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Aim: Environmental drivers and host richness play key roles in affecting herbivore diversity. However, the relative effects of these factors and their effects on lineages characterized by high host specificity are not well known. In this study, we explored the extent to which contemporary climate, Quaternary climate change, habitat heterogeneity, and host plants determine the species richness and endemism patterns of herbivorous eriophyoid mites. Location: Global. Taxon: Eriophyoid mites (Acari: Eriophyoidea). Methods: We compiled a dataset comprising 4,278 eriophyoid mite species from 22,973 occurrence sites based on a comprehensive search of the published literature and the Global Biodiversity Information Facility (GBIF) as a basis for predicting their global distribution patterns. We measured the association of environmental variables and host plant richness with species richness and endemism of eriophyoid mites through multiple regression analyses using a simultaneous autoregressive (SAR) model, an ordinary least squares (OLS) model, and a random forest model. We examined the direct and indirect effects of these environmental variables and the host plant richness on eriophyoid mite diversity using structural equation models (SEMs). Results: The species richness and endemism patterns of eriophyoid mites are concentrated in temperate regions. Contemporary climate, Quaternary climate change, habitat heterogeneity, and host plants all significantly affected eriophyoid mite richness, while Quaternary climate change, habitat heterogeneity, and host plants contributed to the eriophyoid mite endemism. Abiotic factors indirectly influenced the species richness and endemism of eriophyoid mites, via biotic factors—host plants. Main conclusions: The species richness and endemism of eriophyoid mites peak in temperate regions, opposite to the patterns of plants and some other organisms. Complex interactions among biotic and abiotic factors shape the current eriophyoid mite species diversity.

The ozone (O3) susceptibility of cv. Williams was tested in nine independently controlled and monitored open-top chambers (OTC) built at the UK University of Exeter’s TropOz Research facility located at James Cook University’s Environmental Research Complex (ERC) on the Nguma-bada campus in far-north Queensland, Australia (www.tropoz.org). The plants (27 cv. Williams) were grown under O3 fumigation in OTCs for about three months. At the end of the O3 fumigation period and when the plants were on average 97 cm in height, two leaves were collected from every plant, specifically the third most recently expanded and therefore newly mature leaf (new leaf) and the eighth-most recently expanded (old leaf) both new and old leaves having fully developed under O3 fumigation. From each leaf, two mid-lamina leaf sections ~300 cm2 from both sides of the midrib were taken and measured for total fresh weight. After weighing, and scanning to determine area, one section was, wrapped in tinfoil, snap-frozen in liquid N2 and stored at –20°C before freeze-drying for biochemical analyses; the other was dried at 70 °C to account for leaf mass lost to sampling. At the end of the experiment, leaves, midrib, pseudostem, corm and small suckers were harvested separately, and dried in an oven at 70 °C until constant weight for biomass determination. For all lamina samples collected from the OTC experiment, leaf mass per area (LMA) was calculated using the leaf dry mass (DM) obtained on freeze-dried samples and the leaf area (LA) determined by image analyser software (Image-J, NIH, Bethesda, Maryland, USA). LMA was calculated as LMA= DM/LA in units of g m−2. Freeze-dried leaf samples were subsequently ground into fine powder (Rocklabs Bench Top Ring Mill) and stored in airtight vials until determination of leaf biochemistry and stable isotope concentrations. Powdered leaf samples (~30 mg) were extracted in cold 50% acetone (Ritmejerytė et al. 2019). Total antioxidant capacity (TAC) was determined in the leaf extract by the ferric reducing antioxidant power (FRAP) assay. The assay was carried out according to Benzie and Strain (1996) with some modifications. Ascorbic acid was used as the standard and TAC was expressed as ascorbic acid equivalents (mg AAE g−1 dry weight). Total phenolic content (TPC) was measured in the same leaf extract by the Folin–Ciocalteau method (Cork and Krockenberger 1991; Singleton and Rossi 1965) with some modifications (Ritmejerytė et al. 2019). Gallic acid was used as a standard and TPC was expressed as Gallic acid equivalents (mg GAE g–1 dry weight). The carbon stable isotope ratio (δ13C, ‰) and weight percent (%C) were determined using a Costech Elemental Analyser fitted with a zero-blank auto-sampler coupled via a ConFloIV to a ThermoFinnigan DeltaVPLUS using Continuous-Flow Isotope Ratio Mass Spectrometry (EA-IRMS) at James Cook University’s Advanced Analytical Centre. Stable isotope results are reported as per mil (‰) deviations from the VPDB reference. Precisions (S.D.) on internal standards were better than 0.1‰ for δ13C. The iWUE was calculated from δ13C according to the equation of Farquhar et al. (1989). Environmental variables such as air temperature (T), air relative humidity (RH), shortwave radiation and photosynthetically active radiation (PAR) were monitored using a single meteorological monitoring station (Campbell Scientific, Logan, UT, USA) established in the central OTC. Hourly values of O3 and meteorological conditions were measured for the DO3SE (Deposition of O3 for Stomatal Exchange) model. The DO3SE model was used to estimate the O3 flux into leaves. References Benzie IF, Strain JJ (1996). The ferric reducing ability of plasma (FRAP) as a measure of “antioxidant power”: the FRAP assay. Analytical biochemistry, 239(1), 70-76. Cork SJ, Krockenberger AK (1991). Methods and pitfalls of extracting condensed tannins and other phenolics from plants: insights from investigations on Eucalyptus leaves. Journal of chemical ecology, 17(1), 123-134. Farquhar GD, Ehleringer JR, Hubick KT (1989). Carbon isotope discrimination and photosynthesis. Annual review of plant biology, 40(1), 503-537. Ritmejerytė E, Boughton BA, Bayly MJ, Miller RE (2019). Divergent responses of above-and below-ground chemical defence to nitrogen and phosphorus supply in waratahs (Telopea speciosissima). Functional Plant Biology, 46(12), 1134-1145. Singleton VL, Rossi JA (1965). Colorimetry of total phenolics with phosphomolybdic-phosphotungstic acid reagents. American Journal of Enology and Viticulture, 16(3), 144-158.  # Examining ozone susceptibility in the genus Musa (bananas) [https://doi.org/10.5061/dryad.fbg79cp26](https://doi.org/10.5061/dryad.fbg79cp26) The ozone (O3) susceptibility of cv. Williams was tested in nine independently controlled and monitored open top chambers (OTC) built at the UK University of Exeter’s TropOz Research facility located at James Cook University’s Environmental Research Complex (ERC) on the Nguma-bada campus in far-north Queensland, Australia ([www.tropoz.org](https://tropoz.org/)). The plants (27 cv. Williams) were grown under O3 fumigation in OTCs for about three months. At the end of the O3 fumigation period, two leaves were collected from every plant, specifically the third most recently expanded and therefore newly mature leaf (new leaf) and the eighth-most recently expanded (old leaf) both new and old leaves having fully developed under O3 fumigation from every plant for the determination of leaf functional traits such as leaf mass per area (LMA), intrinsic water-use-efficiency (iWUE), total antioxidant capacity (TAC), and total phenolic contents (TPC). LMA was calculated using the leaf dry mass (DM) obtained on freeze-dried samples and the leaf area (LA) determined by image analyser software (Image-J, NIH, [Bethesda, Maryland](https://en.wikipedia.org/wiki/Bethesda,_Maryland), USA). LMA was calculated as LMA= DM/LA in units of g m−2. TAC was determined in the leaf extract by the ferric reducing antioxidant power (FRAP) assay. TAC was expressed as ascorbic acid equivalents (mg AAE g−1 dry weight). Ascorbic acid was used as the standard. TPC was measured in the same leaf extract by the Folin–Ciocalteau method with some modifications. Gallic acid was used as a standard and TPC was expressed as Gallic acid equivalents (mg GAE g–1 dry weight). The carbon stable isotope ratio (δ13C, ‰) was determined using a Costech Elemental Analyser fitted with a zero-blank auto-sampler coupled via a ConFloIV to a ThermoFinnigan DeltaVPLUS using Continuous-Flow Isotope Ratio Mass Spectrometry (EA-IRMS) at James Cook University’s Advanced Analytical Centre. The δ13C values were used to calculate the iWUE. At the end of the experiment, leaves, midrib, pseudostem, corm and small suckers were harvested separately, and dried in an oven at 70 °C until constant weight for biomass determination. Values (dataset 1) represent OTC of three plants (n=3). Environmental variables such as air temperature (T), air relative humidity (RH), shortwave radiation and photosynthetically active radiation (PAR) were monitored using a single meteorological monitoring station (Campbell Scientific, Logan, UT, USA) established in the central OTC. Hourly values of O3 concentration and meteorological conditions were measured for the DO3SE (Deposition of O3 for Stomatal Exchange) model. The DO3SE model was used to estimate the O3 flux into leaves. ## Description of the data and file structure Dataset was uploaded in three different excel sheets, Data1, Data2 and Data3 with their metadata (Data1\_Metadata, Data2\_Metadata, and Data3\_Metadata). Metadata sheets represents the parameter names, description, and units. Data1 sheet contains open top chamber averages data. Data2 sheet contains environmental variables during experimental period. Data3 sheet contains hourly values of O3 concentration and meteorological conditions that were measured for the DO3SE (Deposition of O3 for Stomatal Exchange) model. ## Sharing/Access information Data was produced from our own experimental open top chambers (OTC) built at the UK University of Exeter’s TropOz Research facility located at James Cook University’s Environmental Research Complex (ERC) on the Nguma-bada campus in far-north Queensland, Australia ([www.tropoz.org](https://tropoz.org/)). ## Code/Software Tropospheric ozone (O3) is a global air pollutant that adversely affects plant growth and productivity. While the impacts of O3 have previously been examined for some tropical commodity crops, no information is available for the pantropical crop, banana (Musa spp.). In this study, we exposed Australia’s major banana cultivar, Williams, to a range of [O3] in open-top chambers. In addition, we examined 46 diverse Musa lines growing in a common garden for variation in traits that are hypothesized to shape responses to O3: leaf mass per area, intrinsic water-use-efficiency, and total antioxidant capacity. Banana cv. Williams showed substantial susceptibility to O3. Combined our results from open-top chambers and common garden conditions suggest a substantial risk of O3 to banana production and food security throughout the tropics.

During June/ July 2000 a hydraulic stimulation program was conducted in the well GPK2. The stimulation was performed over the open hole section of GPK2 from 4431 m (measured depth) to the bottom of the well at around 5084 m. A total of 27 800 m3 of water were injected. At the start of the stimulation 700m3 of brine were injected in an effort to encourage downward growth of the stimulated region. The stimulation covered a period of six days. After a break of seven days a post-stimulation injection test was performed. Microseismic emissions have been monitored and recorded throughout the experiment and continued for a further 11 days after the injection test.

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.ScenarioMIP.CAS.FGOALS-g3.ssp370' 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 FGOALS-g3 climate model, released in 2017, includes the following components: atmos: GAMIL3 (180 x 80 longitude/latitude; 26 levels; top level 2.19hPa), land: CAS-LSM, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run by the Chinese Academy of Sciences, Beijing 100029, China (CAS) in native nominal resolutions: atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

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.ScenarioMIP.CNRM-CERFACS.CNRM-ESM2-1.ssp434' 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 CNRM-ESM2-1 climate model, released in 2017, includes the following components: aerosol: TACTIC_v2, atmos: Arpege 6.3 (T127; Gaussian Reduced with 24572 grid points in total distributed over 128 latitude circles (with 256 grid points per latitude circle between 30degN and 30degS reducing to 20 grid points per latitude circle at 88.9degN and 88.9degS); 91 levels; top level 78.4 km), atmosChem: REPROBUS-C_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA1, tripolar primarily 1deg; 362 x 294 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: Pisces 2.s, seaIce: Gelato 6.1. The model was run by the CNRM (Centre National de Recherches Meteorologiques, Toulouse 31057, France), CERFACS (Centre Europeen de Recherche et de Formation Avancee en Calcul Scientifique, Toulouse 31057, France) (CNRM-CERFACS) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

This revised version of the dataset contains all necessary information (wind turbine locations, vessel properties, probability values used) to replicate the case study described in "Decision Support Tool for Offshore Vessel Routing Under Uncertainty" by R. Dawid, D. McMillan & M. Revie.

Slip model and Relative Source Time Functions for the the 2013 Mw 3.3 St. Gallen earthquake

Dataset of Noninvasive beam diagnosis based on the TM010 mode Dataset of Noninvasive beam diagnosis based on the TM010 mode

This dataset and codebook correspond to the second round of survey data gathered in Latvia in 2023, within the project FULFILL - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes. As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from six countries: Denmark, France, Germany, Italy, Latvia, and India. The first round of the survey, consisted of recruiting a representative sample of approximately 2000 households in each country. In this second survey round, we recruit around 500 respondents from the initial survey round, ensuring representativity is maintained. This survey is very similar to the survey in the first round and includes a lot of identical items, including a quantitative assessment of the carbon footprint in the housing, mobility, and diet sectors, socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. Furthermore, the survey includes measures of quality of life, encompassing aspects such as health and well-being, environmental quality, financial security, and comfort. New for this second round, we have incorporated questions regarding the measures respondents adopted in response to the 2022 energy crisis.