The aim of this work was to check its presence in wildlife and to confirm the possible role of reservoir wildlife may play in the propagation of the parasite

Species distribution models (SDMs) are vital tools in ecology and conservation. The integration of increasingly available citizen science data with planned survey data offers a significant opportunity to enhance species distribution estimates. While integrated SDMs often combine presence-only and abundance data, the interdependence between the conditional distributions of these outcomes remains to be elucidated. This study proposes a Bayesian spatial fusion modelling framework to jointly analyse presence-only and abundance data for the African baobab in Benin. The aim was to understand and map the spatial variation in the species’ distribution. We briefly reviewed process-based models for count and point process data and explored various data fusion strategies using Integrated Nested Laplace Approximations (INLA) and Stochastic Partial Differential Equations (SPDE) for inference. The results revealed a heterogeneous baobab distribution across Benin, characterised by a spatial autocorrelation range of 34.4 km (95% Bayesian credible interval, BCI = 27.59-42.52). Key drivers of this distribution include environmental factors such as annual temperature, rainfall of the driest month, soil texture (silt/clay fractions), and slope. A spatial fusion model incorporating shared latent components and common covariates' effects demonstrated the highest performance level, surpassing alternative fusion approaches. The model achieved the highest mean composite scores for the Area Under the ROC Curve (AUC) (0.85±0.02), accuracy (0.77±0.02), and True Skill Statistics (TSS) (0.66±0.05). The shared component model has the capacity to explain datasets' interdependence, estimate covariate effects missed by separate models, and enhance prediction precision. Despite relying on the assumption of an identically shared spatial signal across target responses, this research underscores the potential of spatial fusion modelling for integrating disparate data sources. The findings contribute to advancing SDM inference, particularly in data-limited contexts, and have wider applicability to spatial regression problems involving multisource outcomes.

The variation of climatic parameters influences the distribution of several indigenous fruit species in Benin. This research evaluates the spatio-temporal dynamics of favorable habitats to the cultivation of Cola nitida under current climatic conditions and by 2085 in Benin. To do this, 230 occurrences of C. nitida were combined with the environmental layers using the MaxEnt algorithm in order to highlight its favorable habitats. The AUC (0.969) and SEDI (0.918) results suggest a very good performance of the model. The precipitation of the driest quarter (43.6%) and the maximum temperature of warmest month (41.8%) are the variables that most influenced the geographical distribution of C. nitida in Benin. More than 87% of the national territory remains less favorable to the conservation of C. nitida. Very favorable habitats went from 5.21% (present climatic conditions) to 1.29% (RCP 4.5) i.e., a loss of 3.92%, and occupy only 0.16% of the national territory (RCP 8.5) either a regression of 5.05%. Furthermore, the priority conservation habitats resilient to climate change occupy only 9.69% of the territory. Thus, it is important to implement reforestation projects in priority natural habitats resilient to climate change (classified forest of Pahou, Ouèdo, Djigbé, Itchèdè-Toffo, Niaouli and the reforestation perimeter of Abomey) which are entirely suitable for the conservation of C. nitida. The promotion of C. nitida in agroforestry parks should be encouraged for sustainable production and management.

Understanding how countries’ socio-economic, environmental, health status, and climate factors have influenced the dynamics of COVID-19 is essential for public health, particularly in Africa. This study explored the relationships between African countries’ COVID-19 cases and deaths and their socio-economic, environmental, health, clinical, and climate variables. It compared the performance of Ordinary Least Square (OLS) regression, the spatial lag model (SLM), the spatial error model (SEM), and the conditional autoregressive model (CAR) using statistics such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and coefficient of determination ($R^2$). Results showed that the SEM with the 10-nearest neighbours matrix weights performed better for the number of cases, while the SEM with the maximum distance matrix weights performed better for the number of deaths. For the cases, the number of tests followed by the adjusted savings, Gross Domestic Product (GDP) per capita, dependence ratio, and annual temperature were the strongest covariates. For deaths, the number of tests followed by malaria prevalence, prevalence of communicable diseases, adjusted savings, GDP, dependence ratio, Human Immunodeficiency Virus (HIV) prevalence, and moisture index of the moistest quarter play a critical role in explaining disparities across countries. This study illustrates the importance of accounting for spatial autocorrelation in modelling the dynamics of the disease while highlighting the role of countries’ specific factors in driving its dynamics.