Judges’ Queries and Presenter’s Replies

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Presentation Discussion

  • Icon for: Leslie Ruyle

    Leslie Ruyle

    May 22, 2012 | 09:24 a.m.

    What a great example of interdisciplinary collaboration!

  • Icon for: Brian Drayton

    Brian Drayton

    May 22, 2012 | 09:47 a.m.

    Very interesting work! These two data sets are nice and long. How long a time series do you need for your results to be reliable, do you think? I presume that for many species this will best be thought of in terms of generations rather than years…

  • Icon for: Trevor Hefley

    Trevor Hefley

    May 22, 2012 | 02:29 p.m.

    Hi Dr. Drayton,

    In these two examples we could have used shorter time series and successfully predicted the threshold using our indicator. Based on an unpublished simulation, it appears that longer time series data are needed when the population is far from a threshold and approaching it slowly. Intuitively, this result occurs because LSV increases very rapidly just prior to the threshold (see Fig. 2). So, our methods may be highly relevant for populations that are close to and approaching a threshold rapidly.

    With regard to the sample interval, a sampling interval less than a generation would be relatively uninformative, however, any sampling interval more than a generation is informative. The main dynamic is captured when the environmental change driving the extinction is occurring slowly compared to the generation time and sampling interval. For example, the diatom data in our study is an integrated population estimate sampled at 10 year intervals. This interval is many generations of diatoms, but is relatively high resolution sampling with respect to climate change, which occurs over thousands of years.

  • May 22, 2012 | 12:42 p.m.

    Excellent collaborative, applied, integrated, and outreaching research project!

  • Icon for: Valerie Egger

    Valerie Egger

    May 22, 2012 | 04:03 p.m.

    Nicely done, Trisha and Trevor! I really enjoy seeing real research collaboration come out of the IGERT program that otherwise would not have happened.

  • Icon for: Michael Waite

    Michael Waite

    May 23, 2012 | 04:19 p.m.

    Really nice video and a great introduction to your research. What a great example of collaborative research, too!

  • Further posting is closed as the competition has ended.

  1. Trisha Spanbauer
  2. http://www.igert.org/profiles/3932
  3. Graduate Student
  4. Presenter’s IGERT
  5. University of Nebraska at Lincoln
  1. Trevor Hefley
  2. http://www.igert.org/profiles/3979
  3. Graduate Student
  4. Presenter’s IGERT
  5. University of Nebraska at Lincoln

Modeling critical transitions in natural systems: Can extinctions be predicted?

Understanding population dynamics in response to slowly changing environmental drivers such as climate and land use changes is essential for conservation efforts and for understanding the resilience of an ecosystem. Current efforts have focused on anticipating thresholds in populations that result in transitions to alternative dynamical states. This can be done by fitting complex statistical models to the time series data. An alternative approach is to monitor statistical indicators, but this method is not well tested. We used both statistical models and indicators to determine if extinction thresholds were present and detectable in environmental data sets. We applied these methods to both paleolimnological data of a known local extinction of a dominant algal species (Cymbella cymbiformis) driven by climate change and monitoring data of a declining population of game bird, the bobwhite quail (Colinus virginianus) that is experiencing habitat deterioration due to land use change. In both cases, we found that a common extinction threshold was present; the crossing of the threshold could have been predicted by monitoring suitable statistical indicators and with time series analysis. Both the model and the statistical indicators correctly predicted that C. cymbiformis was committed to a local extinction evidenced in the paleorecord and the model suggests that C. virginianus crossed the extinction threshold in 2011(2003–2032, 95% CI) and will undergo a local extinction in the near future. Our results show that both methods are useful in determining extinction thresholds despite drastically differing taxa, drivers of extinction, and time frames being evaluated.