New Guinea is the world’s largest tropical island, boasting vast tracts of lowland forest and rich faunal assemblages. Many lowland bird species are commonly “lumped” into a single species because their habitat is continuous and assumed to be homogeneous. Emerging phylogeographic research by Dr. Jack Dumbacher, Chairman and Assistant Curator of the Department of Ornithology and Mammalogy at the Academy and Kristy Deiner, Ph.D. student in the Ecology Graduate Group at UC Davis has identified previously unrecognized diversity among New Guinea lowland birds.
          CBR partnered with Dr. Dumbacher and Deiner to test their genetic hypothesis for The Little Shrike-Thrush (Colluricincla megarhyncha) using georeferenced museum collection localities, GIS, and predicted distribution modeling software. Our ecological niche models help reinforce their phylogenetic hypotheses of taxonomic differentiation within large continuous basins. By combining these research tools; taxonomy, niche modeling, and geographic barriers, we can begin to efficiently identify conservation priority areas in New Guinea.

          Invasive alien species (IAS) are the second most important cause of biodiversity loss next to habitat alteration and climate change. In Latin America, economic losses from IAS amount to billions of dollars annually, but strategies to minimize the damage of IAS are generally underdeveloped. Together with researchers from The Nature Conservancy (TNC), and the Centro de Analisis Espacial of the Universidad Mayor de San Andres in Bolivia, CBR is using ensemble distribution modeling to generate composite maps that predict the distribution of 150 of the most threatening IAS in South America.
          To build these maps, species occurrence data, derived from both museum specimens and observations; obtained from the TNC Invasive Species Initiative, the IABIN Invasive Species Information Network (I3N), and the Global Biodiversity Information Facility (GBIF) are used. Researchers combine these data with global environmental data layers in models to identify IAS ‘hotspots’ and plan to stop invasions before they begin.

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