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926 results for “Uganda”
Infrastructure Climate Resilience Assessment Data Starter Kit for Uganda
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2023)</li> <li>railways (OpenStreetMap, 2023)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=11539">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
National Checklists: Uganda Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda
<p>Datasets underlying the publication "Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda":</p> <p><strong>Figure 6: </strong>Number of children enrolled in the Patient Surveillance System (grey bars), and percentage of these children being administered rectal artesunate (RAS), by country.</p> <p><strong>Figure 8:</strong> Overall case fatality ratio (CFR) in patients with danger signs and a positive malaria test at enrolment across the entire study period, by enrolment location and country. Data for Uganda excludes enrolments at PHCs (N=34).</p>
Fault database for the George and NE Edward Rifts, Uganda
<p>This database is associated with a master's research project at the University of Bristol. The initial geospatial database documents the geometry of 152 faults identified through digital mapping, along with associated attributes. This represents the first detailed fault mapping conducted in the area. The version of record is in .geojson format, however data is also available in .kmz and .shp formats. Attributes are adapted from Williams <em>et al. </em>(2022) and the Global Earthquake Model Global Active Faults Database (GEM-GAFD; Styron & Pagani, 2020).</p> <table> <caption>List and brief description of the attributes in the fault database ('EGR_faults')</caption> <thead> <tr> <th scope="col">Attribute</th> <th scope="col">Data Type</th> <th scope="col">Description</th> <th scope="col">Notes</th> </tr> </thead> <tbody> <tr> <td>FAULT_ID</td> <td>integer</td> <td>Unique numerical reference ID</td> <td> </td> </tr> <tr> <td>CONFIDENCE</td> <td>integer</td> <td>Certainty of whether the fault exists there</td> <td>1 = moderate, 2 = high</td> </tr> <tr> <td>FAULT_NAME</td> <td>string</td> <td>Name of fault</td> <td>Not all faults are named. Assigned based on previous mapping or local geographic features.</td> </tr> <tr> <td>DIP_DIR</td> <td>string</td> <td>Compass quadrant of fault dip direction</td> <td> </td> </tr> <tr> <td>NOTES</td> <td>string</td> <td>Remaining miscellaneous information about the fault</td> <td>e.g. cross-cutting, segmentation, channel incision.</td> </tr> <tr> <td>LENGTH_TT</td> <td>integer</td> <td>Tip-to-tip fault length</td> <td> </td> </tr> <tr> <td>MAX_GRAD</td> <td>real number</td> <td>Estimated maximum gradient of scarp</td> <td>Calculated by taking fault-normal topographic profile at steepest gradient indicated by slope map.</td> </tr> <tr> <td>LENGTH</td> <td>real number</td> <td>Along-trace fault length</td> <td> </td> </tr> </tbody> </table> <p>Fault-normal topographic profiles were then used to measure scarp heights for 130 of the 152 mapped faults. The resulting displacement-length data for each fault is documented in 'slip_profile_data.zip'. A summary of the analysis of fault slip (displacement-length) profiles is outlined in 'EGR_slip_profiles_summary.xlsx'.</p> <table> <caption>List and brief description of the attributes in the slip profile summary ('EGR_slip_profiles_summary')</caption> <thead> <tr> <th scope="col">Attribute</th> <th scope="col">Description</th> <th scope="col">Notes</th> </tr> </thead> <tbody> <tr> <td>Fault ID</td> <td>Unique numerical reference ID</td> <td> </td> </tr> <tr> <td>Length</td> <td>Along-trace fault length</td> <td>To nearest 100m</td> </tr> <tr> <td>D<sub>max</sub></td> <td>Maximum measured displacement along fault scarp</td> <td> </td> </tr> <tr> <td>D<sub>max</sub>/L</td> <td>Maximum displacement divided by fault length</td> <td> </td> </tr> <tr> <td>Profile type</td> <td>First order shape of slip profile</td> <td>Following classifications outlined by Manighetti <em>et al. </em>(2001): <table> <tbody> <tr> <td>L = linear</td> </tr> <tr> <td>HR = half-restricted</td> </tr> <tr> <td>TR = tip-restricted</td> </tr> <tr> <td>DTR1 = both tips restricted, symmetrical</td> </tr> <tr> <td>DTR2 = both tips restricted, asymmetric</td> </tr> <tr> <td>DTR3 = half-restricted and tip-restricted</td> </tr> <tr> <td>QE = quasi-elliptical</td> </tr> <tr> <td>ET = elliptical with tapers</td> </tr> <tr> <td>U = unclear/intermediate pattern</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Dominant propagation direction</td> <td>Direction that fault is preferentially propagating in</td> <td>For unilaterally-propagating faults (tip- or half-restricted) as well as DTR and elliptical-with-taper faults that show a strong sense of preferred propagation</td> </tr> <tr> <td>Clarity rating</td> <td>How well profile is fit by first-order shape</td> <td> <table> <tbody> <tr> <td>1 - profile clearly fit by first order shape</td> </tr> <tr> <td>2 - first order shape with moderate smaller-scale peturbations</td> </tr> <tr> <td>3 - first order shape determined with some difficultly, may be obscured by segmentation, channel incision etc. </td> </tr> <tr> <td>4 - profile not fit by any first order shape, or intermediate, or impossible to differentiate</td> </tr> </tbody> </table> </td> </tr> <tr> <td>Segments</td> <td>Estimated number of segments that fault is formed from</td> <td>Interpreted from displacement minima and mapping observations.</td> </tr> <tr> <td>Notes</td> <td>Remaining miscellaneous information about the fault</td> <td>e.g. channel incision, anomalous topographic features</td> </tr> </tbody> </table>
Dataset for Starting at the community: Treatment seeking pathways of children with suspected severe malaria in Uganda
<p>Dataset for the publication <strong>"Starting at the community: Treatment seeking pathways of children with suspected severe malaria in Uganda".</strong></p> <p>Data from the Community Access to Rectal Artesunate for Malaria (CARAMAL) Project, 2018-2021</p> <p>Descriptive analysis of treatment-seeking pathways of and antimalarial treatment provision for children under 5 years with suspected severe malaria in three districts of Northern Uganda. All children first sought treatment from a community health worker before being referred to a higher-level facility.</p>
Pre- and post-intervention responses to a knowledge, attitudes, and practices survey for the study, "Disseminating vaccination information in baby soap products increases knowledge and vaccine uptake in central Uganda: A non-randomized controlled trial"
<p>This dataset contains responses to the pre- and post-intervention knowledge, attitudes, and practices surveys utilized for the study, "Disseminating vaccination information in baby soap products increases knowledge and vaccine uptake in central Uganda: A non-randomized controlled trial."</p>
Supplementary data and code: Human–Wildlife Interactions Predict Febrile Illness in Park Landscapes of Western Uganda
<p>Data at code used for analyses.</p> <p>Salerno J, Ross N, Ghai R, et al (2017) Human–Wildlife Interactions Predict Febrile Illness in Park Landscapes of Western Uganda. EcoHealth. doi: 10.1007/s10393-017-1286-1<br> </p>
Potential Natural Vegetation of Eastern Africa (Burundi, Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia): raster and vector GIS files for each country
<p>The map of potential natural vegetation of eastern Africa (V4A) gives the distribution of potential natural vegetation in Ethiopia, Kenya, Tanzania, Uganda, Rwanda, Burundi, Malawi and Zambia.</p> <p>The map is based on national and local vegetation maps constructed from botanical field surveys - mainly carried out in the two decades after 1950 - in combination with input from national botanical experts. Potential natural vegetation (PNV) is defined as “vegetation that would persist under the current conditions without human interventions”. As such, it can be considered a baseline or null model to assess the vegetation that could be present in a landscape under the current climate and edaphic conditions and used as an input to model vegetation distribution under changing climate.</p> <p>Vegetation types are defined by their tree species composition, and the documentation of the maps thus includes the potential distribution for more than a thousand tree and shrub species, see the documentation (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fspecies.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=aeHdnF4n19CbTTznMdObr91vfZys%2FY1PrK1OxI%2BHif0%3D&reserved=0">https://vegetationmap4africa.org/species.html</a>)</p> <p>The map distinguishes 48 vegetation types, divided in four main vegetation groups: 16 forest types, 15 woodland and wooded grassland types, 5 bushland and thicket types and 12 other types. The map is available in various formats. The online version (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fvegetation_map.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=VKVkjZ8lTKMyoU9luZLAYFDwY5sbwDrGXceVEQAeGIQ%3D&reserved=0">https://vegetationmap4africa.org/vegetation_map.html</a>) and for PDF versions of the map, see the documentation (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fdocumentation.html&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=FIsoem3dYG4%2FIQFMPlM8B2Vf9Doqf2CS7p2fevpAwx0%3D&reserved=0">https://vegetationmap4africa.org/documentation.html</a>). Version 2.0 of the potential natural vegetation map and the woody species selection tool was published in 2015 (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fvegetationmap4africa.org%2Fdocs%2Fversionhistory%2F&data=05%7C02%7Cjpbl%40ign.ku.dk%7Ca3280e568f104b9a26b308dc4e62f67b%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638471434157657534%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=J1aJt1D0dUhDd2fF9uEo8k1uu%2F7josYZCnQG%2FXWj5Ks%3D&reserved=0">https://vegetationmap4africa.org/docs/versionhistory/</a>). The original data layers include country-specific vegetation types to maintain the maximum level of information available. This map might be most suitable when carrying out analysis at the national or sub-national level.</p> <p>When using V4A in your work, cite the publication: Lillesø, J-P.B., van Breugel, P., Kindt, R., Bingham, M., Demissew, S., Dudley, C., Friis, I., Gachathi, F., Kalema, J., Mbago, F., Minani, V., Moshi, H., Mulumba, J., Namaganda, M., Ndangalasi, H., Ruffo, C., Jamnadass, R. & Graudal, L. 2011, Potential Natural Vegetation of Eastern Africa (Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda and Zambia). Volume 1: The Atlas. 61 ed. Forest & Landscape, University of Copenhagen. 155 p. (Forest & Landscape Working Papers; 61 - as well as this repository using the DOI <<span><a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.11125645&data=05%7C02%7Cjpbl%40ign.ku.dk%7C82eb48688be64612c08108dc70c1b2e9%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638509224465318531%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=BtOVb3lPqZXp45K%2BWKUaLQEK3CTn0uMg8ysuQh5aVpo%3D&reserved=0">https://doi.org/10.5281/zenodo.11125645</a></span>>.</p> <p>The development of V4A was mainly funded by the Rockefeller Foundation and supported by University of Copenhagen</p> <p>If you want to use the potential natural vegetation map of eastern Africa for your analysis, you can download the spatial data layers in raster format as well as in vector format from this repository <<span><a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.11125645&data=05%7C02%7Cjpbl%40ign.ku.dk%7C82eb48688be64612c08108dc70c1b2e9%7Ca3927f91cda14696af898c9f1ceffa91%7C0%7C0%7C638509224465318531%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=BtOVb3lPqZXp45K%2BWKUaLQEK3CTn0uMg8ysuQh5aVpo%3D&reserved=0">https://doi.org/10.5281/zenodo.11125645</a></span>></p> <p>A simplified version of the map can be found on <u>Figshare <https://doi.org/10.6084/m9.figshare.1306936.v1>. </u>That version aggregates country specific vegetation types into regional types. This might be the better option when doing regional-level assessments.</p> <p> </p>
Fig. 1 in The Rhipicephalus appendiculatus tick vector of Theileria parva is absent from cape buffalo (Syncerus caffer) populations and associated ecosystems in northern Uganda
Fig. 1 Map showing the sampling sites. The three national parks are indicated with red dots and the cattle sampling sites adjacent to the parks depicted as green dots
Improving the application of Important Plant Areas to conserve threatened habitats: a case study of Uganda
<p><strong>This data set relates to the publication: Richards, S. L., Kalema, J., Ojelel, S., Williams, J., & Darbyshire, I. (2024). Improving the application of Important Plant Areas to conserve threatened habitats: A case study of Uganda. Conservation Science and Practice, e13246. https://doi.org/10.1111/csp2.13246<br></strong></p> <p><strong>Abstract:</strong></p> <p>Important Plant Areas (IPAs) are a successful method of identifying priority areas for plant conservation. Assessment of IPAs, however, often relies on criteria related to species, while incorporation of habitats has been less consistent. Using Uganda as a case study, we test the application of the threatened habitat criterion – criterion C. We identified nationally threatened habitats using Red List of Ecosystems criteria and assess, for the first time, how differing application of thresholds under IPA criterion C can influence IPA network outcomes. Eleven threatened habitats were identified, with declines switching from predominantly forest to savanna after the mid-20<sup>th</sup> century. Significantly, we found current IPA guidance on use of criterion C needlessly limits the number of sites that qualify as IPAs. The “five best sites” IPA threshold is reserved for countries where quantitative data is unavailable, however, the application of the relevant numerical thresholds (site contains ≥10% of national resource or site is among the best quality examples required to collectively prioritisie up to 20% of the national resource) to quantitative data largely generated fewer than five IPAs, comparably limiting conservation opportunities identified. We recommend, therefore, that the “five best” threshold is available for application on both qualitative and quantitative data. This will bolster the value of IPAs in conserving and restoring threatened and ecologically important habitats under the Kunming-Montreal Global Biodiversity Framework.</p> <p><strong>Dataset:</strong></p> <p>Within this dataset is a shapefile of the estimated extent of threatened habitats in Uganda. Each polygon represents a single "site" for each threatened habitat, with methodology for site identification given in the manuscript. Feature area and percentage national resource are given for each site, enabling users to identify those that trigger the different IPA criterion C thresholds.</p> <p><strong>In this study, we have preliminarily identified the threatened habitats and IPAs for Uganda. However, it is important to seek the expertise and views of stakeholders, consider other IPA criteria met and any complementarity between sites when identifying IPAs. In addition, ground-truthing or more localised data could validate the threat status of a vegetation type as well as identifying which sites are best to conserve these habitats. </strong></p>
Fig. 2 in Lophorrhina garnieri, a new fruit chafer species from north-eastern Uganda (Coleoptera: Scarabaeidae, Cetoniinae)
Fig. 2 – Lophorrhina garnieri sp. nov., holotype male: A, clypeus; B, pygidium; C, parameres, lateral view; D, parameres, dorsal view; E, parameres, frontal view. Photographs: A, C-E, Lynette Clennell; B, Kit Chang.
Fig. 1 in Lophorrhina garnieri, a new fruit chafer species from north-eastern Uganda (Coleoptera: Scarabaeidae, Cetoniinae)
Fig. 1 – Lophorrhina garnieri sp. nov., holotype male: A, dorsal habitus; B, ventral habitus; C, lateral habitus. Photographs by Lynette Clennell.
Fig. 5 in Lophorrhina garnieri, a new fruit chafer species from north-eastern Uganda (Coleoptera: Scarabaeidae, Cetoniinae)
Fig. 5 – Lophorrhina pentachordia (Klug, 1835): A, male specimen in its natural habitat; B, typical female clypeus; C, distribution within the Afrotropical Region in relation to the type locality of the new species (map adapt- ed from geology.com). Photographs:A, Reiner Wendt; B, Keita Matsumoto.
Fig. 3 in Lophorrhina garnieri, a new fruit chafer species from north-eastern Uganda (Coleoptera: Scarabaeidae, Cetoniinae)
Fig. 3 – Lophorrhina pentachordia (Klug, 1835), male specimen: A, dorsal habitus; B, ventral habitus; C, lateral habitus. Photographs by Lynette Clennell.
Fig. 4 in Lophorrhina garnieri, a new fruit chafer species from north-eastern Uganda (Coleoptera: Scarabaeidae, Cetoniinae)
Fig. 4 – Lophorrhina pentachordia (Klug, 1835), male specimen:A, clypeus; B, pygidium; C, parameres, lateral view; D, parameres, dorsal view; E, parameres, frontal view. Photographs: A, C-E, Lynette Clennell; B, Kit Chang.
Figure 42 in A review of the subgenus Pandinus Thorell, 1876 with descriptions of two new species from Uganda and Ethiopia (Scorpiones: Scorpionidae)
Figure 42: Distribution map of the subgenus Pandinus. Localities of P. imperator and P. gambiensis are after Prendini (2004: 255, fig. 13), localities of other species are new records except Hargeysa (type locality of P. smithi). Type locality of P. phillipsii corresponds with new records (Goolis Mts.). Other localities of P. phillipsii and P. smithi cited by older authors (Borelli, 1919; Pocock, 1896, 1897, and 1900) are not included in the map. Exact distributions of these species need to be further investigated.
Figures 33–41 in A review of the subgenus Pandinus Thorell, 1876 with descriptions of two new species from Uganda and Ethiopia (Scorpiones: Scorpionidae)
Figures 33–41: 33–36. Metasoma and telson in lateral views. 33. Pandinus ugandaensis sp. n., ♂ holotype. 34. Pandinus ugandaensis sp. n., ♀ allotype. 35. Pandinus mazuchi sp. n., ♀ holotype. 36. Pandinus imperator (C. L. Koch, 1841), ♂ (115 mm), Ghana, FKCP. 37–41. Tarsomere II of 4th leg. 37. Pandinus imperator (C. L. Koch, 1841), ♂ (115 mm), Ghana, FKCP. 38. Pandinus ugandaensis sp. n., ♂ holotype. 39. Pandinus mazuchi sp. n., ♀ holotype. 40. Pandinus phillipsii (Pocock, 1896), ♀ (109 mm), Somaliland, Sheikh, 09°57'25.9"N 45°09'52.2"E, 1492 m a.s.l., FKCP. 41. Pandinus smithi (Pocock, 1897), ♂ (107 mm), Ethiopia, 55 km of Degehbur, 07°49'27.2"N 43°41'56.3"E, 1053 m a.s.l., FKCP.
Figures 10–13 in A review of the subgenus Pandinus Thorell, 1876 with descriptions of two new species from Uganda and Ethiopia (Scorpiones: Scorpionidae)
Figures 10–13: Pandinus phillipsii (Pocock, 1896). 10–11. ♂ (125 mm), dorsal and ventral views, Somaliland, 15 km N of Sheikh, Goolis Mts., 09°58.927'N 45°10.377'E, 1247 m a.s.l. 12–13. ♀ (109 mm), dorsal and ventral views, Somaliland, Sheikh, 09°57'25.9"N 45°09'52.2"E, 1492 m a.s.l.
Figures 14–16 in A review of the subgenus Pandinus Thorell, 1876 with descriptions of two new species from Uganda and Ethiopia (Scorpiones: Scorpionidae)
Figures 14–16: Pandinus phillipsii (Pocock, 1896). Juvenile (80 mm) at the locality Somaliland, Sheikh, Goolis Mts., 09°56'23"N 45°11'14.2"E, 1439 m a.s.l. The large photo (14) is an overall view after overturning a smaller boulder. The smallest photo (15) shows a shallow depression under the boulder in which the scorpion was hiding and underwent an ecdysis (the same spot is shown in the large photo by an arrow). Small arrow points to a partial exuvia of this specimen. The lower photo (16) shows the entire juvenile after removal from the hiding place.
Figures 30–32 in A review of the subgenus Pandinus Thorell, 1876 with descriptions of two new species from Uganda and Ethiopia (Scorpiones: Scorpionidae)
Figures 30–32: Pandinus ugandaensis sp. n. 30. ♂ (97 mm) holotype. 31. ♀ (105 mm) allotype. 32. Uganda, Kaabong env., type locality and native collector.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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