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145 results for “climatic differences”

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zenodo36/100

A Factor Two Difference in 21st-Century Greenland Ice Sheet Surface Mass Balance Projections from Three Regional Climate Models for a Strong Warming Scenario (SSP5-8.5)

1km regridded Greenland Ice Sheet SMB / Runoff / Melt projection until 2100. Projections from MAR, RACMO, HIRHAM forced by CESM2 (SSP5-8.5).

opencc-by-4.0Aug 2024View details →

dryad36/100

Dispersal syndromes are poorly associated with climatic niche differences in the Azorean seed plants

Aim: Environmental niche tracking is linked to the species ability to disperse. While well investigated on large spatial scales, dispersal constraints also influence small-scale processes and may explain the difference between the potential and the realized niche of species at small-scales. Here we test whether niche size and niche fill differ systematically according to dispersal syndrome within isolated oceanic islands. We expect species with higher dispersal abilities (anemochorous or endozoochorous) will have a higher niche fill, despite of their environmental niche size. Location: Azores archipelago Taxon: Native seed plants Methods: We combined a georeferenced database of the species distribution within the archipelago (Azorean Biodiversity Portal/GBIF) with an expert-based dispersal syndrome categorization and a high-resolution climatic grid (CIELO model). Using four climatic variables (Annual Mean Temperature, Mean Diurnal Range, Annual Precipitation, Precipitation Seasonality), we calculated a 4-dimensional hypervolume to estimate the niche size of each species. Niche fill was quantified as the suitable climatic space of the island that was occupied by the focal species. Results: Endozoochorous species display higher niche fill compared to epizoochorous and hydrochorous species, and larger niches than anemochorous and epizoochorous. Differences among the remaining groups are not significant neither for niche fill nor for niche size. Main Conclusions: Although endozoochorous species track their niche more efficiently at small-scales than other dispersal syndromes, the differences between dispersal syndromes are not consistent. The ability of a species to track its niche at small-scales is not tightly related to its dispersal syndrome. Although intuitively appealing, dispersal syndrome classifications might not be the most appropriate tools for understanding dispersal processes at small-scales.

opencc-zeroMay 2022View details →

zenodo36/100

The versatility of pulses: Are consumption and consumer perceptions in different European countries related to the actual climate impact of different pulse types? Author links open overlay panel

Pulses support sustainable production and consumption. Their culinary versatility creates a wide range of possibilities for new products, bridging consumers’ preparation barriers. However, this potential is often intangible for consumers who have little knowledge about plant-based foods. Based on an online survey in Denmark, Germany, Poland, Spain, and the United Kingdom (N = 4,226), this study aimed to investigate consumer utilization and perception of pulses as a versatile, low-carbon food relative to objective life cycle assessment (LCA) measures of 12 pulse types. The most popular pulse types, with specific preferences across countries, were lentils, kidney beans, and chickpeas, typically consumed at home and purchased in dried or canned form. Respondents associated pulses with being healthy and natural, but sustainability was not an essential attribute related to the perception of pulses. LCA revealed a low environmental impact caused by pulse production and consumption, with marginal variations between types and produce. Respondents were unaware of the nuances in the environmental impact of different pulse types, generally perceiving uncommon pulses to be relatively more sustainable than others. In conclusion, a low consumption combined with a misconception of pulses’ environmental impact may demand different promotional strategies including clear communication to inform consumers.

opencc-by-4.0Nov 2022View details →

zenodo36/100

Model outputs and species-level data for "Functional traits and climate drive interspecific differences in disturbance-induced tree mortality"

This repository is divided in three sub-directories:  <ul> <li>sensitivity contains the posterior of each parameter estimated by the bayesian mortality model in a rdata file. This file was generated by the script https://github.com/jbarrere3/SalvageModel/tree/withFinland</li> <li>climate contains for each tree species the climatic variables (mean annual temperature, minimum annual temperature and annual precipitation) extracted from CHELSA and the disturbance-related climatic indices (Fire Weather Index, Snow Water Equivalent and Gust Wind Speed)</li> <li>traits contains the traits calculated directly with NFI data (bark thickness, height to dbh ratio, maximum growth), and a text file with the Species and Trait ID to request to TRY database. </li> </ul> The content of this repository can be used to reproduce the analyses of the paper, with the script stored in  in https://github.com/jbarrere3/DisturbancePaper Edit (19/09/2023): A minor coding error was found in the pre-formatted data of the paper, which did not affect the main results but led to minor change in the value of the posterior estimates. An updated version of the posterior estimates of this dataset was made available at https://zenodo.org/record/8358921.

opencc-by-4.0Feb 2023View details →

dryad36/100

Orchid-mycorrhizal fungi interactions reveal a duality in their network structure in two European regions differing in climate

Network analysis is an effective tool to describe and quantify the ecological interactions between plants and root-associated fungi. Mycoheterotrophic plants, such as orchids, critically rely on mycorrhizal fungi for nutrients to survive, therefore, investigating the structure of those intimate interactions brings new insights into the plant community assembly and coexistence. So far, there is little consensus on the structure of those interactions, described either as nested (generalist interactions), modular (highly specific interactions) or of both topologies. Biotic factors (e.g., mycorrhizal specificity) were shown to influence the network structure, while there is less evidence of abiotic factor effects. By using next-generation sequencing of the orchid mycorrhizal fungal (OMF) community associated with 238 plant individuals belonging to 17 orchid species, we assessed the structure of four orchid-OMF networks in two European regions under contrasting climatic conditions (Mediterranean vs Continental). Each network contained four to 12 co-occurring orchid species, including up to eight species shared among the sites. All four networks were both nested and modular, and fungal communities were different between co-occurring orchid species, despite multiple sharing of fungi across some orchids. Co-occurring orchid species growing in Mediterranean climates were associated with more dissimilar fungal communities, consistent with a greater modular structure compared to the Continental ones. The OMF diversity was comparable among orchid species since most orchids were associated with multiple rarer fungi and with only a few highly dominant ones in the roots. Our results provide useful highlights on potential factors involved in structuring plant-mycorrhizal fungi interactions in different climatic conditions.

opencc-zeroMar 2023View details →

zenodo36/100

Deglacial climate changes as forced by different ice sheet reconstructions - model ouputs

This dataset contains the model output corresponding to the paper entitled "Deglacial climate changes as forced by different ice sheet reconstructions" submitted to Climate of the Past. For the description of the model and simulations we refer to this article.   Simulations: degla_P_bathy_500yr_is_SH_nobathy = with ICE_6G_C, fixed bathymetry degla_P_bathy_500yr_is_SH = with ICE_6G_C, evolving bathymetry degla_P_bathy_500yr_is_SH_bis = with ICE_6G_C, evolving bathymetry, mask modified degla_T_bathyT_100yr_is_SH_nobathy = with GLAC-1D, fixed bathymetry degla_T_bathyT_100yr_is_SH = with GLAC-1D, evolving bathymetry degla_T_bathyT_100yr_is_SH_FWF = with GLAC-1D, evolving bathymetry, fresh water flux degla_T_bathyT_100yr_is_SH_FWFtest3 = with GLAC-1D, evolving bathymetry, fresh water flux with intensity divided by 3 degla_T_bathyT_100yr_is_SH_FWFtest4 = with GLAC-1D, evolving bathymetry, fresh water flux with intensity divided by 4   Variables and corresponding files: Evolution of ocean volume (m3): volume_ocean_degla_P_bathy_500yr_is_SH.txt volume_ocean_degla_T_bathyT_100yr_is_SH.txt Evolution of ocean surface area (1e6 km2): surface_area_degla_P_bathy_500yr_is_SH.txt surface_area_degla_T_bathyT_100yr_is_SH.txt Sea land masks for time slices: tmask_bathy_P_0yr_SH_CC_PI.nc tmask_degla_P_bathy_500yr_is_SH_21ka.nc tmask_degla_T_bathyT_100yr_is_SH_21ka.nc tmask_degla_P_bathy_500yr_is_SH_12ka.nc tmask_degla_T_bathyT_100yr_is_SH_12ka.nc tmask_degla_P_bathy_500yr_is_SH_9ka.nc tmask_degla_T_bathyT_100yr_is_SH_9ka.nc Evolution of global mean temperature (degree C): Temperature_evolution_degla_P_bathy_500yr_is_SH_nobathy.txt Temperature_evolution_degla_T_bathyT_100yr_is_SH_nobathy.txt Temperature_evolution_degla_P_bathy_500yr_is_SH.txt Temperature_evolution_degla_P_bathy_500yr_is_SH_bis.txt Temperature_evolution_degla_T_bathyT_100yr_is_SH_nobathy.txt Temperature_evolution_degla_T_bathyT_500yr_is_SH.txt Temperature_evolution_degla_T_bathyT_100yr_is_SH.txt Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWF.txt Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWFtest3.txt Temperature_evolution_degla_T_bathyT_100yr_is_SH_FWFtest4.txt Temperature maps for time slices: temp_degla_P_bathy_500yr_is_SH_nobathy_21ka.nc temp_degla_T_bathyT_100yr_is_SH_nobathy_21ka.nc temp_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc temp_degla_T_bathyT_100yr_is_SH_nobathy_10ka.nc Evolution of salinity: iLOVECLIM_salinity_ICE-6G_C.nc iLOVECLIM_salinity_GLAC-1D.nc Temperature evolution at NGRIP location: iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH_nobathy.nc iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH.nc iLOVECLIM_t2m_NGRIP_degla_P_bathy_500yr_is_SH_bis.nc iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_nobathy.nc iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH.nc iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWF.nc iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWFtest3.nc iLOVECLIM_t2m_NGRIP_degla_T_bathyT_100yr_is_SH_FWFtest4.nc Temperature evolution at EDC location: iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH.nc iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_nobathy.nc iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH_nobathy.nc iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH.nc iLOVECLIM_t2m_EDC_degla_P_bathy_500yr_is_SH_bis.nc iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWF.nc iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWFtest3.nc iLOVECLIM_t2m_EDC_degla_T_bathyT_100yr_is_SH_FWFtest4.nc Evolution of surface albedo (all globe): iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH_nobathy.nc iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH.nc iLOVECLIM_alb_all_degla_P_bathy_500yr_is_SH_bis.nc iLOVECLIM_alb_all_degla_T_bathyT_100yr_is_SH_nobathy.nc iLOVECLIM_alb_all_degla_T_bathyT_100yr_is_SH.nc Evolution of surface albedo (Northern Hemisphere): iLOVECLIM_alb_NH_degla_T_bathyT_100yr_is_SH_nobathy.nc iLOVECLIM_alb_NH_degla_T_bathyT_100yr_is_SH.nc iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH_nobathy.nc iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH.nc iLOVECLIM_alb_NH_degla_P_bathy_500yr_is_SH_bis.nc Evolution of surface albedo (Southern Hemisphere): iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH_nobathy.nc iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH.nc iLOVECLIM_alb_SH_degla_P_bathy_500yr_is_SH_bis.nc iLOVECLIM_alb_SH_degla_T_bathyT_100yr_is_SH_nobathy.nc iLOVECLIM_alb_SH_degla_T_bathyT_100yr_is_SH.nc Evolution of sea ice area in the Northern Hemisphere (1e12 km2): iLOVECLIM_sea_ice_NH_degla_P_bathy_500yr_is_SH_nobathy.nc iLOVECLIM_sea_ice_NH_degla_P_bathy_500yr_is_SH.nc iLOVECLIM_sea_ice_NH_degla_T_bathyT_100yr_is_SH_nobathy.nc iLOVECLIM_sea_ice_NH_degla_T_bathyT_100yr_is_SH.nc Evolution of sea ice area in the Southern Hemisphere (1e12 km2): iLOVECLIM_sea_ice_SH_degla_P_bathy_500yr_is_SH_nobathy.nc iLOVECLIM_sea_ice_SH_degla_P_bathy_500yr_is_SH.nc iLOVECLIM_sea_ice_SH_degla_T_bathyT_100yr_is_SH_nobathy.nc iLOVECLIM_sea_ice_SH_degla_T_bathyT_100yr_is_SH.nc Winter sea ice fraction and mixed layer depth (m) at time slices: iLOVECLIM_sea_ice_mld_bathy_P_21000yr_SH_21ka.nc iLOVECLIM_sea_ice_mld_bathy_T_21000yr_SH_21ka.nc iLOVECLIM_sea_ice_mld_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc iLOVECLIM_sea_ice_mld_degla_T_bathyT_100yr_is_SH_nobathy_10ka.nc iLOVECLIM_sea_ice_mld_degla_P_bathy_500yr_is_SH_10ka.nc iLOVECLIM_sea_ice_mld_degla_T_bathyT_100yr_is_SH_10ka.nc Evolution of the maximum strength of AMOC: iLOVECLIM_AMOC_degla_P_bathy_500yr_is_SH_nobathy.nc iLOVECLIM_AMOC_degla_P_bathy_500yr_is_SH.nc iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_nobathy.nc iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH.nc iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWF.nc iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWFtest3.nc iLOVECLIM_AMOC_degla_T_bathyT_100yr_is_SH_FWFtest4.nc Meridional overtunring circulation at time slices: MOC_degla_P_bathy_500yr_is_SH_21ka.nc MOC_degla_P_bathy_500yr_is_SH_10ka.nc MOC_degla_P_bathy_500yr_is_SH_nobathy_10ka.nc MOC_degla_T_bathyT_100yr_is_SH_21ka.nc MOC_degla_T_bathyT_100yr_is_SH_10ka.nc

opencc-by-4.0Apr 2023View details →

dryad36/100

Data for: Diversity and specialization responses to climate and land use differ between deadwood fungi and bacteria

This dataset contains data from a field study conducted in 2019 and described in the manuscript "Diversity and specialization responses to climate and land use differ between deadwood fungi and bacteria". To test the effects of land use and climate on diversity and community specialization (H2') of deadwood-inhabiting microbes, we exposed branches of four different tree species at 179 study sites, distributed over a spatial extent of 300 km x 300 km and 1000 m in elevation. Study sites were established in four local land-use types: forests, grasslands, arable sites, and settlements, embedded in near-natural, agricultural, or urban landscapes. We used negative-binomial generalized linear models for diversity and calculated community specialization on host trees by using the standardized two-dimensional Shannon entropy (H2') and beta-regression models. Our results show that host identity and hence specialization strongly exceed the effects of climate and land use for fungal but not bacterial communities. This suggests contrasting responses between microbial taxa to climate change and land-use intensification with further consequences on deadwood diversity interactions and hence decomposition processes.

opencc-zeroJul 2023View details →

zenodo36/100

Prediction of potential suitable areas for Phoebe zhennan in future different climate scenarios

This dataset includes sample collection data of existing Phoebe zhennan in China, as well as historical climate data and future climate data (with a resolution of 2.5 minutes and using the BCC-CSM2-MR GCM model) collected by Worldclim, along with geographical elevation data. These data are used to predict the potential distribution range of Phoebe zhennan in the future.

opencc-by-4.0Jul 2023View details →

dryad36/100