Network science: applications for sustainable agroecosystems and food security

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Essays and Perspectives

Network science: Applications for sustainable agroecosystems and food security

Fredric M. Windsor

^a,∗

, Dolors Armenteras

^b

, Ana Paula A. Assis

^c

, Julia Astegiano

^d

, Pamela C. Santana

^c

, Luciano Cagnolo

^d

, Luísa G. Carvalheiro

^e,f

, Clive Emary

^g

, Hugo Fort

^h

, Xavier I. Gonzalez

ⁱ

,

James J.N. Kitson

^a

, Ana C.F. Lacerda

^j

, Marcelo Lois

^k

, Viviana Márquez-Velásquez

^l,m

,

Kirsten E. Miller

^a

, Marcos Monasterolo

ⁿ

, Marina Omacini

^o

, Kate P. Maia

^c

, Tania Paula Palacios

^k

, Michael J.O. Pocock

^p

, Santiago L. Poggio

^o

, Isabela G. Varassin

^q

, Diego P. Vázquez

^r

, Julia Tavella

^k

, Débora C. Rother

^c

, Mariano Devoto

^k,s

, Paulo R. Guimarães Jr.

^c

, Darren M. Evans

^a,∗

aSchoolofNaturalandEnvironmentalSciences,NewcastleUniversity,NewcastleuponTyne,UK

bLaboratoriodeEcologíadelPaisajeyModelacióndeEcosistemasECOLMOD,DepartamentodeBiología,FacultaddeCiencias,UniversidadNacionaldeColombia,SedeBogotá, Colombia

cDepartamentodeEcologia,InstitutodeBiociências,UniversidadedeSãoPaulo,SãoPaulo,Brazil

dInstitutoMultidisciplinariodeBiologiaVegetalFCEFyN,UniversidadNacionaldeCordoba,CONICET,Cordoba,Argentina

eDepartamentodeEcologia,UniversidadeFederaldeGoiás,CampusSamambaia,Goiânia,Brazil

fCentreforEcology,EvolutionandEnvironmentalChanges(cE3c),UniversityofLisboa,Lisbon,Portugal

gSchoolofMathematics,StatisticsandPhysics,NewcastleUniversity,NewcastleuponTyne,UK

hGrupodeSistemasComplejosyFísicaEstadística,FacultaddeCiencias,UniversidaddelaRepública,Montevideo,Uruguay

iUniversidaddeBuenosAires,FacultaddeIngeniería,DepartamentodeGestión,BuenosAires,Argentina

jDepartamentodeSistemáticaeEcologia,CentrodeCiênciasExatasedaNatureza,UniversidadeFederaldaParaíba,JoãoPessoa,Brazil

kUniversidaddeBuenosAires,FacultaddeAgronomía,CátedradeBotánicaGeneral,BuenosAires,Argentina

lProgramadePós-Graduac¸ãoemCiênciasBiológicas(Zoologia),UniversidadeFederaldaParaíba,JoãoPessoa,Brazil

mIRISResearchGroup,InnovationforResilience,InclusionandSustainability,LaboratoryofAnimalEcology,UniversidadeFederaldaParaíba,CampusIV,RioTinto,Brazil

nCentrodeInvestigacionesyTransferenciadeCatamarca,CONICET-UNCA,SanFernandodelValledeCatamarca,Catamarca,Argentina

oIFEVA,UniversidaddeBuenosAires,CONICET,FacultaddeAgronomía,CátedradeProducciónVegetal,BuenosAires,Argentina

pUKCentreforEcology&Hydrology,Wallingford,UK

qDepartamentodeBotânica,UniversidadeFederaldoParaná,Curitiba,Paraná,Brazil

rInstitutoArgentinodeInvestigacionesdelasZonasÁridas,CONICETandUniversidadNacionaldeCuyo,Mendoza,Argentina

sConsejoNacionaldeInvestigacionesCientíﬁcasyTécnicas(CONICET),Argentina

h i g h l i g h t s

•Wereviewedtheuseofnetworksci- enceinsustainableagriculture.

•Network science can be used to understand,harnessandrestoreeco- logicalprocessesinagriculturalsys- tems.

•Social, economic and ecological aspects of agriculture can be incorporatedusingnovelmethods.

•Agriculturalsystemscanbemanaged usinganetwork-basedframework.

g ra p h i c a l a b s t r a c t

∗ Correspondingauthor.

E-mailaddresses:fredric.windsor@ncl.ac.uk(F.M.Windsor),darren.evans@ncl.ac.uk(D.M.Evans).

https://doi.org/10.1016/j.pecon.2022.03.001

creativecommons.org/licenses/by/4.0/).

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a rt i c l e i nf o

Articlehistory:

Received4August2021 Accepted4March2022 Availableonline30April2022

Keywords:

Agriculture Biodiversity Ecologicalnetworks Socialnetworks Crops Foodproduction Resilience

a b s t ra c t

Theglobalchallengeoffeedingtwobillionmorepeopleby2050,usingmoresustainableagricultural practiceswhilstdealingwithuncertaintiesassociatedwithenvironmentalchange,requiresatransfor- mationoffoodsystems.Wepresentanewperspectiveforhowadvancesinnetworksciencecanprovide novelwaystobetterunderstand,harness,andrestoremultipleecologicalprocessesinagriculturalenvi- ronments.Wedescribe:(i)anetwork-focusedframeworkformanagingagro-ecosystemsthataccounts forthemultipleinteractionsbetweenbiodiversityandassociatedecosystemservices;(ii)guidancefor incorporatingsocio-economicfactorsintoecologicalnetworks;and(iii)thepotentialtoupscalenetwork methodstoinformeffortstobuildresilience,includingglobalfood-supplychains.Indoingsoweaimto facilitatetheapplicationofnetworkscienceasasystems-basedwaytotacklethechallengesofsecuring anequitabledistributionoffood.

“The naturallivingworldisarrangedinverycomplex channels ofsupplythatareknownasfood-chains.[...]Butwithlandin cultivation,whetherpastoral,ploughed,orgardened,theearnest desireofmanhasbeentoshortenfood-chains,reducetheirnumber, andsubstitutenewonesforold.[...]Theenormousproblemstill istomanage,control,andwherenecessaryalter thepatternof food-chainsintheworld,withoutupsettingthebalanceoftheir populations.Itisthislastproblemthathasnotbyanymeansbeen solved.”CharlesElton(1958)

Introduction

Oversixbillionpeoplearedirectlydependentuponagriculture fortheirlivelihoodsworldwide(FoodandAgricultureOrganization oftheUnitedNations,2013),withoneintenpeople(690million) undernourished and themajority oftheworld’s hungry people living in developing countries (United Nations, 2020). Feeding nine billionpeopleby2050,usinglessenvironmentallydamag- ingagriculturalpracticeswhilstalsodealingwiththeuncertainties posed by climate change,is a signiﬁcant undertaking(Godfray etal.,2010).Thescenarioisespeciallychallengingifweconsider that52%ofagriculturallandiscurrentlymoderatelyorseverely degradedasaresultofintensiveagriculturalpractices(Foodand AgricultureOrganizationoftheUnitedNations,2019).Thenexus betweenfooddemandandsupplyposesamajorchallengetocon- temporaryfoodsystemsthatappearunder-equippedtodealwith future challenges.Ultimatelytheymaylackresiliencetopoten- tial perturbations and long-term changes (i.e.,climate change), whichthreatentheintegrityofglobalfoodproduction,foodqual- ity, environmental stability and human health (Cottrell et al., 2019).

Addressingtheseissuesalongside‘bendingthecurveofbiodi- versityloss’(Leclèreetal.,2020)associatedwiththeunsustainable intensivefarmingpracticesandinequitabledistributionandcon- sumptionoffood,requiresaradicaltransformationoffoodsystems fromlocaltoglobalscales(WorldWideFundforNature,2020).

“Bending the curve” is now a major policy driver for many nationsinthequestforsustainableagriculture(e.g.,theEuropean Union’s Farm to Fork Strategy).However, thesigniﬁcantsocio- ecologicalcomplexityincontemporaryagriculture,coupledwith theglobalmovementofgoodsandlabour,inequitabledistribution ofwater andrapidchangesinenvironmentalconditions,means thatfutureagriculturalchallengesaremultifaceted,interconnected and require large amounts of information in order to succeed (HazellandWood,2008).TheUnitedNationsSustainableDevelop- mentGoals(SDGs)weredevelopedtoaddresssuchinterconnected challenges(Chaudharyetal.,2018)andtheecosystemservicespro- videdbyagriculturalsystemsdependonthestronginteractionsand feedbacksbetweenhumans,theenvironment,andfoodsystems.

Therefore,wholesystems-basedapproachesarerequiredtounder-

standandeffectivelymanagetheselayersofcomplexityandface thechallengesimposedbytheSDGs(McGowanetal.,2019).We contendthatcomplexityscience(Table1),inparticularadvancesin networkscience,canprovideasystems-basedframeworkwithin whichthechallengestocontemporaryagriculturalandfoodsys- temscanbeaddressedatmultiplespatialandtemporalscalesand usingdifferentinformationtypes.

Networkscience,whenappliedtothestudyofecosystems,has beenusedtodescribeandunderstandthepatternsofinteractions inassemblagesofinteractingspecies,characterisingtheunderly- ingstructureofecologicalcommunities.Thenetworkstructureof ecologicalsystems,combinedwithmathematicalmodelling,can enhanceour understandingofecological and evolutionarypro- cesses(e.g.,co-evolutionof antagonistsandmutualists), energy andmatterflowinecosystems,theecosystemservicesprovidedby biodiversity,anddemographicdynamicsinspecies,duetoenviron- mentalchange(Montoyaetal.,2006).Inrecentdecades,significant advancesin ourunderstandingof thestructure and robustness ofecologicalnetworkshavebeenderivedfromempiricalstudies conductedinagro-ecosystems,allowingustobetterunderstand therelationshipsbetweenthestructureandfunctionofcomplex ecosystems(SupplementaryTableS1).Inturn,thishasenablednew guidanceforthesustainablemanagementandrestorationofimpor- tantandbeneficialgroupsoforganismsacrossagriculturalregions (seebelow).

As well as proving useful for studying ecological dynamics, network-based thinking enables an understanding of the socio-ecologicalinteractionspresentinagriculturalenvironments (Bohan etal., 2013).This isparticularlyimportant fordevelop- ingfuture management,conservationand restoration strategies tobuild resilience in both crop and non-crop habitats in agri- culturalregions(Bullocketal.,2017).Byscaling-upfurther,the same network approaches can be developed to examine and improvetheresilienceofglobalfoodsupplychains(Pumaetal., 2015).

Here, we present a state-of-the-art for network science in agriculturalsystems,fromspecies interactionnetworksinfields throughtoglobaltradenetworks.Wehighlightrecentdevelop- ments in the field, identify future advances required to build resilienceintofoodsystemsandultimatelydemonstratehowwe canusenetworkspredictivelytogenerateefficientandsustainable agriculture.Withafocusonarangeofdifferentterrestrialcrop- pingsystems(e.g.,intensivetoextensivemanagement),ouraims arefourfold:(i)tosummarisethetheoryandapplicationofnet- workscienceinagro-ecosystems;(ii)todemonstratetheuseof networkapproachestounderstandandmanageagro-ecosystems acrossscales;(iii)toidentifyimportantresearchthemesinagricul- turalnetworks;and(iv)toelucidatefutureadvancesrequiredto understandanddevelopresilienceinagro-ecosystemsacrossthe globe.

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Table1

Glossaryoftermsrelatingtocomplexityandnetworkscienceinagriculture.

Term Description

Generalterms

Complexityscience Thestudyofcomplexsystems,thosecomposedofmultiplecomponentsthatmayinteractwithoneanotherto produceemergentproperties,localconditions,non-linearityandadaptation,whichshapethecollectivebehaviours.

Complexityisobservedinavarietyofsystems.

Networkscience Thestudyoftheinteractionsbetweendiscreteelementsandtheirpotentialimplications.

Network Asystemofinterconnectedorlinkedelements,alsocalleda‘graph’inmathematics.

Ecologicalnetwork Asystemofinterconnectedecologicalunits(e.g.,individuals,populations,species)ataparticulartemporalandspatial scale(e.g.,ahabitat,acommunity,anecosystem).

Networkterms

Nodes(orvertices) Elementswithinanetwork.Nodesmaybepeople,animals,plants,ideas,places,ecosystemservices,habitatsora rangeofotherdiscreteelements.

Links(oredges) Interactionsorrelationshipsbetweentwoormorenodeswithinanetwork(seeEcologicalinteraction).Thiscouldbe weighted(e.g.,visitationfrequencyofapollinatorornumberofcaterpillarsattackedbyparasitoids)orunweighted (e.g.,present/absent).Furthermore,edgescanbedirected(i.e.,fromonenodetotheother)orundirected(i.e.,where therelationbetweenthetwonodesissymmetrical).

Ecologicalinteraction Afunctionallink(seeLinks)betweentwoorganisms(seeNodes)inanecologicalnetwork.Thismaybemutualistic (e.g.,pollination,seeddispersalandendosymbiosis),competitive,antagonistic(e.g.,predationandparasitism), commensalistic(e.g.,someplantsandarbuscularmycorrhizae)oramensalistic(e.g.,tramplingofplantsand invertebratesbylargeanimals).SeeEcologicaltermsforfulldeﬁnitionsofdifferentecologicalinteractiontypes.

Directinteractions Theeffectsofonenodeonanotherthataremediatedbyalinkbetweenthesetwonodes(e.g.,exchangeofgoods, predation,parasitism).

Indirectinteractions Theeffectsofonenodeonanotherwhichismediatedbyoneormoreintermediarynodes.

Unipartitenetwork Anetworkcomprisedofonesetofnodesinwhichinteractionsoccurbetweenallnodes(e.g.,foodwebs).

Bipartitenetwork Anetworkcomprisedoftwosetsofnodes(e.g.,plantsandpollinators,herbivoresandparasitoidsorfarmersand policymakers)inwhichinteractionsonlyoccurbetweennodesofdifferentsets.

Multilayernetwork Anetworkwithmultiplelayersdescribingdifferenttypesoflinks,nodes(e.g.,mutualisticandantagonisticorsocial andecological),orspatiotemporalvariationinthesystem.

Networkstructure Thepatternsofinteractionofagivennetwork(e.g.,thearrangementofnodesandlinks).

Robustness Thetoleranceofnetworkstructuretonodeextinctions.

Ecologicalterms

Amensalism Aninteractioninwhichonespeciesisnegativelyaffected,yettheotherreceivesnocostorbeneﬁt(e.g.,tramplingofa plantbyananimal).

Competition Aninteractionbetweentwospeciesoveracommonresourcethatisinlimitedsupply(e.g.,twopollinatorsvyingfor nectarandpollenfromthesameﬂower).

Apparentcompetition Anindirect,interspeciﬁcinteractionwheretheabundanceofonespeciesisreducedbytheincreaseofotherspecies abundance,withoutdirectinteractionoccurring(e.g.,abundanceofonepreyspeciesisreducedthroughpredation fromafood-limitedgeneralistpredatorsharedwithanotherorganism,orabundanceofonespeciesisreducedinthe presenceofaparasitesharedwithacompetitorspecies).

Mutualism Aninteractioninwhichbothpartnersbeneﬁt(e.g.,plant-pollinatorinteractions).

Antagonism Aninteractioninwhichonepartnerbeneﬁtsattheexpenseofanother(e.g.,predator-prey,plant-herbivoreand herbivore-parasitoidinteractions).

Spillovereffects Ecologicalprocessesoccurringinonehabitatspilloverintotheneighbouringhabitats(e.g.,ﬁeldmarginsenhance pollinationinneighbouringarableﬁelds).

Resilience Theamountofdisturbancethatanecosystemcanwithstandwithoutchangingself-organisedprocessesand structures(Holling,1973)

Ecosystemfunctions Theecologicalprocessesthatregulatethetransfersofenergyandmaterialsthroughanecosystem.

Ecosystemservices Thebeneﬁtsprovidedbyecosystemstohumanwellbeing,i.e.,Nature’scontributiontopeople.Therearefourclasses:

provisioning(products,i.e.,food,freshwater,fuel,wood),regulating(regulationofprocesses,i.e.,climate,ﬂood, disease,pollination),supporting(supportotherecosystemservices,i.e.,nutrientcycling,soilformation,primary production)andcultural(non-materialbeneﬁts,e.g.,recreation,educational,culturalheritage).

Networkapplicationsforagriculture

Agro-ecosystemshavetypicallybeenconsideredascropmono- cultures with a few associated plant and invertebrate species residing inasingleﬁeld, buttheyareclearly farmore complex (Bohan et al., 2013). The relative contributions of biodiversity andspeciesinteractionsintheprovisionoftheseservices,however,remainsunder-appreciatedinagro-ecosystems.Agricultural plantsandanimalsareembeddedincomplexnetworksofabove- andbelow-groundspeciesinteractions(includingfungi,bacteria, virusesandawholehostofotherorganisms;Fig.1),whereorgan- isms are co-dependent(de Vriesand Wallenstein,2017).These ecologicalinteractionsprovideawiderangeofecosystemservices, suchascroppollinationbyinsects(Kleinetal.,2007),naturalpest control(Deroclesetal.,2014)andbeneﬁcialeffectsofsoilmicro- bialactivity(e.g.,promotionofplantgrowth;Bennettetal.,2019).

Inthissense,afundamentalproblemtosolveis,howtocharac- terisetheseservicesiftheydo notoccurin isolationbutrather affecteachotherthroughdirectandindirectinteractionsbetween organisms.

Networkscienceallowsustoexplore notjustthedirect and indirectinteractions betweenbiodiversityincropandnon-crop systems,but alsoincorporatehumandecision-making informa- tionbyfarmers, consumers,regulatorsandlandmanagersfrom local,regionalandglobalscales(Fig.2).Byexploringthedirectand indirectinteractions amongthecomponentsof social-ecological systems,networkscienceprovidessystems-basedapproachesthat cancharacterise,analyseandpotentiallydesignsustainablefood systemsatdifferentscales.

Fieldscale

Recentstudies have provided a better understandingof the structureofmutualisticandantagonisticinteractionsincrop,non- cropandotherhabitatsaroundtheworld.Collectively,thiswork hasprovidednewinsightsintoanumberofecologicalandevolu- tionaryproblems(e.g.,communityassemblyandtraitevolution) andhasalsoindicatedhowtoharnessecosystemservices;mostly pollinationandnaturalpest-control(SupplementaryTableS1).In agro-ecosystems,thechallengeaheadistodescribethesesystems

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Fig.1.Multilayernetworksinagro-ecosystems.(a)Adiagrammaticrepresentationofaconceptualecologicalnetworkataﬁeldscale.(b)Agraphicalrepresentationofthe networkofmultiplespeciesinteractionsin(a),demonstratinghowapparentintractablebiologicalcomplexitycanbesystemisedformathematicalanalysis.(c)Amultilayer representationofthenetworkwhereintra-layerinteractionsarethesameasin(b),yetinter-layerinteractionsarepresentconnectingthesamepopulationsoforganisms interactingacrossthemultiplelayers.Thelayershererepresentdifferenttypesofinteraction.Layers,however,couldalsorepresentdifferentﬁelds,habitats,seasons,yearsor otherdiscretespatialortemporalunits.Nodecolourindicatestheroleoftheorganismswithinthenetwork(e.g.,symbiont,pathogen,pollinator,plant,herbivore,parasitoid, detritivoreandpredator).Linkcoloursandlinetypesrepresentdifferenttypesofintra-andinter-layerinteractions.Notallpotentialintra-layerinteractionshavebeen includedin(a),(b)or(c)forillustrativepurposes,forexampleantagonisticinteractionsbetweenpredators,parasitoids,pollinatorsanddecomposers.

inwaysthatthefullsuiteofimportantdirectandindirectinterac- tionsthatoccurwithinthelandscapeareincorporated(Hutchinson etal.,2019).Indeed,currentassessmentsofchangesinfarmman- agementoverlook importantprocessesderivedfrominteracting species,suchaschangesintraitdistributionsatthespecies-level maybecompensatedforathigherlevelsofbiologicalorganisation (Maetal.,2019).Furthermore,awiderangeofotherinteractions thatmayimpactthedesignofagro-ecosystemshavebeenlesswell studied,includingspilloverofpollinationfromnon-croptocrop plantspecies andbeneﬁcialinteractions betweensoilandplant communities.Althoughwecancontinue toobtaininsightsfrom studyingtheseinteractionsinisolation,theorypredictsthatjusta fewlinksconnectingelementsmaymarkedlychangethedynamics ofwidersystems(WattsandStrogatz,1998).

Conceptually,mergingecologicalnetworksofdifferentinterac- tiontypesisstate-of-the-art.Inanagriculturalcontext,multiple direct and indirect interaction types with crops, both above and below-ground, canbequantiﬁed(Fig.1).The simultaneous quantiﬁcationofthemultipleecosystemservicesprovidedbybio- diversitycanprovideinsightson:(i)trade-offsincropandnon-crop

managementpractices;and(ii)theoften-unexpecteddynamical consequencesofcouplingecologicalinteractions(e.g.,emergent effects,indirecteffectswhichtranscendinteractionnetworksand plasticityintheroleofspecieswithinmultilayernetworks);both ofwhichfacilitatethetaskofimprovingsustainableyields.Pocock etal.(2012)weretheﬁrsttoshowtheusefulnessofa‘network ofecologicalnetworks’atafarm-scalebyanalysinghowdifferent interconnectedgroupsoforganismsrespondtospeciesloss.This workenhancedourunderstandingoftherobustnessandresilience ofcombinedgroupsofplantsandanimalstoenvironmentalchange, hence identifying vulnerable groups and demonstrating novel methods to restoreecosystem functioning based in identifying importantplantsandhabitats.

Landscapescale

Networksat theﬁeld or habitat scale areinherently nested withina wider landscapeof complexinteractions (Evanset al., 2013).Thesenetworksconnecthabitatsatarangeofscales,with connections resulting either from the movement of individual

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Fig.2. Anexampleofamergedagriculturalnetwork.Symbolsrepresentthreatstoecosystems(redsquares),managementinterventionsoractions(orangehexagons), socialactors(bluepentagons),ecologicalnetworks(circles,whereindividualsymbolsrepresentindividualspecies)andtheecosystemservices(greenstars)andeconomic compartments(yellowstar).Theﬁgurecoversonlyasubsetofthepotentialsocialactors,economicpressures,threats,managementactionsandecosystemsservicesthat arepresentinagriculturalsystems.Threatscandirectlylinktoecosystemservices,butalsothreatentheirprovisionthroughecologicalnetworks.Managementcanoccur inresponsetothreatsandtranslatestoecosystemservicesthroughimpactingthestructureandfunctionoftheecologicalnetwork.Differenttypesofinteractionsrequire differentdata,forexampleinteractionsbetweenmanagementandthreatscanbequantiﬁedbyassessingthesuccessofpreviousmanagementinterventionsusingmonitoring.

organismsinspace(i.e.,theutilisationofmultiplehabitatsbythe sameorganismorlong-distancedispersalamongpopulations),by theuseofdifferenthabitatsbydifferentpopulationsofthesame species,orbythetransferandﬂuxofmaterialsandenergyacross thelandscapethroughexistingnetworksofspecies(Massoland Petit,2013).Researchonmeta-communitiesshowsthat,duetothe inherentheterogeneityofecologicalsystemsacrossspace,thenet- workofnetworksaffectstheresponseofboththeoverallnetwork anditssubnetworkstoperturbations(Holyoaketal.,2005).

Severalattemptshavebeenmadetounderstandtheinterac- tionsbetweennetworksatthelandscapescaleinagriculture.These studieshavelookedatthesharedinteractionsbetweenhabitatsin ordertounderstandthepotentialbeneﬁtsof,forexample,shared parasitoids of pest herbivoresin both cropand non-crop habitats(Deroclesetal.,2014).Theydrawonhowconceptssuchas apparentcompetition,spillovereffects,andtop-down/bottom-up effectsproliferateintocropsystems,potentiallyimprovingyields.

Forinstance,thepotentialoftop-downeffects,otherwiseknown astrophiccascades,toregulatepestsmaybeachievedbymanipu- latinghabitatheterogeneity.Habitatheterogeneitycauseschanges in thefeedingbehaviourof generalistpredators,leading tothe presenceofpredatorsfeedingona greaternumber ofpreytaxa (Staudacheretal.,2018).Generalistpredators,inturn,areknownto shiftecosystemstatesthroughexertingpredationpressureonpest species(e.g.,fruit-eatingbirdsinorchards),generatinganincreased productivityofplantsinnaturalsystemsandcrops(Shaveetal., 2018).

Therearearangeofmethodsforlinkingandanalysingthese spatial networks in agricultural regions. The concept of meta- networks,anextensionofpreviousmeta-population,community and ecosystemtheory,hasrecentlybeendevelopedtolinkpre- viously isolated habitat-speciﬁc networks (Emer et al., 2018).

Furthermore, spatial networks including ecological, social and managementsystems,havebeenproposed(Deeetal.,2017).This ﬁeldisdevelopingrapidly,yetfurtheradvancesarerequiredbefore we fully understand how networks at intermediate, landscape

scales interact with one another to affect ecological processes (Poisot et al.,2015).Initial studiesconstructing and investigat- ing networks at broad geographical scales (e.g., Great Britain;

Redheadetal.,2018)demonstratethepotentialforconstructing spatiallyconnectednetworksatbroadscalesusingcombinations ofmethods,bothnewandexisting.Weexploresomeofthenew developmentsandadvancesinthisareaofnetworkecologyinsub- sequentsections(seeConnectingthe agriculturallandscape using networks).

Globalscale

Ecologicalnetworksareyettobefullyexploredattheglobal scale,butthereareeffortstocollateinternationaldatasetstohelp answermacro-ecologicalquestions.Nevertheless,advancesinnet- work sciencemore generally are increasingly beingapplied to agriculturalsystemsdata,inparticulartheglobaltradeofgoods, services (Anderson, 2010) and ecosystem services (Silva et al., 2021).Anappreciationofmultiplescalesisparticularlyimportant inagriculturalsystems,especiallyconsideringthatthemovement andtradeofagriculturalgoods,aswellascurrentandfutureglobal environmentalchangesand biological variationat broadscales, inﬂuencetheagriculturalprocesses occurringatsmallerspatial scales(e.g.,landscapesandﬁelds).Thesestudiesareparticularly importantwhenconsideringtheriskspresentedbyeconomicand environmentalshocks (Challinor et al., 2016).For example, an analysisofnetworksoftradeincerealcropsfrom1986to2013 showedthatthreeentangled,time-invariantsub-networkswere responsibleforthemajorityoftrade,buttherewerealsotransient subnetworksthatdisplayedexponentialgrowthoverintermediate timescales(Dupasetal.,2019).Transientstructures,althoughcon- tributingrelativelylittletothetotaltradeofcereals,maypresent abufferagainstperturbationsorshocksintheshort-term.Little, however,isknownabouttheresilienceofthesenetworks—asub- stantialgapinourunderstandingofagriculturalnetworksandtheir abilitytorespondtofuturescenarios(butseePumaetal.,2015).

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Attemptshavealsobeenmadetolinktogetheragriculturaltrade andsocialnetworks.Forexample,atriadicanalysisframeworksuc- cessfully classiﬁedcountriesbasedontheirlevelofconnectivity totradepartnersandindicatedadistinctdevelopmenttrajectory associated withan increase in the levels of global agricultural importsandexportsforacountry(ShuttersandMuneepeerakul, 2012).

Much canbe learned fromother disciplineswhere network approacheshavebeenusedtoexaminearangeofglobalphenom- ena, includingbankingsystems(MayandArinaminpathy,2010) and diseasetransmission(Pastor-Satorrasetal.,2015).Froman agriculturalperspectiveitmaybepossibletolinktogetherpro- cessesattheglobalscale(i.e.,tradeandcommunity)tothoseatﬁeld and landscapescales.Forexample, changesintheinternational tradeofpesticidesandfertiliserswillaffectaccesstotheseprod- uctsbyfarmers,inturnenhancingorrestrictingtheirapplication atlandscapeandﬁeldscales,andthusalteringthestructureand functionofecologicalnetworksatlocalscalesthroughtheeffects ofeitherhigherorlowerlevelsofchemicalapplication.

Includingpeopleinagriculturalnetworksacrossscales

Humansarerarelyincludedinagro-ecosystemnetworks.Thisis surprisinggiventhat:(i)peoplearethedirectbeneﬁciariesofagri- culturaloutputs;and(ii)agriculturalnetworksarecharacterised bytop-downstructuring,throughaseriesofknowledge-exchange anddecision-makingnetworksinthehumancompartmentofsys- tems(Mansonetal.,2016).Agriculturalsocialnetworkscombine arangeofstakeholdersinvolvedinthemanagementoftheland- scape,suchasfarmers,landmanagers,consumersandregulators (Jarosz,2000).Theinteractionsbetweenthesestakeholders(e.g., informationandequipmentsharing,co-managementoflandscapes andcommonresponsestopolicyorlegislation),andthelandman- agementdecisionsthatarise,areamajorinﬂuenceonecological networks in theagriculturalenvironment (Nelsonet al., 2009).

Likewise,itislogicalthatchangestotheecologicalnetwork(e.g., asaresultofdrought,pestoutbreaksorpollinatordeclines)will affectdecisionmakinginsocialnetworksandthiscanpropagate acrossspatialscales.We contendthatmergingsocial,economic andecologicalnetworkshasconsiderablescopeforbetterunder- standingandmanagementofhuman–ecosystemrelationshipsand feedbacks.Mergingsuchnetworksisamajorchallengebutnowfea- sible,andthebeneﬁtsforbuildingresilienceintofood-systemsare considerable(seeMergingsocialandecologicalnetworkstounder- stand howmanagement decisionsaffect agro-ecosystemsandvice versa).

Advancesrequiredtooperationalisenetworksciencein agriculture

Collectingmorenetworkdatatoenableawholesystem understandingofagro-ecosystems

Agreatervolumeofdataondifferentagriculturalnetworksis requiredacrossmanydifferentnetworktypesinagro-ecosystems, includingecological,sociologicalandeconomic.Here,wefocuson ecologicalnetworksduetorecentdevelopmentssurroundingthe collectionofspecies-interactiondata.

Comprehensiveanddetailedecologicalnetworkscanbecon- structed using a range of techniques. Yet recent advances in DNA-basednetworkconstructionmethodsprovideunprecedented opportunities to scale-up the construction of highly resolved ecological networks (Derocles et al., 2018; Evans et al., 2016;

Srivathsan etal.,2021).Thesetechniquesareparticularlyappli- cabletonetworkconstructionwhereinteractionsmaybecryptic,

infrequent,short-livedordifﬁculttoobserve(Vacheretal.,2016).

Furthermore,theyallowtheinclusion of evolutionaryinforma- tion(e.g.,phylogeneticdata),thatisparticularlyimportantinthe contextof usingnetworks predictivelyfor restoration purposes (Raimundo et al., 2018). Within agro-ecosystems, examples of potentialapplicationsofeco-evolutionarynetworksinclude:linkingabove- and below-groundnetworks (Toju and Baba, 2018), incorporatingcropvirusesandpathogensintoecologicalnetworks andinvestigatingtheroleofintra-speciﬁccompetition.Although DNA-basedmethodsshow great promise,withlow per sample costs(<10cents;Srivathsanetal.,2021)and theability topro- cesslargenumbersofsamplesrapidly,werecognisethesemethods havelimitations (Cuff etal.,2022)andtheiraccessibilityvaries geographically.Arangeofalternativemethodsforconstructingnet- worksexistandaresuitableforcreatingagro-ecosystemnetworks.

Foramorecomprehensivereviewofnovelmethodsofconstruct- ingreplicatedspeciesinteractionnetworksoverlargespatialscales seesectionsinWindsoretal.(inrevision).

Anincreaseintheamountofavailablenetworkdata,combined witha focusonthefunctional implicationsofsuchinteractions (e.g., understanding the wider role of soil microbiota in agro- ecosystems),willenableabetterunderstandingoftheresponses ofagriculturalnetworkstonaturaloranthropogenicperturbations.

Suchknowledge providesoptions formanagement inextensive sustainablesystemsand/orbuildingresilienceintothosefoodsys- temswhichappearatriskofnegativeeffects(seebelow).

Makingthemostofexistingagriculturaldatabyinferring ecologicalinteractions

Inference networks, constructed from co-occurrence data and/orexistinginformationonspeciesinteractions(e.g.,Pocock etal.,2020), provideapotentiallyvaluableresourceforinvesti- gatingnetworksoverbroadspatialandtemporalscales.Indeed, thismethodofnetworkconstructionisgainingmomentumwhen communitydataisgenerated usingenvironmentalDNA(eDNA) methods,although co-occurrenceis not necessarilyevidence of ecological interactions (Blanchet et al., 2020). These methods havenumerousapplications,includingreconstructionofhistorical speciesinteractionnetworks,constructionofnetworksinremote regionswithpoorinteractiondatasetsanddevelopmentofnet- works across broad spatial and temporal scales that allow for testingglobalenvironmentalchangehypotheses.Furthermore,the potentialapplicationtoexistinglong-termbiomonitoringprojects is an area of interest (Bohan et al., 2017), particularly in the contextofexaminingpestandbeneﬁcialinsectinteractionsinagro- ecosystems(Petsopoulosetal.,2021).

Linkingabove-andbelow-groundenvironmentstocreate completeecologicalnetworksinagro-ecosystems

We currently have limited knowledge regarding how biotic interactionsbetweenplantsandbelow-groundmicro-organisms influenceecosystemproductivityandfluxesofmatterandenergy throughtrophiclevels,limitingourabilitytounderstandhowagro- ecosystemsrespondtoenvironmentalchange.Again,DNA-based methodscombinedwithnetworkanalysisoffernewwaystoover- comesomeofthehithertodifficultiesinstudyingmorecomplex plant–microbeinteractions(Bennettetal.,2019)andunearthinga rangeofecosystemservicesthatcanbeharnessed(e.g.,arbuscu- larmycorrhizalfungiandotherrootsymbiontsforplantgrowth).

Indeed,agriculturalplantsdonotonlyrelyondirectdefenceswhen attacked,buttheycanalsorecruitpestantagonistssuchaspreda- torsandparasitoids,bothaboveandbelow-ground,mainlyviathe releaseofvolatileorganiccompounds(i.e.,indirectdefences).How- ever,fromanecologicalnetworkperspectiveitismechanistically

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unclearhow changes inonetrophic level (e.g.,soilarthropods) affectinteractionsinanother(e.g.,insectpollinators),andviceversa (Fig.2),northepotentialtrade-offsfor focalplants(e.g.,crops).

Assuch,wedonotknowwhatdrivesthemultidirectionalﬂuxes betweentheabove-andbelow-groundcomponentsoftheagri- culturalnetworksatmultiplescales.Thisadvanceisrequiredto understandhowthesecomponentsofthesysteminteracttoalter productivityandecosystemserviceswithintheagriculturalenvi- ronment.

Usingmultilayernetworkstoidentifykeyecosystemservicesthat spilloverbetweencropandnon-crophabitats

Usingnon-crophabitats,forexampleﬁeldmargins,hedgerows, uncultivatedlandandwoodland,toinﬂuencethenetworkstruc- tureofplant–animalnetworksinarableagricultureisacommon techniquetoenhancetheprovisionofecosystemservicesinsome regionsoftheglobe.Assessmentsandmanagementinterventions, however,donotoftenconsiderthesimultaneousprovisioningof multipleecosystemservices(Gabaetal.,2015).Itisnevertheless importanttoconsiderthepotentialsynergiesandtrade-offsasso- ciatedwithdecisionssurroundingagriculturalsystems,especially considering that different non-crop habitats enhance different ecosystemservicesinacontext-dependentmanner(Albrechtetal., 2020).

Developingmethodsthatallowforanunderstandingandman- agementofmultipleecosystemservicesisanimportantchallenge thatneedstobeaddressed.Thisadvancecanbeachievedusing anadaptationofcurrentnetwork methodsforselectingoptimal mixturesoforganismstomaximisetheabundanceand/orspecies richnessofinteractingorganisms.Forexample,itispossibletouse machinelearningalgorithmsappliedtonetworkdatatoidentify speciescontributingtodifferentecosystemservicesacrosshabitats anddetermineoptimalmixesofspeciestoprovidesimultaneous ecosystemserviceprovision(Windsoretal.,2021).

Incorporatingdifferentaspectsofagricultureinamultilayer frameworkthroughdevelopingnewtools

Although studies on multilayer networks are still in their infancy (Hutchinson et al.,2019), in agro-ecosystems there are severalclearlydeﬁned‘layers’thatcanbeincorporatedintomul- tilayer network frameworks.These include,but are notlimited to; plant–herbivore, herbivore–parasitoid, herbivore–predator, plant–frugivore,plant–human(e.g.,removalofweeds,diversifying plantspeciesinﬁeldmargins)andherbivore–human(e.g.,pesticide use, enhancingtheabundanceofnaturalenemiesofpestherbi- vores).Throughincorporatingthisarrayofnetworktypesintoone framework,wesuggestthatitispossibletogainabetterunder- standingofthemechanisticprocessesthroughwhichmanagement decisions ata range ofscales altertheecosystem servicespro- videdbytheecologicalnetworksembeddedwithintheagricultural landscape.Inturn,thiswouldallowforanimproveddesignofman- agement strategies, incorporating allpotential knock-oneffects acrosstheecosystemandallowingformaximumecosystemservice provisionwhilstminimisingenvironmentalharm.

Thecurrenttoolboxavailableforanalysingecologicalmultilayer networksislimitedtoasmallbutgrowingnumberofanalyses,such asqualitativemeasuresofrobustness(Pilosofetal.,2017).Thereare alsochallengesassociatedwithlinkingtogetherlayerswithdiffer- entlinkinformationin ameaningfulmanner.Forexample,itis oftennotappropriatetocombineindividualweightednetworks intoasingleweightedmultilayernetworkasthemethodsusedto collectdatadonotlendthemselvestodirectcomparisons.There arealsoissueswithlinkinglargenumbersofdisparatelayersin aparsimoniousandcomputationallyefﬁcientwaythatﬁtswithin

currentmathematicalframeworks.Toaddressthischallenge,we believeabottom-up,plant-focusedapproachincroppedsystems providesasuitablestartingpointtoinvestigatetheroleofdifferent interactionswithinagro-ecosystems.Byalteringplantcommuni- ties there area range of consequences that caninﬂuence even thoseorganismsthatarenotdirectlyconnectedtotheminthenet- work(Scherberetal.,2010).Leadingonfromthisstartingpoint,it wouldbepossibletolinkin-andoff-crophabitatsaswellasabove- andbelow-groundcomponentsoftheagriculturalnetworksusing multilayernetworks.Doingsowillallowforthedevelopmentof managementtechniquesthatenhancetheprovisionofecosystem servicesthroughmakinguseofecologicalprocessessuchasappar- entcompetition(seeAroadmapforusingnetworkspredictivelyin agriculture).

Applyingadaptiveanddynamicnetworkstoprovidenewinsights inagriculturalsystems

Moststudiestodatehaveanalysedthestructureofecological networks(ora‘snapshot’ofspeciesinteractionsintime)andignore importanttemporaldynamics.Yetthepropertiesofecologicalnet- workscanvarydrasticallyatarangeofspatialandtemporalscales (Poisotetal.,2015).Todealwiththedynamicnatureofecological systems,anovelsuiteofnetworkmodelshasrecentlybeendevel- oped,incorporatingadditionalinformationonthephylogenyand traitsoftheorganismsthatcompriseagivenecologicalnetwork (Raimundo etal., 2018).These adaptivenetwork models incor- poratepotentialfunctionalorco-evolutionarychangesthatmight manifestthemselvesinresponsetochangesinthewidernetwork, addinggreaterdetail—e.g.,informationonchangesintheabun- danceandtraitsoforganismsovertime(Deroclesetal.,2018).The aimofsuchmethodsistoallowforanimprovedunderstanding ofhowsystemsrespondtoextinctionsandreductions,whichisin starkcontrasttoclassicalapproachesofrobustnesswithabinary responseofpersistenceorextinctionbasedonwhetherallprevi- ouslyobservedlinksareremoved,e.g.,completedisconnection.A remainingchallengesurroundingadaptivenetworks,however,is thatofparametrisationdatawhich remainslimited.DNA based methodsmay present a wayforward providing anopportunity togaininformationontheevolutionaryhistoryoforganismsand usefulinformationonpotentialinteractionrewiring(seeCollect- ingmorenetworkdatatoenableawholesystemunderstandingof agro-ecosystems).

Furtherdevelopingadaptive network modelswithinagricul- turalsystemsprovidessigniﬁcantpotential,especiallyinresearch investigatinghowhumanmanagementmayaffectotheraspects oftheagriculturalsystem.Examplesofpotentialresearchinclude understandingthe response of network structure and function topesticideresistance,climatechangeandreductionsinpollina- tordiversity,aswellasdevelopingprecisionfertiliserapplication andinvestigatingtheoptimalcombinationsofnon-cropplantsin organicagriculturalsystems.Bydevelopingthesemethodsofanal- ysis,wecangainmoreinformationabouttheecologicalnetworks andsuitablemanagementpracticescanbedevelopedtomakeuse ofeco-evolutionaryprinciples(Loeuilleetal.,2013).Ultimatelythis willallowadaptivenetworkstobeusedpredictivelyfortargeted managementoutcomes(Raimundoetal.,2018).

Developingspatialnetworkstoconnecttheagriculturallandscape anddevelopabroaderpicture

Currently, it is not possible to accurately identify, quantify and understand how management decisions at different scales affectthe structure and dynamics of ecological and social networks, and thushow these processes impactupon agricultural productivity,sustainability and resilience, across thesurround-

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inglandscape(MassolandPetit,2013).Assuch,ameta-network framework is required to understandthe interactions between agriculturalnetworksatthelandscapescale.Todatetherearefew studiesinvestigatingspatialmeta-networks,yetthisconceptcanbe appliedwidely,acrossmultiplenetworktypes(i.e.,socialandeco- logical),tounderstandhowmanagementorperturbationsaffect theprovisionof differentecosystemservicesattheagricultural landscape scale. Meta-network theory couldprovide a suitable frameworkwithinwhichagriculturalquestionsatthelandscape scale can beanswered. Thishasbeen shown in previouswork investigatingplant–bumblebeeinteractionsinforestrysystems, whichdemonstratedthevariableeffectsofhabitatpatchsizeand ﬂightdistancesonthepotentialeffectivenessofconservationand restorationstrategies(Devotoetal.,2014).

Substantialﬁeldandlaboratoryworkisrequiredtoupscalenet- work assessmentsin agricultural environmentsto allowfor an understandingofhowspatialvariationandheterogeneityinsocial and ecologicalprocessesalterstheecosystem servicesprovided acrossthelandscape.Theseeffortshavestartedwiththeoretical models(Loeuilleetal.,2013),butempirical(ﬁeld-basedandexper- imental)studiesarenowrequiredtodrivefurtheradvancesinour understanding.

Mergingsocialandecologicalnetworkstounderstandhow managementdecisionsaffectagro-ecosystemsandviceversa

Agriculturalsystemsareacombinationofsocialandecological networks(Fig.2),withtop-downeffectsofmanagementaltering thenatureofecologicalsystemsandnetworks(Lescourretetal., 2015). Yet,until recently these two interlinked networks have beenanalysed in relativeisolation. Newstudieshave soughtto combinethese(andmultipleother)typesofnetworkstoprovide insightsintohowtheinterfacebetweenmanagementandbiodiver- sityaltersecosystemfunctioning(Felipe-Luciaetal.,2021).These effortshaveincludedagriculturalenvironments(Hutchinsonetal., 2019), andtheorysurroundingsocio-ecologicalnetworksis suf- ﬁcientlymature foractionable interdisciplinaryresearch(Bodin etal.,2017).Puttingmultilayernetworkstoworkinthecontext ofagriculturemayimproveourunderstandingofthemechanistic basisthroughwhichmanagementdecisionstranslatetochangesin production,orotherservicesprovidedbytheagro-ecosystems.

Examples at different scales demonstrate the importance of combining social and ecological networks in agriculture to understandwherechanges insocialnetworksimpactecological networks,andviceversa.Atthelandscapescale,agriculturalenvi- ronmentsarecomposedofdifferenttypesoffarmers,rangingfrom familyfarmers throughtointensivecommercialfarmmanagers, who formnodes within a farmer-biota network. In this exam- plenetwork,farmersarelinkedtogetherthroughtheireffectson thewiderbiodiversityofthelandscape,i.e.,agriculturalmanage- mentbyonefarmermayaffectbiotaatthelandscapescaleand inturnhaveimpactsonotherfarmers.Anexampleofthiswould betheapplicationofpesticidesreducingpollinatordiversityand thusreducingtheprobabilityofpollinationacrossotherfarmsand crops—eventhosewherepesticidesarenotapplied.Acrosscon- tinentalandglobalscales,ecologicalandsocialnetworksatlocal scalescanbelinkedtogetherthroughnationalandinternational tradenetworks,ashasbeenthecasein recentworkinvestigat- ingvirtualpollinationtradenetworks(Silvaetal.,2021).Inthese networks,effectsonpollinatorsatlocalscaleswillaltertheyields ofpollinator-dependentcropsandthushavesocialandeconomic consequencesinotherregionsoftheglobewithwhichthosecrops aretraded.Linkingtogethersystemsatlargescalesisparticularly important(seeFurtheringglobalagriculturalnetworkanalysestohelp enhancetheresilienceoffoodsystems).

Usingthediscoursessurroundingecosystemservicesandnat- ural capital appears to have consolidated efforts linking social andecologicalnetworksacrossotherecosystems.Such applica- tionhasallowedfor thefusion of informationon management strategies and ecosystemservice provisionwith detailed socio- ecologicalnetworks(Deeetal.,2017).Forexample,recentwork thatintegrated ecosystem servicesinto coastalsalt marshfood websfound indirectrisks toservicesthrough species loss, and that vulnerability acrossservices was predictable(Keyes et al., 2021).Usinganecologicalnetwork,thisstudylinkstogethersocial aspectsofsystems:(i)threats,theanthropogenicstressorsimposed ontheecosystembyhumanactors(e.g.,climatechangeorover- exploitationofspecies); and(ii)ecosystemservices,thehuman beneﬁtsderivedfromthenaturalworld.Wecontendthatasimilar

‘threats-network-ecosystemservices’advanceisatangiblestart- ingpoint for operationalisingworkin agro-ecosystems (Fig.2), buildingonabottom-upspecies-interactionapproach.Here,using robustnessanalyses,itwouldbepossibletoexaminehowagricul- turalmanagementand/orthreatstospeciesinteractingwithplants propagatethroughthenetworktoimpactmultipleecosystemser- vices,andthussocio-economicactivities.

Goingforward, a signiﬁcantchallenge for theintegration of socialandecologicalnetworkssurroundsthecollectionofappro- priatedata.Speciﬁcally,collectingandcollatingdataoftheright kind(i.e.,weightedlinkswithcomparableorinteractableunits), at the correct resolution (e.g., seasonal management decisions and knowledge sharing by farmers). Many methods exist for generatingsocial datato constructnetworks; however, current methodsarequalitative (i.e.,usingecosystem serviceasanode linkedtospecieswithoutameasureoftheeffectsofaspecieson serviceprovision)and/orcollectdatainaninappropriatespatial ortemporalresolutionsforintegrationwithecologicalnetworks (Felipe-Luciaetal.,2021).

Thereappeartobethreeoptionsforcombiningsocialandeco- logical networks: (i) include social networks in the framing of ecologicalnetworkquestions(i.e.,useresearchquestionsdriven bysocial networks— forexample decisionmaking and/orland managementatthelandscapescale);(ii)reducetheresolution(i.e., levelofdetail)ofbothsocialandecologicalnetworksandusean existinganalyticalframework(e.g.,Bayesiannetworksandgame theoryhavepreviouslybeenappliedtomulti-agentecosystemser- vicemodelling;Mulazzanietal.,2017);or(iii)developacompletely newmethod/frameworktolinksocialandecologicalsystems.

Furtheringglobalagriculturalnetworkanalysestohelpenhance theresilienceoffoodsystems

Determiningthe resilienceofglobal agriculturetoperturba- tions, shocks and disturbances is crucial and a policy priority (Challinoretal.,2016),especiallyconsideringtheglobalnatureof agriculturalfoodsupplyandthepotentialforfuturefoodshortages (Silvaetal.,2021).Althoughdataexistattheglobalscale,forexam- pleFoodandAgricultureOrganisation (FAO)cropandlivestock productsdata(FoodandAgricultureOrganizationoftheUnited Nations,2018),thereremainslimitednetwork-basedassessments oftheglobaltradeofagriculturalproductsandresearchopportu- nities(Pumaetal.,2015).Currentdataprovideastrongbasisfor potentialanalysesasthereisdetailedinformationonthevalueof annualimportsandexportsofdifferentagriculturalproducts,as wellasthequantityofproductsmovingbetweencountries.Using thesedata,understandingtherobustness ofthetrade network, forexampleifaglobalhealthcrisispreventsexports(Adayand Aday,2020)orifclimateconditionsreduceproductionofagricul- turalgoodswithinageographicregion,isafruitfulplacetobegin.

Furthermore,buildingnetwork-basedanalysesintocurrentglobal andregionalagriculturalmonitoringschemes(e.g.,GrouponEarth

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Fig.3.Agriculturalnetworksatdifferentspatialandtemporalscales.Eachsectionoftheﬁguresummarisesexamplesofnetworkscienceapplicationsatdifferentscales.

Atthefieldscale(1)thecolourofthenodesindicatesthedifferenttaxonomicgroupsandtrophiclevelsrepresentedinthevariousnetworks.Thenetworksatthisscalecan varyintheircomplexityfrombipartite(interactionsbetweentwogroups,i.e.,plantsandherbivores)throughtomultilayerormeta-networkapproacheswhichincludeeither multiplehabitats,networksthroughtime(linkedbythesameorganismsorpopulationsoforganisms)ormultipleinteractiontypes.Acrosslandscapes(2)whichcanrange inscalefrommultiplefieldstoentireregions,networkscantakemanyformsandberepresentedbyclassicnodeandlinkdiagrams(2aand2b),orspatiallyusinglattices representingspace(2c).Inthelattercase,adjacentblocksdirectlyinteractwithoneanother,andotherblocksfurtherafieldmayinteractthroughindirectinteractions.The spreadoforganismsacrossthelandscapematricescanbeestimatedusinganetworkoftheblockswithinthelandscapeandthedirectandindirectinteractionsbetween them.Attheglobalscale(3),networksareequallydiverse,yetgoodexamplesexistfortradenetworks.Subnetworksoftrademaybeparticularlyimportantforagricultural goodsandthelossofproductioninthesesubsetsofnodes(countries)mayleadtoalossofrobustnessinglobalagriculturaltrade.Forspecificterminologyrefertothemain textandTable1.ExamplesofmanynetworksinagricultureandassociatedprocessesareprovidedinTableS1.

ObservationsGlobalAgriculturalMonitoring[GEOCLAM],Anomaly HotSpots ofAgriculturalProduction[ASAP],GlobalInformation and Early Warning System[GIEWS]and Famine Early Warning Systems Network[FEWSNET];Fritzetal.,2019),wouldfurther enhancetheabilitytoprovideanearly-warningsystemforglobal foodsystemsandpromoteenhancedfoodsecurity.

Aroadmapforusingnetworkspredictivelyinagriculture

Usingnetworkspredictivelyforspeciﬁcmanagementoutcomes, suchasbuildingresiliencetoclimatechange,resistancetoinvasion fromnon-nativespecies,orachievinggreatercropyieldswhilst supportingincreasesinthelevelsofnativebiodiversity,couldbe achievable.However,usingtheprinciplesofnetworkecologyto predictthepotentialeffects of farmmanagement interventions