Wednesday, August 8, 2007

PS 54-198: Predictors of succession in a chronosequence of Imperata-infested communities

Akane M. Nishimura, University of California, Los Angeles

Lowland tropical rainforests are rapidly disappearing due to fire, illegal and commercial logging, and cash crop and smallholder agriculture. Research and conservation efforts are urgently needed, especially in understudied areas like Borneo, Indonesia. Borneo is the world’s third largest island and contains an incredibly high diversity of plants and is home to the endangered orangutan, which further increases the ecological imperative to study this area. Succession and reforestation efforts in Southeast Asian rainforests have been hindered by the invasion of Imperata cylindrica, which is one of the ten worst weedy species in the world and often creates a fire climax community. Most research on Imperata control has focused on restoring the land to create commercial plantations. By contrast, little research has been done on natural succession in Imperata grasslands or on restoring these grasslands to secondary forest. For this study, plots have been established in a chronosequence of Imperata infested sites in Tanjung Puting National Park in Central Kalimantan. The study sites are lowland forest areas with differing intensities of land-use, which have all led to a heavy infestation of Imperata. It will be determined if land-use intensity is a good predictor of succession in these communities.  Also, some plots have been planted with indigenous tree species using different reforestation methods to assess their potential in preventing temporary suppression or permanent deflection of succession. Preliminary data shows planted seedling survival rates of up to 94%. The El-Niño Southern Oscillation (ENSO) event during the 2006 dry season, however, seems to have taken a heavy toll on the remaining seedlings. Plots will continue to be monitored for seedling survival and plant cover change. Additionally, climate data and GIS and remote sensing will be used to assess spatiotemporal patterns and to predict future land-use change on a regional basis.