L’objectif  de  cette  étude  est  d’évaluer au Bénin l’efficacité du réseau des aires protégées dans la conservation des habi- tats favorables et prioritaires de certaines espèces ligneuses d’importance socio-éco- nomique. Il s’agit de Afzelia africana, Anogeissus leiocarpa, Burkea africana, Daniellia oliveri, Detarium microcarpum, Prosopis africana et Khaya senegalensis. Les techniques basées sur le principe d’en- tropie maximale (Maxent) combinées avec les SIG ont été utilisées pour projeter les habitats favorables de ces sept espèces ; le logiciel Zonation a été utilisé pour la modé- lisation des habitats prioritaires. Les points de présence des espèces ont été collectés et associés aux variables bioclimatiques dérivées de la température mensuelle et de la pluviométrie obtenues à partir de la base de données en ligne de AfriClim, ainsi qu’à la variable édaphique (sol). En terme de déterminisme environnemental, les variables bioclimatiques telles que l’écart diurne moyen de la température  (Bio2), les précipitations annuelles moyennes (Bio12), l’évapotranspiration potentielle (ETP) et la variable biophysique sol, sont prédictives pour les distributions des sept espèces. Les habitats protégés plus favo- rables aux sept essences dans la zone gui- néenne commencent aux limites de la forêt classée de Kétou (7°43’N), dans la zone soudano-guinéenne, à partir de la lati- tude de la forêt classée d’Agoua (8°30’N), et dans la zone  soudanienne  à partir  de la latitude de la Pendjari (10°35’N).  Pour  la conservation des habitats prioritaires, l’étude de représentation révèle que, dans les zones soudaniennes (9°75’-12°27’N), guinéenne (6°50’-7°40’N) et soudano-gui- néenne, les aires protégées sont respec- tivement efficaces, peu efficaces et non efficaces.

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 agedependent 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.

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Species distribution models have become tools of great importance in ecology since the advanced knowledge of suitable habitat of species is needed in the process of the world's biodiversity conservation. Models that use presence-only data are of great interests and are widely used in ecology due to their easy access. However, these models do not estimate accurately the true spatial species distribution based solely on presence-only data since they do not account for biases induced by the sampling techniques used and imperfect detection. To address this gap, Hierarchical integrated models have been recently introduced. Through this study, we assessed the relative performance of these new SDMs models using simulated data. The performance of the models was tested by comparing the estimates of parameters of the distribution models they provide with parameters used to simulate the distribution of the virtual species. The best model was the one whose estimates were close to the true distribution parameters of the virtual species. Results showed that analyzing Presence-only data in conjunction with Point-counts data through the Dorazio's Hierarchical model produced estimates of the coefficients of the species intensity models with high precision and less bias while the Koshkina integrated model showed poor performance. Site-occupancy data, being not informative of species abundance, did not allow reducing biases in Presence-only data. The Dorazio's Hierarchical model produced estimates with high precision even with low detection probability. We have also found that the species rarity tends to inflate the variability of the models' estimates making modelling abundant species to be more accurate than modelling less abundant species. Hence, to model the species distribution with high precision based on Presence-only data, additional Point-counts data are required to account for sampling bias and imperfect detection.

Species distribution models have become tools of great importance in ecology since the advanced knowledge of suitable habitat of species is needed in the process of the world's biodiversity conservation. Models that use presence-only data are of great interests and are widely used in ecology due to their easy access. However, these models do not estimate accurately the true spatial species distribution based solely on presence-only data since they do not account for biases induced by the sampling techniques used and imperfect detection. To address this gap, Hierarchical integrated models have been recently introduced. Through this study, we assessed the relative performance of these new SDMs models using simulated data. The performance of the models was tested by comparing the estimates of parameters of the distribution models they provide with parameters used to simulate the distribution of the virtual species. The best model was the one whose estimates were close to the true distribution parameters of the virtual species. Results showed that analyzing Presence-only data in conjunction with Point-counts data through the Dorazio's Hierarchical model produced estimates of the coecients of the species intensity models with high precision and less bias while the Koshkina integrated model showed poor performance. Site-occupancy data, being not informative of species abundance, did not allow reducing biases in Presence-only data. The Dorazio's Hierarchical model produced estimates with high precision even with low detection probability. We have also found that the species rarity tends to in ate the variability of the models' estimates making modelling abundant species to be more accurate than modelling less abundant species. Hence, to model the species distribution with high precision based on Presence-only data, additional Point-counts data are required to account for sampling bias and imperfect detection.