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36 results for “energy budgets”
Data from: Accelerometers can measure total and activity-specific energy expenditure in free-ranging marine mammals only if linked to time-activity budgets
Energy expenditure is an important component of foraging ecology, but is extremely difficult to estimate in free-ranging animals and depends on how animals partition their time between different activities during foraging. Acceleration data have emerged as a new way to determine energy expenditure at a fine scale but this needs to be tested and validated in wild animals. This study investigated whether vectorial dynamic body acceleration (VeDBA) could accurately predict the energy expended by marine predators during a full foraging trip. We also aimed to determine whether the accuracy of predictions of energy expenditure derived from acceleration increased when partitioned by different types of at-sea activities (i.e. diving, transiting, resting and surface activities). To do so, we equipped 20 lactating northern (Callorhinus ursinus) and 20 lactating Antarctic fur seals (Arctocephalus gazella) with GPS, time-depth recorders and tri-axial accelerometers and obtained estimates of field metabolic rates using the doubly labelled water (DLW) method. VeDBA was derived from tri-axial acceleration, and at-sea activities (diving, transiting, resting and surface activities) were determined using dive depth, tri-axial acceleration and travelling speed. We found that VeDBA did not accurately predict the total energy expended by fur seals during their full foraging trips (R2 = 0·36). However, the accuracy of VeDBA as a predictor of total energy expenditure increased significantly when foraging trips were partitioned by activity and when activity-specific VeDBA was paired with time-activity budgets (R2 = 0·70). Activity-specific VeDBA also accurately predicted the energy expenditures of each activity independent of each other (R2 > 0·85). Our study confirms that acceleration is a promising way to estimate energy expenditures of free-ranging marine mammals at a fine scale never attained before. However, it shows that it needs to be based on the time-activity budgets that make up foraging trips rather than being derived as a single measure of VeDBA applied to entire foraging trips. Our activity-based method provides a cost-effective means to accurately calculate energy expenditures of fur seals using acceleration and time-activity budgets, that can be transfered to studies on other species.
Script and data of "Role of Frictional Processes in Mesoscale Eddy Available Potential Energy Budget in the Global Ocean"
<p>% File description:</p> <p>1. Cal_conversions.m: a set of functions calculating the EAPE-EKE and EAPE-EKE conversion terms with CESM output data in B-grid</p> <p>2. smooth2a.m: function of boxcar filtering</p> <p>3. CONV_u100_2d.mat: data of the global distribution of upper 100 m averaged conversion terms used in Figure 2 of the manuscript<br> % Variables inside the file:<br> CONVa_H_u100: MAPE-EAPE conversion driven by frictional process<br> CONVo_H_u100: MAPE-EAPE conversion driven by non-frictional process<br> CONVa_V_u100: EAPE-EKE conversion driven by frictional process<br> CONVo_V_u100: EAPE-EKE conversion driven by non-frictional process</p> <p>4. CONV_profile.mat: data of the vertical profiles of global and regional averaged EAPE-EKE conversion terms used in Figure 3 of the manuscript<br> % Variables inside the file:<br> % Vertical profiles of quasi-global-averaged EAPE-EKE conversion <br> CONVa_V_GLO_profile: driven by frictional process<br> CONVo_V_GLO_profile: driven by non-frictional process<br> CONVttw_V_GLO_profile: reproduced by TTW balance <br> <br> % Vertical profiles of EAPE-EKE conversion averaged in western boundary current regions<br> CONVa_V_WBCE_profile: driven by frictional process<br> CONVo_V_WBCE_profile: driven by non-frictional process<br> CONVttw_V_WBCE_profile: reproduced by TTW balance </p> <p> % Vertical profiles of EAPE-EKE conversion averaged in subtropical gyres<br> CONVa_V_STG_profile: driven by frictional process<br> CONVo_V_STG_profile: driven by non-frictional process<br> CONVttw_V_STG_profile: reproduced by TTW balance <br> <br> % Vertical profiles of EAPE-EKE conversion averaged in subpolar gyres<br> CONVa_V_SPG_profile: driven by frictional process<br> CONVo_V_SPG_profile: driven by non-frictional process<br> CONVttw_V_SPG_profile: reproduced by TTW balance </p> <p> % Vertical profiles of EAPE-EKE conversion averaged in the Southern Ocean<br> CONVa_V_SO_profile: driven by frictional process<br> CONVo_V_SO_profile: driven by non-frictional process<br> CONVttw_V_SO_profile: reproduced by TTW balance </p> <p>5. CONV_SeasDiff.mat: data of the seasonal difference (winter minus summer) of global and regional averaged conversion terms used in Figure 3 of the manuscript<br> % Variables inside the file:<br> % Vertical profiles of the seasonal difference of quasi-global-averaged EAPE-EKE conversion <br> CONVa_V_GLO_SeasDiff: driven by frictional process<br> CONVo_V_GLO_SeasDiff: driven by non-frictional process<br> CONVttw_V_GLO_SeasDiff: reproduced by TTW balance <br> <br> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in western boundary current regions<br> CONVa_V_WBCE_SeasDiff: driven by frictional process<br> CONVo_V_WBCE_SeasDiff: driven by non-frictional process<br> CONVttw_V_WBCE_SeasDiff: reproduced by TTW balance </p> <p> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in subtropical gyres<br> CONVa_V_STG_SeasDiff: driven by frictional process<br> CONVo_V_STG_SeasDiff: driven by non-frictional process<br> CONVttw_V_STG_SeasDiff: reproduced by TTW balance <br> <br> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in subpolar gyres<br> CONVa_V_SPG_SeasDiff: driven by frictional process<br> CONVo_V_SPG_SeasDiff: driven by non-frictional process<br> CONVttw_V_SPG_SeasDiff: reproduced by TTW balance </p> <p> % Vertical profiles of the seasonal difference of EAPE-EKE conversion averaged in the Southern Ocean<br> CONVa_V_SO_SeasDiff: driven by frictional process<br> CONVo_V_SO_SeasDiff: driven by non-frictional process<br> CONVttw_V_SO_SeasDiff: reproduced by TTW balance </p> <p>6. Coord_lon_lat_zw.mat: coordinate information for the variables in "CONV_u100_2d.mat", "CONV_profile.mat"and "CONV_SeasDiff.mat"<br> % Variables inside the file:<br> lon: longitude for the global distributions of the conversion terms<br> lat: latitude for the global distributions of the conversion terms<br> z_w: depth of each vertical level for vertical profiles of conversion terms</p>
Velocity, density and energy budget statistics from the article: Validation and application of the lattice Boltzmann algorithm for a turbulent immiscible Rayleigh-Taylor system
<p>We develop a multicomponent lattice Boltzmann (LB) model for the 2D Rayleigh--Taylor turbulence with a Shan-Chen pseudopotential implemented on GPUs. Accuracy of the LB model is tested both for early and late stages of instability. For the developed turbulent motion we analyze the balance between different terms describing variations of the kinetic and potential energies. Then, we analyze the role of interface in the energy balance, and also the effects of the vorticity induced by the interface in the energy dissipation. Statistical properties are compared for miscible and immiscible flows.</p> <p>In this Dataset, we have included the files for the statistics of the energy budget for the miscible and immiscible Rayleigh-Taylor flows studied in the referred article.</p> <p>We also included the examples of the density and velocity fields showed in the Figures 1 and 2 of the article.</p>
Micro data from the Greek Household Budget Survey on energy consumption
<p>The dataset is the end product of compiling a number of datasets from the Greek Household Budget Survey, covering 13 years between 2004 and 2020. The variables included are basic socio-economic, demographic and housing characteristics, along with quantity and cost data of household energy sources.</p>
Velocity, density and energy budget statistics from the article: Validation and application of the lattice Boltzmann algorithm for a turbulent immiscible Rayleigh-Taylor system
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Data from: Regulation of reproductive processes with Dynamic Energy Budgets
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Data from: Accelerometers can measure total and activity-specific energy expenditure in free-ranging marine mammals only if linked to time-activity budgets
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Data from: Substantial intraspecific variation in energy budgets: biology or artefact?
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Data from: Hummingbirds budget energy flexibly in response to changing resources
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Data sets used in the manuscript titled "Ecosystem-level energy and water budgets are resilient to canopy mortality in sparse semi-arid biomes"
<p><span>This data set reports water and energy fluxes, soil water content and sap fluxes measured at two adjacent pinon-juniper woodlands in central New Mexico from January 2009 to December 2016. This data set was used for the analysis in the manuscript titled "Ecosystem-level energy and water budgets are resilient to canopy mortality in sparse semi-arid biomes" submitted to JGR-Biogeosciences. </span></p>
Supporting model data for paper: Measuring the impact of a new snow model using surface energy budget process relationships
<p>Supporting model data for paper: Measuring the impact of a new snow model using surface energy budget process relationships which has been submitted to the Journal of Advances in Modelling Earth Systems: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020MS002144</p> <p>The experiment id h3hh corresponds to simulations with the ECMWF IFS with a single layer snow model. h3eg corresponds to the experimental 5-layer snow model.</p> <p>The timeseries are made by concatenating hourly data from day2 of forecasts initialised at 00UTC each day between Dec 1st 2013 and 1 June 2014.</p>
Comprehensive result data for a pathway for the German energy sector compatible with a 1.5°C carbon budget
<p>This data set covers the results of an energy scenario for Germany within a 1.5°C carbon budget. It represents all relevant results from an energ system modelling excersise, coupling two complementary models.</p> <p>The data set consists of two excel files</p> <ul> <li>Comprehensive results of the energy balance based Energy System Model (ESM) for the heat, transport and power sectors for Germany, disaggregated by consumption sectors (residential, service & commerce, industry, transport)</li> <li>Comprehensive results of the linear optimization energy system model REMix for power, heat and sector coupling</li> </ul> <p>Methodology, models and scenario assumption are detailed in:</p> <p>Simon, S., Xiao, M., Harpprecht, C., Pregger, T., Gardian, H., & Sasanpour, S. (submitted). A pathway for the German energy sector compatible with a 1.5°C carbon budget. <em>Sustainability</em>.</p>
Lean mass dynamics in hibernating bats and implications for energy and water budgets
<p>Hibernation requires balancing energy and water demands over several months with no food intake for many species. Many studies have considered the importance of fat for hibernation energy budgets because it is energy dense and can be stored in large quantities. However, protein catabolism in hibernation has received less attention and whole animal changes in lean mass have not previously been considered. We used quantitative magnetic resonance body composition analysis to measure fat and lean mass in two systems of hibernating bats, emphasizing the importance of lean mass for energy and water budgets. For cave myotis ( Myotis velifer ), lean mass represented 38 and 25% (male and female respectively) of pre-hibernation mass gain. In Townsend's big-eared bats ( Corynorhinus townsendii ), lean mass accounted for 18 – 35% of mass change during hibernation, but lean only contributed 3 – 7% of the energy budget. Water is produced from the catabolism of both fat and lean, but net water production is much less than gross water production when accounting for the water required to excrete urea. Although most mammals can't rely on protein catabolism for metabolic water production due to the water cost of excreting urea, we propose a variation on the protein-for-water strategy whereby hibernators could temporally compartmentalize the benefits of protein catabolism to periods of torpor, and the water cost to periodic arousals when free drinking water is typically available. Combined, our analyses demonstrate that lean mass is dynamic in hibernation, with important functional consequences for both energy and water budgets. </p>
Lean mass dynamics in hibernating bats and implications for energy and water budgets
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Data sets used in the manuscript titled "Ecosystem-level energy and water budgets are resilient to canopy mortality in sparse semi-arid biomes"
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Measured wave, eddy-covariance and energy budget data
<p>Measurement data of wave, eddy-covariance and energy budget in Lake Balaton, Hungary</p> <p>May - Oct 2019</p>
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