Victor Mose,
Deputy Director and Head of Biostatistical Services,
30th March 2019.
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As commercial software become  increasingly expensive, many government institutions across Africawide are turning to opensource applications for data management and analysis. The adoption of opensource technology, raises the challenge of technical skills needed to customize the software to fit organizations’ data needs.
The Amboseli Conservation Program (ACP) has over the years developed opensource technologies for application to various research needs (download paper here). In March 2019 the ACP team in collaboration with the South-Rift Association of Land Owners (SORALO) and the Uaso Ngiro Baboon Project (UNBP) trained the Department of Regional Surveys and Remote Sensing (DRSRS) in the applications of these software tools. The training sessions covered data management in R, Rstudio, statistical significance testing, mapping and estimations of animal populations and distributions from aerial survey data conducted in Kajiado County, southern Kenya. The training took place at African Conservation Centre offices in Karen.
The tools, which integrate data entry, processing and mapping, can be easily adapted to processing countrywide species population estimates from aerial surveys conducted by DRSRS. Historical counts can also be rapidly processed for spatial trends and subsequent policy advice.
Looking ahead, ACP will expand the training for other conservation organizations to include web applications in R shiny for data analysis, management and mapping. The application requires users to have basic computer skills hence allowing non-technical personnel to interact with and process available institutional data.

Surveys conducted across sections of the pastoral Maasai of Kenya show a wide variety of values for wildlife, ranging from utility and medicinal uses to environmental indicators, commerce, and tourism. Attitudes toward wildlife are highly variable, depending on perceived threats and uses. Large carnivores and herbivores pose the greatest threats to people, livestock, and crops, but also have many positive values. Attitudes vary with gender, age, education, and land holding, but most of all with the source of livelihood and location, which bears on relative abundance of useful and threatening species. Traditional pastoral practices and cultural views that accommodated coexistence between livestock and wildlife are dwindling and being replaced by new values and sensibilities as pastoral practices give way to new livelihoods, lifestyles, and aspirations. Human-wildlife conflict has grown with the transition from mobile pastoralism to sedentary livelihoods. Unless the new values offset the loss of traditional values, wildlife will continue to decline. New wildlife-based livelihoods show that continued coexistence is possible despite the changes underway.
Full article available at  https://link.springer.com/article/10.1007/s10745-019-0065-8

​In March, Victor Mose gave a presentation on Spatial and social ecological dynamics of human wildlife interactions at the Institute of Research and Development (IRD) stand at the United Nations Environment Assembly (UNEA-4) in Nairobi. His talk highlighted the need to factor in the social aspects such as human attitudes towards wildlife in modelling their interactions. The presentation is part of ACP’s multidisciplinary approach to understanding the drivers of human wildlife interactions and implication for species conservation and coexistence.

​Using exploratory data analysis to visualize and test hypothesis is fast becoming a method of choice for many researchers working across disciplines. Data visualization allows team members to work together in building ideas about how ecological systems work. Victor Mose outlined the benefits of this approach at the third annual International Biometric Society (IBS)–Kenya chapter held at the Strathmore University in Nairobi. He used the long-term Amboseli data to show how spatial exploratory data analysis can reduce complex ecological hypothesis to simple visual presentations with cross-cutting research applications. The approach requires no prior knowledge about the data and can be rapidly applied to formulate and test hypothesis in visual  form with wide applications.