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This readme file provides all data and R codes used to perform the analyses presented in Figs. 2-4 of the main text and Supplementary Information Figures S1-S2-S3. FIGURE 2 - Seasonally_dated_GDs.txt: Contains information on the timing (Season) of rockfall (GD) in a given tree (Id) and a given year (yr) over the past 100 years. Inv refers to the operators which analyzed growth disturbances in the tree-ring series. Lat / Long refers to the position of the tree in CH1903/ Swiss Grid projection. Intensity (1-4) refers to (1), intermediate (2) and strong (3) GD. Intensity 4 was attributed to injuries (I). Only the 408 GD rated 3 (strong TRD) and 4 (injuries) were used in Fig. 2. Acronyms used for Response_type read as follows: TRD: Tangential rows of traumatic resin ducts; I: Injuries. Acronyms used for Season refer to Dormancy (1_D), early (2_EE), middle (3_ME) and late (4_LE) earlywood, whereas a GD found in the latewood was attributed to either the early (5_EL) or late (6_LL) latewood. - Trends_in_seasonality_R1.R: The data contained in "Seasonally_dated_GDs" were processed with the R script "Trends_in_Seasonality.R". This seasonal trend analysis code is inspired by work published by Schlögl et al. (2021; https://doi.org/10.1016/j.crm.2021.100294) and Heiser et al. (2022; https://doi.org/10.1029/2011JF002262). FIGURE 3-4-S1 - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - Rockfall_frequency_climate.R: The data contained in "Tasch_GD.txt" were processed with the R script "Rockfall_frequency_climate.R". - The temperature (Imfeld23_tmp.txt) and precipitation (Imfeld23_prc.txt) data used in Fig. 3 are from the Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Täschgufer site. - The script set with temperature series enables to compute Fig. 4 (l.149:216) and Fig. 3 (l. 216:330); the script set with precipitation series enables to compute Fig. S1 FIGURE S2 - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) at the Täschgufer site in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - Rockfall_frequency_borehole.R: is adapted from "Rockfall_frequency_climate.R" to work with the borehole dates. - Corvatsch0_6R1: Contains the Corvatsch borehole temperature series (2000-2020, 0.6m depth) (Hoelzle, M. et al. https://doi.org/10.5194/essd-14-1531-2022, 2022). FIGURE S3 - Plattje_GD.txt: Contains the raw data on rockfall impacts (GD) at the Plattje site in a given year (yr) as found all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - - Rockfall_frequency_climate_Plattje.R: The data contained in "Plattje_GD.txt" were processed with the R script "Rockfall_frequency_climate_Plattje.R". - The temperature (Imfeld23_tmp_Plattje.txt) and precipitation (Imfeld23_prc_Plattje.txt) data used in Fig. 3 are from Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Plattje site.

Dataset of process simulations results of the natural gas sweetening and flue gas treatment (first and second sheet, respectively as indicated by the sheet name in the .xlsx file). The dataset refers to the publication Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies by V. Negri, Vàzquey D., Sales-Pardo, Marta, Guimerà, R. and Guillén-Gosàlbez, G. The training and testing dataset are used to generate the figures in the main manuscript and supplementary information.

Aim: Climate warming and biodiversity loss both alter plant productivity, yet we lack an understanding of how biodiversity regulates the responses of ecosystems to warming. In this study, we examine how plant diversity regulates the responses of grassland productivity to experimental warming using meta-analytic techniques. Location: Global Major taxa studied: Grassland ecosystems Methods: Our meta-analysis is based on warming responses of 40 different plant communities obtained from 20 independent studies on grasslands across five continents. Results: Our results show that plant diversity and its responses to warming were the most important factors regulating the warming effects on plant productivity, among all the factors considered (plant diversity, climate and experimental settings). Specifically, warming increased plant productivity when plant diversity (indicated by effective number of species) in grasslands was lesser than 10, whereas warming decreased plant productivity when plant diversity was greater than 10. Moreover, the structural equation modelling showed that the magnitude of warming enhanced plant productivity by increasing the performance of dominant plant species in grasslands of diversity lesser than 10. The negative effects of warming on productivity in grasslands with plant diversity greater than 10 were partly explained by diversity-induced decline in plant dominance. Main Conclusions: Our findings suggest that the positive or negative effect of warming on grassland productivity depends on how biodiverse a grassland is. This could mainly owe to differences in how warming may affect plant dominance and subsequent shifts in interspecific interactions in grasslands of different plant diversity levels.

This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

Dataset Information This dataset presents long-term term indoor solar harvesting traces and jointly monitored with the ambient conditions. The data is recorded at 6 indoor positions with diverse characteristics at our institute at ETH Zurich in Zurich, Switzerland. The data is collected with a measurement platform [3] consisting of a solar panel (AM-5412) connected to a bq25505 energy harvesting chip that stores the harvested energy in a virtual battery circuit. Two TSL45315 light sensors placed on opposite sides of the solar panel monitor the illuminance level and a BME280 sensor logs ambient conditions like temperature, humidity and air pressure. The dataset contains the measurement of the energy flow at the input and the output of the bq25505 harvesting circuit, as well as the illuminance, temperature, humidity and air pressure measurements of the ambient sensors. The following timestamped data columns are available in the raw measurement format, as well as preprocessed and filtered HDF5 datasets: V_in - Converter input/solar panel output voltage, in volt I_in - Converter input/solar panel output current, in ampere V_bat - Battery voltage (emulated through circuit), in volt I_bat - Net Battery current, in/out flowing current, in ampere Ev_left - Illuminance left of solar panel, in lux Ev_right - Illuminance left of solar panel, in lux P_amb - Ambient air pressure, in pascal RH_amb - Ambient relative humidity, unit-less between 0 and 1 T_amb - Ambient temperature, in centigrade Celsius The following publication presents and overview of the dataset and more details on the deployment used for data collection. A copy of the abstract is included in this dataset, see the file abstract.pdf. L. Sigrist, A. Gomez, and L. Thiele. "Dataset: Tracing Indoor Solar Harvesting." In Proceedings of the 2nd Workshop on Data Acquisition To Analysis (DATA '19), 2019. Folder Structure and Files processed/ - This folder holds the imported, merged and filtered datasets of the power and sensor measurements. The datasets are stored in HDF5 format and split by measurement position posXX and and power and ambient sensor measurements. The files belonging to this folder are contained in archives named yyyy_mm_processed.tar, where yyyy and mm represent the year and month the data was published. A separate file lists the exact content of each archive (see below). raw/ - This folder holds the raw measurement files recorded with the RocketLogger [1, 2] and using the measurement platform available at [3]. The files belonging to this folder are contained in archives named yyyy_mm_raw.tar, where yyyy and mmrepresent the year and month the data was published. A separate file lists the exact content of each archive (see below). LICENSE - License information for the dataset. README.md - The README file containing this information. abstract.pdf - A copy of the above mentioned abstract submitted to the DATA '19 Workshop, introducing this dataset and the deployment used to collect it. raw_import.ipynb [open in nbviewer] - Jupyter Python notebook to import, merge, and filter the raw dataset from the raw/ folder. This is the exact code used to generate the processed dataset and store it in the HDF5 format in the processed/folder. raw_preview.ipynb [open in nbviewer] - This Jupyter Python notebook imports the raw dataset directly and plots a preview of the full power trace for all measurement positions. processing_python.ipynb [open in nbviewer] - Jupyter Python notebook demonstrating the import and use of the processed dataset in Python. Calculates column-wise statistics, includes more detailed power plots and the simple energy predictor performance comparison included in the abstract. processing_r.ipynb [open in nbviewer] - Jupyter R notebook demonstrating the import and use of the processed dataset in R. Calculates column-wise statistics and extracts and plots the energy harvesting conversion efficiency included in the abstract. Furthermore, the harvested power is analyzed as a function of the ambient light level. Dataset File Lists Processed Dataset Files The list of the processed datasets included in the yyyy_mm_processed.tar archive is provided in yyyy_mm_processed.files.md. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums. Raw Dataset Files A list of the raw measurement files included in the yyyy_mm_raw.tar archive(s) is provided in yyyy_mm_raw.files.md. The markdown formatted table lists the name of all files, their size in bytes, as well as the SHA-256 sums. Dataset Revisions v1.0 (2019-08-03) Initial release. Includes the data collected from 2017-07-27 to 2019-08-01. The dataset archive files related to this revision are 2019_08_raw.tar and 2019_08_processed.tar. For position pos06, the measurements from 2018-01-06 00:00:00 to 2018-01-10 00:00:00 are filtered (data inconsistency in file indoor1_p27.rld). v1.1 (2019-09-09) Revision of the processed dataset v1.0 and addition of the final dataset abstract. Updated processing scripts reduce the timestamp drift in the processed dataset, the archive 2019_08_processed.tar has been replaced. For position pos06, the measurements from 2018-01-06 16:00:00 to 2018-01-10 00:00:00 are filtered (indoor1_p27.rld data inconsistency). v2.0 (2020-03-20) Addition of new data. Includes the raw data collected from 2019-08-01 to 2019-03-16. The processed data is updated with full coverage from 2017-07-27 to 2019-03-16. The dataset archive files related to this revision are 2020_03_raw.tar and 2020_03_processed.tar. Dataset Authors, Copyright and License Authors: Lukas Sigrist, Andres Gomez, and Lothar Thiele Contact: Lukas Sigrist (lukas.sigrist@tik.ee.ethz.ch) Copyright: (c) 2017-2019, ETH Zurich, Computer Engineering Group License: Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) References [1] L. Sigrist, A. Gomez, R. Lim, S. Lippuner, M. Leubin, and L. Thiele. Measurement and validation of energy harvesting IoT devices. In Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017. [2] ETH Zurich, Computer Engineering Group. RocketLogger Project Website, https://rocketlogger.ethz.ch/. [3] L. Sigrist. Solar Harvesting and Ambient Tracing Platform, 2019. https://gitlab.ethz.ch/tec/public/employees/sigristl/harvesting_tracing Appears in the Proceedings of the 2nd Workshop on Data Acquisition To Analysis (DATA '19)

This dataset contains statistics of the sonnendach.ch dataset at the national level. See README.md for more information.

Historic seeds were acquired for 32 species from stored collections in ex-situ seed banks at The Australian PlantBank and the Australian National Botanic Garden. This included four herbaceous species, ten shrubs, seven shrub-trees and eleven trees where all shrubs, shrub-trees and trees were evergreen species (See Everingham et al 2021, Ecology and Dryad dataset https://doi.org/10.5061/dryad.4f4qrfj83 for more information of seed collection). Matched modern seeds from the same species as the historic seeds were collected in the same location, at the same time of year as their historic counterparts. The amount of time between the historic and modern seed collections ranged from 29 years to 40 years. Seeds were germinated on water agar (0.7% w.v.) in controlled incubators. Most species were germinated at 20°C with a 12-hour light, 12-hour dark cycle, but some species required specific germination treatments such as gibberellic acid (GA3), smoke water (1%) or specific temperature and light treatments (see Everingham et al 2021, Ecology and Dryad dataset https://doi.org/10.5061/dryad.4f4qrfj83 for full germination treatment methods). Treatments were always kept constant for modern and historic seeds of each species. After germination, we transferred up to 50 germinated seeds to trays made up of 24-cells each measuring 4 cm (depth) by 2 cm2 (square area) cells. The seedlings grew for two weeks in the trays to ensure early seedling survival before being transferred to individual 1.9 L pots. Potting soil comprised of 33% Australian Native Landscape supply of “Organic Garden Mix”, 33% washed river sand and 33% Cocopeat as well as a general slow-release fertiliser added at 200 mL per 75 L of soil. Plants were grown in a glasshouse at UNSW, Sydney for six months with an overhead irrigation system. Pots were randomised each month to reduce position effects. After the six-month growing period, we measured a range of morphological leaf traits including leaf area, leaf roundness, leaf margin complexity and leaf thickness following standard protocols from Perez-Harguindeguy 2013, Australian Journal of Botany. To measure leaf shape, leaf area and leaf mass per unit area (LMA), we collected three fresh leaves (excluding the petiole) from each individual plant at the end of the six-month growing period. For two species (Acacia georgensis and Acacia concurrens), due to their seedling size, we were not able to measure area on three leaves and one to two leaves were sampled. Images of these fresh leaves were captured on a Flatbed Scanner and their area and shape metrics were calculated using values measured in image analysis software, ImageJ. Leaf surface area was calculated as the average of the three leaves’ total surface area. ImageJ provided a measurement for each leaf of the maximum length (longest axis of the smallest possible rectangle drawn around the leaf) and width (longest axis perpendicular to the determined maximum length). From these measurements we calculated leaf roundness as the average ratio of width to length of the three leaves whereby the leaves with roundness measurements closer to zero would be longer, thinner leaves and the leaves closer or equal to 1 would be rounder leaves. We calculated the margin complexity as the average of the ratio of perimeter length (cm) to surface area (cm2) from the perimeter of the leaf and the area analysed in ImageJ. To calculate leaf mass per unit area we used the leaf surface area calculations measured in ImageJ. The leaves were then dried to a constant temperature using a drying oven at 60° C for 72 hr. Oven dry mass (g) for the leaves was measured by weighing on a microbalance (Mettler Toledo© AG204 microbalance, 1 x 10-4 accuracy). LMA was calculated as oven-dry mass divided by fresh area. We measured leaf thickness by sampling one leaf from each individual modern and historic plant from all species (the third leaf from the growing tip, counted from the first fully developed/unfolded leaf). On these leaves we measured fresh leaf thickness (mm) at two points on adjacent sides of the mid-vein using a micrometer. An average for leaf thickness was taken from the two measurements for each individual plant. Finally, we calculated stomatal density using the clear nail polish peel method. Clear nail polish peels were performed on the first mature leaf closest to the growing apical tip from each plant. Clear nail polish was painted on the top and underside of the leaf on fresh tissue, away from the mid-vein or any prominent veins. We allowed the nail polish to dry for approximately 60 seconds before removing and mounting on a microscope slide with a coverslip. The peels were then imaged using a Leica© microscope. Stomata in each image were counted manually for the top of the leaf and the bottom of the leaf and the average stomatal density (stomata.cm-2) was calculated for each plant and use in further analysis. We measured physiological variables including leaf photosynthetic rate, intrinsic water use efficiency (iWUE) and leaf nitrogen content. To obtain photosynthetic measurements, we used portable infrared gas analysers (LICOR 6400XT, Lincoln, Nebraska) on well-watered, non-root-bound, non-flowering individuals. We randomly selected a subset of ten historic plants and ten modern plants from each species. Some species had fewer than ten plants available, and some species were excluded from photosynthetic measurements because their leaves were not large enough to fit into the gas chamber without damage to the majority of the seedling. We took infrared gas measurements on the youngest fully expanded mature leaf following standard protocols [66] between the hours of 10:00 to 14:00 (Australian Eastern Standard Time) on days with no visible cloud cover. We ensured that for each species, infrared gas exchange measurements were taken on historic and modern plants at random within a 30-minute period to minimise changes in light or temperature. Our measurements were made under constant saturating light conditions (1800 μmol m-2 s-1) provided from a constant light source in the LICOR chamber. The chamber CO2 concentration was set at 400ppm and the temperature set at 25° C. We took five consecutive measurements approximately two seconds apart and used the average of these five measurements. We recorded the light-saturated photosynthetic rate (Asat; μmol CO2 m-2 s-1) and the stomatal conductance (gs; mol H2O m-2 s-1), and then calculated the intrinsic water use efficiency (iWUE) as the ratio between photosynthetic rate and stomatal conductance. To quantify leaf nitrogen, we harvested leaves at six months, dried them for 72 hr at 60°C, pooled and homogenised each species’ individual modern leaves and individual historic leaves separately and then ground the dried leaf tissue. For each species we sent a pooled sample of historic ground leaf tissue and a pooled sample of modern ground leaf tissue to the Environmental Analysis Laboratory at Southern Cross University, Lismore, Australia for nitrogen analysis. Climate change metrics were determined for each species’ historic and modern seed collection based geographically on modern seed collection site location data (which was collected typically at the same location as the historic data or within a 1 km radius) and were obtained from the Australian Gridded Climate Data at 5 km2 resolution following methods from Everingham et al. 2021, Ecology. The processing code is freely available at https://github.com/SEveringham/ClimateData. The amount of change in all climate metrics was calculated across the five years before historic and modern seed collection to capture longer-term climate change responses of the species without extending to a period of climate that may become non-meaningful or overlap with modern climate data. The amount of change in precipitation metrics and heatwave duration were calculated using the log-transformed ratio of means. Change in all temperature metrics was calculated as the difference between the modern and historic climate metrics. We used different scaling methods because a difference of a few degrees Celsius of temperature has a much higher biological impact than a difference of a few millimetres of precipitation as precipitation has a much larger range of measurement than temperature. None of the climate change metrics were significantly correlated with one another (as all correlation coefficients were below 0.6) and therefore no climate metrics were excluded from our analyses. The climate change metrics we used included the change between the modern and historic seed collections in mean monthly temperature (calculated as the daily median temperature in the month prior to the seed collection and averaged across the previous five years before the seed collection was made) and mean monthly precipitation (an average of precipitation from the month prior to seed collection and then averaged across the 5 years prior to collection). Both the change in the range of temperature and the range of precipitation were calculated as the change (between historic to modern collections) in the difference between the yearly maximum and minimum temperature or precipitation averaged across the five years prior to each seed collection. We also used metrics for change in temperature variability and change in precipitation variability, both of which were calculated as the coefficient of variation (standard deviation divided by the mean) of the temperature or precipitation of the month prior to seed collection averaged across the five years prior. The change in maximum and minimum precipitation of the season before collection were calculated to determine the effects of seasonal rainfall and these were an average across five prior years of collection of the maximum rainfall in the 4 months prior to seed collection (bound by wet season in the subtropics or autumn, winter, spring, summer seasons in the mid-latitudes). We used the change in vapour pressure deficit (VPD) as an indication of the change in atmospheric aridity between the historic and modern seed collections. Finally, metrics of change in extreme climate events included the calculation of maximum heatwave duration (the longest heatwave across all seasons in the 5 years prior to collection whereby heatwaves were defined based on exceptionally high air temperature following the relative extreme heat index metric) and maximum dry spell duration (following the same protocol as maximum heatwave duration but instead with dry spells as calculated from an “extreme dryness index” using VPD measurements). All of the above raw data is available in the leaf measurement file and the climate variable file. We performed all data transformation analysis in R, version 3.6.0 with code freely available at https://github.com/SEveringham/leaf-trait-responses-to-climate-change. All transformed data is available in the full leaf analysis data file provided. Change in traits or gas exchange variables was calculated for all morphological, photosynthetic and leaf economic traits or variables using the log-transformed ratio of means per species using the escalc function in the metafor package. To determine if leaf economic spectra were related to changes in climate, we used a Principal Components Analysis (PCA) to obtain metrics that combined the change in inverse LMA, photosynthetic rate and nitrogen content. The inverse of LMA (specific leaf area [SLA]) was used as it is negatively related to leaf economy (i.e. leaves that have a larger surface area per unit mass will have a lower LMA and are typically on the ‘faster' end of the leaf economic spectrum). The PCA was achieved using the prcomp function in base R and used imputed data as not all species had measurements for all three variables (imputation was done using the imputePCA function in the missMDA package). Adaptation to changing conditions is one of the strategies plants use to survive climate change. Here, we ask whether plants’ leaf morphological and physiological traits/gas exchange variables have changed in response to recent, anthropogenic climate change. We grew seedlings from resurrected historic seeds from ex-situ seed banks and paired modern seeds in a common-garden experiment. Species pairs were collected from regions that had undergone differing levels of climate change using an emerging framework – Climate Contrast Resurrection Ecology, allowing us to hypothesise that regions with greater changes in climate (including temperature, precipitation, climate variability and climatic extremes) there would be greater trait responses in leaf morphology and physiology over time. Our found that in regions where there were greater changes in climate, there were greater changes in average leaf area, leaf margin complexity, leaf thickness and leaf intrinsic water use efficiency. Changes in leaf roundness, photosynthetic rate, stomatal density and the leaf economic strategy of our species were not correlated with changes in the climate. Our results show that leaves do have the ability to respond to changes in climate, however, there are greater inherited responses in morphological leaf traits than in physiological traits/variables, and greater responses to extreme measures of climate than gradual changes in climatic means. It is vital for accurate predictions of species’ responses to impending climate change to ensure that future climate change ecology studies utilise knowledge about the difference in both leaf trait and gas exchange responses, and the climate variables that they respond to. # Data from: Leaf morphological traits show greater responses to changes in climate than leaf physiological traits and gas exchange variables These are the data available for the study pertaining to the manuscript Leaf morphological traits show greater responses to changes in climate than leaf physiological traits and gas exchange variables by Everingham et al. The methods for data collection are available here on Dryad and also in the methods section of the manuscript. There are four datasets available: 1.Leaf_trait_measurement_data.xlsx: the raw data of all leaf trait measurements in the study 2.Climate_Data.xlsx: the raw data of all climate data used in the study 3.growthform.csv: the raw growth form data of each species in the study 4.LeafDataFullUsedinAnalyses.csv: transformed data from the raw data which is then used in all main analyses in the study All datasets have a tab for metadata ("Metadata") where each variable in each dataframe is explained in detail with units provided. Datasets 1,3 and 4 contain NA values - this NA indicates a value that was not measured on the given species due to survival constraints or measurements constraints. The code used to transform the raw data (datasets 1,2,3) to create data 4 are openly available at: [https://github.com/SEveringham/leaf-trait-responses-to-climate-change](https://github.com/SEveringham/leaf-trait-responses-to-climate-change) For more information contact the corresponding author/data collector Suz Everingham ([suz.everingham@gmail.com](mailto:suz.everingham@gmail.com)) Data files can be opened in microsoft excel or any program that can read xlsx files