[1] Characteristic vegetation and biofuels in major ecosystems of southern Africa were sampled during summer and autumn 2000 and burned under semicontrolled conditions. Elemental compositions of fuels and ash and emissions of CO 2 , CO, CH 3 COOH, HCOOH, NO X , NH 3 , HONO, HNO 3 , HCl, total volatile inorganic Cl and Br, SO 2 and particulate C, N, and major ions were measured. Modified combustion efficiencies (MCEs, median = 0.94) were similar to those of ambient fires. Elemental emissions factors (EF el ) for CH 3 COOH were inversely correlated with MCEs; EF el s for heading and mixed grass fires were higher than those for backing fires of comparable MCEs. NO X , NH 3 , HONO, and particulate N accounted for a median of 22% of emitted N; HNO 3 emissions were insignificant. Grass fires with the highest EF el s for NH 3 corresponded to MCEs in the range of 0.93; grass fires with higher and low MCEs exhibited lower EF el s. NH 3 emissions for most fuels were poorly correlated with fuel N. Most Cl and Br in fuel was emitted during combustion (median for each = 73%). Inorganic gases and particulate ions accounted for medians of 53% and 30% of emitted Cl and Br, respectively. About half of volatile inorganic Cl was HCl indicating significant emissions of other gaseous inorganic Cl species. Most fuel S (median = 76%) was emitted during combustion; SO 2 and particulate SO 4 2À accounted for about half the flux. Mobilization of P by fire (median emission = 82%) implies large nutrient losses from burned regions and potentially important exogenous sources of fertilization for downwind ecosystems.

No abstract

No abstract

No abstract

Wetlands in West Africa are among the most vulnerable ecosystems to climate change. West African wetlands are often freshwater transfer mechanisms from wetter climate regions to dryer areas, providing an array of ecosystem services and functions. Often wetland-specific data in Africa is only available on a per country basis or as point data. Since wetlands are challenging to map, their accuracies are not well considered in global land cover products. In this paper we describe a methodology to map wetlands using well-corrected 250-meter MODIS time-series data for the year 2002 and over a 360,000 km 2 large study area in western Burkina Faso and southern Mali (West Africa).A MODIS-based spectral index table is used to map basic wetland morphology classes. The index uses the wet season near infrared (NIR) metrics as a surrogate for flooding, as a function of the dry season chlorophyll activity metrics (as NDVI). Topographic features such as sinks and streamline areas were used to mask areas where wetlands can potentially occur, and minimize spectral confusion. 30-m Landsat trajectories from the same year, over two reference sites, were used for accuracy assessment, which considered the area-proportion of each class mapped in Landsat for every MODIS cell. We were able to map a total of five wetland categories. Aerial extend of all mapped wetlands (class "Wetland") is 9,350 km 2 , corresponding to 4.3% of the total study area size. The classes OPEN ACCESSRemote Sens. 2010, 2 1752"No wetland"/"Wetland" could be separated with very high certainty; the overall agreement (KHAT) was 84.2% (0.67) and 97.9% (0.59) for the two reference sites, respectively. The methodology described herein can be employed to render wide area base line information on wetland distributions in semi-arid West Africa, as a data-scarce region. The results can provide (spatially) interoperable information feeds for inter-zonal as well as local scale water assessments.

BackgroundKnowledge of vector ecology is important in understanding the transmission dynamics of vector borne disease. In this study, we determined the distribution and diversity of mosquitoes along the major nomadic livestock movement routes (LMR) in the traditional pastoral ecozone of northeastern Kenya. We focused on the vectors of Rift Valley fever virus (RVFv) with the aim of understanding their ecology and how they can potentially influence the circulation of RVFv.MethodsMosquito surveys were conducted during the short and long rainy seasons from November 2012 to August 2014 using CO2-baited CDC light traps at seven sites selected for their proximity to stopover points that provide pasture, water and night bomas (where animals spend nights). We compared mosquito abundance and diversity across the sites, which were located in three ecological zones (IV, V and VI), based on the classification system of agro-ecological zones in Kenya.ResultsOver 31,000 mosquitoes were trapped comprising 21 species belonging to 6 genera. Overall mosquito abundance varied significantly by ecological zones and sites. Mansonia species (Ma. uniformis and Ma. africana) were predominant (n = 12,181, 38.3 %). This was followed by the primary RVF vectors, Ae. ochraceus and Ae. mcintoshi comprising 17.9 and 14.98 %, respectively, of the total captures and represented across all sites and ecological zones. The Shannon diversity index ranged from 0.8 to 2.4 with significant zone, site and seasonal variations. There was also significant species richness of RVF vector across ecological zones.ConclusionOur findings highlight differential occurrence of RVFv vectors across ecological zones and sampling sites, which may be important in determining areas at risk of emergence and circulation of RVFv. Moreover, the vector distribution map along LMR generated in this study will guide potential interventions for control of the disease, including strategic vaccination for livestock.

No abstract

Bee keeping is indispensable to global food production. It is an alternate income source, especially in rural underdeveloped African settlements, and an important forest conservation incentive. However, dwindling honeybee colonies around the world are attributed to pests and diseases whose spatial distribution and influences are not well established. In this study, we used remotely sensed data to improve the reliability of pest ecological niche (EN) models to attain reliable pest distribution maps. Occurrence data on four pests (Aethina tumida, Galleria mellonella, Oplostomus haroldi and Varroa destructor) were collected from apiaries within four main agro-ecological regions responsible for over 80% of Kenya's bee keeping. Africlim bioclimatic and derived normalized difference vegetation index (NDVI) variables were used to model their ecological niches using Maximum Entropy (MaxEnt). Combined precipitation variables had a high positive logit influence on all remotely sensed and biotic models' performance. Remotely sensed vegetation variables had a substantial effect on the model, contributing up to 40.8% for G. mellonella and regions with high rainfall seasonality were predicted to be high-risk areas. Projections (to 2055) indicated that, with the current climate change trend, these regions will experience increased honeybee pest risk. We conclude that honeybee pests could be modelled using bioclimatic data and remotely sensed variables in MaxEnt. Although the bioclimatic data were most relevant in all model results, incorporating vegetation seasonality variables to improve mapping the 'actual' habitat of key honeybee pests and to identify risk and containment zones needs to be further investigated.