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On the use (and misuse ) of models in ecological research

On the use (and misuse ) of models in ecological research. Nicolas Delpierre , ESE (UMR 8079) nicolas.delpierre@u-psud.fr. Ecology in English , October 2013. Models are central in current global change research. models + data. models. IPCC WG1 – published 2013, Sep. 13 th.

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On the use (and misuse ) of models in ecological research

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  1. On the use (and misuse) of models in ecologicalresearch Nicolas Delpierre, ESE (UMR 8079) nicolas.delpierre@u-psud.fr Ecology in English, October 2013

  2. Models are central in current global change research models + data models IPCC WG1 – published 2013, Sep. 13th

  3. Models are central in current global change research data models Pereira et al., 2010, Science

  4. A tentative chronology of ecologicalmodelling Mathematicalmodels are the foundation of modern ecologicaltheory

  5. A tentative chronology of ecologicalmodelling Mathematicalmodels are the foundation of modern ecologicaltheory Population ecology Malthus (1798), Verhulst (1838), Lotka (1925), Leslie (1945) Biogeography / ecologicalcommunities Mc Arthur & Wilson (1967), Hubbel (2001) Food webs Elton (1927) Evolutionaryecology Wallace & Darwin (1858) Ecosystemproductivity Lieth (1972)

  6. A tentative chronology of ecologicalmodelling Mathematicalmodels are the foundation of modern ecologicaltheory Population ecology Malthus (1798), Verhulst (1838), Lotka (1925), Leslie (1945) Biogeography / ecologicalcommunities Mc Arthur & Wilson (1967), Hubbel (2001) Food webs Elton (1927) Evolutionaryecology Wallace & Darwin (1858) Ecosystemproductivity Lieth (1972) The complexity of ecological / biologicalsystems prevents the discovery of simple yetpowerfulmodels

  7. Differentkinds of models Empiricalmodels Mechanistic / deterministicmodels Theoreticalmodels

  8. Differentkinds of models Empiricalmodels statistical phenomenological Mechanistic / deterministicmodels based on the representation of (known and described) biological / physicalprocesses Theoreticalmodels generic, simple

  9. How simple a model needs to be ? «simple» means « general » means « good »… William of Ockham 14th c.

  10. How simple a model needs to be ? «simple» means « general » means « good »… (?) William of Ockham 14th c. «  some of the theoretical conclusions [from the model] canbepleasinglysupported by hard data, whileothersremain more speculative» (May and Anderson, 1979)

  11. How simple a model needs to be ? «simple» means « general » means « good »… (?) William of Ockham 14th c. «  some of the theoretical conclusions [from the model] canbepleasinglysupported by hard data, whileothersremain more speculative» (May and Anderson, 1979) « The generality of simple modelsisoftensuperficialbecausetheyonlydemonstrate possible explanationsratherthanprovideactual instances of explanation » (Evans et al., 2013, TREE)

  12. How simple a model needs to be ? Simple modelsmaysometimesbemisleading Simple model (Eisinger & Thulke 2008): « 70% of the population needsimmunization » Spatially explicit model (Anderson 1981): «  60% …» A difference of 15 M€ per annuum Anderson Eisinger & Thulke Eisinger & Thulke, 2008

  13. How to build a model ?

  14. How to build a model ? Model Formulating equations parameterisation parameterisation Evaluation Knowledge of processes and pre-existingmodels data Simulations observations Hypotheses

  15. How to build a model ? Model Formulating equations parameterisation parameterisation Evaluation Knowledge of processes and pre-existingmodels data Simulations observations New hypotheses

  16. How manyprocessesshould i consider ? Is there a limit to the reductionnistapproach ? « We have a tendency to incorporate more and more processesintomodels to improve fitness betweensimulated and observed data. »

  17. How manyprocessesshould i consider ? Is there a limit to the reductionnistapproach ? Model 1 « We have a tendency to incorporate more and more processesintomodels to improve fitness betweensimulated and observed data. Complicatedmodelsmayintegrate more processknowledge but make more parametersless identifiable given certain data sets. » (Luo, 2009) Model 1 bis Identifiability Whenparameterscanbeconstrained by a set of data with a given model structure, they are identifiable. Equifinality differentmodels / parameter values of the same model may fit the data equallywell Model 2 Medlyn et al., 2005, TP Beven, 2006 Luo, 2009, Ecol. Appl.

  18. How to parameterize / validate a model ?

  19. How to parameterize / validate a model ? A question of (parameters and data) uncertainty…

  20. How to parameterize / validate a model ? A question of (parameters and data) uncertainty… Parameteruncertainty: differentexperimental sources report different values for the sameparameter Kattge et al., 2011 Hollinger & Richardson, 2005

  21. How to parameterize / validate a model ? A question of (parameters and data) uncertainty… Parameteruncertainty: differentexperimental sources report different values for the sameparameter • Data uncertainty: • Samplingerror • Measurementprecision / accuracy Theseuncertainties must beconsideredwhenparameterizing / validating the model Kattge et al., 2011 Hollinger & Richardson, 2005

  22. How to parameterize / validate a model ? An example of data-assimilation techniques Bayesianoptimisation approach Likelihoodfunction = probability of the data given the model output generatedthrough the parametervectorq = « measurement of the predictionerror » prior parameter distribution posterior parameter distribution Van Oijen et al., 2005 Martin & Delpierre, 2011 Keenan et al., 2012

  23. How to parameterize / validate a model ? An example of data-assimilation techniques Bayesianoptimisation approach Definition of the costfunction Van Oijen et al., 2005 Martin & Delpierre, 2011 Keenan et al., 2012

  24. How to parameterize / validate a model ? An example of data-assimilation techniques Bayesianoptimisation approach prior parameter distribution posterior parameter distribution Simulations + uncertainty Van Oijen et al., 2005 Martin & Delpierre, 2011 Keenan et al., 2012 Parameter value

  25. How to parameterize / validate a model ? The more correlated… the less identifiable Kuppel, 2013, PhDThesis

  26. How to parameterize / validate a model ? How many data do i need ? Keenan et al., 2013, Ecol. Appl.

  27. How to parameterize / validate a model ? Beware of relyingcompletely on the model ! Keenan et al., 2013, Ecol. Appl.

  28. Föobar model CO2 Solar radiation temperature GPP Reco Radiation interception Global PAR Photosynthesis Stomatal Cond. Carbon Allocation C leaves Growth Respiration Heterotrophic Respiration C aerial wood Maintenance Respiration C reserves C litter C surface C coarse roots C deep C fine roots Keenan et al., unpubl.

  29. Need for consideringuncertainty in projected trends Assimilating more data reduces the uncertainty of projections Keenan et al., 2012

  30. Need for consideringuncertainty in projected trends Alternative model formulations… yielddifferenttrajectories in future projections Vitasse et al., 2011, AFM

  31. How to identify the « best » of 2 (n) models ? Use the Akaike information criterion! HirotuguAkaike 1973 William of Ockham 14th c. The lowest the AIC, the best accuracy-parsimonytrade-off

  32. How to identify the « best » of 2 (n) models ? Use the Akaike information criterion! HirotuguAkaike 1973 William of Ockham 14th c. The lowest the AIC, the best accuracy-parsimonytrade-off

  33. What a datasetwill not tell…

  34. Do experimentsprovidereliable data for informingmy model ? Wolkovich et al., 2012, Nature

  35. Can model parametersbetreated as constants ? acclimation processes Wythers et al., 2005, GCB

  36. Using a model for prospective studies

  37. My model cansaymanythings… depending on what i ask ! Principles of niche modelling Slidefrom Chris Yesson (Zoological Society of London)

  38. My model cansaymanythings… depending on what i ask ! Principles of niche modelling Slidefrom Chris Yesson (Zoological Society of London)

  39. My model cansaymanythings… depending on what i ask ! Principles of niche modelling Slidefrom Chris Yesson (Zoological Society of London)

  40. My model cansaymanythings… depending on what i ask ! Principles of niche modelling Slidefrom Chris Yesson (Zoological Society of London)

  41. My model cansaymanythings… depending on what i ask ! Principles of niche modelling Slidefrom Chris Yesson (Zoological Society of London)

  42. My model cansaymanythings… depending on what i ask ! Principles of niche modelling Slidefrom Chris Yesson (Zoological Society of London)

  43. My model cansaymanythings… depending on what i ask ! Objective of the paper : to assignspecies to extinction risk categories based on projected declines in population size. Under a time scale of 80 years

  44. My model cansaymanythings… depending on what i ask ! What’s the problemwiththat ? Thuiller et al., 1005, PNAS Akçakaya et al., 2006, GCB

  45. My model cansaymanythings… depending on what i ask ! Simulation of Oakproductivitydepends on the resolution of climate forcings Martin et al., unpublishedresults

  46. Take home ideas • Howeverdetailed, models are idealizedrepresentations of the world • Simple models are most of the time general… and not so good • Complexmodelsmay not beparameterizable(… howevercomplicated the data assimilation technique) • Model forecasts are conditional on: • model structure and parameters (and uncertainties) • model forcings • Modelscanonlyanswer questions that one asks

  47. Supplementarymaterial

  48. On the use and misuse of models in ecological / global change research Keenan et al. rate my data, validation GCB (fails) Medlyn et al. 2005 perils and pitfalls Evans et al. 2013 «  Simple meansgeneralmeans good» Whatis a model? Whatisitused for? How valid are inferencesfrom model simulations ?

  49. Plan Models are central in current global change research Examples last ipcc report Examplesspp extinction frompereira et al. 2011 Used for projections of whatmayhappen Raises the question of reliability of the models… and of theiruncertainties Whatis a model ? (we’re not going to center on statisticalmodels) How simple needs a model to be ? Does simple meangeneralmeantrue ? (Evans) I’m a researcher : how can i buildmy model ? (where do i startfrom ?) The question / problem of parameterisation. Data also are uncertain ! Dealingwith multiple uncertainties : MDF frameworks My model isbuilt. How can i check thatitspredictions are reliable? Future trends : what do i need for running my model ? How accurate are the input data (Zhao, Nico + Evea) Simulations in a future / modifiedclimate : what indexes of changes should i use (Akcakaya) What a model can’t do : rate my data…

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