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1,751 results for “Future”
Mechanism of the 2017 Mw 6.3 Pasni earthquake and its significance for future major earthquakes in the eastern Makran
<p>On 7th February 2017, a moment magnitude (M<sub>w</sub>) 6.3 earthquake rattled offshore Pasni in the eastern Makran and triggered a small tsunami. Using a combination of seismicity, multibeam bathymetry, seismic profile, InSAR measurements, and tide gauge observation, we conduct an in-depth investigation into the seismogenic structure, coseismic deformation, and tsunami characteristics of this event. Our results indicate that (1) the earthquake occurred on the shallow-dipping (3-4°) megathrust; (2) the megathrust co-seismically slipped 15 cm and caused ~2-4 cm ground subsidence and uplift at Pasni; (3) our tsunami modeling reproduces the observed 5-cm-high small tsunami waveforms. The Pasni earthquake rupture partially overlaps the 1851 and 1945 earthquake (M>8) slip patches, releasing estimated 3% and 7% of accumulative strain since then. With such stress perturbation, the Pasni earthquake could promote failure of megathrust in the future. This study calls for more preparedness in mitigating earthquake and associated hazards in the eastern Makran. </p>
Future Heat Stress Indicators for Johannesburg and Ekurhuleni
<p>This dataset is part of the scientific paper: Souverijns, N., De Ridder, K., Veldeman, N., Lefebre, F., Kusambiza-Kiingi, F., Memela, W., Jones, N.K.W., 2022. Urban heat in Johannesburg and Ekurhuleni, South Africa: A meter-scale assessment and vulnerability analysis. Urban Climate, 46, 101331. <a href="https://doi.org/10.1016/j.uclim.2022.101331">https://doi.org/10.1016/j.uclim.2022.101331</a></p> <p>Heat stress indicators for present and future climates for the cities of Johannesburg and Ekurhuleni (South Africa).at 30m spatial resolution calculated with UrbClim (De Ridder et al., 2015) for the following scenarios:</p> <p>- 2001-2020: present time (ERA5 input data)</p> <p>- 2021-2040 RCP4.5 (CMIP5 ensemble)</p> <p>- 2021-2040 RCP8.5 (CMIP5 ensemble)</p> <p>- 2041-2060 RCP4.5 (CMIP5 ensemble)</p> <p>- 2041-2060 RCP8.5 (CMIP5 ensemble)</p> <p>For each of the 23 indicators, an overview image is available at high resolution. Furthermore, georeferenced GeoTiffs are available to visualize & analyse the indicators on the users preferred system. The indicators are available in EPSG4326/WGS84. A description of each indicator is given:</p> <p>- Cooling degree hours: Annual number of hours during which the temperatures rises over 25°C, multiplied by the number of degrees the temperature rises above 25°C. This is an international standard to estimate the energy demand for air conditioning use.</p> <p>- CSDI: Cold spell duration index. Annual number of days with at least six consecutive days when the night temperature < 10th percentile</p> <p>- Heatwave days: Number of heatwave days per year calculated following the definition of the South African Weather Service. If the maximum temperature at a particular town is expected to meet or exceed 5 degrees C above the average maximum temperature of “the hottest month” for that particular place (this is 32°C for Johannesburg & Ekurhuleni), as well as persisting in that mode for 3 days or more (https://www.weathersa.co.za/home/weatherques)</p> <p>- T2M_daily_mean_max: Average daily maximum temperature</p> <p>- T2M_daily_mean_max_topography: Daytime Urban Heat Island corrected for topography</p> <p>- T2M_daily_mean_min: Average daily minimum temperature</p> <p>- T2M_daily_mean_min_topography: Nighttime Urban Heat Island corrected for topography</p> <p>- T2M_dayover25: Annual number of days attaining a maximum temperature > 25°C</p> <p>- T2M_dayover25_duetourban: Annual number of days attaining a maximum temperature > 25°C that are caused by urban canopy</p> <p>- T2M_dayover30: Annual number of days attaining a maximum temperature > 30°C</p> <p>- T2M_dayover30_duetourban: Annual number of days attaining a maximum temperature > 30°C that are caused by urban canopy</p> <p>- T2M_max: Absolute maximum temperature</p> <p>- T2M_mean: Absolute average temperature</p> <p>- T2M_min: Absolute minimum temperature</p> <p>- T2M_nightover20: Number of nights attaining a minimum temperature not dropping below 20°C</p> <p>- T2M_nightover25: Number of nights attaining a minimum temperature not dropping below 25°C</p> <p>- TN10P: Annual number of days when the night temperature < 10th percentile</p> <p>- TN90P: Annual number of days when the night temperature > 90th percentile</p> <p>- TN_max: Annual minimum nighttime warmest temperature</p> <p>- TX10P: Annual number of days when the day temperature < 10th percentile</p> <p>- TX90P: Annual number of days when the day temperature > 90th percentile</p> <p>- TX_min: Annual maximum daytime coolest temperature</p> <p>- WSDI: Warm Spell Duration Index. Annual number of days with at least six consecutive days when daytime temperatues > 90th percentile</p>
Wind Data for Station-wise assessment of wind speed and direction under future climates across the United States
<p>This study employs statistical techniques to evaluate climate model performance in wind speed and direction and their projected future changes under the representative concentration pathway (RCP) 8.5 scenario over inland and offshore across the Continental United States (CONUS). It extends the scope of existing studies by characterizing the changes of the full range of the joint wind speed and direction distribution via a conditional approach. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the statistical significance of the future projections. The proposed conditional approach provides a better way to characterize the directional wind speed distributions that offers additional insights for the joint assessment of speed and direction. </p> <p>WRF data: We focus on seasonal (December-January-February (winter hereafter) and June-July-August (summer hereafter) statistics computed from the 3-hourly RCM outputs on both wind speed and direction over ten locations with different local topological features. We use three WRF simulations driven by Community Climate System Model 4 (CCSM4), the Geophysical Fluid Dynamics Laboratory Earth System Model 2 (GFDL-ESM2G), and the Hadley Centre Global Environment Model version 2 (HadGEM2-ES). These three GCMs represent a range of climate sensitivities that encompasses most of the coupled model intercomparison project phase 5 (CMIP5) GCMs when projecting future temperature changes. In this work, we focus on RCP 8.5 scenario for future projections. A 16-member ensemble of one-year of RCM simulation using bias corrected CCSM-driven WRF is also generated for analyzing the uncertainty due to the RCM's internal variability (IV). </p> <p>Benchmark data: Reanalysis data are used as a verification dataset in order to evaluate the RCMs' wind conditions under study for the historical time period. For the seven inland locations, we use the second phase of the multi-institution North American Land Data Assimilation System project, phase 2, at a spatial resolution of 12 km and hourly resolution. NLDAS-2 is an offline data assimilation system featuring uncoupled land surface models driven by observation-based atmospheric forcing. The non-precipitation land surface forcing fields for NLDAS-2 are derived from the analysis fields of the NCEP North American Regional Reanalysis (NARR). NARR analysis fields are at a 32-km spatial resolution and 3-hourly temporal frequency.</p> <p>In-situ measurement: Since reanalysis data can present errors and uncertainties, ground measurements and offshore buoy measurements are used to consolidate the evaluation of RCMs' wind conditions for inland and offshore locations in historical climates. Observational data are extracted from the Automated Surface Observing System (ASOS) network that consists stations covers the U.S. territory, available at ftp://ftp.ncdc.noaa.gov/pub/data/asos-onemin. The offshore downscaled wind speeds from the historical decade are compared with National Data Buoy Center (NDBC) buoy observations of near-surface wind velocities available at https://www.ndbc.noaa.gov. The observed winds at the NBDC anemometers are adjusted to 10-m above ground height and at 3-hourly rate. </p> <p> </p> <p> </p>
Dataset: Experimental carbon emissions from degraded Mediterranean seagrass (Posidonia oceanica) meadows under current and future summer temperatures.
<p> Experimental carbon emissions from degraded Mediterranean seagrass (<em>Posidonia oceanica</em>) meadows.</p> <p> </p> <p>Guillem Roca, Javier Palacios, Sergio Ruíz-Halpern, Núria Marbà</p> <p>Contact details: Guillem Roca, guillemrocac@gmail.com</p> <p>Issue date:</p> <p>Identifier:</p> <p> </p> <p>Citation: Roca, Guillem; Palacios, Javier; Ruíz-Halpern, Marbà, Núria;</p> <p>Experimental carbon emissions from degraded Mediterranean seagrass (<em>Posidonia oceanica</em>) meadows. [Dataset]</p> <p> </p> <p>Abstract: The dataset provides data on sediment C0<sub>2 </sub>efflux rates (μmol CO<sub>2 </sub>m<sup>-2 </sup>s<sup>-1</sup>), carbon emissions during the experiment (gm<sup>-2</sup>), % Organic Carbon, Organic Matter content (g m<sup>-2</sup>) of the <em>Posidonia oceanica</em> seagrass sediments collected in Pollença bay (North of Mallorca Island). Sediments were cultivated in 5 different seawater temperature treatments and two different agitation conditions.</p> <p> </p> <p>Keywords: C0<sub>2 </sub>efflux rates, C0<sub>2</sub> emissions, Sediment, Seagrass, <em>Posidonia Oceanica</em>, experiment, temperature treatment, Sediment suspension Blue carbon, Organic Carbon.</p> <p> </p> <p>Description: The dataset contains data on sediment C0<sub>2 </sub>efflux rates, carbon emissions during the experiment (gm<sup>-2</sup>), % Organic Carbon, Organic Matter content of the <em>Posidonia oceanica</em> seagrass sediments collected in Pollença bay (North of Mallorca Island). Sediments were cultivated in 5 different seawater temperature treatments and two different agitation conditions. Sediments used in the experiment were extracted in October 2017 from the <em>P. Oceanic</em>a meadow of Pollença in Mallorca Island at six-meter depth Figure (1). Sediments were sampled in October 2017 using sediment cores (9 cm ID and 30cm long) and directly transported to the laboratory. Only the top 10 cm of the sediment cores were used since this fraction is the most susceptible to erosion. Living seagrass tissues (roots, rhizomes, and leaves) were removed and sediment was mixed and homogenized. 40ml of sediments were poured into glass containers of 750ml with 500ml of seawater. Finally, each recipient contained a sediment layer of approximately 1.1cm in each container. Containers were placed at five different temperature baths (26,27.5, 29, 30.5, 32 ºC) simulating summer temperatures in the bay (Garcias-Bonet et al., 2019) at different agitation regimes (agitation/repose) to simulate exposed and sheltered conditions.10 containers were sampled right after the experiment started to provide initial sediment conditions. Five containers per temperature and agitation treatment were removed 7, 21, 43, 67, and 98 days from the experiment start, to analyse sediment organic matter and CaCO<sub>3</sub> content. CO<sub>2</sub> incubations were run 5, 14, 56, and 91 days from the experiment start. Sampling times were distributed considering that organic matter remineralisation was likely to follow an exponential trend, including a rapid phase of loss of the more labile material followed by a slower loss of more recalcitrant substrates (Arndt et al., 2013). The experiment was run in the dark to avoid photosynthesis in an isothermal chamber at 21ºC.</p> <p> </p> <p><strong>Organic Carbon analysis</strong></p> <p>In each sampling time, organic matter content in sediments (OM %DW) was estimated as the percentage weight loss of dry sediment sample after combustion at 550ºC for 4 hours. Organic carbon (Corg) was calculated from OM content using the relation described in (Mazarrasa et al., 2017b)</p> <p> </p> <p>y = 0.29x – 0.64; (R2=0.98, p< 0.0001, n=60)</p> <p> </p> <p>OM and POC stocks along the experiment (mg OM ml-1 and mg POC ml-1) were estimated by multiplying the OM and POC (%DW) by the sediment dry weight (mg) remaining in each experimental unit and standardized to the initial volume of sediment (40 ml) introduced in every glass container. Inorganic carbon was estimated as the percentage weight loss of already combusted sediment (550ºC) after combustion at 1000ºC.</p> <p> </p> <p><strong>Sediment CO<sub>2</sub> production</strong></p> <p>Container headspace CO<sub>2</sub> gas concentration was measured during 20 minutes continuum incubations (4 replicates) in each temperature and agitation treatment in all sampling times. CO<sub>2</sub> air concentration measures were carried out using an Infra Red Gas Analyser EGM4 from PPSystems. Concentration of dissolved CO<sub>2</sub> in seawater (in μmol CO<sub>2</sub> L<sup>−1</sup>) was calculated from the concentration of CO<sub>2</sub> (in ppm) measured in headspace air samples after equilibration as described in (Garcias-Bonet and Duarte, 2017; Wilson et al., 2012). Briefly, we calculate the dissolved CO<sub>2</sub> remaining in seawater after equilibration with the air phase ([CO<sub>2</sub>]SW−eq) by,</p> <p> </p> <p>[CO<sub>2</sub>]SW−eq = 10−6 β [C CO<sub>2</sub>]Air P</p> <p> </p> <p>where β is the Bunsen solubility coefficient of CO<sub>2</sub>, calculated according to Wiesenburg and Guinasso (1979), as a function of seawater temperature and salinity; [CO<sub>2</sub>]Air is the CO<sub>2</sub> concentration measured in containers headspace air (in ppm) and P is the atmospheric pressure (in atm) of dry air that was corrected by the effect of multiple sampling applying Boyle’s Law. Then, the initial CO<sub>2</sub> concentration in seawater before the equilibrium ([CO<sub>2]SW</sub>−before eq) was calculated (in ml CO<sub>2</sub> /ml H<sub>2</sub>O) by,</p> <p> </p> <p>[CO<sub>2</sub>]<sub>SW−before eq</sub> = ([CH<sub>4</sub>]<sub>SW−eq</sub> V<sub>Sw</sub> + 10−6 ([CO<sub>2</sub>]Air −[CO<sub>2</sub>]<sub>Air background</sub>) V<sub>Air</sub>)/V<sub>SW</sub></p> <p> </p> <p>Where V<sub>Sw</sub> is the volume of seawater in the core or in the seawater closed circuit, [CO<sub>2</sub>]<sub>Air background</sub> is the atmospheric CO<sub>2</sub> background level and V<sub>Air</sub> is the volume of the headspace or the closed air circuit. Finally, the initial CO<sub>2</sub> concentration was transformed to µmol CH<sub>4</sub> L<sup>−1</sup> by applying the ideal gas law.</p> <p>CO<sub>2</sub> efflux values were calculated from CO<sub>2</sub> variation per time unit. Then, we converted the rates to aerial (taking in account container surface) base, and thickness (in μmol m<sup>-2 </sup>s<sup>-1</sup>).</p> <p> </p> <p> </p> <p> </p> <p> </p>
Water security in an uncertain future: contrasting realities from an availability-demand perspective
<p>Here we provide the climate change data from the Coupled Model Intercomparison Project phase 6 (CMIP6) used in an assessment of future water security of two Brazilian basins: Guariroba River basin and Jaguari River basin responsible for supplying water to Campo Grande city and the São Paulo Metropolitan Region, respectively. The basins' hydrological response was simulated using the SWAT+ model considering three climate change scenarios from a CMIP6 multimodel ensemble: SSP2-4.5 (medium forcing), SSP3-7.0 (high forcing), and SSP5-8.5 (high forcing). The ensemble was constructed using seven General Circulation/Earth System models (variant ID r1i1p1f1 and further processed for bias correction using quantile delta mapping.</p> <p>The manuscript of "<em>Water security in an uncertain future: contrasting realities from an availability-demand perspective</em>" has been published in <strong>Water Resources Management</strong>, please find it <a href="https://doi.org/10.1007/s11269-022-03160-x">here</a>.</p>
Future-proofing the koala: synergizing genomic and environmental data for effective species management
<p><span>Climatic and evolutionary processes are inextricably linked to conservation. Avoiding extinction in rapidly changing environments often depends upon a species' capacity to adapt in the face of extreme selective pressures. Here, we employed exon capture and high-throughput next-generation sequencing to investigate the mechanisms underlying population structure and adaptive genetic variation in the koala (<em>Phascolarctos cinereus</em>), an iconic Australian marsupial that represents a unique conservation challenge because it is not uniformly threatened across its range.</span> <span>An examination of 250 specimens representing 91 wild source locations revealed that five major genetic clusters currently exist on a continental scale. The initial divergence of these clusters appears to have been concordant with the Mid-Brunhes Transition (</span><span>∼</span><span> 430–300 kya), a major climatic reorganization that increased the amplitude of Pleistocene glacial-interglacial cycles. While signatures of polygenic selection and environmental adaptation were detected, strong evidence for repeated, climate-associated range contractions and demographic bottleneck events suggests that geographically isolated refugia may have played a more significant role in the survival of the koala through the Pleistocene glaciation than <em>in situ</em> adaptation. Consequently, the conservation of genome-wide genetic variation must be aligned with the protection of core koala habitat to increase the resilience of threatened populations to accelerating anthropogenic threats. Finally, we propose that the five major genetic clusters identified in this study should be accounted for in future koala conservation efforts (e.g. guiding translocations), as existing management divisions in the states of Queensland and New South Wales do not reflect historic or contemporary population structure.</span></p>
The effect of harbor developments on future high-tide flooding in Miami, Florida. Accompanying data.
<p>List of available data: </p> <p>- Historical documents with tidal range measurements in the Biscayne Bay</p> <p>- Geo-referenced tif files of US Coast Survey chart no. 165 (1895) and NOAA US Coast Survey chart no. 11468 (2017)</p> <p>- Water level series projected through 2100 at the South Florida Water Management District gauge MRMS4 (mat files)</p> <p> </p> <p> </p> <p> </p>
South-East US historical and projected population per county for FUTURES urban growth modeling in GRASS GIS
<p>South East US historical (2001-2019) and projected (2020-2100) population per county for 6 states (NC, SC, TN, GA, AL, FL). Historical data come from The National Vital Statistics System (https://seer.cancer.gov/popdata/download.html) and future data are projected by Hauer 2019 (https://doi.org/10.1038/sdata.2019.5) for SSP2 scenario. Data are formatted for r.futures.demand module, which is a GRASS GIS addon for computing future land demand for FUTURES urban growth model. </p>
Past, present and future of chamois science
<p><span>The chamois <em>Rupicapra</em> spp. is the most abundant mountain ungulate of Europe and the Near East, where it occurs as two species, the Northern chamois <em>R. rupicapra</em> and the Southern chamois <em>R. pyrenaica</em>. Here, we provide a state-of-the-art overview of research trends and the most challenging issues in chamois research and conservation, focusing on taxonomy and systematics, genetics, life history, ecology and behavior, physiology and disease, management, and conservation. Research on <em>Rupicapra</em> has a longstanding history and has contributed substantially to the biological and ecological knowledge of mountain ungulates. Although the number of publications on this genus has markedly increased over the past two decades, major differences persist with respect to knowledge of species and subspecies, with research mostly focusing on the Alpine chamois <em>R. r. rupicapra</em> and, to a lesser extent, the Pyrenean chamois <em>R. p. pyrenaica</em>. In addition, a scarcity of replicate studies of populations of different subspecies and/or geographic areas limits the advancement of chamois science. Since environmental heterogeneity impacts behavioral, physiological and life history traits, understanding the underlying processes would be of great value from both an evolutionary and conservation/management standpoint, especially in the light of ongoing climatic change. Substantial contributions to this challenge may derive from a quantitative assessment of reproductive success, investigation of fine-scale foraging patterns, and a mechanistic understanding of disease outbreak and resilience. Improving conservation status, resolving taxonomic disputes, identifying subspecies hybridization, assessing the impact of hunting and establishing reliable methods of abundance estimation are of primary concern. Despite being one of the most well-known mountain ungulates, substantial field efforts to collect paleontological, behavioral, ecological, morphological, physiological and genetic data on different populations and subspecies are still needed to ensure a successful future for chamois conservation and research.</span></p>
Data: Clouds drive differences in future surface melt over the Antarctic ice shelves (Kittel et al., 2022)
<p>Outputs used in:</p> <p><em>Kittel, C., Amory, C., Hofer, S., Agosta, C., Jourdain, N. C., Gilbert, E., Le Toumelin, L., Gallée, H., and Fettweis, X.: Clouds drive differences in future surface melt over the Antarctic ice shelves, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-263, accepted, 2021.</em></p> <ul> <li>MAR outputs with summer values of melt, surface energy budget components, and cloud properties over the Antarctic ice sheet (1980--2100)</li> <li>Grid file used in MAR simulations</li> </ul> <p>If you need other variables or output frequencies from MAR, write me (c2kittel@gmail.com) and I will be glad to help you. I will also be happy to share the scripts I have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR outputs.<br> <br> Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below that contained both informations related to MAR. In order to document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact C. Kittel to add their works in the list of MAR-related publications. </p> <p>"We thank C. Kittel and the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations. "</p> <p>You should also refer to and cite the following paper:</p> <p><em>Kittel, C., Amory, C., Hofer, S., Agosta, C., Jourdain, N. C., Gilbert, E., Le Toumelin, L., Gallée, H., and Fettweis, X.: Clouds drive differences in future surface melt over the Antarctic ice shelves, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2021-263, accepted, 2021.</em></p>
Beyond sex and aggression: Testosterone rapidly matches behavioral responses to social context and tries to predict the future
<p>Although androgens are widely studied in the context of aggression, androgenic influences on prosocial behaviors have been less explored. We examined testosterone's (T) influence on prosocial and aggressive responses in a positively-valenced social context (interacting with a pairbond partner) and a negatively-valenced context (interacting with an intruder) in socially monogamous Mongolian gerbils. T increased and decreased prosocial responses in the same individuals towards a pairbond partner and an intruder, respectively, both within 30 minutes, but did not affect aggression. T also had persistent effects on prosocial behavior; males in which T initially increased prosocial responses towards a partner continued to exhibit elevated prosocial responses towards an intruder male days later until a second T injection rapidly eliminated those responses. Thus, T surges can rapidly match behavior to current social context, as well as prime animals for positive social interactions in the future. Neuroanatomically, T rapidly increased hypothalamic oxytocin, but not vasopressin, cellular responses during interactions with a partner. Together, our results indicate that T can facilitate and inhibit prosocial behaviors depending on social context, that it can influence prosocial responses across rapid and prolonged time scales, and that it affects oxytocin signaling mechanisms that could mediate its context-dependent behavioral influences.</p>
The simulated monthly runoff data in the historical period and under future climate scenarios of the Yarlung Zangbo River Basin
<p>This data provides the simulated monthly runoff data under the historical period (1979-2014) and future (2049-2084) climate scenarios for four sub-basins of the Yarlung Zangbo River Basin, including Nugexia, Nuxia, Lasha, and Rikaze.<br> This runoff data is simulated based on the GR4J model coupled with a simple degree-day snow module. The GR4J_SNOW performs parameterization and calculates runoff on each grid cell, and the gridded simulated runoff then converges to the outlet of the sub-basin.<br> Time series of the daily records for meteorological forcing data (precipitation, air temperature, vapor pressure, wind speed, downward long-wave radiation, and downward short-wave radiation) from 1979-2014 was provided by China Meteorological Forcing Dataset (CMFD). <br> Future climate scenarios were generated using the combined climate forcing data together with scaling factors obtained from empirical downscaling of 30 available CMIP5 models (28 GCMs for RCP4.5 and 29 GCMs for RCP8.5). The simulated runoff under RCP4.5 and RCP8.4 are the ensemble averages of 28 and 29 simulated runoff results, respectively.</p>
Data from: Trading water for carbon in the future: effects of elevated CO2 and warming on leaf hydraulic traits in a semiarid grassland
<p class="MsoNormal"><a name="_Hlk96844723"></a><span>The effects of climate change on plants and ecosystems are mediated by plant hydraulic traits, including interspecific and intraspecific variability of trait phenotypes. Yet, integrative and realistic studies of hydraulic traits and climate change are rare. In a semiarid grassland, we assessed the response of several plant hydraulic traits to elevated CO<sub>2</sub> (+200 ppm) and warming (+1.5</span><span><span> to </span></span><span><span>3</span></span><span><span>℃;</span></span><span><span> day to night). For leaves of five dominant species (three graminoids, two forbs), and in replicated plots exposed to seven years of elevated CO<sub>2</sub>, warming, or ambient climate, we measured: stomatal density and size, xylem vessel size, turgor loss point, and water potential (pre-dawn). Interspecific differences in hydraulic traits were larger than intraspecific shifts induced by elevated CO<sub>2</sub> and/or warming. Effects of elevated CO<sub>2</sub> were greater than effects of warming, and interactions between treatments were weak or not detected. The forbs showed little phenotypic plasticity. The graminoids had leaf water potentials and turgor loss points that were 10 to 50% less negative under elevated CO<sub>2</sub>; thus, climate change might cause these species to adjust their drought resistance strategy away from tolerance and toward avoidance. The C4 grass also reduced allocation of leaf area to stomata under elevated CO<sub>2</sub>, which helps explain observations of higher soil moisture. The shifts in hydraulic traits under elevated CO<sub>2</sub> were not, however, simply due to higher soil moisture. Integration of our results with others' indicates that common species in this grassland are more likely to adjust stomatal aperture in response to near-term climate change, rather than anatomical traits; this contrasts with apparent effects of changing CO<sub>2</sub> on plant anatomy over evolutionary time. Future studies should assess how plant responses to drought may be constrained by the apparent shift from tolerance (via low turgor loss point) to avoidance (via stomatal regulation and/or access to deeper soil moisture).</span></span></p>
Future Food Security in Africa under Climate Change
<p>This excel file contains data tables (S2-S3) also found in the supplementary materials of the publication titled "Future Food Security in Africa under Climate Change". The tables included here include a regional breakdown of African countries (Table S2), available calories for direct or indirect human consumption under diverse food loss and waste pathways (Table S3), and data on national caloric deficits under different scenarios (Table S4). </p>
Predicting past and future SARS-CoV-2-related sick leave using discrete time Markov modelling
<p><strong>Background: </strong>Prediction of SARS-CoV-2-induced sick leave among healthcare workers (HCWs) is essential for being able to plan the healthcare response to the epidemic.</p> <p><strong>Methods: </strong>During first wave of the SARS-Cov-2 epidemic (April 23<sup>rd </sup>to June 24<sup>th</sup>, 2020), the HCWs in the greater Stockholm region in Sweden were invited to a study of past or present SARS-CoV-2 infection. We develop a discrete time Markov model using a cohort of 9449 healthcare workers (HCWs) who had complete data on SARS-CoV-2 RNA and antibodies as well as sick leave data for the calendar year 2020. The one-week and standardized longer term transition probabilities of sick leave and the ratios of the standardized probabilities for the baseline covariate distribution were compared with the referent period (an independent period when there were no SARS-CoV-2 infections) in relation to PCR results, serology results and gender.</p> <p><strong>Results:</strong> The one-week probabilities of transitioning from healthy to partial sick leave or full sick leave during the outbreak as compared to after the outbreak were highest for healthy HCWs testing positive for large amounts of virus (ratio: 3.69, (95% confidence interval, CI: 2.44-5.59) and 6.67 (95% CI: 1.58-28.13), respectively). The proportion of all sick leaves attributed to COVID-19 during outbreak was at most 55% (95% CI: 50%-59%).</p> <p><strong>Conclusions: </strong>A robust Markov model enabled use of simple SARS-CoV-2 testing data for quantifying past and future COVID-related sick leave among HCWs, which can serve as a basis for planning of healthcare during outbreaks.</p>
Data for: Forecasting shifts in habitat suitability of three marine predators suggests a rapid decline in inter-specific overlap under future climate change
<p><strong><span>Aim:</span></strong><span> To estimate spatiotemporal changes in habitat suitability and inter-specific overlap among three marine predators: Baltic grey seals (<em>Halichoerus grypus grypus</em>), harbour seals (<em>Phoca vitulina</em>), and harbour porpoises (<em>Phocoena phocoena</em>) under contemporary and future conditions.</span></p> <p><strong><span>Location: </span></strong><span>The southwestern region of the Baltic Sea, including the Danish Straits and the Kattegat, one of the fastest-warming semi-enclosed seas in the world.</span></p> <p><strong><span>Methods: </span></strong><span>Location data (>200 tagged individuals) were analysed within the </span><span>maximum entropy (MaxEnt) </span><span>algorithm to estimate changes in total area size and overlap of species-specific habitat suitability between 1997-2020 and 2091-2100. A total of eleven candidate predictor variables were considered </span><span>representing anthropogenic activity, environmental, and climate sensitive oceanographic conditions in the area. Sea surface temperature and salinity</span><span> data were taken from </span><span>representative concentration pathways [RCPs] scenarios 6.0 and 8.5</span><span> to forecast potential </span><span>climate change effects</span><span>.</span></p> <p><strong><span>Results:</span></strong><span> Model output suggests that habitat suitability of Baltic grey seals will decline drastically over space and time, largely driven by changes in sea surface salinity and a loss of currently available haulout sites following sea level rise in the future. A similar though weaker response was observed for harbour seals, while suitability of habitat for harbour porpoises was predicted to remain fairly stable over space and time. Inter-specific overlap in highly suitable habitat was predicted to increase slightly under RCP scenario 6.0 when compared to contemporary conditions but to largely disappear under RCP scenario 8.5.</span></p> <p><strong><span>Main conclusions:</span></strong><strong> </strong><span>Marine predators in the southwestern Baltic Sea and adjacent waters may respond differently to future climatic conditions, leading to divergent shifts in habitat suitability that are likely to decrease inter-specific overlap.<strong> </strong>We, therefore, conclude that climate change can lead to a marked redistribution of area use by marine predators in the region, which may influence local food-web dynamics and ecosystem functioning.</span></p>
Data from "Projections of leaf turgor loss point shifts under future climate change scenarios" (Tordoni et al. 2022 Global Change Biology)
<p>The dataset includes four sheets representing the average turgor loss point (tlp) values at grid cell level (tlp_data) and the climatic variables and related climate change scenarios derived from the three models used in this study (HadGEM2-ES-RACMO22E, EC-EARTH_RACMO22E, EC-EARTH_CCLM4-8-17, respectively).</p> <p>The sheet "tlp_data" reports the cell ID (OGU) and the average tlp values for each taxonomic group considered in this study (gymnosperms, angiosperms, herbaceous and woody angiosperms). </p> <p>Each of the other three sheets reports the cell ID (OGU), coordinates of the cell centroid (Long, Lat) and a set of six climatic variables: 95<sup>th</sup> percentiles of average temperature (BIO1.95, °C), temperature seasonality (BIO4, °C), annual consecutive frost days where temperature was ≤ 0 °C (CFD.ann, n° days), annual consecutive dry days where precipitation was < 1 mm (CDD.ann, n° days), 5<sup>th</sup> percentiles of cumulate annual precipitation (BIO12.5, mm), and precipitation seasonality (BIO15, %). For each model, "hist" refers to historical data encompassing the period 1970-2005, whereas "RCP2.6" and "RCP8.5" reports the average value of future projections for the period 2080-2100 in two representative concentration pathway (RCP) scenarios (RCP2.6 and RCP8.5).</p> <p> </p>
Data from: Access to resources buffers against effects of current reproduction on future ability to provide care in a burying beetle
<p>Studies investigating the trade-off between current and future reproduction often find that increased allocation to current reproduction is associated with a reduction in the number or quality of future offspring. In species that provide parental care, this effect on future offspring may be mediated through a reduced future ability to provide care. Here, we test this idea in the burying beetle <em>Nicrophorus vespilloides</em>, a species in which parents shift the cost of reproduction towards future offspring and provide elaborate parental care. We manipulated brood size to alter the costs females experienced in association with current reproduction and measured the level of parental care during a subsequent breeding attempt. Given that these beetles breed on carcasses of small vertebrates, it is important to consider confounding effects due to benefits associated with resource access during breeding. We therefore manipulated access to carrion and measured the level of parental care during a subsequent breeding attempt. We found that females provided the same level of care regardless of previous brood size and resource access, suggesting that neither affected future ability to provide care. This may reflect that parents feed on carrion during breeding, which may buffer against any costs of previous breeding attempts. Our results show that increased allocation to current reproduction is not necessarily associated with a reduction in future ability to provide care. Nevertheless, this may reflect unique aspects of our study system, and we encourage future work on systems where parents do not have access to a rich resource during breeding.</p>
The current and future distribution of the yellow fever mosquito (Aedes aegypti) on Madeira Island [data set].
<p><strong>Additional data for manuscript:</strong> "The current and future distribution of the yellow fever mosquito (<em>Aedes aegypti</em>) on Madeira Island" published in PLOS Neglected Tropical Diseases by José Maurício Santos, César Capinha, Jorge Rocha, Carla Alexandra Sousa.</p> <p><strong>Corresponding authors:</strong> José Maurício Santos (josemauriciosantos@campus.ul.pt) & César Capinha (cesarcapinha@campus.ul.pt).</p> <p> </p> <p> </p> <p> </p>
Data from: Hindcast-validated species distribution models reveal future vulnerabilities of mangroves and salt marsh species
<p>Rapid climate change threatens biodiversity via habitat loss, range shifts, increases in invasive species, novel species interactions, and other unforeseen changes. Coastal and estuarine species are especially vulnerable to the impacts of climate change due to sea level rise and may be severely impacted in the next several decades. Species distribution modeling can project the potential future distributions of species under scenarios of climate change using bioclimatic data and georeferenced occurrence data. However, models projecting suitable habitat into the future are impossible to ground truth. One solution is to develop species distribution models for the present and project them to periods in the recent past where distributions are known to test model performance before making projections into the future. Here, we develop models using abiotic environmental variables to quantify the current suitable habitat available to eight Neotropical coastal species: four mangrove species and four salt marsh species. Using a novel model validation approach that leverages newly available monthly climatic data from 1960-2018, we project these niche models into two time periods in the recent past (i.e., within the past half-century) when either mangrove or salt marsh dominance was documented via other data sources. Models were hindcast-validated and then used to project the suitable habitat of all species at four time periods in the future under a model of climate change. For all future time periods, the projected suitable habitat of mangrove species decreased, and suitable habitat declined more severely in salt marsh species.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.