Gastrointestinal symbionts of chimpanzees in Cantanhez National Park, Guinea-Bissau with respect to habitat fragmentation

One of the major factors threatening chimpanzees (Pan troglodytes verus) in Guinea-Bissau is habitat fragmentation. Such fragmentation may cause changes in symbiont dynamics resulting in increased susceptibility to infection, changes in host specificity and virulence. We monitored gastrointestinal symbiotic fauna of three chimpanzee subpopulations living within Cantanhez National Park (CNP) in Guinea Bissau in the areas with different levels of anthropogenic fragmentation. Using standard coproscopical methods (merthiolate-iodine formalin concentration and Sheather's flotation) we examined 102 fecal samples and identified at least 13 different symbiotic genera (Troglodytella abrassarti, Troglocorys cava, Blastocystis spp., Entamoeba spp., Iodamoeba butschlii, Giardia intestinalis, Chilomastix mesnili, Bertiella sp., Probstmayria gombensis, unidentified strongylids, Strongyloides stercoralis, Strongyloides fuelleborni, and Trichuris sp.). The symbiotic fauna of the CNP chimpanzees is comparable to that reported for other wild chimpanzee populations, although CNP chimpanzees have a higher prevalence of Trichuris sp. Symbiont richness was higher in chimpanzee subpopulations living in fragmented forests compared to the community inhabiting continuous forest area. We reported significantly higher prevalence of G. intestinalis in chimpanzees from fragmented areas, which could be attributed to increased contact with humans and livestock.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.