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Data from: Mortality limits used in wind energy impact assessment underestimate impacts of wind farms on bird populations
<p>In this archive we share the data and R code used for the construction of population models for seven bird species (Common Starling, Black-tailed Godwit<strong>,</strong> Marsh Harrier, Eurasian Spoonbill, White Stork, Common Tern and White-tailed Eagle) for our assessment of the effects of wind farms (Schippers et al. 2020). In most cases we parameterized our population models based on species-specific survival and reproduction rates from scientific articles and reports, but in the case of the Western Marsh Harrier we analyzed previously unpublished nest success and capture-mark-resighting data. Below we first describe per species which data we used for model parameterization, and then describe per data file what each variable represents.</p> <p>We selected populations of seven species based on the availability of data, considerable likelihood to collide with wind turbines and contrasting ages of first reproduction. For species for which long time series of demographic data were available with population trends clearly changing over time, we separately assessed periods with contrasting population trends, as detailed in the species descriptions below. Mean survival and reproduction rates, standard deviations and additional information like the age of first reproduction can be found in the accompanying paper by Schippers et al. (2020). </p> <p> </p> <p><strong>Common Starling</strong></p> <p>On the fast-slow continuum of reproductive capacity, the common starling is the fastest of the seven species we selected: it starts reproducing at an age of one year. We used the mean survival and reproductive rates for the whole Dutch breeding population (Versluijs et al. 2016), distinguishing three separate periods: 1960-1978, 1978-1990 and 1990-2012. In the first period (1960-1978) the population grew at 10% per year. This was followed by a period where the population was relatively stable (1978-1990). During the last period (1990-2012) the population declined strongly.</p> <p> </p> <p><strong>Black tailed Godwit</strong></p> <p>Kentie et al. (2017) studied two Dutch populations of the Black-tailed Godwit in southwestern Fryslân (Skriezekrite and Kuststrook) over four to five annual transitions (Kentie et al. 2017). Godwits started reproducing at age two, but only had 0.5-0.6 fledglings per breeding pair per year. The adults are rather long-lived with an 86% annual survival rate. We construct separate matrix models for the two populations.</p> <p> </p> <p><strong>Marsh Harrier</strong></p> <p>Mean vital rates of the Dutch breeding population of Marsh Harriers were estimated for 1997-2015 using respectively ring recoveries available at the Dutch Centre for Avian Migration and Demography NIOO-KNAW and reproduction data from the Dutch Raptor Working Group. Annual survival of Marsh Harriers was analyzed using live re-sightings and dead recoveries of 12,059 birds ringed as nestling between 1991 and 2016 and 74 birds ringed as ‘adult’ in the same period (due to low sample sizes, birds ringed in their first and second calendar year were lumped with older birds in the ‘adult’ category; see ‘marshHarrierSurvival.csv’ below). Nest success was estimated using data of 1914 nests, which were followed from the beginning to the end of the nest cycle, in the Netherlands between 1997 and 2015 (see ‘marshHarrierReproduction.csv’ below; we thank Rob G. Bijlsma for making the data available). </p> <p> </p> <p><strong>Spoonbill</strong></p> <p>For each year in the 1994-2008 period, age-specific (first-year, second-year, third-year, older) annual survival rates were derived for the Dutch Spoonbill population from van der Jeugd et al. (2014). Participation in the breeding population was 0% in the first three years and went up from 63% at age four to 95% at age 6 and older.</p> <p> </p> <p><strong>White Stork</strong></p> <p>Schaub et al. (2004) analyzed demographic data on White Storks in Switzerland from 1977 till 2000. Here we extracted annual survival and reproduction rates from the COMADRE Animal Matrix Database (version 2.0.1; Salguero-Gómez et al., 2016). Storks start reproducing at age 3, with breeding participation increasing with age from 48% to 100%. </p> <p> </p> <p><strong>Common Tern</strong></p> <p>For the Common Tern we used mean vital rate estimates published by van der Jeugd et al. (2014) for the Dutch Waddenzee population, including the Northern part of the IJsselmeer, between 2000 and 2010 (van der Jeugd et al. 2014). The total Waddenzee and IJsselmeer population is estimated at 7,630 pairs (average population 2010-2014), constituting approximately 40% of the Dutch breeding population of about 20,000 pairs (Sovon 2016). </p> <p> </p> <p><strong>White-tailed Eagle</strong></p> <p>Krüger et al. (2010) published demographic data on White-tailed Eagles in Schleswig-Holstein, Germany, over the period 1947 till 2008. Following these authors, and based on the two matrices in COMADRE v.2.0.1 (Salguero-Gómez et al., 2016), we used separate matrix models for the early period (stable population dynamics) and from 1975 onwards (population growth). These eagles start reproducing at age five. </p> <p> </p> <p>Here we describe the archived files:</p> <p> </p> <p><strong>matrices.R</strong></p> <p>This annotated R file details how the vital rate estimates are used to construct age-structured, post-breeding-census, one-year-timestep population matrix models. In these so-called post-breeding census models the birds in the first class were 0 years old (Caswell 2001).</p> <p> </p> <p><strong>commonstarling19602012.csv</strong></p> <p>Mean survival and reproductive rates for the whole Dutch breeding population of Common Starlings for the time period 1960-2012. </p> <p>year = start year</p> <p>juvSurv = first-year survival of fledgelings</p> <p>adultSurv = annual survival of older birds</p> <p>fec = number of fledgelings per pair (which have a 1:1 sex ratio)</p> <p> </p> <p><strong>blacktailedgodwit20112016.csv</strong></p> <p>Mean survival and reproduction rates of the Black-tailed Godwit in southwestern Fryslân (populations Skriezekrite and Kuststrook) over four to five annual transitions in the period 2011-2016. </p> <p>pop = population</p> <p>startYear = start year</p> <p>adultSurv = annual survival of older birds</p> <p>chickSurv = first-year survival of chicks</p> <p>nestSuc = probability that a nest is successful</p> <p> </p> <p><strong>marshharrier19972015.csv</strong></p> <p>Mean vital rates of the Dutch breeding population of Western Marsh Harriers for 1997-2015.</p> <p>year = start year</p> <p>r = number of fledgelings per pair</p> <p>s1 = first-year survival of fledgelings</p> <p>s2 = annual survival of older birds</p> <p> </p> <p><strong>marshharrierreproduction.csv</strong></p> <p>Western Marsh Harrier nest record data of in the Netherlands.</p> <p>year = year</p> <p>clutchSize = number of eggs</p> <p>young = number of chicks (if known)</p> <p>fledgelings = number of fledgelings</p> <p> </p> <p> </p> <p> </p> <p><strong>marshharriersurvival.csv</strong></p> <p>Ringing and resighting data (using EURING coding) on Western Marsh Harriers in the Netherlands. </p> <p>ringID = ring identifier</p> <p>date = observation date</p> <p>metalRingInformation</p> <p>1 = Metal ring added (where no metal ring was present), position (on tarsus or above) unknown or unrecorded.</p> <p>2 = Metal ring added (where no metal ring was present), definitely on tarsus.</p> <p>3 = Metal ring added (where no metal ring was present), definitely above tarsus.</p> <p>4 = Metal ring is already present.</p> <p>condition </p> <p>0 = Condition completely unknown.</p> <p>1 = Dead but no information on how recently the bird had died (or been killed).</p> <p>2 = Freshly dead – within about a week.</p> <p>3 = Not freshly dead – information available that it had been dead for more than about a week.</p> <p>4 = Found sick, wounded, unhealthy etc. and known to have been released (including ring or other mark identified on a bird in poor condition without the bird having being caught).</p> <p>5 = Found sick, wounded, unhealthy etc. and not released or not known if released.</p> <p>6 = Alive and probably healthy but taken into captivity.</p> <p>7 = Alive and probably healthy and certainly released (including ring or other mark identified on a healthy bird without the bird having being caught).</p> <p>8 = Alive and probably healthy and released by a ringer (including ring or other mark identified on the bird by a ringer without the bird having being caught). </p> <p>ageReported </p> <p>0 = Age unknown, i.e. not recorded.</p> <p>1 = Pullus: nestling or chick, unable to fly freely, still able to be caught by hand.</p> <p>2 = Full-grown: able to fly freely but age otherwise unknown.</p> <p>3 = First-year: full-grown bird hatched in the breeding season of this calendar year.</p> <p>4 = Afer first-year: full-grown bird hatched before this calendar year; year of hatching otherwise unknown.</p> <p>5 = 2<sup>nd</sup> year: a bird hatched last calendar year and now in its second calendar year.</p> <p>6 = Afer 2<sup>nd</sup> year: full-grown bird hatched before last calendar year; year of hatching otherwise unknown.</p> <p>7 = 3<sup>rd</sup> year: a bird hatched two calendar years before, and now in its third calendar year.</p> <p>8 = Afer 3<sup>rd</sup> year: a full-grown bird hatched more than three calendar years ago (including present year as one); year if bird otherwise unknown.</p> <p>9 = 4<sup>th</sup> year: a bird hatched three calendar years before, and now in its fourth calendar year.</p> <p>A = Afer 4<sup>th</sup> year: a bird older than category 9 – age otherwise unknown.</p> <p>sexReported</p> <p>U = Unknown</p> <p>M = Male</p> <p>F = Female</p> <p> </p> <p><strong>eurasianspoonbill19942008.csv</strong></p> <p>For each year in the 1994-2008 period, age-specific (first-year, second-year, third-year, older) annual survival rates are given for the Dutch Spoonbill population.</p> <p>year = start year</p> <p>fled = number of fledgelings per breeding pair</p> <p>s1 = first-year survival rate</p> <p>s2 = second-year survival rate</p> <p>s3 = third-year survival rate</p> <p>s4 = older birds' annual survival rate</p> <p> </p> <p><strong>whitestork19772000.csv</strong></p> <p>Demographic data on White Storks in Switzerland from 1977 till 2000.</p> <p>year = start year</p> <p>fled = number of fledgelings per pair</p> <p>sj = first-year survival of fledgelings</p> <p>sa = annual survival of older birds</p> <p> </p> <p><strong>commontern19942009.csv</strong></p> <p>Mean vital rate estimates for the Common Tern for the Dutch Waddenzee population, including the Northern part of the IJsselmeer, between 2000 and 2010.</p> <p>year = start year</p> <p>r = number of daughter fledgelings per adult female</p> <p>s1 = first-year survival rate</p> <p>s2 = second-year survival rate</p> <p>sA = older birds' annual survival rate</p> <p> </p> <p><strong>whitetailedeaglepmat1.csv</strong></p> <p><strong>whitetailedeaglepmat2.csv</strong></p> <p><strong>whitetailedeaglefmat1.csv</strong></p> <p><strong>whitetailedeaglefmat2.csv</strong></p> <p>White-Tailed Eagle age-specific survival (Pmat) and reproduction (Fmat) matrices as found in COMADRE v.2.0.1, for Schleswig-Holstein, Germany, studied over the period 1947-2008. Period 1 lasts upto 1975, period 2 from 1975. </p>
Energy consumption and greenhouse gas emissions data of activated carbon production using different biomass
<p>This dataset includes the energy consumption and Greenhouse Gas emissions data of activated carbon production using 73 different types of woody biomass.</p> <p>Understanding the environmental implications of activated carbon (AC) produced from diverse biomass feedstocks is critical for biomass screening and process optimization for sustainability. Many studies have developed Life Cycle Assessment (LCA) for biomass-derived AC. However, most of them either focused on individual biomass species with differing process conditions or compared multiple biomass feedstocks without investigating the impacts of feedstocks and process variations. Developing LCA for AC from diverse biomass is time-consuming and challenging due to the lack of process data (e.g., energy and mass balance).</p> <p>This study addresses these knowledge gaps by developing a modeling framework that integrates artificial neural network (ANN), a machine learning approach, and kinetic-based process simulation. The integrated framework is able to generate Life Cycle Inventory data of AC produced from 73 different types of woody biomass with 250 characterization data samples. The results show large variations in energy consumption and GHG emissions across different biomass species (43.4–277 MJ/kg AC and 3.96–22.0 kg CO<sub>2</sub>-eq/kg AC). The sensitivity analysis indicates that biomass composition (e.g., hydrogen and oxygen content) and process operational conditions (e.g., activation temperature) have large impacts on energy consumption and GHG emissions associated with AC production.</p>
Smart Energy Use Case Clustering Dataset - II
<p>Cluster data set for multilayered and contextualized smart energy blockchain traces</p>
Data from: Linking size spectrum, energy flux and trophic multifunctionality in soil food webs of tropical land-use systems
1. Many ecosystem functions depend on the structure of food webs, which heavily relies on the body size spectrum of the community. Despite that, little is known on how the size spectrum of soil animals responds to agricultural practices in tropical land-use systems and how these responses affect ecosystem functioning. 2. We studied land-use induced changes in belowground communities in tropical lowland ecosystems in Sumatra (Jambi province, Indonesia), a hotspot of tropical rainforest conversion to rubber and oil palm plantations. The study included ca. 30,000 measured individuals from 33 high-order taxa of meso- and macrofauna spanning eight orders of magnitude in body mass. Using individual body masses we calculated the metabolism of trophic guilds and used food-web models to calculate energy fluxes and infer ecosystem functions, such as decomposition, herbivory, primary and intraguild predation. 3. Land-use change was associated with reduced abundance and taxonomic diversity of soil invertebrates, but strong increase in total biomass and moderate changes in total energy flux. These changes were due to increased biomass of large-sized decomposers in soil, in particular earthworms, with their share in community metabolism increasing from 11% in rainforest to 59-76% in jungle rubber, and rubber and oil palm plantations. Decomposition, i.e. the energy flux to decomposers, stayed unchanged, but herbivory, primary and intraguild predation decreased by an order of magnitude in plantation systems. Intraguild predation was very important, being responsible for 38% of the energy flux in rainforest according to our model. 4. Conversion of rainforest into monoculture plantations is associated by an uneven loss of size classes and trophic levels of soil invertebrates resulting in sequestration of energy in large-sized primary consumers and restricted flux of energy to higher trophic levels. Pronounced differences between rainforest and jungle rubber reflect sensitivity of rainforest soil animal communities to moderate land-use changes. Soil communities in plantation systems sustained high total energy flux despite reduced biodiversity. The high energy flux into large decomposers but low energy fluxes to other trophic guilds suggests that trophic multifunctionality of belowground communities is compromised in plantation systems.
Data from: Energy and the scaling of animal space use
Daily animal movements are usually limited to a discrete home range area that scales allometrically with body size, suggesting that home-range size is shaped by metabolic rates and energy availability across species. However, there is little understanding of the relative importance of the various mechanisms proposed to influence home-range scaling (e.g., differences in realm productivity, thermoregulation, locomotion strategy, dimensionality, trophic guild, and prey size) and whether these extend beyond the commonly studied birds and mammals. We derive new home-range scaling relationships for fishes and reptiles and use a model-selection approach to evaluate the generality of home-range scaling mechanisms across 569 vertebrate species. We find no evidence that home-range allometry varies consistently between aquatic and terrestrial realms or thermoregulation strategies, but we find that locomotion strategy, foraging dimension, trophic guild, and prey size together explain 80% of the variation in home-range size across vertebrates when controlling for phylogeny and tracking method. Within carnivores, smaller relative prey size among gape-limited fishes contributes to shallower scaling relative to other predators. Our study reveals how simple morphological traits and prey-handling ability can profoundly influence individual space use, which underpins broader-scale patterns in the spatial ecology of vertebrates.
Data from: Energy benefits and emergent space use patterns of an empirically parameterized model of memory-based patch selection
Many species frequently return to previously visited foraging sites. This bias towards familiar areas suggests that remembering information from past experience is beneficial. Such a memory-based foraging strategy has also been hypothesized to give rise to restricted space use (i.e. a home range). Nonetheless, the benefits of empirically derived memory-based foraging tactics and the extent to which they give rise to restricted space use patterns are still relatively unknown. Using a combination of stochastic agent-based simulations and deterministic integro-difference equations, we developed an adaptive link (based on energy gains as a foraging currency) between memory-based patch selection and its resulting spatial distribution. We used a memory-based foraging model developed and parameterized with patch selection data of free-ranging bison Bison bison in Prince Albert National Park, Canada. Relative to random use of food patches, simulated foragers using both spatial and attribute memory are more efficient, particularly in landscapes with clumped resources. However, a certain amount of random patch use is necessary to avoid frequent returns to relatively poor-quality patches, or avoid being caught in a relatively poor quality area of the landscape. Notably, in landscapes with clumped resources, simulated foragers that kept a reference point of the quality of recently visited patches, and returned to previously visited patches when local patch quality was poorer than the reference point, experienced higher energy gains compared to random patch use. Furthermore, the model of memory-based foraging resulted in restricted space use in simulated landscapes and replicated the restricted space use observed in free-ranging bison reasonably well. Our work demonstrates the adaptive value of spatial and attribute memory in heterogeneous landscapes, and how home ranges can be a byproduct of non-omniscient foragers using past experience to minimize temporal variation in energy gains.
Data from: Food restriction and chronic stress alter energy use and affect immunity in an infrequent feeder
Glucocorticoids are important mediators of energy utilization for key physiological processes, including immune function. Much work has focused on the effects of energy limitation and stress for key physiological processes such as reproduction and immunity. However, it is unclear how stress alters energy use across different energy states, and the physiological ramifications of such effects are even less clear. In this study, we altered energy and stress states of an infrequent feeder, the Terrestrial Gartersnake (Thamnophis elegans), using fasting and repeated restraint stress (Chronic Stressors) to test how these challenges interacted to affect immune function, energy metabolites, and glucocorticoid reactivity (a traditional indicator of stress state) to restraint stress, a standardized, acute stressor. After this acute stressor, the snakes which had received chronic stress had increased glucocorticoid reactivity, and both treatments altered energy metabolite use and storage. Evidence of interaction of food restriction and chronic stress treatments on innate immune function and energy metabolites (triglycerides and glycerol) suggests that stress alters energy use in a manner dependent on the energy state of the animal. Snakes have a remarkable ability to maintain functionality of key physiological processes under stressful conditions but are still susceptible to multiple simultaneous stressors, a situation increasingly prevalent in our ever-changing environment.
Primate Daily Energy Use
<p>Daily energy requirements of primates collated from the literature, with sample size, body weight, mean species brain weight (from Stephan 1981), basal metabolic rate, source, and measurement method.</p>
High resolution, interactive, or animated versions of illustrations used in the paper "Quantifying the Dunkelflaute: An analysis of variable renewable energy droughts in Europe"
Open the record for dataset details and reuse information.
Strong-ground motion for the city of Santiago (Chile) by using the Heterogeneous Energy-Based method
<p>Information pertaining to the generation of strong ground motion in the city of Santiago, Chile, using the Heterogeneous Energy-Based method proposed by Venegas-Aravena (2023) for the San Ramón Fault.</p>
Data from: Mining the in-use stock of energy-transition materials for closed-loop e-mobility
<p>Material flow analysis dataset for energy-transition materials developed within the Spoke11 - CNMS MOST - WP2:Design for Sustainability</p>
Energy and forces annotated atomic structures of platinum using first-principles calculations
<p>Dataset for training machine learning potential of platinum.</p> <p>Detailed explanation of data generation is described in the preprint. below</p> <p>Chun, H., Kang, J., Kang, D., Heo, J., Cho, H., Heo, J., … & Han, B. (2022). Tracking the 3d atomic structures during thermal treatment and catalytic activity of individual pt nanoparticles.. https://doi.org/10.21203/rs.3.rs-1441062/v1</p>
Studying energy materials under operating conditions by X-ray absorption spectroscopy using the new OÆSE endstation at BESSY II
<div>For the "Predicted undulator gap offset", ID of binary files in "undulator gap detuning" folder are:</div> <div>[474,475,476,477,478] for oxygen K-edge measurements with corresponding offset gap values of [20,15,10,5,-1.672]</div> <div>[466,468,469,470,472,473] for nickel L23-edge measurements with corresponding offset gap values of [30,43,35,10,20,-1.672]</div> <div> <div> </div> <div>For the "Beam blocking between data acquisition" data, files uploaded for "maximum flux" ("high_dose_5MH3PO3.txt") and "beam blocker" ("low_dose_5MH3PO3.txt")</div> <div> </div> <div>For the "Operation of the cell at different temperatures" data, files uploaded for 25°C ("CV_planarPt_5MH3PO3_25C.txt") and for 72°C ("CV_planarPt_5MH3PO3_72C.txt")</div> <div> <div> </div> <div> <div>For the "Detuning the undulator gap minimizes the radiation-induced oxidation" plot, in the "Undulator detuning P K-edge" folder the files are:</div> </div> </div> <div> <div>["w_AuMesh,Ugap=6p0_I0=58nA_t=10min.txt",</div> <div>"w_Aumesh_Ugap=6p15_I0=9nA_t=20min.txt",</div> <div>"w_AuMesh_Ugap=6p11_I0=18nA_t=30min.txt",</div> <div>"w_AuMesh_Ugap=6p05_I0=40nA_t=40min.txt"] with [100,69,31,16]% of maximum flux respectively</div> <div>"aqueous_1M_H3PO3_exp_PK_XAS.txt" and "aqueous_1M_H3PO4_exp_PK_XAS.txt" are the reference spectra of aqueous 1M H3PO3 and 1M H3PO4 respectively</div> <div> </div> <div> <div>For the "Variation of the position of the irradiated sample", in the "NCA degradation damage" folder the files are:</div> <div> <div>[1271,1272,1273] for oxygen K-edge measurement for 1st, 2nd and 3rd scan in the same position</div> <div>[1297,1298,1299] for nickel L3-edge measurement for 1st, 2nd and 3rd scan in the same position</div> <div>[1304] for nickel L3-edge spectra for measurement in a new position</div> </div> </div> <div> </div> <div>For the "Cu samples electrodeposited under different conditions " in the "Different Cu electrodeposition protocols" folder, the files are:</div> <div>at the Cu L3-edge:</div> <div> <div>electrolyte = "Scan_1107_2022_10_08_aq_CuSO4.sdat"</div> <div>Cu2O = "Scan_2020_2022_11_02_Cu2O_in_CuSO4.sdat"</div> <div>Cu = "Scan_2028_2022_11_02_Cu_in_CuSO4.sdat"</div> <div>at the Cu K-edge:</div> <div> <div>electrolyte = "Scan_2007_2022_11_02_aq_CuSO4.dat"</div> <div>Cu2O = "Scan_2021_2022_11_02_Cu2O_in_CuSO4.dat"</div> <div>Cu = "Scan_2033_2022_11_02_Cu_in_water.dat"</div> <div>and the electrochemistry:</div> <div>"scan_2027" for the Cu electrodeposition</div> <div>"3_ED_CA_XXX_CuSO4_RHE_YYY.mpr" for the 5 files related to the Cu2O electrodeposition</div> </div> </div> </div> <div> </div> <div>The spectromicrographs are the files:</div> <div>1247 for the TFY mode</div> <div>1248 for the PFY mode</div> <div> </div> <div>The FEXRAV signals are given by the following IDs:</div> <div> <div> <div>[1998,2002,2009] for the chemical species ["sXAS-Main peak (Cu2O)","sXAS-Secondary peak (Cu(0))","hXAS-Main peak"]</div> </div> </div> <div> </div> </div>
Supplementary material for "Comprehensive framework for dynamic energy assessment of building systems using IFC graphs and Modelica"
<p>The provided files include the following:</p> <ul> <li>The IFC model of the demo building used in Section 2.</li> <li>The parsed space boundaries graph.</li> <li>The resulting fragmented space boundaries graph.</li> </ul>
Case studies used in the test of the MCDA-MSS for energy systems analysis, described according to its 156 features
<p>Case studies used in the test of the MCDA-MSS, described according to its 156 features.</p>
Using weather radar to help minimize wind energy impacts on nocturnally migrating birds
<p>As wind energy rapidly expands worldwide, information to minimize impacts of this development on biodiversity is urgently needed. Here we demonstrate how data collected by weather radar networks can inform placement and operation of wind facilities to reduce collisions and minimize habitat-related impacts for nocturnally migrating birds. We found over a third of nocturnal migrants flew through altitudes within the rotor-swept zone surrounding the North American Great Lakes, a continentally important migration corridor. Migrating birds concentrated in terrestrial stopover habitats within 20-km from shorelines, a distance well beyond the current guidelines for construction of new land-based facilities, and their distributions varied seasonally and at local and regional scales, creating predictable opportunities to minimize impacts from wind energy development and operation. Networked radar data are available across the U.S. and other countries and broad application of this approach could provide information critical to bird-friendly expansion of this globally important energy source.</p>
Diet and temperature modify the relationship between energy use and ATP production to influence behaviour in zebrafish (Danio rerio)
<p>Food availability and temperature influence energetics of animals, and can alter behavioural responses such as foraging and spontaneous activity. Food availability, however, is not necessarily a good indictator of energy (ATP) available for cellular processes. The efficiency of energy transduction from food-derived substrate to ATP in mitochondria can change with environmental context. Our aim was to determine whether the interaction between food availability and temperature affects mitochondrial efficiency and behaviour in zebrafish (Danio rerio). We conducted a fully factorial experiment to test the effects of feeding frequency, acclimation temperature (three weeks to 18 or 28°C), and acute test temperature (18 and 28°C) on whole-animal oxygen consumption, mitochondrial bioenergetics and efficiency (ADP consumed per oxygen atom; P:O ratio), and behaviour (boldness and exploration). We show that infrequently fed (once per day on four days per week) zebrafish have greater mitochondrial efficiency than frequently fed (three times per day on five days er week) animals, particularly when warm-acclimated. The interaction between temperature and feeding frequency influenced exploration of a novel environment, but not boldness. Both resting rate of producing ATP and scope for increasing it were positively correlated with time spent exploring and distance moved in standardised trials. In contrast, behaviour was not associated with whole-animal aerobic (oxygen consumption) scope, but exploration was positively correlated with resting oxygen consumption rates. We highlight the importance of variation in both metabolic (oxygen consumption) rate and efficiency of producing ATP in determining animal performance and behaviour. Oxygen consumption represents energy use, and P:O ratio is a variable that determines how much of that energy is allocated to ATP production. Our results emphasise the need to integrate whole-animal responses with subcellular traits to evaluate the impact of environmental conditions on behaviour and movement. --</p>
Primary Fossil Energy Use and Costs as used for "Savings and Avoided Costs of Living Carbon Negative"
<p>This Excel file provides the primary fossil energy use and costs data and calculations as used for "Savings of Living Carbon Negative".</p>
The BAU Scenario Clic Sand , R.E Scenario Clic Sand and an integrated BAU and R.E Visualization Results for Energy Policy for Uganda Using OSeMOSYS
<p>Business As Usual (BAU) Scenario , Renewable Energy (R.E) Scenario Clic Sand are used as data sets to run the model. The integrated BAU and R.E. visualization result templates contain the findings of the models. For instance, power generation graphs, installed energy capacity graphs, annual capital cost graphs, annual carbon dioxide emissions graphs , capital cost and any other parameters of interest related to the model can be found in the result visualization template.</p>
Data used in the publication "High Energy Emissions induced by air density fluctuations of discharges"
<p>This data was used to generate the Figures in the publication "High Energy Emissions induced by air density fluctuations of discharges".</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.