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2,260 results for “Climatic change”
Leaving disturbance legacies conserves boreal conifers and maximizes net CO2 absorption under climate change and more frequent and larger windthrow regimes
<p>No description provided.</p>
Fig. 3 in Den phenology and reproductive success of polar bears in a changing climate
Fig. 3.—Den locations for female polar bears (Ursus maritimus) that were observed with (dens that produced cubs) or without cubs (dens that did not produce cubs) following den emergence. Females were observed on average 37 days after emergence from dens.
Fig. 2 in Den phenology and reproductive success of polar bears in a changing climate
Fig. 2.—Examples of control charts with temperature data measured by thermistors onboard satellite collars fitted to adult female polar bears (Ursus maritimus) used to identify denning and to estimate entrance into and emergence from dens. Entrance and emergence dates were estimated as the median date between observations within and above control limits at the start and end of a denning event (shown as horizontal dashed lines).
Fig. 1 in Den phenology and reproductive success of polar bears in a changing climate
Fig. 1.—Locations of maternal dens of polar bears (Ursus maritimus) identified using temperature-sensor data collected from satellite radiocollars deployed on adult female polar bears in the Chukchi Sea and Beaufort Sea subpopulations. Red lines indicate IUCN Polar Bear Specialist Group identified subpopulation boundaries.
Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change (Data)
<p>This repository contains the data necessary to reproduce the study developed in Legrand et al. (2023) "Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change"</p>
"The main message is that sustainability would help" – Reflections on takeaway messages of climate change data visualizations.
<p>As part of a larger project investigating expert opinions on public climate change communication and the role of data visualizations1, we conducted semi-structured interviews with 17 experts in the fields of climate change, science communication, or data visualization. We also interviewed six members of the general public with no professional background in either of these areas, who we refer to as lay participants. In the following, lay participants are identified as L-1 to L-6 and expert participants as E-1 to E-17.</p> <p>We used two example visualizations from online news sources (if preferred, translated to German) as a discussion basis and asked participants about the main takeaway message the visualization author wanted to convey. Example visualization 1 was also shown to 17 experts, resulting in a total of 23 formulated takeaway messages. Example visualization 2 was shown to all six lay participants and to seven experts, resulting in a total of 13 messages. For both example visualizations and an overall count of 36 formulated takeaway messages for thematic analysis, we have observed variations in the included contents and the length/abstraction of messages, as well as in the sensemaking process itself among the two participant groups, but also between lay and expert participants. This document provides an overview of those 36 takeaway messages formulated by the study participants.</p>
African Climate Hazard Assessment; The impacts of climate change and the vulnerability of African nations
<p>The ACHA Index assesses the vulnerability to climate hazards for African nations. The uploaded datasets include the final index rankings as well as the individual aggregations of each hazard.</p>
Bold Park reptile species capture data for: Decadal abundance patterns in an isolated urban reptile assemblage: Monitoring under a changing climate
<p class="MsoNormal"><span>Fenced pitfall trapping in four sampling sites <span>representing different habitats and fire history</span> over the primary reptile activity period for 35 consecutive years with over 17000 individuals captured during 3300 days of sampling; the trapping regime was modified for the last 28 years.</span></p>
Results of Climate change impact on sediment discharge using a large ensemble rainfall dataset
<p>Calculation results for each resolution of Climate change impact on sediment discharge using a large ensemble rainfall dataset</p>
Developing a Physics-informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments
<p>This data archive includes the source code of EXP-HYDRO, standard DL, hybrid-J, and hybrid-Z models, as well as simulated daily runoff (mm/d) of all five models in the paper at the three subbasins in the source region of the Yellow River. For more details please see the publication.</p> <p>Please cite the paper as follows:</p> <p>Zhong, L., Lei, H., & Gao, B. (2023). Developing a physics-informed deep learning model to simulate runoff response to climate change in Alpine catchments. Water Resources Research, 59, e2022WR034118. https://doi. org/10.1029/2022WR034118</p> <p> </p>
Fig. 2 A in Multigenic resistance to Xylella fastidiosa in wild grapes (Vitis sps.) and its implications within a changing climate
Fig. 2 A Manhattan plot of the V. arizonica genome showing markers associated with bacterial load. The plot denotes each of the 19 chromosomes for haplotype 1. Each circle represents a SNP with a corresponding p value, based on EMMAX genome-wide association analysis. The 25 SNPs that were detected in two separate GWA analyses are circled in red and define the 8 peaks of association, which are numbered as P1, P2, etc., and referred to in the text. In addition to SNPs, the locations of significantly associated kmers and CNVs are provided when they overlap with a SNP-defined peak. The colored horizontal lines represent the cut-off p-values (P <0.05, Bonferroni corrected) for the different marker types. Significant (P <0.05, Bonferroni corrected) kmers and CNVs are represented by red and blue triangles, respectively.
Fig. 1 Vitis arizonica sampling and phenotypes. A in Multigenic resistance to Xylella fastidiosa in wild grapes (Vitis sps.) and its implications within a changing climate
Fig. 1 Vitis arizonica sampling and phenotypes. A map of the Southwestern United States and Northern Mexico indicates sampling locations of the n = 167 V. arizonica accessions used in this study. The color of sample locations (circles) are colored according to their resistance phenotype, as measured by bacterial load (CFU/mL). The histogram of phenotypes (in CFU/mL) is to the right of the map. Map generation relied on information from GADM, a publicly available database (http:gadm.org).
Fig. 5 in Multigenic resistance to Xylella fastidiosa in wild grapes (Vitis sps.) and its implications within a changing climate
Fig. 5 Relationships among resistance, genetic markers and bioclimatic data. a The estimated relative importance, from GF modeling, of each of the bioclimatic variables tested. The y-axis is a measure of the importance of various variables to explain the model - i.e., the relative importance of each bioclimatic variable for predicting changes in allele frequency across the landscape. Each boxplot denotes the average inferred importance of the bioclimatic variable, with the whiskers plotting the standard deviation of 1000 separate analyses (gray dots). BIO8 was estimated to have the biggest impact on the model in all 1000 analyses. b The turnover function showing the temperature range of BIO8 on the x-axis and the change in the genetic composition on the y-axis. The circles represent individuals that are colored by resistance (gray) or susceptible (white). c Individual predictors in a linear model to predict resistance levels (CFU/ml). The label score_ref represents sets of 1000 randomly chosen sets of 25 SNPs; K1 and K2 are the proportion of the assignments to each admixture group for each individual. The other predictors include bioclimatic variables and genomic data, as listed in the text, each evaluated 1000 times with bootstrapped datasets. Each boxplot reports the second and third quartiles, with median values in the square and circles showing outliers. The barplot whiskers report standard deviation, and the dashed horizontal line reflects the median value of 1000 replicates of the Rpd score. d The density distribution of BIO8 for a global database of locations of Xylella fastidiosa detection.
Fig. 4 in Multigenic resistance to Xylella fastidiosa in wild grapes (Vitis sps.) and its implications within a changing climate
Fig. 4 The presence of resistance and susceptibility kmers in different data sets. a Analyses within the V. arizonica sample set. The top-left graph indicates the 99 different resistance (R-kmers) kmers across the x-axis, with their detection frequency across the resistant (CFU/mL <13) accessions. The top-right graph plots the average detection frequency of susceptibility kmers (S-kmers). The bottom-left and bottom-right graph are similar, they but show R-kmer and S-kmer detection frequencies among susceptible accessions. b The same graphs as in A, but the top graphs plot R-kmer and S-kmer detection frequencies for the five V. vinifera cultivars bred for PD resistance by backcrossing to V arizonica, while the bottom graphs represent susceptible V. vinifera cutlivars. c. Plots of kmer frequencies in six Vitis species. The species phylogeny is shown on the left, with the average detection frequency of R-kmers shown in red dot. The gray dots represent average detection frequencies of randomly chosen kmers that had similar population frequencies in V. arizonica as the set of R kmers. Whiskers denote 95% confidence intervals.
Climate change impact on vernacular and archaeological cultural heritage building materials in Europe and Latin America
<p>The analysis and interpretation of past climate data and simulations of climate models for future periods will allow us to study the impacts of climate change on cultural Heritage. The H2020 SCORE project (Sustainable COnservation and REstoration of built cultural heritage – 2020-2024) centres on two types of cultural heritage that differ by their geographical location and therefore their climatic conditions, as are vernacular cultural heritage in Europe (6 sites in Denmark, France, Italy and Spain) and archaeological sites in Latin America (2 sites in Mexico). All the cases study share a fundamental similarity in terms of the use of materials and construction techniques.</p> <p>One objective of the project is to quantify the impacts of continuous climate and pollution changes on building materials of cultural heritage under future IPCC socioeconomic scenarios with high and low mitigation measures at years 2030, 2050 and 2070, using peer-reviewed dose-response equations. We also focus on the degradation effects due to extreme events (heatwave, dry spells and extreme rainfall/flood) of each of the selected regions of our cases study. We apply these climatic conditions past and future) in different models, based on scientific literature, that allow estimate the weathering of the materials employed in the construction of cultural heritage buildings.</p> <p>Finally, we deliver preliminary results for a “cocktail of extreme events” based on the literature review and experiment in laboratory specifically designed to quantify the damages and degradation of building materials due to a realistic series of adverse climate and pollution events.</p> <p>We present here some results of future weathering (2081-2100) compared to near par (2001-2020) for different weathering process under the SSP5- RCP 8.5 scenario. The evolution of each process is different and it is different form one site to the other.</p> <p> </p>
Data for the manuscript: Enabling Climate Change Adaptation in Coastal Systems. A Systematic Literature Review
<p>This dataset includes the list of publications, framework and dataset used for the paper "Enabling Climate Change Adaptation in Coastal Systems. A Systematic Literature Review"</p>
Amplified drought induced by climate change reduces seedling emergence and increases seedling mortality for two Mediterranean perennial herbs
<div>Seedling recruitment is a bottleneck for population dynamics and range shift. The vital rates linked to recruitment by seed are impacted by amplified drought induced by climate change. In the Mediterranean region, autumn and winter seedling emergence and mortality may have strong impact on the overall seedling recruitment. However, studies focussing on the temporal dynamic of recruitment during these seasons are rare.</div> <div> </div> <div>This study was performed in a deciduous Mediterranean oak forest located in southern France and quantifies the impact of amplified drought conditions on autumn and winter seedling emergence and seedling mortality rates of two herbaceous plant species with meso-Mediterranean and supra-Mediterranean distribution (respectively, <em>Silene italica</em> and <em>S. nutans</em>). Seedlings were followed from October 2019 to May 2020 in both undisturbed and disturbed plots where the litter and the aboveground biomass has been removed to create open microsites.</div> <div> </div> <div>Amplified drought conditions reduced seedling emergence and increased seedling mortality for both <em>Silene </em>species but these negative effects were dependent on soil disturbance conditions. Emergence of <em>S. italica</em> decreased only in undisturbed plots (-7%) whereas emergence of <em>S. nutans </em>decreased only in disturbed plots (-10%) under amplified drought conditions. The seedling mortality rate of <em>S. italica </em>was 51% higher under amplified drought conditions in undisturbed plots while that of <em>S. nutans</em> was 38% higher in disturbed plots.</div> <div> </div> <div>Aridification due to lower precipitation in the Mediterranean region will negatively impact the seedling recruitment of these two <em>Silene </em>species. Climate change effects on early vital rates may likely have major negative impacts on the overall population dynamic.</div>
Identifying 'climate keystone species' as a tool for conserving ecosystem functioning under climate change
<p><strong>Aim</strong>: Climate change affects ecological communities via impacts on species. The community's response to climate change can be represented as the temporal trend in a climate-related functional property that is quantified using a relevant functional trait. Noteworthy, some species influence this response in the community more strongly than others.</p> <p><strong>Innovation</strong>: Leveraging on the concept of keystone species, we propose that species with a strong effect on the community's functional response to climate change beyond their relative abundance can be considered as 'climate keystone species'. We develop a stepwise tool to determine species' effects on a community's climate response and identify climate keystone species. We quantify the species-specific effect by measuring the difference in the community's climate response with and without the species. Next, we identify climate keystone species as those with a strong residual effect after weighting with their relative abundances in the community.</p> <p><strong>Main</strong> <strong>Conclusions</strong>: To illustrate the use of the stepwise tool with empirical data, we identify climate keystone species that have a strong effect on the change in the average temperature niche in North American bird communities over time and find the identification tool ecologically relevant. Identification of climate keystone species can serve as an additional conservation method to efficiently protect ecosystem functions.</p>
Data for "Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China"
<p>The data contains some of the data necessary for this paper. and partly simulation results<br> </p>
UrbAlytics - Remote Sensing tools for Urban Heat Island Assessment and Climate Change Adaptation through Nature-Based Solutions
<p>Urban Heat Island (UHI) is considered one of the significant problems posed to human beings due to the urbanization and industrialization of human civilization. The leading causes of UHI are the vast amounts of heat urban structures produce as they absorb and re-radiate solar radiation and anthropogenic heat sources. The issue mainly affects cities or metropolises with a vast population and a thriving economy. The problem will worsen significantly in the future due to the predicted three billion people living in urban areas worldwide. Due to the severity of the problem, accessing up-to-date information layers that can support city planners and decision-makers in the context of climate resilience is a demanding problem nowadays.</p> <p><strong>UrbAlytics</strong> is an experimental sub-project of the H2020-funded project <a href="https://ai4copernicus-project.eu/"><strong>AI4Copernicus</strong></a> that aims to bridge Artificial Intelligence with Earth Observations, producing information layers that can support city planners and decision-makers in the context of climate resilience and related challenges in urban areas. This research investigates, thanks to the joint expertise of the partners <a href="https://www.latitudo40.com/"><strong>Latitudo 40</strong></a> and <a href="https://www.landsrl.com/land-research-lab"><strong>LAND Research Lab®</strong></a>, the Urban Heat Island (UHI) effect, evaluating its impacts on cities, assessing Ecosystem Services provided by Blue and Green Infrastructures and proposing a set of Nature-Based Solutions (NBS) for climate adaptation and extreme heat mitigation. </p> <p><strong>The dataset</strong></p> <p>This dataset is the tool's output of a fully automated workflow realized during the project and tested for the cities of <strong>Milan</strong> and <strong>Naples</strong>, pilot users of the experiment. The choice of Milan and Naples allows for different readiness levels, data availability, and urban-climatic conditions.<br> For each city, the dataset contains the following layers for the analysis period 2018-2022.</p> <p><strong> HEATWAVE POTENTIAL RISK (HPR)</strong></p> <p>Risk Assessment mapping concerning extreme heat, considering the severity of the heat island phenomenons, the exposure of sensitive age groups and the vulnerability due to city morphology and surface materials. The risk assessment is the first step in defining a methodology that aims to assess the effectiveness of mitigation and adaptation strategies to climate extremes. It's a value in [0,1], where the higher the value higher the risk.</p> <p><strong> MICROCLIMATIC PERFORMANCE INDEX (MPI)</strong></p> <p>The role of vegetation in the city in abating the Heat Island effect has been widely demonstrated. In this context, deploying Urban Green Infrastructure is recognized as one of the most important strategies to mitigate UHI and promote a resilient city environment. The significance of the mitigation role of the Heat Island phenomenon that vegetation assumes makes it necessary to map Urban Green Infrastructure to estimate a cooling potential. Estimating the microclimatic performance of urban vegetation is crucial to plan adaptation and mitigation actions for the UHI effect. In this work, up-to-date Tree Cover Density and Land Cover maps have been produced using machine learning applied to Sentinel-2 satellite imagery. Those maps have been interpolated and combined, creating 20 Blue and Green Infrastructures classes. Each category's microclimatic performance score was attributed based on evapotranspiration potential, shading and albedo. The output is a map with integer values in [1, 20], where the lower the value higher the microclimatic performance. </p> <p><strong> PARK COOL ISLANDS (PCI)</strong></p> <p>Park Cool Islands layer identifies the most performing areas during extreme summer heatwaves, according to their size and relevant characteristics, providing reliable information to citizens and urban planners about the safest and coolest areas during extreme heatwaves. Since the green areas' type and composition can influence their cooling effects, we considered both the size and composition of urban parks to identify the most performing green areas in terms of the Park Cool Island effect. <strong> </strong>The layer distinguishes between major and minor Park Cool Islands. <em>Major PCI</em> includes areas covered by at least 50% of tree canopy coverage and bigger than 2 hectares with an estimated cooling distance of 300 m buffer<strong>. </strong><em>Minor PCI</em> includes green areas whose surface is between 1 and 2 hectares as well as those green areas bigger than 2 hectares but covered by less than 50% of tree canopy coverage, with an estimated cooling distance of 100 m buffer.</p> <p> </p> <p><strong>Contact Information</strong></p> <p>If you would like further information about the dataset or if you experience any issues downloading files, please contact us <a href="mailto:giovanni.giacco@latitudo40.com">giovanni.giacco@latitudo40.com</a>, <a href="mailto:giulia.castellazzi@landsrl.com">giulia.castellazzi@landsrl.com</a></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.