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1,104 results for “arid”
Figure 5 in A new species of the troglobitic genus Spelaeogammarus da Silva Brum, 1975 (Amphipoda: Artesiidae) from a cave in the Brazilian semi-arid region, with new records of its congener, Spelaeogammarus spinilacertus Koenemann and Holsinger, 2000
Figure 5. Outcrops and pastures close to the Gruna da Serra Verde cave (A); cave gallery in the dry season (B) and detail of the small pool formed by the upper phreatic aquifer, where specimens of Spelaeogammarus ginae sp. nov. were found (C); map indicating the state of Bahia, the Gruna da Serra Verde cave, and the land use and land cover of the area (D). Photographs: M.E. Bichuette.
Figure 3 in A new species of the troglobitic genus Spelaeogammarus da Silva Brum, 1975 (Amphipoda: Artesiidae) from a cave in the Brazilian semi-arid region, with new records of its congener, Spelaeogammarus spinilacertus Koenemann and Holsinger, 2000
Figure 3. Spelaeogammarus ginae sp. nov., Gruna da Serra Verde cave, Serra do Ramalho, state of Bahia, Brazil. Paratype male, body length 11.31 mm; gnathopod 1 (A); gnathopod 2 (B); pereopod 3 (C); pereopod 4 (D); pereopod 5 (E); pereopod 6 (F); pereopod 7 (G). Scale bars: A, B = 400 μm; C–G = 1 mm.
Figure 7 in A new species of the troglobitic genus Spelaeogammarus da Silva Brum, 1975 (Amphipoda: Artesiidae) from a cave in the Brazilian semi-arid region, with new records of its congener, Spelaeogammarus spinilacertus Koenemann and Holsinger, 2000
Figure 7. Lake formed by upper phreatic aquifer at Canoa Quebrada cave (A); small pool of upper phreatic aquifer at Lapa Doce System (B); distribution of Spelaeogammarus spinilacertus Koenemann and Holsinger, 2000, indicating caves with records of the species showing the land use and land cover of the area (C). Caption: LD = Lapa Doce System, GU = Gruta da Umburana cave, LS = Gruta da Lagoa Seca cave, BC = Buraco do Cão cave, CQ = Canoa Quebrada cave, BS = Baixa do Salitre cave, and BJ = Jaburu cave. Photographs: (A) Adriano Gambarini, (B) Jonas E. Gallão.
Figure 1 in A new species of the troglobitic genus Spelaeogammarus da Silva Brum, 1975 (Amphipoda: Artesiidae) from a cave in the Brazilian semi-arid region, with new records of its congener, Spelaeogammarus spinilacertus Koenemann and Holsinger, 2000
Figure 1. Preserved (A) and live specimen (B) of Spelaeogammarus ginae sp. nov., Gruna da Serra Verde cave, Serra do Ramalho karst area, state of Bahia, Brazil. Photographs: (A) T. Zepon, (B) M.E. Bichuette.
Data for: "High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula"
<p>Data accompanying the publication: High-resolution Soil Moisture Evolution in Hyper-arid Regions: A Comparison of InSAR, SAR, Microwave, Optical, and Data Assimilation Systems in the southern Arabian Peninsula. For filenames starting with T: Exponential fit parameters time0 and mag0 for InSAR coherence data. they are binary files, where fit = a*exp(-b*x); a = -log(mag0); b = 1/time0. timeerr contains the uncertainty of the time0 parameter, and maghigh/maglow contain the high and low uncertainty for the mag0 parameter, respectively. For for each frame or overlap region (T101, T28, T130, T28_T101, T130_T28), there is a vrt file (T..._20180524.time0.vrt), which is the metadata file applicable to all files of the same frame. Files starting with mags_times: Exponential fit parameters for ASCAT/SMAP/GLDAS data. the same parameters (time0, timeerr, mag0, maghigh, maglow) can be found in these matlab structure files. In addition, the .mat files contain the offset parameter and related uncertainty, as well as lat/lon information. </p>
Trait functional diversity explains mixture effects on litter decomposition at the arid end of a climate gradient
<p><span>Litter decomposition is controlled by climate, litter quality and decomposer communities. Because the decomposition of specific litter types is also influenced by the properties of adjacent types, mixing litter types may result in non-additive effects on overall decomposition rates. The strength of these effects seems to depend on the litter functional diversity. However, it is unclear which functional traits or combination of traits explain litter mixture effects and if these depend on the range of trait values and the ecosystems involved. These uncertainties hamper our ability to predict decomposition in plant communities. </span></p> <p><span>We aimed at understanding whether and how functional diversity (measured as functional dispersion, FDis) influences litter decomposition, and how this influence varies among different climates and across decomposition stages. We calculated FDis based on litter traits related to nutrient concentrations or to litter recalcitrance, and tested whether these diversity measures and climatic parameters (soil moisture and temperature) explained litter mixture effects on decomposition. </span></p> <p><span>Additive mixture effects (i.e. decomposition of mixtures equalling the mean decomposition of the single litter types) were common in most of the evaluated climates. Non-additive, negative effects were mainly restricted to the driest and warmest sites, and decreased with time. Non-additive effects increased in magnitude with the mixtures' FDis, with positive effects being related to FDis in nutrient traits and negative effects being related to FDis in recalcitrance traits. </span></p> <p><span>Synthesis: Litter mixing did not have strong effects on decomposition rates across the studied climatic gradient overall, and the direction and intensity of the mixture effects were context-dependent. The effects were stronger and more negative in the dryer ecosystems. Where effects were found, functional diversity calculated from selected groups of traits (related to nutrients or litter recalcitrance) predicted mixture effects, especially where trait ranges were broad, though much of the variation remains unexplained. We propose that functional diversity metrics based on litter traits that are mechanistically relevant, applied to diverse site-specific litter mixtures in different climates, can help to better understand under which conditions and in which direction litter diversity affects decomposition.</span></p>
Data for Holdrege et al: Precipitation intensification increases shrub dominance in arid, not mesic, ecosystems
<p>Data and code for the Holdrege et al. (2022) manuscript entitled 'Precipitation intensification increases shrub dominance in arid, not mesic, ecosystems' published in <em>Ecosystems </em>(doi: 10.1007/s10021-022-00778-1). This repository contains the results from simulations of sagebrush dominated plant communities conducted across 200 sites in the western United States. Simulations were run for ambient conditions as well as increased precipitation intensity and warming. Refer to the manuscript for details on the methods. This repository contains the summarized model output (data) and the R code that uses this data to compute the statistics and create the figures presented in the manuscript. See the README.md and data_dictionary.md files for more information on the code and data, respectively. Note, the .git subfolder provided here contains the git (version control history) files, and the users can ignore these. </p>
Astronomically controlled aridity in the Sahara since at least 11 million years ago: Dataset
<p>This data is associated with the manuscript:</p> <p>Crocker, A.J., Naafs, B.D.A., Westerhold, T. <em>et al.</em> Astronomically controlled aridity in the Sahara since at least 11 million years ago. <em>Nat. Geosci.</em> (2022). https://doi.org/10.1038/s41561-022-00990-7</p> <p>All data are from Ocean Drilling Project (ODP) Site 659 and include:</p> <ul> <li>Elemental compositions measured by X-ray fluorescence</li> <li>Dust flux estimates</li> <li>Carbon isotopic signatures of <em>n</em>-alkanes</li> <li>Strontium and neodymium isotopic signatures of lithogenic material</li> <li>Lithogenic grain size distributions and end member compositions</li> <li>Splice and age model information</li> </ul>
Text-fig. 10. Extant pans east of Inhaminga (18°26′28″'S: 35°35′45″E) surrounded by woodland. The pans typically have an arid, vegetation-free, marginal zone and a water-logged sump. Some pans are connected to each other by shallow overflow valleys. Image modified from Google Earth. in Stratigraphy, Chronology And Palaeontology Of The Tertiary Rocks Of The Cheringoma Plateau, Mozambique
Text-fig. 10. Extant pans east of Inhaminga (18°26′28″'S: 35°35′45″E) surrounded by woodland. The pans typically have an arid, vegetation-free, marginal zone and a water-logged sump. Some pans are connected to each other by shallow overflow valleys. Image modified from Google Earth.
Figure 4. Right IX in A New Genus and Species of Earthworm (Oligochaeta: Megascolecidae) from Semi-Arid Australia
Figure 4. Right IX spermatheca, diverticulum dissected from bodY wall of Aridulodrilus molesworthae gen. et sp. nov.
Compiled dataset and posterior results for NEMo analyses on Acacia tolerance to aridity and salinity
<p>The dataset contains the compiled data for the aridity and salinity conditions at <em>Acacia</em> species' presence and absence locations, factors on sampling bias, as well as <em>Acacia</em> phylogeny. The data file is the input to the NEMo analyses. The dataset also contains the result files from the analyses.</p>
FIGURE 4 in Population structure of the seahorse Hippocampus reidi (Syngnathiformes: Syngnathidae) in a Brazilian semi-arid estuary
FIGURE 4 | Proportion of color patterns (A) and holdfast use (B) of Hippocampus reidi in the Pacoti River estuary, Ceará, Brazil, between December 2017 and November 2018.
FIGURE 3 in Population structure of the seahorse Hippocampus reidi (Syngnathiformes: Syngnathidae) in a Brazilian semi-arid estuary
FIGURE 3 | Spatial variation in the proportion of pregnant males of Hippocampus reidi along the salinity gradient in the Pacoti River estuary, Ceará, Brazil, between December 2017 and November 2018. Y = pregnant male record, N = non-pregnant male record.
FIGURE 2 in Population structure of the seahorse Hippocampus reidi (Syngnathiformes: Syngnathidae) in a Brazilian semi-arid estuary
FIGURE 2 | Temporal variation of environmental variables: (A) salinity and (B) water transparency (cm), and Hippocampus reidi population variables: (C) population density (ind.m-2), (D) proportion of pregnant males (Y = pregnant male record, N = non-pregnant male record) and (E) individual height (cm), in the Pacoti River estuary, Ceará, Brazil, between December 2017 and November 2018. The months of the rainy season are highlighted in blue.
FIGURE 1 in Population structure of the seahorse Hippocampus reidi (Syngnathiformes: Syngnathidae) in a Brazilian semi-arid estuary
FIGURE 1 | Geographic location of the Pacoti River estuary, Ceará, Brazil (A, B), indicating Hippocampus reidi sampling locations (A to K) (C).
Figure 1 in Plant diversity in Sabkha ecosystems of arid region: spatial and environmental drivers
Figure 1. The first two axes of canonical analysis of principal coordinates based on discriminant analysis (CAP) of plant species composition in the three Sabkha ecosystems. Ash: Al-Oshaziyah; Qas: Qasab; Shaq: Shaqa. F-value= 2.879 and P-value=0.003 of the CAP model.
Figure 4 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 4. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) with different surrounding habitats (sugarcane + sesame, monoculture, sesame) during cropping season of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 3 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 3. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) at different crop developmental stages (crop phenology) of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 2 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 2. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) during cropping season of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 1 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 1. Percent numbers of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) during 2018 at Layyah, Punjab, Pakistan.
ScienceDex guides
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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.