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2,260 results for “Climatic change”
Figure 1 in Climate Changes of the Temperature of the Surface and Level of the Black Sea by the Data of Remote Sensing at the Coast of the Krasnodar Krai and the Republic of Abkhazia
Figure 1. The Black Sea coast of the Krasnodar Krai and the Republic of Abkhazia.
Data from: Climate drives body mass changes in a mountain ungulate: Shorter winters lead to heavier Alpine ibex
<p>Climate affects seasonality and plant phenology, which can influence seasonal body mass dynamics of herbivores in temperate environments. We investigated long-term trends of seasonal body mass changes in male Alpine ibex (<em>Capra ibex</em>). We used SEM to test direct and indirect relationships between body mass, mass changes and environmental and climatic variables. Individually recognizable Alpine ibex were weighed repeatedly between 2000 and 2022 in Gran Paradiso National Park (Italy). Autumn mass increased substantially over these two decades, up to 15% in some age classes. Over the same time frame, both summer mass gain and winter mass loss decreased, suggesting that heavier autumn body mass was due to the cumulative effects of reduced mass loss over several winters. The environmental factor with the strongest effects on winter mass changes was the starting date of vegetation green-up at low altitude, where ibex gather after winter to feed on new growth vegetation. Early springs led to lower winter mass loss, likely because ibex relied on stored fat for a shorter period and had greater access to forage. High population density also increased winter mass loss. Environmental conditions and resource availability, possibly also influenced by density in winter and early spring seem therefore to directly affect the body mass dynamics of male Alpine ibex, while the effect of summer conditions appears less relevant. By affecting seasonal body mass dynamics, climate change may have consequences for life history and population dynamics of mountain herbivores, for example via earlier access of young males to reproduction.</p>
AkiraSMori/BiodProd-ProtectArea: Analyses for "Biodiversity protection and its future benefits to society are intertwined with climate change action"
<div> <h2>Abstract</h2> <a href="https://github.com/AkiraSMori/BiodProd-ProtectArea/blob/main/README.md#abstract"></a></div> <p>Biodiversity loss and climate change are incontrovertibly intertwined, yet the nuanced interplay between these global challenges is often understated in policy dialogues. Here, we illustrate that conservation through protected areas can effectively preserve primary productivity and carbon capture in forests worldwide, which directly depend on tree diversity. However, we also discover that failing to mitigate future climate change has the potential to diminish the effectiveness of terrestrial protected areas in conserving tree diversity-dependent forest productivity, especially in warmer biomes. This holds true even under the most optimized selection of protected areas designed to meet the global biodiversity target of 30% protection by 2030. Thus, climate change mitigation is critical for the success of many conservation actions aimed at achieving global targets; otherwise, existing and future efforts to conserve biodiversity and their benefits to society could be in vain. Addressing anthropogenic climate change will sustain the many biodiversity-derived ecosystem benefits to society.</p>
Fig. 4 in The effects of short-term climate change on the range of species: the case of the expanding European dwarf mantis Ameles spallanzania in northern Italy (Mantodea: Amelidae)
Fig. 4 – Suitability maps of Ameles spallanzania referred to each decade.
Fig. 5 in The effects of short-term climate change on the range of species: the case of the expanding European dwarf mantis Ameles spallanzania in northern Italy (Mantodea: Amelidae)
Fig. 5 – Boxplots of suitability per decade referred to the historical range and northern Italy.
Fig. 6 in The effects of short-term climate change on the range of species: the case of the expanding European dwarf mantis Ameles spallanzania in northern Italy (Mantodea: Amelidae)
Fig. 6 – Extent of predicted presence areas derived from binary maps.
Fig. 2 in The effects of short-term climate change on the range of species: the case of the expanding European dwarf mantis Ameles spallanzania in northern Italy (Mantodea: Amelidae)
Fig. 2 – Land use in the occurrences of the current period.
Fig. 3 in The effects of short-term climate change on the range of species: the case of the expanding European dwarf mantis Ameles spallanzania in northern Italy (Mantodea: Amelidae)
Fig. 3 – Importance of climatic variables used to model the distribution of Ameles spallanzania.
Data for: A harmonized database of European forest simulations under climate change
<p>This repository contains the database presented in the publication "A harmonized database of European forest simulations under climate change". It contains a collection of harmonized forest simulation model outputs from 17 different models covering 1.1 million individual simulation runs, over 136 million simulation years across over 13,599 unique locations in Europe.</p> <p>Detailed description can be found in the publication (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.dib.2024.110384" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dib.2024.110384</a>). The file "forest_simulation_db_v1.7z" contains all raw simulation outputs and a metadata table of all simulations including information about locations and harmonized soil conditions for those locations. Simulation outputs with harmonized climate data are stored in one SQLite database per climate scenario.</p> <p>The code that was used to create the database, as well as to access and explore the data can be found here: https://github.com/magrueni/forest_simulation_database.git</p> <p>Note: Please be cautious with the use of the simulations with unique identifiers 1037-1047. There were some abrupt species compositions changes reported that suggest that in a small number of the original simulations there was an underlying issue in the compilation of the raw simulation data.</p> <p> </p> <p>--- Please use the updated version 1.1 ---</p> <p>Unfortunately we found an bug in the daily climate extraction process of the previous version, leading to inconsistencies in the harmonized climate data. We corrected the harmonized daily climate data for all scenarios. Additionally, the LAI values in the raw data of the simulations with the unique identifier 1016 were calculated wrongly and therefore corrected in this version. Please use the updated version for all analyses. We apologize for any inconveniences.</p> <p> </p> <p>--- Please use the updated version 1.1 ---</p>
Data to support 'Deforestation amplifies climate change effects on warming and cloud level rise in African montane forest'
<p>This respository contains output data to support the manuscript titled 'Deforestation amplifies climate change effects on warming and cloud level rise in African montane forest' by Temesgen Alemayehu Abera, Janne Heiskanen Eduardo Eiji Maeda, Mohammed Ahmed Muhammed, Netra Bhandari, Ville Vakkari, Binyam Tesfaw Hailu, Petri K.E. Pellikka, Andreas Hemp, Pieter G. van Zyl, and Dirk Zeuss </p>
Fig. 2 in Predicting the potential distribution of the subalpine broad-leaved tree species, Betula ermanii Cham. under climate change in South Korea
Fig. 2. ROC curve and AUC value in the current period (1970- 2000).
Fig. 1 in Predicting the potential distribution of the subalpine broad-leaved tree species, Betula ermanii Cham. under climate change in South Korea
Fig. 1. The location of occurrence points of Betula ermanii in South Korea (N = 162).
Investigating the resilience of termite communities to logging and climate change in Borneo
<b>Description: </b><p>This project set out to quantify the tolerance of termite communities to climate change, or more specifically, temperature and humidity change, two climatic variables that have been hypothesised to drive species distributions (particularly for small ectotherms such as termites). The data presented here are the tolerances of termites to increasing temperatures, and decreasing humidities. <br><br>The thermal data was recorded by inserting termites into individual glass vials, placing those sealed vials into a water bath, and increasing the temperature until they could no longer function. This temperature was recorded, and taken as CTmax (Critical Thermal Maximum), for each individual termite. These data can be found in the TemperatureData worksheet. <br><br>The humidity data was recorded slightly differently. Groups of termites (of the same genus) were weighed and placed in one of two types of glass vial. Dessicated vials also contained silica gel (and a barrier to prevent termite interaction with the gel) which reduced the humidity to an average of 30%. Control vials did not contain any silica gel and had an average humidity of 85%. These vials were removed at one of 5 time points, and the termites were weighed again, and weight change was recorded. This weight change was attributed to water loss. <br><br>The body water data was used to calculate the proportion of body mass that was water, for multiple termite genera. This was done so that percentage of body water lost could be calculated for the humidity experiment, rather than an absolute value of water loss (as termites vary in size, using absolute values would cause false conclusions). </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/31"><b>Investigating the resilience of termite communities to logging and climate change in Borneo</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=33">here</a></p><p><b>Data worksheets: </b>There are 3 data worksheets in this dataset:</p><ol><li><p><b>Thermal tolerance data</b> (Worksheet TemperatureData)</p><p>Dimensions: 1256 rows by 17 columns</p><p>Description: Thermal tolerance data of termites</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that each termite was taken from (Field type: ID)</li><li><b>Day</b>: The day on which the experiment took place (Field type: ID)</li><li><b>Termite_no</b>: The unique termite number, missing numbers are due to non-experimental deaths (Field type: ID)</li><li><b>Experiment</b>: Whether it was the first or second experiment from the same colony (Field type: Replicate)</li><li><b>Family</b>: The family of the termite (Field type: ID)</li><li><b>Genus</b>: The genus of the termite (Field type: ID)</li><li><b>Species</b>: The species (where known) of the termite (Field type: ID)</li><li><b>Name</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>CTmax</b>: The critical thermal maximum of the termite, or the temperature that it died at (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Nest_type</b>: The type of nest that the termite builds (Field type: Categorical Trait)</li><li><b>Nest_Layer</b>: The layer within the forest that the nest is built (Field type: Categorical Trait)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>Caste</b>: Which behavioural caste the termite belonged to (Field type: Categorical Trait)</li></ul><br></li><li><p><b>Humidity tolerance data</b> (Worksheet HumidityData)</p><p>Dimensions: 169 rows by 16 columns</p><p>Description: Humidity tolerance data of 4 termite genera</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that the termites were taken from (Field type: ID)</li><li><b>Tube</b>: The unique tube number that the termites were placed in (Field type: ID)</li><li><b>Taxa</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>Time</b>: The five time points that the tubes were removed at (Field type: Numeric)</li><li><b>Treatment</b>: Whether the termites were placed in a control or desiccated tube (Field type: Categorical)</li><li><b>Initial</b>: Weight of the group of termites at the start of the experiment (Field type: Numeric Trait)</li><li><b>Finish</b>: Weight of group of termites when removed from experiment (Field type: Numeric Trait)</li><li><b>Percentage_lost</b>: Proportion of body mass change (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>No_termites</b>: Number of termites placed in the tube (Field type: Numeric)</li><li><b>No_dead</b>: Number of termites that were dead at the point of the second weighing (Field type: Numeric)</li><li><b>Percentage_Dead</b>: Percentage of termites that are dead at point of second weighing (Field type: Numeric)</li></ul><br></li><li><p><b>Termite total body water data</b> (Worksheet BodyWaterData)</p><p>Dimensions: 38 rows by 12 columns</p><p>Description: Data calculating the total body water of 4 termite genera, this data was used in the humidity data to calculate the percentage of body water lost during the experiment</p><p>Fields: </p><ul><li><b>Nest_no</b>: The unique nest (colony) number that the termites were taken from (Field type: ID)</li><li><b>Tray_no</b>: The unique tray number that the termites were placed in (Field type: ID)</li><li><b>Genus</b>: The species (where known) of the termite (Field type: Taxa)</li><li><b>Weight_start</b>: Weight of the group of termites at the start of the experiment (Field type: Numeric Trait)</li><li><b>Weight_end</b>: Weight of group of termites when removed from experiment (Field type: Numeric Trait)</li><li><b>Percentage</b>: Percentage of body mass that is water (Field type: Numeric Trait)</li><li><b>Sampling_zone</b>: Sampling area, LFE corresponds to the area at the SAFE project, and OP is Oil Palm (Field type: Categorical)</li><li><b>Forest_type</b>: Forest type sampled from within the sampling area, OG corresponds to Old Growth (or pristine) forest (Field type: Categorical)</li><li><b>Food_group</b>: The food group that the termite belongs to, categorised using the latest literature (Field type: Categorical Trait)</li><li><b>Body</b>: The level of sclerotisation of the termite (Field type: Categorical Trait)</li><li><b>Caste</b>: Which behavioural caste the termite belonged to (Field type: Categorical Trait)</li></ul><br></li></ol><p><b>Date range: </b>2016-02-01 to 2016-07-01</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Arthropoda<br> -  - Insecta<br> -  -  - Isoptera<br> -  -  -  -  - <i>Homallotermes</i><br> -  -  -  -  -  - [<i>Homallotermes foraminifer</i>]<br> -  -  -  - Kalotermitidae<br> -  -  -  -  - <i>Glyptotermes</i><br> -  -  -  -  -  - [Glyptotermes sp.]<br> -  -  -  - Rhinotermitidae<br> -  -  -  -  - <i>Coptotermes</i><br> -  -  -  -  -  - [Coptotermes sp.]<br> -  -  -  -  - <i>Parrhinotermes</i><br> -  -  -  -  -  - [<i>Parrhinotermes pygmaeus</i>]<br> -  -  -  -  - <i>Schedorhinotermes</i><br> -  -  -  -  -  - [<i>Schedorhinotermes sarawakensis</i>]<br> -  -  -  -  -  - [Schedorhinotermes sp.]<br> -  -  -  - Termitidae<br> -  -  -  -  - <i>Bulbitermes</i><br> -  -  -  -  -  - [Bulbitermes sp.]<br> -  -  -  -  - <i>Dicuspiditermes</i><br> -  -  -  -  -  - [Dicuspiditermes sp.]<br> -  -  -  -  - <i>Globitermes</i><br> -  -  -  -  -  - [<i>Globitermes globosus</i>]<br> -  -  -  -  - <i>Hospitalitermes</i><br> -  -  -  -  -  - [<i>Hospitalitermes hospitalis</i>]<br> -  -  -  -  -  - [<i>Hospitalitermes bicolour</i>]<br> -  -  -  -  - [Lacessitermes sp.]<br> -  -  -  -  - <i>Longipeditermes</i><br> -  -  -  -  -  - [Longipeditermes sp.]<br> -  -  -  -  - <i>Macrotermes</i><br> -  -  -  -  -  - [<i>Macrotermes gilvus</i>]<br> -  -  -  -  - <i>Microcerotermes</i><br> -  -  -  -  -  - [Microcerotermes sp.]<br> -  -  -  -  - <i>Nasutitermes</i><br> -  -  -  -  -  - [<i>Nasutitermes havilandi</i>]<br> -  -  -  -  -  - [Nasutitermes sp.]<br> -  -  -  -  - <i>Odontotermes</i><br> -  -  -  -  -  - [Odontotermes sp.]<br></div><p></p>
Dataset: Climate change effects on early stages of Quercus ariifolia (Fagaceae), an endemic oak from seasonally dry forests of Mexico
<p>This repository contains the files associated with the following article:</p> <p>Badano EI, FA Guerra-Coss, EJ Sánchez-Montes de Oca, CI Briones-Herrera & SM Gelviz-Gelvez. Climate change effects on early stages of <em>Quercus ariifolia</em> (Fagaceae), an endemic oak from seasonally dry forests of Mexico. Acta Botanica Mexicana, 126, Article e1466. <a href="https://doi.org/10.21829/abm126.2019.1466">https://doi.org/10.21829/abm126.2019.1466</a></p> <p>The first Microsoft Excel file (Additional data 01 - Microclimate.xlsx) contains microclimate data gathered within control plots and climate change simulation plots (CCS plots) during the field experiment. These data include air temperature (measured every hour in 10 experimental units of each climate treatment with dataloggers - HOBO U23-Pro-V2, Onset Computer Corporation, USA), rainfall (measured at each rainfall event in 5 experimental units of each climate treatment with automatized pluviometers - HOBO S-RGB-M002, Onset Computer Corporation, USA) and soil water content (measured every week in 10 experimental units of each climate treatment with a time-domain reflectometer - FieldScout TDR 300, Spectrum Technologies, USA). Values of each of these variables are provided in different spreadsheets. The second Microsoft Excel file (Additional data 02 - Seedling responses) contains the data used to calculate the emergence rates and survival rates of <em>Quercus ariaefolia</em> during the experimental period a each experimental treatment. This file also contains the data gathered at the end of the experiment about chlorophyll content (in SPAD Units) and chlorophyll fluorescence (the spreadsheet contains the values of these variables measured on each seedling leaf; the computation of averages across leaves of each seedling is provided on the side), and the other functional traits measured on seedlings from controls and CCS plots.</p>
Data output for: Climate change stimulated agricultural innovation and exchange across Asia, in review.
<p>The GitHub repository for this project does not contain the output<br> generated by the script—3.2 GB of compressed data. All output data is<br> available as this Zenodo archive.</p> <p>The `vignettes/` directory contains all data generated by the<br> `guedesbocinsky2018.Rmd` RMarkdown vignette:</p> <p> - `data/raw_data` contains data downloaded from web sources for this<br> analysis<br> - `data/derived_data/` contains tables of the raw site chronometric<br> data without locational information, and the modeled chronometric<br> probability and niche information for each site.<br> - `data/derived_data/models/` contains R data objects describing the<br> Kriging interpolation models across the study area<br> - `data/derived_data/recons/` contains NetCDF format raster bricks of<br> the model output (i.e., the reconstructed crop niches)<br> - `figures/` contains all figures output by the script, including<br> videos of how each crop niche changes over time<br> - `figures/site_densities/` contains figures of the estimated<br> chronometric probability density for each site in our database<br> - `submission/` contains all of the figures, tables, movies, and<br> supplemental datasets included with d’Alpoim Guedes and Bocinsky<br> (2018)</p>
Projecting boreal bird responses to climate change: the signal exceeds the noise
<p>Current and projected future potential boreal bird densities (4-km resolution)</p> <p>Citation for journal article associated with this dataset:<br> --------------------<br> Stralberg, D., S. M. Matsuoka, A. Hamann, E. M. Bayne, P. Sólymos, F. K. A. Schmiegelow, X. Wang, S. G. Cumming, and S. J. Song. 2015. Projecting boreal bird responses to climate change: the signal exceeds the noise. Ecological Applications 25:52-69. http://dx.doi.org/10.1890/13-2289.1</p> <p>Coordinate System<br> ------------------<br> Projection: Lambert Conformal Conic<br> False Easting: 0.00000000<br> False Northing: 0.00000000<br> Central Meridian: -95.00000000<br> Standard Parallel 1: 49.00000000<br> Standard Parallel 2: 77.00000000<br> Latitude Of Origin: 0.00000000<br> Linear Unit: Meter<br> Datum: D WGS 1984</p> <p>Summary<br> -------<br> The boreal forest biome provides a resource-rich environment for breeding birds, supporting high species diversity and bird numbers. These birds are likely to shift their distributions northward in response to rapid climate change over the next century. We used a comprehensive dataset of avian point-count surveys from across boreal Canada and Alaska, combined with interpolated climate data, to develop bioclimatic niche models of current avian distribution and density for 80 boreal-breeding songbird species. We then used a downscaling of projected future climates to assess the potential for these species to change their distribution and abundance in response to climate change. Note that projections represent potential densities based on climatic conditions, land use and topography. They do not account for physiographic barriers such as the northern extent of the Rocky Mountains that may prevent colonization of otherwise suitable habitat. Therefore current species’ distributions may be over-estimated in certain regions, particularly in Alaska.</p> <p>Boosted regression tree models of species distribution were averaged across two sets of covariates (climate-only and climate + land use + topography), 11 bootstrap samples, and four global climate models. Mean projections and uncertainty estimates (coefficient of variation) are available for the current period (based on climate data from 1961-1990) and three future time periods (2011–2040, 2041­–2070, 2071–2100). Climate data layers available at tinyurl.com/ClimateNA.</p> <p>Contact<br> -------<br> Diana Stralberg, University of Alberta (stralber@ualberta.ca)<br> Boreal Avian Modelling Project (borealbirds.ca)</p> <p>Project sponsors<br> ----------------<br> Boreal Avian Modelling (BAM) Project<br> Alberta Biodiversity Management and Climate Change Adaptation Project</p> <p>Avian data providers<br> --------------<br> http://www.borealbirds.ca/index.php/data_partners<br> USGS Breeding Bird Survey<br> Breeding Bird Atlases of Canada</p> <p>BAM founding organisations and funders<br> --------------------------------------<br> Environment Canada<br> University of Alberta<br> Canadian BEACONs Project</p> <p>Financial supporters<br> --------------------<br> USFWS Neotropical Migratory Bird Conservation Act<br> Vanier Canada Graduate Scholarships</p> <p>Alberta Biodiversity Monitoring Institute<br> Alberta Innovates Technology Futures<br> Alberta Pacific Forest Industries Inc.<br> Climate Change and Emissions Management Corporation<br> Joint Canada-Alberta Implementation Plan for Oil Sands Monitoring<br> Killam Trusts<br> Landscape Conservation Cooperatives<br> National Fish and Wildlife Foundation<br> Université Laval</p> <p>Species code definitions<br> ------------------------<br> Code Common name (Scientific name)<br> ALFL Alder Flycatcher (Empidonax alnorum) ‡<br> AMCR American Crow (Corvus brachyrhynchos)<br> AMGO American Goldfinch (Spinus tristis)<br> AMPI American Pipit (Anthus rubescens) ‡<br> AMRE American Redstart (Setophaga ruticilla)<br> AMRO American Robin (Turdus migratorius) ‡<br> ATSP American Tree Sparrow (Spizella arborea) ‡<br> BAWW Black-and-white Warbler (Mniotilta varia)<br> BBWA Bay-breasted Warbler (Setophaga castanea)<br> BCCH Black-capped Chickadee (Poecile atricapillus) ‡<br> BHCO Brown-headed Cowbird (Molothrus ater)<br> BHVI Blue-headed Vireo (Vireo solitarius)<br> BLBW Blackburnian Warbler (Setophaga fusca)<br> BLJA Blue Jay (Cyanocitta cristata)<br> BLPW Blackpoll Warbler (Setophaga striata) ‡<br> BOCH Boreal Chickadee (Poecile hudsonicus) ‡<br> BRBL Brewer’s Blackbird (Euphagus cyanocephalus)<br> BRCR Brown Creeper (Certhia americana) ‡<br> BTNW Black-throated Green Warbler (Setophaga virens)<br> CAWA Canada Warbler (Cardellina canadensis)<br> CCSP Clay-colored Sparrow (Spizella pallida)<br> CEDW Cedar Waxwing (Bombycilla cedrorum)<br> CHSP Chipping Sparrow (Spizella passerina) ‡<br> CMWA Cape May Warbler (Setophaga tigrina)<br> COGR Common Grackle (Quiscalus quiscula)<br> CONW Connecticut Warbler (Oporornis agilis)<br> CORA Common Raven (Corvus corax) ‡<br> CORE Common Redpoll (Acanthis flammea) ‡<br> COYE Common Yellowthroat (Geothlypis trichas)<br> CSWA Chestnut-sided Warbler (Setophaga pensylvanica)<br> DEJU Dark-eyed Junco (Junco hyemalis) ‡<br> EAKI Eastern Kingbird (Tyrannus tyrannus)<br> EAPH Eastern Phoebe (Sayornis phoebe)<br> EVGR Evening Grosbeak (Coccothraustes vespertinus)<br> FOSP Fox Sparrow (Passerella iliaca) ‡<br> GCKI Golden-crowned Kinglet (Regulus satrapa) ‡<br> GCTH Gray-cheeked Thrush (Catharus minimus) ‡<br> GRAJ Gray Jay (Perisoreus canadensis) ‡<br> HETH Hermit Thrush (Catharus guttatus) ‡<br> HOLA Horned Lark (Eremophila alpestris) ‡<br> LCSP Le Conte's Sparrow (Ammodramus leconteii)<br> LEFL Least Flycatcher (Empidonax minimus)<br> LISP Lincoln's Sparrow (Melospiza lincolnii) ‡<br> MAWA Magnolia Warbler (Setophaga magnolia)<br> MOWA Mourning Warbler (Geothlypis philadelphia)<br> NAWA Nashville Warbler (Oreothlypis ruficapilla)<br> NOWA Northern Waterthrush (Parkesia noveboracensis) ‡<br> OCWA Orange-crowned Warbler (Oreothlypis celata) ‡<br> OSFL Olive-sided Flycatcher (Contopus cooperi) ‡<br> OVEN Ovenbird (Seiurus aurocapilla)<br> PAWA Palm Warbler (Setophaga palmarum)<br> PHVI Philadelphia Vireo (Vireo philadelphicus)<br> PIGR Pine Grosbeak (Pinicola enucleator) ‡<br> PISI Pine Siskin (Spinus pinus) ‡<br> PUFI Purple Finch (Carpodacus purpureus)<br> RBGR Rose-breasted Grosbeak (Pheucticus ludovicianus)<br> RBNU Red-breasted Nuthatch (Sitta canadensis) ‡<br> RCKI Ruby-crowned Kinglet (Regulus calendula) ‡<br> REVI Red-eyed Vireo (Vireo olivaceus)<br> RUBL Red-winged Blackbird (Agelaius phoeniceus) ‡<br> RWBL Rusty Blackbird (Euphagus carolinus) ‡<br> SAVS Savannah Sparrow (Passerculus sandwichensis) ‡<br> SOSP Song Sparrow (Melospiza melodia)<br> SWSP Swamp Sparrow (Melospiza georgiana)<br> SWTH Swainson's Thrush (Catharus ustulatus) ‡<br> TEWA Tennessee Warbler (Oreothlypis peregrina)<br> TRES Tree Swallow (Tachycineta bicolor) ‡<br> VATH Varied Thrush (Ixoreus naevius) ‡<br> VESP Vesper Sparrow (Pooecetes gramineus)<br> WAVI Warbling Vireo (Vireo gilvus)<br> WCSP White-crowned Sparrow (Zonotrichia leucophrys) ‡<br> WETA Western Tanager (Piranga ludoviciana)<br> WEWP Western Wood-Pewee (Contopus sordidulus) ‡<br> WIWA Wilson's Warbler (Cardellina pusilla) ‡<br> WIWR Winter Wren (Troglodytes hiemalis)<br> WTSP White-throated Sparrow (Zonotrichia albicollis)<br> WWCR White-winged Crossbill (Loxia leucoptera) ‡<br> YBFL Yellow-bellied Flycatcher (Empidonax flaviventris)<br> YRWA Yellow-rumped Warbler (Setophaga coronata) ‡<br> YWAR Yellow Warbler (Setophaga petechia) ‡</p> <p>‡ symbols denote the 38 species currently breeding in the Alaskan boreal region.<br> </p>
Data archive for Towards understanding potential atmospheric contributions to abrupt climate changes: characterizing changes to the North Atlantic eddy-driven jet over the last deglaciation
<p>This archive contains simulated North Atlantic eddy-driven jet latitude and tilt data generated using the PlaSim model to accompany Andres and Tarasov (Climate of the Past, accepted), DOI: https://doi.org/10.5194/cp-15-1-2019.</p> <p>Paper abstract is as follows:</p> <p>"Abrupt climate shifts of large amplitudes were common features of the Earth’s climate as it transitioned into and out of the last full glacial state approximately 20 000<br> years ago, but their causes are not yet established. Midlatitude atmospheric dynamics may have played an important role in these climate variations through their effects on heat and precipitation distributions, sea ice extent, and wind-driven ocean circulation patterns. This study characterizes deglacial winter wind changes over the North Atlantic (NAtl) in a suite of transient deglacial simulations using the PlaSim Earth system model (run at T42 resolution) and the TraCE-<br> 21ka (T31) simulation. Though driven with yearly updates in surface elevation, we detect multiple instances of NAtl jet transitions in the PlaSim simulations that occur within 10<br> simulation years and a sensitivity of the jet to background climate conditions. Thus, we suggest that changes to the NAtl jet may play an important role in abrupt glacial climate changes.</p> <p>We identify two types of simulated wind changes over the last deglaciation. Firstly, the latitude of the NAtl eddy-driven jet shifts northward over the deglaciation in a sequence of distinct steps. Secondly, the variability in the NAtl jet gradually shifts from a Last Glacial Maximum (LGM) state with a strongly preferred jet latitude and a restricted latitudinal range to one with no single preferred latitude and a range that is at least 11 ◦ broader. These changes can significantly affect ocean circulation. Changes to the position of the NAtl jet alter the location of the wind forcing driving oceanic surface gyres and the limits of sea ice extent, whereas a shift to a more variable jet reduces the effectiveness of the wind forcing at driving surface ocean transports.</p> <p>The processes controlling these two types of changes differ on the upstream and downstream ends of the NAtl eddy-driven jet. On the upstream side over eastern North America, the elevated ice sheet margin acts as a barrier to the winds in both the PlaSim simulations and the TraCE-21ka experiment. This constrains both the position and the latitudinal variability in the jet at LGM, so the jet shifts in sync with ice sheet margin changes. In contrast, the downstream side over the eastern NAtl is more sensitive to the thermal state of the background climate. Our results suggest that the presence of an elevated ice sheet margin in the south-eastern sector of the North American ice complex strongly constrains the deglacial position of the jet over eastern North America and the western North Atlantic as well as its variability."</p> <p> </p>
Prediction and Rehabilitation of Highway Embankment Slope Failures in Changing Climate
<p>Corresponding data set for Tran-SET Project No. 17GTLSU04. Abstract of the final report is stated below for reference:</p> <p>"Highway slopes constructed with clayey soil are prone to desiccation cracks due to wetting and drying weather cycle, which allows greater moisture infiltration into the embankment from precipitation. Fissures formed due to extended wetting and drying cycles allow water to seep deeper into the soil than surficial wetting and increase the water content. This increases the moisture content in the soil and results in reduction in shear strength to the fully softened strength. On the other hand, development of desiccation cracks and reduction of the soil matric suction ultimately result in higher hydraulic conductivity value which causes development of higher pore water pressure. As the moisture content of the clayey soil increases, the strength reduces to a fully softened shear strength that causes frequent shallow and medium slope failures that are oriented approximately parallel to the surface of the embankment. Hence, the fully softened strength of Louisiana and Texas soils need to be quantified to develop a predictive tool for identifying high-risk zones of highway embankments. Given the documented failures in Texas and Louisiana, this research project is focused on investigating past failures to develop lessons learned and guidelines that can be implemented in the predictive framework."</p>
Laboratory modeling of gap-leaping and intruding western boundary currents under different climate change scenarios
<p>Western boundary currents (WBCs), such as, the Kuroshio and the Gulf Stream, are very intense currents flowing along the western boundaries of the oceans.<br>WBCs -and their respective extensions- have an important effect on climate because of their huge heat transports, the corresponding air–sea interactions and the role they play in sustaining the global conveyor belt. It is therefore very relevant to analyze WBC dynamics not only through observations and numerical modelling, but also by means of laboratory experiments; to this respect several rotating tank experiments have been performed in recent years.<br>The new laboratory experiments proposed here for the Hydralab+ 19GAPWEBS project are aimed at analyzing the interactions of a WBC with gaps located along the western coast. Examples of such processes include the Gulf Stream leaping from the Yucatan to Florida and the Kuroshio leaping, and partly penetrating, through the South and East China Seas and through the wider gap separating Taiwan to Japan. In the experiments the WBC is produced by a horizontally unsheared current flowing over a topographic beta slope; along the western lateral boundary a sequence of gaps of different widths simulate the openings present in the above mentioned locations.</p>
Code and data to reproduce the results of the paper: "Land Use Patterns and Climate Change---A Modeled Scenario of the Late Bronze Age in Southern Greece"
<p>Code and data to reproduce the results of Knitter et al. (2019): Land Use Patterns and Climate Change---A Modeled Scenario of the Late Bronze Age in Southern Greece. ERL.</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.