The Seasonal Distribution of Wild Camels (Camelus ferus) in Relation to Changes of the Environmental Conditions in Mongolia

Abstract

The wild camel (Camelus ferus) is a critically endangered large ungulate, surviving in just three distinct populations located in the Taklamakan Desert, China; deserts near Lop Nuur, China; and in China and Mongolia within and adjacent to Mongolia’s Great Gobi Strictly Protected Area (GGSPA). The population surviving in Mongolia remains poorly researched, but as few as 500 individuals may survive, although its distribution has remained relatively constant over the past 30 - 50 years. This study aimed at identifying potentially important environmental factors that influence the distribution of wild camels in Mongolia and predicting seasonal movement. We predicted distribution by season using presence only data and selected environmental predictors, including land surface temperature, normalized difference vegetation indices (NDVI), water sources, vegetation and soil. Model predictions revealed that land surface temperature in summer correlated significantly with wild camel distribution, with camels occurring in cooler areas. Abundance of biomass did not significantly correlate with camel distribution. Camels occurred in areas with intermediate levels of NDVI in most seasons, implying that they may base foraging decisions on forage quantity, not quality. Positive correlations of camel distribution with higher NDVI in summer (P = 0.03) suggests that they may prefer herbaceous species that appear after rainfall. Models indicate distance to water sources may be critical for camel distribution in all seasons. Camel occurrence correlated with areas containing shallow mountain soils in summer. Camels displayed no significant habitat correlations in other seasons, yet ranges differed among all seasons. Camels used a common region in spring, summer and autumn that we believe represents the core of the species’ annual range. Wild camel distribution during winter varied significantly from other seasons. Our modelling led to a predicted distribution range that was consistent with ranges described by previous research, indicating consistency between survey data and satellite tracking data.