Biology Seminar Spring 2020

  • 1/30 Introduction
  • 2/6 Wendy Turner, SUNY-Albany. Anthrax Transmission in
    Herbivorous African Wildlife
  • 2/13 Shari Wiseman, Nature Neuroscience. Perspectives on Scientific Publishing
  • 2/20 Felicia Keesing Bard College, Biology Program. The Ecology of an African Savanna
  • 2/27 Krista Caballero, Bard College, Experimental Humanities Program. Birding the Future
  • 3/5 Michael Gitlin, Hunter College. Carriers of Meaning: Some Research Threads in The Night Visitors, a film about moths by Michael Gitlin
  • ——— All in-person seminars below this point are cancelled because of the coronavirus pandemics. We will update this page once we have a better plan. Stay tuned!
  • 3/12 Samantha Monier ‘12, CUNY. Changing Climate and the Winter Seabird Community of South Georgia
  • 3/19 Megan Gall, Vassar College. Hear, Here? The What and Where of Northern Saw-whet Owl Auditory Processing
  • 3/26 SPRING BREAK
  • 4/2 Rachit Neupane ‘13, MIT. BMI1 is a Context-dependent Tumor Suppressor that is a Barrier to Dedifferentiation in Non-small Cell Lung Adenocarcinoma
  • 4/9 Min Shinn ‘14, Washington University-St. Louis. Allosteric Effect of Single-stranded DNA Binding Proteins (SSB) C-terminal Tails on E. coli RecO Binding to DNA
  • 4/16 Erika Crispo, Pace University. Adaptive States versus Evolutionary Processes: A Meta-Analysis Contrasting Conservation Approaches
  • 4/23 Colin Aitken, Vassar College. Investigating the Mechanism of Translation Initiation on the Ribosome and Across the Transcriptome
  • 4/30 ADVISING WEEK
  • 5/7 Student talks (TBC)

Keesing lab: a mathematical model for livestock management

Biology professor Felicia Keesing and her colleagues published a paper in Scientific Reports that describes a mathematical model that can be used to manage livestock on grazing lands around the world. While previous models to manage livestock grazing exist, they require a lot of data and those data are hard to collect, making the models less useful. Keesing’s team developed a model that requires very little data yet makes sophisticated predictions, including estimating how much grass would be left over to support wild grazers. The model successfully predicted grass abundance at the team’s field sites in Kenya.