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We used data on number of carcasses of wildlife species sold in 79 bushmeat markets in a region of Nigeria and Cameroon to assess whether species composition of a market could be explained by anthropogenic pressures and environmental variables around each market. More than 45 mammal species from 9 orders were traded across all markets; mostly ungulates and rodents. For each market, we determined median body mass, species diversity (game diversity), and taxa that were principal contributors to the total number of carcasses for sale (game dominance). Human population density in surrounding areas was significantly and negatively related to the percentage ungulates and primates sold in markets and significantly and positively related to the proportion of rodents. The proportion of carnivores sold was higher in markets with high human population densities. Proportion of small-bodied mammals (<1 kg) sold in markets increased as human population density increased, but proportion of large-bodied mammals (>10 kg) decreased as human population density increased. We calculated an index of game depletion (GDI) for each market from the sum of the total number of carcasses traded per annum and species, weighted by the intrinsic rate of natural increase (rmax ) of each species, divided by individuals traded in a market. The GDI of a market increased as the proportion of fast-reproducing species (highest rmax ) increased and as the representation of species with lowest rmax (slow-reproducing) decreased. The best explanatory factor for a market's GDI was anthropogenic pressure-road density, human settlements with >3000 inhabitants, and nonforest vegetation. High and low GDI were significantly differentiated by human density and human settlements with >3000 inhabitants. Our results provided empirical evidence that human activity is correlated with more depleted bushmeat faunas and can be used as a proxy to determine areas in need of conservation action., (© 2015 Society for Conservation Biology.)

We analysed the main geographical trends of terrestrial mammal species richness (SR) in Argentina, assessing how broad-scale environmental variation (defined by climatic and topographic variables) and the spatial form of the country (defined by spatial filters based on spatial eigenvector mapping (SEVM)) influence the kinds and the numbers of mammal species along these geographical trends. We also evaluated if there are pure geographical trends not accounted for by the environmental or spatial factors. The environmental variables and spatial filters that simultaneously correlated with the geographical variables and SR were considered potential causes of the geographic trends. We performed partial correlations between SR and the geographical variables, maintaining the selected explanatory variables statistically constant, to determine if SR was fully explained by them or if a significant residual geographic pattern remained. All groups and subgroups presented a latitudinal gradient not attributable to the spatial form of the country. Most of these trends were not explained by climate. We used a variation partitioning procedure to quantify the pure geographic trend (PGT) that remained unaccounted for. The PGT was larger for latitudinal than for longitudinal gradients. This suggests that historical or purely geographical causes may also be relevant drivers of these geographical gradients in mammal diversity.

West Nile virus (WNV) is an emergent arthropodborne virus that is transmitted from bird to bird by mosquitoes. Spillover events occur when infected mosquitoes bite mammals. We created a geopositioned database of WNV presence in Africa and considered reports of the virus in all animal components: reservoirs, vectors, and nonhuman dead-end hosts. We built various biogeographic models to determine which drivers explain the distribution of WNV throughout Africa. Wetlands of international importance for birds accounted for the detection of WNV in all animal components, whereas human-related drivers played a key role in the epizootic cases. We combined these models to obtain an integrative and large-scale perspective of the areas at risk for WNV spillover. Understanding which areas pose the highest risk would enable us to address the management of this spreading disease and to comprehend the translocation of WNV outside Africa through avian migration routes. [ABSTRACT FROM AUTHOR]

Observed species richness (OSR) is a widely used and well-studied biodiversity metric. However, non-observed species in favorable ecosystems are also relevant. Two metrics that include observed and potential species were recently defined: potential biodiversity (hereafter potential species richness—PSR) and geometric mean of favorabilities (GMF). We used these metrics to evaluate the national park network of mainland Spain at two time periods (2002 and 2015), using terrestrial mammals on a UTM 100-km2 grid. PSR and GMF are based on the favorability function, a species distribution model that assesses how favorable an area is for the presence of a species, over and above its prevalence in the study area. For each park and for the whole network, we calculated the mean and sum of OSR, PSR, and GMF in each time period, as well as changes between periods. OSR and PSR were higher inside than outside the park network in both time periods. Thus, although the network covers a very small proportion of the country, it performs well for the representation of mammal species and their favorable areas. However, mean PSR was lower in 2015 than in 2002 inside the national park network, whereas the opposite was the case outside the network. Mountainous Parks generally not only concentrated highly favorable areas for mammals, but they also showed less favorable areas in 2015 compared to 2002, although the reduction was moderate to low. This is a result to consider for future analyses because if the tendency increases, it may have consequences for the conservation of mammals and for the adequacy of the national park network. [ABSTRACT FROM AUTHOR]

Ebola virus is responsible for the fatal Ebola virus disease ( EVD)., Identifying the distribution area of the Ebola virus is crucial for understanding the risk factors conditioning the emergence of new EVD cases. Existing distribution models have underrepresented the potential contribution that reservoir species and vulnerable species make in sustaining the presence of the virus., In this paper, we map favourable areas for Ebola virus in Africa according to environmental and zoogeographical descriptors, independent of human-to-human transmissions. We combine two different biogeographical approaches: analysis of mammalian distribution types (chorotypes), and distribution modelling of the Ebola virus., We first obtain a model defining the distribution of environmentally favourable areas for the presence of Ebola virus. Based on a review of mammal taxa affected by or suspected of exposure to the Ebola virus, we model favourable areas again, this time according to mammalian chorotypes. We then build a combined model in which both the environment and mammalian distributions explain the favourable areas for Ebola virus in the wild., We demonstrate that mammalian biogeography contributes to explaining the distribution of Ebola virus in Africa, although vegetation may also underscore clear limits to the presence of the virus. Our model suggests that the Ebola virus may be even more widespread than previously suspected, given that additional favourable areas are found throughout the coastal areas of West and Central Africa, stretching from Cameroon to Guinea, and extend further East into the East African Lakes region., Our findings show that the most favourable area for the Ebola virus is significantly associated with the presence of the virus in non-human mammals. Core areas are surrounded by regions of intermediate favourability, in which human infections of unknown source were found. The difference in association between humans and other mammals and the virus may offer further insights on how EVD can spread. [ABSTRACT FROM AUTHOR]

The article discusses the protected African rainforest mammals and climate change.