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Ecological
Modelling
j ourna l h o me pa g e: w w w . e l s e v i e r . c o m / l o c a t e / e c o l m o d e l
Aquatic
food
webs
of
the
oxbow
lakes
in
the
Pantanal:
A
new
site
for
fisheries
guaranteed
by
alternated
control?
Ronaldo
Angelini
a,∗,
Ronny
José
de
Morais
b,
Agostinho
Carlos
Catella
c,
Emiko
Kawakami
Resende
c,
Simone
Libralato
daDepartamentodeEngenhariaSanitáriaeAmbiental,UniversidadeFederaldoRioGrandedoNorte,UFRN,BR-101,CampusUniversitário,Natal,RN59078-970,Brazil bMestreemEcologiaeEvoluc¸ão,UniversidadeFederaldeGoiás,Goiânia,GO,Brazil
cCentrodePesquisaAgropecuáriadoPantanal,EMBRAPA,Corumbá,MS,Brazil dIstitutoNazionalediOceanografiaediGeofisicaSperimentale–OGS,Trieste,Italy
a
r
t
i
c
l
e
i
n
f
o
Articlehistory:
Received25January2012
Receivedinrevisedform2January2013 Accepted4January2013
Available online 19 February 2013 Keywords:
Foodwebcontrol Floodpulse,Toppredator Keystonespecies Lindex Kemptonindex
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b
s
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Floodpulseandbioticinterrelationshipscontrolthefoodwebdynamicsofriverfloodplainsystems.The PantanalPlainintheParaguayRiverBasin(Brazil)occupies140,000km2ofperiodicallyfloodedareas
andisdividedinto12subregionswithdifferentcharacteristicsrelatedtothefloodpulseduration,the vegetation,thetypeofsoil,andtheresourcesusedinactivities,particularlyfishing.Inthisstudy,we usedEcopathwithEcosim(EwE)tomodelthreeoxbowlakesintheSouthPantanalPlain,wherethere isnofishingactivity,totestthesimilarityoftheecosystems,toidentifythekeystonespeciesandthe typesoffoodwebcontrols,andtodeterminewhethertheseenvironmentscansupportmoderatedfishing pressure.Wefoundthatthefoodwebsoftheoxbowlakesaresimilartoeachotherbecause,although theydependmainlyonthepresenceorabsenceofpredators,floodpulsessimilarlyhomogenizethelakes. Theresultshighlighttheimportanceofdetritusinthesefoodwebs.Inaddition,thehighestvaluesofthe keystonenessspeciesindexinthethreemodelshighlighttheroleoftoppredators(Hopliasmalabaricus, Serrasalmusspp.,Pseudoplatystomareticulatum,birds,andmammals).Therefore,wesuggestthatthefood websinthethreesystemsaresubjectedtoanalternatedcontrolprocess:detrituscontrolsthefoodweb duringthefloodseasonandbythetoppredatorsduringthedryseason.Thesimulationoutputsindicate thattheseoxbowlakescansustainonlymoderatefishingbecauseincreasingthefishingpressurereduces thebiodiversityandcannegativelyimpactthetoppredators.
© 2013 Elsevier B.V. All rights reserved.
1. Introduction
Thehydrologicalregimeinriverfloodplainsystemsis consid-eredthekeyfactorthatdeterminesthestructureandfunctioning of the communities in these areas (Junk et al., 1989; Jepsen and Winemiller, 2002; Agostinho et al., 2007). This assump-tion is true because the flood pulse determines the exchange of nutrients among the terrestrial and the aquatic environ-ments,andthisexchangeinfluencestheproductivity,allowsthe migrationofspecies,and thusincreasesthepossibilityof inter-actionsbetweenspecies(ZeugandWinemiller,2008;Alho,2008; Hamilton,2010).
Inadditiontothefloodpulse,thebioticinterrelationshipsalso regulatethedynamicsofthecommunitiesofriverfloodplain sys-tems(Thomazetal.,2007;Luz-Agostinhoetal.,2008),andthefood
∗ Correspondingauthor.Tel.:+558487433020.
E-mailaddresses:ronangelini@yahoo.com.br(R.Angelini),
ronnybio@hotmail.com(R.J.deMorais),catella@cpap.embrapa.br(A.C.Catella),
emiko@cpap.embrapa.br(E.K.Resende),slibralato@ogs.trieste.it(S.Libralato).
webmaybeaffectedbydifferentfoodwebcontrols(Hunterand Price,1992;Paceetal.,1999;Curyetal.,2000;Hunter,2001;Hunt andMcKinnell,2006;YaraginaandDolgov,2009).
Inatop-downcontrolsystem,thehighertrophic levels(TLs) determine the bulk of the lower TLs through direct and indi-recteffects(Carpenteretal.,1985;Leiboldetal.,1997;Dyerand Letourneau,2003;Mooreetal.,2003;DinnenandRobertson,2010). Inabottom-upcontrolsystem,thebasisofthefoodchain (produc-ersordetritivores)regulatestheproductivityandabundanceofthe higherTLs(Nielsen,2001;Lorentsenetal.,2010).Thespeciesin theintermediateTLsmayexertatypeofcontrolcalledwasp-waist, inwhichchangesintheabundancesofthesespeciesaffectsboth theirpredatorsandtheirprey(Curyetal.,2000;Shannonetal., 2004).However,thedynamics ofsomecommunities are deter-minedthroughamixedcontrol,i.e.,withsomeinfluencefromboth theproducersandthepredators(AcháandFontúrbel,2003;Hunt andMcKinnell,2006).
The species that mostinfluence these types of controls are consideredkeyspeciesbecausetheyplayadominantroleinthe developmentof theecosystem. Thisrole is independent ofthe biomass(Millsetal.,1993;Pirainoetal.,2002;Libralatoetal.,2006;
0304-3800/$–seefrontmatter © 2013 Elsevier B.V. All rights reserved.
Gasallaetal.,2010)orTLofthekeyspecies(Paine,1995;Power etal.,1996;Bond,2001;Davic,2003;Jordanetal.,2007).
Throughaquantitativeanalysisoffoodwebsand,inparticular, oftheinteractionsofkeyspecies,itispossibletopredictthe stabil-ityoffoodwebsthatfacevariousimpacts,suchasfishing(Travers etal.,2010).Thisinformationcansupportthemanagementof fish-ingactivities(Angelinietal.,2006;AngeliniandVelho,2011)to ensurethesustainabilityandconservationoftheecosystem pro-ductivity(Colletal.,2008a).
TheHighParaguayRiverBasinoccupiesanareaof496,000km2
in themidwestern regionof Brazil and comprises 140,000km2
of periodicallyflooded areasof thePantanal Plain atits center andsurroundingnon-floodedareas(Junketal.,2006).Morethan 270fishspeciesarefoundin thisarea(Britskietal.,2007), and thefishing of mainly a few largespecies is an important eco-nomic and social activity that is practiced under professional, non-professional,sport,andsubsistencemodalities(Catellaetal., 2008).Thefishingactivitytakesplacepredominantlyinthe south-ernandnorthernareasofPantanal.Thus,itisofinteresttoimprove thespatialdistributionof thefishingefforttoavoidoverfishing andtoensurethepresenceofspeciesandenvironmentsthatare currentlyunderusedorhavelowcommercialvalue(Catella,2003). Thisstudyaims toquantifythetrophic interactions andthe energy/matterflows of three oxbow lakes that are situated in thebasinsoftheMirandaandAquidauanaRiversinthePantanal floodplain.Thereiscurrentlynofishingactivityintheselakes.In addition,thisstudyinvestigatesthefollowingecologicalissues: (a)Theseenvironmentspresentsimilarecosystemattributes.Our
propositionisthatthesestudiedenvironmentsareverysimilar systemsbecausetheyhavethesamephysicalcharacteristics (e.g.,riverdistancesanddepths).
(b)Thefoodwebisalternativelycontrolled,i.e.,bybothtop preda-torsanddetritus.Despitethefloodpulsebeingafundamental controllingfactorinthewetseason,thepremiseforthis suppo-sitionisthatthebioticcontrolbythetoppredators(keyspecies) alsocontributestothestructuringofthecommunitiesinthese areas,particularlyduringthedryseason.
(c)These environmentscan supportmoderate fishingpressure. Pantanalfishingismainlyconcentratedonlargespeciesand toppredators.Thus,thereareunexploitedspeciesinthe stud-iedenvironmentsthathavenotbeenpreviouslyexplored,and foodweb modelingsimulationspermittheevaluationofthe potentialeffectsoffishingontheseoxbowlakes.
2. Materialsandmethods
ThePantanalischaracterizedbycomplexdrainageduetothe differenttypesofrivers,lakes,temporarystreams,andmarshland that arefoundin thearea. Thisregion iscomposedof 12 sub-regions withdifferentcharacteristics related tothe floodpulse duration,thevegetation,andthetypeofsoil(Súarezetal.,2004). Theaveragefishingcatchrecordedbetween2000and2005inthe SouthPantanalwasequivalentto667t:40%wascapturedthrough professionalfishingand60%wasobtainedthroughsportsfishing. In2005,theeightmostcapturedspeciesrepresented82%ofthe catch:sixweretopchaincarnivores(Pseudoplatystomacorruscans, Pseudoplatystomareticulatum,Paulicealuetkeni,Salminus brasilien-sis,Pygocentrusnattereri,andPinirampuspirinampu)andtwowere omnivorespecies(PiaractusmesopotamicusandLeporinus macro-cephalus)(AlbuquerqueandCatella,2009).NetoandMateus(2009) comparedprofessionalandsportfishingintheNorthPantanaland showedthatthesesamespecieswereresponsiblefor95%ofthe total catch. Twospecies represented50% ofthe catchobtained throughprofessional(P.corruscansandP.reticulatum)andsport
Fig.1.Mapofregionandlocalizationofthreeoxbowlakes(indetails):(1)Baíada Onc¸a(ONCmodel);(2)Brac¸oMortoAcima(MAB);(3)Brac¸oMortoAbaixo(MAC).
fishing(P.corruscansand theomnivore “pacu-peva”,which isa groupofthreespeciesoftheMileynaefamily).
Thethreeoxbowlakesinvestigatedinthisstudy(BaiadaOnc¸a, Brac¸oMortoAcima,andBrac¸oMortoAbaixo)aresituatedsouthof thePantanalinMato-GrossodoSulState(Brazil).AsshowninFig.1, theseareoxbowlakesoftheMirandaRiverandofitslargest tribu-tary,theAquidauanaRiver.Bothoftheseriversexhibitmeandering coursesandareassociatedwithapproximately260marginallakes, whicharelocallyreferredtoas“Baias”,along thelast353kmof theircourses.
The Baia da Onc¸a (with an area of 0.153km2) joins the
AquidauanaRiverduringfloodingperiods(December–January)and isdisconnectedAprilthroughMay.Ithasamaximumdepthof3m andincludesnoaquaticmacrophytesbecauseitissurroundedby along-termsemi-deciduousriparianforest(CatellaandPetrere, 1996).TheBrac¸oMortoAcima(withanareaof0.2875km2)and
theBrac¸oMortoAbaixo(withanareaof0.3365km2)aresituated
neartheMirandaRiverandjointheriverinJanuaryandare discon-nectedfromitinmonthsofdrought.Thedepthofthesesystems variesbetween1m(drought)and3m(flood),andthereisa pre-dominanceofriparianforestandaquaticmacrophytes(Resende, 2000).Theaveragewatertemperatureinthethreewaterbodies isapproximately26◦Candexhibitslittleannualvariability.These environmentswerechosenduetotheirpermanentpattern,thefact thattheyareequallydistantfromrivers,andtheirrelativelyeasy access,i.e.,theselakesareeasiertostudybecausetheapproachto mostoftheaquaticecosystemsinthePantanalfloodplainisquite difficult,regardlessoftheseason.Moreover,thesesystems repre-sentunexploitedlakesofthePantanalandthusrepresentpotential newareasforfisheryactivities.
2.1. Model
EcopathwithEcosim(EwE,versions6.0and5.2forpreliminary balancingprocesses),whichisbasedonthemass-balance assump-tion,wasusedfortheelaborationandquantificationofmodelsof thethreeenvironments.Inthebasicequation,theconsumptionby apredator(orgroup)leadstothemortalityofitsprey(orgroup)due topredation.Thisismathematicallydescribedbylinearequations (ChristensenandPauly,1992):
Bi×PBi×EEi−
j
whereBiisthebiomassofgroupi;PBiistheProduction/Biomass
rateofi,whichisequaltothetotalmortalityZ(Allen,1971);EEi
istheecotropicefficiencyofi,whichvariesfrom0to1and repre-sentsthefractionoftheproductionofthegroupthatistransferred tohigherTLsorexportedfromthesystem;Bjisthebiomassof
predatorj;QBjisthefoodconsumptionperunit ofbiomassfor
predatorj;DCji isthefractionofiinthedietofj;andEXiisthe
exportofiandrepresentsthebiomassthatiscaughtthrough fish-ingand/orthatmigratestootherecosystems.Thespeciesofthe investigatedlakesarenotinvolvedinreproductivemigrationsor fishing;thus,thevalueofEXiofallfishspecieswasinitiallyset
toverylowvalues(1%ofbiomass)torepresentoccasionalfishing. Thebiomasseswereexpressedastwwkm−2 (metrictonsofwet weightpersquarekilometer),andtheflowsinthefoodwebwere expressedintwwkm−2year−1.
Foranecosystemwithngroups(compartments),themodelwill haveasystemofnlinearequations.InthedevelopmentofanEwE model,itisnecessarytoincludeatleastthreeofthefourmain inputparameters(Bi,PBi, QBi,andEEi)becauseit ispossibleto
estimatethemissingparameterbyconnectingtheproductionof onegroupwiththeconsumptionbytheothergroupsbasedonthe assumptionthattheproductionofonegroupisutilizedbyanother groupinsidethesystem(ChristensenandPauly,1992).Inthethree modelsdevelopedfortheoxbowlakesofthePantanal,EEiwasthe
parameterthatwasestimatedbyEwE,withfewexceptions(see below).
2.2. Dataset
2.2.1. Fishcompartments
Thesamplingsoftheichthyofaunainthethreeenvironments wereconducted using seining nets. Nine collections were per-formedattheBaiadaOnc¸abetween1988and1989(Catellaand Petrere,1996),whereas15collectionswereconductedintheother twosystemsbetween1998and2000(ResendeandPereira,1998; Resendeetal., 1998,2000a,b).Allofthefishsamplescollected in thethree ecosystems were standardized through theuse of a 66-m wide trawling net (stretched mesh size=13.3mm, net height=4m).InBaiadaOnc¸a,thesampleswerecollectedoncea monthfromSeptembertoDecember.InSeptember,twotrawling perdaywereconductedtosampletheichthyofauna(inthe morn-ingandintheafternoon).Inthefollowingmonths,threehaulsper day(morning,midday,andafternoon)wereconducted.The trawl-ingwasperformedusingarunningcanoe(0.5ms−1)andencircling atotalareaofapproximately4845m2(CatellaandPetrere,1996).
Similarproceduresandmeshsizeswereusedinthethreehauls thatwereperformedeachdayinBrac¸oMortoAbaixoandBrac¸o MortoAcimaduringthe1weekofsamplingthatwasconductedin OctoberandNovember;thesamplingareascomprisedatotalarea of4500m2foreachsystem.Thesweptareawasassumedtohave
theshapeofhalfofanellipse(EllipseArea=˘×a×b,whereais 1/4ofthelengthofthetrawlingnet(16.5m)andbisthedistance (m)betweenthestartpositionoftrawlingandtheendposition). Therefore,assumingaconstantandevencatchabilityamongthe species,thebiomass(B) valuewascalculatedasthesumofthe individualweightsoffishdividedbythesweptareas,whichgave thecorrespondingdensities(biomassperunitofarea).
Duringthesamplingperiodofthisstudy(1988–2000),Pantanal floodingwashigherthanthehistoricalmeans(Junketal.,2006and Fig.3).Inthesecondsamplingperiod(1998–2000),theriverlevel wasslightlylowerthanin1988,althoughtheinundationpatternsin thePantanalremainedrelativelyconstant(Hamilton,2002)despite theENSOeventsinthisperiod,whichdidnotinfluencethe Pan-tanalhydrologicdynamics(Clarkeetal.,2003).Thus,duringboth periodsandintheyearsbetweensamplings,thethreelakeswere regularlyfloodedtoasimilarextentandremainedisolatedfrom
theriversduringthesametimeperiods.Therefore,wecouldsafely assumethatthelocationandthetemporaldifferencesdidnot indi-catedissimilarecosystemdynamics,whichpermitsthecomparison ofthesemodeledenvironments.
Oftheapproximately270fishspeciesfoundin thePantanal (Britskietal.,2007),75,90,and80fishspecieswerecollectedinthe BaiadaOnc¸a,theBrac¸oMortoAcima,andtheBrac¸oMortoAbaixo, respectively.Allofthesespeciesbelongto20families.Allofthe speciessampledinBrac¸oMortoAbaixowerealsocaughtinBrac¸o MortoAcima.InBaiadaOnc¸a,onlytwodifferentspecieswere sam-pled.Thus,thesamplingrevealedahighsimilarityamongthethree oxbowlakes.
Ineachmodel,themainfishspeciesandthemostimportant speciesinthebiomasswererepresentedbyasinglecompartment: therewere31fishspeciesintheBaiadaOnc¸a(ONCmodel),whereas 29fishspecieswerefoundintheBrac¸oMortoAcima(MAC)andin theBrac¸oMortoAbaixo(MAB).The29speciesthatwerefoundin boththeMACandtheMABwerealsofoundintheONC; Cynopota-muskincaidiandHypophthalmusedentatuswereonlyfoundinthe BaiadaOnc¸a.ThespeciesthatconstitutedtheONC,MACandMAB modelsrepresentedatleast97%ofthetotalfishbiomassineach system.
TheProduction/Biomass(PB) wascalculated using thePauly (1980) equation; the equation parameters were obtainedfrom studiesontropicalfish(Cunhaetal.,2007;AngeliniandAgostinho, 2005a;Capistrano-Santanaetal.,2004;Vazzoleretal.,1997)or fromFishBase(FroeseandPauly,2009).PalomaresandPauly(1998) equationwasusedtodeterminetheConsumption/Biomassratio (QB)usingparametersthatwereestimatedbasedonoursamplings, includingthecaudalfinareaindex.
Thedietcompositionmatrixwasdeterminedthroughan analy-sisofthestomachcontentsofthecollectedindividuals(Resende and Pereira, 1998; Resende et al., 1998, 2000a,b). Novakowski etal.(2008)estimatedthedietofafishcommunityinthissame regionandfoundverysimilarvalues.Althoughseasonalityisa dis-cerniblecharacteristicinthePantanalregion,negligibleseasonal differenceswereobservedinthedietcompositionofthestudied species;this similarity supportedthedevelopment ofonly one annualmodelforeachenvironment.Thedietcompositionofthe modelsisshowninthesupplementarymaterials(AnnexA).
2.2.2. Non-fishcompartments
Thenon-fishcompartmentsusedinthethreemodelswere phy-toplankton,zooplankton,aquaticinsects,terrestrialinsects,birds, caimans,mammals,anddetritus.Aquaticmacrophyteswereused onlyforthemodelsoftheMACandtheMABbecausethese orga-nismsarenotfoundintheBaiadaOnc¸a(CatellaandPetrere,1996). The B and PB values for caimans (Caimancrocodilus yacare) wereestimated accordingtothemethod suggestedby Campos etal.(2006),whostudiedthemovementofcaimansinanother regionofthePantanal.Thedietcompositionusedwasobtained fromBorteiroetal.(2009),whostudiedcaimansofthesamebasin (Uruguay),andtheQBvaluewasobtainedfromAfricancrocodiles inVillanuevaetal.(2006a).ThevalueofBforthebirds (includ-ingArdeaalba,Egrettathula,Phalacrocoraxbrasilianus,andSterna superciliaris)wasestimatedaccordingtotheresearchperformed byFigueiraetal.(2006),whostudiedthebirdsintheentire Pan-tanalregion;thevaluesofPBandQBwereobtainedfromthestudy conductedbyVillanuevaetal.(2006a),whostudiedfreshwater lakesinAfricaandfoundthatthecompositionofthedietofbirds wasgenerallydistributedamongsmallfishspecies.ThevalueofB formammalswasestimatedbasedonthestudybyDesbiezetal. (2010),whereasthePB,QB,andDCvalueswereestimatedaccording totheresearchconductedbyWaldemarinetal.(2007),whostudied PteronurabrasiliensisandLontralongicaudisinthePantanal.
Duetothelackoflocalinformationfortheothercompartments (phytoplankton,macrophytes,zooplankton,andinvertebrates),the valuesofBforthesegroupswereestimatedusingthePBEcopath inputs,whichwereobtainedfromAngeliniandAgostinho(2005b), whomodeledafloodplainnearthesiteofthisstudy.TheEEvalues forthesegroupswereobtainedfromAngelinietal.(2006),who quantifiedafoodwebintheAmazonasRiver,whichhasa simi-larhydrologicalregimeastheoneinthePantanal.TheBvaluefor detrituswasestimatedusingthefollowingempiricalregression: log detritus=0.954×logprimaryproduction
+0.863×log euphoticzone−2.41 (2) wherethePrimaryProductionunit isgCm−2 (valueswere esti-matedfromColeetal.,1988)andtheunitsfortheeuphoticzone aremeters(2m,CatellaandPetrere,1996).
2.2.3. Balanceandvalidationofmodels
ThePedigreeIndex(IPdgr)wascalculatedtodeterminethe qual-ityassociatedwiththeinputvaluesofeachparameter(B,PB,QB, andthedietmatrixelements;Christensenetal.,2005).InEwE,the userattributesaqualityvaluebetween0(low)and1(high)foreach parameteraccordingtothesourceoftheinformation(lowfor gen-eralmodel-basedinputvaluesandhighforvaluesobtainedthrough experimentaldata).Then,theaverageofthequalityvaluesofallof theparametersinallofthegroupsiscalculatedtoobtainageneral syntheticindexofthemodelinputdataquality(Christensenetal., 2005;Morissetteetal.,2006).
TheEcorangerroutine(EwEversion5.2)wasusedtotestthe consistencyofthemodeloutputsbecauseitallowsanevaluation ofthequalityoftheinputdatausingthePedigreeIndex.The Eco-rangerroutineconsiderstheinputvaluesthemeanofanormal distributionwithauser-definedconfidenceinterval(Christensen etal.,2005).Thus,Ecorangerre-samplestheseinputvalues,runs themodels,andteststhereliabilityofthemodeloutputs,which reducestheinherentimprecisionoftheinputvaluesusinga least-squarescriteriontoobtainthemodelwiththeminimumresidual. Consequently,when thedataare notcompletely accurate,EwE helpsfindthemodelthatbestfitsthemostrobustsetofavailable data(Paulyetal.,2000).
2.2.4. Outputmodels
TheEwEmodelcalculatestheseriesofindicatorsandproperties ofeach functionalgroupthatareusefulfortheecological anal-ysisofthefoodweb.Inparticular,EwEcalculatesthefractional trophiclevel(TLi)ofeachcomponentbasedonitsdietcomposition
(ChristensenandPauly,1993)andthevarianceofpreyacrossTLs, whichiscalledtheOmnivoryIndex(OI;Christensenetal.,2005).
Moreover,usingtheMixedTrophicImpact(MTI)matrix,which isusedtoassessthedirectandindirectinteractionsbetweenthe speciesinafoodweb,Libralatoetal.(2006)developeda keystone-nessspeciesindex(KSi):
KSi=log[ei(1−pi)] (3)
where ei is equal to
m2ij (mij represents the interactionbetweentheimpactinggroupiandtheimpactedgroupj;this mea-sureiscalculatedbasedontheMTI);therefore,eiisameasureof
theeffectsofeachgrouponalloftheothergroupsofthefoodweb (includingtheindirectones).Theeffectofthechangeonthegroup biomassitself(i.e.,mii)isnotincluded;piisequalto(Bi/totalB),
whereBiisthebiomassoftheimpactedgroupandtotalBisthe
totalbiomass(excludingdetritus).Throughtheevaluationofthe positiveandnegativecontributionstothevalueofei,itispossible
tomakeinferencesregardingthetypeofcontrolthatgovernsthe analyzedecosystem(Libralatoetal.,2006).
Globalpropertiesofecosystemswereusedtodescribethestate ofdevelopmentoftheenvironmentsandtosupportthe compar-isons among them(Odum, 1969):(a)TotalSystemThroughput (TST),whichisthesumofallflowsinthesystem(consumption, exports,respiration,andflowstodetritus);(b)TotalPrimary Pro-duction/TotalRespiration(PPT/RT),whichdescribesthematurityof systems,i.e.,itincreasestovaluescloseto1asthesystemsbecomes moremature;(c)Biomass/TotalSystemThroughput(B/TST),which wasusedtoevaluatethebiomassmaintainedbyaunitofflowin thesystemandisexpectedtoincreasewithmaturity(Christensen, 1995);(d)FinnCyclingIndex(Finn%),whichrepresentsthe propor-tionofrecycledflowsinthesystem(Finn,1976)andcanbeused asameasureofresilience(Vasconcellosetal.,1997);(e)thepath length(PL),whichiscalculatedasthetotalsystemflowsdivided bythesumofthetotalexportandthetotalrespirationflowsandis directlyconnectedtothematurityoftheecosystem(Christensen andPauly,1993);(f)theConnectanceIndex(CI),whichistheratio ofthenumberofactuallinkstothenumberofpossiblelinksinthe modeledfoodwebsuchthatagreaterconnectivitytendsto indi-catea highersystemmaturity(ChristensenandWalters,2004a; Christensenet al.,2005);(g)theSystemOmnivory Index(SOI), whichaggregatesthevariationsbetweenthedietsofthe compo-nentsand isnot affectedby theaggregationmethod(Libralato, 2008),i.e.,valuesofSOIcloseto0indicateadominanceof spe-cialistconsumers,whereasvaluescloseto1indicateadominance ofgroupswithhighfoodplasticity(Paulyetal.,1993;Christensen etal.,2000);and(h)ascendancyandoverhead,whichare emer-gentproperties(Ulanowicz,2009)thatcorrespondtoameasureof systemmaturityandameasureofecosystemstability,respectively (Christensen,1995).
EwE also aggregates the biomasses and flows into integer TLs,therebyrepresentingthesystemusingthe“LindemanSpine” (Libralatoetal.,2010),whichallowstheestimationofthe Trans-ferEfficiency(TE)astheratiobetweentheproductionsobtainedat twoconsecutiveintegerTLs.TheLindemanSpineallowsabetter understandingoftheeffectivenessofenergytransferinthe sys-tem(Lindeman,1942)becauseitseparatelytakesintoaccountthe detritus-basedandthegrazingfoodchainsandallowsthe perfor-manceofbiomassanalysisbyTL.
2.2.5. Fisheries-relatedoutputsandderivedmeasures
AnotherEwEoutputisthePrimaryProductionRequired(PPR) tosustainthebiomassproductionofthefoodweborganisms.This measurewasmainlyusedtoquantifythepressureoffishingon theecosystemsbycalculating thePPRtosustaincatches(Pauly andChristensen,1995).BasedonthePPR,Libralatoetal.(2008) developedthelossofsecondaryproductionindex(Lindex):
L= −1
P1×lnTE×
(PPRi×TETLi−1) (4)
whereP1=PP(CalculatedNetPrimaryProduction)+FLDET(Flows toDetritus)andindicatestheautotrophicproductionand detri-tusproductionbythefoodweb(tkm−2year−1),TEistheaverage efficiencyoftransferbetweentheTLs(%),PPRiisthePrimary
Pro-ductionRequiredforcomponenti(tkm−2year−1),andTLiistheTL
ofcomponenti.
Libralatoetal.(2008)usedthetrophiclevelofthecatches(TLc) inEq.(4)anddevelopedtheLindextomeasurethelossofsecondary productionduetofishing.TheauthorsalsocalculatedtheLindex in51marineecosystemsthatwereclassifiedassustainablyand notsustainablyexploitedbyfishing.Basedonthefrequency distri-butionoftheLvalues,theseresearchersproposedreferencelevels toclassifytheprobabilityofthesustainabilityoffishing(psust). Therefore,thereferencevaluesfortheLindexthatindicatesa sus-tainability(psust)between75% and 95%would beL75%=0.021 (standard deviation (s.d.)=0.013) and L95%=0.007 (s.d.=0.007)
(Colletal.,2008a).TheLindexofthethreemodelsinvestigated inthisstudywasestimatedconsideringaverysmallcatchinthe basemodels,whichwasequivalentto1%ofthebiomassofeach fishspecies(F=0.01),andsuccessivelyincreasingthecatchineach additionalsetofsimulations(seebelow).
TheTotalPrimaryProductionRequired(TPPR)dependsonthe cyclesthatoccurbetweenthehigherTLsthatmagnifythe produc-tionthataffectsTL1.Therefore,itisimportanttocompareTPPR withP1toobtainanestimateofthesystem’sautocatalysis poten-tial(sensuUlanowicz,2009),i.e.,tomeasurethepositivefeedbacks thathelpthesystemdeveloponitsownthroughrecyclingcycles. Therefore,theindex[1−(P1/TPPR)],whichvariesfrom0to1,was estimated:values closetounity indicatethatthesystem main-tainsitselfthroughhighautocatalysiswithalowproduction(P1), whereasvaluesclosetozeroindicatethatthehigherTLsarenot exertingpositivefeedbackonthesystem,whichthustendstobe unabletounfolditself,i.e.,inlowautocatalysisconditions,all pro-ductionisusedintheupperTLsandonlyasmallamountofmaterial isrecycled.
TheKemptonindex(Q)includesspeciesorfunctionalgroupsat aTL3orhigher(KemptonandTaylor,1976).Qisarelativeindex ofbiomassdiversitythatwasderivedfromKempton’sQ75index, whichwasalsodevelopedasanindicatorofbiodiversityevenness (AinsworthandPitcher,2006).AlowervalueofQimpliesalow evennessandrichnessandhighergroupdominance.TheKempton QindexisautomatedinEwEasadynamicoutputinthesimulations (ChristensenandWalters,2004b).
2.2.6. Fisheriesandsimulations
ThetrophodynamicmoduleofEwE(i.e.,Ecosim)usessettings fromthemass-balancemodule(i.e.,Ecopath)astheinitial condi-tionsandparameterdefinitions(ChristensenandWalters,2004b). Namely,thesystemequation(Eq.(1))istransformedintoasystem ofordinarydifferentialequationasfollows(Waltersetal.,1997, 2000): dBi/dt=gi×
j Qij− j Qij+Ii−(MOi+Fi+ei)×Bi (5)wheredBi/dtisthechangeinBofgroupiovertimet,giisthenet
growthefficiency,Qjiistheconsumptionofgroupjbygroupi,n
isthenumberof preygroups,Qij istheconsumption ofgroupi
bygroupj,misthenumberofpredatorgroups,Iiistheextentof
immigrationofgroupi,MOiisthenon-predationrateofnatural
mortalityofgroupi,Fiisthefishingmortalityofgroupi,andeiis
theemigrationofgroupi.
InEq.(5),thequantityofpreyiconsumedbypredatorj(Qij)
isanonlinearrelationshipbasedonthe“foragingarenaconcept” (Waltersetal.,2000;WaltersandChristensen,2007;Ahrensetal., 2011):
Qij=aij×
v
ij×Bi×Bj 2×v
ij×aij×Bj(6) whereaijistheeffectivesearchrateofpredatorjfeedingonprey
i,Biisthebiomassoftheprey,Bjisthepredatorbiomass,and
v
ijisthevulnerabilityofpreyitopredatorj.ThefoundationofEq.(6)is the“foragingarenaconcept”,inwhichthepreybiomassisdivided intovulnerableandnon-vulnerablecomponentsandthetransfer ratebetweenthesetwocomponentsisthevulnerabilityrate(
v
ij),whichdetermineswhethertheflowcontrolistop-down(
v
ij>2),bottom-up(
v
ij<2),ormixed(v
ij=2;thedefaultsvaluesinEwE).Asystemofdifferentialequations,suchasEqs.(5)and(6), per-mits thesimulation of foodweb dynamics and canbe usedto investigatefisheriesunderseveralmanagementscenarios.
Toanswerourthirdecologicalissue(i.e.,whetherthese envi-ronmentscansupportmoderatefishingpressure),weperformeda
setoffishingsimulationsontheoxbowlakestoassessthe ecosys-temchangesinducedbyfishingandtoverifytheperformanceof theKemptonandLindexes.Witheachof thethreemodels,we performed20simulations,eachofwhichrepresented30-year sce-narios withincreasing fishingmortalityforall fishspecies.The simulationswereperformedusingtheEcosimroutine(Christensen andWalters,2004a)andmaintainingtheinitialEcopathfishing mortality(F0=0.01,i.e.,1%oftheinitialEcopathbiomass)forthe
first4yearsofthesimulation,thenlinearlyincreasingthefishing mortalityinyears5–13fromF0toF=F0×f(wherefisamultiplier),
andthenmaintainingthelastvalueofFforthelast13yearsofthe simulation.Atotalof20simulationswereperformedforvaluesof fequalto10,20,30,40,50,60,70,80,90,100,200,300,400,500, 600,700,800,900,and1000.Thus,thesimulationsexploredthe effectsoffishingmortality(F)rangingfrom0.01to10.
TheEcosim vulnerabilityparameters are usuallydetermined throughthebestfitwiththeobservedtimeseries(see,forexample, Freireetal.,2008).However,notime-seriesdatawereavailable in thepresent study toperform anauthentic calibration;thus, weusedtheEcosimdefaultvulnerabilityparameters(Christensen andWalters,2004a;
v
=2,whichindicatesamixedfoodweb con-trol),andonlythefishingmortalitywaschangedinthedifferent simulations.Thephytoplanktonproductionwasmaintained con-stantduringthesimulations(“maxrelP/B”=1).Ifthebiomassofa compartmentdecreasedtolessthan20%oftheoriginalbiomass, thecompartmentwasnotconsideredintheanalysisofthemodel outputs.Theoutputsofthe30-yearsimulationswereusedto com-paretheKemptonQindexes,thenumbersofcompartmentsthat remained in the system(S), the TL dynamics, and the L index betweenthethreemodels.3. Results
3.1. BasicestimatesandPedigreeIndex
Of thethree proposedmodels, theBaia da Onc¸a (ONC) and theBrac¸oMortoAcima(MAC) werebalancedwithouttheneed to change theoriginal input values. Five components (Psectro-gaster curviventris,Moenkhausia spp., Trachydoras paraguayensis, Curimatellaspp.,andPimelodellaspp.)oftheBrac¸oMortoAbaixo (MAB)model hadEEvalues higherthan1,whichindicatesthat theflowswerenotbalanced(theconsumption flowsofagroup werehigherthanitsproductionflows).Themodelwasbalanced usingtheautomaticmassbalanceroutine(Christensenetal.,2005; Kavanaghetal.,2004),which,throughaseriesofiterativeruns, minimizesthechangesintheinputvaluesforthoseparameters withalowcontributiontothePedigreeIndextoproduceamodel withEEvalueslessthan1.0.
TheglobalPedigreeIndexeswere0.71,0.61,and0.59forthe ONC,MAC,andMABmodels,respectively.Thesevaluesindicate that the quality of the input data for the models is generally goodfor ecosystem analysis(Christensenet al.,2000).Through theEcorangerroutine,theONCmodelshowedalargernumberof acceptableexecutions(200/10,000),withaminimumresidualsum of159.07.TheMAChad85acceptableexecutionsoutof10,000, withaminimumresidualsumof172.38,whereastheMABhad27 acceptableexecutionsoutof10,000,withaminimumresidualsum of202.46.Thesevaluesareconsideredacceptableforthepurposes ofthisstudy(Villanuevaetal.,2006b).
The values of Production/Biomass (PB) and Consump-tion/Biomass(QB)forthefishspecieswerethesameinthethree models(Tables1–3).However,thevaluesofBforthe phytoplank-ton,macrophytes, aquatic,andterrestrial insectscompartments thatwerecalculatedbyEwEvaried.Thematrixofthediet com-positionineachsystemisshowninTablesA1–A3ofAppendixA;
Table1
Basicinputsparametersandoutputs(inbold)forEcopathoftheBaíadaOnc¸a(ONCmodel).TL,trophiclevel(TL);B,biomass(tkm−2);PB,Production/Biomass(tkm−2year−1); QB,Consumption/Biomass(tkm−2year−1);EE,EcotrophicEfficiency;OI,OmnivoryIndex;PN,PathNumber.
Compartimentos TL B PB QB EE OI PN Catch 1 Phytoplankton 1.00 3.82 250.00 0.334 2 Mammals 4.08 0.41 1.50 3.65 0.000 0.306 2085 3 Caymans 4.16 0.10 0.25 0.80 0.000 0.243 2023 4 Birds 3.62 0.10 0.30 47.20 0.000 0.275 155 5 Zooplankton 2.00 3.03 55.00 250.00 0.530 0.000 2 6 Aquaticinsects 2.00 4.28 10.40 40.00 0.620 0.096 1 7 Terrestrialinsects 2.00 2.42 25.00 250.00 0.620 0.128 1 8 Schizodonborellii 2.17 0.66 3.47 55.99 0.315 0.324 1 0.006 9 Sturisomarobustum 2.00 0.40 1.19 55.78 0.982 0.007 1 0.004 10 Tetragonopterusargenteus 2.94 0.11 3.20 18.73 0.996 0.062 5 0.001 11 Chaetobranchopsisaustralis 2.57 0.27 1.90 15.71 0.922 0.241 5 0.002 12 Pseudoplatystomareticulatum 3.43 0.74 2.82 10.00 0.228 0.444 798 0.007 13 Hemiodusorthonops 2.10 0.28 1.46 69.66 0.774 0.205 4 0.003 14 Hypostomusspp. 2.00 1.33 2.46 33.20 0.641 0.000 1 0.013 15 Psectrogastercurviventris 2.00 3.63 1.40 62.81 0.998 0.009 1 0.036 16 Pseudoplatystomacorruscans 3.50 0.10 2.82 10.00 0.062 0.483 489 0.001 17 Moenkhausiaspp. 2.99 5.36 5.00 25.84 0.432 0.180 6 0.053 18 Loricariaspp. 2.00 0.15 4.50 26.06 0.212 0.013 1 0.001 19 Potamorhinasquamoralevis 2.00 1.08 1.24 40.28 0.994 0.001 1 0.010 20 Auchenipterusnuchalis 2.98 0.29 3.00 14.56 0.398 0.023 5 0.003 21 Leporinusspp. 2.04 0.17 2.60 20.00 0.324 0.043 3 0.001 22 Plagioscionternetzi 3.31 0.32 1.41 8.77 0.993 0.270 108 0.003 23 Acestrorhynchuspantaneiro 3.41 0.15 2.20 24.64 0.401 0.390 248 0.001 24 Hemisorubimplatyrhynchos 3.34 0.11 1.30 10.32 0.465 0.191 108 0.001 25 Hypophthalmusedentatus 2.25 0.73 1.85 8.14 0.298 0.184 3 0.007 26 Cynopotamuskincaidi 3.14 0.26 1.75 14.54 0.939 0.286 66 0.002 27 Trachydorasparaguayensis 2.65 0.10 2.35 25.67 0.816 0.222 6 0.001 28 Hopliasmalabaricus 3.50 1.23 2.67 8.53 0.411 0.388 358 0.012 29 Serrasalmusspp. 3.30 0.58 1.62 14.64 0.995 0.413 68 0.006 30 Gymnogeophagusbalzanii 2.56 0.12 1.94 17.63 0.985 0.242 6 0.001 31 Pimelodusspp. 2.74 1.78 1.99 11.70 0.397 0.269 5 0.017 32 Loricariichthysspp. 2.00 0.80 1.17 25.51 0.991 0.006 1 0.008 33 Roeboidesspp. 3.20 1.06 2.12 21.48 0.947 0.424 11 0.010 34 Steindachnerinaspp. 2.00 1.82 1.30 52.78 0.988 0.000 2 0.018 35 Curimatelladorsalis 2.00 5.83 1.48 18.43 0.998 0.001 1 0.058 36 Triportheusspp. 2.72 0.79 2.11 14.30 0.960 0.325 6 0.008 37 Pimelodellaspp. 2.93 0.42 1.40 12.73 0.943 0.241 32 0.004 38 Astyanaxsp 2.59 0.12 2.40 15.27 0.855 0.313 4 0.001 39 Detritus 1.00 30.00 0.966 0.391
thesematrices are evidenceof thesimilarity of thediet ofthe
speciesinthethreeenvironments.
3.2. Outputmodels
IntheONCmodel (Table1), thehighestTL wasobtainedfor
caimans(4.16).Amongthefishspecies,thehighestTLvalueswere obtainedforHopliasmalabaricus(3.50),P.corruscans(3.50),andP. reticulatum(3.43).IntheMACmodel(Table2),thehighestTLvalue was4.31(mammals);amongthefishspecies,thehighestTLvalues wereobtainedfor P.reticulatum(3.33),P.ternetzi(3.33),andH. malabaricus(3.32).Mammalswerealsothegroupwiththehighest TL(4.32)intheMABmodel(Table3);thefourfishspecieswiththe nexthighestTLvalueswerethefollowing:H.malabaricus(3.47),P. ternetzi(3.41),Acestrorhynchuspantaneiro(3.33)andSerrasalmus spp.(3.33).
Ingeneral,thetoppredatorshavethelargernumberofpaths thatleadtothem.AmongthegroupswithaTL>3,nine,six,and threeofthegroupsintheONC(Table1),MAB(Table2),andMAC (Table3)models,respectively,exhibitedanOmnivoryIndex(OI) greaterthan0.25.Inthethreemodels,thespeciesAstyanax alti-paranae,Triportheusspp.,Pimelodusspp.,Pimelodellaspp.,Roeboides spp.,andH.edentatusfromtheintermediateTLs(between2and3) hadanOIgreaterthan0.25duetoahighlydiversifieddietdespite thelownumberofpaths.
ThezeroEEvalueforthecaimans,birds,andmammals com-partmentsinthethreemodelsreflectstheabsenceofpredation onthesegroupsbecausetheseareconsideredtoppredatorsinthe
system(Christensenetal.,2005).TheEEvaluesintheother com-partmentswerehigh,demonstratingtheimportanceofallofthese groupsaspreyand/orpredators.Theestimatedvaluesofthe zoo-planktonbiomass(between2.24and3.62)arecomparabletothose thatwereobservedinthePantanalfloodplainbyFantin-Cruzetal. (2010).
Ingeneral,thevaluesoftheecosystemattributes(Table4)are verysimilaramongthethreeenvironments,whichshowahigh overhead,highrecyclingflows(Finnindex),andlikelyahigh matu-rity(PPT/RTcloseto1),althoughtheascendancyvaluesarelow.The MABandMACmodelshaveahighertotalbiomassthantheONC modelduetothepresenceofaquaticmacrophytes.Thebiomass ofthefishgroupsintheONCmodelissmaller(30.79tkm−2)than intheMAB(36.03tkm−2)andtheMAC(43.97tkm−2)models,but thesebiomassdifferencesdonotsignificantlyalterthevaluesof theattributesoftheecosystems.
Thegeometricaverageoftheenergytransferamong theTLs (Table4)ishigherintheMAB(9.4%)modelcomparedwiththe ONCandMACmodels(8.1%inboth).Thesevaluesaresimilarto eachotherandclosetothoseobtainedwithotherenvironments (Christensenetal.,2005;Villanuevaetal.,2006b).
TheTransferEfficiency(TE)betweenTLs2and3ishigherina grazingfoodchainthaninadetritus-basedfoodchainfortheONC model(Fig.2),whichistheoxbowlakewithasignificantlylower biomassofprimaryproducers(withoutamacrophytes compart-ment).FortheotherTLs,theefficiencyofthegrazingfoodchain washigherthanthecorrespondingefficiencyofthedetritus-based chain.Inaddition,therearedifferencesin themagnitudeofthe
Table2
Basicinputsparametersandoutputs(inbold)forEcopathoftheBrac¸oMortoAcima(MACmodel).TL,trophiclevel(TL);B,biomass(tkm−2);PB,Production/Biomass (tkm−2year−1);QB,Consumption/Biomass(tkm−2year−1);EE,EcotrophicEfficiency;OI,OmnivoryIndex;PN,PathNumber;Catch,1%ofbiomass.
Compartments TL B PB QB EE OI PN Catch 1 Phytoplankton 1.00 4.17 250.00 0.401 2 Macrophytes 1.00 133.23 10.00 0.300 3 Mammals 4.31 0.43 1.50 3.65 0.000 0.005 1526 4 Caymans 4.16 0.10 0.25 0.80 0.000 0.156 1569 5 Birds 3.52 0.02 0.30 47.20 0.000 0.105 122 6 Zooplankton 2.00 3.80 55.00 250.00 0.536 0.000 2 7 Aquaticinsects 2.00 12.08 10.40 40.00 0.570 0.096 2 8 Terrestrialinsects 2.00 2.85 25.00 250.00 0.616 0.147 1 9 Schizodonborellii 2.17 1.91 3.47 55.99 0.302 0.214 3 0.019 10 Sturisomarobustum 2.00 0.52 1.21 55.78 0.943 0.007 1 0.005 11 Tetragonopterusargenteus 2.93 0.30 3.16 18.73 0.966 0.064 7 0.003 12 Chaetobranchopsisaustralis 2.57 0.50 1.95 15.71 0.996 0.241 6 0.005 13 Pseudoplatystomareticulatum 3.33 2.26 2.82 10.00 0.102 0.339 490 0.022 14 Hemiodusorthonops 2.03 0.59 1.46 69.66 0.904 0.029 6 0.006 15 Hypostomusspp. 2.00 2.59 2.46 33.20 0.643 0.000 1 0.026 16 Psectrogastercurviventris 2.00 3.14 1.40 62.81 0.985 0.006 1 0.031 17 Pseudoplatystomacorruscans 3.07 0.34 2.82 10.00 0.121 0.189 39 0.003 18 Moenkhausiaspp. 2.76 3.32 5.00 25.84 0.944 0.184 8 0.033 19 Loricariaspp. 2.00 0.27 4.50 26.06 0.280 0.007 1 0.002 20 Potamorhinasquamoralevis 2.00 2.33 1.23 40.28 0.975 0.001 1 0.023 21 Auchenipterusnuchalis 2.98 0.20 2.71 14.56 0.944 0.023 6 0.002 22 Leporinusspp. 2.03 0.21 2.60 12.72 0.473 0.033 5 0.002 23 Plagioscionternetzi 3.33 0.47 1.40 8.77 0.996 0.194 378 0.004 24 Acestrorhynchuspantanaeiro 3.29 0.14 2.22 24.64 0.429 0.108 42 0.001 25 Hemisorubimplatyrhynchos 3.27 0.27 1.30 10.32 0.643 0.122 345 0.002 26 Trachydorasparaguayensis 2.65 3.70 2.35 25.67 0.870 0.222 7 0.037 27 Hopliasmalabaricus 3.32 3.02 2.67 8.53 0.105 0.297 590 0.030 28 Serrasalmusspp. 3.24 0.43 1.58 14.64 0.994 0.367 407 0.004 29 Gymnogeophagusbalzanii 2.56 0.58 1.94 17.63 0.999 0.242 7 0.006 30 Pimelodusspp. 2.54 1.56 1.99 11.70 0.397 0.250 6 0.015 31 Loricariichthysspp. 2.00 2.51 1.16 25.51 0.972 0.006 1 0.025 32 Roeboidesspp. 3.02 1.05 2.09 21.48 0.996 0.301 32 0.010 33 Steindachnerinaspp. 2.00 3.17 1.33 52.78 0.969 0.000 2 0.031 34 Curimatelladorsalis 2.00 6.74 1.47 18.43 0.987 0.000 2 0.067 35 Triportheusspp. 2.73 1.46 2.11 14.30 0.830 0.210 7 0.014 36 Pimelodellaspp. 2.84 4.18 1.40 12.73 0.988 0.212 42 0.042 37 Astyanaxsp 2.62 0.40 2.43 15.27 0.991 0.266 6 0.004 38 Detritus 1.00 30.00 0.638 0.317
flowsfromthefoodchains:theenergy thatarrives attheTL 2
intheONCmodelarisesfromdetritus(1224.0twwkm−2year−1)
and producers (318.7twwkm−2year−1), resulting in a
detriti-vore:herbivoreratio(D:H)of3.8:1.IntheMACmodel,thisratio
is smaller (D:H=1.9:1) and similar to that of the MAB model
(D:H=1.82:1).TheseratiosdecreasedathigherTLsandexhibited
meanvaluesof3.06,1.4,and1.46intheONC,MAC,andMAB
mod-els,respectively(Fig.2).
Themammals,birds,andtop-predatorfishcompartments(H. malabaricus,P.reticulatum,Serrasalmusspp.,andPimelodellaspp.) hadthehighestKSi(Table5)inthethreeenvironments.
3.3. Quantificationandanalysisoffishingeffects
TheTotal PrimaryProduction Required(TPPR) surpassed P1 (energyavailableatTL1),whichisthesumofCalculatedNet Pri-maryProduction(PP) withFlowsto Detritus(FLTED)(Table6). ThedifferencesbetweenTPPRandP1,whichisestimatedbythe [1−(P1/TPPR)]index(Table6),showvalues closeto0.5forthe MACandMABmodelsand0.66fortheONCmodel.
TheLindexvaluesweresmallandsimilarbetweenthethree environments(Table6).Thisfindingindicatesthatfishingisa sus-tainableactivityinthesteady-statemodels.Theincreasedfishing mortalityinthesimulationsraisedtheLindexvalues,butthese increasedLindex values indicated ahighprobability of fishery sustainability(Fig.3).
Thesimulationsofthemodelsofthethreelakesindicatedthatan increaseinthefishingeffortreducedtheQindexandthenumber ofcompartments(Fig.4).FortheONCmodel(Fig.4a andb),an
“optimumKempton”wasfoundatf=7,i.e.,atthisvalue,thefishing activityis likelytoimprovethebiodiversity evenness.Forf>70 withtheONCmodel(Fig.4b)andf>50withtheMACandtheMAB models(Fig.4dandf),theKemptonvaluestendedtoinfinityand cannotbeusedtoevaluatethefoodwebstructure.Tomaintain theKemptonindexvalueclosetoitsinitialvalue(withoutfishing mortality),thefvalueshavetoremainatlessthan7fortheONC modelandatlessthan4fortheMACandtheMABmodels.
Ingeneral,thesimulationswithhighfishingpressureresulted intheremovalofthehighestTLspecies/group(Table7),suchas Hemisorubimplatyrhynchos(TL=3.34),mammals(TL=4.32),A. pan-taneiro(TL=3.33),andcaimans(TL=4.16).Mostoftheremovals were registered in the simulation with f=20 (13–14 compart-ments).TheTLsofthecompartmentsforthecommunity(excluding TL=1)decreased(Fig.5a)astheTLoftheremainingfish compart-mentsincreased(Fig.5b)togreaterthan2.5(Table7):Moenkhausia spp. (TL=2.76), Triportheus spp. (TL=2.59), and Tetragonopterus argenteus (TL=2.93). Auchenipterus nuchalis (2.98) and Pimelo-dusssp. (TL=2.74)alsopersisted intheONCmodel,whereas T. paraguayensis(TL=2.65)remainedintheMACmodeland Gymno-geophagusbalzanii(TL=2.56)remainedintheMABmodel. 4. Discussion
Themodels(ONC,MAC, andMAB)of thethree oxbowlakes integrated different biological information and sources of data, includingsomethatareavailableonlyinthegrayliterature.The goodqualityoftheinputvalues,particularlythebiomassestimates forthefishandtheirdietcompositions,werereflectedinthehigh
Table3
Basicinputsparametersandoutputs(inbold)forEcopathoftheBrac¸oMortoAbaixo(MABmodel).TL,trophiclevel(TL);B,biomass(tkm−2);PB,Production/Biomass (tkm−2year−1);QB,Consumption/Biomass(tkm−2year−1);EE,EcotrophicEfficiency;OI,OmnivoryIndex;PN,PathNumber;Catch,1%ofB.
Compartments TL B PB QB EE OI PN Catch 1 Phytoplankton 1.00 2.88 250.00 0.400 2 Macrophytes 1.00 123.26 10.00 0.300 3 Mammals 4.32 0.09 1.50 3.65 0.000 0.019 2892 4 Caymans 3.82 0.02 0.25 0.80 0.000 0.384 2774 5 Birds 3.54 0.02 0.30 47.20 0.000 0.104 137 6 Zooplankton 2.00 2.66 55.00 250.00 0.535 0.000 2 7 Aquaticinsects 2.00 12.98 10.40 40.00 0.627 0.096 2 8 Terrestrialinsects 2.00 2.18 25.00 250.00 0.626 0.144 1 9 Schizodonborellii 2.17 0.83 3.40 55.99 0.227 0.214 3 0.009 10 Sturisomarobustum 2.00 0.22 1.22 55.78 0.926 0.002 1 0.002 11 Tetragonopterusargenteus 2.93 0.39 3.12 18.73 0.988 0.064 7 0.004 12 Chaetobranchopsisaustralis 2.54 0.44 1.91 15.71 0.996 0.242 6 0.004 13 Pseudoplatystomareticulatus 3.20 1.31 2.82 10.00 0.050 0.539 591 0.013 14 Hemiodusorthonops 2.03 0.36 1.46 69.66 0.956 0.029 6 0.003 15 Hypostomusplecostomus 2.00 0.43 2.46 33.20 0.009 0.000 1 0.004 16 Psectrogastercurviventris 2.00 4.53 1.40 62.81 0.916 0.001 1 0.045 17 Pseudoplatystomacorruscans 3.14 0.04 2.82 10.00 0.094 0.455 119 0.000 18 Moenkhausiaspp. 2.76 1.28 5.00 25.84 0.904 0.184 8 0.012 19 Loricariaspp. 2.00 0.50 4.50 26.06 0.278 0.002 1 0.005 20 Potamorhinasquamoralevis 2.00 1.70 1.24 40.28 0.992 0.001 1 0.017 21 Auchenipterusnuchalis 2.98 0.74 2.71 14.56 0.901 0.023 6 0.007 22 Leporinusspp. 2.03 0.79 2.60 12.72 0.347 0.030 5 0.008 23 Plagioscionternetzi 3.41 0.43 1.46 8.77 0.998 0.290 465 0.004 24 Acestrorhynchuspantaneiro 3.33 0.05 2.22 24.64 0.880 0.196 1014 0.005 25 Hemisorubimplatyrhynchos 3.20 0.32 1.30 10.32 0.559 0.096 428 0.003 26 Trachydorasparaguayensis 2.65 3.25 2.35 25.67 0.906 0.222 7 0.032 27 Hopliasmalabaricus 3.47 1.17 2.67 8.53 0.266 0.501 1451 0.011 28 Serrasalmusspp. 3.30 1.89 1.59 14.64 0.978 0.354 731 0.019 29 Gymnogeophagusbalzanii 2.56 0.38 1.95 17.63 1.000 0.242 7 0.004 30 Pimelodusspp. 2.48 1.57 1.99 11.70 0.382 0.242 6 0.015 31 Loricariichthysspp. 2.00 2.06 1.26 25.51 0.920 0.006 1 0.020 32 Roeboidesspp. 2.74 0.94 2.08 21.48 0.999 0.256 32 0.009 33 Steindachnerinaspp. 2.00 0.85 1.33 52.78 0.985 0.000 2 0.008 34 Curimatellaspp. 2.00 2.04 1.47 18.43 0.917 0.000 2 0.020 35 Triportheusspp. 2.59 1.65 2.11 14.30 0.999 0.235 7 0.016 36 Pimelodellaspp. 2.85 4.53 1.40 12.73 0.902 0.205 42 0.045 37 Astyanaxsp 2.52 1.34 2.40 15.27 0.903 0.245 6 0.013 38 Detritos 1.00 30.00 0.603 0.304 Table4
Ecosystemattributesvaluesforthreemodels:BaíadaOnc¸a(ONC),Brac¸oMortoAcima(MAC),Brac¸oMortoAbaixo(MAB).
Attributes ONC MAC MAB
TotalSystemThroughput(tkm−2year−1) 5333 9075 7210
TotalPrimaryProduction/TotalRespiration(dimensionless) 0.60 1.05 1.12
Totalbiomass/totalthroughput(year) 0.008 0.023 0.025
Totalbiomass(excludingdetritus)(tkm−2) 44.94 204.83 180.14
ConnectanceIndex(CI;dimensionless) 0.146 0.151 0.164
Totalcatchessimulated(tkm−2year−1) 0.304 0.471 0.355 Meantrophiclevelofsimulatedcatches(dimensionless) 2.51 2.49 2.52 SystemOmnivoryIndex(SOI;dimensionless) 0.170 0.126 0.144 Throughputcycled(includingdetritus)(tkm−2year−1) 494.51 547.68 417.97
TransferEfficiency(%) 8.1 8.10 9.40
Finn’sCyclingIndex(%) 9.27 6.03 5.80
Ascendency(%) 26.40 26.90 27.10
Overhead(%) 73.60 73.10 72.90
Pathlength[Tf/(TEx+TRe)](dimensionless) 3.25 2.88 2.85
PathNumber(dimensionless) 6621 5671 10,765
Table5
Topfivespecieswithhigherkeystonenessspeciesindex(KSi)inthethreeoxbowlakes:BaíadaOnc¸a(ONC),Brac¸oMortoAcima(MAC)andBrac¸oMortoAbaixo(MAB).
Rank ONC MAC MAB
Oxbowlakes(KSi)
1 Birds(0.194) Mammals(0.139) Serrasalmusspp.(0.374)
2 H.malabaricus(0.108) H.malabaricus(0.122) H.malabaricus(0.345) 3 P.reticulatum(0.105) P.reticulatum(0.0375) Mammals(−0.002) 4 Mammals(0.094) Serrasalmusspp.(−0.0122) Aquaticinsects(−0.0462) 5 Aquaticinsects(−0.060) Aquaticinsects(−0.0328) Pimelodellaspp.(−0.0477)
Fig.2. ModifiedLindemanSpineofthreeoxbowlakesinPantanal(Brazil):BaíadaOnc¸a–ONC,Brac¸oMortoAcima–MACandBrac¸oMortoAbaixo–MAB.Pforprimary producersandDfordetritusidentify,respectively,detritus-basedandgrazingfoodchainsforeachmodel.Romannumbersstandforintegertrophiclevel.TransferEfficiency inpercentage.Flowsintwwkm−2year−1andbiomassintwwkm−2.
Table6
EstimatestocalculateLindexforthreeoxbowlakesinBrazilianwetlandPantanal:BaíadaOnc¸a(ONC),Brac¸oMortoAcima(MAC),Brac¸oMortoAbaixo(MAB).
Attributes ONC MAC MAB
PrimaryProduction–PP(tkm−2year−1) 954.25 2374.19 1953.58
FlowtoDetritus(tkm−2year−1) 1267 2456 1990
P1=PP+FlowtoDetritus 2221.25 4830.19 3943.58
TotalPrimaryProductionRequired–TPPR(tkm−2year−1) 6652.08 9521.24 7696.99
1−(P1/TPPR) 0.666 0.493 0.488
P1/TPPR(%) 33.39 50.73 51.24
PPRfromPrim.Prod.(tkm−2year−1) 1521.06 3415.56 2867.38 PPRfromdetritus(tkm−2year−1) 5131.01 6105.63 4829.61 Lindex(forcatchof1%ofinitialbiomass) 0.000075 0.000084 0.000052
Table7
Numberofthecompartments(seeTables1–3)extinctandrespectivemeantrophiclevelsbysimulationswithincreasingoffishingmortality(F).
F ONC MAC MAB
No.oftheextinctcomp. MeanTL No.oftheextinctcomp. MeanTL No.oftheextinctcomp. MeanTL
4 24 3.33 5 6 24 3.34 3 4.31 3 4.32 7 2 4.08 25 3.27 25 3.20 8 9;13;16 2.53 4;24 3.72 4 3.82 9 3 4.16 17 3.07 23 3.41 10 12;29 3.37 10 2.00 20 11;15;19;22;23;25;26; 28;32;34;35;37 2.59 10;12;13;14;20;21;23; 27;28;29;31;33;34 2.57 12;13;14;15;16;17;20; 21;27;28;30;31;33;34 2.51 30 4;8;14;21;27;30;33 2.61 3;9;15;16;22;30 2.38 5;9;22 2.58 40 1 1 32 3.02 32;36 2.66 50 38 2.59 19;37 2.26 60 36 2.84 70 18 2 19;37 2.31 26 2.65 Comp.remaining 5;6;7;10;17;20;31;36; 39 2.54 1;2;6;7;8;11;18;26;35; 38 2.44 1;2;6;7;8;11;18;29;35; 38 2.32
valuesofthePedigreeIndexes,whichweresimilartothosefound
byVillanuevaetal.(2006b),Pedersenetal.(2008),Tsagarakisetal. (2010),andGubianietal.(2011).Thelowsumofsquaresdifference betweenthereferencemodelsandthesuccessfulrunsofthe Eco-rangerroutineshowedthatthemodelsrealisticallydescribethe foodwebsoftheseenvironments(Christensenetal.,2005).
However,thelackoflocalandquantitativeinformationforthe non-fishcompartmentsrestrictsthemodelsbuiltinthisstudy.For example,theparametersforthecaimans(PBandQB)werebased oninformationobtainedwithcrocodiles,andthebiomasswas esti-matedbytuningtheMABmodel.Nevertheless,theintegrationof alloftheecosystem informationhighlightedthecompartments andregionsforwhichmoredetailedandquantitativestudiesare needed.Indeed, although it is one of thelargest floodplainsin theworld,thePantanalstill requiresthe accumulationofbasic informationtoprovideinsightsthatwouldproveusefultoits man-agement(daSilvaetal.,2001;Junketal.,2006).
Furthermore,thelackoftime-seriesdata(groupbiomassand landing)thatmight haveallowedthecalibrationof the ecosys-temdynamicsagainstobservedvaluesrepresentsalimitationof themodelresults.Therefore,theconsistencyofthemodeloutputs usingtheEcorangerroutinerepresentedthebestpossibletestofthe
Fig.3. Lindexdynamicswithdifferentvaluesoffishingmortality(F)simulatedfor thethreemodelsONC,MACandMAB.
availableinformation.Thus,oursimulationsneedtobeconsidered withcautionandwillcertainlyrequireadditionaldata.
Notably,theseoxbowlakeshavethesamedistances,depths,and timesofisolationastheriver;thesearetheabioticvariablesthat mostinfluencethefishspeciesrichnessinthelakesofthePantanal (Súarezetal.,2004).Accordingly,thethreelakemodelsserveasa goodbasisforthecomparisonoftheseenvironments.
Althoughthereissimilarityamongthespeciescompositions, somespeciesoccupydifferentTLsineachenvironment;for exam-ple,theTLofP.corruscansis3.5intheONC,3.07intheMAC,and3.14 intheMAB.Thisfindingindicatesthat,despitethesimilarityofthe habitatsanddiets,thereisstillaconsiderablesmall-scalespatial variability(Girardetal.,2010),whichindicatesthatthespeciesact moreinatrophiccontinuumthaninconstantTLs(Wantzenetal., 2002)andhighlightstheneedforsite-specificevaluations.
The range in species diets may alsofavor theircoexistence andincreasethetolerancetoenvironmentalstress(Méronaand Mérona,2004;Pouillyetal.,2006).Inthestudiedlakes,the vari-anceinthedietsacrossTLs,i.e.,theOI,waslowcomparedtothat obtainedinothersystems(AngeliniandAgostinho,2005b).This variabilityisanunusualsituationintropicalenvironments,where mostofthefishspeciesaregeneralists(Hahnetal.,2004),andmay beaneffectof theaggregationof functionalgroups withlower TLs(zooplankton,insects,andprimaryproducercompartments), whichconsequentlyreducestheOIoftheirpredators.
ThelowglobalvaluesforSOIandCIresultfromsystemsthatare moredependentondetritusasasourceofenergy(Heymansetal., 2004);systemsthatareless dependentondetritushavehigher CIandSOI valuesbecausetheorganismsneedtodiversifytheir energysources(Christensen,1995;Vasconcellosetal.,1997).Thus, thehighdependenceoftheorganismsonthedetritusfoodchain simplifiesthefoodwebs.
Theimportanceofdetritusisconfirmedbythehigherenergy flowfromdetritus(Fig.2)toTL2,whichdemonstrates thehigh degreeofdependenceofthesystemonthehydrologicalseasonality thatbringsthedetritus(Silvaetal.,2010).Thisresultissupported bytheecologicaltheoryfortropicallakes,inwhichthemainenergy flowpathinshallowwatersflowsthroughdetritus(Bowen,1983). Matureecosystemsaremoredependentondetritivorythanon her-bivory(Odum,1969),andtheD:Hratio(detritivory:herbivory)in themodels(Fig.2)thatweredevelopedinthisstudywasalways high,particularlyintheONCmodel.
The role of detritus from flood pulses could beeven larger because it maintains the energy flow; stabilizes the food web
Fig.4.KemptonQindexandnumberofcompartmentssimulatedwithfishingincreasingforthreemodels.LeftpanelsrepresenttheKemptonQandthenumberofspecies attheendof30yearssimulationsforONC(a),MAC(c)andMAB(e).RightpanelsrepresentKemptonQindexmonthlydynamicsduringthesimulationswithdifferentvalues offishingmortality(F)simulated(seetext),formodelsONC(b);MAC(d)andMAB(f).
(Mooreetal.,2004)whenthephysicalmodificationofthehabitat andofitsvariables,suchasdissolvedoxygen,pH,waterclarity,and waterspeedareinduced(Silvaetal.,2010;Williamsonetal.,1999); andplaysafundamentalroleintheorganizationandmaintenance ofecosystems(CyrandPace,1993;Junketal.,2006;Luz-Agostinho etal., 2008;Hamilton,2010).Fish consumptiononthe Baiada
Onc¸ashowsthatdetritusandalgaearethemainfooditemsthat areconvertedintofishbiomass(46.5%),andtheyarefollowedby fish(8.4%),insects(6.5%),andmicrocrustaceans(3.9%).Thisfinding shortensthefoodwebandincreasestheeffectivenessofthe com-munity(CatellaandPetrere,1996)despitethefactthatthevalues oftrophicTransferEfficiency(TE)werelower(8.1%fortheONCand
Fig.5.Trophiclevel(TL)dynamicsduringthesimulationswithdifferentvaluesoffishingmortality(F)simulated(seetext)formodelsONC,MACandMAB.(a)TLforall speciesremained;(b)TLforfishspeciesremained.
theMACmodelsand9.4%fortheMABmodel)thanexpected(10%; PaulyandChristensen,1995).
Thedependenceondetritusmaintainstheresiliencyofthe sys-tembyprovidinga highsourceof reservedenergy becausethe overheadvaluesarehigh(Christensen,1995)and maintainsthe flexibilityandlikelythematurityofthesystembecausethePPT/RT valuesareclosetoone.TheONCmodelhasaPPT/RTsmallerthan onebecausenoaquaticmacrophytesareincludedinthismodel. Asaresult,thedetritusissuppliedthroughthefloodpulses,and itsinternalcycling,whichisestimatedbytheFinnindex,ishigher thanthatintheothermodels,regardlessofthesmallerflowof detritus.Thesimilaroutputsamongtheoxbowlakesconfirmthat thefoodwebsofthelakesareanalogoustoeachother;although thelakesfollowdistinctpathsintimesofdrought,whichdependon thepresenceorabsenceofpredators,thelakesarehomogenizedby thehigh-tidefloodpulse(Thomazetal.,2007),whichlikelycauses anintermediatedisturbancethatlimitsthematuringprocessofthe lakes(modelshavelowascendancyvalues).
Thehighvaluesofthekeystonenessspeciesindexinthethree modelshighlighttheimportantroleofthetoppredators(H. mal-abaricus, Serrasalmus spp., P.reticulatum, birds, and mammals). Therefore,inadditiontothedetritus(bottom-upprocesses),the toppredatorsalsotendtobeveryinfluentialintheconfigurationof thefoodweb,whichsuggeststhatanemergingfeatureofthethree systemsistheactionofamixedoralternatedcontrolsystem.Infact, theseparationoftop-downandbottom-upprocessesbecomes par-ticularlydifficultinaflooding-proneenvironment,whichdoesnot necessarilyinducethepredominanceofanyoftheseprocesses.The strongrelationshipbetweenbiotic(predators)andabiotic compo-nents(detritus)inthedeterminationofthefishcommunitiesin thePantanal,hasalsobeenstressedbySilvaetal.(2010).Itislikely thatthetoppredatorshaveahigherlevelofactioninthedry sea-son(Wantzenetal.,2002;Thomazetal.,2007)andthatdetritus hasahigherlevelofactionduringfloods.Thisexplainsandsupports thepropositionthatanalternatedcontrolsystemdeterminesthe ecosystemprocessesinthePantanallakes.Thefactthatthemodels usedinthisstudyrepresentannualbudgetsdoesnotallowthefull confirmationofthissuggestion,whichappearsquiteplausible,and providesatopicforfuturestudiesandanalyses.
AnimportantfractionoftheTotalPrimaryProductionRequired (TPPR)returnstodetritusandenhancestherecyclingcausedbythe predatoryspeciesinthehigherTLs,whichresultsinvaluesofTPPR thatarehigherthantwicetheP1(sumofPrimaryProduction(PP)
withFlowtoDetritus).ThedifferencebetweenTPPRandP1 indi-catesthatthehigherTLscreatedahighlevelofpositivefeedback inthesesystems,especiallyintheONCmodel,whichhasalower biomassofprimaryproducers.Therefore,thetoppredators cre-ateaneffectivesetofpositiveparallelfeedbacksthatgeneratean indeterminationofflowsandenrichthesystem,therebyallowing ittogrow,tobeflexible,andtowithstanddisturbances(Ulanowicz, 2009).
Increasingtheeffectoffishinginthesimulationsresultedinan increaseintheLindexvalues(Fig.3).However,thehighestvalues oftheLindexthatwereobtainedwerenotsufficienttomodifythe probabilityofsustainablefishery,whichwasverycloseto1, regard-lessofthefishingpressure.Thisresultimpliesthesustainabilityof fisheries(Libralatoetal.,2008).TheKemptonindexexhibited dif-ferenttrendsdependingonthesystem(Fig.4).IntheONCmodelthe Qindexreachedan“optimumvalue”atf=7,whichindicatesthat thelossofH.platyrhynchosandmammalslikelyincreasedthe even-nessoftheremainingspecies.Incontrast,theKemptonQindexfor theMACandtheMABmodelsdecreasedbeforethefirstremovals fromtheecosystem(Fig.4),likelybecauseincreasingthefishing mortality(F)decreasestheevennessamongthecompartments.The Kemptonindexforthemodelsofthethreeoxbowlakesconfirmed thatthefishingactivitycouldconsiderablymodifythefish commu-nity,ashasbeenobservedinotherecosystems(Colletal.,2008b). Inaddition,whenmanycompartmentsbecamelocallyextinct,the Kemptonvaluestendedtoincreasenotably(notethatEwElimits thecomputationfortheKemptonQwhenitsvalueishigherthan twiceitsinitialEcopathvalue)inresponsetoasubstantialchange inthefoodweb.
Thesimulatedaveragevalueofthetrophiclevelofthe commu-nity(TLco)atincreasingvaluesoffshowedtwooppositepatterns: theTLcodecreasedforallspecies(excludingTL=1)andincreased for fishspecies asthefishing pressureincreases. Inall models, the zooplankton, aquatic, and terrestrial insects (TL=2.0) com-partmentsremainedand exhibitedincreasedbiomasswhen the weightedTLdecreased.Whenitwascalculatedonlyforthefish compartments, theTLco increasedbecause allof thefish com-partments withTL=2.0 wereremoved,particularlywhen F=20 (Table7).Attheendofthesimulation,onlythosefish compart-mentswithTL>2.5remainedinthethreemodels(Table7).
Themammals’compartmentwastheonlykeyspecies(Table5) that were first removed in the MAC and the MAB models (Table7; compartment 3).In general,these removalscould be
consideredlocalextinctions.Thecompartments(orspecies)that rapidlybecamelocallyextinctcanbecalled “non-impacted-food-web-dependent” (sensible toimpacts). Moreover,these species generallyhavesimilarTLscomparedwiththekeyspecies. How-ever,withtheexceptionofmammalsandP.reticulatumintheONC model,whichbecameextinctwithF=10,theotherkeyspecieswere extinctonlywhenF>20,whichisaveryhighvalueforthe commu-nitiesintheseareas.Thisresultindicatesthatthekeyspecieshave ahighcapacitytoremaininimpactedenvironmentsandattempt tomaintaintheirinteraction strengthswiththecomponentsof another system even under impact. However, this assumption requiresfutureinvestigation.
Themainecologicalroleoftheseoxbowlakesistoprovidea habitatforthegrowthofmanysmallspecies,whicharepreyedon bythespeciesthatarethemaintargetsoffishing.However,someof thosespeciesthatarenotthemaintargetsoffishing(e.g.,Hoplias sp.,Hypostomussp.,andPotamorrhynasp.)haveahighpotential tobedirectlyusedintheproductivesector(non-professionaland evenindustrial)aslongasvalueisaddedtothemduringthefish processing(Laraetal.,2008).Nevertheless,theresultsalsoindicate thatonlymoderatefishingcanbesustainedbecausethereduction ofthebiomassofthesespeciesnegativelyimpactsthetop preda-torsandincreasestheirprobabilitytobecomelocallyextinct.This findingis inaccordancewiththeimportanceof“foragefish” in supportinglargerpiscivores(Waltersetal.,2005).
5. Conclusion
Considering that there are approximately 260 oxbow lakes inthestudiedregion,which have a total watersurface areaof 14.05km2andonaverage37.4toffishperkm2(Tables1–3),one
couldexpectthetotalfishbiomassintheoxbowlakestobe approx-imately525t.Afishingmortalityof3,whichwouldcorrespondto catchingalmost10tyear−1inthethreesystemssimulatedinthis studyorcloseto140tyear−1inalloftheoxbowlakes,wouldnot affectthefishstockorlocalbiodiversity.Thisfishingamount cor-respondstohalfoftheannualprofessionalfishingaverageof270t thatwasrecorded intheentirePantanalintheperiodbetween 2000and2005(AlbuquerqueandCatella,2009).Ourresultagrees withtheguidelinesproposedbyCatella(2003)forthelocalfishing policytoincreasetheutilizationofunderexploitedspeciesthatare presentintheseenvironmentsandtosimultaneouslyreducethe fishingeffortonlargetargetspeciesinthePantanal,whichdoes notcurrentlyoccurintheenvironmentsanalyzedinthisstudy.The useoftheseunderexploitedfishesbyprofessionalfishermencould evencontributetoareductionintheconflictsofinterestregarding theuseoffishingresourcesthatexistamongnon-professional fish-ermenandthetouristicfishingsectorinthePantanal(Catellaetal., 2008).Animprovedandmorecautiousapproachtowardthefish stockandthePantanalbiodiversitywoulduseanadaptive man-agementapproach(MarttunenandVehanen,2004;Walters,1986), whichimpliesagradualincreaseoffishing:forthefirst5years,the fishingmortalityshouldbeF=1,andifthefishingandpopulation monitoringshowresultsthatareinagreementwithourfindings, themanagerscouldincrease thecatches.However,it wouldbe necessarytodefine,togetherwiththeusers andtheregulatory agencies,therulesforexploitation:(a)permittedgearsandmesh size(onlythehookiscurrentlyallowed), (b)community-based datamonitoring(Carvalhoetal.,2009),and(c)suitableprocessing technologiestoavoiddiscardsandsupplythemarketdemand.
Theresultsof theexploratorysimulation analysis presented hereshouldbeconsideredwithappropriatecaution.Themodels developedhighlightedtheinputdatathatrequirelocalestimates for thebiomass and parameters of somegroups. Similarly, the lackoftime-seriesdatadidnotallowapropermodelcalibration.
Nevertheless, the models developed include the best available informationandhavebeencheckedforconsistency.Moreover,the modelresultssubstantiateknownecologicalfeatures.Theissues investigatedindicatethepotentialforecosystemmodelinginthis biomeandidentifiedareasinwhichfurtherinvestigationandthe collectionofadditionaldatawouldallowfillingthegapsidentified inthisstudytoultimatelyimproveboththeecologicalknowledge andthemodelingapproach.Itisworthmentioningthattheseare thefirstquantifiedfoodwebaquaticmodelselaboratedforthe Pan-tanaloxbowlakes:themodelshighlighttheecologicalimportance ofthisapproachandprovideinsightsforfuturestudiesthatwould helpunderstandtheeffectofthefisheryontheenvironmentsof thePantanalthroughecosystemmodeling.
TheEwEapproach providedanunderstandingof theaquatic food webs of three marginal lakes in the Pantanal, which are supposedtobestructurallycharacterizedbyalternatedcontrol, throughtheuseofbasalcompartments:detritus,whichentersthe systemthroughfloodpulses(wetseason),andtoppredators,such asH.malabaricus,Serrasalmusspp.,P.reticulatum,birds,and mam-mals(dryseason).Theoxbowlakesmaybeconsideredresilient systemsduetotheirecosystemattributesandareableto sustain-ablywithstandalowfishingpressure,particularlyofspeciesthat arenotcurrentlyexploited,becausethisfisherywouldbe moni-toredandevaluatedwiththeoutputsfromourmodels.
Acknowledgments
ThispaperisbasedontheMaster’sDegreedissertationofR.J. MoraisunderthesupervisionofR.AngeliniintheEcology Post-GraduateProgramatUniversidadeFederaldeGoiás(Brazil).The authorsthanktheeditorandthetwoanonymousreviewerswho improvedthemanuscript,especiallyreviewer#3,whosuggested theexpression“alternatedcontrol”.TheauthorsalsothankAJEfor theEnglishrevision.
AppendixA. Supplementarydata
Supplementarydataassociatedwiththisarticlecanbefound, in the online version, at http://dx.doi.org/10.1016/j.ecolmodel. 2013.01.001.
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