Correlates of bushmeat in markets and depletion of wildlife.

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.

Metapopulation Patterns of Iberian Butterflies Revealed by Fuzzy Logic.

Metapopulation theory considers that the populations of many species are fragmented into patches connected by the migration of individuals through an interterritorial matrix. We applied fuzzy set theory and environmental favorability (F) functions to reveal the metapopulational structure of the 222 butterfly species in the Iberian Peninsula. We used the sets of contiguous grid cells with high favorability (F ≥ 0.8), to identify the favorable patches for each species. We superimposed the known occurrence data to reveal the occupied and empty favorable patches, as unoccupied patches are functional in a metapopulation dynamics analysis. We analyzed the connectivity between patches of each metapopulation by focusing on the territory of intermediate and low favorability for the species (F < 0.8). The friction that each cell opposes to the passage of individuals was computed as 1-F. We used the r.cost function of QGIS to calculate the cost of reaching each cell from a favorable patch. The inverse of the cost was computed as connectivity. Only 126 species can be considered to have a metapopulation structure. These metapopulation structures are part of the dark biodiversity of butterflies because their identification is not evident from the observation of the occurrence data but was revealed using favorability functions.

Applying fuzzy logic to assess the biogeographical risk of dengue in South America.

BACKGROUND: Over the last decade, reports about dengue cases have increase worldwide, which is particularly worrisome in South America due to the historic record of dengue outbreaks from the seventeenth century until the first half of the twentieth century. Dengue is a viral disease that involves insect vectors, namely Aedes aegypti and Ae. albopictus, which implies that, to prevent and combat outbreaks, it is necessary to understand the set of ecological and biogeographical factors affecting both the vector species and the virus. METHODS: We contribute with a methodology based on fuzzy logic that is helpful to disentangle the main factors that determine favorable environmental conditions for vectors and diseases. Using favorability functions as fuzzy logic modelling technique and the fuzzy intersection, union and inclusion as fuzzy operators, we were able to specify the territories at biogeographical risk of dengue outbreaks in South America. RESULTS: Our results indicate that the distribution of Ae. aegypti mostly encompasses the biogeographical framework of dengue in South America, which suggests that this species is the principal vector responsible for the geographical extent of dengue cases in the continent. Nevertheless, the intersection between the favorability for dengue cases and the union of the favorability for any of the vector species provided a comprehensive map of the biogeographical risk for dengue. CONCLUSIONS: Fuzzy logic is an appropriate conceptual and operational tool to tackle the nuances of the vector-illness biogeographical interaction. The application of fuzzy logic may be useful in decision-making by the public health authorities to prevent, control and mitigate vector-borne diseases.