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527 results for “Climate Response”
FIGURE 4 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 4. Site-specific data for lamina length and lamina width plotted for each taxon. 4A: Lamina length for Platanus neptuni. 4B: Lamina width for P. neptuni. 4C: Lamina length for Eotrigonobalanus furcinervis. 4D: Lamina width for E. furcinervis. 4E: Lamina length for Daphnogene cinnamomifolia. 4F: Lamina width for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 1 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 1. Map showing the locations of the considered sites, which are numbered according to Table 1.
FIGURE 3 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 3. Site-specific data for lamina area and lamina perimeter plotted for each taxon. 3A: Lamina area for Platanus neptuni. 3B: Lamina perimeter for P. neptuni. 3C: Lamina area for Eotrigonobalanus furcinervis. 3D: Lamina perimeter for E. furcinervis. 3E: Lamina area for Daphnogene cinnamomifolia. 3F: Lamina perimeter for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 6 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 6. Site-specific data for lamina centroid and leaf length-to-width ratio (LWR) plotted for each taxon. 6A: Lamina centroid for Platanus neptuni. 6B: LWR for P. neptuni. 6C: Lamina centroid for Eotrigonobalanus furcinervis. 6D: LWR for E. furcinervis. 6E: Lamina centroid for Daphnogene cinnamomifolia. 6F: LWR for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 2 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 2. Plasticity index (PI) of various leaf traits, for the considered sites and taxa. 2A: PI for lamina area. 2B: PI for lamina length. 2C: PI for lamina perimeter. 2D: PI for lamina width. 2E: PI for lamina circularity. 2F: PI for lamina centroid. Squares: Platanus neptuni. Circles: Daphnogene cinnamomifolia. Triangles: Eotrigonobalanus furcinervis. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 5 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 5. Site-specific data for lamina circularity and lamina roundness plotted for each taxon. 5A: Lamina circularity for Platanus neptuni. 5B: Lamina roundness for P. neptuni. 5C: Lamina circularity for Eotrigonobalanus furcinervis. 5D: Lamina roundness for E. furcinervis. 5E: Lamina circularity for Daphnogene cinnamomifolia. 5F: Lamina roundness for D. cinnamomifolia. The boxes span the 50% interquartile. The horizontal lines within the boxes indicate the median values. The "whiskers" mark the highest and lowest values. Outliers located at a distance of up to 1.5 times the quartile span outside the whiskers are drawn as asterisks, and extreme outliers are drawn as circles. Different minuscule letters indicate statistically significant differences among sites. Colors indicate deposit type. Colors indicate deposit type. Green: fluviatile. Red: volcanic. Blue: marine. Lines delimit age groups. LE: Late Eocene. EO: Early Oligocene. LO: Late Oligocene. EM: Early Miocene. For site numbers and dating see Table 1.
FIGURE 7. Age-specific discriminant analysis using all morphometric parameters for all sites. 7A in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 7. Age-specific discriminant analysis using all morphometric parameters for all sites. 7A: Eocene. 7B: Oligocene. Triangles: Platanus neptuni. Squares: Eotrigonobalanus furcinervis. Circles: Daphnogene cinnamomifolia.
FIGURE 8 in Taxon-specific variability of leaf traits in three long-ranging fossil-species of the Paleogene and Neogene: Responses to climate?
FIGURE 8. Circularity plotted against LWR. Blue circles: Platanus neptuni. Red squares: Eotrigonobalanus furcinervis. Yellow diamonds: Daphnogene cinnamomifolia. Black line: Relationship between circularity and length-towidth ratio of an ellipse. Please note that this relationship was calculated by using an approximate equation for the perimeter of an ellipse, which causes the slight deflection of the curve for high circularity values. As approximation, the following equation for the ellipse perimeter (EP) was used: EP = π* [2 * (a2 + b2)1/2].
Bumble bee responses to climate and landscapes: Investigating habitat associations and species assemblages across geographic regions in the United States of America
<p><span>Bumble bees are integral pollinators of native and cultivated plant communities, but species are undergoing significant changes in range and abundance on a global scale. Climate change and land cover alteration are key drivers in pollinator declines; however, limited research has evaluated the cumulative effects of these factors on bumble bee<em> </em>assemblages. This study tests bumble bee assemblage (calculated as richness and abundance) responses to climate and land use by <span>modeling </span>species-specific habitat requirements, and assemblage-level responses across geographic regions. <span>We integrated species richness, abundance, and distribution data for 18 bumble bee species with site-specific bioclimatic, landscape composition, and landscape configuration data to evaluate</span> the effects of multiple environmental stressors <span>on bumble bee assemblages throughout</span> 433 agricultural fields in<span> Florida, Indiana, Kansas, Kentucky, Maryland, South Carolina, Utah, Virginia, and West Virginia from 2018 to 2020. Distinct east vs. west groupings emerged when evaluating species-specific habitat associations, prompting a detailed evaluation of bumble bee assemblages by geographic region. Maximum temperature of warmest month and precipitation of driest month had a positive impact on bumble bee assemblages in the Corn Belt/Appalachian/northeast, southeast, and northern plains regions, but a negative impact in the mountain region. Further, </span>forest land cover surrounding agricultural fields was highlighted as supporting more rich and abundant bumble bee assemblages<span>. Overall, climate and land use combine to drive bumble bee assemblages, but how those processes operate is idiosyncratic and spatially contingent across regions. From these findings, we suggested regionally specific management practices to best support rich and abundant bumble bee assemblages in agroecosystems. </span>Results from this study contribute to a better understanding of climate and landscape factors affecting bumble bees and their habitats throughout the USA. </span></p>
Variable species establishment in response to microhabitat indicates different likelihoods of climate-driven range shifts
<p>Climate change is causing geographic range shifts globally, and understanding the factors that influence species' range expansions is crucial for predicting future biodiversity changes. A common, yet untested, assumption in forecasting approaches is that species will shift beyond current range edges into new habitats as they become macroclimatically suitable, even though microhabitat variability could have overriding effects on local population dynamics. We aim to better understand the role of microhabitat in range shifts in plants through its impacts on establishment by Q1) examining microhabitat variability along large macroclimatic (i.e., elevational) gradients, Q2) testing which of these microhabitat variables explain plant recruitment and seedling survival, and Q3) predicting microhabitat suitability beyond species range limits. We transplanted seeds of 25 common tree, shrub, forb, and graminoid species across and beyond their current elevational ranges in the Washington Cascade Range, USA, along a large elevational gradient spanning a broad range of macroclimates. Over five years, we recorded recruitment, survival, and microhabitat (i.e., high resolution soil, air, and light) characteristics rarely measured in biogeographic studies. We asked whether microhabitat variables correlate with elevation, which variables drive species establishment, and whether microhabitat variables important for establishment are already suitable beyond leading range limits. We found that only 30% of microhabitat parameters covaried with elevation. We further observed extremely low recruitment and moderate seedling survival, and these were generally only weakly explained by microhabitat. Moreover, species and life stages responded in contrasting ways to soil biota, soil moisture, temperature, and snow duration. Microhabitat suitability predictions suggest that distribution shifts are likely to be species-specific, as different species have different suitability and availability of microhabitat beyond their present ranges, thus calling into question low-resolution macroclimatic projections that will miss such complexities. We encourage further research on species responses to microhabitat and including microhabitat in range shift forecasts.</p>
Fig. 3 in Regional uniqueness of tree species composition and response to forest loss and climate change
Fig. 3 | Response of tree species to climate change across biomes. The median absolute latitude and median elevation shift among species, fraction of lost and gained species, and change in taxonomic and phylogenetic composition under climate change were computed for each forest ecoregion. The boxplots show statistics for n = 239 ecoregions for Tropical Moist Broadleaf Forests, n = 14 ecoregions for Tropical Coniferous Forests, n = 55 ecoregions for Tropical Dry Broadleaf Forests, n = 26 ecoregions for Boreal Forests, n = 91 ecoregions for Temperate Broadleaf Forests, n = 49 ecoregions for Temperate Conifer Forests and n = 61 ecoregions for Mediterranean Forests. The center line of the boxplots shows the median, the box limits the quartiles, the whiskers 1.5 times the interquartile range, and the points the outliers.Changes are computed between predicted distributions with climate variables for 1981-2010 and climate projections for 2071-2100 under climate change scenario SSP 5.85. Changes in composition are computed as the Euclidean distance between scaled NMDS and evoPCA values computed at the ecoregion level. Source data are provided as a Source Data file.
Fig. 2 in Regional uniqueness of tree species composition and response to forest loss and climate change
Fig. 2 | Species occupancy range distribution and loss. a Distributions of species occupancy range sizes globally (gray) and constrained to forests (at least 10% tree cover, color) for species in each forest biome. b Boxplot of relative range reduction across species in each forest biome with the center line showing the median, the box limits the quartiles, the whiskers 1.5 times the interquartile range, and the points the outliers. The distributions and boxplots are computed for n = 6810 species for Tropical Moist Broadleaf Forests, n = 588 species for Tropical Coniferous Forests, n = 1101 species for Tropical Dry Broadleaf Forests, n = 54 species for Boreal Forests, n = 1744 species for Temperate Broadleaf Forests, n = 178 species for Temperate Conifer Forests and n = 580 species for Mediterranean Forests. c Global map of median species range size constrained to forests, created with QGIS110. The gray base map corresponds to all areas for which model predictors were available. d Plot of species' median latitude against range size constrained to forests, colored by point density, where red indicates the highest density. Source data are provided as a Source Data file.
Fig. 1 in Regional uniqueness of tree species composition and response to forest loss and climate change
Fig. 1 | Gradients in taxonomic and phylogenetic composition show a near- a, c. Scatter plot of taxonomic and phylogenetic ordinations in environmental unique biodiversity signature of every single location on the planet. Taxonomic space, a 2-dimensional space made up of the 2 first axes of a PCA of the environcomposition is represented by a 3-axis non-metric dimensional scaling (NMDS) and mental variables used for species distribution modeling: mean annual temperature phylogenetic beta-diversity is represented by the 3 first axes of a phylogenetic (MAT), temperature seasonality (T season), annual precipitation (Annual P), preordination (evoPCA). Both the taxonomic and phylogenetic ordinations are com- cipitation seasonality (P season), growing season length (GSL), net primary proputed on the global community matrix derived from the modeled distributions of ductivity (NPP),silt content (Silt),coarse fragments (CF),and soil pH (pH).b, d. Map n = 10,590 tree species sampled at a resolution of 100 km, resulting in n = 12,548 of taxonomic and phylogenetic ordinations in geographical space. Source data are sites. The 3 axes of each ordination are mapped to red, green, and blue with provided as a Source Data file. The maps were created with QGIS110 and the gray minimum and maximum values corresponding to the 10th and 90th percentiles. base map corresponds to all areas for which model predictors were available.
Data for "Africa's Climate Response to Marine Cloud Brightening Strategies is Highly Sensitive to Deployment Region"
<p>Community Earth System Model 2 simulation data with marine cloud brightening perturbations used to compute climate impact metrics in "Africa's Climate Response to Marine Cloud Brightening Strategies is Highly Sensitive to Deployment Region" by Romaric C. Odoulami, Haruki Hirasawa, Kouakou Kouadio, Trisha D. Patel, Kwesi A. Quagraine, Izidine Pinto, Temitope S. Egbebiyi, Babatunde J. Abiodun, Christopher Lennard, and Mark G. New. Simulation descriptions can be found in Hirasawa et al., 2023 <em>Geophysical Research Letters</em> doi.org/10.1029/2023GL104314.</p>
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 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>
Dataset for "Comparison of the Fast and Slow Climate Response to Three Radiation Management Geoengineering Schemes"
<p>Reproducible dataset for "Comparison of the Fast and Slow Climate Response to Three Radiation Management Geoengineering Schemes"</p>
Raw data sets from Jones et al. 2018 QSR publication: A multi-proxy approach to understanding complex responses of saltlake catchments to climate variability and human pressure: A Late Quaternary case study from south-eastern, Spain
<p>Attached are the raw data sets containing the pollen data, DXR, Grain size and C14 ages from the recent publication: Jones et al. 2018 QSR publication: A multi-proxy approach to understanding complex responses of saltlake catchments to climate variability and human pressure: A Late Quaternary case study from south-eastern, Spain.</p> <p>Note that these data sets do contain hiatuses and a major age-reversal due to erosian which have likely been caused by increased seasonal wetness at the onset of the Holocene. A full explanation is provided in our 2018 publication. If you do wish to use the data, it is essential that you read the publication inorder to interpret the results correctly. We also require that when using this data that you correctly cite it (Bibliographic reference and the doi number of the data set). There were some problems uploading the XRF (geochemical) data sets, so I haven't included these yet, but hopefully will do eventually. </p> <p>Below I have also included the abstract from our publication, which provides an overview of the purpose of our work and a brief summary of the main findings.</p> <p>Abstract of Jones et al. 2018:</p> <p>The article focuses on a former salt lake in the upper Vinalopo Valley in south-eastern Spain. The study spans the Late Pleistocene through to the Late Holocene, although with particular focus on the period between 11 ka cal BP and 3000 ka cal BP (which spans the Mesolithic and part of the Bronze Age). High resolution multi-proxy analysis (including pollen, non pollen palynomorphs, grain size, X-ray fluorescence, and X-ray diffraction) was undertaken on the lake sediments. The results show strong sensitivity to<br> both long term and small changes in the evaporation/precipitation ratio, affecting the surrounding vegetation composition, lake-biota and sediment geochemistry. To summarise the key findings the main general trends identified include: 1) Hyper-saline conditions<br> and low lake levels at the end of the Late Glacial 2) Increasing wetness and temperatures which witnessed an expansion of mesophilic woodland taxa, lake infilling and the establishment of a more perennial lake system at the onset of the Holocene 3) An increase in solar insolation after 9 ka cal BP which saw the re-establishment of pine forests 4) A continued trend towards increasing dryness (climatic optimum) at 7 ka cal BP but with continued freshwater input 5) An increase in sclerophyllous open woody vegetation (anthropogenic?), and increasing wetness (climatic?) is represented in the lake record between 5.9 and 3 ka cal BP 6) The Holocene was also punctuated by several aridity pulses, the most prominent corresponding to the 8.2 ka cal BP event. These events, despite a paucity of well dated archaeological sites in the surrounding area, likely altered the carrying capacity of this area both regionally and locally, particularly during the Mesolithic-Neolithic transition, in terms of fresh water supply for human/animal consumption, wild plant food reserves and suitable land for crop growth.</p>
Figure 3. The effective population size through recent time for 3 in Comparative analyses of past population dynamics between two subterranean zokor species and the response to climate changes
Figure 3. The effective population size through recent time for 3 clades of Gansu zokor (Eospalax cansus).
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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.