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Aim Despite an increasing number of studies highlighting the impacts of climate change on boreal species, the main factors that will drive changes in species assemblages remain ambiguous. We study how species community composition would change following anthropogenic and natural disturbances. We determine the main drivers of assemblage dissimilarity for bird and beetle communities. Location Côte-Nord, Québec, Canada. Methods We quantify two climate-induced pathways based on direct and indirect effects on species occurrence under different harvest management scenarios. The direct climate effects illustrate the impact of climate variables while the indirect effects are reflected through habitat-based climate change. We develop empirical models to predict the distribution of more than 100 species over the next century. We analyze the regional and the latitudinal species assemblage dissimilarity by decomposing it into 'balanced variation in species occupancy and occurrence' and 'occupancy and occurrence gradient'. Results Both pathways increased dissimilarity in species assemblage. At the regional scale, both effects have an impact on decreasing the number of winning species. Yet, responses are much larger in magnitude under mixed climate effects (a mixture of direct and indirect effects). Regional assemblage dissimilarity reached 0.77 and 0.69 under mixed effects versus 0.09 and 0.10 under indirect effects for beetles and birds, respectively, between RCP8.5 and baseline climate scenarios when considering harvesting. Latitudinally, assemblage dissimilarity increased following the climate conditions pattern. Main conclusions The two pathways are complementary and alter biodiversity, mainly caused by species turnover. Yet, responses are much larger in magnitude under mixed climate effects. Therefore, the inclusion of climatic variables considers aspects other than just those related to forest landscapes, such as life cycles of animal species. Moreover, we expect differences in occupancy between the two studied taxa. This could indicate the potential range of change in boreal species concerning novel environmental conditions.

Les paysages arctiques se caractérisent par la présence de nombreux lacs et étangs qui possèdent des propriétés physico-chimiques et biologiques distinctes. Depuis 2018, nous étudions les communautés zooplanctoniques de plus de 22 lacs et 13 étangs d'eau douce situés au sud de l'Île Victoria à Cambridge Bay, Nunavut (69 ° N, 105 ° O). Pour chacun des lacs et étangs échantillonnés les communautés de zooplancton ont été récoltées et les spécimens ont été identifiés jusqu'à l'espèce. Au total, plus de 77 espèces différentes ont été identifiées incluant 56 rotifères, 6 copépodes, 11 cladocères, 2 crevettes arctiques, une espèce appartenant à la famille des Mysidacea et une crevette têtard. Arctic landscapes are characterized by the presence of many lakes and ponds that exhibit distinct physico-chemical and biological properties. Since 2018, we have been studying the zooplankton communities of more than 22 lakes and 13 freshwater ponds located on southern Victoria Island, Cambridge Bay, Nunavut (69°N, 105°W). For each of the lakes and ponds sampled, zooplankton communities were collected and specimens were identified to species. In total, more than 77 different species were found, including 56 rotifers, 6 copepods, 11 cladocerans, 2 fairy shrimps, a mysid and a tadpole shrimp.

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.NUIST.NESM3.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 NUIST ESM v3 climate model, released in 2016, includes the following components: atmos: ECHAM v6.3 (T63; 192 x 96 longitude/latitude; 47 levels; top level 1 Pa), land: JSBACH v3.1, ocean: NEMO v3.4 (NEMO v3.4, tripolar primarily 1deg; 384 x 362 longitude/latitude; 46 levels; top grid cell 0-6 m), seaIce: CICE4.1. The model was run by the Nanjing University of Information Science and Technology, Nanjing, 210044, China (NUIST) in native nominal resolutions: atmos: 250 km, land: 2.5 km, ocean: 100 km, seaIce: 100 km.

Climate change affects species' spatio-temporal distribution deeply. However, how climate affects the spatio-temporal distribution pattern of related species on the large scale remains largely unclear. Here, we selected two closely related species in the Taxus genus, Taxus chinensis and Taxus mairei, to explore their distribution pattern. Four environmental variables were employed to simulate the distribution patterns using the optimized Maxent model. The results showed that the highly suitable area of T. chinensis and T. mairei in the current period was 1.616 × 105 km2 and 3.093 × 105 km2, respectively. The distribution area of T. chinensis was smaller than that of T. mairei in different periods. Comparison of different periods shows that the distribution area of the two species was almost in stasis from LIG to the future periods. Temperature and precipitation were the main climate factors that determined the potential distribution of the two species. The centroids of T. chinensis and T. mairei were in Sichuan and Hunan provinces in current period, respectively. In the future, the centroid migration direction of the two species would shift towards the northeast. Our results revealed that the average elevation distribution of T. chinensis was higher than that of T. mairei. This study sheds new insights into the habitat preference and limiting environmental factors of the two related species and provides a valuable reference for the conservation of these two threatened species.

This dataset includes data from two experiments.

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.GMMIP.CAS.FGOALS-f3-L' 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-f3-L climate model, released in 2017, includes the following components: atmos: FAMIL2.2 (Cubed-sphere, c96; 360 x 180 longitude/latitude; 32 levels; top level 2.16 hPa), land: CLM4.0, 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: 100 km, land: 100 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.DAMIP.MPI-M.MPI-ESM1-2-LR' 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 MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

Research data and scientific software related to spatio-temporal estimations of ecological soil moisture with available data covering the whole territory of Germany and the Kellerwald National Park (Hesse). Temporal trends of modelled soil moisture for the time period 1961–2070 were statistically analyzed. Soil moisture changes (drying-out) at both national and regional levels were mapped. {"references": ["Nickel S, Schr\u00f6der W 2017. Fuzzy modelling and mapping soil moisture for observed and future periods. An alternative for dynamic modelling at the national and regional scale? Annals of Forest Science 74:71"]}

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.