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527 results for “Climate Response”
Species diversity and plant dominance influence grassland stability in response to extreme climatic events and anthropogenic drivers across three LTER sites: Cedar Creek, Konza Prairie, and Kellogg Biological Station, 1982-2023.
The data in this package is associated with the analysis for a manuscript titled "Multiple community properties drive ecosystem resistance and resilience to extreme climate events across mesic grasslands". The files include compiled data on plant biomass production, species abundance, experimental treatments, extreme climate event values, and calculated diversity and stability measures from grassland plots in experiments at CDR, KBS, and KNZ LTER sites.
Predator Contributions to Belowground Responses to Climate Warming at Harvard Forest 2014
Identifying the factors that control soil CO2 emissions will improve our ability to predict the magnitude of climate change-soil ecosystem feedbacks. Despite the integral role of invertebrates in belowground systems, they are excluded from climate change models. Soil invertebrates have consumptive and non-consumptive effects on microbes, whose respiration accounts for nearly half of soil CO2 emissions. By altering the behavior and abundance of invertebrates that interact with microbes, invertebrate predators may have indirect effects on soil respiration. We examined the effects of a generalist arthropod predator on belowground respiration under different warming scenarios. Based on research suggesting invertebrates may mediate soil CO2 emission responses to warming, we predicted that predator presence would result in increased emissions by negatively affecting these invertebrates. We altered the presence of wolf spiders (Pardosa spp.) in mesocosms containing a forest floor community. To simulate warming, we placed mesocosms of each treatment in ten open-top warming chambers ranging from 1.5 to 5.5° C above ambient at Harvard Forest, MA. As expected, CO2 emissions increased under warming and we found an interactive effect of predator presence and warming, though the effect was not consistent through time. The interaction between predator presence and warming was the inverse of our predictions: mesocosms with predators had lower respiration at higher levels of warming than those without predators. Carbon dioxide emissions were not significantly associated with microbial biomass. We did not find evidence of consumptive effects of predators on the invertebrate community, suggesting that predator presence mediates response of microbial respiration to warming through non-consumptive means. In our system we found a significant interaction between warming and predator presence that warrants further research into mechanism and generality of this pattern to other systems.
CBC02 Winter-spring survival and response of birds to variable climate using mist-net captures at Konza Prairie
This dataset includes captures of small-bodied landbirds captured via passive mist-netting efforts. The objectives are to (a) initiate a long-term survey of the non-breeding birds of the site, (b) understand the behavioral and physiological mechanisms that allow birds to cope with the unpredictable, variable, and often harsh conditions during winter months, and (c) provide a training platform for students. The collection of this dataset is fully integrated into the teaching of “Wild Bird Research” (an undergraduate hands-on research course in the Division of Biology) and less formal instruction in bird research methods for graduate students. Additionally, the banding efforts have benefited from the engagement of Konza Prairie docents and frequently hosts class visits and other visitors interested in witness bird banding operations.
Ecosystem responses to changes in climate and carbon dioxide in twelve mature ecosystems ranging from prairie to forest and from the arctic to the tropics
We use the Multiple Element Limitation (MEL) model to examine the responses of twelve ecosystems - from the arctic to the tropics and from grasslands to forests - to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in annual precipitation. The ecosystems we simulated include moist acidic tundra, shrub tundra, and wet sedge tundra near Toolik Lake, Alaska, alpine dry meadow tundra near Niwot Ridge, Colorado, restored tallgrass prairie near Kellogg Biological Station, Michigan, native tallgrass prairie at the Konza Prairie, Kansas, upland and lowland boreal forest near Bonanza Creek, Alaska, temperate coniferous forest in HJ Andrews Experimental Forest, Oregon, a northern hardwood forest in Hubbard Brook Experimental Forest, New Hampshire, a transition oak-maple forest in Harvard Forest, Massachusetts, and lowland tropical rainforest near Caxiuanã National Forest, Pará, Brazil. For each of the twelve sites, we run six 100-year simulations beginning from the calibrated steady state (72 simulations total). The six simulations are: (1) increasing CO2 from 400 to 800 μmol mol-1, (2) warming from current temperatures to current plus 3.5oC, (3) decreasing precipitation from 100% to 80% of the current annual rate, (4) increasing precipitation from 100% to 120% of the current annual rate, (5) doubling of CO2, 3.5oC warming, and 20% decrease in precipitation, and (6) doubling of CO2, 3.5oC warming, and 20% increase in precipitation. This dataset consists of the MEL model Windows executable, the driver and parameter file for each site, and the output files for each of the six simulations listed above.
Mangrove leaf physiological response to local climate at Key Largo, Watson River Chickee, Taylor Slough, and Little Rabbit Key, South Florida (FCE) from July 2001 to August 2001
Determine the red mangrove leaf physiological response to the local climate to understand the local controls on plant physiology. Data were collected in the Key Largo Ranger Station, Watson River Chickee and Taylor Slough research Sites, South Florida.
warmXtrophic: plant community responses to the individual and interactive effects of climate warming and herbivory across multiple years at Kellogg Biological Station Long-Term Ecological Research Sites (KBS LTER), Michigan, USA, and University of Michigan Biological Station (UMBS), Michigan, USA.
Climate change has both direct and indirect effects on ecological communities. Whereas most climate change ecology experiments manipulate abiotic drivers to measure direct effects of climate on species or communities, fewer quantify the indirect effects through biotic interactions, especially over multiple sites and years. In this factorial experiment we manipulate temperature through open-top chambers, and the level of insect herbivory through insecticide. At two early successional field sites separated by 3 degrees of latitude and 3°C of mean annual temperature (University of Michigan Biological Station, Pellston, MI and Kellogg Biological Station, Hickory Corners, MI), 6 replicate 1-m2 plots per treatment were installed in May 2015. 12 plots per site are at ambient temperature, 12 are warmed with year-round non-UV filtering polycarbonate and wood frame construction OTCs for tall-stature plants (Welshofer et al. 2018 MEE). Insecticide reduces insect herbivory in half the plots (Welshofer et al. 2018 Oecologia). Over the course of the experiment, OTCs warmed the plant communities by 1.9°C-3.0°C on average over the growing season. Each year, through 2021, plant traits and community responses were measured at the species level: plant phenology (green-up, flowering, flowering duration, seed set); plant percent cover (aerial % cover of the 1m2 plot); plant traits (specific leaf area, C and N content), herbivory damage to leaves, and plant species biomass (only in 2021). Further methodological details are found within each response variable metadata. This experiment is ongoing and further data package updates are planned. L0 data is available upon request. R scripts can be found here: https://github.com/SpaCE-Lab-MSU/warmXtrophic. The biotic and abiotic community context and relative strengths of direct vs. indirect effects may yield ecological surprises under climate change unless addressed together. Large-scale experiments like this one can improve our ability to unde
PALEODEM/Late Glacial and Early Holocene human demographic responses to climatic and environmental change in Atlantic Iberia
<p>This data files and R markdown scripts have been used in the meta-analysis of chronological and subsistence patterns of Atlantic hunter-gatherer groups between Late Glacial and Early Holocene in Atlantic Iberia.</p> <p>They correspond to the following reference: </p> <p>McLaughlin, T.R., Gómez-Puche, M., Cascalheira, J., Bicho, N.F., Fernández-López de Pablo, J. 2020. Late Glacial and Early Holocene human demographic responses to climatic and environmental change in Atlantic Iberia. <em>Phil. Trans. R. Soc. B. </em>(revised submitted version 29/04/2020)</p> <p>We specify the content of each file further down:</p> <ol> <li>Analysis_markdown.Rmd – R markdown file with the scripts to reproduce the analyses.</li> <li>Analysis_markdown.pdf – R markdown file in pdf format to reproduce the analyses.</li> <li>database_references.docx –A separate text file that comprises the extended bibliographic references used as source of the archaeological radiocarbon archaeological and isotopic data sets analyzed.</li> <li>Datelist.csv – spreadsheet that contains the 371 radiocarbon dates used as raw data to run the scripts. The last column of the table includes the bibliographical reference of the archaeological data compiled.</li> <li>ngrip.csv – NGRIP GICC05 paleotemperature record based on oxygen isotope series from Rasmussen SO <em>et al.</em>2006 A new Greenland ice core chronology for the last glacial termination. <em>J. Geophys. Res. Atmos.</em><strong>111</strong>. (doi:10.1029/2005JD006079) and Andersen KK <em>et al.</em>2006 The Greenland Ice Core Chronology 2005, 15–42ka. Part 1: constructing the time scale. <em>Quat. Sci. Rev.</em>25, 3246–3257. </li> <li>Pailler_and_Bard_42.csv­­ – Sea surface temperature data of the Atlantic margin of Iberia based on the paper: Pailler D, Bard E. 2002 High frequency palaeoceanographic changes during the past 140 000 yr recorded by the organic matter in sediments of the Iberian Margin. <em>Palaeogeogr. Palaeoclimatol. Palaeoecol.</em>181, 431–452. (doi:https://doi.org/10.1016/S0031-0182(01)00444-8)</li> <li>Paleodiet.csv – spreadsheet containing the published palaeodietary isotopic information of the human remains considered in this study.</li> <li>src.r – source r code of custom functions called upon this analysis by the R.markdown files. </li> </ol> <p>To reproduce analyses reported in the McLaughlin et al Phil Trans paper, donwload R_scripts and csv_files into the same folder. Open the *.rmd scripts in RStudio (https://www.rstudio.com), and run the scripts. </p> <p>The csv files can also be imported into R and used by the scripts. </p> <p> </p> <p> </p> <p> </p>
SLP01 Leaf physiology in response to fire and climate within Cornus drummondii shrubs at Konza prairie
Woody encroachment threatens the loss of remaining grasslands. Clonal shrubs are of particular concern because of their ability to resprout after disturbance, spread vegetatively, and share resources among interconnected stems. These traits contribute to the encroachment of deep-rooted clonal shrubs in tallgrass prairie. In this study, we investigated how leaf physiological traits differ among interconnected stems within a dominant encroaching shrub in tallgrass prairie, Cornus drummondii. Accounting for intra-clonal differences among stems in response to disturbance may be useful to more accurately parameterize models that predict the effects of shrub encroachment on ecosystem processes. Gas exchange rates, water potential, carbon isotopes, and leaf traits were collected from the periphery to the center of discrete C. drummondii shrubs. Measurements took place in the summers of 2015 and 2018
WAT02 Climate legacies determine grassland responses to future rainfall regimes
Climate variability and periodic droughts have complex effects on carbon (C) fluxes, with uncertain implications for ecosystem C balance under a changing climate. Responses to climate change can be modulated by persistent effects of climate history on plant communities, soil microbial activity, and nutrient cycling (i.e., legacies). To assess how legacies of past precipitation regimes influence tallgrass prairie C cycling under new precipitation regimes, we modified a long-term irrigation experiment that simulated a wetter climate for >25 years. We reversed irrigated and control (ambient precipitation) treatments in some plots and imposed an experimental drought in plots with a history of irrigation or ambient precipitation to assess how climate legacies affect aboveground net primary productivity (ANPP), soil respiration, and selected soil C pools. Legacy effects of elevated precipitation (irrigation) included higher C fluxes and altered labile soil C pools, and in some cases altered sensitivity to new climate treatments. Indeed, decades of irrigation reduced the sensitivity of both ANPP and soil respiration to drought compared with controls. Positive legacy effects of irrigation on ANPP persisted for at least 3 years following treatment reversal, were apparent in both wet and dry years, and were associated with altered plant functional composition. In contrast, legacy effects on soil respiration were comparatively short-lived and did not manifest under natural or experimentally-imposed “wet years,” suggesting that legacy effects on CO2 efflux are contingent on current conditions. Although total soil C remained similar across treatments, long-term irrigation increased labile soil C and the sensitivity of microbial biomass C to drought. Importantly, the magnitude of legacy effects for all response variables varied with topography, suggesting that landscape can modulate the strength and direction of climate legacies. Our results demonstrate the role of climate his
CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia: Dataset
<p>This repository is linked to the paper "CO2 fertilization, transpiration deficit and vegetation period drive the response of mixed broadleaved forests to a changing climate in Wallonia" submitted to Annals of Forest Science and written by Louis DE WERGIFOSSE (corresponding author), Frédéric ANDRE, Hugues GOOSSE, Steven CALUWAERTS, Lesley DE CRUZ, Rozemien DE TROCH, Bert VAN SCHAEYBROECK and Mathieu JONARD.</p> <p>The files stored in the repository are the input files that should be used in the model HETEROFOR to retrieve the results displayed in the study and the corresponding results themselves. The source code of the model HETEROFOR can be freely accessed and downloaded (https://doi.org/10.5281/zenodo.3591348). Additional information on the model can be found in the following description papers: Jonard et al., 2020 (https://doi.org/10.5194/gmd-13-905-2020) and de Wergifosse et al., 2020 (https://doi.org/10.5194/gmd-13-1459-2020).</p> <p>The repository contains three directories. The first (HETEROFOR_input_files) comprises the additional files to those in the model repository presented in the previous paragraph needed to run the model for the purpose of this study. The second directory (Simulation_outputs_raw) contains the data directly provided by the model without any processing. The third directory (Simulation_outputs_raw) includes the model outputs after processing.</p> <p>The directory "HETEROFOR_input_files" is constituted of two directories called "Climate_files" and "Stand_files". "Climate_files" is subdivided in three sub-directories. Sub-directory "Original_downscaled_CORDEX_timeseries" contains the climate projections of the four sites and scenarios described in the study. These downscaled timeseries have been produced by the Royal Meteorological Institute of Belgium under the program CORDEX.be, which is part of EURO-CORDEX. A bias correction has been further applied to these climate timeseries that are stored in the "Bias_corrected_timeseries" sub-directory. The files of these two sub-directories should be used in HETEROFOR as "Meteorological data" input files. The "CO2_concentrations" sub-directory includes the yearly averaged projected concentrations for the three RCP scenarios described in the paper. In HETEROFOR, they should be put as input in the "Atmospheric CO2 concentration" part after selecting the option "Variable over time". The second directory called "Stand files" contain the six inventory files described in the study for which a thinning has been applied. They should be used in HETEROFOR as "Inventory data" input files.</p> <p>The directory "Simulation_outputs_raw" is divided similarly to the study into two simulation experiments. The "First simulation experiment" directory is further subdivided into constant and time-dependent CO2 concentrations like in the study and contains one file for the regular modality and one for the thinning modality. All the files are constructed the same way with, for each tree and site (or stand, soil and climate), annual values of Net Primary Production (NPP) in kg of carbon, transpiration and potential transpiration in L under the different climate scenarios. In addition, the "Phenology" directory contains, for each day and under all climate scenarios, the green proportion (proportion of green leaves comprised between 0 and 1) for the two tree species considered in the study (Common oak and European beech).</p> <p>Finally, the directory "Simulation_outputs_processed" is constructed similarly to "Simulation_outputs_raw" but all the data are integrated in one file at the yearly time step. However, the units change with the NPP expressed in gC/m2 and transpiration and potential transpiration in mm (or L/m2) while the vegetation period is averaged according to the percentage of species occurrence<br> in the different stands.</p> <p><br> For more information concerning this repository or the study, please do not hesitate to contact Louis DE WERGIFOSSE (louis.dewergifosse@uclouvain.be) or Mathieu JONARD (mathieu.jonard@uclouvain.be).</p>
Global dataset for evaluating impact of topographic factors on hydrologic response to climate variability
<p>The dataset contained here was used to document the biomes in the world that show high sensitivity in their hydrologic response to interannual changes in climatic forcing during the 2001-2016 period, while evaluating the role of major topoclimatic factors in modulating these responses. To do this we generated a hydrologic sensitivity index (HSi). HSi evaluates the absolute ratio between the changes of the climatic conditions (dryness index, DI) and hydrologic response (evaporative index, EI<sub>R</sub>) between consecutive years (e.g. HSi= |∆ EI<sub>R</sub> /∆ DI|). HSi was computed for every successive pair of years from 2001 to 2016. A total of 15 HSi maps were obtained representing the HSi for each consecutive pair of years. For each map, where HSi >1, regions are classified as <strong><em>Sensitive</em></strong> and for HSi ≤1, <strong><em>Resilient</em></strong>. To provide a synthesis of the general trend of global hydrologic sensitivity, we display the frequency of HSi, showing the recurrence of HSi >1 for every non-ocean location with a range of 0 (low frequency) to 15 (high frequency). Regions where frequency HSi≥7 are considered highly recurring and as such are deemed as the most hydrologically sensitive. </p> <p><strong>This dataset includes the code and raster data to evaluate the effect of the topography on HSi to plot the average frequency HSi for all elevations, aspects, and slope steepness against latitudinal change.</strong> We used global digital elevation models (DEMS) from the Shuttle Radar Topography Mission (SRTM) data (90 m resolution; version 4, for latitudes < 60◦ N and GTOPO30 (1◦ resolution; http://lta.cr.usgs.gov/GTOPO30) for latitudes > 60◦ N. Slope and aspect maps were derived from the DEMs using standard GIS-based methods in ArcMap 10.7.Elevation range used is [0,7000] meters above sea level (m.a.s.l), aspect (N, NE, E, SE, S, SW, W, NW) specifically above slope values greater than 10-degrees (no flat areas used), and slope [0,90] degrees.</p> <p><strong>Contents:</strong></p> <ul> <li>1 MATLAB with the code ready to use</li> <li>1 PDF file with the same code</li> <li>27 geotiff files for elevation (dem#1-27.tif)</li> <li>27 geotiff files for frequency HSi (freq#1-27.tif) </li> </ul> <p>Note: the following files of slope and aspect could not upload in repository due to exceedance in storage limit: 50MG. The DEM files must be run in ArcMap using slope and aspect tool to produce the following files with the following names.</p> <ul> <li>27 geotiff files for slope (slope#1-27.tif)</li> <li>27 geotiff files for aspect (aspect#1-27.tif)</li> </ul>
Fifty-year changes of the world ocean's surface layer in response to climate change
<p>This object includes two files. One (GlobalML_Trend_1970_2018.mat) contains the 1970-2018 trends of mixed-layer depth, 0-200 stratification, and pycnocline stratification, as described in: Sallée, J.B., Pellichero, V., Akhoudas, C., Pauthenet, E., Vignes, L., Schmidtko, S., Naveira Garabato, A., Sutherland, P., Kuusela, M., 2020, Fifty-year changes of the world ocean’s surface layer in response to climate change, 591, 592–598, https://doi.org/10.1038/s41586-021-03303-x. The second one (<a href="https://zenodo.org/api/files/15c80d5f-a6f9-4b7b-bde1-12de43732195/GlobalML_Climato_1970_2018.mat">GlobalML_Climato_1970_2018.mat</a>) contains a climatology of mixed-layer depth based on the same methodology as in Sallée et al., 2021 (nature; doi:https://doi.org/10.1038/s41586-021-03303-x) but without regressing a trend. The climatological field is therefore different than in the paper; more robust in region where trends are unphysical (e.g. winter high latitude)</p>
Data for "Phenotypic responses to climate change are significantly dampened in big-brained birds"
<p>Anthropogenic climate change is rapidly altering local environments and threatening biodiversity throughout the world. Although many wildlife responses to this phenomenon appear largely idiosyncratic, a wealth of basic research on this topic is enabling the identification of general patterns across taxa. Here we expand those efforts by investigating how avian responses to climate change are affected by the ability to cope with ecological variation through behavioral flexibility (as measured by relative brain size). After accounting for the effects of phylogenetic uncertainty and interspecific variation in adaptive potential, we confirm that although climate warming is generally correlated with major body size reductions in North American migrants, these responses are significantly weaker in species with larger relative brain sizes. Our findings suggest that cognition can play an important role in organismal responses to global change by actively buffering individuals from the environmental effects of warming temperatures.</p>
Evolution of Indian Ocean Paleoceanography and South-East Asian Climate during the Miocene in response to change in regional topography
<p>This directory contain outputs of 9 paleo-climate simulations performed with the IPSL-CM5A2 and PISCES-v2 models. The simulations have used in a paper to be published in Nature Geoscience (2022) entitled "Divergent South Asian Monsoon Rainfall and Wind Histories due to topography effects" (Sarr et al.) that investigates the co-evolution of Arabian Sea upwelling and South Asian Monsoon rainfall and winds over the Miocene. It includes simulations with both early Miocene and late Miocene paleogeography.</p> <p>SimulationsOutputs.tar directory contains NetCDF files with ocean, ocean biogeochemistry and atmosphere variables. Data are monthly average over the last 100 years of each simulation.</p> <p>TopoMiocene.tar contains the paleogeographies used for the simulations.</p> <p> More informations on output contents can be find in README_detailsOutput.md document as well as within the Methods section of the publication.</p> <p>PISCES_update.tar contains updated routines for the PISCES-offline model (Aumont et al., 2015) that have been used for the publication. It contains a REAME.md file that explain how to include those updates within the reference code.</p> <p> </p>
Data set for "The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices"
<p>This data set was used for the modelling in the article M. Kölbach, O. Höhn, K. Rehfeld, M. Finkbeiner, J. Barry, and M. M. May, “The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices”<strong><em>,</em></strong> <em>Sustainable Energy Fuels</em>, <strong>2022</strong>, <strong>6</strong>, 4062-4074, <a href="https://doi.org/10.1039/D2SE00561A">https://doi.org/10.1039/D2SE00561A</a>.</p> <p>It contains the External Quantum Efficiency (EQE) data of a wafer-bonded AlGaAs//Si dual-junction solar cell for several top absorber compositions, angle of incidences, and temperatures modelled using the OPTOS formalism (see <a href="https://doi.org/10.1364/OE.24.0A1083">https://doi.org/10.1364/OE.24.0A1083</a> , <a href="https://doi.org/10.1364/OE.23.0A1720">https://doi.org/10.1364/OE.23.0A1720</a> , and <a href="http://doi.org/10.1109/JPHOTOV.2021.3064562"> https://doi.org/10.1109/JPHOTOV.2021.3064562</a>). Moreover, the data set includes hourly resolved direct and diffuse solar spectra for a location near the Neumayer station in Antarctica (-70.67°/-8.28°) that were modelled using the libRadtran software package for the year 2021 (see <a href="https://doi.org/10.1140/epjconf/e2009-00912-1">https://doi.org/10.1140/epjconf/e2009-00912-1</a> and <a href="http://doi.org/10.5194/acp-5-1855-2005">https://doi.org/10.5194/acp-5-1855-2005</a>). The modelling of the spectra was performed employing the predefined “subarctic summer” and “subarctic winter” atmosphere datasets assuming a tilt angle of 70° and 1-axis tracking. For the sake of simplicity, no cloud cover was assumed over the course of the whole year. Finally, the input files required for modelling the climatic response of solar water splitting devices for the selected location in Antarctica using the “climatic_response_function” of YaSoFo (see <a href="http://doi.org/10.5281/zenodo.5257492">https://doi.org/10.5281/zenodo.5257492</a> for an extended example) are included in the data set.</p>
Changes in above- versus belowground biomass distribution in permafrost regions in response to climate warming
<p>Permafrost regions contain approximately half of the carbon stored in land ecosystems and have warmed at least twice as much as any other biome. This warming has influenced vegetation activity, leading to changes in plant composition, physiology, and biomass storage in aboveground and belowground components, ultimately impacting ecosystem carbon balance. Yet, little is known about the causes and magnitude of long-term changes in the above- to belowground biomass ratio of plants (η). Here, we analyzed η values based on 3,013 plots and 26,337 plant-specific measurements representing eight sites across the Tibetan Plateau from 1995 to 2021. Our analysis revealed distinct temporal trends in η for three vegetation types: a 17% increase in alpine wetlands, and a decrease of 26% and 48% in alpine meadows and alpine steppes, respectively. These trends were primarily driven by temperature-induced growth preferences rather than shifts in plant species composition. Our findings indicate that in wetter ecosystems climate warming promotes aboveground plant growth, while in drier ecosystems, such as alpine meadows and alpine steppes, plants allocate more biomass belowground. Four process-based biogeochemical models failed to simulate the observed changes in η, which highlights the importance of improved process understanding of the processes driving the response of biomass distribution to climate warming, which is crucial for predicting the future carbon trajectory of permafrost ecosystems.</p>
Classification and frequency of climate change drivers and responses of small-scale fishers found in literature review
<p>Climate change hazards were classified into resource availability and fishing operations or both following the framework proposed by Cheung et al (2012). Response units were firstly classified into overarching responses and then categorized as suggested by the adaptive-transformative framework of Barnes et al.<sup> </sup>(2020). Adaptation units that did not represent an active adaptation response were classified as remaining. More than one hazard could be attributed to each fishers' response.</p>
Data: Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing
<p>The dataset contains supporting data for the paper submitted to The Cryosphere "Projecting the Response of Greenland's Peripheral Glaciers to Future Climate Change: Glacier Losses, Sea Level Impact, Freshwater Contributions, and Peak Water Timing".<br><br>OGGM_area_projections.nc contains data for Figure 3.<br>OGGM_volume_projections contains data for Figure 4.</p> <p>OGGM_MassLoss_SLR_projections_regions.nc contains data for Figure 5.</p> <p>OGGM_solid_ice_discharge_regions.nc contains data for Figure 6.</p> <p>OGGM_freshwater_runoff_magnitude_composition_timings_projections.nc & OGGM_freshwater_runoff_projections_regions.nc contain data for Figure 7.</p> <p>OGGM_PeakWaterYear_projections_regions.nc contains data for Figure 8.</p>
Expansion of coccidioidomycosis (Valley fever) endemic regions in the United States in response to climate change: projections of disease incidence
<p>This file contains estimations of coccidioidomycosis (Valley fever) incidence data in cases per 100,000 population per year for the contemporary time period and projections throughout the 21st century in response to RCP4.5 and RCP8.5 climate scenarios, associated with the publication:</p> <p>Gorris, M. E., Treseder, K. K., Zender, C. S., and Randerson, J. T. (2019). Expansion of coccidioidomycosis endemic regions in the United States in response to climate change. <em>GeoHealth</em>. </p> <p>The data is reported for each county in the conterminous US with its associated FIPS code (Column 1), county name (Column 2), state FIPS code (Column 3), and state name (Column 4). Column 5 contains the estimation of mean annual Valley fever incidence averaged from 2000-2015. Column 6-8 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for RCP4.5 climate scenario. Likewise, Columns 9-11 contain the estimations of mean annual Valley fever incidence for the 11-year averages surrounding years 2035, 2065, and 2095 for the RCP8.5 climate scenario. </p> <p>Details about how the incidence data was calculated may be read in the Methods subsection of the paper under "Modeling of current and future mean annual Valley fever incidence". The data provided here was used to create Figure 7 and Supporting Information Figure S5. Counties that have non-zero incidence are considered endemic by our climate-constrained niche model, so this data may also be used to create portions of Figures 3, 4, and S3. </p>
Dataset for: Infectious disease responses to human climate change adaptations
<p>Original and derived data products referenced in the original manuscript are provided in the data package.</p> <h3>Description of the data and file structure</h3> <p><em>Original data:</em></p> <p><code>Table_1_source_papers.csv</code>: Papers that met review criteria and which are summarized in Table 1 of the manuscript.</p> <ol> <li><strong>ID</strong>: The paper identification number</li> <li><strong>Topic</strong>: The broad topic (i.e., each row of Table 1)</li> <li><strong>Authors:</strong> The names of the authors of the paper</li> <li><strong>Article Title</strong>: The title of the paper</li> <li><strong>Source Title</strong>: The name of the journal in which the paper was published</li> <li><strong>Abstract</strong>: The paper's abstract, retrieved from the Web of Science search</li> <li><strong>study_type:</strong> Classification of the study methodology/approach. "A" = a designed study that shows effect ,"B" = a pre/post study, "C" = a comparison of health outcomes or pathogen risk relative to a 'control/comparison' area, "D" = some quantitative effect but no control, "E" = qualitative comments but little supporting evidence, and/or a qualitative review.</li> <li><strong>pathogen_broad</strong>: Broad classification of the type of pathogen discussed in the paper.</li> <li><strong>transmission_type</strong>: Categorization of indirect, direct, sexual, vector, or other transmission modes.</li> <li><strong>pathogen_type</strong>: Categorization of bacteria, helminth, virus, protozoa, fungi, or other pathogen types.</li> <li><strong>country:</strong> Country in which the study was performed or results discussed. When countries were not available, regions were used. NA values indicate papers in which a geographic region was not relevant to the study (i.e., a methods-based study).</li> </ol> <p><em>Derived data:</em></p> <p><code>change_livestock_country.csv:</code> A dataframe containing values used to generate Figure 4a in the manuscript.</p> <ol> <li><strong>County Name</strong>: The name of the county in Kenya</li> <li><strong>Sheep and goats 1980</strong>: The estimated number of sheep and goats in 1980</li> <li><strong>Sheep and goats 2016</strong>: The estimated number of sheep and goats in 2016</li> <li><strong>pct_change_shoat</strong>: The percent change in sheep and goat numbers from 1980 to 2016</li> <li><strong>Cattle 1980</strong>: The estimated number of cattle in 1980</li> <li><strong>Cattle 2016</strong>: The estimated number of cattle in 2016</li> <li><strong>pct_change_cattle</strong>: The percent change in cattle numbers from 1980 to 2016</li> <li><strong>Camel 1980</strong>: The estimated number of camels in 1980</li> <li><strong>Camel 2016</strong>: The estimated number of camels in 2016</li> <li><strong>pct_change_camel</strong>: The percent change in camel numbers from 1980 to 2016</li> <li><strong>human_pop 1980</strong>: The estimated human population in the county in 1980</li> <li><strong>human_pop 2016</strong>: The estimated human population in the county in 1980</li> <li><strong>pct_change_human</strong>: The percent change in the human population from 1980 to 2016</li> <li><strong>area_sq_km</strong>: The land area of the county</li> <li><strong>change_ind_per_sq_km_shoat:</strong> Absolute change in number of sheep and goats from 1980 to 2016</li> <li><strong>change_ind_per_sq_km_cattle:</strong> Absolute change in number of cattle from 1980 to 2016</li> <li><strong>change_ind_per_sq_km_camel:</strong> Absolute change in number of camels from 1980 to 2016</li> </ol> <p><code>country_avg_schist_wormy_world.csv</code>: A dataframe containing values used to generate Figure 3 in the manuscript.</p> <ul> <li><strong>Country:</strong> The country in which the schistosome prevalence studies were performed.</li> <li><strong>Latitude:</strong> The latitute in decimal degrees</li> <li><strong>Longitude:</strong> The longitute in decimal degrees</li> <li><strong>Maximum.prevalence:</strong> The mean maximum schistosomiasis prevalence of studies conducted within each country.</li> </ul> <p><code>kenya_precip_change_1951_2020.csv</code>: A dataframe containing values used to generate Figure 4b in the manuscript.</p> <ul> <li><strong>Precipitation (mm):</strong> Binned annual precipitation values</li> <li><strong>1951-1980:</strong> The density of observations for each annual precipitation value for the 1951-1980 period</li> <li><strong>1971-2000:</strong> The density of observations for each annual precipitation value for the 1971-2000 period</li> <li><strong>1991-2020:</strong> The density of observations for each annual precipitation value for the 1991-2020 period</li> </ul> <h3>Sharing/Access information</h3> <p>Data were derived from the following sources:</p> <ul> <li> <p>Ogutu, J. O., Piepho, H.-P., Said, M. Y., Ojwang, G. O., Njino, L. W., Kifugo, S. C., & Wargute, P. W. (2016). Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: What are the causes? <em>PloS ONE</em>, <em>11</em>(9), e0163249. https://doi.org/10.1371/journal.pone.0163249</p> </li> <li> <p>London Applied & Spatial Epidemiology Research Group (LASER). (2023). <em>Global Atlas of Helminth Infections: STH and Schistosomiasis</em> [dataset]. London School of Hygiene and Tropical Medicine. https://lshtm.maps.arcgis.com/apps/webappviewer/index.html?id=2e1bc70731114537a8504e3260b6fbc0</p> </li> <li> <p>World Bank Group. (2023). <em>Climate Data & Projections—Kenya</em>. Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/kenya/climate-data-projections</p> </li> </ul>
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.