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Effects of Temporal variability of two ecological processes

13c1e126e91944499df10d649c4aeec9?s=47 jatalah
October 24, 2005
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Effects of Temporal variability of two ecological processes

13c1e126e91944499df10d649c4aeec9?s=128

jatalah

October 24, 2005
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  1. Javier Atalah Supervisors : Dr. Mark Costello Dr. Marti Anderson

    Effect of temporal variability of two ecological processes on benthic communities
  2. Overview  Introduction  Factors affecting diversity  Role of

    temporal variability  Physical disturbance experiment  Grazing experiment  Summary
  3. Introduction  Nature is variable in  Time  space.

     Variation in  population abundance  Species composition  Factors causing variation (e.g. predation, disturbance)  Understanding such variations is a primary objective of ecology
  4. An external force which results in loss of biomass (Grime

    1977) o Physical: storms o Biological: grazing, predation. o Anthropogenic: fishing, tramping Disturbance Diversity
  5. Area Intensity Frequency Temporal spacing of the events have been

    ignored! Many experiments have been focused on the effect of disturbance regimes
  6. Temporal occurrence A larva A competent After succession Temporal occurrence

    B Larva B competent After succession
  7. Objective  To test the effects of the temporal variability

    of disturbance on the diversity of macrobenthic assemblages
  8. Methods  PVC panels (15x15 cm) were used as settlement

    substrata.  90 d maturing phase  150 d experimental phase
  9. Study sites Ti Point

  10. . . . . Chile Brasil Australia New Zealand .

    Kiel
  11. Experimental design  Multi-factorial ANOVA :  Temporal variability: constant,

    low and high  Sequence: nested in low and high levels  Block: five rings at each sites  Response variables:  Species richness, multivariate data, taxa cover.  Analysis: PERMANOVA
  12. Disturbance regimes Variability Sequence constant 1 1 2 3 1

    2 3 Time low high Frequency = 10 events/150 d Mean time interval = 1 event/15 days Constant sd = 0 Low sd = 5.77 High sd = 16.33
  13. Disturbance event  Complete removal of 20% of the community

     Random position
  14. Sampling  Visual estimation of percentage cover of each taxa

     3 sampling dates: day 0, 75 and 150.
  15. Results: Community composition Leigh Harbour Ti Point Green algae 3

    4 Brown algae 3 5 Red algae 4 5 Sponges 0 2 Cnidarians 2 2 Polychaetes 3 3 Crusteceas 4 4 Bryozoans 3 4 Ascidians 1 2 Total 23 31
  16. Number of taxa 0 2 4 6 8 10 12

    14 Leigh Harbour TiPoint Constant Low High Undisturbed Levels of variability
  17. Taxa % cover Constant Low High Undisturbed 0 20 40

    60 80 100 Biofilm Ulvella sp. Ralfsia verrucosa Crustose coralline Balanus trigonus
  18. Community structure Stress=0.14 Constant Low High Undisturbed

  19. Time Abundance μconst = μvariable σ2 const << σ2 variable

    Temporal heterogeneity of disturbance may increase abundance fluctuations over time Sampling μoverall constant Variable
  20. 2nd Experiment: Variable grazing  Effect of intensity and temporal

    variability of grazing on macroalgae assemblages  Communities were exposed to regimes of grazing by the gastropod Cantharidus purpureus
  21. Methods  Caging experiment  Weekly sampling

  22. Experimental design Low Constant G G G G G G

    G Variable 2G 2G 2G 2G High Constant 2G 2G 2G 2G 2G 2G 2G Variable 4G 4G 4G 4G Week 0 1 2 3 4 5 6
  23. 0 40 80 120 160 0 1 2 3 4

    5 6 Week Cover (%) High-Constant High-Variable Low-Constant Low-Variable Undisturbed Percentage cover by treatment
  24. 0 200 400 600 800 1000 1200 Temporal variance HC

    HV LC LV Undisturbed Effect on the temporal variance of cover
  25. Stress = 0.05 Community structure Undisturbed Constant Variable

  26. Summary  Experimental design was novel in assessing the effect

    of temporal variability in disturbance  No consistent effect of variability of physical disturbance  Variable grazing regimes did change community structure  Reduce percentage cover  Increase temporal variance
  27. Acknowledgments  Saskia Otto  Leigh Marine Lab staff 

    IFM – GEOMAR, University of Kiel  All researcher from GAME network  NZODA Study Awards scholarship  Foundation Mercator