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11,983 results for “africa”
Data from: Architectural differences associated to functional traits among 45 coexisting tree species in central Africa
1. Architectural traits that determine the light captured in a given environment are an important aspect of the life-history strategies of tropical tree species. In this study, we examined how interspecific variation in architectural traits is related to the functional traits of 45 coexisting tree species in central Africa. 2. At the tree level, we measured tree diameter, total height and crown dimensions for an average of 30 trees per species (range 14–72, total 968 trees) distributed over a large range of diameters (up to 162 cm). Using log-log models, we fitted species-specific allometric relationships between tree diameter, height and crown dimensions. At the species level, we derived architectural traits (height and crown dimensions) at 15 cm and maximum diameters from species-specific allometries. The architectural traits were then related to functional traits, including light requirements, wood density, leaf habit, and dispersal mode. 3. Among the 45 coexisting tree species, we identified strong variations in height and crown allometries, along with architectural traits derived from these species-specific allometries. There was a positive correlation among architectural traits, suggesting that large-statured canopy species were taller and had larger and deeper crowns than small-statured understory species at all ontogenic stages. The relationships between architectural and functional traits highlighted a continuum of species between the large-statured canopy species and the small-statured understory species. In this moist and seasonal forest, large-statured canopy species tended to be light-demanding, wind-dispersed, deciduous and large contributors to forest biomass (high basal area), while small-statured understory species tended to be shade-tolerant, animal-dispersed, evergreen and most abundant in terms of stem density. 4. Our results highlighted strong architectural differences among coexisting tropical tree species in central Africa. The relationships between architectural and functional traits provided insights into the life-history strategy of tropical tree species.
Model inputs and results from FastScape landscape evolution model runs for southern Africa
<p>These are the input files and results for the models presented in the revised version of the paper "Constraining plateau uplift in southern Africa by combining thermochronology, sediment flux, topography, and landscape evolution modeling" submitted to JGR:Solid Earth in October 2020 and revised in May 2017. The corresponding code needed to run the inputs can be found here: http://doi.org/10.5281/zenodo.4150333. The readme.txt file contained here explains the included data and results, as well as simple instructions for how to run the models.</p>
Data from: Population genomics and morphometric assignment of western honey bees (Apis mellifera L.) in the Republic of South Africa
Backgrounds: Apis mellifera scutellata and A.m. capensis (the Cape honey bee) are western honey bee subspecies indigenous to the Republic of South Africa (RSA). Both bees are important for biological and economic reasons. First, A.m. scutellata is the invasive "African honey bee" of the Americas and exhibits a number of traits that beekeepers consider undesirable. They swarm excessively, are prone to absconding (vacating the nest entirely), usurp other honey bee colonies, and exhibit heightened defensiveness. Second, Cape honey bees are socially parasitic bees; the workers can reproduce thelytokously. Both bees are indistinguishable visually. Therefore, we employed Genotyping-by-Sequencing (GBS), wing geometry and standard morphometric approaches to assess the genetic diversity and population structure of these bees to search for diagnostic markers that can be employed to distinguish between the two subspecies. Results: Apis mellifera scutellata possessed the highest mean number of polymorphic SNPs (among 2,449 informative SNPs) with minor allele frequencies >0.05 (Np = 88%). The RSA honey bees generated a high level of expected heterozygosity (Hexp = 0.24). The mean genetic differentiation (FST; 6.5%) among the RSA honey bees revealed that approximately 93% of the genetic variation was accounted for within individuals of these subspecies. Two genetically distinct clusters (K = 2) corresponding to both subspecies were detected by Model-based Bayesian clustering and supported by Principal Coordinates Analysis (PCoA) inferences. Selected highly divergent loci (n = 83) further reinforced a distinctive clustering of two subspecies across geographical origins, accounting for approximately 83% of the total variation in the PCoA plot. The significant correlation of allele frequencies at divergent loci with environmental variables suggested that these populations are adapted to local conditions. Only 17 of 48 wing geometry and standard morphometric parameters were useful for clustering A.m. capensis, A.m. scutellata, and hybrid individuals. Conclusions: We produced a minimal set of 83 SNP loci and 17 wing geometry and standard morphometric parameters useful for identifying the two RSA honey bee subspecies by genotype and phenotype. We found that genes involved in neurology/behavior and development/growth are the most prominent heritable traits evolved in the functional evolution of honey bee populations in RSA.
UGANDAN AFRICA - FREE PBR MODEL
IVORY MUSEUM : a test of water animation from blender to sketchfab in PBR shaders textured in substance painter . NOTE : you can hold alt + right mouse button to change the direction of lighting of the scene . Source: Objaverse 1.0 / Sketchfab
FIGURE 11 in Onuphi s and Mooreonuphis (Annelida: Onuphidae) from West Africa with the description of three new species and the reinstatement of O. landanaensis Augener, 1918
FIGURE 11. Comparison of the progression of hooks in Onuphis hanneloreae sp. nov. juveniles.
FIGURE 1. Pelvicachromis rubrolabiatus, holotype. NMW 94835 in cichlid species (Teleostei, Perciformes) from Guinea, West Africa
FIGURE 1. Pelvicachromis rubrolabiatus, holotype. NMW 94835, male, 59.5 mm SL; Guinea: Badi River.
FIGURE 4 in cichlid species (Teleostei, Perciformes) from Guinea, West Africa
FIGURE 4. Geographical distribution of P. rubrolabiatus and P. signatus.
FIGURE 6 in cichlid species (Teleostei, Perciformes) from Guinea, West Africa
FIGURE 6. Pelvicachromis signatus, male, aquarium specimen, not preserved, Guinea: Kolente region.
FIGURES 6 – 8 in A remarkable new lichenophilous Pamexis species from the Hantam Karoo of South Africa (Neuroptera: Myrmeleontidae: Palparini)
FIGURES 6 – 8. Pamexis namaqua. Resting position on lichen background. Photos: R. W. Mansell ©
Figure 10. - ATruncatoflabellumtruncum, holotype, Eltanin 283, Strait of Magellan B Truncatoflabellumformosum, USNM 91757, Vityaz 2635, off Mozambique C Truncatoflabellumcarinatum, USNM 92806, Taiwan D Truncatoflabellumgardineri, USNM 91736, holotype, Anton Bruun 7-3905, South Africa. Scale bars: all 10 mm.
Figure 10. - ATruncatoflabellumtruncum, holotype, Eltanin 283, Strait of Magellan B Truncatoflabellumformosum, USNM 91757, Vityaz 2635, off Mozambique C Truncatoflabellumcarinatum, USNM 92806, Taiwan D Truncatoflabellumgardineri, USNM 91736, holotype, Anton Bruun 7-3905, South Africa. Scale bars: all 10 mm.
Figure 3. - ATruncatoflabellumzuluense, paratype, USNM 91751, MD ZK-20, South Africa B Truncatoflabellumpusillum, holotype, USNM 81978, Albatross 5178, Philippines C Truncatoflabellumangustum, USNM 98894, MUSORSTOM 8-1016, Vanuatu D Truncatoflabellumangiostomum, USNM 96643, Cape Jaubert, Western Australia. Scale bars: all 10 mm, except for basal scar views, which are 5 mm.
Figure 3. - ATruncatoflabellumzuluense, paratype, USNM 91751, MD ZK-20, South Africa B Truncatoflabellumpusillum, holotype, USNM 81978, Albatross 5178, Philippines C Truncatoflabellumangustum, USNM 98894, MUSORSTOM 8-1016, Vanuatu D Truncatoflabellumangiostomum, USNM 96643, Cape Jaubert, Western Australia. Scale bars: all 10 mm, except for basal scar views, which are 5 mm.
Figure 8. - ATruncatoflabellumcumingi, neotype, USNM 81976, Te Vega 1-54, Indonesia B Truncatoflabellumvanuatu, holotype, USNM 71860, Pleistocene of Vanuatu C Truncatoflabellumduncani, paratype, USNM M353592, Balcombe's Bay, Victoria (Balcombian = Middle Miocene) D Truncatoflabellummultispinosum, paratype, USNM 91741, South Africa. Scale bars: all 10 mm, except for basal scar views of B and C.
Figure 8. - ATruncatoflabellumcumingi, neotype, USNM 81976, Te Vega 1-54, Indonesia B Truncatoflabellumvanuatu, holotype, USNM 71860, Pleistocene of Vanuatu C Truncatoflabellumduncani, paratype, USNM M353592, Balcombe's Bay, Victoria (Balcombian = Middle Miocene) D Truncatoflabellummultispinosum, paratype, USNM 91741, South Africa. Scale bars: all 10 mm, except for basal scar views of B and C.
Load and renewable generation time series for selected regions in Africa and Eurasia
<p>The data set consist of three groups of yearly time series for 2003-2012 used in grid modelling. All time series are presented in .csv format. The data covers wind and PV power generation and load of 12 regions. The regions have 2 letter identifier and include the following:</p> <p>EU - EU countries</p> <p>NA - Algeria, Egypt, Libya, Morocco, Tunisia</p> <p>WA - Cameroon, Ghana, Nigeria</p> <p>EA - Ethiopia, Kenya, Tanzania, Uganda</p> <p>SA - South Africa</p> <p>RU - European part of Russia, Belarus, Ukraine</p> <p>MS - Arabic countries, Israel, Turkey</p> <p>IP - Bangladesh, India, Pakistan, Sri Lanka</p> <p>SB - Asian part of Russia, Kazakhstan, Turkmenistan, Uzbekistan</p> <p>IC - Indonesia, Malaysia, Philippines, Thailand, Vietnam</p> <p>CN - China</p> <p>FE - South Korea, Japan</p> <p> </p> <p>Wind power time series are derived from MERRA wind speed data for Enercon E-126 wind turbine.</p> <p>PV power time series are derived from MERRA solar surface irradiance data.</p> <p>Load time series are generated with the periodical function.</p> <p> </p> <p>Data for EU was derived within the project RESTORE 2050 as described in</p> <p>Kies, A., Chattopadhyay, K., von Bremen, L., Lorenz, E., & Heinemann, D. (2016). Simulation of renewable feed-in for power system studies.</p> <p>Data for all other nodes was derived as described in</p> <p>Krutova, M. et al., The smoothing effect for renewable resources in an Afro-Eurasian power grid, Adv. Sci. Res., 2017</p> <p>Load for EU is obtained from ENTSO-E and is not included in this data set.</p>
The dust source points from the west coast of South Africa from 2000 to 2021
<p>This data set describes the location and land use of the dust source points from 2000 and 2021 on the west coast of South Africa from succulent Karoo shrubland, bare areas, and dry pans. The data has been derived daily from MODIS images and the land cover was determined by the South African National Land Cover (SANLC) provided by the Department of Forestry, Fisheries and the Environment.</p>
A list of alien taxa for South Africa
<p>Zengeya TA, Faulkner KT, Mtileni MP, Fernández Winzer L, Kumschick S, McCulloch-Jones EJ, Miza-Tshangana SA, Robinson TB, Sifuba A, Engelbrecht W, van Wilgen BW, Wilson JRU (preprint) A checklist of alien taxa for South Africa. bioRxiv: 2025.2005.2022.655507. doi:10.1101/2025.05.22.655507</p> <p>This is an updated version of SANBI and CIB 2023. Appendix 2 to "The Status of Biological Invasions and their Management in South Africa in 2022"—The species list. South African National Biodiversity Institute, Kirstenbosch and DSI-NRF Centre of Excellence for Invasion Biology, Stellenbosch. http://dx.doi.org/10.5281/zenodo.8217197</p> <p>For more details see: http://iasreport.sanbi.org.za</p> <p>For the report itself see: http://dx.doi.org/10.5281/zenodo.8217182</p> <p>For a list of the references used (for v20250520) see: http://dx.doi.org/10.5281/zenodo.17185376</p> <p>The South African Department of Forestry, Fisheries and the Environment (DFFE) are thanked for funding noting that this dataset does not necessarily represent the views or opinions of DFFE or its employees</p>
Tshikombeni knowledge of national biodiversity symbols in South Africa
<p>Most countries have declared one or more animal or plant species to be amongst their national symbols, termed here national biodiversity symbols. National biodiversity symbols are the species formally or informally recognised by societies and countries as having meaning to one or more of national identity, values and unity.</p> <p>It has been proposed previously that national biodiversity symbols can be used as flagship species to advance habitat conservation in their respective countries. However, this assumes that the symbols are well known and revered by the citizens of the country concerned. We examined this assumption via direct interviews with 382 urban residents in four towns in South Africa, which is a mega-biodiversity country with five national biodiversity symbols (a national tree, flower, animal, bird and fish).</p> <p>We found that less than 3 % of the urban respondents could name all five species, ranging from 6 % for the national tree to 40 % for both the national flower and national animal. Knowledge of other national symbols (flag and anthem) were equally low. The number of national biodiversity symbols known increased with income and education level of respondents. Despite limited knowledge of which species were the national biodiversity symbols, almost two-thirds of respondents felt that having national biodiversity symbols was important for promoting national identity.</p> <p>These findings show that from a heritage perspective a great deal more awareness needs to be developed in South Africa around the national biodiversity symbols. From a conservation perspective, it indicates that the national biodiversity symbols are unlikely, at this stage at least, to be useful as flagship species for habitat conservation programmes.</p>
« Lend your Money, Lose your Friend? » - Chinese Official Lending and Bilateral Political Alignment: The Case of Africa
<p>This database provides a set of 45 variables related to UNGA voting affinity vis-à-vis China, official lending, and other bilateral economic and political indicators for China and 43 African countries over the period 2000-2020. The indicators are grouped into four categories: voting data, loan and debt, economic indicators, and political indicators. </p><p>This dataset was compiled in order to conduct research and econometric work for a journal article entitled: </p><p><strong>« Lend your Money, Lose your Friend? » - Chinese Official Lending and Bilateral Political Alignment: The Case of Africa</strong></p><p>, written by Clément Durif, Junior Resarch Fellow at the Asia Centre <a href="mailto:clement.durif@sciencespo.fr">clement.durif@sciencespo.fr</a><br><br>The status of this journal article is pending submittal and acceptation from a Journal Publication</p>
- Wings with dark patterns, yellowish to black (a, b); tropical Africa ………………………………4 in A review of the Afrotropical Rhyssinae (Hymenoptera: Ichneumonidae) with the descriptions of five new species
- Wings with dark patterns, yellowish to black (a, b); tropical Africa ………………………………4
A dataset of community perspectives on living conditions and disaster risk management in informal settlements: A case study in KwaZulu-Natal Province, South Africa
<p>This article describes a dataset of community perspectives on living conditions and disaster risk management in Khan Road, a non-serviced informal settlement, located in Pietermaritzburg, the capital of KwaZulu-Natal province in South Africa. The data were collected by local community researchers via a structured questionnaire of 159 participants conducted between August and September 2022, using mobile phones via KoboToolbox. The dataset was analysed using exploratory data analysis (EDA) techniques. This household survey is part of a research project aiming to develop an evidence base of opportunities, risks and vulnerabilities related to housing construction and resource management in incremental upgrading of informal settlements in South Africa. This dataset can be used by local practitioners and policymakers involved in decision-making for informal settlement upgrading and help them prioritise resources and upgrading interventions based on what informal dwellers need. Furthermore, this cleaned dataset could support the analysis of further South African data guiding the development of digital platforms as a real-time resource management tool or guide the enhancement of existing theoretical frameworks in the field of participatory design and co-production used by academic scholars. </p>
Datasets, scripts and log files to PHIRST respiratory syncytial virus analysis (South Africa, 2017-2018)
No description provided.
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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)
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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.