retrieval. This may be the result of an underestimation in the source strength of the biomass burning aerosol, a result that is a common feature in many global aerosol models (Kinne et al., 2003). Furthermore, the emission inventories used in the model are from the early 1980s. Emission strengths may certainly have increased in the years where the satellite data is taken (2000–2003). Deficiencies in how the model transports the aerosol may also contribute to the low optical depths, although such assessments are beyond the scope of this article. Correcting the GCM data with the factor derived from the AERONET climatology brings the spatial pattern into much better agreement with the MODIS distribution. However, whereas there is a single peak off the western coast of southernAfrica in the model data, the MODIS data has a secondary peak inland. Over oceanic regions the corrected GCM distribution tends to produce higher values of aerosol optical depth than the remotely sensed data, especially off the eastern coast of Africa. The higher values are likely to
The overestimation of trees and underestimation of grasses compared to observations could be due either to intrinsic bi- ases of the DGVM, or to the anthropogenic influence on the modern southern African vegetation. The diverse parameter- izations used in the simulation of processes governing veg- etation dynamics or in the fire module may not be optimal for southernAfrica, especially at high spatial resolution. In a similar simulation performed over Europe (data not shown) the model results show the same type of biases and over- estimates forests in the Mediterranean region, which sug- gests that the impact of a large amplitude in the hydrologi- cal seasonal cycle on vegetation is not adequately simulated in the model. The impact of fire on tree development, which has been suggested to be a determining factor in southernAfrica (Westfall et al., 1983; Titshall et al., 2000; Bond et al., 2003a, b), may also be underestimated. The improvement of the model calibration may be possible, but requires further tests and comparisons with observation data at a local scale from regions without current anthropogenic impact, such as national parks.
The Bantu-speaking populations nowadays inhabiting southernAfrica are quite diverse linguistically and culturally, comprising pastoralists, agro-pastoralists, and agriculturalists who speak languages belonging to several different subgroups of both Eastern and Western Bantu; these populations share the same territory and are often involved in trade. From a genetic perspective, these populations appear to be relatively homogenous, with little differences even among linguistically distinct populations [6,11,12,19]. The main genetic signal characterizing the people at the southernmost edges of the Bantu expansion is the degree of admixture with the autochthonous populations; this can be explicitly measured by the presence of the characteristic mtDNA haplogroups L0d and L0k and Y-chromosomal haplogroups A- M51, A-M23, and B-M112 [20–22]. Admixture with autochtho- nous peoples in Bantu-speaking populations is detectable predom- inantly in the maternal line, in accordance with sex-biased gene flow [20,23]. The level of admixture differs considerably among populations; in particular, substantial proportions of mtDNA haplogroups L0d and/or L0k are observed in the pastoralist Kuvale from southwestern Angola , in the Fwe of southwest- ern Zambia , and in the Zulu and Xhosa from South Africa . In contrast, in populations from eastern Zambia, Zimbabwe, and Mozambique these characteristic autochthonous haplogroups are found at a frequency of at most 3% [19,25,26].
than one Khoisan group have not included enough populations to form a clear picture of the pattern of sub-structure and population relationships within southernAfrica 17,18 .
Here we present a high-resolution study of the genomic rela- tionships of southern and eastern African populations who speak languages characterized by heavy use of click consonants. Our study capitalizes on three novel resources: (1) a unique collection of southern African DNA samples encompassing most of the linguistic and cultural diversity of Khoisan groups; (2) a single nucleotide pol- ymorphism (SNP) array that is the first to include polymorphisms discovered in Khoisan; and (3) new methods of statistical analysis, some of which we introduce here for the first time, that allow us to make inferences about historical relationships even in the presence of admixture.
precipitation is the most important climatic driver for fuel load production, and that the southern African region in general, and special areas in particular such as the Etosha National Park region [Du Plessis, 1997] are known to be very heterogeneous, precipitation should present the most accurate spatial and temporal coverage possible. This could be realized by coupling higher resolution ground data to remote sensed data for instance. Potential factors such as temperature, grazing, soil type and nutrient availability, or fire regime are known to influence, sometimes in a synergic way, primary production [O’Coonor and Bredenkamp, 1997] so they should not be discarded. The lack of data on fine woody production in the tropical savanna ecosys- tems made us develop a simple twig load production based on a regional empirical relationship involving only the TC percentage. However, these loads reflect multiyear produc- tion. This should be improved in next stages. The imple- mentation of the model to simulate productivity over several years would be valuable in order to catch the effect of inter- annual variability and to avoid the reset action that occurs at the beginning of September, when some grass and litter fuel should be still on the ground. This multiyear product could provide insight on the effect of climate change over savanna ecosystems, and more particularly the influence of the 18- year rainfall oscillation (known as successive spells of 9 wet and 9 dry years) that is known to affect southernAfrica [Tyson, 1986].
[ 1 ] A 45,000-year high-resolution sedimentary record
from Lake Masoko in Tanzania shows the climate in this part of East Africa to have been characterized by a short and less severe dry season during the Last Glacial Maximum. Moisture and lake-level proxies, pollen assemblages and magnetic susceptibility, indicate that rainfall in the Masoko area was strongly controlled by low-latitude insolation (i.e., precessional forcing). Such observations contrast with the climatic patterns previously reconstructed in East Africa for the last glacial. Indeed, widespread dry conditions are generally observed and are attributed to the predominant control of high latitude glacial forcing over insolation forcing on the tropical hydrology. However, during the Younger Dryas cold event, out of phase with regional insolation behaviour, wetter conditions prevailed in this area, suggesting that the rainfall belt over Africa was probably shifted to the South, bringing moisture to the southernmost tropics. Citation: Garcin, Y., A. Vincens, D. Williamson, J. Guiot, and G. Buchet (2006), Wet phases in tropical southernAfrica during the last glacial period, Geophys. Res. Lett., 33, L07703, doi:10.1029/2005GL025531.
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Table 4. September monthly mean surface and top of atmosphere direct radiative e ffect [Wm −2 ] over southernAfrica for a series of models runs that vary the input parameters, using the MODIS derived horizontal distribution of biomass burning aerosol. Also shown is the percent- age change from the base case monthly mean value, and the range [Wm −2 ] in the radiative forcing for each model run.
Both participate in the scientific and executive committees of the various CPWF projects.
IWMI research capacities in South Africa are insufficient to allow the Institute to carry out by itself the many projects in which it is involved. Moreover, one of the CPWF objectives is to conduct research in partnership with other CGIAR Centers, national research institutions, nongovernmental organizations, government agencies and representatives of civil society. The researchers made available to IWMI thus play a major role in establishing relations and building partnerships with researchers and French research teams, most of whom are connected to the Gestion de l’eau, acteurs et usages (G-EAU) joint research unit (unité mixte de recherche, or UMR) combining CEMAGREF, IRD, CIRAD, ENGREF), the universities of southernAfrica, and the other actors mentioned above.
This article examines the convergence of real GDP per capita in the Common Market for Eastern and SouthernAfrica (COMESA) during the period 1950- 2003. Income departures across countries were evaluated from several panel data unit root tests, especially we consider the absolute and conditional convergence. We find no evidence supporting the existence of convergence process for the in- come in the COMESA. Nevertheless, applying economic development criterion allows to identity two absolute convergence clubs into the COMESA, one for the most four developed countries (Egypt, Libya, Mauritius, Seychelles), and one other for the fourteen less developed ones. Thus, we show that most economies of COMESA are locked into a sustained poverty trap process.
campestris and can be considered as candidate species for its biocontrol. Apart from this species, most Cuscuta of southernAfrica are rare and red-listed species that do not threaten crops.
I wish to thank Robert Anderson (CMNC), Max Barclay (BMNH), Johannes Bergsten (NHRS), Roman Borovec (FFWS), Lourdes Chamorro (NMNH), Enzo Colonnelli, Roberto Casalini (MCZ), Madougou Garba (DGPVN, Niger), Elizabeth Grobbelaar, Riaan Stals (SANC), Ruth Müller (TMSA), Hélène Perrin (MNHN) and Simon van Noort (SAMC) for providing access to collections and for the loan of specimens that made this study possible. I thank Stuart Hall (Stellenbosch Botanical Garden, Republic of South Africa) for his help with the location and identification of plant associations for Smicronyx. I also thank Paula Strauss, Gary Beukman and Michael Lutzeyer for access to the Grootbos Private Nature Reserve and their friendly assistance during sampling and Laure Benoit (CIRAD) who assisted with the production of Barcode sequences. The following services are acknowledged for providing permits for insect sampling: Western Cape Province (Western Cape Nature Conservation Board [permit No. CN44-30-4229] and the Cape Research Centre [Republic of South African National Parks, CRC/2019- 2020/012--2012/V1]); KwaZulu-Natal Province (Ezemvelo KZN Wildlife permits office, Collecting Permit KZN: OP1382-2019); Namibia (Collecting and MET delivered by Gobabeb in the course of the International Diptera Conference IDC9-2018). Finally, I acknowledge the collections platform of the CBGP (Emmanuelle Artige) for access to collections and imaging systems and Peter Biggins (CIRAD) who reviewed the English.
All projections show fairly high predictive success rates, with AUC often >0.9, the only exception being the projections for Rhabdomys as a genus (Table 3). Suitability maps pro- duced using MAXENT result in differential distributions for all taxa (Fig. 4). Using Kappa Maximizing Threshold (KMT) to determine the threshold for presence–absence predictions usually results in an excellent specificity (i.e., little error rate in absence predictions) but sometimes a poor sensitivity (i.e., a poor error rate in presence predictions) (Table 3). We there- fore present occurrence predictions based on MDT (Fig. 5), which resulted in a more balanced sensitivity and specificity (Table 3). Occurrence maps based on the KMT strategy are provided in Appendix 3. Under our prediction, R. pumilio presents the largest range as it occurs in the western (drier) part of the distribution range, but also penetrates further into the central part of southernAfrica. While R. dilectus range appears to cover the eastern (wettest) part of southernAfrica, with R. d. chakae occupying a larger area than R. d. dilectus, the latter being restricted to the northeastern part of our study area. Zones of overlap are predicted between the three taxa (Fig. 6).
Control of African swine fever (ASF) in countries in Eastern, Central and SouthernAfrica (ECSA) is particularly complex owing to the presence of all three known epide‐ miological cycles of maintenance of the virus, namely an ancient sylvatic cycle involv‐ ing the natural hosts and vectors of the disease as well as domestic cycles with and without involvement of natural vectors. While the situation is well documented in some of the countries, for others very little information is available. In spite of the unfavourable ASF situation, the pig population in the sub‐region has grown exponen‐ tially in recent decades and is likely to continue to grow in response to rapid urban growth resulting in increasing demand for animal protein by populations that are no longer engaged in livestock production. Better management of ASF will be essential to permit the pig sector to reach its full potential as a supplier of high quality protein and a source of income to improve livelihoods and create wealth. No vaccine is cur‐ rently available and it is likely that, in the near future, the sub‐region will continue to rely on the implementation of preventive measures, based on the epidemiology of the disease, to avoid both the devastating losses that outbreaks can cause and the risk the sub‐region poses to other parts of Africa and the world. The current situation in the ECSA sub‐region is reviewed and gaps in knowledge are identified in order to support ongoing strategy development for managing ASF in endemic areas.
buettikoferi, Eidolon helvum and Lissonycteris angolensis), throughout Western, Central and SouthernAfrica and which were found to be positive for the Zaïre strain Ebolavirus. Population genetics studies will be achieved by single-nucleotide polymorphism obtain ed by genotyping by sequencing method. Our research will give a better knowledge on the mobility of these species as well as on their genetic structures and population relationships. This information will be also essential to identify networks of contacts between bat populations and communities as well as interactions between humans and bats , in order to estimate risks of transfer of filoviruses among African regions. This project is integrated in the EU funded EBO-SURSY project supervised by the World Organization for Animal Health (OIE) aiming to better understand the problematic of Ebola in Africa.
Elodie Hut, The Hugo Observatory (University of Liège, Belgium)
The 2014-2016 El Nino-related drought, the 2014 Malawi floods, and the 2017 Cyclone Dineo are examples of disasters that have displaced hundreds of thousands of people across the Southern African region in the recent years. Most disaster responses so far have been reactive rather than preemptive, highlighting the need to promote resilience and preparedness in the face of both slow-onset and sudden-onset disasters. Whilst on the global level governance frameworks are being pushed to improve the integration of climate and disaster-induced displacement across the world (e.g. the Platform for Disaster Displacement, the Warsaw International Mechanism for Loss & Damage’s Task Force on Displacement, the soon-to-be-adopted Global Compact on Migration, as well as global instruments of the 2030 Agenda for Sustainable Development, including the Sendai Framework for Disaster Risk Reduction - SFDRR), implementation at the local and regional scales remains slow and challenging. This paper will take stock of the initiatives that have been introduced at the national and regional levels to prevent and better manage disaster displacement in the Southern African region, measure their success, and interrogate the opportunity to further mainstream displacement into the four priorities for action of the SFDRR, the underlying objectives being to improve the decision-making capacity of policy-makers and practitioners in the sub-region, as well as to reduce the overall vulnerability of Southern African populations to disaster risk.
prehistory | population genetics | migration
H unter–gatherer populations have inhabited southernAfrica for tens of thousands of years (1). Within approximately the last 2,000 y, these populations were joined by food-producing groups (both pastoralists and agriculturalists), and a culturally diverse set of populations occupy the region today. Because written history was unavailable until recently in southernAfrica, inferences about the migration patterns leading to the present distribution of pop- ulations have largely been informed by archaeology and linguistics. Genetic data are an additional source of information about population history, but extracting this information remains chal- lenging. Studies of diversity in southernAfrica have highlighted the influence of precolonial population admixture on the genetic structure of populations in the region (2–4) but have come to different conclusions about the historical scenarios that led to this admixture. In particular, although there is agreement that the arrival of Bantu-speaking agriculturalist populations had a major demographic impact in many populations, the importance of population movements from other parts of Africa or the world is unclear. Schlebusch et al. (2) argued for eastern African an- cestry specifically in the Nama, a pastoralist population, and Pickrell et al. (3) raised this possibility not just for the Nama but for several Khoe-speaking populations. Identifying the sources of non-Khoisan ancestry in southernAfrica could shed light on the historical processes that led to the extensive linguistic and cul- tural diversity of the region.