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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.

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.

Changes in late rice phenology during 1981–2009 were investigated using observed phenological data from agro-meteorological stations across China. This dataset contains 1) details of late rice agrometeorological experiment stations; 2) mean date of late rice phenology date and trend in phenology date during the period of 1981–2009; 3) trends in length of late rice growing period during the period of 1981-2009. Changes in late rice phenology during 1981–2009 were investigated using observed phenological data from agro-meteorological stations across China. This dataset contains 1) details of late rice agrometeorological experiment stations; 2) mean date of late rice phenology date and trend in phenology date during the period of 1981–2009; 3) trends in length of late rice growing period during the period of 1981-2009.

Widespread shrub encroachment is profoundly impacting the structures and functions of global drylands, and precipitation change is assumed to be one of the most critical factors affecting this phenomenon. However, there is little evidence to show how precipitation changes will affect the process. In this study, we conducted a 6-year precipitation manipulation experiment (-30%, ambient, +30%, and +50%) to investigate the effects of precipitation changes on the growth of shrubs and herbaceous plants in a shrub-encroached grassland in Inner Mongolia. We found that the increasing precipitation significantly increased the mean height, coverage, and aboveground biomass of herbaceous species, while the growth of shrub species did not exhibit a significant response to precipitation changes. With increasing precipitation, the relative coverage of shrubs decreased, while that of herbs increased. The native dominant herbaceous plant (Leymus chinensis) with more sensitive maximum photosynthetic rate to the precipitation change, showed higher photosynthetic nitrogen use efficiency and water use efficiency than those of the encroached shrub species (Caragana microphylla) at high soil moisture contents, reflecting that the ecophysiological characteristics of L. chinensis might provide it a competitive advantage under increased precipitation. Our findings suggest that increasing precipitation may slow down shrub encroachment by facilitating herbaceous growth in Mongolian grasslands, and consequently affect the forage value and carbon budget in these ecosystems. 

Livestock grazing is among the most intensive land-use activities in grasslands and can affect plant communities directly or indirectly via grazing-induced soil legacies. Under climate change, grasslands are threatened globally by recurrent drought. However, the extent to which drought influences grazing-induced soil legacy effects on plant biomass production and community composition remains largely unexplored. We grew five naturally co-occurring plant species (three dominants and two subordinates) in mixed communities in a glasshouse experiment in live and sterilized soil that had or had not been subjected to 19 years of grazing; these plant communities were then exposed to a subsequent drought. We tested the treatment effects on plant community biomass, proportional aboveground biomass of individual species, arbuscular mycorrhizal (AM) fungal root colonization, and soil nutrient availability. Under drought-free conditions, soils from grazed plots produced significantly higher plant aboveground and total community biomass compared to soils from ungrazed plots. In contrast, plant aboveground and total community biomass were similar between grazed and ungrazed soils under drought conditions. Similarly, soils from grazed plots increased the proportional biomass of dominant species but decreased the proportion of subordinate species; however, the proportional biomass of dominant and subordinate species was similar between grazed and ungrazed soils under drought conditions. Soil NO3--N in grazed soil was significantly higher compared to ungrazed soil. Drought dramatically increased soil NO3--N in sterilized soil and had a more pronounced increase in grazed soil than in ungrazed soil. Arbuscular mycorrhizal fungal root colonization from grazed soil was lower compared to ungrazed soil. Drought significantly increased the soil available phosphorus concentration, as well as plant community AM fungal root colonization. Synthesis. Our study suggests that drought can neutralize positive grazing effects on plant community biomass production via altered plant-soil interactions. Also, we found that drought can alleviate the negative effects of grazing legacies on subordinate species by reducing the competitiveness of dominant species. Our study provides new insights for understanding the underlying mechanisms of grazing effects on grassland productivity under climate change. Please see the README document and the accompanying published article: Duan, DD., Tian, Z., Wu, NN., Feng, XX., Hou, FJ., Nan, ZB., Kardol, P., and Chen, T. 2023. Drought neutralizes positive effects of long-term grazing on grassland productivity through altering plant-soil interactions. Functional Ecology. 

This dataset contains the underlying data for the following publication: Li, R., Perdana, S., Vielle, M. (2021), Potential integration of Chinese and European emissions trading market: welfare distribution analysis, Mitigation and Adaptation Strategies for Global Change, 26:22 https://doi.org/10.1007/s11027-021-09960-7.

Prediction of the potentially suitable areas of Leonurus japonicus habitability zones with maxent occurrence points：By sorting out the information of Leonurus japonicus specimens recorded in the Chinese Digital Herbarium (CVH, http://www.cvh.ac.cn/), and combining with the L. japonicus presence points in the Global Biodiversity Information Platform (GBIF, https://www.gbif.org/), the existing distribution positions of L. japonicus were preliminarily obtained, and then the corresponding latitude and longitude coordinates of each distribution point were obtained by Baidu coordinate system. All were used for modeling. environmental variables：Species’ ecoloical niches are affected by climate, topography, biology, and other factors. In consideration of the comprehensiveness and complexity of ecological factors, 34 environmental variables which could reflect species’ ecoloical niches were selected. The list included 19 bioclimatic factors, 14 soil factors and a topographic factor (altitude).The current (1970–2000), 2050s (2041–2060), and 2090s (2081–2100) bioclimatic factor data used in this research were derived from the world climate database Worldclim (http://www.worldclim.Org), and the pixel size of the data was 2.5 arc-minutes (-5 km). The climate data of the 2050s and 2090s were obtained from the Beijing Climate Center-Climate System Model-Medium Resolution (BCC-CSM2-MR), one of the Coupled Model Inter-Comparison Project Phase 6 (CMIP6) datasets, which included three scenarios: sustainable development (SSP126), intermediate development (SSP245) and conventional development (SSP585). SSP scenarios have a high accuracy and separation rate and can integrate local development factors, and so are more convincing than CMIP5 data. The data of soil factors and topographic factors were obtained form the World Soil Database (HWSD) of the FAO (http://www.fao.org/faostat/en/#data), and the provincial national vector map were from China’s Ministry of Natural Resources (http://www.mnr.gov.cn/). The environmental variables is in ASCii format. ASCii can be viewed using standard GIS software such as: environmental variables\\climate\\50126\\bio1.asc Naming convention: Type Variables Description UNITS Bio1 Annual Mean Temperature ℃×10 Bioclimatic Bio2 Mean Diurnal Range ℃×10 Variables Bio3 Isothermality 1 Bio4 Temperature Seasonality 1 Bio5 Max Temperatur ℃×10 Bio6 Min Temperature of Coldest Month ℃×10 Bio7 Temperature Annual Range ℃×10 Bio8 Mean Temperature of Wettest Quarter ℃×10 Bio9 Mean Temperature of Driest Quarter ℃×10 Bio10 Mean Temperature of Warmest Quarter ℃×10 Bio11 Mean Temperature of Coldest Quarter ℃×10 Bio12 Annual Precipitation mm Bio13 Precipitation of Wettest Month mm Bio14 Precipitation of Driest Month mm Bio15 Precipitation Seasonality 1 Bio16 Precipitation of Wettest Quarter mm Bio17 Precipitation of Driest Quarter mm Bio18 Precipitation of Warmest Quarter mm Bio19 Precipitation of Coldest Quarter mm T\_GRAVEL Topsoil Gravel Content %vol. Top Soil Variable T\_SAND Topsoil Sand Fraction % wt. T\_SILT Topsoil Silt Fraction % wt. T\_CLAY Topsoil Clay Fraction % wt. T\_USDA\_TEX\_CLASS Topsoil USDA Texture Classification name T\_REF\_BULK\_DENSITY Topsoil Reference Bulk Density kg/dm3 T\_OC Topsoil Organic Carbon % weight T\_PH\_H2O Topsoil pH (H2O) -log(H+) T\_CEC\_CLAY Topsoil CEC (clay) cmol/kg T\_CEC\_SOIL Topsoil CEC (soil) cmol/kg T\_BS Topsoil Base Saturation % T\_TEB Topsoil TEB cmol/kg T\_ESP Topsoil Sodicity (ESP) % T\_ECE Topsoil Salinity (Elco) dS/m Terrain ELEV Elevation m ENMeval package: To avoid overfitting due to the high complexity of the model constructed with the default parameters, which may cause the predicted distribution of the potential habitat of L. japonicus to deviate too much from the actual situation, this study used the ENMeval package in R 4.3.1, and adjusted the two most important parameters, namely, regularization multiplier (RM) and feature combination (FC), to improve the prediction accuracy of the model. CoordinateCleaner：The R software package ‘CoordinateCleaner’ was used to removing records without coordinate precision and suspected outliers. Based on the ‘subset’ ‘clean\_coordinates’ operation in CoordinateCleaner, we obtained the results of bias corrections on the datasets. SpThin package: Spatial thinning of species occurrence records can help address problems associated with spatial sampling biases. Ideally, thinning removes the fewest records necessary to substantially reduce the effects of sampling bias, while simultaneously retaining the greatest amount of useful information. Spatial thinning can be done manually; however, this is prohibitively time consuming for large datasets. Using a randomization approach, the ‘thin’ function in the spThin R package returns a dataset with the maximum number of records for a given thinning distance, when run for sufficient iterations. geosphere package:The geosphere package in the R environment was used to calculate the centroid range shift distance of L. japonicus under different climate change scenarios. SDMTools：The package in R language was used to calculate the location of centroid in the suitable area of Leonurus japonicus under 6 different economic paths in the current and future periods. VIF package: The usdm package provides a set of functions to support dealing with problematic situations in species distribution modelling (e.g., multicollinearity, positional uncertainty).To detect whether predictor variables are subjected to multicollinearity, you may use vif (variance inflation factor) metric, and some methods implemeted in this package including vifstep or vifcor (a stepwise procedure to identify collinear variables). Leonurus japonicus Houtt. is a traditional Chinese medicinal plant with high medicinal and edible value. Wild L. japonicus resources have been reduced dramatically in recent years. This study predicted the response of distribution range of L. japonicus to climate change in China, which provided the scientific basis for the conservation and utilization. In this study, 489 occurrence points of L. japonicus were selected based on GIS technology and spThin package. The default parameters of the Maxent model were adjusted by using ENMeva1 package of the R environment, and the optimized Maxent model was used to analyze the distribution of L. japonicus. When the feature combination in the model parameters is hing and the regularization multiplier is 1.5, the Maxent model has a higher degree of optimization. With the AUC of 0.830 our model showed a good predictive performance The results showed that L. japonicus was widely distributed in the current period. The maximum temperature of the warmest month, the minimum temperature of the coldest month, the precipitation of the wettest month, the precipitation of the driest month and altitude were the main environmental factors affecting the distribution of L. japonicus. Under the three climate change scenarios, the suitable distribution area of L. japonicus will range-shift to high latitudes, indicating that the distribution of L. japonicus has a strong response to climate change. The regional change rate is the lowest under the SSP126-2090s scenario and the highest under the SSP585-2090s scenario.

The aim of this study was to quantify direct and indirect relationships between soil microbial community properties (potential basal respiration, microbial biomass) and abiotic factors (soil, climate) in three major land-cover types. Location: Europe Time period: 2018 Major taxa studied: Microbial community (fungi and bacteria) We collected 881 soil samples from across Europe in the framework of the Land Use/Land Cover Area Frame Survey (LUCAS). We measured potential soil basal respiration at 20ºC and microbial biomass (substrate-induced respiration) using an O2-microcompensation apparatus. Climate and soil data were obtained from previous LUCAS surveys and online databases. Structural equation modeling (SEM) was used to quantify relationships between variables, and equations extracted from SEMs were used to create predictive maps. Fatty acid methyl esters were measured in a subset of samples to distinguish fungal from bacterial biomass. Soil microbial properties in croplands were more heavily affected by climate variables than those in forests. Potential soil basal respiration and microbial biomass were correlated in forests but decoupled in grasslands and croplands, where microbial biomass depended on soil carbon. Forests had a higher ratio of fungi to bacteria than grasslands or croplands. Soil microbial communities in grasslands and croplands are likely carbon-limited in comparison with those in forests, and forests have a higher dominance of fungi indicating differences in microbial community composition. Notably, the often already-degraded soils of croplands could be more vulnerable to climate change than more natural soils. The provided maps show potentially vulnerable areas that should be explicitly accounted for in coming management plans to protect soil carbon and slow the increasing vulnerability of European soils to climate change. [Methods] Soil samples were collected during the 2018 LUCAS soil sampling campaign. Soil chemical and physical properties were measured at the Joint Research Centre in Ispra, Italy (Orgiazzi et al., 2018). Soil microbial respiration and biomass, as well as water content and water holding capacity, were measured in the Eisenhauer lab of the German Centre for Integrative Biodiversity Research. Fungi/Bacteria was measured by fatty acid analysis by Felipe Bastida at CEBAS CSIC. Climate and geographical data were harvested from various databases, which are listed in Appendix 1 (data sources) of the associated paper. For more details on the soil sampling and physical and chemical properties, see: Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A., & Fernández-Ugalde, O. (2018). LUCAS Soil, the largest expandable soil dataset for Europe: a review. European Journal of Soil Science, 69(1), 140-153. https://doi.org/10.1111/ejss.12499 For more details on the measurements of soil microbial respiration and biomass, fatty acids, and water holding capacity, see the supplementary methods of the associated paper (Appendix 2). [Usage Notes] Fatty acid analysis was performed for a subset of 267 samples. Water holding capacity and associated measurements of basal respiration was analyzed in a subset of 100 samples. The samples that were not in these subsets have NA values for the columns associated with these measurements. In order to protect the precise locations of the LUCAS sampling sites, latitude and longitude values could not be given. The approximate location of each sampling site is instead described by the NUTS3 region. If you wish to replicate the structural equation modeling described in the paper, for which latitude is required, please get in touch. A description of each column is available in the associated metadata file. Deutsche Forschungsgemeinschaft, Award: FZT 118-202548816. European Research Council, Award: 694368. European Commission. Directorate-General for the Environment. Direction Générale Opérationnelle Agriculture, Ressources Naturelles et Environnement du Service Public de Wallonie. Eurostat. Peer reviewed

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