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152 results for “climate resilience”
Data from: Resilience of an integrated crop-livestock system to climate change: a simulation analysis of cover crop grazing in southern Brazil
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Data from: Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States
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Synchronization of seasonal acclimatization and short-term heat hardening improves physiological resilience in a changing climate
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Importance of mega-environments in evaluation and identification of climate resilient maize hybrids (Zea mays L.)
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Long-term resilience of primary sex ratios in a species with temperature dependent sex determination after decades of climate warming
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Data from: Resistance and resilience of soil microbiomes under climate change
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Data from: Climatic resilience after extreme drought in Mediterranean shrubland plant communities
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Resilience of seagrass populations to thermal stress does not reflect regional differences in ocean climate
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Data from: navigating uncertainty: managing herbivore communities enhances savanna ecosystem resilience under climate change
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Data from: Landscape complexity promotes resilience of biological pest control to climate change
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Spatial, climate and ploidy factors drive genomic diversity and resilience in the widespread grass Themeda triandra
<p>This data set was used to assess the climate resilience of <i>Themeda triandra</i>, a foundational species and the most widespread plant in Australia, by assessing the relative contributions of spatial, environmental, and ploidy factors to contemporary genomic variation. Reduced-representation genome sequencing on 472 samples from 52 locations was used to test how the distribution of genomic variation, including ploidy polymorphism, supports adaptation to hotter and drier climates.</p>
Data from: The importance of using topographic features to predict climate-resilient habitat for migratory forest landbirds: an example for the Rusty Blackbird, Olive-sided Flycatcher, and Canada Warbler
<p>Maintaining a functionally-connected network of high-quality habitat is one of the most effective responses to biodiversity loss. However, the spatial distribution of suitable habitat may shift over time in response to climate change. Taxa such as migratory forest landbirds are already undergoing climate-driven range shifts. Therefore, patches of climate-resilient habitat (also known as "climate refugia") are especially valuable from a conservation perspective. Here, we performed maximum entropy (Maxent) species distribution modeling to predict suitable and potentially climate-resilient habitat in Nova Scotia, Canada, for three migratory forest landbirds: Rusty Blackbird (Euphagus carolinus), Olive-sided Flycatcher (Contopus cooperi), and Canada Warbler (Cardellina canadensis). We used a reverse stepwise elimination technique to identify covariates that influence habitat suitability for the target species at broad scales, including abiotic (topographic control of moisture and nutrient accumulation) and biotic (forest characteristics) covariates. As topography should be relatively unaffected by a changing climate and helps regulate the structure and composition of forest habitat, we posit that the inclusion of appropriate topographic features may support the identification of climate-resilient habitat. Of all covariates, Depth to water table was the most important predictor of relative habitat suitability for the Rusty Blackbird and Canada Warbler, with both species showing a strong association with wet areas. Mean canopy height was the most important predictor for the Olive-sided Flycatcher, whereby the species was associated with taller trees. Our models, which comprise the finest scale species distribution models available for these species in this region, further indicated that, for all species, habitat (1) remains relatively abundant and well distributed in Nova Scotia and (2) is often located in wet lowlands (a climate-resilient topographic landform). These findings suggest that opportunities remain to conserve breeding habitat for these species despite changing temperature and precipitation regimes.</p>
Data from: Ecological disturbance influences adaptive divergence despite high gene flow in golden perch (Macquaria ambigua): implications for management and resilience to climate change
Populations that are adaptively divergent but maintain high gene flow may have greater resilience to environmental change as gene flow allows the spread of alleles that have already been tested elsewhere. In addition, populations naturally subjected to ecological disturbance may already hold resilience to future environmental change. Confirming this necessitates ecological genomic studies of high dispersal, generalist species. Here we perform one such study on golden perch (Macquaria ambigua) in the Murray-Darling Basin (MDB), Australia using a genome-wide SNP dataset. The MDB spans across arid to wet and temperate to sub-tropical environments, with low to high ecological disturbance in the form of low to high hydrological variability. We found high gene flow across the basin and three populations with low neutral differentiation. Genotype-environment association analyses detected adaptive divergence predominantly linked to an arid region with highly variable riverine flow, and candidate loci included functions related to fat storage, stress and molecular or tissue repair. The high connectivity of golden perch in the MDB will likely allow locally adaptive traits in its most arid and hydrologically variable environment to spread and be selected in localities that are predicted to become arid and hydrologically variable in future climates. High connectivity in golden perch is likely due to their generalist life history and efforts of fisheries management. Our study adds to growing evidence of adaptation in the face of gene flow, and highlights the importance of considering ecological disturbance and adaptive divergence in biodiversity management.
Data from: Genomic basis for coral resilience to climate change
Recent advances in DNA-sequencing technologies now allow for in-depth characterization of the genomic stress responses of many organisms beyond model taxa. They are especially appropriate for organisms such as reef-building corals, for which dramatic declines in abundance are expected to worsen as anthropogenic climate change intensifies. Different corals differ substantially in physiological resilience to environmental stress, but the molecular mechanisms behind enhanced coral resilience remain unclear. Here, we compare transcriptome-wide gene expression (via RNA-Seq using Illumina sequencing) among conspecific thermally sensitive and thermally resilient corals to identify the molecular pathways contributing to coral resilience. Under simulated bleaching stress, sensitive and resilient corals change expression of hundreds of genes, but the resilient corals had higher expression under control conditions across 60 of these genes. These "frontloaded" transcripts were less up-regulated in resilient corals during heat stress and included thermal tolerance genes such as heat shock proteins and antioxidant enzymes, as well as a broad array of genes involved in apoptosis regulation, tumor suppression, innate immune response, and cell adhesion. We propose that constitutive frontloading enables an individual to maintain physiological resilience during frequently encountered environmental stress, an idea that has strong parallels in model systems such as yeast. Our study provides broad insight into the fundamental cellular processes responsible for enhanced stress tolerances that may enable some organisms to better persist into the future in an era of global climate change.
Infrastructure Climate Resilience Assessment Data Starter Kit for French Guiana
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2025)</li> <li>railways (OpenStreetMap, 2025)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola et al., 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=12278">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2025) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Code to run the analyses of "Forest storm resilience depends on the interplay between functional composition and climate - insights from European-scale simulations" by Barrere et al. (2024).
<p>Repository containing the code to run the model and statistical analyses of the paper "Forest storm resilience depends on the interplay between functional composition and climate - insights from European-scale simulations" by Julien Barrere, Björn Reineking, Maxime Jeaunatre and Georges Kunstler, accepted by Functional Ecology in 2024.</p><p> </p><p>The code requires prior installation of the <a href="https://github.com/gowachin/matreex">matreex</a> R package, developped by Maxime Jeaunatre (INRAE), and of the ```targets``` package. The data folder, required to run the code, can be made available upon request to julienbarrere3@gmail.com</p><p> </p><p>Once the packages are installed and the data folder is placer in the main folder, just run ```targets::tar_make()``` from R and the script will download the other packages required and run the analyses.</p><p> </p><p>A version of this code is also available in the github <a href="https://github.com/jbarrere3/FunDiv_ipm">repository</a></p><p> </p><p> </p>
Infrastructure Climate Resilience Assessment Data Starter Kit for Morocco
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2023)</li> <li>railways (OpenStreetMap, 2023)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=11539">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Data and supporting information from: Higher metabolic plasticity in temperate compared to tropical lizards suggests increased resilience to climate change
<p>We investigate thermal acclimation of three species of <i>Takydromus</i> lizards distributed along a broad latitudinal gradient in China. The dataset are including metabolic rates, organ size, mitochondrial respiration, Cox activities, proteomic and metabolomic regulations of three <em>Takydromus </em>lizards after warm and cold acclimation in the laboratory. See details in related paper.</p>
Planning for Resilience: Incorporating scenario and model uncertainty and trade-offs when prioritizing management of climate refugia
<p>Climate change has become the greatest threat to the world's ecosystems. Locating and managing areas that contribute to the survival of key species under climate change is critical for the persistence of ecosystems in the future. Here we identify "Climate Priority" sites as coral reefs exposed to relatively low levels of climate stress that will be more likely to persist in the future. We present the first analysis of uncertainty in climate change scenarios and models, along with multiple objectives, in a marine spatial planning exercise and offer a comprehensive approach to incorporating uncertainty and trade-offs in any ecosystem. We first described each site using environmental characteristics that are associated with a higher chance of persistence (larval connectivity, hurricane influence and acute and chronic temperature conditions in the past and the future). Future temperature increases were assessed using downscaled data under four different climate scenarios (SSP1 2.6, SSP2 4.5, SSP3 7.0 and SSP5 8.5) and 57 model runs. We then prioritized sites for intervention (conservation, improved management or restoration) using robust decision-making approaches that select sites that will have a benign climate under most climate scenarios and models. The modeling work is novel because it solves two important issues. 1) It considers trade-offs between multiple planning objectives explicitly through Pareto analyses; and 2) It makes use of all the uncertainty around future climate change. Priority intervention sites identified by the model were verified and refined through local stakeholder engagement including assessments of local threats, ecological condition and government priorities. The workflow is presented for the Insular Caribbean and Florida, and at the national level for Cuba, Jamaica, Dominican Republic and Haiti. Our approach allows managers to consider uncertainty and multiple objectives for climate smart spatial management in coral reefs or any ecosystem across the globe.</p>
Thermal ecology and baseline energetic requirements of a large-bodied ectotherm suggest resilience to climate change
<p>Most studies on how rising temperatures will impact terrestrial ectotherms have focused on single populations or multiple sympatric species. Addressing the thermal and energetic implications of climatic variation on multiple allopatric populations of a species will help us better elucidate how a species may be impacted by altered climates. We used eight years of thermal and behavioral data collected from four populations of Pacific rattlesnakes (<em>Crotalus oreganus</em>) living in climatically distinct habitat types (inland and coastal) to determine the field-active and lab-preferred body temperatures, thermoregulatory metrics, and maintenance energetic requirements of snakes from each population. Physical models showed that thermal quality was best at coastal sites, but inland snakes thermoregulated more accurately despite being in more thermally constrained environments. Projected increases of 1 and 2 ºC in ambient temperature result in an increase in overall thermal quality at both coastal and inland sites. Population differences in modeled standard metabolic rate estimates were driven by body size and not field-active body temperature, with inland snakes requiring 1.6x more food annually than coastal snakes. All snakes thermoregulated with high accuracy, suggesting that small increases in ambient temperature are unlikely to impact the maintenance energetic requirements of individual snakes and that some species of large-bodied reptiles may be robust to modest thermal perturbations under conservative climate change predictions.</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.