1 / 20

Benjamin Planque and Ulf Lindstrøm

Defining reference states for ecosystems - an approach through stochastic dynamic foodweb modeling (SDF). Benjamin Planque and Ulf Lindstrøm. Temporal changes in ecosystems to measure resilience. Resilience ~ the ability to maintain structure and function

harding-vinson
Download Presentation

Benjamin Planque and Ulf Lindstrøm

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Defining reference states for ecosystems -an approach through stochastic dynamic foodwebmodeling (SDF) Benjamin Planque and Ulf Lindstrøm

  2. Temporal changes in ecosystems to measure resilience Resilience ~ the ability to maintain structure and function substantial changes in structure or function ~ Lack of resilience Peterson et al., 1998

  3. Ecosystem regime shifts sudden, high-amplitude, infrequent events, which persist over decadal to multidecadal time scales and are evident on multiple trophic levels (Lees et al., 2006) Regime shifts in the North Pacific (Hare and Mantua, 2000)

  4. Trophic controls Changes in trophic controls in the Barents Sea Positive correlation: Bottom-up control Negative correlation: Top-down control (Johannesen et al., 2012)

  5. Evidence for regime shifts and trophic shifts • What do these examples mean? • Do we have an ecosystem reference state to tell us whether these changes are the rule of the exception?

  6. A simple SDF model for the Barents Sea ecosystem Super complex Non-linear Adaptive Not fully observable Simple and linear Stochastic (trophic flows) Constrained (physiology, life history, mass-balance) Poster A:19

  7. SDF model produces realistic features Diet fractions Trophic functional relationships Density dependence Biomass time series

  8. Ecosystem regime shift detection • 3000y SDF simulation • Principal Component Analysis (PCA) on biological time series (6) • Change point analysis on the first component. • At least 10y between regime shifts • Frequency of regime shifts

  9. regime shift detection: results PC1 variance explained: 30% Frequency of regime shift detection: 18y-1

  10. Changes in trophic control • 3000y SDF simulation • Correlation analysis on biomass • 15y sliding window • Frequency of top-down vs. bottom-up inversions

  11. trophic shifts: results

  12. trophic shifts: results

  13. Conclusions • Regime/trophic shifts are observed in real systems • The Stochastic Dynamic Food-web model shows that these are ‘expected’ features of ecosystems, given few reasonable assumptions… • … and without exceptional events in the climate forcing • In the Barents Sea, the ‘null expectation’ is that substantial changes in ecosystem structure and functions occur every few decades

  14. Thank you for your attention

  15. (Johannesen et al., 2012)

  16. Variability in fish population abundance Atlantic bluefin tuna Ravier and Fromentin 2001

  17. Variability in fish population abundance Relative abundance (log) Years Ravier and Fromentin 2001

  18. Variability in fish population abundance Pacific sardine

  19. Variability in fish population abundance number of scales 1000cm2 / year Biomass (106 tons) Years Baumgartner, Soutard et Ferreira-Batrina 1992

More Related