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The active field of connectivity conservation has provided numerous methods to identify wildlife corridors with the aim of reducing the ecological effect of fragmentation. Nevertheless, these methods often rely on untested hypotheses of animal movements, usually fail to generate fine-scale predictions of road crossing sites, and do not allow managers to prioritize crossing sites for implementing road fragmentation mitigation measures. We propose a new method that addresses these limitations. We illustrate this method with data from southwestern Gabon (central Africa). We used stratified random transect surveys conducted in two seasons to model the distribution of African forest elephant (Loxodonta cyclotis), forest buffalo (Syncerus caffer nanus), and sitatunga (Tragelaphus spekii) in a mosaic landscape along a 38.5 km unpaved road scheduled for paving. Using a validation data set of recorded crossing locations, we evaluated the performance of three types of models (local suitability, local least-cost movement, and regional least-cost movement) in predicting actual road crossings for each species, and developed a unique and flexible scoring method for prioritizing road sections for the implementation of road fragmentation mitigation measures. With a data set collected in <10 weeks of fieldwork, the method was able to identify seasonal changes in animal movements for buffalo and sitatunga that shift from a local exploitation of the site in the wet season to movements through the study site in the dry season, whereas elephants use the entire study area in both seasons. These three species highlighted the need to use species- and season-specific modeling of movement. From these movement models, the method ranked road sections for their suitability for implementing fragmentation mitigation efforts, allowing managers to adjust priority thresholds based on budgets and management goals. The method relies on data that can be obtained in a period compatible with environmental impact assessment constraints, and is flexible enough to incorporate other potential movement models and scoring criteria. This approach improves upon available methods and can help inform prioritization of road and other linear infrastructure segments that require impact mitigation methods to ensure long-term landscape connectivity. [ABSTRACT FROM AUTHOR]

Abstract Background Hepatitis E virus (HEV), an enterically transmitted pathogen, is highly endemic in several African countries. Pregnant women are at particularly high risk for acute or severe hepatitis E. In Gabon, a central African country, the prevalence of antibodies to HEV among pregnant women is 14.1%. Recent studies have demonstrated unusual patterns of hepatitis E (chronic hepatitis, cirrhosis) among immunodeficient patients. Findings We investigated the prevalence of antibodies to HEV among pregnant women infected with HIV-1 or HTLV-1 in Gabon. Of 243 samples collected, 183 were positive for HIV-1 and 60 for HTLV-1; 16 women (6.6%) had IgG antibodies to HEV. The seroprevalence was higher among HIV-1-infected women (7.1%) than HTLV-1-infected women (5.0%). Moreover, the HIV-1 viral load was significantly increased (p ≤ 0.02) among women with past-HEV exposure (1.3E+05 vs 5.7E+04 copies per ml), whereas no difference was found in HTLV-1 proviral load (9.0E+01 vs 1.1E+03 copies per ml). Conclusions These data provide evidence that HIV-1-infected women are at risk for acute or severe infection if they are exposed to HEV during pregnancy, with an increased viral load.