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2,260 results for “climate change”
Fig. 3 in Phytoplankton Responses To Climate Change In The Large Lakes Of The Baltic Sea Basin
Fig. 3. Phytoplankton biomass structure in the Lake Peipsi (Lake Peipsi - left and Lake Pihkva - right column) for August 2004-2014.
Fig. 5 in Phytoplankton Responses To Climate Change In The Large Lakes Of The Baltic Sea Basin
Fig. 5. Average monthly (a – July, b – August) summer air temperature over Lake Peipsi catchment area for 1989–2014.
Data for: Environment-dependent relationships between corticosterone and energy expenditure during reproduction: insights from seabirds in the context of climate change
<p>We studied the relationship between baseline levels of the steroid hormone corticosterone and daily energy expenditure (DEE) in the little auk (<em>Alle alle</em>), an Arctic sea bird that is experiencing mounting energetic challenges due to climate change. We specifically investigated the hypothesis that there might be environment-dependent relationships between baseline corticosterone, DEE, time activity budgets, diving behavior and fitness-related traits (chick provisioning rate, adult body condition). Furthermore, we also examined whether mercury (Hg) contamination might interfere with corticosterone production, and hence potentially the capacity to upregulate DEE. In addition, we performed a phylogenetically controlled analysis across breeding seabird species to assess the relationship between baseline corticosterone and DEE, which we estimated via <span>a model derived from a phylogenetically controlled meta-analysis, </span><span>available within a <span>web-based app (‘Seabird FMR Calculator’, </span></span><span><a href="https://ruthedunn.shinyapps.io/seabird_fmr_calculator/"><span>https://ruthedunn.shinyapps.io/seabird_fmr_calculator/</span></a></span><span>) (Dunn et al. 2018). These datasets contain information on corticosterone levels, DEE, TABs and Hg in little auks, and the data used in our phylogenetically controlled analysis. Please see the READ me file for details.</span></p>
Dataset: How do news about a heatwave affect public prioritization of climate change adaptation and mitigation behaviors?
<p><span>These datasets contain survey data that was used to evaluate the effect of the exposure to heatwave news texts on people’s preference for climate mitigation and adaptation actions, as presented in the manuscript titled “<em>How do news about a heatwave affect public prioritization of climate change adaptation and mitigation behaviors?</em>”. Three versions of the dataset are available:</span></p> <ol> <li><strong>Original dataset</strong>: This version contains choice text as data points and includes all finished survey responses that passed the attention check questions (n=1209).</li> <li><strong>Original recoded dataset</strong>: This version was generated by recoding choice text into numerical values. The 'Income' variable, representing household income levels for both Canadian and US residents, was added by converting reported income ranges to a unified scale based on exchange rate equivalencies. The "Income_Canadians" and "Income_US" columns were subsequently removed to avoid repetitions. </li> <li><strong>Final dataset</strong>: This version excludes observations from participants who completed the survey in under four minutes and those who selected the same response for every item within each matrix-style question (also known as straight-lining). Additionally, responses with missing values in questions regarding political views, gender, and household income, as well as responses where participants identified as non-binary or indicated that their gender was not listed, were omitted (see “Methods” for more details). Dependent variables have been added based on the original responses, including personal-level mitigation and adaptation likelihoods, personal-level mitigation preference, and both non-weighted and weighted collective-level mitigation preference. Furthermore, the dataset includes a 'Climate Change Concern' variable, derived through principal component analysis of thirteen variables expressing participants’ climate change attitudes and efficacy beliefs concerning climate actions. Variables not used in the subsequent data analysis were removed. Age, political views, education, and income columns were standardized. The final dataset was used for the data analysis presented in the manuscript.</li> </ol> <p>The following variables/columns can be found across the three versions of the dataset:</p> <ul> <li>Dependent variables: <ul> <li>Starting with “<em>Personal_Mitigation</em>”: participant’s self-reported likelihood of taking selected personal-level climate change mitigation actions</li> <li>Starting with “<em>Personal_Adaptation</em>”: participant’s self-reported likelihood of taking selected personal-level climate change adaptation actions</li> <li>Starting with “<em>Collective_Mitigation</em>”: participant’s ranking of the collective-level climate change mitigation initiatives</li> <li>Starting with “<em>Collective_Adaptation</em>”: participant’s ranking of the collective-level climate change adaptation initiatives</li> <li><em>Personal_Mitigation_Likelihood</em>: personal-level mitigation likelihood (present only in the final dataset)</li> <li><em>Personal_Adaptation_Likelihood</em>: personal-level adaptation likelihood (present only in the final dataset)</li> <li><em>Personal_Preference</em>: personal-level mitigation preference (present only in the final dataset)</li> <li><em>Collective_Preference_Unweighted</em>: non-weighted collective-level mitigation preference (present only in the final dataset)</li> <li><em>Collective_Preference_Weighted</em>: weighted collective-level mitigation preference (present only in the final dataset)</li> </ul> </li> <li>Independent variables: <ul> <li><em>Group</em>: group that the participant was assigned to as part of the experimental intervention</li> <li><em>Distance</em>: indicates whether the participant was assigned to read about a heatwave occurring in their community or a city 6,000 km away (for experimental groups only)</li> <li><em>Severity</em>: indicates whether the participant was prompted to read about a heatwave without or with the mention of associated causalities (for experimental groups only)</li> </ul> </li> <li>Covariates and supporting variables: <ul> <li><em>Gender</em>: gender identity</li> <li><em>Identity</em>: ethnic and/or racial identity</li> <li><em>Age</em>: age</li> <li><em>Political_Views</em>: position on the liberal-conservative continuum</li> <li><em>Education</em>: highest level of education</li> <li><em>Country</em>: country of residence</li> <li><em>Canada_Province</em>: province or territory of residence (for Canadian participants only)</li> <li><em>US_State</em>: state of residence (for US participants only)</li> <li><em>Duration_Residence</em>: duration of residence in the current community</li> <li><em>Income_Canadians</em>: annual household income in Canadian dollars (for Canadian participants only)</li> <li><em>Income_US</em>: annual household income in US dollars (for US participants only)</li> <li><em>Income</em>: annual household income for both Canadian and US residents derived by converting reported income ranges to a unified scale based on exchange rate equivalencies</li> <li><em>Efficacy_Mitigation_Personal</em>: belief regarding the response efficacy of personal-level climate change mitigation actions</li> <li><em>Efficacy_Mitigation_Collective</em>: belief regarding the response efficacy of collective-level climate change mitigation actions</li> <li><em>Efficacy_Adaptation_Personal</em>: belief regarding the response efficacy of personal-level climate change adaptation actions</li> <li><em>Efficacy_Adaptation_Collective</em>: belief regarding the response efficacy of collective-level climate change adaptation</li> <li><em>Climate_Change_Importance:</em> perception of climate change as a personally important issue</li> <li>Climate_Change_Worry: level of worry about climate change</li> <li>Starting with “<em>Climate_Risk</em>”: beliefs regarding the degree of harm that climate change will cause to plants and animal species (Climate_Risk_Animals_Plants), future generations of people (Climate_Risk_Future_Generations), people in developing countries (Climate_Risk_Developing_Countries), people in participant’s country (Climate_Risk_Country), people in participant’s community (Climate_Risk_Community), and the participant personally (Climate_Risk_Personal)</li> <li>Climate_Change_Onset_Time: belief regarding when climate change will start harming people in their community</li> <li><em>Six_Americas_Segment</em>: the Global Warming's Six Americas segment participant aligns with derived based on the Six Americas Short SurveY (SASSY) Group Scoring Tool</li> <li><em>Climate_Change_Concern</em>: variable derived through PCA of thirteen variables expressing participants' climate change attitudes and efficacy beliefs pertaining to climate actions (present only in the final dataset)</li> <li><em>Survey_Duration_Seconds</em>: The amount of time it took the respondent to complete the survey</li> </ul> </li> </ul>
Data from: Climate change could fuel urinary schistosomiasis transmission in Africa and Europe
<p>This dataset contains primary, intermediate, and output data for "Climate change could fuel urinary schistosomiasis transmission in Africa and Europe". In this paper, we use mechanistic and correlative modelling to predict the distribution of schistosomiasis intermediate host snail <em>Bulinus truncatus.</em> Model projections suggest the suitable habitat for <em>B. truncatus</em> will increase by 17%, with new suitable habitat in Southern Europe and Central Africa, and a reduction in suitable habitat in the Sahel region.</p>
Figure 4 in Climate Changes of the Temperature of the Surface and Level of the Black Sea by the Data of Remote Sensing at the Coast of the Krasnodar Krai and the Republic of Abkhazia
Figure 4. Spatial variability of the climatic rate of the Black Sea level change (cm/yr) for period from 1993 to 2015.
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.
Data from: Integrating genomic data and simulations to evaluate alternative species distribution models and improve predictions of glacial refugia and future responses to climate change
<p>Climate change poses a threat to biodiversity, and it is unclear whether species can adapt to or tolerate new conditions, or migrate to areas with suitable habitats. Reconstructions of range shifts that occurred in response to environmental changes since the last glacial maximum from species distribution models (SDMs) can provide useful data to inform conservation efforts. However, different SDM algorithms and climate reconstructions often produce contrasting patterns, and validation methods typically focus on accuracy in recreating current distributions, limiting their relevance for assessing predictions to the past or future. We modeled historically suitable habitat for the threatened North American tree green ash (<em>Fraxinus pennsylvanica</em>) using 24 SDMs built using two climate models, three calibration regions, and four modeling algorithms. We evaluated the SDMs using contemporary data with spatial block cross-validation and compared the relative support for alternative models using a novel integrative method based on coupled demographic-genetic simulations. We simulated genomic datasets using habitat suitability of each of the 24 SDMs in a spatially-explicit model. Approximate Bayesian Computation (ABC) was then used to evaluate the support for alternative SDMs through comparisons to an empirical population genomic dataset. Models had very similar performance when assessed with contemporary occurrences using spatial cross-validation, but ABC model selection analyses consistently supported SDMs based on the CCSM climate model, an intermediate calibration extent, and the generalized linear modeling algorithm. Finally, we projected the future range of green ash under four climate change scenarios. Future projections using the SDMs selected via ABC suggest only minor shifts in suitable habitat for this species, while some of those that were rejected predicted dramatic changes. Our results highlight the different inferences that may result from the application of alternative distribution modeling algorithms and provide a novel approach for selecting among a set of competing SDMs with independent data.</p>
Fig. 1 in The effects of short-term climate change on the range of species: the case of the expanding European dwarf mantis Ameles spallanzania in northern Italy (Mantodea: Amelidae)
Fig. 1 – Distribution of Ameles spallanzania in Italy across a, past period and b, current period. Confirmed data refer to already known presence cells in the previous time interval.
Research data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems"
<p><strong>Research Data related to the article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em></strong></p> <p>Dear reader,</p> <p>reasearch data are provided for the research article "Understanding Climate Change and Anthropogenic Impacts on the Salinization of Low-lying Coastal Groundwater Systems" by Seibert et al. (2024) published in <em>Earth's Future</em>. The authors hope that the research data allows for a better understanding of the modeling workflow. Questions regarding the modeling approach etc. can be directed to the authors, see contact details below.</p> <p>The research data covers the following files:</p> <ul> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for the n=566 model variants. Subfolders for each model variant and corresponding files are stored in the subfolder 'model_variants'. An overview regarding the set-up of the model variants is presented in the .xlsx spreadsheet 'model_variants_overview.xlsx' in the folder 'model_variants'.</li> <li>Base data files, used as input files to iMOD-WQ (Verkaik et al., 2021), stored in the subfolder 'imod_input'. However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consider the corresponding meta-data files and/or get in touch with one of the authors for further information.</li> <li>Post-processed model output data, which was further used for model evaluation, stored in the subfolder 'model_output'.</li> <li>Figure files and the corresponding .py scripts, stored in the subfolder 'figures'.</li> </ul> <p>Meta-data files are provided with data files in the different subfolders for clarification.</p> <p>iMOD-WQ (Verkaik et al., 2021) input and .run-files were executed on the University Oldenburg High-Performance Cluster 'Rosa', funded by DFG through its Major Research Instrumentation Program, INST 184/225-1 FUGG, and the Ministry of Science and Culture (MWK) of the Lower Saxony State.</p> <p>Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/</p> <p>The DFG is thanked for SALTSA project funding (DFG project number MA 3274/9-1) within the Priority Programme ‘Regional Sea Level Change and Society (SeaLevel)’. Research related to this article further benefited from funding of the projects WAKOS (BMBF; support code 01LR2003E) and the DFG research unit FOR 5094: The dynamic deep subsurface of high-energy beaches (DynaDeep).</p> <p>Literature:</p> <p>Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092.</p> <p>Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/</p> <p>Seibert, S. L., Greskowiak, J., Oude Essink, G. H. P., & Massmann, G. (2024). Understanding climate change and anthropogenic impacts on the salinization of low‐lying coastal groundwater systems. Earth's Future, 12, e2024EF004737. https://doi.org/10.1029/2024EF004737<br><br><strong>Contact one of the authors if you have further questions</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de), Gualbert H.P. Oude Essink (Gualbert.OudeEssink@deltares.nl) or Gudrun Massmann (gudrun.massmann@uol.de)</p>
Fig. 3 in Exploiting parallels between livestock and wildlife: Predicting the impact of climate change on gastrointestinal nematodes in ruminants
Fig. 3. In marginal grazing systems in Europe sheep often occupy separate summer and winter grazing areas, analogous to the summer and winter ranges of migratory ruminants. In the uplands of Wales, UK, (shown here) sheep are often grazed on extensive areas of land at low stocking densities over the summer period, and sent to lowland dairy farms for winter grazing at higher stocking densities. (Photo: Rose, H.).
Fig. 2 in Exploiting parallels between livestock and wildlife: Predicting the impact of climate change on gastrointestinal nematodes in ruminants
Fig. 2. The relative seasonal incidence of ovine parasitic gastroenteritis (PGE) in the Southwest of England, UK, based on monthly diagnoses of (a) Nematodosis (NOS = species not otherwise specified), (b) Haemonchosis and (c) Nematodirosis (van Dijk et al., 2008).
Fig. 1 in Exploiting parallels between livestock and wildlife: Predicting the impact of climate change on gastrointestinal nematodes in ruminants
Fig. 1. Comparison of the instantaneous daily development rate of Ostertagia ostertagi (grey) and O. gruehneri (black) at a range of constant temperatures. Instantaneous daily development rates were estimated from the time to 50% development of L3, derived from data published in the literature (O. ostertagi: Rose, 1961; Pandey, 1972; Young et al., 1980) and original data (O. gruehneri: Hoar, 2012) as described by Azam et al. (2012).
Fig. 6 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect
Fig. 6. Podolica cattle in the Gallipoli Cognato Regional Park, Basilicata, southern Italy. These cattle move freely within the park's territory, helping in disseminating Ixodes ricinus to different altitudes (from 200 m to over 1000 m).
Fig. 5. A in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect
Fig. 5. A male of the winter tick Haemaphysalis inermis collected in a cold winter day in January 2010 in Basilicata, southern Italy.
Fig. 4 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect
Fig. 4. Shanghai, China: the largest city proper by population in the world. China is the world's largest carbon emitter; it accounted for 29% of global total emissions in 2012 (Olivier et al., 2013).
Fig. 3 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect
Fig. 3. Deforestation of Atlantic rainforest for the establishment of banana tree plantations in Amaraji, north-eastern Brazil.
Fig. 1 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect
Fig. 1. Climate change is contributing to sea level rise. The Boa Viagem beach is a tourist destination in Recife, north-eastern Brazil. If current trends in sea level rise persist, cities like Recife may be literally swallowed the sea in the coming decades.
Fig. 2 in Climate change, biodiversity, ticks and tick-borne diseases: The butterfly effect
Fig. 2. Sloth found on a road that crosses a region of Atlantic rainforest in Aldeia, north-eastern Brazil. Crab-eating foxes (Cerdocyon thous) and other wild animals are commonly seen crossing this road and are frequently victims of car crashes.
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.