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Speciesdistribu-onmodellinginthemarineenvironment:opportuni-es

anddangers

DerekTi:ensor11thOctober2009

QuebecCity

Outline

1.Introduc-on2.Examplesofpresence‐onlymarinemodels

3. Methods

4. WhenshouldIuseapresence‐onlymodel?

5.Challenges&Dangers

6.Conclusions

1.Introduc-on

Typesofmodel

observeddistribu-on

environmentalfactors

predicteddistribu-on+ =

Significantextradatarequirements/assump-ons

• Canbebroadlybrokendownintopresence‐onlyandpresence‐absence,bydatarequirements

P‐O

P‐A

or

Typesofdistribu-ondata Typesofdistribu-ondata

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Whyusepresenceonlymodels?

Idealscenario

‐  Goodspa-alcoverage

‐  Reliableabsencedata

‐  Comparablelevelofeffortbetweencells(loca-ons)

Whyusepresenceonlymodels?

Idealscenario

‐  Goodspa-alcoverage

‐  Reliableabsencedata

‐  Comparablelevelofeffortbetweencells

Reality*

‐  Sparsedata‐  O]enpoten-alfalseabsences

‐  Frequentlynotstandardisedeffort

‐  Verydifficulttoproveabsence*Atleastinthemarinerealm

fundamentalniche:poten2altosurvive,grow,reproduce•  physiologicaltolerance(abio-c)•  resources(bio-c&abio-c)

realisedniche:actualsurvival,growth,reproduc-on•  compe-tors,predators,parasites&pathogens(bio-c)

  non‐normalresponsecurves  occurrence≠op-malcondi-ons  poten-allyseveralnicheconfigura-ons

Nicheconcepts Examplemodellingmethods

EnvelopeModels•  BIOCLIM,DOMAIN,Mahalanobisdistance,RES/AquaMaps

CanonicalMethods•  ENFA,discriminantanalysisRegressionTechniques•  GLM,GAM,generalizeddissimilaritymodels,(boosted)regressiontrees,MARS

Machinelearningmethods•  GARP,ar-ficialneuralnetworks,MAXENT

•  Speciesdistribu-onmodellingissomewhatlessfrequentinthemarinerealm

Terrestrialvs.marine

•  84of995(<8.5%)ofSDMpapersfrom1991to2008were‘marine’(Macpherson,pers.comm.)

•  Whyisthis?

‐ Samplingmorechallenging,anddatarequirementsmoredifficulttomeet?

2.Examplesofpresence‐onlymarine

models

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Aquamaps

Kaschneretal.(2008)

•  Automa-callygeneratedmapsfor>9,000marinespecies

•  Mapscanbereviewedandverifiedbyexperts

•  Basedon(supplemented)environmentalenvelopes(modifiedRESmodel)

•  Developedforpar-cularlydata‐poorsitua-ons

Aquamaps

Kaschneretal.(2008)

Cold‐waterstonycoralsonseamounts

•  MaxentandENFA•  Modelledhabitatsuitabilityfordeep‐seastonycoralsonseamounts

•  Three‐dimensionalapproach

Ti:ensoretal.(2009)

500‐750mdepthlayer

Mappedtoseamountsummits

Effectsofoceanacidifica-ononcold‐watercorals:seamountrefugia

Ti:ensoretal.(inreview)

LOW HIGH

Habitatsuitability

Percen

tageofcells

Present‐day

2099

Atlan-cbenthos>30N

Atlan-cseamountsummits>30N

Presentdaybenthos

2099benthos

Impactsofclimatechangeonmarinebiodiversity

•  AnotherexampleTBA

Cheungetal.(2009)•  Bioclimateenvelopemodel

3.Methods

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BIOCLIM

•  requiresonlypresence•  rec-linearenvelope

  doesnotcopewellwithcollinearityorinterac-onsamongpredictors

•  h:p://www.diva‐gis.org/

presence environment

environmentalpredictor

cumula-

ve

presen

ce

e.g.Farber&Kadmon2003EcologicalModelling160:115‐130

RES/AquaMaps

RES:Kaschneretal.(2006)Aquamaps:Kaschneretal.(2008)

Slide:J.Ready

•  Trapezoidalresponsecurve

•  Developedspecificallyformarineenvironmentanddata

DOMAIN

•  requiresonlypresence•  ellip-cenvelope

Mahalanobisdistance:D2=(x–m)TC‐1(x–m)NB:assumesnormality

•  Rbase:mahalanobis()

e.g.Carpenteretal.1993Biodiversity&Cons.2:667‐680

Mahalanobisdistance

e.g.Corsietal.1999Conserva-onBiology13:150‐159

•  requiresonlypresence•  point‐similarityenvelope

Gowermetric:

•  h:p://www.diva‐gis.org/

centroid

variance‐adjusteddistance

EnvironmentalNicheFactorAnalysis(ENFA)

•  Speciesnicheisasubsetoftheglobalenvironment.

•  SpeciessetofEGVdiffersfromglobalsetby:–  Marginality(devia-onfromtheglobalmean)–  Specialisa-on(nichebreadth)

σG

µG

Frequency

Altitude

GlobalSpecies

σS

µS

EnvironmentalNicheFactorAnalysis(ENFA)

•  comparescondi-onsatpresencelocali-esto‘global’condi-ons

•  computesanalterna-vesetofaxes(factors):Marginalitymaximizesdifferenceinmeans

Specialisa2onmaximizesvariancera-o

•  NB:assumesnormality•  www.unil.ch/biomapper/

presence&background environment

variable1

variable2

marginality

specialisa-on

re-projection

e.g.Brotonsetal.2004Ecography27:437‐448

Gene-cAlgorithmforRule‐setPredic-on(GARP)

•  usesdataonpresence&(pseudo)absence

•  genes=rules/func-onschromosome=ruleset

•  3ruletypes:atomic(threshold),range&logit

•  non‐parametric•  binaryoutput•  predictors’contribu-onsto

solu-onunknown•  www.lifemapper.org/desktopgarp/

initial random set of chromosomes (sets of rules)

chromosome replication based on fitness (prediction success)

gene mutation cross-over joining

selection for fitness & dissimilarity

test against data

next ite

ration

e.g.Petersonetal.1999Science285:1265‐1267

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Maximumentropymodels(MAXENT)

Distn.ofmax.entropy(unconstrained)

Distn.ofmax.entropy(cointoss)

Phillipsetal.(2006)Phillipsetal.(2008)

•  BasedonShannon’sentropy

•  Presenceandbackgrounddata

•  Iden-fiessta-s-caldistribu-onthatbestfitsobserveddatawhileminimizingconstraints(maximizingentropy)

•  Maximumlikelihoodapproachwithop-malsolu-on

Maxent(con-nued)

•  5constrainttypes:–  Linear–  Quadra-c–  Product–  Threshold–  Binary

•  Thuscanfitawidevarietyofdistribu-onfunc-ons

•  Goodathandlingcorrelatedpredictorvariables

3.WhenshouldIuseapresence‐onlymodel?

Albacoretuna1960s&1990s

Japaneselongliningdata

E.g.Myers&Worm(2003)Wormetal.(2005)

Japaneselongliningdata

•  >20billionhooksfrom1950to1999!

•  Yets-llsome5degcellsinwhichspecieswerenotcaught,butarelistedaspresentbyFAO

Therela-onshipbetweeneffortandpresence

Scheidat,Gillesetal,(unpublisheddata)

Ti:ensor&Worm(inprep)

Albacorecatch(1960s)

Albacorecatch(1990s)

Logeffort(hooks)10^2 10^8

Logcatch

010

^8

Logcatch

HarbourPorpoise

Effort(km^2)025

Num

.sigh-

ngs

0

60

Logcatch

010

^8

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Whenshouldyouuseapresence‐onlymodel?

•  Ifyouhavereliableabsencedata,itisbe:ertouseaP/Amodel(Elithetal.2006)

•  However,itisbe:ertouseapresence‐onlymodelratherthanaP/Amodelwithproblema-cabsencedata

•  Otherwiseyoucanbeinaccuratelyrepresen-ngspeciesniche

Presence‐onlymodelvalida-on

•  Howtotestmodelperformance?•  Fieldisevolvingextremelyrapidly

•  Threshold‐independentmetricshaverecentlybeendeveloped(e.g.AUC,Phillipsetal.2006)

•  Cross‐valida-onimportanttopreventover‐fi{ng

Builtmodelsusingpresenceorpresence‐inferredabsencedata

Testedusingtruepresence‐absencedata

Elithetal.(2006)

Howdopresence‐onlymodelsperformincomparisontoP/Amodels?

GOODINTERMEDIATE

LEASTGOOD

AquaMaps/RES

Readyetal.(accepted)

• Compareswellwithexis-ngmethods• Inclusionofexpertknowledgetendstoimprovepredic-ons

WhichmethodshouldIuse?

•  Dependsonyourproblem•  IammostfamiliarwithENFAandMaxentandbothcomplementoneanother–ENFAiseasilyinterpretable,Maxenttendstoperformbe:erundercross‐valida-on

•  Iwouldthusadviseimplemen-ngmul-plemethodstogetarobustunderstandingofspecies‐environmentrela-onships.

5.Challengesanddangers

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Presence‐onlychallenges

•  Spa-alautocorrela-onnotyetabletoberesolvedinpresence‐onlymodels(Dormannetal.2007)

Marine‐specificchallenges

•  Inherentlythree‐dimensionalenvironment

•  Bestapproachdependsonorganism–benthic,pelagic,mid‐water?

•  Modelthevolumeinwhichaspecieslives,notjustthe‘area’

Mismatchbetweenseamountsamplesandbathymetry

Depthofsample–depthofbathymetry‐3000 0 6000

Num

berofsam

ples

0

700

0

More(marine)challenges

•  Highlycorrelatedenvironmentalvariablescanpresentmodeliden-fiabilityissues

Seasurfacetemperature

SeasurfacedissolvedO2

Generalmodellingchallenges

•  Poten-alscalemismatchesbetweendriversandobserva-ons

•  Bio-cinterac-ons

•  KEYASSUMPTION:Samplescovertherangeofenvironmentalspaceoccupiedbyaspecies

Dangers

•  Over‐fi{ng•  So]warepackagesareterrificallyeasytouse(whichmeanstheyareo]enappliedwithinsufficientthought)

•  Modellingpoten-alvs.realizedniche,andunderstandingthatdifference.

•  Insertsasquatchpaper

•  Modellingwithoutusingbiological&ecologicalknowledgeoforganism(s)understudyisfoolish

(2009)

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6.Conclusions

Inconclusion

•  Presence‐onlymethodsareveryusefulwhenabsencedataareunreliable

•  Idealfordatacompiledfromnon‐standardisedoreffort‐correctedsources(e.g.museumcollec-ons,mul-plesurveyswithdifferentmethodologies)

Inconclusion

•  Performancecompareswelltopresence‐absencemodels

•  Manyopportuni-esasstudiesinthemarineenvironmentarelimited.

•  Fieldisevolvingveryrapidly,soimportanttokeepaneyeontheliterature.

Thankyou

•  Kris-nKaschner•  JanaMacPherson•  BorisWorm•  UNEP•  FMAP•  LenFestfounda-on

•  Ihavekeypapersandso]wareinarangemodellinglibraryonmyflashdriveforanyonewhowants

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AquaMaps:Environmentalenvelopes

Probability

Absoluteminimum

Absolutemaximum

Op-malrange

Environmentallayer

Depth:derivedfrompublishedinforma-onabouthabitatusage

METHOD AquaMaps:Environmentalenvelopes

Probability

Observedmin

Observedmax

Op-malrange10thto90thpercen-les

Environmentallayervalues

SST,Salinity,PP,Seaiceconcentra-on:derivedfrompointdata

75thpercen-le+(IQR*1.5)

25thpercen-le‐(IQR*1.5)

IQR=interquar-lerange

Slide:courtesyJ.Ready

METHOD

AquaMaps:ExpertinputMETHOD