Ritter Atoundem Guimapi
Research Scientist
Attachments
CV 2023Biography
Ritter A. Guimapi works as research scientist in the Division of Biotechnology and plant health, under the department of Pests and Weeds in Forestry, Agriculture and Horticulture. His main area of responsibility includes: Mathematical modeling for plant protection; computer-based simulation of insect pest dynamic and their interaction with their beneficial organisms, for use among other things, in warning services.
Ritter’s academic background consists of a PhD in ecological modelling and computer science, a master's in computer science and a bachelor's in mathematics and computer science. He has extensive experience developing models to understand and predict the risk of the dynamics and invasion of various pests across the African and Asian continents. Furthermore, he has many years of international experience using model to explore the effect of both environmental and climatic factors on the dynamics of agroecological processes in relation to plant protection, and to optimize the timing of field implementation of environmentally friendly pest control solutions. Many of these models are integrated into Decision Support System like VIPS (Varsling Innen PlanteSkadegøjøre: https://www.vips-landbruk.no/ ) and are in use to help address pest (insects and plants) management challenges for the wellbeing of smallholder farmers.
Authors
Beatrice T. Nganso Komi Mensah Agboka Salvador D. Atagong Sidonie Fameni Topé Tchouzeube Massing Tobias Landmann Subramanian Sevgan Willy Mwiza Fredrick Odera Emmanuel D. Piiru Z. Ngalo Otieno-Ayayo Victoria Soroker Ritter Atoundem GuimapiAbstract
No abstract has been registered
Authors
Komi Mensah Agboka Frank Thomas Ndjomatchoua Ritter Atoundem Guimapi Luca Rossini Abdelmutalab G. A. Azrag Quinto Juma Meltus Tobias Landmann Sunday Ekesi Elfatih M. Abdel‐RahmanAbstract
ABSTRACT The multitasking lesser mealworm ( Alphitobius diaperinus ) is a special beetle known as a pest in poultry, a resource for waste degradation and an alternative for protein production. This study compares the predictive accuracy of correlative species distribution models (SDMs) with a risk index derived from a mechanistic model. The study derives the mechanistic‐based risk index from the ordinary differential equation that describes the population dynamics of A. diaperinus using the temperature‐dependent bio‐demographic rates, while the ensemble SDM is derived using well‐known algorithms such as maximum entropy, random forest and so forth. We finally propose a hybrid model combining both approaches using a weighted average approach. When overlaid on occurrence data, the predictive accuracy of the mechanistic model globally varied across temporal scales, with the highest performance observed in the October–December quarter (27% of occurrences were predicted correctly). The comparison across geographic regions model had the best performance in Asia (94.4% accuracy), outperforming the two scenario SDMs (78.3%). In contrast, the correlative ensemble SDM performed better in Europe (93%), where we have most of the data, but was very sensitive to data gaps, especially in Africa. Finally, the proposed hybrid model outperforms both individual models in the global scenario (86.5% accuracy). These findings highlight the strengths and limitations of both modelling approaches and provide critical insights to optimise pest management strategies, sustainable utilisation and ecological forecasting by refining SDM through the integration of biological realism and empirical data.
Authors
Frank Thomas Ndjomatchoua Richard Olaf James Hamilton Stutt Ritter Atoundem Guimapi Luca Rossini Christopher A. GilliganAbstract
Empirical field data and simulation models are often used separately to monitor and analyse the dynamics of insect pest populations over time. Greater insight may be achieved when field data are used directly to parametrize population dynamic models. In this paper, we use a differential evolution algorithm to integrate mechanistic physiological-based population models and monitoring data to estimate the population density and the physiological age of the first cohort at the start of the field monitoring. We introduce an ad hoc temperature-driven life-cycle model of Bemisia tabaci in conjunction with field monitoring data. The likely date of local whitefly invasion is estimated, with a subsequent improvement of the model’s predictive accuracy. The method allows computation of the likely date of the first field incursion by the pest and demonstrates that the initial physiological age somewhat neglected in prior studies can improve the accuracy of model simulations. Given the increasing availability of monitoring data and models describing terrestrial arthropods, the integration of monitoring data and simulation models to improve model prediction and pioneer invasion date estimate will lead to better decision-making in pest management.
