Landscape ecology of coffee pests in smallholdings: influence of landscape fragmentation, farming systems and a warming climate in Murang’a County, Kenya.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Coffee production systems have resulted in simplified landscapes with fragments of natural and semi-natural vegetation characterised by loss of biodiversity, high pests and disease incidences and excessive pesticide input. Consequently, the resilience of coffee landscapes against climate change impacts such as high diurnal temperature range, erratic rains, and prolonged droughts is weakened. Equally, controlling pests and diseases using natural enemies is no longer effective due to the unselective use of harmful chemicals. The present study aimed to understand the role of landscape ecology in a typical smallholder coffee-based landscape in creating suitable ecological conditions for the proliferation of coffee pests, specifically, coffee berry borer (CBB), Hypothenemus hampei, and the Antestia bugs Antestiopsis thunbergii (ABT) and A. facetoides (ABF) in an important coffee growing zone in central Kenya. The study also examined the impact of limiting temperature rise to below 2oC on habitat suitability for growing Arabica coffee to guide the implementation of the Paris agreement, which requires countries to stabilize the global mean surface temperature rise to below 1.5oC and in the worstcase scenario, well below 2.0oC above the pre-industrial levels. Firstly, the study explored Sentinel 2, Landsat 8 and PlanetScope datasets to characterise the smallholder coffee-based landscape and the level of fragmentation in each agro-ecological sub-zones of the upper midland (UM) agro-ecological zone. Sentinel 2 provides a robust dataset for land use and land cover (LULC) classification, with shortwave near-infrared and green bands being critical for classifying coffee bushes. Coffee was the dominant cover type in the higher agro-ecological sub-zones of Kenya, whereas annual crops dominated the lower sub-zones. Secondly, the study sought to identify the significant spatial scale and landscape structure that influenced the abundance of the three coffee pests, given that CBB had a low dispersal capacity and vice versa for the antestia bugs. The results showed that the pests foraged within a radius of 300m, with CBB having the shortest optimum foraging distance of 100m. The CBB abundance was strongly influenced by contiguous coffee patches, especially at higher elevations, whereas adjacent patches were more suitable for antestia bugs, especially cropland in the lower agroecological sub-zones. Thirdly, the shade and edge effect on microclimate and coffee pest abundance were examined. Generally, CBB preferred shaded coffee in the lower sub-zones and full-sun coffee in the higher sub-zones. For Antestia bugs, ABT preferred shaded coffee in all the agro-ecological sub-zones, whereas ABF preferred full-sun coffee, especially in the low sub-zones. Notable also was the influence of the edge effect of agroforest in lowering the mean temperature of full-sun coffee plots. Finally, the study looked at the impact of limiting v temperature rise to below 2oC under the Representative Concentration Pathways (RCP) 2.6 scenario on habitat suitability for growing Arabica coffee. The results showed that the area under coffee will increase, especially in 2070, and the coffee suitable range will shift to lower sub-zones. Overall, the study revealed that the existing landscape structure in smallholder coffee agrosystems favours coffee pests proliferation. Pest pressure at the lower sub-zones is high, especially in coffee plots without shade. However, implementing climate-friendly policies will reverse the current trend, making the lower sub-zones more suitable for growing Arabica coffee. An increase in acreage for planting coffee will translate to more yields, which could alleviate poverty and grow Kenya’s gross domestic product. The study underscores the urgency for smallholder farmers to shift their coffee production systems to climate-smart options such as increasing shade in their plots. This will increase their landscape resilience against climate change and pest control. Additionally, policy makers need to implement climate policies and promote clean energy development to limit temperature rise by the end of the century.Author name on WMS appears as: Gladys Mosomtai

Prediction of insect pest distribution as influenced by elevation: Combining field observations and temperature-dependent development models for the coffee stink bug, Antestiopsis thunbergii (Gmelin)

The antestia bug, Antestiopsis thunbergii (Gmelin 1790) is a major pest of Arabica coffee in Africa. The bug prefers coffee at the highest elevations, contrary to other major pests. The objectives of this study were to describe the relationship between A. thunbergii populations and elevation, to elucidate this relationship using our knowledge of the pest thermal biology and to predict the pest distribution under climate warming. Antestiopsis thunbergii population density was assessed in 24 coffee farms located along a transect delimited across an elevation gradient in the range 1000–1700 m asl, on Mt. Kilimanjaro, Tanzania. Density was assessed for three different climatic seasons, the cool dry season in June 2014 and 2015, the short rainy season in October 2014 and the warm dry season in January 2015. The pest distribution was predicted over the same transect using three risk indices: the establishment risk index (ERI), the generation index (GI) and the activity index (AI). These indices were computed using simulated life table parameters obtained from temperature-dependent development models and temperature data from 1) field records using data loggers deployed over the transect and 2) predictions for year 2055 extracted from AFRICLIM database. The observed population density was the highest during the cool dry season and increased significantly with increasing elevation. For current temperature, the ERI increased with an increase in elevation and was therefore distributed similarly to observed populations, contrary to the other indices. This result suggests that immature stage susceptibility to extreme temperatures was a key factor of population distribution as impacted by elevation. In the future, distribution of the risk indices globally indicated a decrease of the risk at low elevation and an increase of the risk at the highest elevations. Based on these results, we concluded with recommendations to mitigate the risk of A. thunbergii infestation

Applications of ecological niche modelling for mapping the risk of Rift Valley fever in Kenya

Rift Valley Fever (RVF) is a viral zoonotic disease of economic importance caused by a virus of the Phlebovirus genus, Bunyaviridae family. The disease occurs cyclically between 5 to 15 years which is associated with El Nino/Southern Oscillation weather phenomenon. Various studies have been done to map RVF distribution using a variety of approaches including the use of disease occurrence maps, statistical models which uses presence and absence data such as logistic regression method, etc. However, acquiring correct absence data is not easy and hence maps generated from standard statistical models might not be a true representation of the disease distribution. In this study Ecological Niche Modeling was used to determine the distribution of RVF in Kenya using GARP algorithm which uses presence-only data. Occurrence of RVF data were obtained by geo-referencing all the known hotspots in the country based on historical data acquired from the Directorate of Veterinary Services (DVS). The environmental variables that were used as the input data included: land use, soil type, elevation, vegetation index acquired from MODIS satellite spanning from October 2006 to March 2007, rainfall and temperature for the same period of time as the satellite imagery. Of the sampled data 70% was used to train the model while 30% to test the model. The result mapped the actual distribution of RVF in Kenya with an AUC of 0.82. A model evaluation was done using Partial ROC which had a 1.77 indicating that the model predicted well. The results will be used to improve the already existing maps and for better planning of mitigation measures. It will also be used together with socio-economic variables to evaluate vulnerability indices in all the divisions across the country

Using ecological niche modelling for mapping the risk of Rift Valley fever in Kenya

Introduction Rift valley fever (RVF) is a viral zoonotic disease of economic importance caused by a virus of the Phlebovirus genus, Bunyaviridae family. The disease occurs cyclically between 5 to 15 years which is associated with El Nino weather phenomenon. Various studies have been done to map RVF distribution using a variety of approaches including the use of disease occurrence maps, statistical models which uses presence and absence data such as logistic regression method, etc. However, acquiring correct absence data is not easy and hence maps generated from standard statistical models might not be a true representation of the disease distribution. Materials and Methods In this study Ecological Niche Modeling was used to determine the distribution of RVF in Kenya using GARP algorithm which uses presence-only data. RVF occurrence data were obtained by geo-referencing all the known hotspots in the country based on historical data acquired from the Directorate of Veterinary Services (DVS). The environmental variables that were used as the input data included: landuse, soil type, elevation, vegetation index acquired from MODIS satellite spanning from October 2006 to march 2007, rainfall and temperature for the same period of time as the satellite imagery. Of the sampled data 70% was used to train the model while 30% to test the model. Results The result mapped the actual distribution of RVF in Kenya with an AUC of 0.82. A model evaluation was done using Partial ROC which had a 1.74 indicating that the model predicted well. Conclusion and Recommendations The results will be used to improve the already existing maps and for better planning of mitigation measures. It will also be used together with socio-economic variables to evaluate vulnerability indices in all the divisions across the country

Prediction of insect pest distribution as influenced by elevation : combining field observations and temperature-dependent development models for the coffee stink bug, Antestiopsis thunbergii (Gmelin)

The antestia bug, Antestiopsis thunbergii (Gmelin 1790) is a major pest of Arabica coffee in Africa. The bug prefers coffee at the highest elevations, contrary to other major pests. The objectives of this study were to describe the relationship between A. thunbergii populations and elevation, to elucidate this relationship using our knowledge of the pest thermal biology and to predict the pest distribution under climate warming. Antestiopsis thunbergii population density was assessed in 24 coffee farms located along a transect delimited across an elevation gradient in the range 1000±1700 m asl, on Mt. Kilimanjaro, Tanzania. Density was assessed for three different climatic seasons, the cool dry season in June 2014 and 2015, the short rainy season in October 2014 and the warm dry season in January 2015. The pest distribution was predicted over the same transect using three risk indices: the establishment risk index (ERI), the generation index (GI) and the activity index (AI). These indices were computed using simulated life table parameters obtained from temperature-dependent development models and temperature data from 1) field records using data loggers deployed over the transect and 2) predictions for year 2055 extracted from AFRICLIM database. The observed population density was the highest during the cool dry season and increased significantly with increasing elevation. For current temperature, the ERI increased with an increase in elevation and was therefore distributed similarly to observed populations, contrary to the other indices. This result suggests that immature stage susceptibility to extreme temperatures was a key factor of population distribution as impacted by elevation. In the future, distribution of the risk indices globally indicated a decrease of the risk at low elevation and an increase of the risk at the highest elevations. Based on these results, we concluded with recommendations to mitigate the risk of A. thunbergii infestation.The Centre de CoopeÂration Internationale en Recherche Agronomique pour le DeÂveloppement (CIRAD), Montpellier, France, https://www.cirad.fr/, in support of PhD student stipend (AGAA); CHIESA project (Climate Change Impacts on Ecosystem Services and Food Security in Eastern Africa) funded by the Ministry of Foreign Affairs of Finland, http://chiesa.icipe. org/, in support of laboratory and field experimentation; The German Academic Exchange Service (DAAD) In-Region Postgraduate Scholarship, in support of student stipends and school fees. International Centre of Insect Physiology and Ecology is core-funded by UK's Department for International Development (DFID), Swedish International Development Cooperation Agency, the Swiss Agency for Development and Cooperation (SDC) and the Kenyan Government.http://www.plosone.orgam2018Zoology and Entomolog

Distribution and abundance of key vectors of Rift Valley fever and other arboviruses in two ecologically distinct counties in Kenya

Background Rift Valley fever (RVF) is a mosquito-borne viral zoonosis of ruminants and humans that causes outbreaks in Africa and the Arabian Peninsula with significant public health and economic consequences. Humans become infected through mosquito bites and contact with infected livestock. The virus is maintained between outbreaks through vertically infected eggs of the primary vectors of Aedes species which emerge following rains with extensive flooding. Infected female mosquitoes initiate transmission among nearby animals, which amplifies virus, thereby infecting more mosquitoes and moving the virus beyond the initial point of emergence. With each successive outbreak, RVF has been found to expand its geographic distribution to new areas, possibly driven by available vectors. The aim of the present study was to determine if RVF virus (RVFV) transmission risk in two different ecological zones in Kenya could be assessed by looking at the species composition, abundance and distribution of key primary and secondary vector species and the level of virus activity. Methodology Mosquitoes were trapped during short and long rainy seasons in 2014 and 2015 using CO2 baited CDC light traps in two counties which differ in RVF epidemic risk levels(high risk Tana-River and low risk Isiolo),cryo-preserved in liquid nitrogen, transported to the laboratory, and identified to species. Mosquito pools were analyzed for virus infection using cell culture screening and molecular analysis. Findings Over 69,000 mosquitoes were sampled and identified as 40 different species belonging to 6 genera (Aedes, Anopheles, Mansonia, Culex, Aedeomyia, Coquillettidia). The presence and abundance of Aedes mcintoshi and Aedes ochraceus, the primary mosquito vectors associated with RVFV transmission in outbreaks, varied significantly between Tana-River and Isiolo. Ae. mcintoshi was abundant in Tana-River and Isiolo but notably, Aedes ochraceus found in relatively high numbers in Tana-River (n = 1,290), was totally absent in all Isiolo sites. Fourteen virus isolates including Sindbis, Bunyamwera, and West Nile fever viruses were isolated mostly from Ae. mcintoshi sampled in Tana-River. RVFV was not detected in any of the mosquitoes. Conclusion This study presents the geographic distribution and abundance of arbovirus vectors in two Kenyan counties, which may assist with risk assessment for mosquito borne diseases

Ecological Niche Modeling for Filoviruses: A Risk Map for Ebola and Marburg Virus Disease Outbreaks in Uganda

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Datasets for mapping pastoralist movement patterns and risk zones of Rift Valley fever occurrence

Rift Valley fever (RVF) is a zoonotic disease affecting humans and animals. It is caused by RVF virus transmitted primarily by Aedes mosquitoes. The data presented in this article propose environmental layers suitable for mapping RVF vector habitat zones and livestock migratory routes. Using species distribution modelling, we used RVF vector occurrence data sampled along livestock migratory routes to identify suitable vector habitats within the study region which is located in the central and the north-eastern part of Kenya. Eleven herds monitored with GPS collars were used to estimate cattle utilization distribution patterns. We used kernel density estimator to produce utilization contours where the 0.5 percentile represents core grazing areas and the 0.99 percentile represents the entire home range. The home ranges were overlaid on the vector suitability map to identify risks zones for possible RVF exposure. Assimilating high spatial and temporal livestock movement and vector distribution datasets generates new knowledge in understanding RVF epidemiology and generates spatially explicit risk maps. The results can be used to guide vector control and vaccination strategies for better disease control. (C) 2017 The Authors. Published by Elsevier Inc

Ecological niche modeling as a tool for prediction of the potential geographic distribution of Bacillus anthracis spores in Tanzania

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Global emergence of Alphaviruses that cause arthritis in humans

Arthropod-borne viruses (arboviruses) may cause severe emerging and re-emerging infectious diseases, which pose a significant threat to human and animal health in the world today. These infectious diseases range from mild febrile illnesses, arthritis, and encephalitis to haemorrhagic fevers. It is postulated that certain environmental factors, vector competence, and host susceptibility have a major impact on the ecology of arboviral diseases. Presently, there is a great interest in the emergence of Alphaviruses because these viruses, including Chikungunya virus, O'nyong'nyong virus, Sindbis virus, Ross River virus, and Mayaro virus, have caused outbreaks in Africa, Asia, Australia, Europe, and America. Some of these viruses are more common in the tropics, whereas others are also found in temperate regions, but the actual factors driving Alphavirus emergence and re-emergence remain unresolved. Furthermore, little is known about the transmission dynamics, pathophysiology, genetic diversity, and evolution of circulating viral strains. In addition, the clinical presentation of Alphaviruses may be similar to other diseases such as dengue, malaria, and typhoid, hence leading to misdiagnosis. However, the typical presence of arthritis may distinguish between Alphaviruses and other differential diagnoses. The absence of validated diagnostic kits for Alphaviruses makes even routine surveillance less feasible. For that purpose, this review describes the occurrence, genetic diversity, clinical characteristics, and the mechanisms involving Alphaviruses causing arthritis in humans. This information may serve as a basis for better awareness and detection of Alphavirus-caused diseases during outbreaks and in establishing appropriate prevention and control measures