Finding optimum climatic parameters for high tomato yield in Benin (West Africa) using frequent pattern growth algorithm.

Tomato is one of the most appreciated vegetables in the world. Predicting its yield and optimizing its culture is important for global food security. This paper addresses the challenge of finding optimum climatic values for a high tomato yield. The Frequent Pattern Growth (FPG) algorithm was considered to establish the associations between six climate variables: minimum and maximum temperatures, maximum humidity, sunshine (Sun), rainfall, and evapotranspiration (ET), collected over 26 years in the three agro-ecological Zones of Benin. Monthly climate data were aggregated with yield data over the same period. After aggregation, the data were transformed into 'low', 'medium', and 'high' attributes using the threshold values defined. Then, the rules were generated using the minimum support set to 0.2 and the confidence to 0.8. Only the rules with the consequence 'high yield' were screened. The best yield patterns were observed in the Guinean Zone, followed by the Sudanian. The results indicated that high tomato yield was associated with low ET in all areas considered. Minimum and maximum temperatures, maximum humidity, and Sun were medium in every Zone. Moreover, rainfall was high in the Sudanian Zone, unlike the other regions where it remained medium. These results are useful in assessing climate variability's impact on tomato production. Thus, they can help farmers make informed decisions on cultivation practices to optimize production in a changing environment. In addition, the findings of this study can be considered in other regions and adapted to other crops.

Residents' perception and impact of COVID-19 on ecotourism in West Africa: The case of Banco National Park in Côte d'Ivoire.

Ecotourism, as a means of fostering socio-economic level of local communities and contributing to the conservation of forest resources, is important for development in low-income countries. This work investigates the extent to which local people support the continuation of ecotourism during the COVID-19 pandemic and their attitudes towards resource conservation in Banco National Park in Côte d'Ivoire using social exchange theory (SET) as a foundation. A closed-ended questionnaire was used to conduct a survey with 150 informants selected among residents around the park. The data were analyzed using descriptive, correlational, and Partial Least Squares Structural Equation Modelling (PLS-SEM) approaches. The results showed that residents' perception of the impacts of ecotourism strongly affects their support for tourism development during the COVID-19 pandemic (ß = 0.918, p < 0.05). The socio-cultural (ß = 0.275, p < 0.05) and environmental (ß = 0.309, p < 0.05) benefits of ecotourism are the key determinants of the residents' perception and their support for ecotourism within the park during COVID-19 pandemic. The findings also revealed that economic benefits from ecotourism are linked to residents' perceptions of the qualities of the tourism place (ß = 0.363, p < 0.05). Overall, local people around Banco Park recognize that ecotourism produces more benefits than detriments. The COVID-19 pandemic, a painful and unexpected event, has not blunted their support for the continuation of ecotourism. This study calls for the integration of local residents' opinions in the development of the ecotourism sector in Côte d'Ivoire. It is a first step in determining residents' attitudes towards ecotourism in West Africa in a post-COVID context, and the results constitute a starting point for future studies.

Habitat range shift and prediction of the potential future distribution of Ricinodendron heudelotii (Baill.) Heckel in Benin (West Africa).

Ricinodendron heudelotii (Baill.) Heckel is an important nutraceutical reservoir. Its Sustainable exploitation requires information on its potential distribution in the current context of rapid population growth and climate change threats. This study aimed to map the suitable areas for its domestication and conservation under current and future climate conditions in Benin. Occurrence data were recorded and combined with the environmental layers of two climatic scenarios (optimistic RCP 4.5 and pessimistic RCP 8.5) following the biodiversity modelling approach (biomod2). Currently, about four percent (5082 Km2) of the country's area mainly located in the sub-humid and the humid zones were potentially suitable for R. heudelotii distribution. Under future climatic conditions the potentially suitable areas were mainly in the sub-humid zone, but almost all the highly suitable areas located in the humid zone will become medium suitable areas by the years 2055 and 2085 horizons. This study shows that, whatever the future climatic scenarios, R. heudelotii will substantially maintain the size of its range across the country. These findings allow undertaking anticipated actions to better adapt to the potential effects of climate change and to better guide policies for the conservation and development of forest resources.

Climate and soil effects on tree species diversity and aboveground carbon patterns in semi-arid tree savannas.

Climatic and edaphic effects are increasingly being discussed in the context of biodiversity-ecosystem functioning. Here we use data from West African semi-arid tree savannas and contrasting climatic conditions (lower vs. higher mean annual precipitation-MAP and mean annual temperature-MAT) to (1) determine how climate modulates the effects of species richness on aboveground carbon (AGC); (2) explore how species richness and AGC relate with soil variables in these contrasting climatic conditions; and (3) assess how climate and soil influence directly, and/or indirectly AGC through species richness and stand structural attributes such as tree density and size variation. We find that greater species richness is generally associated with higher AGC, but more strongly in areas with higher MAP, which also have greater stem density. There is a climate-related influence of soils on AGC, which decreases from lower to higher MAP conditions. Variance partitioning analyses and structural equation modelling show that, across all sites, MAP, relative to soils, has smaller effect on AGC, mediated by stand structural attributes whereas soil texture and fertility explain 14% of variations in AGC and influence AGC directly and indirectly via species richness and stand structural attributes. Our results highlight coordinated effects of climate and soils on AGC, which operated primarily via the mediation role of species diversity and stand structures.

Assessing the Impact of Vaccination on the Dynamics of COVID-19 in Africa: A Mathematical Modeling Study.

Several effective COVID-19 vaccines are administered to combat the COVID-19 pandemic globally. In most African countries, there is a comparatively limited deployment of vaccination programs. In this work, we develop a mathematical compartmental model to assess the impact of vaccination programs on curtailing the burden of COVID-19 in eight African countries considering SARS-CoV-2 cumulative case data for each country for the third wave of the COVID-19 pandemic. The model stratifies the total population into two subgroups based on individual vaccination status. We use the detection and death rates ratios between vaccinated and unvaccinated individuals to quantify the vaccine's effectiveness in reducing new COVID-19 infections and death, respectively. Additionally, we perform a numerical sensitivity analysis to assess the combined impact of vaccination and reduction in the SARS-CoV-2 transmission due to control measures on the control reproduction number (Rc). Our results reveal that on average, at least 60% of the population in each considered African country should be vaccinated to curtail the pandemic (lower the Rc below one). Moreover, lower values of Rc are possible even when there is a low (10%) or moderate (30%) reduction in the SARS-CoV-2 transmission rate due to NPIs. Combining vaccination programs with various levels of reduction in the transmission rate due to NPI aids in curtailing the pandemic. Additionally, this study shows that vaccination significantly reduces the severity of the disease and death rates despite low efficacy against COVID-19 infections. The African governments need to design vaccination strategies that increase vaccine uptake, such as an incentive-based approach.

Lag-time effects of vaccination on SARS-CoV-2 dynamics in German hospitals and intensive-care units.

Background: The Efficacy and effectiveness of vaccination against SARS-CoV-2 have clearly been shown by randomized trials and observational studies. Despite these successes on the individual level, vaccination of the population is essential to relieving hospitals and intensive care units. In this context, understanding the effects of vaccination and its lag-time on the population-level dynamics becomes necessary to adapt the vaccination campaigns and prepare for future pandemics. Methods: This work applied a quasi-Poisson regression with a distributed lag linear model on German data from a scientific data platform to quantify the effects of vaccination and its lag times on the number of hospital and intensive care patients, adjusting for the influences of non-pharmaceutical interventions and their time trends. We separately evaluated the effects of the first, second and third doses administered in Germany. Results: The results revealed a decrease in the number of hospital and intensive care patients for high vaccine coverage. The vaccination provides a significant protective effect when at least approximately 40% of people are vaccinated, whatever the dose considered. We also found a time-delayed effect of the vaccination. Indeed, the effect on the number of hospital patients is immediate for the first and second doses while for the third dose about 15 days are necessary to have a strong protective effect. Concerning the effect on the number of intensive care patients, a significant protective response was obtained after a lag time of about 15-20 days for the three doses. However, complex time trends, e.g. due to new variants, which are independent of vaccination make the detection of these findings challenging. Conclusion: Our results provide additional information about the protective effects of vaccines against SARS-CoV-2; they are in line with previous findings and complement the individual-level evidence of clinical trials. Findings from this work could help public health authorities efficiently direct their actions against SARS-CoV-2 and be well-prepared for future pandemics.

Intensity and lag-time of non-pharmaceutical interventions on COVID-19 dynamics in German hospitals.

Introduction: Evaluating the potential effects of non-pharmaceutical interventions on COVID-19 dynamics is challenging and controversially discussed in the literature. The reasons are manifold, and some of them are as follows. First, interventions are strongly correlated, making a specific contribution difficult to disentangle; second, time trends (including SARS-CoV-2 variants, vaccination coverage and seasonality) influence the potential effects; third, interventions influence the different populations and dynamics with a time delay. Methods: In this article, we apply a distributed lag linear model on COVID-19 data from Germany from January 2020 to June 2022 to study intensity and lag time effects on the number of hospital patients and the number of prevalent intensive care patients diagnosed with polymerase chain reaction tests. We further discuss how the findings depend on the complexity of accounting for the seasonal trends. Results and discussion: Our findings show that the first reducing effect of non-pharmaceutical interventions on the number of prevalent intensive care patients before vaccination can be expected not before a time lag of 5 days; the main effect is after a time lag of 10-15 days. In general, we denote that the number of hospital and prevalent intensive care patients decrease with an increase in the overall non-pharmaceutical interventions intensity with a time lag of 9 and 10 days. Finally, we emphasize a clear interpretation of the findings noting that a causal conclusion is challenging due to the lack of a suitable experimental study design.

Modeling COVID-19 dynamics in the sixteen West African countries.

The COVID-19 pandemic is currently causing several damages to the world, especially in the public health sector. Due to identifiability problems in parameters' estimation of complex compartmental models, this study considered a simple deterministic susceptible-infectious-recovered (SIR)-type model to characterize the first wave and predict the future course of the pandemic in the West African countries. We estimated some specific characteristics of the disease's dynamics, such as its initial conditions, reproduction numbers, true peak and peak of the reported cases, with their corresponding times, final epidemic size and time-varying attack ratio. Our findings revealed a relatively low proportion of susceptible individuals in the region and the different countries ( 1.2 % across West Africa). The detection rate of the disease was also relatively low ( 0.9 % for West Africa as a whole) and < 2 % for most countries, except for Gambia (12.5 %), Cape-Verde ( 9.5 % ), Mauritania ( 5.9 % ) and Ghana ( 4.4 % ). The reproduction number varied between 1.15 (Burkina-Faso) and 4.45 (Niger), and most countries' peak time of the first wave of the pandemic was between June and July. Generally, the peak time of the reported cases came a week (7-8 days) after the true peak time. The model predicted for the first wave, 222,100 actual active cases in the region at the peak time, while the final epidemic size accounted for 0.6 % of the West African population (2,526,700 individuals). The results showed that COVID-19 has not severely affected West Africa as in other regions. However, current control measures and standard operating procedures should be maintained over time to accelerate a decline in the observed trends of the pandemic.

Bayesian spatial modelling of malaria burden in two contrasted eco-epidemiological facies in Benin (West Africa): call for localized interventions.

BACKGROUND: Despite a global decrease in malaria burden worldwide, malaria remains a major public health concern, especially in Benin children, the most vulnerable group. A better understanding of malaria's spatial and age-dependent characteristics can help provide durable disease control and elimination. This study aimed to analyze the spatial distribution of Plasmodium falciparum malaria infection and disease among children under five years of age in Benin, West Africa. METHODS: A cross-sectional epidemiological and clinical survey was conducted using parasitological examination and rapid diagnostic tests (RDT) in Benin. Interviews were done with 10,367 children from 72 villages across two health districts in Benin. The prevalence of infection and clinical cases was estimated according to age. A Bayesian spatial binomial model was used to estimate the prevalence of malaria infection, and clinical cases were adjusted for environmental and demographic covariates. It was implemented in R using Integrated Nested Laplace Approximations (INLA) and Stochastic Partial Differentiation Equations (SPDE) techniques. RESULTS: The prevalence of P. falciparum infection was moderate in the south (34.6%) of Benin and high in the northern region (77.5%). In the south, the prevalence of P. falciparum infection and clinical malaria cases were similar according to age. In northern Benin children under six months of age were less frequently infected than children aged 6-11, 12-23, 24-60 months, (p < 0.0001) and had the lowest risk of malaria cases compared to the other age groups (6-12), (13-23) and (24-60): OR = 3.66 [2.21-6.05], OR = 3.66 [2.21-6.04], and OR = 2.83 [1.77-4.54] respectively (p < 0.0001). Spatial model prediction showed more heterogeneity in the south than in the north but a higher risk of malaria infection and clinical cases in the north than in the south. CONCLUSION: Integrated and periodic risk mapping of Plasmodium falciparum infection and clinical cases will make interventions more evidence-based by showing progress or a lack in malaria control.

Household access to basic drinking water, sanitation and hygiene facilities: secondary analysis of data from the demographic and health survey V, 2017-2018.

BACKGROUND: In Benin, access to water, sanitation and hygiene (WASH) remains an issue. This study aims to provide an overview of household access to basic WASH services based on nationally representative data. METHOD: Secondary analyses were run using the 'HOUSEHOLD' dataset of the fifth Demographic and Health Survey 2017-2018. The dependent variables were household access to individual and combined basic WASH services. The characteristics of the household head and those related to the composition, wealth and environment of the household were independent variables. After a descriptive analysis of all study variables, multivariate logistic regression was performed to identify predictors of outcome variables. RESULTS: The study included 14,156 households. Of these, 63.98% (95% CI = 61.63-66.26), 13.28% (95% CI = 12.10-14.57) and 10.11% (95% CI = 9.19-11.11) had access to individual basic water, sanitation and hygiene facilities, respectively. Also, 3% (95% CI = 2.53-3.56) of households had access to combined basic WASH services. Overall, the richest households and few, and those headed by people aged 30 and over, female and with higher levels of education, were the most likely to have access to individual and combined basic WASH services. In addition, disparities based on the department of residence were observed. CONCLUSION: The authors suggest a multifactorial approach that addresses the identified determinants.

Food and medicinal uses of Annona senegalensis Pers.: a country-wide assessment of traditional theoretical knowledge and actual uses in Benin, West Africa.

BACKGROUND: The growing interest for more natural products in food and health industries has led to increasing research on traditional knowledge related to plants. While theoretical knowledge (TK) on the uses of a species informs on the wide spectrum of potential uses of that species, actual uses (AU) highlight their potential being actually used. Distinguishing between the two is important when reporting ethnobotanical studies. However, studies often equated AU and TK, sometimes misleading conclusions, and decision-making. This study assessed TK, AU, and difference between TK and AU of Annona senegalensis and how each is related to factors such as age, sex, sociolinguistic group, and main activity in Benin republic. METHODS: Data were collected through semi-structured individual interviews (n = 755) and analyzed using among others, relative frequency of citation (RFC), and use-value (UV). RESULTS: A total of 168 theoretical uses were recorded but only 92 were "actually" practiced, of which four were food and 88 medicinal uses. TK and AU were positively correlated. As expected, TK was also significantly higher than AU, indicating that some potential uses of the species are still not valued. Sociolinguistic group and main activity, not age and sex, were the main factors influencing TK, AU, and difference between TK and AU. The highest TK was found with Bariba sociolinguistic group and the highest AU with Otamari. Fruits (100%) and flowers (10%) were the most used organs for food, while leaves (40%) and roots (7%) were mostly used for medicinal purposes. The most common food uses were consumption of the ripe fruits (100%), and food seasoning with flowers (10%). The most cited diseases were malaria (28%) and intestinal worms (8%). CONCLUSIONS: The study illustrated the importance of differentiating between TK and AU. It documented  the wide range of the uses of A. senegalensis, while highlighting its most common uses, and the need to better valorize and sustainably manage the species.

Predicting the potential impacts of climate change on the endangered Caesalpinia bonduc (L.) Roxb in Benin (West Africa).

Caesalpinia bonduc (L.) Roxb is a medicinal plant with high therapeutic values but declared extinct in the wild in Benin. This study explored the potential distribution and climatic suitability of the species under the present-day and future conditions in Benin, based on two Representative Concentration Pathways (RCP4.5 and RCP8.5) at the 2055-time horizon. The occurrence data were recorded in the distribution area of the species in Benin. These data were supplemented with those from the Global Biodiversity Information Facility (GBIF, www.gbif.org) website and the literature. A total of 23 environmental variables (15 bioclimatic data and 8 biophysical data) were used. The Bioclimatic variables for temperature and humidity were downloaded from Africlim site at 1 km resolution. The biophysical variables concern population, elevation, slope, landcover, wetland, distance to river, soil and distance to dwellings data that are downloaded respectively from DIVA-GIS, ISRIC and SEDAC website at different resolution. A correlation test has been applied to eliminate the highly correlated variables (r ≥ 0.9) using Pearson correlation coefficient. Species distribution modelling data were processed using five algorithms namely Random Forest (RF), Boosted Regression Trees (BRT), Maximum entropy (MAXENT), Generalized Linear Models (GLM) and Generalized Additive Models (GAM). The results showed that all models performed well with the area under the curve (AUC) values greater than 0.9. The RF, GLM, and GAM models predicted an increase in the suitable areas for the cultivation of the species. BRT and MaxEnt showed a substantial decrease in the suitable areas based on the two scenarios but this reduction is more observed with the MaxEnt model. These results show that climate change and human pressures will have significant effects on the distribution of C. bonduc throughout Benin. Sustainable management measures are necessary for C. bonduc and should be integrated in development policies to preserve the population of the species from total extinction in Benin.

Effects of seed provenance, pre-treatment and mass on germinability and seedling growth of Balanites aegyptiaca (L.) Delile and Ricinodendron heudelotii (Bail.) Pierre in Benin (West Africa).

Balanites aegyptiaca (L.) Delile and Ricinodendron heudelotii (Bail.) Pierre are socioeconomically important species in sub-Saharan Africa. This study was conducted to assess the seed germinability and seedling growth of those species based on several treatments and to define proper conservation and domestication strategies in Benin. The seeds were randomly collected in their natural habitats. The experiment was conducted using a split-split plot design and the data was analyzed using the generalized linear mixed and survival models. The heaviest seeds (B. aegyptiaca seed mass ≥3 g and R. heudelotii ≥ 1.50 g) provided the highest germination rates (73.60 ± 5.19% and 62.50 ± 5.71%, respectively) when seeds were scarified with a hammer. For B. aegyptiaca seedlings, the seeds from the phytodistrict of North Borgou scarified with a hammer and the heaviest seeds showed the highest total height (36.43 ± 1.03 cm), basal diameter (2.84 ± 0.03 mm), the greatest number of leaves (32), and ramifications. The heaviest seeds of R. heudelotii had also the highest value for total height at the day-28 after sowing (26.73 ± 13.56 cm) until the day-105 (151.97 ± 6.37 cm). The heaviest seeds of R. heudelotii from the phytodistrict of Pobe showed the highest basal diameter (12.53 ± 1.47 mm) and the greatest number of leaves (14), with almost no ramification during the trial period. These findings constitute a step forward in upscaling the reproduction of these species for better contribution to economies while serving in restoration plans.

Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm.

Binary Generalized Linear Mixed Model (GLMM) is the most common method used by researchers to analyze clustered binary data in biological and social sciences. The traditional approach to GLMMs causes substantial bias in estimates due to steady shape of logistic and normal distribution assumptions thereby resulting into wrong and misleading decisions. This study brings forward an approach governed by skew generalized t distributions that belong to a class of potentially skewed and heavy tailed distributions. Interestingly, both the traditional logistic and probit mixed models, as well as other available methods can be utilized within the skew generalized t-link model (SGTLM) frame. We have taken advantage of the Expectation-Maximization algorithm accelerated via parameter-expansion for model fitting. We evaluated the performance of this approach to GLMMs through a simulation experiment by varying sample size and data distribution. Our findings indicated that the proposed methodology outperforms competing approaches in estimating population parameters and predicting random effects, when the traditional link and normality assumptions are violated. In addition, empirical standard errors and information criteria proved useful for detecting spurious skewness and avoiding complex models for probit data. An application with respiratory infection data points out to the superiority of the SGTLM which turns to be the most adequate model. In future, studies should focus on integrating the demonstrated flexibility in other generalized linear mixed models to enhance robust modeling.

A Hybrid Modeling Technique of Epidemic Outbreaks with Application to COVID-19 Dynamics in West Africa.

The widely used logistic model for epidemic case reporting data may be either restrictive or unrealistic in presence of containment measures when implemented after an epidemic outbreak. For flexibility in epidemic case reporting data modeling, we combined an exponential growth curve for the early epidemic phase with a flexible growth curve to account for the potential change in growth pattern after implementation of containment measures. We also fitted logistic regression models to recoveries and deaths from the confirmed positive cases. In addition, the growth curves were integrated into a SIQR (Susceptible, Infective, Quarantined, Recovered) model framework to provide an overview on the modeled epidemic wave. We focused on the estimation of: (1) the delay between the appearance of the first infectious case in the population and the outbreak ("epidemic latency period"); (2) the duration of the exponential growth phase; (3) the basic and the time-varying reproduction numbers; and (4) the peaks (time and size) in confirmed positive cases, active cases and new infections. The application of this approach to COVID-19 data from West Africa allowed discussion on the effectiveness of some containment measures implemented across the region.

On the reliability of predictions on Covid-19 dynamics: A systematic and critical review of modelling techniques.

Since the emergence of the novel 2019 coronavirus pandemic in December 2019 (COVID-19), numerous modellers have used diverse techniques to assess the dynamics of transmission of the disease, predict its future course and determine the impact of different control measures. In this study, we conducted a global systematic literature review to summarize trends in the modelling techniques used for Covid-19 from January 1st, 2020 to November 30th, 2020. We further examined the accuracy and precision of predictions by comparing predicted and observed values for cumulative cases and deaths as well as uncertainties of these predictions. From an initial 4311 peer-reviewed articles and preprints found with our defined keywords, 242 were fully analysed. Most studies were done on Asian (78.93%) and European (59.09%) countries. Most of them used compartmental models (namely SIR and SEIR) (46.1%) and statistical models (growth models and time series) (31.8%) while few used artificial intelligence (6.7%), Bayesian approach (4.7%), Network models (2.3%) and Agent-based models (1.3%). For the number of cumulative cases, the ratio of the predicted over the observed values and the ratio of the amplitude of confidence interval (CI) or credibility interval (CrI) of predictions and the central value were on average larger than 1 indicating cases of inaccurate and imprecise predictions, and large variation across predictions. There was no clear difference among models used for these two ratios. In 75% of predictions that provided CI or CrI, observed values fall within the 95% CI or CrI of the cumulative cases predicted. Only 3.7% of the studies predicted the cumulative number of deaths. For 70% of the predictions, the ratio of predicted over observed cumulative deaths was less or close to 1. Also, the Bayesian model made predictions closer to reality than classical statistical models, although these differences are only suggestive due to the small number of predictions within our dataset (9 in total). In addition, we found a significant negative correlation (rho = - 0.56, p = 0.021) between this ratio and the length (in days) of the period covered by the modelling, suggesting that the longer the period covered by the model the likely more accurate the estimates tend to be. Our findings suggest that while predictions made by the different models are useful to understand the pandemic course and guide policy-making, some were relatively accurate and precise while other not.

Traditional medicinal knowledge of woody species across climatic zones in Benin (West Africa).

ETHNOPHARMACOLOGICAL RELEVANCE: Plant parts are often used by local people to treat their affections. This study addressed the Traditional Medicinal Knowledge of woody species in Benin and the dependence of medicinal use of woody species on climatic zones. AIM OF THE STUDY: It reports (i) the main diseases categories treated with medicinal use of woody species in Benin and changes across climatic zones by inferring their epidemiological status, and (ii) the woody species involved and their distribution according to climate conditions. MATERIALS AND METHODS: Ethnobotanical interviews were undertaken using a semi-structured questionnaire. Five hundred and ninety medicinal plant professionals (healers, traders …) were interviewed in the whole country. Frequency of citation and informant consensus factor were calculated to highlight the main human diseases categories and woody species used for their treatment. A principal component analysis was performed to determine the occurrence of diseases categories in different climatic zones. RESULTS: About 94% of international diseases categories were treated using medicinal woody species in Benin. Nighty-seven ailments in 16 diseases categories were identified. Among them, 5 diseases categories (General and unspecified, Digestive, Skin, Neurological, and Musculoskeletal) were highlighted as important. The Sudano-Guinean zone showed the highest diseases frequencies, whereas the Sudanian zone showed the lowest. The epidemiological status of some phytodistricts was worrisome. In our study, 123 woody species belonging to 93 genera and 35 families were reported, and among them, 16 were the most used as treatments. CONCLUSIONS: There is a lack of consensus among traditional healers about which woody species to use. Many different species were used to treat a given diseases category. Also, information concerning their organ composition was not available in the literature, for the majority of species. Biological and chemical investigations are thus needed for a better valorization of the most frequently used plants in the future.

On the use of growth models to understand epidemic outbreaks with application to COVID-19 data.

The initial phase dynamics of an epidemic without containment measures is commonly well modelled using exponential growth models. However, in the presence of containment measures, the exponential model becomes less appropriate. Under the implementation of an isolation measure for detected infectives, we propose to model epidemic dynamics by fitting a flexible growth model curve to reported positive cases, and to infer the overall epidemic dynamics by introducing information on the detection/testing effort and recovery and death rates. The resulting modelling approach is close to the Susceptible-Infectious-Quarantined-Recovered model framework. We focused on predicting the peaks (time and size) in positive cases, active cases and new infections. We applied the approach to data from the COVID-19 outbreak in Italy. Fits on limited data before the observed peaks illustrate the ability of the flexible growth model to approach the estimates from the whole data.

Revisiting biotic and abiotic drivers of seedling establishment, natural enemies and survival in a tropical tree species in a West Africa semi-arid biosphere reserve.

Biotic and abiotic drivers of seedling establishment and survival are fundamental not only for elucidating processes occurring at plant early life stages, but also for assisting species natural regeneration. Keystone, multipurpose and economically important tree species such as Afzelia africana Sm. are reportedly facing recruitment constraints, yet little is known about how abiotic and biotic factors shape the species seedling dynamics. Here, we monitored the species seedlings over one year across three seasons in West Africa savannahs to determine how conspecific and heterospecific biotic neighborhood and habitat heterogeneity correlate with initial seedling density, leaves' fungal infection and herbivory and how all these factors combined, influence the species seedling survival. Seedling densities increased with increasing conspecific adult densities, and were highest in tree savannahs and on sandy-silt soils. Leaves' fungal infection and herbivory were also positively associated with conspecific adult density, but were more abundantly observed in tree savannahs than in shrub savannahs. Seedling survival was constrained on higher slope, and negatively affected by conspecific adult density, especially in shrub savannahs. There was a strong evidence for negative density-dependence effects of conspecific adults on seedling survival, which operated through negative effects of herbivory and fungal infection. Habitat heterogeneity was also an important driver, which modulated biotic factors' effects on seedling survival: tree savannahs promote positive conspecific density-dependence of seedling fungal infection and herbivory more than shrub savannahs. Nonetheless, seedlings were more sensitive to natural enemies in shrub savannahs, suggesting increased negative conspecific density-dependence effects on seedling survival in less dense vegetation, possibly as a result of enhanced specialization of predators and pathogens on a limited set of species. The study brings important insights into the mechanisms that drive the establishment and survival of the species seedling, which should be considered in the design of management activities aiming at the conservation of this endangered species.

Phylogenomic approaches reveal how climate shapes patterns of genetic diversity in an African rain forest tree species.

The world's second largest expanse of tropical rainforest is in Central Africa, and it harbours enormous species diversity. Population genetic studies have consistently revealed significant structure across Central African rainforest plants. In particular, previous studies have repeatedly demonstrated a north-south genetic discontinuity around the equatorial line, in a continuous expanse of rainforest where a climatic inversion is documented. Here, we took a phylogeographic approach by sequencing 351 nuclear markers in 112 individuals across the distribution of the African rainforest tree species Annickia affinis (Annonaceae). We showed for the first time that the north-south divide is the result of a single, major colonization event across the climatic inversion from an ancestral population located in Gabon. We suggested that differences in ecological niche of populations located on either side of this inversion may have contributed to this phylogenetic discontinuity. We found evidence for inland dispersal, predominantly in northern areas, and variable demographic histories among genetic clusters, indicating that populations responded differently to past climate change. We show how newly developed genomic tools can provide invaluable insights into our understanding of tropical rainforest evolutionary dynamics.