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The aggregation of data concerned 103 Italian cities and for each city 45 indicators were considered

Conservation of thermally sensitive species depends on monitoring organismal and population-level responses to environmental change in real time. Epigenetic processes are increasingly recognized as key integrators of environmental conditions into developmentally plastic responses, and attendant epigenomic datasets hold potential for revealing cryptic phenotypes relevant to conservation efforts. Here, we demonstrate the utility of genome-wide DNA methylation (DNAm) patterns in the face of climate change for a group of especially vulnerable species, those with temperature-dependent sex determination (TSD). Due to their reliance on thermal cues during development to determine sexual fate, contemporary shifts in temperature are predicted to skew offspring sex ratios and ultimately destabilize sensitive populations. Using reduced-representation bisulfite sequencing, we profiled the DNA methylome in blood cells of hatchling American alligator (Alligator mississippiensis), a TSD species lacking reliable markers of sexual dimorphism in early life-stages. We identified 120 sex-associated differentially methylated cytosines (DMCs; FDR < 0.1) in hatchlings incubated under a range of temperatures, as well as 707 unique temperature-associated DMCs. We further developed DNAm-based models capable of predicting hatchling sex with 100% accuracy (in 20 training samples and 4 test samples) and past incubation temperature with a mean absolute error of 1.2˚C (in 4 test samples) based on the methylation status of 20 and 24 loci, respectively. Though largely independent of epigenomic patterning occurring in the embryonic gonad during TSD, DNAm patterns in blood cells may serve as non-lethal markers of hatchling sex and past incubation conditions in conservation applications. These findings also raise intriguing questions regarding tissue-specific epigenomic patterning in the context of developmental plasticity. 

The ecological impacts of multiple stressors are hard to predict but important to understand. When multiple stressors influence foundation species, the effects can cascade throughout the ecosystem. Gulf of Mexico seagrass ecosystems are currently experiencing a suite of novel stressors, including warmer water temperatures and increased herbivory due to tropicalization and conservation efforts. We investigated the impact of warming temperatures and grazing history on plant performance, morphology, and palatability by integrating a mesocosm study using the seagrass Thalassia testudinum with feeding trials using the sea urchin Lytechinus variegatus. Warming temperatures negatively impacted T. testudinum tolerance traits, reducing belowground biomass by 34%, productivity by 74%, shoot density by 10%, and the number of leaves per plant by 24%, and negatively impacted resistance traits through 13% lower toughness of young leaves and a trend for reduced leaf carbon:nitrogen. Lytechinus variegatus individuals preferred to consume plants grown under heated conditions, which supports findings of enhanced palatability. Simulated turtle grazing impacted more plant traits than grazing by other herbivores, potentially diminishing plant resilience to future disturbances through reduced rhizome non-structural carbohydrate concentrations and increasing palatability through reduced fiber content and 23% lower leaf carbon:phosphorus. Simulated turtle, simulated parrotfish, and urchin grazing reduced leaf carbon:nitrogen by 11%, also potentially increasing nutritive value. Interactions between warming temperatures and grazers on plant traits were additive for 16 out of 19 response variables. However, the stressors non-additively impacted the number of leaves per plant, fiber content, and epiphyte load. We suggest that the impacts of grazers on leaf turnover rate and leaf age may vary based on water temperature, potentially driving these interactions. Overall, increased temperatures and grazing pressure will likely reduce seagrass resilience, structure, and biomass, potentially impacting feedback systems and producing negative consequences for seagrass cover, associated species, and ecosystem services.

Climate trends during maize growing period and their impacts on spring maize yield in North China was investigated. This dataset contains: 1) information of stations in cultivation region for spring maize in North China; 2) Trend in temperature and its effect on yield in cultivation region for spring maize in North China; 3) Trend in radiation and its effect on yield in cultivation region for spring maize in North China; 4) Trend in precipitation and its effect on yield in cultivation region for spring maize in North China. Climate trends during maize growing period and their impacts on spring maize yield in North China was investigated. This dataset contains: 1) information of stations in cultivation region for spring maize in North China; 2) Trend in temperature and its effect on yield in cultivation region for spring maize in North China; 3) Trend in radiation and its effect on yield in cultivation region for spring maize in North China; 4) Trend in precipitation and its effect on yield in cultivation region for spring maize in North China.

This repository contains postprocessed results that are part of the paper "Identification of reliable locations for wind power generation through a global analysis of wind droughts" published in Communications Earth & Environment. The results are provided on a latitude-longitude grid, except where specified, and include: mean wind speed, annual mean wind speed, mean wind power density, annual mean wind power density, minumum annual mean wind power density, energy deficits for seasonal variability, energy deficits for weather variability, energy deficits for wind droughts, wind speed time series at Lat 53.00 Lon 3.00. Code and instructions required to reproduce these results are available in the GitHub repository at https://github.com/eantonini/Global_wind_droughts.

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.

This dataset provides the data applied in the case studies of the manuscript "Backcasting the Techno-economic Targets For Constructing Low-carbon Power Systems". Both the modified Garver’s 6-bus and realistic Northwest China power system are presented here, in two excel files respectively. The datasets include detailed information about buses, units, existing corridors, and candidate corridors.Average cost variations and load growth rate over the planning period are also provided.

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.

To quantify the seasonal transition dates, we used NDVI derived from Sentinel-2 MultiSpectral Instrument (Level-1C) images during 2016–2020 based on Google Earth Engine (https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2). We performed an atmospheric correction (Yin et al., 2019) on the images before calculating NDVI. The months from May to October were set as the study period each year. The quality control process includes 3 steps: (i) the cloud was masked according to the QA60 band; (ii) images were removed if the number of pixels with NDVI values outside the range of -1–1 exceeds 30% of the total pixels while extracting the median value of each date; (iii) NDVI outliers resulting from cloud mask errors (Coluzzi et al., 2018) and sporadic snow were deleted pixel by pixel. NDVI outliers mentioned here appear as a sudden drop to almost zero in the growing season and do not form a sequence in this study (Komisarenko et al., 2022). To identify outliers, we iterated through every two consecutive NDVI values in the time series and calculated the difference between the second and first values for each pixel every year. We defined anomalous NDVI differences as points outside of the percentiles threshold [10 90], and if the NDVI difference is positive, then the first NDVI value used to calculate the difference will be the outlier, otherwise, the second one will be the outlier. Finally, 215 images were used to reflect seasonal transition dates in all 5 study periods of 2016–2020 after the quality control. Each image was resampled with 32 m spatial resolution to match the resolution of the ArcticDEM data and SnowModel outputs. To detect seasonal transition dates, we used a double sigmoid model to fit the NDVI changes on time series, and points where the curvature changes most rapidly on the fitted curve, appear at the beginning, middle, and end of each season (Klosterman et al., 2014). The applicability of this phenology method in the Arctic has been demonstrated (Ma et al., 2022; Westergaard-Nielsen et al., 2013; Westergaard-Nielsen et al., 2017). We focused on 3 seasonal transition dates, i.e., SOS, NDVImax day, and EOF. The NDVI values for some pixels are still below zero in spring and summer due to topographical shadow. We, therefore, set a quality control rule before calculating seasonal transition dates for each pixel, i.e., if the number of days with positive NDVI values from June to September is less than 60% of the total number of observed days, the pixel will not be considered for subsequent calculations. As verification of fitted dates, the seasonal transition dates in dry heaths and corresponding time-lapse photos acquired from the snow fence area are shown in Fig. 2. Snow cover extent is greatly reduced and vegetation is exposed with lower NDVI values on the SOS. All visible vegetation is green on the NDVImax day. On EOF, snow cover distributes partly, and NDVI decreases to a value close to zero. # Data from: Drivers of contemporary and future changes in Arctic seasonal transition dates for a tundra site in coastal Greenland The dataset includes all original images used in this study to extract seasonal transition dates and corresponding results. ## Description of the data and file structure Datasets included: (1) The spatial distribution of NDVI values for this study region (168 rows and 166 columns). Each file is named in the form of '' year-month-day''. For example, a file named "2016-05-02'' represents the data for 2nd, May of 2016. The normal NDVI values in each file range from -1 to 1, and NaN represents no valid value. The folder named 'unique_date_NDVI' refers to the spatial distribution of NDVI for all available dates, directly acquired from satellite images. The folder named 'unique_date_NDVI_rm_outlier' refers to the spatial distribution of NDVI after quality correction for each date using the described method. (2) The extracted phenology indicators for each pixel in this study region. Five tables named 'Phe_pixel_XXXX.xlsx' include the extracted seasonal transition dates during 2016–2020, pixel by pixel. There are 9 columns in each table, they are row number and column number (used to describe the specific location of pixel), year, start of spring, middle of spring, end of spring, start of fall, middle of fall, and end of fall. ## Sharing/Access information All functions regarding the extraction of seasonal transition dates can be found here: * All parameters and associated functions regarding the SnowModel can be found here: * All original meteorological data in this study is from: * Climate change has had a significant impact on the seasonal transition dates of Arctic tundra ecosystems, causing diverse variations between distinct land surface classes. However, the combined effect of multiple controls as well as their individual effects on these dates remains unclear at various scales and across diverse land surface classes. Here we quantified spatiotemporal variations of three seasonal transition dates (start of spring, maximum Normalized Difference Vegetation Index (NDVImax) day, end of fall) for five dominant land surface classes in the ice-free Greenland and analyzed their drivers for current and future climate scenarios, respectively.