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# Heatwave grazing kelp microbes sequences [https://doi.org/10.5061/dryad.vhhmgqns7](https://doi.org/10.5061/dryad.vhhmgqns7) We experimentally simulated ocean warming and marine heatwaves (MHWs) to quantify effects on two dominant temperate seaweed species and their microbiota, as well as grazing by a tropical herbivore. The kelp *Ecklonia radiata*’s microbiota in sustained warming and MHW treatments were enriched with microorganisms associated with seaweed disease and tissue degradation. In contrast, the fucoid *Sargassum linearifolium*’s microbiota was unaffected by temperature\*.\* Consumption by the tropical sea-urchin *Tripneustes gratilla* was greater on *Ecklonia* where the microbiota had been altered by higher temperatures, while *Sargassum*’s consumption was unaffected. Elemental traits (carbon, nitrogen), chemical defences (phenolics) and tissue bleaching of both seaweeds were generally unaffected by temperature. ## Description of the data and file structure Juvenile *Ecklonia radiata* (length \~15cm; N=140) and *Sargassum linearifolium* (length \~10cm; N=140) were collected haphazardly (>2m apart) at Cronulla rocky reef, Sydney, Australia. We exposed seaweeds to one of four temperature profiles over seven weeks: Ambient, Warming, marine heatwave MHW, MHW variable. After seven weeks of exposure to temperature treatments, a subset of individuals from each species/temperature treatment (*Ecklonia*: n=4-6; *Sargassum*: n=3) were randomly selected. Sterile cotton swabs were used to sample microbiota on algal surfaces, with the same area (20cm2) and swabbing time (30s) sampled for all individuals. Swabs were immediately stored in liquid nitrogen and transported to the University of New South Wales (UNSW, Sydney) and kept at -80°C until DNA extraction. DNA was extracted from swabs using the DNeasy PowerSoil Kit (Qiagen) and amplified using Polymerase Chain Reaction (PCR) primers 341F (5’-CCTACGGGNGGCWGCAG-3’) and 785R (5’-GACTACHVGGGTATCTAATCC-3’), targeting the 16S rRNA gene V3-V4 regions (bacteria and archaea), and were sequenced with a 2x250bp MiSeq reagent kit v2 on the Illumina MiSeq2000 Platform. The range-expansion of tropical herbivores due to ocean warming can profoundly alter temperate reef communities by overgrazing the seaweed forests that underpin them. Such ecological interactions may be mediated by changes to seaweed-associated microbiota in response to warming, but empirical evidence demonstrating this is rare. We experimentally simulated ocean warming and marine heatwaves (MHWs) to quantify effects on two dominant temperate seaweed species and their microbiota, as well as grazing by a tropical herbivore. The kelp Ecklonia radiata’s microbiotain sustained warming and MHW treatments were enriched with microorganisms associated with seaweed disease and tissue degradation. In contrast, the fucoid Sargassum linearifolium’s microbiota was unaffected by temperature. Consumption by the tropical sea-urchin Tripneustes gratilla was greater on Ecklonia where the microbiota had been altered by higher temperatures, while Sargassum’s consumption was unaffected. Elemental traits (carbon, nitrogen), chemical defences (phenolics) and tissue bleaching of both seaweeds were generally unaffected by temperature. Effects of warming and MHWs on seaweed holobionts (host plus its microbiota) are likely species-specific. The effect of increased temperature on Ecklonia’s microbiota and subsequent increased consumption suggest that changes to kelp microbiota may underpin kelp-herbivore interactions, providing novel insights into potential mechanisms driving change in species’ interactions in warming oceans.

Public acceptance (PA) is nowadays essential for the sustainable development of nuclear energy and becomes animportant issue for research community. Although some studies had investigated the factors influencing PA ofnuclear energy, few researches were founded to verify the impact of cultural values. This research proposed atheoretical model to explore how individualism and collectivism, as an important dimension of culture, moderated the relevance between perceived risk/benefit and PA. A questionnaire survey was conducted nationwidein China whose number of under-construction nuclear power plants ranks first in the world, and received 887valid responses. The analysis of moderating effect showed individualism weakened the relevance betweenperceived benefit and PA, whereas collectivism had no significant moderating role on the relevance betweenperceived benefit and PA. Collectivism strengthened the relevance between perceived risk and PA, whereasindividualism had no significant moderating role on the relevance between perceived risk and PA. Moreover,perceived benefit was confirmed to be a more important predictor for PA than perceived risk. The abovementioned findings could not only provide new insights that help to understand the difference in energy policiesbetween China and the developed countries, but also provide new reference and guidance for the future policymaking. Public acceptance (PA) is nowadays essential for the sustainable development of nuclear energy and becomes animportant issue for research community. Although some studies had investigated the factors influencing PA ofnuclear energy, few researches were founded to verify the impact of cultural values. This research proposed atheoretical model to explore how individualism and collectivism, as an important dimension of culture, moderated the relevance between perceived risk/benefit and PA. A questionnaire survey was conducted nationwidein China whose number of under-construction nuclear power plants ranks first in the world, and received 887valid responses. The analysis of moderating effect showed individualism weakened the relevance betweenperceived benefit and PA, whereas collectivism had no significant moderating role on the relevance betweenperceived benefit and PA. Collectivism strengthened the relevance between perceived risk and PA, whereasindividualism had no significant moderating role on the relevance between perceived risk and PA. Moreover,perceived benefit was confirmed to be a more important predictor for PA than perceived risk. The abovementioned findings could not only provide new insights that help to understand the difference in energy policiesbetween China and the developed countries, but also provide new reference and guidance for the future policymaking.

Studies have shown that food chain length is governed by interactions between species richness, ecosystem size, and resource availability. While redundant trophic links may buffer impacts of species loss on food chain length, higher extinction risks associated with predators may result in bottom-heavy food webs with shorter food chains. The lack of consensus in earlier empirical studies relating species richness and food chain length reflects the need to account robustly for the factors described above. In response to this, we conducted an empirical study to elucidate impacts of land-use change on food chain length in tropical forest streams of Southeast Asia. Despite species losses associated with forest loss at our study areas, results from amino acid isotope analyses showed that food chain length was not linked to land use, ecosystem size or resource availability. Correspondingly, species losses did not have a significant effect on occurrence likelihoods of all trophic guilds except herbivores. Impacts of species losses were likely buffered by high levels of initial trophic redundancy, which declined with canopy cover. Declines in trophic redundancy were most drastic amongst invertivorous fishes. Declines in redundancy across trophic guilds were also more pronounced in wider and more resource-rich streams. While our study found limited evidence for immediate land-use impacts on stream food chains, the potential loss of trophic redundancy in the longer term implies increasing vulnerability of streams to future perturbations, as long as land conversion continues unabated.

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.BCC.BCC-ESM1.amip' 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 BCC-ESM 1 climate model, released in 2017, includes the following components: atmos: BCC_AGCM3_LR (T42; 128 x 64 longitude/latitude; 26 levels; top level 2.19 hPa), atmosChem: BCC-AGCM3-Chem, land: BCC_AVIM2, ocean: MOM4 (1/3 deg 10S-10N, 1/3-1 deg 10-30 N/S, and 1 deg in high latitudes; 360 x 232 longitude/latitude; 40 levels; top grid cell 0-10 m), seaIce: SIS2. The model was run by the Beijing Climate Center, Beijing 100081, China (BCC) in native nominal resolutions: atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 50 km, seaIce: 50 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.CAS.FGOALS-g3' 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.

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.

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.CAMS.CAMS-CSM1-0.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 CAMS-CSM 1.0 climate model, released in 2016, includes the following components: atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb), land: CoLM 1.0, ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m), seaIce: SIS 1.0. The model was run by the Chinese Academy of Meteorological Sciences, Beijing 100081, China (CAMS) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

Although many studies have tested the direct effects of drought on alien plant invasion, less is known about whether drought affects alien plant invasion indirectly via interactions of plants with other groups of organisms such as soil mesofauna. To test for such indirect effects, we grew single plants of nine naturalized alien target species in pot-mesocosms with a community of five native grassland species under four combinations of two drought (well-watered vs drought) and two soil-mesofauna-inoculation (with vs without) treatments. We found that drought decreased the absolute and the relative biomass production of the alien plants, and thus reduced their competitive strength in the native community. Drought also decreased the abundance of soil mesofauna, particularly soil mites, but did not affect the abundance and richness of soil herbivores. Soil-fauna inoculation did not affect biomass of the alien plants but increased biomass of the native plant community, and thereby decreased the relative biomass production of the alien plants. This increased invasion resistance due to soil fauna, however, tended (p = 0.09) to be stronger for plants growing under well-watered conditions than under drought. Synthesis. Our multispecies experiment thus shows that soil fauna might help native communities to resist alien plant invasions, but that this effect might be weakened under drought. In other words, soil mesofauna may buffer the negative effects of drought on alien plant invasions. The file archives 'SoilFauna_Drought_PlantInvasion_Date_YJL.tar' include three dataset, one named 'SoilFauna_Drought_PlantInvasion.csv' (Biomass data), one named 'SoilFaunaData.csv' (Soil Fauna data), and one named 'SoilNitrogenData.csv' (soil nitrogen data). The file 'SoilFauna_Drought_PlantInvasion.Rmd' is the R script, and its output is 'SoilFauna_Drought_PlantInvasion.html'. All data were collected from a greenhouse expeirment at the Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences.