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Study species and populations Hypericum maculatum (Crantz), H. montanum (L.) and H. perforatum (L.) are perennial herbs native to Europe. The study species share many characteristics, such as yellow flowers and leaf arrangement. The native ranges of H. maculatum and H. perforatum extend from southern to Northern Europe and they are commonly found in grassland habitats (GBIF Secretariat, 2022; GBIF Secretariat, 2022). Compared to H. maculatum and H. perforatum, H. montanum has a more limited range, especially at higher latitudes, and is a habitat specialist occurring more scarcely and mainly in woodlands (GBIF Secretariat, 2022). Out of the three species, H. montanum occurs within the narrowest range (range = 2.65ᵒC, mean = 0.89ᵒC) while having the highest average of annual temperatures (based on average calculated over the years 1970-2000). We obtained seeds, subject to availability, from across the study species´ European distribution areas to represent the trailing (southern distributional edge), core, and leading (northern distributional edge) parts of the species distributions. We chose seed accessions, i.e., seeds collected from the same location at the same time, among the accessions available in managed seed banks (e.g., Millennium Seed Bank, The European Native Seed Conservation Network ENSCONET partners (Eastwood & Rivière, 2009)) and augmented them with populations collected afresh. In total, we included 23 populations in the experiment: six H. maculatum, six H. montanum, and 11 H. perforatum populations. Temperature treatments and greenhouse cultivation We grew the plants under common garden conditions in greenhouses of the Viikki Plant Growth Facilities, University of Helsinki, from December 2021 to May 2022. In May, we transferred the plants outside for seed maturation. We collected seeds from the experimental individuals during summer and weighed them from August to October 2022. In the greenhouses, we grew the plants under four different temperature treatments with two replicates for each, totaling eight greenhouse compartments, and each species on a separate table within each compartment. The daytime (16h) temperatures were set to 16°C (“Cold”), 20°C (“Medium”), 24°C (“Warm”), and 28°C (“Hot”). The choice of temperature treatments was loosely based on data on average summer temperatures at the trailing-, core-, and leading distributional areas of the study species from the climatic information service WorldClim (https://www.worldclim.org). The night-time temperatures (8h) were set at 8°C and 10°C below the daytime temperature at the germination and vegetative stages, respectively. Photoperiod in all treatments was 16/8h light/dark. In addition to the automated temperature settings, we monitored realized temperature conditions at the plant level using temperature loggers (Lascar EL-USB-2-LCD+). The realized temperatures were somewhat higher than the set temperatures, particularly as solar radiation increased with the advancing spring, but the differences between treatments remained approximately equal. On the 1st of December 2021, we sowed 25 seeds per population and replicate into trays filled with the sowing mixture (Kekkilä kylvöseos W HS R8017; KEK31116) with seeds of two populations sown on each tray separated by a border of cardboard and sand. We then placed the trays on a water retaining rug on greenhouse growing tables and its soil topped with coarse sand after sowing the seeds. In the beginning of February 2022, we randomly chose up to ten (depending on availability) of the germinated plants per population, treatment, and replicate to be included in the experiment and transferred them from trays to individual 1L pots filled with soil (Kekkilä Professional Karkea Ruukutusseos; KEK33933) and we then placed the pots on a water retaining rug on greenhouse growing tables. At the same time, we propped up the plants on a support stick if the plant was large enough. We marked all plant individuals with QR-coded ID tags. We separated the individuals of each population into replicates A and B, each with up to ten plants. To avoid microclimatic biases, we periodically rotated both the germination trays and later the plant pots. We regularly fertilized the plants, approximately every 3 weeks with fertilizer solution. Watering was implemented by an automated watering system with treatment-specific schedules to keep all experimental plants equally moist. The watering system of H. perforatum in the “Hot”-treatment, replicate A, broke at the end of March leaving the plants dry for some days, which we take into account in the interpretation of the results. Data collection Starting two months after sowing, we collected flowering phenology data by monitoring the plants two times per week and recording the date of the first observation of an open flower for each plant individual. During a four-week data collection phase at peak flowering (April-May), we counted flowers at different stages (i.e., bud, flowering, flowered, seed capsule), except for H. maculatum and H. montanum populations which could not be counted in the “Medium” and “Cold” treatments due to limited time. For each study individual, up to five seed capsules or withered flowers were marked to ensure a matching collection of seeds after ripening. After the flowering counts, we transferred the plants to outdoor conditions at the Kumpula Botanic Garden to allow the seeds to ripen over the summer, whereafter we collected them. We dried the collected seeds for a minimum of five days in RH 15% and cleaned them using sieves (800µm and 250µm), after which we counted and weighed them. We used average seed mass (total seed mass / total seed number) as a measure of reproductive output, as some of the seeds could have dispersed before the capsules were collected, rendering the total seed number an unreliable proxy measure of fitness. References Eastwood, R., & Rivière, S. (2009). The ENSCONET Virtual Seed Bank. ENSCONEWS, 5. # Higher thermal plasticity in flowering phenology increases flowering output ## Description of the data and file structure "Flowering_time.csv" "Flower_count.csv" "Seed_mass.csv" "ThermalPlasticity.R" "Watering_schedule.docx" "greenhouse_conditions.docx" Data was used in the publication: Kotilainen, A., Mattila, A. L. K., Møller, C., Koivusaari, S., Hyvärinen, M., & Hällfors, M. 2023. Higher thermal plasticity in flowering phenology increases flowering output. Submitted to *Ecology and Evolution*. Non-available data is indicated by "NA" throughout all datasets. ### Flowering\_time.csv | Value | Description | | :---------------- | :------------------------------------------------------------------------------------------------------------------------- | | Format | comma-separated value | | Number of rows | 1431 | | Number of columns | 6 | | Species | Scientific name of species (abbreviated). Hpe = Hypericum perforatum, Hma = Hypericum maculatum, Hmo = Hypericum montanum. | | Treatment | Cold (16°C), Medium (20°C), Warm (24°C), or Hot (28°C) | | Replicate | Replicate distinguishing treatment and species | | Accession | Accession ID based on species and distribution range | | UniqueID | Unique ID for each individual | | DateFlow | Date of flowering (YYYYMMDD) | ### "Flower\_count.csv" | Value | Description | | :---------------- | :------------------------------------------------------------------------ | | Format | comma-separated value | | Number of rows | 744 | | Number of columns | 15 | | Species | Scientific name of species (abbreviated). Hpe = Hypericum perforatum. | | Treatment | Cold (16°C), Medium (20°C), Warm (24°C), or Hot (28°C) | | Replicate | Replicate distinguishing treatment and species | | Accession | Accession ID based on species and distribution range | | UniqueID | Unique ID for each individual | | DateMeasured | Date of the measurement in YYYYMMDD format | | Alive | Alive = 1, Dead = 0 | | Buds\_number | Number of buds per individual at the date of the measurement | | Flowers\_number | Number of flowers per individual at the data of the measurement | | Whithered\_number | Number of whithered flowers per individual at the data of the measurement | | SeedCaps\_number | Number of seed capsules per individual at the date of the measurement | ### Seed\_mass.csv | Value | Description | | :---------------- | :-------------------------------------------------------------------------- | | Format | comma-separated value | | Number of rows | 744 | | Number of columns | 10 | | Species | Scientific name of species (abbreviated). Hpe = Hypericum perforatum. | | Treatment | Cold (16°C), Medium (20°C), Warm (24°C), or Hot (28°C) | | Replicate | Replicate distinguishing treatment and species | | Accession | Accession ID based on species and distribution range | | DateMeasured | Date of the measurement in YYYYMMDD format | | NumbSeedCapsules | Number of seed capsules collected | | TotalSeedMass\_g | Total seed mass for five sampled seed capsules per individuals in grams (g) | | XOSeeds\_Y.N | Seeds, yes (Y) or no (N) | | NumberOfSeeds | Number of seeds counted from the number of seed capsules collected | ## R Code A combined R script for is provided ("ThermalPlasticity.R") for the data analyses used in the publication. ## Word documents Detailed information for the experimental watering schedule ("Watering_schedule.docx"), as well as the temperature settings and realized temperature for the greenhouse chambers conditions ("greenhouse_conditions.docx") are provided. Ongoing climate change poses an increasing threat to biodiversity. To avoid decline or extinction, species need to either adjust or adapt to new environmental conditions or track their climatic niches across space. In sessile organisms such as plants, phenotypic plasticity can help maintain fitness in variable and even novel environmental conditions and is therefore likely to play an important role in allowing them to survive climate change, particularly in the short term. Understanding a species’ response to rising temperature is crucial for planning well-targeted and cost-effective conservation measures. We sampled seeds of three Hypericum species (H. maculatum, H. montanum, and H. perforatum), from a total of 23 populations originating from different parts of their native distribution areas in Europe. We grew them under four different temperature regimes in a greenhouse to simulate current and predicted future climatic conditions in the distribution areas. We measured flowering start, flower count, and subsequent seed weight, allowing us to study variations in the thermal plasticity of flowering phenology and its relation to fitness. Our results show that individuals flowered earlier with increasing temperature, while the degree of phenological plasticity varied among species. More specifically, the plasticity of H. maculatum varied depending on population origin, with individuals from the leading range edge being less plastic. Importantly, we show a positive relationship between higher plasticity and increased flower production, indicating adaptive phenological plasticity. The observed connection between plasticity and fitness supports the idea that plasticity itself may be adaptive. This study underlines the need for information on plasticity for predicting species' potential to thrive under global change and the need for studies on whether higher phenotypic plasticity is currently being selected for as natural populations experience a rapidly changing climate.

Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic Plant Aboveground Biomass Synthesis Dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass grams per meter squared (g/m^2) on 2327 sample plots in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

Dispersal is a key process with crucial implications in spatial distribution, density and genetic structure of species’ populations. Dispersal strategies can vary according to both individual and environmental features, but putative phenotype-by-environment interactions have rarely been accounted for. Melanin-based color polymorphism is a phenotypic trait associated with specific behavioral and physiological profiles and is therefore a good candidate trait to study dispersal tactics in different environments. Here, using a 40 years dataset of a population of color polymorphic tawny owls (Strix aluco), we investigated natal dispersal distance of recruiting gray and pheomelanic reddish-brown (hereafter brown) color morphs in relation to post-fledging winter temperature and individual characteristics. Since morphs are differently sensitive to cold winters, we predicted that morphs’ natal dispersal distances vary according to winter conditions. Winter temperature did not affect the proportion of brown (or gray) among recruits. We found that dispersal distances correlate with winter temperature in an opposite manner in the two morphs. While the gray morph undertakes larger movements in harsher conditions, likely because it copes better with winter severity, the brown morph disperses shorter distances when winters are harsher. We discuss this morph-specific natal dispersal pattern in a context of competition for territories between morphs and in terms of costs and benefits of these alternative strategies. Our results stress the importance of considering the interaction between phenotype and environment to fully disentangle dispersal movement patterns and provide further evidence that climate affects behavior and local distribution of this species.

Data and GrADS (Grid Analysis and Display System) scripts for reproducing the figures and numerical results included in the manuscript "Changes in March mean snow water equivalent since the mid-twentieth century and the contributing factors in reanalyses and CMIP6 climate models". Revised for The Cryosphere in March 2023. In addition to the README file, there are two zipped archives: swe_trends.zip (2.3 GB) includes both the data (mostly as GrADS binaries), the GrADS data descriptor files and the scripts. swe_trends_no_data.zip (74 kB) includes just the scripts and the data descriptor files. Please see the README file for further details on the content and use of the archives.

GE-220-14000_HP_at_138.mat is the hourly power profile (8760) of the GE-220 14 MW turbine in 0.25 degree spatial resolution (720x1400) at a hub height of 138 meters. Each value in the data represents the output in kilowatts.Siemens-167-8000_HP_at_111.5.mat is the hourly power profile (8760) of the Siemens-167 8 MW turbine in 0.25 degree spatial resolution (720x1400) at a hub height of 111.5 meters. Each value in the data represents the output in kilowatts.WDPA40000by20000.mat is the world database on protected areas in 0.009 degree spatial resolution. Each value represents the protected status of the cell, with 1 if protected and 0 otherwise.distanceToShore_21600x43200.mat is a global matrix with distance to shorelines in 0.00833 degree spatial resolution. Each value represents the distance to the nearest shore in kilometers.bathymetry_21600x43200.mat is a global matrix with water depths in 0.00833 degree spatial resolution. Each value represents the elevation above sea in meters, with negative value for depths.

This dataset contains data on the effect of global warming (1.5°C to 4°C) on the current production of 30 major food crops, as well as on the potential diversity of food crops on the current global cropland.The journal article that this dataset is supplement to: https://doi.org/10.1038/s43016-025-01135-w Contents Source data files (.xlsx) for bar plots and scatter plots in the journal article “Climate change threatens crop diversity at low latitudes" Source data files (.tiff) for map image figures in the journal article “Climate change threatens crop diversity at low latitudes". Note: these are parts of the other .tiff files listed below, and it is mentioned in the end of the file descriptions which layer produces each map figure image in the article and supplement. Raster files (.tiff) of the lowest warming levels that would push 25%, 50%, and 75% of current food crop production in grid cell outside the SCS Raster files (.tiff) of the total potential diversity for all food crops and for food crop groups, covering baseline climate conditions and the four global warming levels Raster files (.tiff) of the change in potential diversity compared to baseline for all food crops and for food crop groups at the four global warming levels. Raster files (.tiff) of the areas within and outside the crop specific Safe Climatic Spaces of 30 food crop types Raster files (.tiff) of the difference in areas within the crop specific Safe Climatic Spaces when using seasonal versus annual method for defining the Safe Climatic Space See Data description.docx for detailed descriptions of the data files.

This dataset contains data from measurements of 1) atmospheric trace gas concentrations (O3, NO, NO2, SO2, CO) and 2) meteorology, solar and terrestrial radiation, and 3) aerosols done at SMEAR III research station at Kumpula campus, Helsinki, during years 2005–2021. The data were exported from SMEAR database on the dataset release date. Most recent version of the data are available at https://smear.avaa.csc.fi/.