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Is there a mechanism deficit in ecology?

Is there a mechanism deficit in ecology?. INTECOL 2013. Agenda. Macroecology is mechanism-less? Mechanism in ecology Mechanism in physics Example 1 – distribution & abundance Example 2 - biogeography. Macroecology – a mechanism deficit?.

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Is there a mechanism deficit in ecology?

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  1. Is there a mechanism deficit in ecology? INTECOL 2013

  2. Agenda • Macroecology is mechanism-less? • Mechanism in ecology • Mechanism in physics • Example 1 – distribution & abundance • Example 2 - biogeography

  3. Macroecology – a mechanism deficit? • “If you can do a regression and pull data out of a journal you can do macroecology” Machine Learning ? p<0.05 I did science!

  4. Agenda • Macroecology is mechanism-less? • Mechanism in ecology • Mechanism in physics • Example 1 – distribution & abundance • Example 2 - biogeography

  5. Reductionism Causality • Biology is hierarchical reified • Causality moves up • Mechanism comes from below Ricklefs Sadava et al Potochkin & McGill 2012

  6. Generalized LotkaVolterra

  7. But does it work? McGill 2013 (in The Balance of Nature and Human Impacted. Rohde)

  8. The equilibrial target is always moving!

  9. Agenda • Macroecology is mechanism-less? • Mechanism in ecology • Mechanism in physics • Example 1 – distribution & abundance • Example 2 - biogeography

  10. Physics 1687 • Newton: • F=Ma • F=GM1M2/d2 • Inertia & equal/opposite reactions • Descartes clockwork universe

  11. Physics 2013 • Quantum mechanics • Statistical mechanics

  12. Mechanism in physics • Practical • If you have an equation that is useful/predictive you have a mechanism (or maybe you’re just done and don’t care about mechanism?) • Laddered • Quantum mechanics gives Bohr atom • Physical chemistry gives multi-atom systems • Ideal gas law/statistical mechanics gives relation of macro-properties • Statistical • Quantum mechanics • Statistical mechanics (avoids intermediate numbers problem) • As general as possible • Only occasionally reductionist (more often self-contained) • Context aware (external forcing, environment)

  13. Conclusion • Macroecology • Doesn’t have a mechanism deficit • Has a mechanism recognition deficit Mechanisms in ecology: AWOL or Purloined Letter. Towards a practical view of mechanism. 2010 McGill & Nekola

  14. Agenda • Macroecology is mechanism-less? • Mechanism in ecology • Mechanism in physics • Example 1 – distribution & abundance • Example 2 - biogeography

  15. A thought experiment – sampling from the region Larger local community N=4, S=3 Region Small local community N=2, S=2

  16. We can write sampling idea as equations S, N, Ni, A from region are inputs Pivotal idea is sampling function: P=(ni|Ni,a,A,) Also see: Etienne & Alonso 2005 Green & Plotkin 2007 He & Legendre 2002 Dewdney 1998 Pielou multiple McGill 2011 American Journal Botany

  17. How are we doing? • Surprisingly not too bad, but we’re missing something (too much , not enough )

  18. Need another assumption • Have been using sampling function, , as spatially random (binomial or Poisson form) • We know clumped in nature • Clumping would fix problems (reduce , increase ) • More individuals from same species in sample lowers  • More individuals from same species in one sample, likely to be underrepresented in other sample increases  Condit et al 2000

  19. Clumping fixes it! • =Finite Negative Binomial (Zillio & He 2010)

  20. Sampling works at small scales Stochastic absences Sampling Deterministic absences Moving out of range Scale-break 100km X 100km

  21. Ni(X)=NMAXiexp(||X- mi||2/s2) Abundance si MVPi Range Boundary mi X Spatial extent Spatially explicit, larger scale version RADIUSi mi EXTENT WIDTH A B McGill & Collins 2003 Also see Gauch & Whittaker 1972 Allen & White 2003 WIDTH

  22. 3 assumptions common to many theories 3 Assumptions • Species abundance varies logarithmically • Individuals in 1 species are clumped • All else can be random McGill Ecology Letters 2010

  23. Agenda • Macroecology is mechanism-less? • Mechanism in ecology • Mechanism in physics • Example 1 – distribution & abundance • Example 2 - biogeography

  24. Back to Leibig’s Law?GauseLeibig biogeographic law • Any one variable sets an upper limit according to a Gause’s law (Gaussian bell-curve) • But most sites at the optimum are limited by something else

  25. It appears to be very general

  26. It appears very general

  27. Bruce Martin Wikimedia under CCA

  28. CristianSolari In the lab can home in on limiting factor

  29. Environment and Organisms • What • Dozens of GIS layers of climate that are biologically relevant for use in distribution modelling • Bioagricultural (e.g. degree days, frost free days, drought) • Extreme events (10-year coldest day, 50 year drought) • Topographic (slope, aspect, moisture indices) • Landcover • Traditional climate • Publically served, global 1km co-registered • Status • Funding from 3 organizations, >30 people involved • Pieces starting to become publically available

  30. Non-stationarity is now helpful

  31. Can dopredictions

  32. Summary • Mechanism is not deterministic and reductionist • Mechanism is often stochastic, self-referential, context-sensitive, more general than biology • Macroecology already has many mechanisms! • Sampling w/ clumping • Gause’s normal curve & Liebig’s Law

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