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284 results for “Future climate”
Phenological responses to climate warming in temperate moths and butterflies: species traits predict future changes in voltinism
Changes in the number of generations per year (voltinism) have been among the most common phenological responses to climate warming in insects inhabiting seasonal environments. Nevertheless, numerous species have maintained univoltine (one generation per year) phenology with increasing temperatures, indicating the involvement of phylogenetic, ecological or some other constraints on phenological change. I examined geographic variation in voltinism in moths and butterflies of Northern Europe to identify species traits that might predispose species to univoltine/multivoltine phenology. I focused on species with a wide latitudinal distribution range (15 degrees as a minimum) which makes it unlikely that constraints imposed by season length could preclude multivoltinism across their distribution. Almost half of the 731 moth and butterfly species considered appear to have a single generation throughout their entire European range. A univoltine life-cycle across a wide latitudinal gradient suggests the presence of some constraint that makes additional generations either impossible or at least strongly disadvantageous, which will unlikely change with future climate warming. The scattered distribution of univoltine and multivoltine species across the lepidopteran phylogeny indicates that phylogenetic constraints are not strongly limiting changes in voltinism, and the trait is open to ecologically-driven adaptive evolution. My data show that species with one generation per year are generally larger than multivoltine species, but size forms no absolute constraint to having multiple generations per year. Obligately univoltine species dominate among egg and adult overwinterers (life-histories typical of so-called spring-feeders), whereas species with capacity for multiple generations prevail among pupal overwinterers. Multivoltinism is also infrequent among species feeding on grasses, particularly in endophagous grass-feeders. Larval diet breadth has no discernible effect on voltinism. Given the diverse ecological consequences of voltinism and its changes, accounting for the species' capacity for multivoltinism may be a key to address future challenges in biodiversity conservation and pest management.
Crops and Climate: Shaping Pakistan's Economic Future
<p>The project, Crops and Climate: Shaping Pakistan’s Economic Future, aims to address the<br>challenges posed by climate variability and unsustainable agricultural practices in Pakistan.<br>Agriculture plays a crucial role in the country’s economy, contributing between 19% and 20% of<br>its GDP and employing approximately 38% of the labor force (Government of Pakistan, 2022).<br>However, the sector is vulnerable to unpredictable weather patterns, fluctuating crop yields, and a<br>lack of data-driven decision-making.<br>To mitigate these challenges, the project proposes developing an AI-powered Climate and Crop<br>Economic Analysis System. This system will utilize machine learning algorithms and real-time<br>data analytics to forecast crop yields for key crops such as wheat, rice, cotton, maize, and<br>sugarcane, taking into account historical agricultural data, climate patterns, and production trends.<br>It will provide agricultural stakeholders, policymakers, and industry leaders with informed<br>decision-making tools, thus optimizing resource allocation, enhancing food security, and<br>promoting sustainable agricultural practices.<br>The expected outcome is a modernized agricultural sector that contributes to economic stability,<br>reduces reliance on imports, and increases export potential.By incorporating climate adaptation<br>strategies, the project also aligns with global efforts to mitigate the impacts of climate change on<br>food production (FAO, 2020).</p>
Dataset for Bukovsky et al. (2021): "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections"
<p>This dataset contains derived data and model data necessary for reproducing the results found in "SSP-Based Land Use Change Scenarios: A Critical Uncertainty in Future Regional Climate Change Projections" by Melissa S. Bukovsky, Jing Gao, Linda O. Mearns, and Brian C. O'Neill. This dataset contains data not otherwise available in other public archives, as noted in Bukovsky et al. (2021, Earth's Future; preprint available at https://doi.org/10.1002/essoar.10504141.2). That is, this dataset contains data from the land-use change simulations that are not part of NA-CORDEX (na-cordex.org), but which are complementary to those published in the NA-CORDEX archive.</p>
Variable influence of photosynthetic thermal acclimation on future carbon uptake in Australian wooded ecosystems under climate change
<p><span>Climate change will impact gross primary productivity (GPP), net primary productivity (NPP), and carbon storage in wooded ecosystems. The extent of change will be influenced by thermal acclimation of photosynthesis – the ability of plants to adjust net photosynthetic rates in response to growth temperatures – yet regional differences in acclimation effects among wooded ecosystems are currently unknown. We examined the effects of changing climate on 17 Australian wooded ecosystems with and without the effects of thermal acclimation of C<sub>3</sub> photosynthesis. Ecosystems were drawn from five ecoregions (tropical savanna, tropical forest, Mediterranean woodlands, temperate woodlands, and temperate forests) that span Australia's climatic range. We used the CABLE-POP land surface model adapted with thermal acclimation functions and forced with HadGEM2-ES climate projections from RCP8.5. For each site and ecoregion, we examined a) effects of climate change on GPP, NPP, and live tree carbon storage; and b) impacts of thermal acclimation of photosynthesis on simulated changes. Between the end of the historical (1976–2005) and projected (2070–2099) periods, simulated annual carbon uptake increased in the majority of ecosystems by 26.1 to 63.3% for GPP and 15 to 61.5% for NPP. Thermal acclimation of photosynthesis further increased GPP and NPP in tropical savannas by 27.2% and 22.4% and by 11% and 10.1% in tropical forests with positive effects concentrated in the wet season (tropical savannas) and the warmer months (tropical forests). We predicted minimal effects of thermal acclimation of photosynthesis on GPP, NPP and carbon storage in Mediterranean woodlands, temperate woodlands and temperate forests. Overall, positive effects were strongly enhanced by increasing CO<sub>2</sub> concentrations under RCP8.5. We conclude that the direct effects of climate change will enhance carbon uptake and storage in Australian wooded ecosystems (likely due to CO<sub>2</sub> enrichment) and that benefits of thermal acclimation of photosynthesis will be restricted to tropical ecoregions.</span></p>
Present status, future trends, and control strategies of invasive alien plants in China affected by human activities and climate change
<p>Invasive alien plants (IAPs) have serious environmental and economic impacts, especially in vulnerable areas of China. However, IAP richness distribution patterns, their driving factors, and the dynamic shifts in potential distribution areas remain elusive. We assessed IAP richness distribution patterns and drivers using 402 IAPs recorded in China at 88,926 occurrence points, and then predicted their potential distribution areas. The results show that IAP hotspots were mainly located in southeastern China, especially coastal areas of the South and East and large inland cities. Population density, gross domestic product (GDP), and four climate variables associated with precipitation and temperature jointly influenced the richness distribution pattern of all IAPs. Specifically, population density and GDP impacted the richness distribution pattern of narrow-range IAPs, and population density, GDP, distance to the nearest national highway, and five climate variables affected the richness distribution pattern of widespread IAPs. Only GDP contributed significantly to the richness distribution pattern of the top 5% hotspot grid cells, whereas population density, GDP, and precipitation in the driest month (BIO14) significantly influenced the richness distribution patterns of hotspots for both the top 10% and top 20%. Prediction analysis demonstrated that southeastern China would have a particularly high invasion risk under both current and future climate scenarios. Regions with increases in predicted species richness are more common (44.83%–64.97%) than those with decreases, except under the Representative Concentration Pathway (RCP) 4.5 scenario. Climate change will contribute greatly to the expansion of potential IAP distribution areas under both optimistic (RCP 2.5) and pessimistic scenarios (RCP 8.5). The results of this study provide insights into the priority management of IAPs through developing promising strategies for the control and prevention of IAP invasion.</p>
Calibrated and uncalibrated projection data from the paper "Assessing observational constraints on future European climate in an out-of-sample framework"
<p>Individual uncalibrated and calibrated projections for each of the five methods (A-E) in the following folders:</p> <p>MethodA_proj/</p> <p>MethodB_proj/</p> <p>MethodC_proj/</p> <p>MethodD_proj/</p> <p>MethodE_proj/</p> <p> </p> <p>Also included are the out-of-sample data from the "pseudo-observations" (taken from CMIP6 models) used for the verification (see paper for full details):</p> <p>FUTUREverif/</p>
The potential range of Agrilus planipennis according to current and future climate conditions
<p>This dataset is associated to the following reference:</p> <p>Jean-Pierre Rossi, Raphaëlle Mouttet, Pascal Rousse and Jean-Claude Streito<sup> </sup>(2024) Modelling the potential range of <em>Agrilus planipennis</em> in Europe according to current and future climate conditions. In press. Trees, Forests and People. https://doi.org/10.1016/j.tfp.2024.100559</p> <p>The files correspond to rasters saved as geotiff files. They can be used in geographical information systems.</p> <p><strong>Current climate conditions: 2001-2018. The maps correspond to the whole world.</strong></p> <p>CS_2001-2018_Bart.tif</p> <p>Climate suitability for Agrilus planipennis modelled using the BART algorithm and the reference conditions (2001-2018). The climate suitability ranges from 0 (unsuitable) to 1 (suitable).</p> <p>CS_2001-2018_Brt.tif</p> <p>Climate suitability for Agrilus planipennis modelled using the BRT algorithm and the reference conditions (2001-2018). The climate suitability ranges from 0 (unsuitable) to 1 (suitable).</p> <p>CS_2001-2018_Rf.tif</p> <p>Climate suitability for Agrilus planipennis modelled using the RF algorithm and the reference conditions (2001-2018). The climate suitability ranges from 0 (unsuitable) to 1 (suitable).</p> <p>CS_2001-2018_Bart_pres_abs.tif</p> <p>Reclassified climate suitability maps for Agrilus planipennis modelled using the algorithm BART and the reference conditions (2001-2018). The raster contains 2 classes: suitable conditions (raster value = 1) and unsuitable conditions (raster value = 0).</p> <p>CS_2001-2018_Brt_pres_abs.tif</p> <p>Reclassified climate suitability maps for Agrilus planipennis modelled using the algorithm BRT and the reference conditions (2001-2018). The raster contains 2 classes: suitable conditions (raster value = 1) and unsuitable conditions (raster value = 0).</p> <p>CS_2001-2018_Rf_pres_abs.tif</p> <p>Reclassified climate suitability maps for Agrilus planipennis modelled using the algorithm RF and the reference conditions (2001-2018). The raster contains 2 classes: suitable conditions (raster value = 1) and unsuitable conditions (raster value = 0).</p> <p>CS_2001-2018_committee.tif</p> <p>Map showing the percentage of algorithms indicating suitable climate conditions for Agrilus planipennis. The algorithms (BART, BRT and RF) are projected using the current climate conditions (2001-2018).</p> <p><strong>Future climate conditions: 2041-2060. The maps correspond to Europe.</strong></p> <p> CS_2041-2060_SSP1-2.6_committee.tif</p> <p>Map showing the percentage of algorithms indicating suitable climate conditions for Agrilus planipennis. The algorithms (BART, BRT and RF) are projected using the climate data associated with 11 GCMs for the period 2041-2060 and the SSP1-2.6.</p> <p> CS_2041-2060_SSP2-4.5_committee.tif</p> <p>Map showing the percentage of algorithms indicating suitable climate conditions for Agrilus planipennis. The algorithms (BART, BRT and RF) are projected using the climate data associated with 11 GCMs for the period 2041-2060 and the SSP2-4.5.</p> <p> CS_2041-2060_SSP3-7.0_committee.tif</p> <p>Map showing the percentage of algorithms indicating suitable climate conditions for Agrilus planipennis. The algorithms (BART, BRT and RF) are projected using the climate data associated with 11 GCMs for the period 2041-2060 and the SSP3-7.0.</p> <p> CS_2041-2060_SSP5-8.5_committee.tif</p> <p>Map showing the percentage of algorithms indicating suitable climate conditions for Agrilus planipennis. The algorithms (BART, BRT and RF) are projected using the climate data associated with 11 GCMs for the period 2041-2060 and the SSP5-8.5.</p> <p> </p> <p>BART: Bayesian Additive Regression Trees</p> <p>BRT: Boosted Regression Trees</p> <p>RF: Random Forest</p>
Data set for the study "Interplay between climate and carbon cycle feedbacks could substantially enhance future warming"
<p>This repository contains the data necessary to reproduce the results of the paper: <br>"Interplay between climate and carbon cycle feedbacks could substantially enhance future warming" <br><a href="https://iopscience.iop.org/article/10.1088/1748-9326/adb6be" target="_blank" rel="noopener">https://iopscience.iop.org/article/10.1088/1748-9326/adb6be</a></p> <h3><strong>Data organization:</strong></h3> <p>The Zenodo repository is organized as follows inside of <code>results.zip</code>:</p> <ul> <li>Figure generation are given by "*.pynb" and "*.m" files<br><br></li> <li>Data files as NetCDF output are organized with the following structure inside of <code>data</code>:<br><br> <ul> <li><strong>Experiment/emission scenario</strong>: <code>hist-aer</code>, <code>ssp126</code>, <code>ssp434</code>, and <code>ssp245</code><br><br> <ul> <li><strong>Equilibrium climate sensitivity</strong>: <code>ecs_2.0K</code>, <code>ecs_2.5K</code>, <code>ecs_3.0K</code>, <code>ecs_3.5K</code>, <code>ecs_4.0K</code>, <code>ecs_4.5K</code>, and <code>ecs_5.0K</code><br><br> <ul> <li><strong>Experiment: </strong><code>comp</code>, <code>comp_fix_ch4</code>, <code>comp_fix_co2_ch4</code>, <code>comp_ssp_co2_ch4</code><br><br> <ul> <li><strong>Component</strong>: atmosphere (<code>atm</code>), ocean (<code>ocn</code>), land (<code>lnd</code>), sea ice (<code>sic</code>), carbon dioxide (<code>co2</code>), methane (<code>ch4</code>)<br><br></li> <li><strong>File type</strong>: for some experiments, files are divided into timeseries (<code>*_ts.nc</code>) or 2D data (<code>*.nc</code>)<br><br></li> <li>Note: <code>comp_ssp_co2_ch4</code> are the CLIMBER-X runs which used prescribed concentrations (rather than emissions) and is only available for ECS 3°C<br><br></li> <li>Note: <code>comp_fix_ch4</code> and <code>comp_fix_co2_ch4</code> is only available for ECS 2°C, 3°C, and 5°C (as shown in Fig. 4 in the manuscript)</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Dataset for "Winter inverse lake stratification under historic and future climate change"
<p>Summary results for Woolway et al., Winter inverse lake stratification under historic and future climate change. See README file for specific information on the variables provided.</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Sint-Katelijne-Waver, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Sint-Katelijne-Waver (51°3'25"N 4°11'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Uccle KMI, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Uccle KMI (50°47'49"N, 4°21'29" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven City centre, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Leuven City Centre (50°52'48"N 4°42'0" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Leuven Casa Blanca, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Casa Blanca neighbourhood Leuven (50°52'48"N, 4°43'48"E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations
<p>Data for article "Future Projection of Solar Energy Over China Based on Multi-Regional Climate Model Simulations"</p>
Past wet-dry climate cycles inform Central Asia's future
<p>Here we present the original dat of high-resolution pollen, sediment grain size and stable isotopes for cyclic moisture changes in Lop Nur, northwestern China, Central Asia since the Last Glacial Maximum. The pollen data includes the pollen abundance, concentration and flux of dominant types. The grain size data includes sand, silt, and clay. The stable isotope data comprises C and N. </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>
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>
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>
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>
ScienceDex guides
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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.