• Energy Research
• NL close
• FR close
• DK close
• English close

1. Natural populations deliver a wide range of products that provide income for millions of people and need to be exploited sustainably. Large heterogeneity in individual performance within these exploited populations has the potential to improve population recovery after exploitation and thus help sustaining yields over time. 2. We explored the potential of using individual heterogeneity to design smarter harvest schemes, by sparing individuals that contribute most to future productivity and population growth, using the understorey palm Chamaedorea elegans as a model system. Leaves of this palm are an important non-timber forest product and long-term inter-individual growth variability can be evaluated from internode lengths. 3. We studied a population of 830 individuals, half of which was subjected to a 67 % defoliation treatment for three years. We measured effects of defoliation on vital rates and leaf size – a trait that determines marketability. We constructed integral projection models in which vital rates depended on stem length, past growth rate, and defoliation, and evaluated transient population dynamics to quantify population development and leaf yield. We then simulated scenarios in which we spared individuals that were either most important for population growth or had leaves smaller than marketable size. 4. Individuals varying in size or past growth rate responded similarly to leaf harvesting in terms of growth and reproduction. By contrast, defoliation-induced reduction in survival chance was smaller in large individuals than in small ones. Simulations showed that harvest-induced population decline was much reduced when individuals from size and past growth classes that contributed most to population growth were spared. Under this scenario cumulative leaf harvest over 20 years was somewhat reduced, but long-term leaf production was sustained. A three-fold increase in leaf yield was generated when individuals with small leaves are spared. 5. Synthesis and applications This study demonstrates the potential to create smarter systems of palm leaf harvest by accounting for individual heterogeneity within exploited populations. Sparing individuals that contribute most to population growth ensured sustained leaf production over time. The concepts and methods presented here are generally applicable to exploited plant and animal species which exhibit considerable individual heterogeneity. Vital rate and internode dataThis data file contains annual vital rate data (stem length growth, fruit production, survival and leaf production) of 830 individuals of the understorey palm Chamaedorea elegans, collected in a 0.7 ha plot in Chiapas, Mexico, during the period November 2012 - November 2015. A 2/3 defoliation treatment was repeatedly applied to half of the individuals. The data file also contains measurements of the lengths of all internodes of all individuals.

# Bird\_breeding\_productivity\_data [https://doi.org/10.5061/dryad.fxpnvx0zt](https://doi.org/10.5061/dryad.fxpnvx0zt) This folder contains data sets (**Bird_prod_data.csv, Clim_mean_prod_lin.csv, Clim_mean_prod_poly.csv, Clim_trend_PCA_prod_lin.csv, Clim_trend_PCA_prod_poly.csv**), models (.rds files; see below for their naming scheme) and code (**R-script_bird_prod.R**) related to the article: *Climatic predictors of long-distance migratory birds’ breeding productivity across Europe* ## Description of the data and file structure The data is stored in subfolder "Data" **Bird_prod_data.csv** * *Reg*: breeding region; CZP = the Czech Republic, DEG-DKC = Germany and Denmark, ESP = Spain, FRP_N = northern part of France, FRP_S = central & southern part of France, GBT_N = northern parts of Great Britain – Wales and England, Scotland, Northern Ireland – and Ireland, GBT_S = southern parts of Great Britain – England and Wales, HGB = Hungary, NLA = the Netherlands, SFH = Finland, SVS = Sweden - *EURING*: species code * *Year*: year corresponding to breeding season - *Species*: species name (see also Table 3 in the article) * *Site*: site code - *Ad*: number of adults * *Juv*: number of juveniles - *TotalEPR*: water availability in wintering grounds (called ETr in the article) * *Ad_scaled*: Number of adults standardized to mean = 0 and SD = 1 for each species and site - *T3, T4, T5, T6*: temperature in March, April, May, June * *GDD10_3, GDD10_4, GDD10_5, GDD10_6*: growing degree-days in March, April, May, June - *GOD*: green-up onset date * *Rain_anom_3, Rain_anom_4, Rain_anom_5, Rain_anom_6*: precipitation anomaly in March, April, May, June, abbreviated as ΔR in the article - *R10_5, R10_6*: number of heavy rain days in May, June * *R20_5, R20_6*: number of very heavy rain days in May, June - *R1c_5, R1c_6*: number of consecutive rain days 1mm in May, June * *R2c_5, R2c_6*: number of consecutive rain days 2mm in May, June **Clim_mean_prod_lin.csv** * *reg*: breeding region - *clim_var*: abbreviation of climate variable * *mean_val*: mean value of the climate variable - *Est_prod_lin*: estimate of the linear term in the relationship between breeding productivity and climate variable * *SE_prod_lin*: standard error of the estimate of the linear term in the relationship between breeding productivity and climate variable **Clim_mean_prod_poly.csv** * *reg*: breeding region - *clim_var*: abbreviation of climate variable * *mean_val*: mean value of the climate variable - *Est_prod_poly*: estimate of the quadratic term in the relationship between breeding productivity and climate variable * *SE_prod_poly*: standard error of the estimate of the quadratic term in the relationship between breeding productivity and climate variable **Clim_trend_PCA_prod_lin.csv** * *reg*: breeding region - *clim_change*: climate warming variable derived from the first axis of PCA (Principal Component Analysis), for months of March, April, May, June * *Est_trend*: slope of the linear temporal trend of climate warming variable over the study period **Clim_trend_PCA_prod_poly.csv** * reg: breeding region - clim_change: climate warming variable derived from the first axis of PCA (Principal Component Analysis), for months of March, April, May, June * Est_trend: slope of the quadratic temporal trend of climate warming variable over the study period Fitted models (88 files) are stored in subfolder "Models" Naming scheme of the models is: **Hyp2 or Hyp3**: models for testing Hypothesis 2 or Hypothesis 3, respectively **resp1 or resp2**: response variable of the model was derived from the relationship between breeding productivity and the linear term of the climate variable (i.e. *Est_prod_lin*, see above in Clim_mean_prod_lin.csv) or the quadratic term of the climate variable (i.e. *Est_prod_poly*, see above in Clim_mean_prod_poly.csv), respectively **lin or poly**: models employ linear or polynomial (quadratic) terms of climate variables, respectively **T, GDD10, ΔR, GOD**: climate variables used in testing Hypothesis 2 or Hypothesis 3, i.e. temperature, growing degree-days, precipitation anomaly, and green-up onset date, respectively **3, 4, 5, 6**: months of March, April, May, or June **warm_PCA1** (for Hypothesis 3 only): climate warming variable was derived from the first axis of PCA (Principal Component Analysis), suffixes 3, 4, 5 or 6 means months of March, April, May, and June ## Code/Software The code file "R-script_bird_prod.R" is an R script created by version 4.3.1, allowing to run all our analyses. It consists of the following parts: * loading the libraries * loading the data set Bird_prod_data.csv and preparing the variables for testing Hypothesis 1 * fitting the models for testing Hypothesis 1 * performing the model averaging * extraction of the marginal effects of climate variables * calculation of the temporal variance explained by climate variables * loading the data sets Clim_mean_prod_lin.csv and Clim_mean_prod_poly.csv and preparing the variables for testing Hypothesis 2 * fitting the models for testing Hypothesis 2 * extraction of parameters from the fitted models * loading the data sets Clim_trend_PCA_prod_lin.csv and Clim_trend_PCA_prod_poly.csv and preparing the variables for testing Hypothesis 3 * fitting the models for testing Hypothesis 3 * extraction of parameters from the fitted models Ongoing climate changes represent a major determinant of demographic processes in many organisms worldwide. Birds, and especially long-distance migrants, are particularly sensitive to such changes. To better understand these impacts on long-distance migrants’ breeding productivity, we tested three hypotheses focused on (i) the shape of the relationships with different climate variables, including previously rarely tested quadratic responses, and on regional differences in these relationships predicted by (ii) mean climatic conditions and (iii) by the rate of climate change in respective regions ranging from Spain to Finland. We calculated breeding productivity from constant effort ringing sites from 11 European countries covering 34 degrees of latitude, and extracted temperature- and precipitation-related climate variables from E-OBS and NASA MODIS datasets. To test our hypotheses, we fitted GLMM and Bayesian meta-analytic models. We revealed hump-shaped responses of productivity to temperature, growing degree-days, green-up onset date, and precipitation anomaly, and negative responses to intense and prolonged rains across the regions. The effects of March temperature and April growing degree-days were more negative in cold than in warm regions, except that one with the highest accumulated heat, whereas increasing June precipitation anomalies were associated with higher productivity in both dry and wet regions. The rate of climate warming was unrelated to productivity responses to climate. The influence of climate on bird productivity proved to be frequently non-linear, as expected by ecological theory. To explain the differences between regions, the rate of climate change is less important than regional interannual variability in climate (which is predicted to increase), but this may change with the progression of climate change in the future. Productivity declines in long-distance migratory songbirds are particularly expected if out-of-norm water excess increases in frequency or strength.

The date sets compiled and analyzed the gender and social aspects of development and related policies in Vietnam’s Mekong River Delta.

The aim of this study was to identify the attitudes of the public in the United States and in 12 European countries towards foreign policy issues and transatlantic issues. The survey concentrated on issues such as: United States and European Union (EU) leadership and relations, favorability towards certain countries, institutions and people, security, cooperation and the perception of threat including issues of concern with Afghanistan, Iran, and Russia, energy dependence, economic downturn, and global warming, Turkey and Turkish accession to the EU, promotion of democracy in other countries, and the importance of economic versus military power. Several questions asked of respondents pertained to voting and politics including whether they discussed political matters with friends and whether they attempted to persuade others close to them to share their views on politics which they held strong opinions about, vote intention, their assessment of the current United States President and upcoming presidential election, political party attachment, and left-right political self-placement. Demographic and other background information includes age, gender, race, ethnicity, religious affiliation and participation, age when stopped full-time education and stage at which full-time education completed, occupation, number of people aged 18 years and older living in the household, type of locality, region of residence, prior travel to the United States or Europe, and language of interview. computer-assisted personal interview (CAPI); computer-assisted telephone interview (CATI); paper and pencil interview (PAPI)The original data collection was carried out by TNS, Fait et Opinion -- Brussels on request of the German Marshall Fund of the United States.The codebook and setup files for this collection contain characters with diacritical marks used in many European languages.A split ballot was used for one or more questions in this survey. The variable SPLIT defines the separate groups.For data collection, the computer-assisted face-to-face interview was used in Poland, the paper and pencil interview was used in Bulgaria, Romania, Slovakia and Turkey, and the computer-assisted telephone interview was used in all other countries.Additional information on the Transatlantic Trends Survey is provided on the Transatlantic Trends Web site. (1) Multistage random sampling was implemented in the countries using face-to-face interviewing. Sampling points were selected according to region, and then random routes were conducted within these sampling points. Four callbacks were used for each address. The birthday rule was used to randomly select respondents within a household. (2) Random Digit Dialing was implemented in the countries using telephone interviewing. Eight callbacks were used for each telephone number. The birthday rule was used to randomly select respondents within a household. The adult population aged 18 years and over in 13 countries: Bulgaria, France, Germany, Italy, the Netherlands, Poland, Portugal, Romania, Slovakia, Spain, Turkey, the United Kingdom, and the United States. Smallest Geographic Unit: country Response Rates: The total response rate for all countries surveyed is 23 percent. Please refer to the "Technical Note" in the ICPSR codebook for additional information about response rate. Please refer to the "Technical Note" in the ICPSR codebook for further information about weighting. Datasets: DS1: Transatlantic Trends Survey, 2008

Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format TL;DR: this is a nationally aggregated hourly dataset for the capacity factors per unit installed capacity for storage hydropower plants and run-of-river hydropower plants in the European region. All the data is provided for 30 climatic years (1981-2010). Method Description The hydro inflow data is based on historical river runoff reanalysis data simulated by the E-HYPE model. E-HYPE is a pan-European model developed by The Swedish Meteorological and Hydrological Institute (SMHI), which describes hydrological processes including flow paths at the subbasin level. E-hype only provides the time series of daily river runoff entering the inlet of each European subbasin over 1981-2010. To match the operational resolution of the dispatch model, we linearly downscale these time series to hourly. By summing up runoff associated with the inlet subbasins of each country, we also obtain the country-level river runoff. The hydro inflow time series per country is defined as the normalized energy inflows (per unit installed capacity of hydropower) embodied in the country-level river runoff. A dispatch model can be used to decides whether the energy inflows are actually used for electricity generation, stored, or spilled (in case the storage reservoir is already full). Data coverage This dataset considers two types of hydropower plants, namely storage hydropower plant (STO) and run-of-river hydropower plant (ROR). Not all countries have both types of hydropower plants installed (see table). The countries and their acronyms for both technologies included in this dataset are: Country Run-of-River Storage Austria AT_ROR AT_STO Belgium BE_ROR BE_STO Bulgaria BG_ROR BG_STO Switzerland CH_ROR CH_STO Cyprus CZ_ROR CZ_STO Germany DE_ROR DE_STO Denmark DK_ROR Estonia EE_ROR Greece EL_ROR EL_STO Spain ES_ROR ES_STO Finland FI_ROR FI_STO France FR_ROR FR_STO Great Britain GB_ROR GB_STO Croatia HR_ROR HR_STO Hungary HU_ROR HU_STO Ireland IE_ROR IE_STO Italy IT_ROR IT_STO Luxembourg LU_ROR Latvia LV_ROR the Netherlands NL_ROR Norway NO_ROR NO_STO Poland PL_ROR PL_STO Portugal PT_ROR PT_STO Romania RO_ROR RO_STO Sweden SE_ROR SE_STO Slovenia SI_ROR SI_STO Slovakia SK_ROR SK_STO Data structure description The files is provided in CSV (.csv) format with a comma (,) as separator and double-quote mark (") as text indicator. The first row stores the column labels. The columns contain the following: first column (or A) contains the row number Label: unlabeled Contents: interger range [1,262968] second column (or B) contains the valid-time Label: T1h Contents represent time with text as [DD/MM/YYYY HH:MM]) column 3-52 (or C-AY) each contain the capacity factor for each valid combination of a country and hydropower plant type Label: XX_YYY the two letter country code (XX) and the hydropower plant type (YYY) acronym for storage hydropower plant (STO) and run-of-river hydropower plant (ROR) Contents represent the capacity factor as a floating value in the range [0,1], the decimal separator is a point (.). DISCLAIMER: the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. The raw hydro data was generated as part of 'Evaluating sediment Delivery Impacts on Reservoirs in changing climaTe and society across scales and sectors (DIRT-X)', this project and therefor, Jing hu, received funding from the European Research Area Network (ERA-NET) under grant number 438.19.902. Laurens P. Stoop received funding from the Netherlands Organization for Scientific Research (NWO) under Grant No. 647.003.005.

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.C4MIP.CNRM-CERFACS.CNRM-ESM2-1' 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.

Densification has been carried out for many years, mostly in biomass processing, animal feed production, and pharmaceutical industries. During the years, researchers and engineers attempted to improve the product quality and minimize the production costs. The most important quality parameters of solid bio-materials are the compressive strength, abrasion resistance, impact resistance, moisture adsorption, and density. Various studies used different standard and non-standard methods to characterize these quality parameters. The objective of this paper is twofold: (1) to investigate the state-of-the-art methods and devices used in the quality assessment of densified bio-materials, including a comparison between non-standard and standard methods. (2) to discuss the effect of different factors on the properties of densified bio-materials using an integrated approach. The results show a lack of standard methods for the quality assessment of bio-materials and therefore, there is an emerging need for development of dedicated standards for bio-materials. Moreover, the use of dissimilar methods and devices in the quality assessment of bio-materials gives risk to uncertainties about the effect of different factors on the product quality.

Due to the shift in electrical energy generation from thermal synchronous generators towards various renewable sources, power system stability will become a more pressing issue in the near future. In this thesis, we explore possible improvements to the dynamic grid model currently used by TenneT TSO for large-disturbance stability studies, specifically the addition of motor load, wind, solar PV and HVDC transmission modelling. Thus we pave the way for future, more in-depth research that can contribute to the development of a more sophisticated dynamic grid model for operational and planning use. Our results indicate that motor load modelling has a strong negative influence on grid dynamic performance (compared to a static representation of the same load), and that wind and PV models have a strong positive influence, but that particularly for wind models, the choice of appropriate model parameters remains a challenge. ; Electrical Engineering | Electrical Power Engineering

International audience In today’s consumer society, shopping is the kind of mundane and routine kind of mobility that we all engage in. Yet having a first child or growing old radically changes people’s logistical habits as consumers, what the authors of this book call consumer logistics. Exploring consumer mobility through the lens of life phase and age will deepen the understanding of hitherto under-researched aspects of the ageing process and of mobility, knowledge that is of vital importance for societies striving for sustainable mobility and sustainable cities.