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205 results for “long-term change”
Modeling dataset: Long-term Change in Metabolism Phenology across North-Temperate Lakes, Wisconsin, USA 1979-2019
This dataset includes model configurations, scripts and outputs to process and recreate the outputs from Ladwig et al. (2021): Long-term Change in Metabolism Phenology across North-Temperate Lakes. The provided scripts will process the input data from various sources, as well as recreate the figures from the manuscript. Further, all output data from the metabolism models of Allequash, Big Muskellunge, Crystal, Fish, Mendota, Monona, Sparkling and Trout are included.
Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode
<p>This dataset accompanies Wright et al. (2022): Influence of long-term changes in solar irradiance forcing on the Southern Annular Mode, Climate of the Past.</p> <p>This dataset contains:</p> <ul> <li><strong>Solar constant experiments</strong>: monthly files for sea level pressure (psl), surface stress east (tax), surface stress north (tay), screen temperature (tsc), and temperature at X pressure (t[0-18]) for solar constant experiments, specifically <ul> <li>control</li> <li>S+1</li> <li>S+3</li> <li>S+7</li> <li>S+35</li> <li>S-3</li> <li>S-7</li> <li>S-15</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>Transient experiments</strong>: sea level pressure (psl) and screen temperature (tsc) files covering 1-2000 CE using: <ul> <li>Steinhilber_x2 solar forcing (monthly files)</li> <li>Shapiro solar forcing (monthly files)</li> </ul> </li> </ul> <p>These transient experiments are run as an Orbital-Greenhouse gases-Solar forcing experiment, and complement Phipps et al. (2013) (https://zenodo.org/record/3908927)</p> <p> </p>
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Data to support the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362
<p>Soil organic carbon content and water content at the different pressure points, as measured by Ioanna Panagea for the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362 from the the long term experiments belonging in some of the SoilCare project partners. </p>
Shapefiles showing the locations of long-term climate change refugia and hotspots identified in the FairSeas report "A Climate Resilient Path for Ireland's Marine Protected Areas Network"
<p>Shapefiles created for the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network", an addendum chapter to "Revitalising Our Seas report: Identifying<br>Areas of Interest for Marine Protected Area Designation in Irish Waters"</p> <p>These shapefiles summarise long-term patterns that emerge from the spatial-meta analysis of physical-biogeochemical and species distribution modelling data, providing an overview of the distribution of climate change refugia and climate change hotspots across Ireland's National Marine Planning Framework between 2026 - 2069, and across the two emissions scenarios considered in the report (RCP4.5 and RCP8.5). </p> <p>Filenames refer to the specific analysis each set of shapefiles belong to: Benthic habitats, benthic megafauna, pelagic habitats, pelagic megafauna and forage fish. Details of the modelling datasets used in each of these analyses, the meta-analysis method and shapefile creation can be found in Annex A1 in the report "A Climate Resilient Path for Ireland’s Marine Protected Areas Network".</p>
Change detection technique comparison in long-term wetland monitoring: datasets and maps of the Poitevin Marsh (France)
<h3>For a full description of the methodology and results, please see the following article:</h3> <div> <div>Demarquet, Q., Rapinel, S., Gore, O., Dufour, S., Hubert-Moy, L., 2024. Continuous change detection outperforms traditional post-classification change detection for long term monitoring of wetlands. <em>International Journal of Applied Earth Observation and Geoinformation </em>133, 104142. <a href="https://doi.org/10.1016/j.jag.2024.104142">https://doi.org/10.1016/j.jag.2024.104142</a></div> <div> </div> <div>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</div> </div> <h3># Datasets</h3> <p>Points datasets are projected in WGS84 (EPSG:4326), and are provided in the open source GeoPackage format.</p> <p>The first dataset (<strong>Dataset_1.gpkg</strong>) contains training and validation points for random forest classification of EUNIS habitats in the Poitevin Marsh. This dataset consists of 3360 training and 840 validation points (total: 4200).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>CLASS</em>": EUNIS first level habitat type, classified as following:<br> <ul> <li>1: EUNIS habitat A</li> <li>2: EUNIS habitat B</li> <li>3: EUNIS habitat C1J5</li> <li>4: EUNIS habitat C3</li> <li>5: EUNIS habitat E</li> <li>6: EUNIS habitat G</li> <li>7: EUNIS habitat I</li> <li>8: EUNIS habitat J</li> </ul> </li> <li>"<em>DATE</em>": Date associated with EUNIS habitat sample</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>TYPE</em>": Either training ("<em>train</em>") or validation ("<em>test</em>") sample</li> </ul> <p>The second dataset (<strong>Dataset_2.gpkg</strong>) contains points for the Olofsson correction method. This dataset consists of 326 points where the change classes are classified as following: -10 (wetland loss), 10 (wetland gain), 100 (stable existing wetland), and 200 (stable damaged wetland).<br>Fields description:</p> <ul> <li>"<em>ID</em>": unique identifier</li> <li>"<em>LON</em>": Point longitude in decimal degrees</li> <li>"<em>LAT</em>": Point latitude in decimal degrees</li> <li>"<em>REFERENCE</em>": Change class reference</li> <li>"<em>CCDC</em>": Change class obtained from the Continuous Change Detection and Classification approach</li> <li>"<em>PCCD</em>": Change class obtained from the Post-Classification Change Detection approach</li> </ul> <p>Supplementary layout files (<strong>Dataset_1.qml</strong> and <strong>Dataset_2.qml</strong>) support formatting of the points in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># EUNIS habitat</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. </p> <p>Habitat maps are given for the two approaches in years 1984 and 2022:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_HABITAT_1984.tif</strong> and <strong>CCDC_HABITAT_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_HABITAT_1984.tif </strong>and <strong>PCCD_HABITAT_2022.tif</strong>)</li> </ul> <p>Supplementary layout files (<strong>CCDC_HABITAT_1984.qml, CCDC_HABITAT_2022.qml, PCCD_HABITAT_1984.qml, PCCD_HABITAT_2022.qml</strong>) support formatting of raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># Change detection during the 1984-2022 period</h3> <p>Maps are projected in WGS84 (EPSG:4326), and are provided in the GeoTiff format at 30m of spatial resolution. Raster values follow the classification scheme used in Dataset_2.</p> <p>Change detection maps are given for the two approaches:</p> <ul> <li>CCDC: Continuous Change Detection and Classification (<strong>CCDC_CHANGE_1984_2022.tif</strong>)</li> <li>PCCD: Traditional post-classification approach (<strong>PCCD_CHANGE_1984_2022.tif</strong>)</li> </ul> <p>Supplementary layer files (<strong>CCDC_CHANGE_1984_2022.qml</strong> and<strong> PCCD_CHANGE_1984_2022.qml</strong>) support formatting of the raster layers in QGIS software.</p> <p>--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <h3># GEE repository</h3> <p>To get direct access to GEE scripts and assets, please follow those two links:</p> <p>https://code.earthengine.google.com/?accept_repo=users/demarquetquentin/CCDC_Poitevin</p> <p>https://code.earthengine.google.com/?asset=projects/ee-quen-dem/assets/CCDC_Poitevin</p>
Supplementary material for "Long-term trends of reproductive success of black grouse Lyrurus tetrix in the southern Swiss Alps in relation to changes in climate and land use"
<p><strong>Abstract</strong></p> <p>Breeding success of an Alpine black grouse <em>Lyrurus tetrix</em> population in southern Switzerland was monitored from 1981 to 2020. This long-term dataset allows exploring relationships of reproductive rates with climate and habitat, which have shown marked changes during this period. Over the 40 years, the average elevation of black grouse breeding sites increased by around 100 m in Central/Southern Ticino but showed only a slight increase in Northern Ticino, where black grouse occur at higher elevations. Average reproductive rates in Northern Ticino remained constant throughout the study period but declined in Central/Southern Ticino. Relationships between reproductive success and weather as well as habitat variables were analysed with a multiple regression model. Temperature during the early chick-rearing phase and the time of egg-laying was positively correlated with reproductive rate. Correlations between reproductive rates and precipitation were less clear, and only small proportions of the variance in reproductive rates could be explained by precipitation. Brush forest explained the greatest amount of variation in reproductive rate (6.2%). Forest, alpine agricultural areas, and unproductive vegetation all showed a positive relationship with reproductive rate, but the proportion of the variance explained was small. Year (5.1%) and its interaction with region (2.3%) explained considerable amounts of the variance. While in Northern Ticino reproductive success did not show a negative trend when correcting for weather and habitat changes, there remained a negative trend over the years in Central/Southern Ticino. Despite the positive correlations of reproductive rate with temperature, increasing temperatures do not appear to have improved reproductive success, likely as a result of habitat changes that forced black grouse towards higher elevations. Changes in reproductive success were limited to the southern region, indicating deteriorating conditions at the edge of the distribution range.</p> <p> </p>
Temporally enhanced RSEI and Nighttime Lights Reveal Long-Term Ecological Changes and Effective Protection in China's Inaugural National Parks
<p>China's inaugural national parks play a crucial role in preserving biodiversity and maintaining ecosystem services. These protected areas are characterized by diverse landscapes and sensitive ecological environments. Over recent decades, the interplay between intensified human activities and global climate change has posed significant challenges to the ecological quality of these regions. Accurate and scientific assessment of ecological quality is essential for informed management and policy-making.</p> <p>This dataset is based on multiple MODIS datasets, incorporating NDVI, LST, WET, and NDBSI as indicators. Using principal component analysis (PCA), we produced the Improved Remote Sensing Ecological Index (RSEI) for these parks from 2000 to 2022 at a 500m spatial resolution.</p> <p>The RSEI was calculated using four component indices: greenness, heat, dryness, and wetness. Data for dryness and wetness were derived from the 8-day composite 500m resolution surface reflectance product MOD09A1. Heat was calculated using the 8-day composite 1km resolution land surface temperature product MOD11A2, which was resampled to 500m resolution. Greenness was derived from the 16-day composite 500m resolution vegetation index product MOD13A1.</p> <p>The improved RSEI calculation method enhances the temporal stability and comparability of the data, making it more suitable for long-term ecological monitoring.</p> <p>The improved RSEI effectively integrates dynamic changes of multiple variables and offers better temporal comparability for long-term ecological monitoring. Our results indicate that the ecological environment quality within the inaugural national parks significantly improved over the study period, with more noticeable improvements following the implementation of pilot conservation programs.</p> <p>This dataset provides foundational information for understanding the long-term ecological trends in China's national parks. It serves as a crucial resource for researchers, policymakers, and conservationists dedicated to the sustainable management and development of these vital ecological regions.</p> <p>The dataset contains five RAR compressed files, each corresponding to one of the national parks. These files include the Remote Sensing Ecological Index (RSEI) data from 2000 to 2022 for each respective park:</p> <ul> <li><strong>NTLNP-RSEI.rar</strong>: Contains the RSEI data for the Northeast Tiger and Leopard National Park (NTLNP) from 2000 to 2022.</li> <li><strong>HTRNP-RSEI.rar</strong>: Contains the RSEI data for the Hainan Tropical Rainforest National Park (HTRNP) from 2000 to 2022.</li> <li><strong>WNP-RSEI.rar</strong>: Contains the RSEI data for the Wuyishan National Park (WNP) from 2000 to 2022.</li> <li><strong>SNP-RSEI.rar</strong>: Contains the RSEI data for the Sanjiangyuan National Park (SNP) from 2000 to 2022.</li> <li><strong>GPNP-RSEI.rar</strong>: Contains the RSEI data for the Giant Panda National Park (GPNP) from 2000 to 2022.</li> </ul> <p>Each of these compressed files includes the improved RSEI calculations for the respective national park, providing a comprehensive view of the ecological quality changes over the 22-year period.</p> <p>The details of the data are as follows:</p> <ul> <li><strong>Data Format</strong>: GeoTiff</li> <li><strong>Pixel Values</strong>: Represent RSEI, ranging from 0 to 1, with no units.</li> <li><strong>Compatibility</strong>: The data can be directly opened and processed using remote sensing and GIS software such as ENVI and ArcGIS.</li> <li><strong>Data Quality</strong>: Due to the application of water and snow masks to remove the influence of water bodies and snow/ice on the WET component, there are some missing data areas.</li> </ul> <p>These datasets offer valuable insights into the ecological quality changes within each national park over the specified period, making them essential for researchers, policymakers, and conservationists involved in the sustainable management and development of these protected areas.</p> <p>For using the data and code provided in this dataset, please cite the following paper:</p> <p>Wen, C., Long, T., He, G., Jiao, W., & Jiang, W. (2025). Temporally enhanced RSEI and nighttime lights reveal long-term ecological changes and effective protection in China’s inaugural national parks. <em>Ecological Indicators, 170</em>, 112981. <a href="https://doi.org/10.1016/j.ecolind.2024.112981" target="_new" rel="noopener">https://doi.org/10.1016/j.ecolind.2024.112981</a></p> <p>The calculation of the RSEI is completed using Google Earth Engine. The link to the calculation code is:</p> <p><a href="https://code.earthengine.google.com/fab5452cd224d1f06226aece4c1a1016">https://code.earthengine.google.com/089d74f423e91a0da9490f5098c55021</a></p>
Coral growth data for the research article "Reconstruction of long-term sub-lethal effects of warming on a temperate coral in a climate change hotspot" in Journal of Animal Ecology
<p>This repository contains the coral growth data files used to generate the results for the following article:</p> <p> </p> <p>MJ. Vergotti, JP. D’Olivo, T. Brachert, P. Capdevila, J. Garrabou, C. Linares, P. Spreter, DK. Kersting (2024) Reconstruction of long-term sub-lethal effects of warming on a temperate coral in a climate change hot-spot. <em>Journal of Animal Ecology</em>. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.14225</p> <p> </p> <p><strong>Abstract: </strong>The impact of warming on zooxanthellate corals is widespread, from tropical to temperate seas, with their associated mortalities causing global concern. The temperate coral <em>Cladocora caespitosa</em> is the only zooxanthellate coral with reef-building capacity in the Mediterranean Sea, a climate change hotspot with warming rates triple the global average. Over the past two decades, <em>C. caespitosa</em> populations have suffered severe mortality events associated with marine heatwaves (MHWs). However, with monitoring efforts beginning, at best, in the 2000s, the occurrence of MHWs before to that period, as well as the sub-lethal effects of these events remain poorly understood. Here we use sclerochronology to reconstruct the histories of past stress events and long-term sub-lethal effects on <em>C. caespitosa</em> in three locations within the NW Mediterranean Sea, each with different environmental conditions. Skeletal extension, density and calcification rates were compared to the <em>in situ</em> seawater temperature of each site to assess their relationship. Additionally, we assessed the occurrence of skeletal growth anomalies to reconstruct stress events between 1991 and 2021, a period that encompasses the onset and evolution of warming-related mass mortality events in the NW Mediterranean Sea. Our results reveal a positive association between calcification and temperature, following a latitudinal temperature gradient. However, the evolution of the likelihood distribution of growth rates in the warmest site (Columbretes Islands) since the 1990s indicates a decrease in linear extension and calcification rates during the most recent years. With the increase in the frequency of MHWs and growth anomalies during the last decade, this decline suggests a recurrence in physiological stress events. These results unravel information on the long-term impacts of warming on coral growth and highlight the potential of applying sclerochronology to reconstruct sub-lethal effects of warming using <em>C. caespitosa</em>. </p> <p> </p> <p> </p> <p><strong>Funding</strong>: This research is supported by the Horizon 2020 program of research and innovation of the European Union under the MaCoBioS grant agreement, by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, project no. 401447620) and by the Spanish Ministry of Science, Innovation and Universities under the project UndResCoral (project no. PID2022-137539OA-C22). D.K.K. was supported by a Ramon y Cajal postdoctoral grant funded by the Ministry of Science and Innovation (PEICTI 2021–2023; grant no. RYC2021-033576-I). C.L. acknowledges the support by ICREA Academia. J.G. acknowledges the grant “Severo Ochoa Centre of Excellence” accreditation (CEX2019-000928-S) funded by AEI 10.13039/501100011033.</p>
Database of a study on long-term land cover changes in Thừa Thien Huế Province, Central Vietnam
<p>Data provided here forms the basis of a study which is published in the journal Land Use Policy (year 2023) under the title 'The nature of a ‘forest transition’ in Thừa Thien Huế Province, Central Vietnam – A study of land cover changes over five decades'. The database contains the image files of the maps (and associated legends) shown in the publication. Further data (currently under use for other related research) will eventually be added to the repository.</p>
Lake browning generates a spatiotemporal mismatch between DOC and limiting nutrients, 2018 spatial survey, modeled light limitation and whole-lake productivity changes in long-term Adirondack lake survey 1994-2012
This data set contains information on a spatial survey of dissolved organic matter (DOM) across lakes and wetlands in the Northeast and Midwest, USA and modeled long-term changes in light limitation and whole-lake productivity in a suite of lakes in the Adirondack State Park, New York, USA. Widespread long-term increases in DOM have been observed in many lakes in a process known as browning. This data set enables the assessment of potential changes in dissolved absorbance and dissolved organic nutrients associated with browning. This data set accompanies a manuscript in review at Limnology and Oceanography: Letters.
Fig. 5 in Long-term changes in avian relative abundances in relation to human disturbance in a tropical dry forest in central Myanmar
Fig. 5. Human detections along five, two-kilometre transects, one each in five habitats/subtypes (young dipterocarp forest, flooded dipterocarp forest, mixed deciduous forest, mature dipterocarp forest, and wetlands) per month in Chatthin Wildlife Sanctuary. Effort = number of habitat subtypes * total months sampled per year * 2 km, X-axis= year, Y-axis= human disturbance index (number of human detections per one unit of effort).
Fig. 1 in Long-term changes in avian relative abundances in relation to human disturbance in a tropical dry forest in central Myanmar
Fig. 1. Map of Chatthin Wildlife Sanctuary with land-cover changes; flooded dipterocarp forest (FL), mixed deciduous forest (MD) and young dipterocarp forest (YI) in Chatthin Wildlife Sanctuary between 1999 and 2020 and locations of bird survey points.
Fig 4 in Long-term changes in avian relative abundances in relation to human disturbance in a tropical dry forest in central Myanmar
Fig 4. Seasonal (y-axis) and long-term trends of abundances of six avian guilds in Chatthin Wildlife Sanctuary from 1999 to 2020. Redder = higher abundances, white/paler = lower abundances, and grey = no data.
Data for: Long-term body size change in multiple landbird species, long-term change in temperature and precipitation as well as associations between temperature, precipitation, and morphological change in multiple landbird species, 2004 – 2019, 2021 - 2022.
<p>Six data sets used to look for long-term change in precipitation and temperature, body size change and possible environmental drivers of morphological change in birds captured during spring or fall migration in and around Lackawanna State Park, northeastern Pennsylvania, USA.</p> <p>The file labeled daily_temp_precip.csv contains daily precipitation and average daily temperature data from the Scranton/Wilkes Barre Airport (Avoca, Pennsylvania, USA) and the file called daily_temp_precip_1400 contains daily precipitation and daily temperature data from weather stations within 1,400 km of our study site location (41.6<sup>o</sup>N, 75.7<sup>o</sup>W), bounded by 80<sup>o</sup> W and 70<sup>o</sup>W longitude.</p> <p>The file called band_data_final.csv contains data collected from the first capture of individuals of multiple species during spring or fall migration, the file called all_hy_env_morph.csv contains temperature and precipitation anomaly data from Scranton/Wilkes Barre Airport (Avoca, Pennsylvania, USA), as well as morphological data from the first capture of all fall migrating young of the year.</p> <p>The file called all_hy_env_morph_1400.csv contains temperature and precipitation anomaly data from weather stations within 1,400 km of our study site location (41.6<sup>o</sup>N, 75.7<sup>o</sup>W), bounded by 80<sup>o</sup> W and 70<sup>o</sup>W longitude as well as morphological data from the first capture of all fall migrating young of the year while the file called local_hy_env_morph.csv contains temperature and precipitation anomaly data as well as first capture of local young of the year.</p>
Dataset: Long-term Change in Metabolism Phenology in North Temperate Lakes
<p>Scripts, model configurations and outputs to process the data and recreate the figures from Ladwig, R., Appling, A., Delany, A., Dugan, H.A., Gao, L., Lottig, N., Stachelek, J., Hanson, P.C.: Long-term Change in Metabolism Phenology in North-Temperate Lakes.</p> <p>This repository includes the setup and output from the metabolism model ran on the lakes Allequash, Big Muskellunge, Crystal, Fish, Mendota, Monona, Sparkling and Trout. Scripts to run the model are located under /src (odem_min_indexed_sed.stan, run_odem_chtc_sed.R, stan_utility.R, stan_utility_chtc.R) together with the scripts to create the driver data, 0_Model_Input.R, and the processed results for the discussion of the paper, 1_Postprocessing.R. Addtionally, all scripts to recreate the figures from the manuscript are also included in /src.</p> <p>The figures are located under /Figures and processed output under /Processed_Output.</p>
Fig. 1. The potential distribution map for B in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 1. The potential distribution map for B. bombina under contemporary climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
Fig. 4. The potential distribution map for B. bombina under projected 2050 in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 4. The potential distribution map for B. bombina under projected 2050 climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
Fig 3 in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig 3. Response curve showing how the logistic prediction changes as the environmental variable Bio2 (Mean diurnal temperature range, oC, X-axis) is varied, keeping all other environmental variables at their average sample value. The curve shows the mean response of the 10 replicate Maxent runs (red) and and the mean +/– one standard deviation (blue).
Long-term variations of daily insolation and Quaternary climatic changes
<p>Supplementary material to:</p> <p>Berger A. L. (1978), Long-term variations of daily insolation and Quaternary climatic changes, Journal of the Atmospheric Sciences, (35) 2362-2367 doi:10.1175/1520-0469(1978)035<2362:LTVODI>2.0.CO;2</p> <p>Tables and FORTRAN computer code for the computation of daily mean insolation, according to the solution BER78.</p> <p>This solution is commonly referred to as BER78.</p> <p>Material uploaded to Zenodo by Michel Crucifix on 2022-10-14.</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.