giscoR v. 1.1.0, marmap v. 1.0.12, boot v. 1.3-32, broom v. 1.0.12, geosphere v. 1.6-8, lmerTest v. 3.2-1, corrplot v. 0.95, broom.mixed v. 0.2.9.7, tidymodels v. 1.4.1, multilevelmod v. 1.0.0, performance v. 0.16.0, glmmTMB v. 1.1.14, bbmle v. 1.0.25.1, ggpubr v. 0.6.3, zoo v. 1.8-15. Use the scripts in the following order: 1. Clim_vars_all_sites_years_timeframes.Rmd 2. Clim_corrs_pcas.Rmd 3. Clim_dist_calcs.Rmd 4. Clim_dist_fitness_prep.Rmd 5. Clim_dist_fitness_models.Rmd 6. Total_Fitness.Rmd 7. Clim_dist_fitness_figs.Rmd 8. WklyClim_Mort.Rmd ## Access information Other publicly accessible locations of the data: * [https://github.com/IntBio-UCD/WL2.ClimDist](https://github.com/IntBio-UCD/WL2.ClimDist) 

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5. Yenisei River-South TRISH dataset of tree-ring chronologies (2025)

   https://doi.org/10.18739/a2fj29f7d  

   Panyushkina, Irina (January 2025, NSF Arctic Data Center)

   This is a two-file dataset of 111 tree-ring index chronologies calculated from ITRDB downloaded raw ring data (International Tree-Ring Data Bank at www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets/tree-ring) and tree rings collected by TRISH project (Collaborative Research: Fresh water and heat fluxes to the Arctic Ocean modeled with tree-ring proxies, U.S. NSF OPP award # 1917503) relevant to reconstruction of hydrologic variables for the upper reaches of Yenisei River basin. The data are in specific format suitable for a web-based reconstruction tool called Tree-Ring Integrated System for Hydrology (TRISH, https://trish.sr.unh.edu/). This data is an extended dataset of TRISH tool built-in network of tree rings called "Yenisei ITRDB (TRISH team)" that geographically focused on the upper reaches of the Yenisei River basin.\n File 1: TreeMeta111YeniseiSouthTRISH.txt\n File 2: TreeData111YeniseiSouthTRISH.txt\n READme file with the attributes of dataset."]} 

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