Dengue is a viral disease transmitted by mosquitoes. The rapid spread of dengue could lead to a global pandemic, and so the geographical extent of this spread needs to be assessed and predicted. There are also reasons to suggest that transmission of dengue from non-human primates in tropical forest cycles is being underestimated. We investigate the fine-scale geographic changes in transmission risk since the late 20th century, and take into account for the first time the potential role that primate biogeography and sylvatic vectors play in increasing the disease transmission risk. We apply a biogeographic framework to the most recent global dataset of dengue cases. Temporally stratified models describing favorable areas for vector presence and for disease transmission are combined. Our models were validated for predictive capacity, and point to a significant broadening of vector presence in tropical and non-tropical areas globally. We show that dengue transmission is likely to spread to affected areas in China, Papua New Guinea, Australia, USA, Colombia, Venezuela, Madagascar, as well as to cities in Europe and Japan. These models also suggest that dengue transmission is likely to spread to regions where there are presently no or very few reports of occurrence. According to our results, sylvatic dengue cycles account for a small percentage of the global extent of the human case record, but could be increasing in relevance in Asia, Africa, and South America. The spatial distribution of factors favoring transmission risk in different regions of the world allows for distinct management strategies to be prepared.

Yellow fever is transmitted by mosquitoes among human and non-human primates. In the last decades, infections are occurring in areas that had been free from yellow fever for decades, probably as a consequence of the rapid spread of mosquito vectors, and of the virus evolutionary dynamic in which non-human primates are involved. This research is a pathogeographic assessment of where enzootic cycles, based on primate assemblages, could be amplifying the risk of yellow fever infections, in the context of spatial changes shown by the disease since the late 20th century. In South America, the most relevant spread of disease cases affects parts of the Amazon basin and a wide area of southern Brazil, where forest fragmentation could be activating enzootic cycles next to urban areas. In Africa, yellow fever transmission is apparently spreading from the west of the continent, and primates could be contributing to this in savannas around rainforests. Our results are useful for identifying new areas that should be prioritised for vaccination, and suggest the need of deep yellow fever surveillance in primates of South America and Africa.

Aim: The boundaries of species distributions are often shaped by natural barriers, such as mountains and rivers, but species distribution models usually fail to include these constraints. We tested several approaches that include barriers as explanatory variables in species distribution models.Location: Africa and South America. Time period: Current.Major taxa studied: Primates. Methods:We modelled the ranges of pairs of species separated by a river, taking into account three explanatory components: the environment (ecosystems, topohydrography, climate and human pressure), the spatial structure shaped by history and population dynamics (using a trend-surface approach) and rivers as naturals barriers to dispersal (using a binary cis-trans variable that describes both sides of the river). To assess how the addition of a spatial structure and the barrier could improve distribution models, we used a nested approach by comparing models based on: (a) the environment; (b) the environment and the spatial structure; and (c) the environment, the spatial structure and the river. These models were constructed using favourability functions.Results: There was a decreased occurrence of high-favourability values on the opposite side of the rivers in models that included the spatial structure of distributions compared with models based on the environment alone. This decrease was more marked when the description of the spatial structure was made more flexible.However, model performance was significantly improved by the inclusion of cis-trans variables that identified areas on the opposite side as totally unfavourable. Main conclusions:The performance of distribution models can be improved by the use of approaches that describe barriers. Although adding the location of geographical units in relationship to a river appears to be the most accurate way to define the presence of a barrier, defining this variable may be challenging. A suitable alternative is to analyse the spatial structure of distributions using a flexible approach. K E Y W O R D S

Spatial modelling for predicting potential wildlife distributions and human impacts in the Dja Forest Reserve, Cameroon. Biological Conservation, 230 (230). pp. 104-112.