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1,015 results for “West Africa”
FIGURE 4. A in A new small barb (Cyprininae: Smiliogastrini) from the Louesse, Lekoumou (upper Niari basin), and Djoulou (upper Ogowe basin) rivers in the Republic of Congo, west-central Africa
FIGURE 4. A, Known distributional range of Enteromius walshae; yellow stars indicate collection sites and red star indicates collection locality of holotype (AMNH 266856). B, Type locality of holotype, in a small tributary of the Mandoro River (upper Louesse River basin).
FIGURE 1 in A new small barb (Cyprininae: Smiliogastrini) from the Louesse, Lekoumou (upper Niari basin), and Djoulou (upper Ogowe basin) rivers in the Republic of Congo, west-central Africa
FIGURE 1. Enteromius species sampled (table 1), photographed immediately postmortem or in preservation: A, kuiluensis; B, martorelli; C, prionacanthus; D, rouxi, E, rubrostigma; F, trispilomimus; G, walshae new species; H, nigroluteus; I, brichardi; J, camptacanthus; K, chiumbeensis; L, diamounanganai; M, guirali; N, holotaenia; O, jae; P, catenarius; Q, castrasibutum. No tissues are currently available for E. catenarius. Photograph of E. castrasibutum courtesy of Jon Armbruster.
Fig. 3 in The vascular plant diversity of Burkina Faso (West Africa) - a quantitative analysis and implications for conservation
Fig. 3. – Origin of introduced plant species in Burkina Faso. The majority of introduced species originates in the Americas.
Fig. 6 in The vascular plant diversity of Burkina Faso (West Africa) - a quantitative analysis and implications for conservation
Fig. 6. – Province species richness in relation to province characteristics. Species richness per province is shown dependent on 4 factors.
Fig. 1 in The vascular plant diversity of Burkina Faso (West Africa) - a quantitative analysis and implications for conservation
Fig. 1. – The provinces of Burkina Faso and their assignment to the phytogeographic zones used in this study. The classification of provinces to the PGZs is modified after WHITE (1983) and GUINKO (1984a). [1: Les Balé; 2: Bam; 3: Banwa; 4: Bazègua. 5: Bougouriba; 6: Boulgou; 7: Boulkiemdé; 8: Ganzourgou; 9: Gnagna; 10: Gourma; 11: Houet; 12: Ioba; 13: Kadiogo; 14: Kénédougou; 15: Comoé; 16: Komandjari; 17: Kompienga; 18: Kossi; 19: Koulpélogo; 20: Kouritenga; 21: Kourwéogo; 22: Léraba; 23: Loroum; 24: Mouhoun; 25: Nahouri; 26: Namentenga; 27: Nayala; 28: Oubritenga; 29: Oudalan; 30: Passoré; 31: Sanguié; 32: Sanmatenga; 33: Séno; 34: Sissili; 35: Soum; 36: Sourou; 37: Tapoa; 38: Tuy; 39: Yagha; 40: Yatenga; 41: Ziro; 42: Zondoma; 43: Zoundwéogo; 44: Poni; 45: Noumbiel]
FIG. 5 in Shrews (Mammalia, Eulipotyphla) from a biodiversity hotspot, Mount Nimba (West Africa), with a field identification key to species
FIG. 5. — Plot between the two first axes of the CVA performed upon four external body measurements (head and body, tail, hindfoot, and tail lengths) for 252 shrew specimens from Mount Nimba, Ziama and surrounding areas. Each species is represented by a color dot. Yellow dots indicate class barycenter and ellipses at 0.05% confidence; b, C. buettikoferi Jentink, 1888; d, C. douceti Heim de Balsac, 1958; e, C. eburnea Heim de Balsac, 1958; g, C. grandiceps Hutterer, 1983; j, C. jouvenetae Heim de Balsac, 1958; C. muricauda (Miller, 1900); mu, S. megalura (Jentink, 1888); n, C. nimbae Heim de Balsac, 1956; o, C. obscurior Heim de Balsac, 1958; ol, C. olivieri (Lesson, 1827); sy, C. nimbasilvanus Hutterer, 2003; t, C. theresae Heim de Balsac, 1968.
FIG. 1 in Shrews (Mammalia, Eulipotyphla) from a biodiversity hotspot, Mount Nimba (West Africa), with a field identification key to species
FIG. 1. — Detail of the trapping localities at Mount Nimba with respect to elevation. Sampling sites are shown in black circles. Light and dark grey shading refer to areas above 600 m and 1000 m, respectively. Names of localities used in this work as follows: G, Guinea; L, Liberia, Gbié: G1-A, B; Gouan: G2-A, B; Seringbara:G3; Gblayougouma G4; Ziéla: G5; East Nimba Nature Reserve L1-A, B, C; Bentor: L2; Bonlah: L3; Yekepa: L4; Tailings: L5; Camp4: L6; Liabala: L7; Gbapa: L8; Zolowee: L9; Grassfield: L10; Border (Yekepa): L11.
APPENDIX 8 in Shrews (Mammalia, Eulipotyphla) from a biodiversity hotspot, Mount Nimba (West Africa), with a field identification key to species
APPENDIX 8. — Nimba shrews skins with field numbers: A, MNHN-ZM-2014-900 (LB07) C. buettikoferi Jentink, 1888 Camp 4; B, MNHN-ZM-2012-1079 (NIM217) C. grandiceps Hutterer, 1983 Gouan; C, MNHN-ZM-2012-1158 (NIM201) C. olivieri (Lesson, 1827) Gbié; D, MNHN-ZM-2012-1111 (NIM232) C. muricauda (Miller, 1900) Gouan; E, MNHN-ZM-2012-1180 (NIM 301) C. theresae Heim de Balsac, 1968 Gouan; F, MNHN-ZM-MO-1981-492 C. nimbae Heim de Balsac, 1956 Holotype Zouguepo; G, MNHN-ZM-MO-1981-483 C. eburnea Heim de Balsac, 1958 Mt Tonkui; H, MNHN-ZM-2012-1123 (NIM 219) C. obscurior Heim de Balsac, 1958 Gouan.
FIG. 4 in Shrews (Mammalia, Eulipotyphla) from a biodiversity hotspot, Mount Nimba (West Africa), with a field identification key to species
FIG. 4. — Standard karyotypes from Mount Nimba shrews: A, male C. buettikoferi Jentink, 1888 MNHN-ZM-2012-1071, 2n = 52, NFa = 66; B, male C. grandiceps Hutterer, 1983 MNHN-ZM-2012-1077, 2n = 46, NFa = 64; C, male C. jouvenetae Heim de Balsac, 1958 MNHN-ZM-2012-1091, 2n = 44, NFa = 68; D, male C. olivieri (Lesson, 1827) MNHN-ZM-2012-1164, 2n = 50, NFa = 60; E, female C. theresae Heim de Balsac, 1968 MNHN-ZM-2012-1171, 2n = 50, NFa = 70.
FIG. 7 in Shrews (Mammalia, Eulipotyphla) from a biodiversity hotspot, Mount Nimba (West Africa), with a field identification key to species
FIG. 7. — Comparison of the relative abundance (% on y axis) of shrew species between four different surveys. Nimba this work (N = 226), Taï: Churchfield et al. (2004) (N = 553), Ziama: Nicolas et al. (2009) (N = 2571), Dodo-Haut Cavally: Decher et al. (2005) (N = 15). For authorships of the species, see Table 5.
FIG. 3 in Shrews (Mammalia, Eulipotyphla) from a biodiversity hotspot, Mount Nimba (West Africa), with a field identification key to species
FIG. 3. — Craniodental measurements used for morphometric analyses adapted from Dippenaar (1977) and Hutterer & Kock (2002): a, condyle-incisive length; b, nasal width; c, interorbital width; d, occipital greatest width; e, greatest maxillary width; f, upper tooth row length; g, height of the skull at M2 level; h, greatest braincase height; i, mandibular length; j, lower tooth row length; k, greatest length between extremities of the coronoid and angular processes.
FIG. 2 in Shrews (Mammalia, Eulipotyphla) from a biodiversity hotspot, Mount Nimba (West Africa), with a field identification key to species
FIG. 2. — Examples of habitats where pitfall traps were placed on the Guinean and Liberian Nimba: A, gallery forest and swamp, camp 4 (Liberia); B, pitfall, altitude savannah with Loudetia kagerensis, Mare d'hivernage site (1642 m) (Guinea); C, pitfall, Selingbala (Guinea): mesophyllous secondary forest; D, pitfall, Gbie (Guinea): gallery forest with Parinari excelsa Sabine,1824, Carapa procera DC., 1824 and Pseudospondias microcarpa (A. Rich.) Engl., 1883, Maranthochloa purpurea (Ridl.) Milne-Redh.
FIG. 3. — C. rizetiana Stoff. & M in A new coffee species from South-West Cameroon, the principal hotspot of diversity for Coffea L. (Coffeeae, Ixoroideae, Rubiaceae) in Africa
FIG. 3. — C. rizetiana Stoff. & M.Noirot, sp. nov., fruit showing its basis (right) and top (left) from the Coffea L. collection at Bassin Martin (Réunion, France).
FIG. 2. — C. rizetiana Stoff. & M in A new coffee species from South-West Cameroon, the principal hotspot of diversity for Coffea L. (Coffeeae, Ixoroideae, Rubiaceae) in Africa
FIG. 2. — C. rizetiana Stoff. & M.Noirot, sp. nov.: A, habit; B, node with flowers and petioles; C, detail of flower; D, fruit; E, transection of the fruit with two seeds. Vouchers deposited in BR: A-C, Stoffelen 2045; C, D, Noirot EC66, 30.X.2012 (liquid preserved collection). Scale bars: A, 3 cm; B, 2 cm; C-E, 1 cm.
FIG. 1 in A new coffee species from South-West Cameroon, the principal hotspot of diversity for Coffea L. (Coffeeae, Ixoroideae, Rubiaceae) in Africa
FIG. 1. — Phylogram based on combined trnLF, accD-psa1 and ITS data. Numbers on branches represent Bayesian Posterior Probabilities and Maximum Likelihood Bootstrap Support, respectively. An asterisk indicates a lack of support.
Virtual stations (TeroVIR ) and water level time series (TeroWAT) in West Africa and Arctic regions
<p>The dataset contains a sample of locations across Siberia and Africa, for which water-level time series were automatically derived from Sentinel-3 altimeters (methodology described in Machefer et al. 2022<sup>1</sup>) from year 2016 to year 2021, together with the in-situ station records and the area covered by the altimetry measurements. The purpose of this dataset is validation and exemplification of the methodology. </p> <p>The methodology described produces comprehensive water level records at a global scale based on altimetry satellite data. The validation against in-situ data was assessed in numerous environments in West Africa and complex locations such as Arctic rivers partially covered with ice.<br> <br> This dataset offers a sample of the records at 3 locations in West Africa (Kemacina [Mali], Koulikouro [Mali], Lokoja [Niger]) and in the sub-arctic region (Yakutsk [Russia]). The data are organised by Level 1 of <a href="http://www.hydrosheds.org/">HydroBASINS</a><sup>2 </sup>definition (ex: africa) in two folders, each containing: virtual stations (teroVIR) and insitu stations (insitu) as shapefiles with their associated metadata, the corresponding water level time series (teroWAT) in NetCDF, and the level 3 of HydroBASINS, corresponding to the largest river basins of each continent. Finally, a csv file (validation) presents the computed metrics assessing the accuracy of the processors.</p> <p>N.B.: time series with less than two common date points between insitu and teroWAT have not been assessed. </p> <p>[1] Machefer, M., Perpinyà-Vallès M., Escorihuela M.J., Gustafsson D., Romero L. (2022): Challenges and evolution of water level monitoring towards a comprehensive, world-scale coverage with remote sensing. Earth System Science Data (Under Reviewing)</p> <p>[2] Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org.</p>
Data and scripts for: Green turtles highlight connectivity across a regional marine protected area network in West Africa
<p>Data derivates and analysis scripts (in R) used for the paper on analyzing green turtle MPA coverage and connectivity in West Africa.</p>
Novel Chromobacterium sp. isolated from mosquitos in Burkina Faso, West Africa
<p>Nov. <em>Chrombacterium</em> sp. isolated in wild caught mosquitos in Burkina Faso, West Africa </p>
Figure 25 in Water mites from West Africa (Acari: Hydrachnidia)
Figure 25 Arrenurus (Megaluracarus) geniculatus Koenike, female. A – venter; B – palp. Scale bars: A = 200 µm, B = 50 µm.
Figure 23 in Water mites from West Africa (Acari: Hydrachnidia)
Figure 23 Arrenurus (Megaluracarus) chutteri ankasa n. sp., holotype male A-C, paratype female D. A – dorsum; B – venter; C – palp; D - venter. Scale bars: A-B, D = 200 µm, C = 50 µm.
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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)
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.