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601 results for “Global changes”
Figure 3 in How can global climate change influence the geographic distribution of the eucalyptus yellow beetle? Modeling and prediction for Brazil
Figure 3. Predicting of potential areas to the occurrence of Costalimaita ferruginea in the period of 2061-2080, in two climate change scenarios, Representative Concentration Pathways (RCP) 4.5 e 8.5 (W/m2), using the algorithm Envelope Score (AUC = 0.808). The numbers 1 to 5 represent the Brazilian biomes, being 1 = Amazônia, 2 = Caatinga, 3 = Cerrado, 4 = Pantanal, 5 = Mata Atlântica e 6 = Pampa.
Figure 2 in How can global climate change influence the geographic distribution of the eucalyptus yellow beetle? Modeling and prediction for Brazil
Figure 2. Predicting of potential areas to the occurrence of Costalimaita ferruginea in the period of 2041-2060, in two climate change scenarios, Representative Concentration Pathways (RCP) 4.5 e 8.5 (W/m2), using the algorithm Envelope Score (AUC = 0.808). The numbers 1 to 5 represent the Brazilian biomes, being 1 = Amazônia, 2 = Caatinga, 3 = Cerrado, 4 = Pantanal, 5 = Mata Atlântica e 6 = Pampa.
Correlation of global temperature change and the number of children born in Vienna per year
<p>The datasets contains values from 2002 to 2017<br> The first row of the file contains the column titles (= the header).<br> Every row afterwards contains values separated by a semi-colon:<br> index; year; mean near surface temperature deviation; number of children born in Vienna<br> <br> </p>
Environmental behavior of novel "smart" anti-corrosion nanomaterials in a global change scenario
<div>The present dataset contains dynamic light scattering data and quantification of anions (corrosion inhibitors) and target metals (Zn and Al) in saltwater dispersions aiming to assess and compare the environmental behavior of four anti-corrosion nanomaterials in the following conditions: “temperate seawater” (T=20 ºC, pH=8.0, without HA); “tropical seawater” (T=30 ºC, pH=8.0, without HA), “acidified temperate seawater” (T=20 ºC, pH=7.6, without HA), “acidified tropical seawater” (T=30 ºC, pH 7.6, without HA); “temperate seawater enriched with natural organic matter (NOM)” (T=20 ºC, pH=8.0, with HA); “tropical seawater enriched NOM” (T=30 ºC, pH=8.0, with HA).</div> <div> </div> <div> </div>
Changes in four decades of near-CONUS tropical cyclones in an ensemble of 12km thermodynamic global warming simulations
<p>Snapshot level data of TC extractions from the thermodynamical global warming runs described in "Changes in four decades of near-CONUS tropical cyclones in an ensemble of 12km thermodynamic global warming simulations."</p>
Data and analysis code for Zhou et al. 2024, Global Change Biology
<p>This dataset accompanies the paper:</p> <p>Zhou, J., Zhu, P., Kluger, D.M., Lobell, D.B., and Jin, Z. 2024. Changes in the yield effect of the preceding crop in the US Corn Belt under a warming climate. Global Change Biology</p>
Climate change exposure and vulnerability of the global protected area estate from an international perspective
<p>Aim: Protected areas are essential to conserve biodiversity and ecosystem benefits to society under increasing human pressures of the Anthropocene. Anthropogenic climate change, however, threatens the enduring effectiveness of protected areas in conserving biodiversity and providing ecosystem services, because it modifies and redistributes biodiversity with unknown consequences for ecosystem functioning within protected areas. Here we assess (1) the climate change exposure of the global terrestrial protected area estate and (2) the climate change vulnerability of national protected area estates.</p> <p>Location: Terrestrial protected areas worldwide.</p> <p>Methods: We calculated local climate change exposure as predicted climate anomalies between the present and 2070 using ten global climate models, two emission scenarios (RCP 4.5 and 8.5) and the finest spatial resolution available for global climate projections (approx. 1 km). We estimated the climate change vulnerability of national protected area estates by analysing countrywide relationships between protected areas' climate anomalies and other protected area characteristics, i.e. area, elevation, terrain ruggedness, human footprint and irreplaceability for globally threatened species.</p> <p>Results: We found predicted climate anomalies highest in protected areas of (sub-)tropical countries. The correlations between climate anomalies and protected area characteristics strongly differ between countries. Globally, protected areas showing large climate anomalies tend to be at high elevation and highly irreplaceable for threatened species, increasing climate change vulnerability. These protected areas are relatively large in area, of high topographic heterogeneity and less pressured by humans, decreasing climate change vulnerability.</p> <p>Main conclusion: This study reveals potential hotspots of climate change impact inside the terrestrial protected area estate. It thus supports and guides climate-smart conservation policy and management, particularly national to local authorities, to ensure the future effectiveness of protected areas in preserving biodiversity and ecosystem benefits under climate change.</p>
Data from: Pathways to global-change effects on biodiversity: New opportunities for dynamically forecasting demography and species interactions
<p>In structured populations, persistence under environmental change is threatened when abiotic factors simultaneously negatively affect survival and reproduction of several life-cycle stages. Such effects can then be exacerbated when species interactions generate reciprocal feedbacks between the demographic rates of the different species. Despite the importance of such demographic feedbacks, forecasts that account for them are severely limited as individual-based data on interacting species are perceived to be essential for such mechanistic forecasting - but are rarely available. This dataset is the input to showcase a state-of-the-art Bayesian method to infer and project stage-specific survival and reproduction from abundance data for several interacting species in a Mediterranean shrub community.</p>
Global diversity patterns of larger benthic foraminifera under future climate change
<p><span>Global warming threatens the viability of tropical coral reefs and associated marine calcifiers, including symbiont-bearing larger benthic foraminifera (LBF). The impacts of current climate change on LBF are debated because they were particularly diverse and abundant during past warm periods. Studies on the responses of selected LBF species to changing environmental conditions reveal varying results. </span><span>Based on a comprehensive review of the scientific literature on LBF species occurrences, we applied species distribution modeling using Maxent to estimate present-day and future species richness patterns on a global scale for the time periods 2040–2050 and 2090–2100. </span><span> </span><span>For our future projections, we focus on Representative Concentration Pathway 6.0 from the Intergovernmental Panel on Climate Change, which projects mean surface temperature changes of +2.2°C by the year 2100. This data set comprises all raw data and results. </span>Our results suggest that species richness in the Central Indo-Pacific is two to three times higher than in the Bahamian ecoregion, which we have identified as the present-day center of LBF diversity in the Atlantic. Our future predictions project a dramatic temperature-driven decline in low-latitude species richness and an increasing widening bimodal latitudinal pattern of species diversity. While the central Indo-Pacific, now the stronghold of LBF diversity, is expected to be most pushed outside of the currently realized niches of most species, refugia may be largely preserved in the Atlantic. LBF species will face large-scale non-analogous climatic conditions compared to currently realized climate space in the near future, as reflected in the extensive areas of extrapolation, particularly in the Indo-Pacific. Our study supports hypotheses that species richness and biogeographical patterns of LBF will fundamentally change under future climate conditions, possibly initiating a faunal turnover by the late 21st century.</p>
High trophic level feedbacks on global ocean carbon uptake and marine ecosystem dynamics under climate change (Dupont et al., GBC)
<p>Files used to make the analysis in the paper "High trophic level feedbacks on global ocean carbon uptake and marine ecosystem dynamics under climate change" (Dupont et al., accepted in GBC)</p> <p>- HTL_LTL_figures.ipynb is the python notebook in which are computed the different terms to make the figures of the paper </p> <p>- histrcp85.1-PISAPE-N-OW** and piCtrl2-PISAPE-N-OW** files contain the raw outputs of the one way (OW) simulation</p> <p>- histrcp85.1-PISAPE-N-TW* and piCtrl2-PISAPE-N-TW* files contain the raw outputs of the two way (TW) simulation</p> <p>- all files ending with *rmp_f.nc/ *regrid.nc/ *f20.nc are regridded files to make maps used in the paper. More details can be found in the python noteboook (briefly, dAT_* = change in active export, dDIC_*= change in dissolved inorganic carbon, dEPC200_* = change in carbon export at 200m depth, OW/TW_<a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/TW_dBMapermp_f.nc">dBMape</a>* = OW/TW change in small high trophic levels biomass, OW/<a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/TW_dBMapermp_f.nc">TW_dBM</a>meszo* = OW/TW change in mesozooplankton biomass)</p> <p>- <a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/egestt2_2.nc">egestt2_2.nc</a>, excrett2_2.nc and graztt2_2.nc are the outputs of egestion, excretion and grazing terms used to compute the active export (AT). </p>
Data from: Potential effects of future climate change on global reptile distributions and diversity
<p class="first-paragraph"><span><strong>Aim:</strong></span><span> Until recently, complete information on global reptile distributions has not been widely available. Here, we provide the first comprehensive climate impact assessment for reptiles on a global scale.</span></p> <p class="western"><span><strong>Location:</strong></span><span> Global, excluding Antarctica</span></p> <p class="western"><span><strong>Time period:</strong></span><span> 1995, 2050, 2080</span></p> <p class="western"><span><strong>Major taxa studied:</strong></span><span> Reptiles</span></p> <p class="western"><span><strong>Methods:</strong></span><span> We modelled the distribution of 6,296 reptile species and assessed potential global as well as realm-specific changes in species richness, the change in global species richness across climate space, and species-specific changes in range extent, overlap and position under future climate change. To assess the future climatic impact on 3,768 range-restricted species, which could not be modelled, we compared the future change in climatic conditions between both modelled and non-modelled species.</span></p> <p class="western"><span><strong>Results:</strong></span><span> Reptile richness was projected to decline significantly over time, globally but also for most zoogeographic realms, with the greatest decrease in Brazil, Australia and South Africa. Species richness was highest in warm and moist regions, with these regions being projected to shift further towards climate extremes in the future. Range extents were projected to decline considerably in the future, with a low overlap between current and future ranges. Shifts in range centroids differed among realms and taxa, with a dominating global poleward shift. Non-modelled species were significantly stronger affected by projected climatic changes than modelled species.</span></p> <p class="western"><span><strong>Main conclusions:</strong></span><span> With ongoing future climate change, reptile richness is likely to decrease significantly across most parts of the world. This effect as well as considerable impacts on species' range extent, overlap, and position were visible across lizards, snakes and turtles alike. Together with other anthropogenic impacts, such as habitat loss and harvesting of species, this is a cause for concern. Given the historical lack of global reptile distributions, this calls for a re-assessment of global reptile conservation efforts, with a specific focus on anticipated future climate change.</span></p>
Data for: The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis
<p><strong><em>Data description</em></strong></p> <p>This dataset includes detailed information about five different types of land use change reported in “The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis (Agriculture, Ecosystems and Environment; <a href="https://doi.org/10.1016/j.agee.2023.108402)">https://doi.org/10.1016/j.agee.2023.108402)</a>”. </p> <p> </p> <p>Lists of five different types of land use change</p> <p>1) conversion of primary forest to cropland</p> <p>2) conversion of primary forest to grassland</p> <p>3) conversion of cropland to forest</p> <p>4) conversion of grassland to forest</p> <p>5) conversion of grassland to cropland</p> <p> </p> <p>Lists of detailed information</p> <ul> <li>Land use change (pre-LUC, post-LUC)</li> <li>Country, Location, Geographic position (Longitude, Latitude) </li> <li>Altitude (m)</li> <li>Climate zone</li> <li>Weather [rainfall (mm yr<sup>-1</sup>) and temperature (°C)]</li> <li>Reported time of change (years)</li> <li>Vegetation type (pre-LUC, post-LUC)</li> <li>Fertilizer (pre-LUC, post-LUC: type, application; change)</li> <li>Soil sampling depth (cm)</li> <li>Soil type [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil pH, bulk density, CEC [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil organic carbon [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil total nitrogen [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil total phosphorus [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil C:N [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil C:P [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Soil N:P [units, pre-LUC, post-LUC, change rate (%)]</li> <li>Reference</li> </ul> <p> </p> <p><em><strong>Data collection method</strong></em></p> <p>We analyzed five different types of LUC: 1) conversion of primary forest to cropland, 2) conversion of primary forest to grassland, 3) conversion of cropland to forest, 4) conversion of grassland to forest, and 5) conversion of grassland to cropland.</p> <p>We classified primary forest as forest that had not previously been cleared and used for other land uses. The conversion of cropland or grassland to forest includes naturally generated and intentionally planted forest. Cropland is land used for growing agricultural crops and may include short pasture phases, and grassland is land used continuously for grazing purposes, but may include occasional and repeated pasture-renewal phases.</p> <p>While we tried to make categorical distinctions between these land-use types, land uses are often more fluid in practice, which may not always have been stated in the publications underlying our data compilation.</p> <p>When a paper reported both contents and stocks, we used the stock-based measure. We used reported stocks if the original work had already been corrected to equivalent soil mass (Ellert and Bettany, 1995) or if corrected stocks had been reported in previous reviews or meta-analyses (Don et al., 2011; Poeplau et al., 2011; Guo and Gifford, 2002). Where bulk-density correction had not been applied, we tried to make those corrections to estimate changes to equivalent soil mass if studies provided sufficient information on soil bulk density and depth, using the method of Zhang et al. (2004). If that was not possible, we used the reported SOC, TN, or TP contents.</p> <p> </p> <p><em><strong>Acknowledgements</strong></em></p> <p>We thank scientists who measured, analyzed, and published the data compiled for this study. We are especially grateful to Drs. Axel Don, Christopher Poeplau, Lex Bouwman, and Gaihe Yang, who provided their global meta-data through personal communication. D.-G.K. acknowledges support from the IAEA CRP D15020. M.U.F.K and L.L.L. were supported by the Strategic Science Investment Fund (SSIF) of New Zealand’s Ministry of Business, Innovation and Employment.</p>
Global transportation infrastructure exposure to the change of precipitation in a warmer world
<p>This repository provides the base data to perform a global transport asset exposure analysis for extreme precipitation under climate change.In this study, we comprehensively analyze the exposure of road and railway infrastructure assets to changes in precipitation return periods globally.</p> <p>For more details, please see:</p> <p>Liu, K., Wang, Q., Wang, M. <em>et al.</em> Global transportation infrastructure exposure to the change of precipitation in a warmer world. <em>Nat Commun</em> <strong>14</strong>, 2541 (2023). https://doi.org/10.1038/s41467-023-38203-3</p>
Kilometer-scale global warming simulations and active sensors reveal changes of tropical deep convection
<p>This zip file contains data and codes to reproduce the figures of a manuscript on X-SHiELD.</p> <p>Contact mbolot@princeton.edu for questions.</p>
The practice and promise of temporal genomics for measuring evolutionary responses to global change
<p>Understanding the evolutionary consequences of anthropogenic change is imperative for estimating long-term species resilience. While contemporary genomic data can provide us with important insights into recent demographicic histories, investigating past change using present genomic data alone has limitations. In comparison, temporal genomics studies, defined herein as those that incorporate time series genomic data, leverage museum collections and repeated field sampling to directly examine evolutionary change. As temporal genomics is applied to more systems, species, and questions, best practices can be helpful guides to make the most efficient use of limited resources. Here, we conduct a systematic literature review to synthesize the effects of temporal genomics methodology on our ability to detect evolutionary changes. We focus on studies investigating recent change within the past 200 years, highlighting evolutionary processes that have occurred during the past two centuries of accelerated anthropogenic pressure. We first identify the most frequently studied taxa, systems, questions, and drivers, before highlighting overlooked areas where further temporal genomics studies may be particularly enlightening. Then, we provide guidelines for future study and sample designs while identifying key considerations that may influence statistical and analytical power. Our aim is to provide recommendations to a broad array of researchers interested in using temporal genomics in their work.</p>
Data from: Global change in brain state during spontaneous and forced walk in Drosophila is composed of combined activity patterns of different neuron classes
<p><span>Movement-correlated brain activity has been found across species and brain regions. Here, we used fast whole-brain lightfield imaging in adult <em>Drosophila </em>to investigate the relationship between walk and brain-wide neuronal activity. We observed a global change in activity that tightly correlated with spontaneous bouts of walk. While imaging specific sets of excitatory, inhibitory, and neuromodulatory neurons highlighted their joint contribution, spatial heterogeneity in walk- and turning-induced activity allowed parsing unique responses from subregions and sometimes individual candidate neurons. For example, previously uncharacterized serotonergic neurons were inhibited during walk. While activity onset in some areas preceded walk onset exclusively in spontaneously walking animals, spontaneous and forced walk elicited similar activity in most brain regions. These data suggest a major contribution of walk and walk-related sensory or proprioceptive information to global activity of all major neuronal classes.</span></p>
Disturbance alters transience but nutrients determine equilibria during grassland succession with multiple global change drivers
<p>Disturbance and environmental change may cause communities to converge on a steady state, diverge towards multiple alternative states, or remain in long-term transience. Yet, empirical investigations of successional trajectories are rare, especially in systems experiencing multiple concurrent anthropogenic drivers of change. We examined succession in old field grassland communities subjected to disturbance and nitrogen fertilization using data from a long-term (22-year) experiment. Regardless of initial disturbance, after a decade communities converged on steady states largely determined by resource availability, where species turnover declined as communities approached dynamic equilibria. Species favored by the disturbance were those that eventually came to dominate the highly fertilized plots. Furthermore, disturbance made successional pathways more direct under low nutrients, revealing an important interaction effect between nutrients and disturbance as drivers of community change. Our results underscore the dynamical nature of grassland and old field succession, demonstrating how community properties such as beta-diversity change through transient and equilibrium states.</p>
Dataset - Mumme et al. 2023 Global Change Biology
<p>Dataset used in Mumme et al. (2023) Wherever I may roam – Human activity alters movements of red deer (<em>Cervus elaphus</em>) and elk (<em>Cervus canadensis</em>) across two continents. For full list of funding information, please see the acknowledgement section of the original article (<a href="https://doi.org/10.1111/gcb.16769">https://doi.org/10.1111/gcb.16769</a>).</p>
Data from: Climate change alters global invasion vulnerability among ecoregions
<p><strong>Aim</strong>: We assess climate similarity among global freshwater and terrestrial ecoregions under historical and future climate scenarios to determine where climate change will impact the climate filter of invasion process.</p> <p><strong>Location</strong>: Global.</p> <p><strong>Methods</strong>: We used the Climatch algorithm to conduct a climate-match analysis to quantify the climate similarity between freshwater and terrestrial ecoregions of the world. Climate match was modelled between all freshwater and terrestrial ecoregions. The analysis was conducted under historical climates and projected climates of 2090 under three shared-socioeconomic pathways SSP2-4.5, SSP3-7.0, SSP5-8.5. Climate matches of each ecoregion were presented as mean climate match to all other ecoregions. Friedman's non-parametric rank sum two-way analysis of variance with repeated measures was used to examine differences in climate match between climate scenarios. </p>
Data and code for Global change drives modern plankton communities away from pre-industrial state
<p>Data and R code for "Global change drives modern plankton communities away from pre-industrial state" by Lukas Jonkers, Helmut Hillebrandt and Michal Kucera (https://doi.org/10.1038/s41586-019-1230-3).</p> <p>Compare planktonic foraminifera species assemblages from sediments and sediment traps.</p> <p>Scripts written by Lukas Jonkers</p> <p>DATA SOURCES<br> * HadISST: Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. Journal of Geophysical Research: Atmospheres 108, doi:10.1029/2002JD002670 (2003).<br> * ERSST v5: Huang, B. et al. NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 5. Monthly mean. NOAA National Centers for Environmental Information. doi:10.7289/V5T72FNM. Access date: 14 Sep 2018. (2017).<br> * sediment assemblages: Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. Scientific Data 4, 170109, doi:10.1038/sdata.2017.109 (2017).<br> * sediment traps: citations provided in data files</p> <p>DATA<br> 1. Planktonic foraminifera shell flux time series<br> 1.1. all data: dat_sel.RDS<br> 1.2. time series with >125 and >150 micron data: dat_small.RDS<br> 1.3. shell flux data in csv format: shell_flux_data.csv</p> <p>2. ForCenS core top sediment assemblages<br> 2.1. all data (excluding duplicates and samples with incomplete taxonomy): forcens_trimmed_compare.RDS<br> 2.2. all data, split by region: species_domains_compare.RDS<br> 2.3. indices of samples: domain_indeces_compare.RDS</p> <p>3. SST<br> 3.1. average SST for each sample in ForCenS for 1870-1899 period based on HadISST: forcens_HadSST_1870-1899.RDS<br> 3.2. average SST for each sample in ForCenS for 1854-1883 period based on ERSST v5: forcens_ERSST_1854-1883.RDS<br> 3.3. average SST for each sediment trap site for 1870-1899 period based on HadISST: traps_HadSST_1870-1899.RDS<br> 3.4. average SST for each sediment trap site for 1854-1883 period based on ERSST v5: traps_ERSST_1854-1883.RDS<br> 3.5. average SST for each sediment trap site for deployment period based on HadISST: traps_HadSST_period.RDS<br> 3.6. average SST for each sediment trap site for deployment period based on ERSST v5: traps_ERSST_period.RDS<br> 3.7. linear SST trend between 1870 and 2015 based on HadISST: hadisst_trend_1870-2015.RDS<br> 3.8. average SST for period of sediment trap observations: hadisst_mean_1978-2013.RDS</p> <p>CODE<br> 1. get_ForCenS.R: selection of ForCenS data. Used to generate data 2.1-2.3<br> 2. make_polygons.R: make circles with 100 km radius around trap and core tope sites used in 4 and 5<br> 3. make_polygon_function.R: used by 2<br> 4. extract_HadSST.R: extraction of data 3.1, 3.3, 3.5, 3.7, 3.8<br> 5. extract_ERRSTv5.R: extraction of data 3.2, 3.4, 3.6<br> 6. make_annual_assemblages.R: process shell flux time series (data 1.1 and 1.2)<br> 7. make_annual_fluxes_function.R: used by 6<br> 8. compare_trap_sed_publish.R: code to compare sediment trap and core top assemblages<br> 9. compare_figs.R: code to create figures<br> 10. maps_robinson.R: code to create maps</p>
ScienceDex guides
Understand access before you commit
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.