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Sample collection Samples of FWW were collected from 16 locations across its distribution range in China; 14 of these have previously been used for population genetics analysis using microsatellite markers (Cao et al., 2016). The other two newly collected populations were obtained from the expansion fronts of FWW in 2017-2018. Larvae of FWW were each sampled from different silk webs at each sampling location to reduce the chances of collecting siblings. In total, 306 larvae of FWW were obtained and used for DNA extraction, library construction, and genotyping, with 13-20 individuals per population. Library construction, SNP calling, and filtering Genomic DNA was isolated from larvae individually using a DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany). We used the ddRAD method to develop genome-wide SNPs for FWW (Peterson et al., 2012). Genomic DNA from each individual was digested by the restriction enzymes NlaIII and AciI for 3 hours at 37 °C (Aguilar et al., 1979; Li et al., 2018). Then we used 67.5 µl (1.5×) SpeedBeads (GE) to purify the digested DNA. A pair of uniquely modified Illumina P1 (5 bp) and P2 adapters (4 bp) were ligated to the digested DNA at 16 °C overnight. A heat-deactivation step was used to end the ligating reaction under conditions of 65 °C for 10 min and 22 cycles at 20 °C for 1 min. We pooled ligated products with a unique adapter into one library, followed by a purifying step using (1.5×) SpeedBeads (GE). Fragments of 420 - 540 bp were selected using BluePippin on a 2% gel cassette (Sage Sciences, Beverly, MA, USA) and then amplified using 12 PCR (polymerase chain reaction) amplification cycles. We used 64 µl 0.8× SpeedBeads to purify the amplified libraries. The quantity and quality of each library were evaluated using Qubit 3.0 and Agilent Bioanalyses 2100. The Illumina NovaSeq 6000 platform was used for sequencing to obtain 150-bp paired-end reads. We used Stacks version 2.52 to filter the low-quality sequencing data and call SNPs (Catchen et al., 2013). Raw sequencing reads were demultiplexed and trimmed using the process_radtags. Reads for each individual were mapped to the reference genome of FWW with a size of 510.5 Mb from NCBI (Assembly: GCA_003709505.1 ASM370950v1) (Wu et al., 2018) using Bowtie version 2.3.5.1 (Langmead et al., 2012). SNPs were called using a maximum likelihood framework and filtered with populations implemented in Stacks, VCFtools version 0.1.16 (Danecek et al., 2011), and the R package vcfR (Knaus et al., 2017) based on the following criteria: (a) samples with a mapping rate less than 80% were removed; (b) SNPs with a sequencing depth higher than eight and less than 500 were removed; (c) samples and SNPs with a missing rate higher than 10% in the corresponding dataset were removed; (d) SNPs with a minor allele count lower than 10 were removed; (e) SNPs with observed heterozygosity of > 0.75 across all populations were removed; (f) SNPs with a p-value of Hardy-Weinberg equilibrium (HWE) lower than 10-7 in all populations were removed to generate dataset of neutral SNPs. In order to reduce the influence of linkage on population structure inferences, we retained only SNPs separated by at least 1000 bp (Lowry et al., 2017). References Aguilar, J. D., & Riom, J. (1979). Nemoraea pellucida (Meigen), A new parasite of Hyphantria cunea (Drury) [France; fall webworm]. Bulletin De La Société Entomologique De France, 84, 204-207. Cao, L. J., Wei, S. J., Hoffmann, A. A., Wen, J. B., & Chen, M. (2016). Rapid genetic structuring of populations of the invasive fall webworm in relation to spatial expansion and control campaigns. Diversity and Distributions, 22, 1276-1287. Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A., & Cresko, W. A. (2013). Stacks: an analysis tool set for population genomics. Molecular Ecology, 22, 3124-3140. Danecek, P., Auton, A., Abecasis, G., Albers, C. A., anks, E. B., Depristo, M. A., . . . Sherry, S. T. (2011). The variant call format and VCFtools. Bioinformatics, 27, 2156-2158. Knaus, B. J., & Grünwald, N. J. (2017). VCFR: A package to manipulate and visualize variant call format data in R. Molecular Ecology Resources, 17, 44-53. Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods, 9, 357-359. Li, B. Y., Gao, Q., Cao, L. J., Hoffmann, A. A., Yang, Q., Zhu, J. Y., & Wei, S. J. (2018). Conserved profiles of digestion by double restriction endonucleases in insect genomes facilitate the design of ddRAD. Zoological Systematics, 43, 341-355. Lowry, D. B., Hoban, S., Kelley, J. L., Lotterhos, K. E., Reed, L. K., Antolin, M. F., & Storfer, A. (2017). Breaking RAD: an evaluation of the utility of restriction site-associated DNA sequencing for genome scans of adaptation. Molecular Ecology Resources, 17, 142-152. Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S., & Hoekstra, H. E. (2012). Double digest RADseq: an inexpensive method for de novo SNP discovery and genotyping in model and non-model species. PloS ONE, 7, e37135. Wu, N., Zhang, S., Li, X., Cao, Y., Liu, X., Wang, Q., . . . Zhan, S. (2018). Fall webworm genomes yield insights into rapid adaptation of invasive species. Nature Ecology and Evolution, 3, 105-115. Adaptive evolution following colonization can affect the impact of invasive species. The fall webworm (FWW) invaded China 40 years ago through a single introduction event involving a severe bottleneck and subsequently diverged into two genetic groups. The well-recorded invasion history of FWW, coupled with a clear pattern of genetic divergence, provides an opportunity to investigate whether there is any sign of adaptive evolution following the invasion. Based on genome-wide SNPs, we identified genetically separated western and eastern groups of FWW and correlated spatial variation in SNPs with geographical and climatic factors. Geographic factors explained a similar proportion of the genetic variation across all populations compared to climatic factors. However, when the two population groups were analyzed separately, environmental factors explained more of the variation than geographic factors. SNP outliers in populations of the western group had relatively stronger response to precipitation than temperature-related variables. Functional annotation of SNP outliers identified genes associated with insect cuticle protein potentially related to desiccation adaptation in the western group and genes associated with lipase biosynthesis potentially related to temperature adaptation in the eastern group. Our study suggests that invasive species may maintain evolutionary potential to adapt to heterogeneous environments despite a single invasion event. The molecular data suggest that quantitative trait comparisons across environments would be worthwhile. Here we provided VCF files generated and its population map generated in this study. Three VCF files were included. 1.fww_invariant+SNP_miss20_DP3.GQ20.vcf.gz, includes SNPs and invariant sites of all populations; 2.fww.ddRAD.all.vcf.gz, includes SNPs of all populations; 3.fww.4fds.vcf.gz, includes four degenerated SNPs of all populations; 4.fww_265_popmap.txt, includes a population map of all individuals. The three VCF files and a population map file can be opened by VCFtools and used as input files for population genetic diversity, population genetic structure, demographic inference, and outlier scanning 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.CMIP.INM.INM-CM5-0' 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 INM-CM5-0 climate model, released in 2016, includes the following components: aerosol: INM-AER1, atmos: INM-AM5-0 (2x1.5; 180 x 120 longitude/latitude; 73 levels; top level sigma = 0.0002), land: INM-LND1, ocean: INM-OM5 (North Pole shifted to 60N, 90E. 0.5x0.25; 720 x 720 longitude/latitude; 40 levels; vertical sigma coordinate), seaIce: INM-ICE1. The model was run by the Institute for Numerical Mathematics, Russian Academy of Science, Moscow 119991, Russia (INM) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 50 km, seaIce: 50 km.

The data to plot the figures in the paper. The data to plot the figures in the paper.

The paper "Blue Food policy objectives: an analysis of opportunities and trade-offs" integrates the findings of an initiative to assess the multiple roles of blue foods in food systems worldwide (https://www.bluefood.earth/) and translates them into four policy objectives aimed at realizing the contributions of aquatic foods to more nutritious, just, resilient and environmentally sustainable food systems. This dataset contains the variables used to assess conditions (at the level of nations) when blue food policy objectives are likely to be relevant. The R code used for Boolean analysis is available here: https://github.com/emmywas/BFA_Policy_analysis The paper "Blue Food policy objectives: an analysis of opportunities and trade-offs" is part of the Blue Food Assessment ( https://www.bluefood.earth/ ) a comprehensive examination of the role of aquatic foods in building healthy, sustainable, and equitable food systems. The assessment was supported by the Builders Initiative, the MAVA Foundation, the Oak Foundation, and the Walton Family Foundation. BC also thanks the Erling Persson Family Foundation. The data comes from publicly available (or published) datasets

The published dataset has been created using high quality and fine resolution satellite retrievals of aerosols from the MIDAS climatology (Gkikas et al., 2021), as well as information for clouds from CMSAF (Pfeifroth et al., 2017). The surface solar radiation has been simulated using the libRatran radiative transfer model (Mayer and Kylling, 2005). More information about the methodology used to create the dataset can be found in the corresponding study: I. Fountouakis et al., (2021): Effects of aerosols and clouds on the levels of surface solar radiation and solar energy in Cyprus, submitted to the journal Remote Sensing References Gkikas, A.; Proestakis, E.; Amiridis, V.; Kazadzis, S.; Di Tomaso, E.; Tsekeri, A.; Marinou, E.; Hatzianastassiou, N.; Pérez García-Pando, C. ModIs Dust AeroSol (MIDAS): a global fine-resolution dust optical depth data set. Atmos. Meas. Tech. 2021, 14, 309–334. 10.5194/amt-14-309-2021. Pfeifroth, Uwe; Kothe, Steffen; Müller, Richard; Trentmann, Jörg; Hollmann, Rainer; Fuchs, Petra; Werscheck, M. Surface Radiation Data Set - Heliosat (SARAH) - Edition 2, Satellite Application Facility on Climate Monitoring. 2017. 10.5676/EUM_SAF_CM/SARAH/V002. Mayer, B.; Kylling, A. The libRadtran software package for radiative transfer calculations-description and examples of use. Atmos. Chem. Phys. 2005, 5, 1855–1877.

# Competition mode and soil nutrient status shape the role of soil microbes in the diversity–invasibility relationship [https://doi.org/10.5061/dryad.wh70rxww6](https://doi.org/10.5061/dryad.wh70rxww6) ## Description of the data and file structure Data usage bootstrap: See plant biomass data at: Li et al.\_ plant biomass See soil fungi community data at: Li et al.\_ Fungi See soil FUNGuild function prediction at: Li et al.\_ FUN Guid ### DATA-SPECIFIC INFORMATION FOR: Li et al.\_ plant biomass 1\. Number of variables: 6 2\. Number of cases/rows: 763 3\. Variable List: "species" refers to the classification of plants as either alien or native. "diversity" refers to the level of plant diversity. "soil mixture types" refers to the types of soil mixtures. "interspecific competition" and "intraspecific competition" refer to the modes of competition. "fertilization" and "non-fertilization" refer to the nutrient levels. "total biomass" refers to the total plant biomass (g) under a specific treatment. ### DATA-SPECIFIC INFORMATION FOR: Li et al.\_ Fungi 1\. Number of variables: 28 2\. Number of cases/rows: 2940 3\. Variable List: OTU: operational taxonomic unit sample1: Experimental soil sample naming sample2: Experimental soil sample naming sample4: Experimental soil sample naming sample5: Experimental soil sample naming sample6: Experimental soil sample naming sample7: Experimental soil sample naming sample8: Experimental soil sample naming sample9: Experimental soil sample naming sample10: Experimental soil sample naming sample11: Experimental soil sample naming sample12: Experimental soil sample naming sample13: Experimental soil sample naming sample14: Experimental soil sample naming sample15: Experimental soil sample naming sample16: Experimental soil sample naming sample17: Experimental soil sample naming sample18: Experimental soil sample naming sample19: Experimental soil sample naming sample20: Experimental soil sample naming sample21: Experimental soil sample naming sample22: Experimental soil sample naming sample23: Experimental soil sample naming sample24: Experimental soil sample naming sample25: Experimental soil sample naming sample26: Experimental soil sample naming sample27: Experimental soil sample naming taxonomy: Microbial classification level ### DATA-SPECIFIC INFORMATION FOR: Li et al.\_ FUN Guid 1\. Number of variables: 29 2\. Number of cases/rows: 2940 3\. Variable List: Functional type: sample1: Experimental soil sample naming sample2: Experimental soil sample naming sample4: Experimental soil sample naming sample5: Experimental soil sample naming sample6: Experimental soil sample naming sample7: Experimental soil sample naming sample8: Experimental soil sample naming sample9: Experimental soil sample naming sample10: Experimental soil sample naming sample11: Experimental soil sample naming sample12: Experimental soil sample naming sample13: Experimental soil sample naming sample14: Experimental soil sample naming sample15: Experimental soil sample naming sample16: Experimental soil sample naming sample17: Experimental soil sample naming sample18: Experimental soil sample naming sample19: Experimental soil sample naming sample20: Experimental soil sample naming sample21: Experimental soil sample naming sample22: Experimental soil sample naming sample23: Experimental soil sample naming sample24: Experimental soil sample naming sample25: Experimental soil sample naming sample26: Experimental soil sample naming sample27: Experimental soil sample naming At 8 weeks after the start of the test phase, all aboveground and belowground parts of the test plants were harvested and rinsed with water. Owing to the death of some seedlings during the growth period, 502 R. typhina individuals (interspecific: 239, intraspecific: 263) and 259 A. altissima individuals were harvested. The collected plant tissue was placed in a 70 °C oven to dry for a week and was weighed. The biomass of two R. typhina plants was measured together under intraspecific competition, and the biomass of R. typhina and A. altissima was measured separately under interspecific competition. Soil sampling, DNA extraction, amplicon sequencing, and bioinformatics analysis The 27 mixed fresh soil samples, collected from various diversity treatments and subsequently stored in a refrigerator at −80 °C, were analyzed for soil fungal community composition. The total microbial community DNA was extracted according to the instructions of the E.Z.N.A. Soil DNA Kit (Omega Bio-tek, Norcross, GA). The quality of the extracted DNA was verified using 1% agarose gel electrophoresis, and DNA concentration and purity were measured using a NanoDrop 2000 device (Thermo Fisher Scientific, Waltham, MA). The fungal rRNA internal transcribed spacer (ITS) region was amplified using the primer set ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGCGTTCTTCATCGATGC-3′). Sequencing was performed using a MiSeq PE300/NovaSeq PE250 platform (Illumina, San Diego, CA). The Illumina MiSeq platform has a higher throughput and lower error rate than other high-throughput sequencers (Loman et al., 2012; Frey et al., 2014). Fungal sequences were classified using the UNITE database (version 8.0) and using the USEARCH11-uparse algorithm for clustering. Operational taxonomic unit (OTU) sequence similarity was 0.97. Species classification was performed using the unite8.0/its fungi database, and classification confidence was 0.7. As fungal DNA extraction failed for one sample, we obtained 26 samples of ITS rDNA. Understanding the relationship between plant diversity and invasibility is essential in invasion ecology. Species-rich communities are hypothesized to be more resistant to invasions than species-poor communities. However, while soil microorganisms play a crucial role in regulating this diversity–invasibility relationship, the effects of plant competition mode and soil nutrient status on their role remain unclear. To address this, we conducted a two-stage greenhouse experiment. Soils were first conditioned by growing nine native species separately in them for 1 year, then mixed in various configurations with soils conditioned using one, three, or six species, respectively. Next, we inoculated the mixed soil into sterilized substrate soil and planted the alien species Rhus typhina and native species Ailanthus altissima as test plants. We set up two competition modes (intraspecific and interspecific) and two nutrient levels (fertilization using slow-release fertilizer and non-fertilization). Under intraspecific competition, regardless of fertilization, the biomass of the alien species was higher in soil conditioned by six native species. By contrast, under interspecific competition, the biomass increased without fertilization but remained stable with fertilization in soil conditioned by six native species. Analysis of soil microbes suggests that pathogens and symbiotic fungi in diverse plant communities influenced R. typhina growth, which varied with competition mode and nutrient status. Our findings suggest that the soil microbiome is pivotal in mediating the diversity–invasibility relationship, and this influence varies according to competition mode and nutrient status.

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

Microbes inhabiting deep soil layers are known to be different from their counterpart in topsoil, yet remain under investigation in terms of their structure, function, and how their diversity is shaped. The microbiome of deep soils (> 1 m) is expected to be relatively stable and highly independent from climatic conditions. Much less is known, however, on how these microbial communities vary along climate gradients. Here, we used amplicon sequencing to investigate bacteria, archaea, and fungi along fifteen 18-m depth profiles at 20–50 cm intervals across contrasting aridity conditions in semi-arid forest ecosystems of China's Loess Plateau. Our results showed that bacterial and fungal α diversity and bacterial and archaeal community similarity declined dramatically in topsoil and remained relatively stable in deep soil. Nevertheless, deep soil microbiome still showed the functional potential of N cycling, plant-derived organic matter degradation, resource exchange, and water coordination. The deep soil microbiome had closer taxa-taxa and bacteria-fungi associations and more influence of dispersal limitation than topsoil microbiome. Geographic distance was more influential in deep soil bacteria and archaea than in topsoil. We further showed that aridity was negatively correlated with deep-soil archaeal and fungal richness, archaeal community similarity, relative abundance of plant saprotroph, and bacteria-fungi associations, but increased the relative abundance of aerobic ammonia oxidation, manganese oxidation, and arbuscular mycorrhizal in the deep soils. Root depth, complexity, soil volumetric moisture, and clay play bridging roles in the indirect effects of aridity on microbes in deep soils. Our work indicates that even microbial communities and nutrient cycling in deep soil are susceptible to changes in water availability, with consequences for understanding the sustainability of dryland ecosystems and the whole-soil in response to aridification. Moreover, we propose that neglecting soil depth may underestimate the role of soil moisture in dryland ecosystems under future climate scenarios. The files in Dryad contain the data necessary to reproduce the statistical analyses published in the manuscript "Deciphering microbiomes dozens of meters under our feet and their edaphoclimatic and spatial drivers". The four datasets in the zipped file: 1. Metadata.xlsx: Soil properties, environmental variables and sample information 2. Microbial properties.xlsx: microbial composition and diversity 3. Network properties.xlsx: Soil microbial co-occurrence network properties 4. MST\_assembly.xlsx: The modified stochasticity ratio (MST) was used to infer microbial community assembly processes Variable description * MAP: Mean annual precipitation * Aridity: 1-precipitation/evapotranspiration * Soil volumetric moisture: SVWC, ratio of volume occupied by water in soil to total soil volume * Specific surface area: SSA, total surface area per unit mass of soil sample * Median size: MS, particle size corresponding to 50% of the cumulative percentage of the size distribution * pH: Soil pH * SOC: Soil organic carbon * MBC: Microbial biomass carbon * TN: Soil total nitrogen * NH4+-N: Ammonium nitrogen * NO3--N: Nitrate nitrogen * TP: Soil total phosphorus * AP: Available phosphorus * Root biomass: root dry weight per unit soil area * meanFunction: Soil multi-nutrient cycling index * Station: Abbreviation of sampling site * Afforestation: Ecosystem type of aboveground vegetation * Observed richness: Alpha diversity * PCoA1: The first principal component of PCoA * bac\_MST: Modified stochasticity ratio of bacteria * arc\_MST: Modified stochasticity ratio of archaea * fun\_MST: Modified stochasticity ratio of fungi If you have any other questions about this Dataset, you can directly contact the corresponding authour