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BarEcoRe’s ”Spatial” Workshop . 22 – 24 / 05 / 2012

BarEcoRe’s ”Spatial” Workshop . 22 – 24 / 05 / 2012. Organisators:. Grégoire Certain ; Mette Skern-Mauritzen.

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BarEcoRe’s ”Spatial” Workshop . 22 – 24 / 05 / 2012

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  1. BarEcoRe’s ”Spatial” Workshop. 22 – 24 / 05 / 2012 Organisators: GrégoireCertain; Mette Skern-Mauritzen Benjamin Planque; LisLindalJørgensen; TrudeThangstad; Maria Fossheim; EddaJohannesen; PadminiDalpadado; Cecilie Hansen; Magnus Aune-Wiedmann; Susanne Kortsch; Michaela Aschan; Raul Primicerio Wksparticipants: A GENERAL INTRODUCTION

  2. Ouraim is to answerthequestions: arethere, in theBarentsSea, areas wherethe marine ecosystem is more ”resilient” than in others ?

  3. Resilience is a complexconcept, thatencompassseveralfieldofscience and has beendefined in numerousways. As such, resilience is an ”umbrellaconcept” that hosts manyothers. For empiricalassessment, it is thereforedifficult to useresilienceonly. The concept must be narrowed to a simplerone, workable and tractable. Following Levin & Lubchenco (2008), wepropose: Resilience ~ Stability arethere, in theBarentsSea, areas wherethe marine ecosystem is more stablethan in others ?

  4. Resilience (Stability)ofwhat1 to what2 ? (Carpenter et al. 2001) (1) The BarentsSea Marine Ecosystem, from primaryproductors to top predators and includingbenthic, demersal and pelagicspecies. (2) Oncoming global changes: Climatevariability(i.e. change in value and dynamicenvironmental parameters) and harvesting pressure (i.e. disruptionoftrophicpathways, increasedmortalities, speciesremoval). arethere, in theBarentsSea, areas wherethe marine ecosystem is more stable and mighttherefore be less ”affected” by global change ?

  5. In whichwaysecosystemsare ”affected” by something ? Externalforcing (Climate, Harvesting) = Drivers ofchange Ecosystem Abiotic (physico-chemicalproperties, habitat) Energy flow & cycling* Biotic (Σspecies) Habitat engineering* Regulation*throughecologicalinteractions Howecosystemsustainsthesechanges ? SeveralEmergentproperties*allows to buffer change

  6. Thanks to theseemergentproperties, most ecosystemsarerather stable despitetheconstantnaturalvariability.Butwhatcontrolstheefficiencyoftheseregulationmechanisms ? Abiotic (physico-chemicalproperties, habitat) Ecosystem Biologicaldiversity(Σspecies) affectsthethreekindofemergentproperties: it controlsthelength and structureofthefood web; it controlsthedegree and intensityofecologicalinteractions; It determinestheeffectofthebioticcomponentontheabiotic Biotic (Σspecies) The ”biodiversity – stability” paradigmemerged in the litterature as a miscellaneouscollectionofexperiments, empiricalobservations, theoreticaldevelopments. In short, highly diverse systems are more stable, butthewaysthroughwichthisincreasedstabilityariseare multiples, complex, and context-dependent .

  7. Are there, in theBarentsSea, areas wherethe marine ecosystemdiffers in term ofbiologicaldiversity to thepointthat it mightaffecttheirstability to environmentalperturbationinduced by harvesting activity or global change ? That, wecantry to answer ! But to be reliable, weneed to quantifythebiologicaldiversityoftheBarentsSea in different areas. Ourassessmentshouldcompriseas muchecosystemcomponent as possible, and cover as muchaspectofbiodiversity as possible. Three steps: (1) Definethe Areas (2) Choosethemetrics (3) Definetheframeworkthroughwhichmetricswill be aggregated and interpreted.

  8. (1) Definethe Areas To be discussed… BUT aggregatingprimarily at the Atlantis scale offers thepossibility to re-aggregate as wewish…

  9. (2) Choosethemetrics Ecosystemscale Informationrequired Type ofMetric Pelagic, Demersal Benthos, Top predator abundance / biomasses per sp. Taxonomicdiversity abundance / biomasses per sp. Pelagic, Demersal Benthos, Top predator PhylogeneticDiversity Phylogenetictree abundance / biomasses per sp. Pelagic,Demersal Benthos, Top predator FunctionalDiversity Life historytraitmatrix(demersal) abundance / biomasses per sp. Food web topology Wholeecosystem Topologymatrix(update) Legend: We have /Wecould have

  10. (3) Aggregation and interpretationofthemetrics A setofcovariatereflectingdifferentkindofenvironmental drivers A setofmetricorganisedacrossthreelevels: Metricfamily, Ecosystemscale, Metric type Physico-Chemical Bathymetry Taxon. Slope Benthos Metricsα, β, γ BottomTemperature Mean and range for each Demersal Metricsα, β, γ SurfaceTemperature Pelagic Metricsα, β, γ BottomSalinity Top predator Metricsα, β, γ SurfaceSalinity Icecoverage Phylo. Benthos Metricsα, β, γ MixedLayerDepth … … Potential Energy Deficit Currentstrength Funct. demersal Metricsα, β, γ Pressure … … Trawlingintensity, basedon VMS data Other ? Trophic. All Metricsα, β, γ Biomasses as a 5th metricfamily ? Biomasses ofkeybiologicalcompartments Proposal… SurfaceCHla Zooplanctonsizefraction The wayweaggregateresultsshouldtakesintoaccountthishierarchicalorganisation, thenumberofmetricsdocumented at eachlevel, and theirdegreeofcomplementarity Theseinformationwillprovidethecontextwithinwhichwewill be able to interpret ourresultsonbiodiversity

  11. Crucialtasks to fulfill during theworkshop Discussspatial scaleand possiblepolygon aggregations. Decideonthesetofmetrics and environmentalinformationwewilluse. Whichoneshouldweinclude/exclude ? Decideonthemodalitiesofaggregation and interpretationofmetrics Otherimportantquestions The placeofbiomass-relatedinformation. In whichwayscanwecontribute/benefit to the Atlantis project ?

  12. (it should be 10h right now) We start by a setofshortpresentation from everybody. I propose to organizethe talks as follows: 1/ Invited talk: Cecilie. Curentdevelopmentonthe Atlantis model(~15 min) Discussion(~30 min) 2/ ”Data” talk: Børsheim, Dalpadado, Jorgensen, Johannessen, Mauritzen(~1h15) Discussion(~30 min) 3/ ”Metric” talks: Certain, Aune-Wiedemann, Kortsch(~45 min) Discussion(~30 min) Total talk & discussion ~ 4h End ofthe Day: Group discussion and planning for the Day 2.What do we do, how do weorganizeourselves to reachtheobjectives.

  13. Suggestion for Day 2: forming ”Competencegroups”. Belowaresomeindications in term of starting groupcomposition,butWksparticipantsareencouraged to circulatebetweenthegroups. Group compositioncan be up-datedafterLunch for example. Taxonomicgroup: Edda, Maria, Greg.Compute and refinethetaxonomicindexesonvariouscommunity data. Food web topologygroup: Susanne, Michaela, Cecilie: updatethetopology, test somemetrics at the polygon scale Workshopparticipantscirculatebetweenthe 4 groups Phylogenetic and functionaldiversitygroup: Mette, Magnus, Raul.Investigatethephylogenetcdiversityquestion. Refinefunctionalanalysis Environment & Pressuregroup: Benjamin, Lis, Padmini, Trude. Up-datetheenvironmentmetrics. Link theenvironment to biodiversitymetricsproduced by othergroups

  14. TAXONOMY-BASED METRICS

  15. Most oftheabundance-baseddiversitymetricsarebasedonspeciesfrequencies:

  16. What is themeasurmentunit for diversity ? Spe, the ”effectivespeciesnumber”. Suppose a diversitysample is composedof S species. Ifall speciesareequally abundant, their relative frequenciesequal1/S, and themeanoftheirfrequenciesalsoequals1/S Ifall speciesare not equally abundant, themeanoftheirfrequenciescan be expressed1/Spe. Spe is the ”effectivespeciesnumber”, i.e. thenumberofequally abundant virtualspecies in thedataset. Most diversityindicesareexpressed in term of Spe, or oneofitstransformation

  17. The ”true” diversity, or diversityof order q (Hill, 1973) For a single compositionalunit(sample) of i=1…S species: It is the inverse ofthemeanproportionalabundanceofthe type ofinterests (species). Measurmentunit:Spe It is a generalisationofmanydiversityindexes, dependingon q[0,+∞[: Ifq = 0, it equalsspeciesrichnessand corresponds to 1/weighted harmonicmeanofthepi, (theweightbeingthepithemselves) Ifq -> 1, it equalsexp(shannon) and corresponds to 1/weighted geometricmeanofthepi, (theweightbeingthepithemselves) Ifq = 2, it relates to theSimpson indexand corresponds to 1/weighted arithmeticmeanofthepi, (theweightbeingthepithemselves) Ifq -> ∞, it becomesthemaximumfrequencyin thesample

  18. Effectofchangingq: q=0, speciesrichness q->1, Exp(shannon) q->∞ pi max

  19. N compositionalunits (sample) case: DecomposingHill’s (γ) diversityintoα and βcomponent. Two waysofrelatingmathematicallyα, β and γ Multiplicative: α * β = γ , or Additive: α + β = γ γ β α α βM = γ/αcan be interpreted as a local to regional diversity ratio. It expresseshowmany times as rich in effectivespeciesthedataset is thanoneofitsconstituentcompositionalunits.. β β α βA = γ - αcan be interpreted as species turnover amongthecompositionalunits. It expressestheamount by whichtheeffectivespeciesrichnessoftheentire (regional) datasetexceedsthatof a single sampling unit. βdiversitiescan be basedondifferentαdiversitiesmetrics: Simpson, Shannon, Speciesrichness… . For consistency, Tuomisto (2010) recommandtheuseofHill’s true diversity (1973) with q = 1 or 2.

  20. Formulatingαdiversity as a ”True Diversity” P(i|j)all-1 expressesthemeanspeciesdiversitywithinthe sampling unit. Wecall it αt with An alternative formulation has beenproposed by Jost (2007). Wewillcall it αR = effectivenumberofspecies*samplingunit combination in thedataset (numberofvirtualcells in thesite*speciesmatrix) αR = = effectivenumberof sampling unit in thedataset Theseformulationswill lead to thecorrespondingβdiversitymetric. From αt, βMt and βAtcan be calculated, and similarlyβMR and βARareobtained from αR . Both have slightlydifferentmathematicalproperties.

  21. Indexesderived from ”true” βdiversity: There is in theliteraturesomedebate and proposalsofdifferentwaysofexpressingβdiversity. We’llretain a fewofthese: Lead to generalisationofsimilarityindexes (Jaccard, Sorensen, etc…)

  22. A ”complete” setofindexes for measuringbiodiversity For a given region with N sampling site Within and across sampling unit βMt αt βAt ~ ~ We cover a large part oftheα- and β-relatedmetrics. Wepropose to useeither q=1 or 2 βPt γ ~ ~ αR βMR ~ Wepropose to complementthisset by some more focusedindexes to be computed at the polygon scale. They all capture a specificpropertyoftaxonomicbiodiversity. Compositionalsimilarity (Chao 2008) Regional varianceexcess (Lande 1996) Dominance (McNaughton 1952) Equitability (Smith & Wilson 1996) Rarity (Mac Gill 2002)

  23. Let’sseehowthatlooks like… Method: for each region with N>=20 sampling site, we Randomlyselect (withreplacement) 20 sampling site Wecomputethesetof 12 metrics 999 times to estimateuncertainty Weproceed to a PCA analysis so thattwosummarisesthese 12 measuresalong PC1 and PC2 Welook at the polygon score onthePCs and rank themaccordingly

  24. Median ofthe 999 metricvalues

  25. αdiversity equitability similarity, dominance, rarity β and γdiversity negative score onthe PC1 caninvariably be interpreted as highbiodiversity

  26. ContributionofEigenvectors Eigenvalue for PC1: 0.5-0-7

  27. HighTaxonomicDiversity LowTaxonomicDiversity

  28. Associateduncertainty - Axis 1 Polygon numbers

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