Invasive holoparasitic plants of the genus Cuscuta (dodder) threaten Africa's ecosystems, due to their rapid spread and attack on various host plant species. Most Cuscuta species cannot photosynthesize, hence rely on host plants for nourishment. After attachment through a peg-like organ called a haustorium, the parasites deprive hosts of water and nutrients leading to their death. Despite their rapid spread in Africa, dodders have attracted limited research attention, although data on their taxonomy, host range and epidemiology are critical for their management. Here, we combine taxonomy and phylogenetics to reveal presence of field dodder (Cuscuta campestris) and C. kilimanjari (both either naturalized or endemic to East Africa), and for the first time in continental Africa, presence of the giant dodder (C. reflexa) a south Asian species. These parasites have a wide host range, parasitizing species across 13 angiosperm orders. Evaluating the possibility of C. reflexa to expand this host range to tea, coffee, and mango, crops of economic importance to Africa, revealed successful parasitism, following haustorial formation and vascular bundle connections in all three crops. However, only mango mounted a successful post-attachment resistance response. Furthermore, species distribution models predicted high habitat suitability for all three Cuscuta species across major tea- and coffee-growing regions of Eastern Africa, suggesting an imminent risk to these crops. Our findings provide relevant insights into a little-understood threat to biodiversity and economic wellbeing in Eastern Africa, and providing critical information to guide development of management strategies to avert their spread.

Invasive holoparasitic plants of the genus Cuscuta (dodder) threaten African ecosystems due to their rapid spread and attack on various host plant species. Most Cuscuta species cannot photosynthesize and hence rely on host plants for nourishment. After attachment through a peg-like organ called a haustorium, the parasites deprive hosts of water and nutrients, which negatively affects host growth and development. Despite their rapid spread in Africa, dodders have attracted limited research attention, although data on their taxonomy, host range, and epidemiology are critical for their management. Here, we combine taxonomy and phylogenetics to reveal the presence of field dodder (Cuscuta campestris) and Cuscuta kilimanjari (both either naturalized or endemic to East Africa), in addition to the introduction of the giant dodder (Cuscuta reflexa), a south Asian species, in continental Africa. These parasites have a wide host range, parasitizing species across 13 angiosperm orders. We evaluated the possibility of C. reflexa to expand this host range to tea (Camelia sinensis), coffee (Coffea arabica), and mango (Mangifera indica), crops of economic importance to Africa, for which haustorial formation and vascular-bundle connections in all three crops revealed successful parasitism. However, only mango mounted a successful post-attachment resistance response. Furthermore, species distribution models predicted high habitat suitability for Cuscuta spp. across major tea- and coffee-growing regions of Eastern Africa, suggesting an imminent risk to these crops. Our findings provide relevant insights into a poorly understood threat to biodiversity and economic wellbeing in Eastern Africa, and provide critical information to guide development of management strategies to avert Cuscuta spp. spread.

Ocean circulation, upwelling phenomena and chlorophyll-a concentrations were investigated within the framework of numerical model simulations with 1/12° nested horizontal grid-size, in the tropical western Indian Ocean, along the coasts of Tanzania and Kenya. Ekman driven upwelling exhibited high levels of spatial and temporal variability in the region, characterized by a more vigorous occurrence/intensification during the Northeast than the Southwest Monsoon season. A similar trend was observed for chlorophyll-a distribution, but with an additional strong contribution during the inter-monsoon period from March to April. Trend analysis of a SST-derived coastal upwelling index (CUI) computed over the Pemba Channel and offshore of the East African Coastal Current (EACC), for 24 years (1990 - 2013), revealed a general linear relation of the form CUI(yr) = 2.4x10-7yr – 285, with a steady small annual increase of the upwelling phenomena by 0.0024/year ≃ 4% during the whole period of the simulation, which could be attributed to documented increasing trends of wind intensity and water volume transport in the region. The CUI exhibited the two most dominant peaks of variabilities on the range of annual and semi-annual timescales. The wind-stress southward component and the easting/westing veering of the northward EACC at 6°S revealed that these parameters were moderate and significantly correlated with the CUI (r = - 0.53 and 0.52, p<0.05) respectively, further suggesting its intensification during the Northeast Monsoon season.

This paper focuses on one of the high resolution models used for weather forecasting at Kenya Meteorological Department (KMD). It reviews the skill and accuracy of the Weather Research and Forecasting (WRF)-Environmental Modeling System (EMS) model, in simulating weather over Kenya. The study period was March to May 2011, during the rainy season over Kenya. The model output was compared with the observed data from 27 synoptic stations spread over the study area, to determine the performance of the model in terms of its skill and accuracy in forecasting. The spatial distribution of rainfall and temperature showed that the WRF model was capable of reproducing the observed general pattern especially for temperature. The model has skill in forecasting both rainfall and temperature over the study area. However, the model may underestimate rainfall of more than 10 mm/day and displace its location and overestimate rainfall of less than 1 mm/day. Therefore, during the period of enhanced rainfall especially in the month of April and part of May the model forecast needs to be complemented by other models or forecasting methods before giving a forecast. There is need to improve its performance over the domain through review of the parameterization of small scale physical processes and more observed data need to be simulated into the model.

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