CHASING THE SINGERS: THE TRANSITION OF LONG-SONG (URTYN DUU) IN POST-SOCIALIST MONGOLIA

Long-song (Urtyn duu) is a prominent Mongolian traditional folk song genre that survived throughout the socialist period (1921-1990) and throughout the political transformation of Mongolia from socialism to democratic capitalism after the Soviet Union was dismantled and terminated its aid to Mongolia in 1990. This dissertation, based on research conducted from 2006 to 2010, presents and investigates the traces of singers' stories and memories of their lives, songs, and singing, through the lens of the discourse on change and continuity in, and as, folk tradition. During the socialist period, this genre was first considered backward, and was then subtly transformed into an urban national style, with the formation of a boundary between professionalism and amateurism among long-song singers and with selective performance of certain songs and styles. This boundary was associated with politics and ideology and might be thought to have ended when the society entered its post-socialist period. However, the long-song genre continued to play a political role, with different kinds of political meaning one the one hand and only slight musical modification on the other. It was now used to present a more nostalgic and authentic new Mongolian identity in the post-socialist free market. Through my investigation, I argue that the historical transition of Mongolia encompassed not merely political or economic shifts, but also a deeper transformation that resulted in new cultural forms. Long-song provides a good case study of the complicated process of this cultural change.

Modeling the Effects of Habitat Fragmentation on the Movements of the Mongolian Gazelle in the Eastern Mongolian Steppe

The Mongolian gazelle, Procapra gutturosa, resides in the immense and dynamic ecosystem of the Eastern Mongolian Steppe. The Mongolian Steppe ecosystem dynamics, including vegetation availability, change rapidly and dramatically due to unpredictable precipitation patterns. The Mongolian gazelle has adapted to this unpredictable vegetation availability by making long range nomadic movements. However, predicting these movements is challenging and requires a complex model. An accurate model of gazelle movements is needed, as rampant habitat fragmentation due to human development projects - which inhibit gazelles from obtaining essential resources - increasingly threaten this nomadic species. We created a novel model using an Individual-based Neural Network Genetic Algorithm (ING) to predict how habitat fragmentation affects animal movement, using the Mongolian Steppe as a model ecosystem. We used Global Positioning System (GPS) collar data from real gazelles to “train” our model to emulate characteristic patterns of Mongolian gazelle movement behavior. These patterns are: preferred vegetation resources (NDVI), displacement over certain time lags, and proximity to human areas. With this trained model, we then explored how potential scenarios of habitat fragmentation may affect gazelle movement. This model can be used to predict how fragmentation of the Mongolian Steppe may affect the Mongolian gazelle. In addition, this model is novel in that it can be applied to other ecological scenarios, since we designed it in modules that are easily interchanged.