Hierarchical statistical models in wildlife ecology

Institute Seminar by Rahel Sollmann

  • only online
  • Datum: 16.04.2024
  • Uhrzeit: 10:30 - 11:30
  • Vortragende(r): Rahel Sollmann
  • I studied biology at the University of Cologne and the Rheinische Friedrich-Wilhelms University Bonn, where I obtained my Diploma in 2006. I obtained my PhD from the Free University Berlin and the Leibniz Institute for Zoo and Wildlife Research (IZW) in 2011, with a dissertation on the ecology and conservation of jaguars in the central Brazilian Cerrado savannah. I spent the next 10 years in the USA, first as a post-doc in Dr. Beth Gardner’s lab at North Carolina State University (2011-2015), developing and applying hierarchical statistical models to questions of wildlife ecology and management. This was followed by a 1-year postdoc with the US Forest Service in Davis, CA, using HSMs to study the impact of fire and fire management on wildlife. In 2016, I was hired as an Assistant Professor for Quantitative Ecology at the Department of Wildlife, Fish, and Conservation Biology at UC Davis. I stayed at UC Davis for five years, teaching introductory statistics and principles of sampling wildlife to undergraduates, and working with graduate students on applying HSM to different questions of wildlife ecology and conservation. In 2021 I moved to Berlin for my current position as Senior Scientist in the Department of Ecological Dynamics at the IZW, where I have been continuing my work on HSM in wildlife research.
  • Ort: online
  • Gastgeber: Max Planck Institute of Animal Behavior
  • Kontakt: cmonteza@ab.mpg.de
Knowing how many species or individuals occur at a given place and time is fundamental to many questions in wildlife ecology, conservation and management. Enumerating wildlife, however, is complicated by our imperfect and varying (with method, species, habitat, etc) ability to detect animals. In this seminar, I introduce hierarchical statistical models (HSM) as a tool to deal with imperfect detection. I present two case studies using HSM of different levels of occurrence is estimated from species detection/non-detection data. We use this framework to evaluate how land use and climate change driven conversion to savannah habitat will likely affect terrestrial mammals in the Southern Brazilian Amazon. The second study uses open population spatial capture-recapture (opSCR), a much more complex framework that uses spatially explicit individual-level data collected over multiple surveys to estimate population density and demographic rates. We use opSCR to investigate the sex and age-specific expansion of the Pyrenean brown bear population. These case studies highlight the flexibility of HSM as a tool to account for different sampling scenarios and address a wide range of ecological questions.

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