Dataset Information

The bii4africa dataset of faunal and floral population intactness estimates across Africa's major land uses.

ABSTRACT: Sub-Saharan Africa is under-represented in global biodiversity datasets, particularly regarding the impact of land use on species' population abundances. Drawing on recent advances in expert elicitation to ensure data consistency, 200 experts were convened using a modified-Delphi process to estimate 'intactness scores': the remaining proportion of an 'intact' reference population of a species group in a particular land use, on a scale from 0 (no remaining individuals) to 1 (same abundance as the reference) and, in rare cases, to 2 (populations that thrive in human-modified landscapes). The resulting bii4africa dataset contains intactness scores representing terrestrial vertebrates (tetrapods: ±5,400 amphibians, reptiles, birds, mammals) and vascular plants (±45,000 forbs, graminoids, trees, shrubs) in sub-Saharan Africa across the region's major land uses (urban, cropland, rangeland, plantation, protected, etc.) and intensities (e.g., large-scale vs smallholder cropland). This dataset was co-produced as part of the Biodiversity Intactness Index for Africa Project. Additional uses include assessing ecosystem condition; rectifying geographic/taxonomic biases in global biodiversity indicators and maps; and informing the Red List of Ecosystems.

SUBMITTER: Clements HS

PROVIDER: S-EPMC10861571 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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The bii4africa dataset of faunal and floral population intactness estimates across Africa's major land uses.

Clements Hayley S HS Do Linh San Emmanuel E Hempson Gareth G Linden Birthe B Maritz Bryan B Monadjem Ara A Reynolds Chevonne C Siebert Frances F Stevens Nicola N Biggs Reinette R De Vos Alta A Blanchard Ryan R Child Matthew M Esler Karen J KJ Hamann Maike M Loft Ty T Reyers Belinda B Selomane Odirilwe O Skowno Andrew L AL Tshoke Tshegofatso T Abdoulaye Diarrassouba D Aebischer Thierry T Aguirre-Gutiérrez Jesús J Alexander Graham J GJ Ali Abdullahi H AH Allan David G DG Amoako Esther E EE Angedakin Samuel S Aruna Edward E Avenant Nico L NL Badjedjea Gabriel G Bakayoko Adama A Bamba-Kaya Abraham A Bates Michael F MF Bates Paul J J PJJ Belmain Steven R SR Bennitt Emily E Bradley James J Brewster Chris A CA Brown Michael B MB Brown Michelle M Bryja Josef J Butynski Thomas M TM Carvalho Filipe F Channing Alan A Chapman Colin A CA Cohen Callan C Cords Marina M Cramer Jennifer D JD Cronk Nadine N Cunneyworth Pamela M K PMK Dalerum Fredrik F Danquah Emmanuel E Davies-Mostert Harriet T HT de Blocq Andrew D AD De Jong Yvonne A YA Demos Terrence C TC Denys Christiane C Djagoun Chabi A M S CAMS Doherty-Bone Thomas M TM Drouilly Marine M du Toit Johan T JT Ehlers Smith David A DA Ehlers Smith Yvette C YC Eiseb Seth J SJ Fashing Peter J PJ Ferguson Adam W AW Fernández-García José M JM Finckh Manfred M Fischer Claude C Gandiwa Edson E Gaubert Philippe P Gaugris Jerome Y JY Gibbs Dalton J DJ Gilchrist Jason S JS Gil-Sánchez Jose M JM Githitho Anthony N AN Goodman Peter S PS Granjon Laurent L Grobler J Paul JP Gumbi Bonginkosi C BC Gvozdik Vaclav V Harvey James J Hauptfleisch Morgan M Hayder Firas F Hema Emmanuel M EM Herbst Marna M Houngbédji Mariano M Huntley Brian J BJ Hutterer Rainer R Ivande Samuel T ST Jackson Kate K Jongsma Gregory F M GFM Juste Javier J Kadjo Blaise B Kaleme Prince K PK Kamugisha Edwin E Kaplin Beth A BA Kato Humphrey N HN Kiffner Christian C Kimuyu Duncan M DM Kityo Robert M RM Kouamé N'goran G NG Kouete T Marcel M le Roux Aliza A Lee Alan T K ATK Lötter Mervyn C MC Lykke Anne Mette AM MacFadyen Duncan N DN Macharia Gacheru P GP Madikiza Zimkitha J K ZJK Mahlaba Themb'alilahlwa A M TAM Mallon David D Mamba Mnqobi L ML Mande Claude C Marchant Rob A RA Maritz Robin A RA Markotter Wanda W McIntyre Trevor T Measey John J Mekonnen Addisu A Meller Paulina P Melville Haemish I HI Mganga Kevin Z KZ Mills Michael G L MGL Minnie Liaan L Missoup Alain Didier AD Mohammad Abubakr A Moinde Nancy N NN Moise Bakwo Fils E BFE Monterroso Pedro P Moore Jennifer F JF Musila Simon S Nago Sedjro Gilles A SGA Namoto Maganizo W MW Niang Fatimata F Nicolas Violaine V Nkenku Jerry B JB Nkrumah Evans E EE Nono Gonwouo L GL Norbert Mulavwa M MM Nowak Katarzyna K Obitte Benneth C BC Okoni-Williams Arnold D AD Onongo Jonathan J O'Riain M Justin MJ Osinubi Samuel T ST Parker Daniel M DM Parrini Francesca F Peel Mike J S MJS Penner Johannes J Pietersen Darren W DW Plumptre Andrew J AJ Ponsonby Damian W DW Porembski Stefan S Power R John RJ Radloff Frans G T FGT Rambau Ramugondo V RV Ramesh Tharmalingam T Richards Leigh R LR Rödel Mark-Oliver MO Rollinson Dominic P DP Rovero Francesco F Saleh Mostafa A MA Schmiedel Ute U Schoeman M Corrie MC Scholte Paul P Serfass Thomas L TL Shapiro Julie Teresa JT Shema Sidney S Siebert Stefan J SJ Slingsby Jasper A JA Sliwa Alexander A Smit-Robinson Hanneline A HA Sogbohossou Etotepe A EA Somers Michael J MJ Spawls Stephen S Streicher Jarryd P JP Swanepoel Lourens L Tanshi Iroro I Taylor Peter J PJ Taylor William A WA Te Beest Mariska M Telfer Paul T PT Thompson Dave I DI Tobi Elie E Tolley Krystal A KA Turner Andrew A AA Twine Wayne W Van Cakenberghe Victor V Van de Perre Frederik F van der Merwe Helga H van Niekerk Chris J G CJG van Wyk Pieter C V PCV Venter Jan A JA Verburgt Luke L Veron Geraldine G Vetter Susanne S Vorontsova Maria S MS Wagner Thomas C TC Webala Paul W PW Weber Natalie N Weier Sina M SM White Paula A PA Whitecross Melissa A MA Wigley Benjamin J BJ Willems Frank J FJ Winterbach Christiaan W CW Woodhouse Galena M GM

Scientific data 20240212 1

Sub-Saharan Africa is under-represented in global biodiversity datasets, particularly regarding the impact of land use on species' population abundances. Drawing on recent advances in expert elicitation to ensure data consistency, 200 experts were convened using a modified-Delphi process to estimate 'intactness scores': the remaining proportion of an 'intact' reference population of a species group in a particular land use, on a scale from 0 (no remaining individuals) to 1 (same abundance as the ...[more]

PMID: 38346970

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Project description:BackgroundThe spatial modeling of infectious disease distributions and dynamics is increasingly being undertaken for health services planning and disease control monitoring, implementation, and evaluation. Where risks are heterogeneous in space or dependent on person-to-person transmission, spatial data on human population distributions are required to estimate infectious disease risks, burdens, and dynamics. Several different modeled human population distribution datasets are available and widely used, but the disparities among them and the implications for enumerating disease burdens and populations at risk have not been considered systematically. Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example.MethodsThe recent construction of a global map of P. falciparum malaria endemicity enabled the testing of different gridded population datasets for providing estimates of PAR by endemicity class. The estimated population numbers within each class were calculated for each country using four different global gridded human population datasets: GRUMP (~1 km spatial resolution), LandScan (~1 km), UNEP Global Population Databases (~5 km), and GPW3 (~5 km). More detailed assessments of PAR variation and accuracy were conducted for three African countries where census data were available at a higher administrative-unit level than used by any of the four gridded population datasets.ResultsThe estimates of PAR based on the datasets varied by more than 10 million people for some countries, even accounting for the fact that estimates of population totals made by different agencies are used to correct national totals in these datasets and can vary by more than 5% for many low-income countries. In many cases, these variations in PAR estimates comprised more than 10% of the total national population. The detailed country-level assessments suggested that none of the datasets was consistently more accurate than the others in estimating PAR. The sizes of such differences among modeled human populations were related to variations in the methods, input resolution, and date of the census data underlying each dataset. Data quality varied from country to country within the spatial population datasets.ConclusionsDetailed, highly spatially resolved human population data are an essential resource for planning health service delivery for disease control, for the spatial modeling of epidemics, and for decision-making processes related to public health. However, our results highlight that for the low-income regions of the world where disease burden is greatest, existing datasets display substantial variations in estimated population distributions, resulting in uncertainty in disease assessments that utilize them. Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling. In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions.

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Dataset Information

The bii4africa dataset of faunal and floral population intactness estimates across Africa's major land uses.

Publications

The bii4africa dataset of faunal and floral population intactness estimates across Africa's major land uses.

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