Disentangling the role of floral sensory stimuli in pollination networks

13 Pages·2017·0.86 MB·English

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DOI:10.1038/s41467-018-03448-w
fl
Disentangling the role of oral sensory stimuli in
pollination networks
Aphrodite Kantsa 1,6, Robert A. Raguso2, Adrian G. Dyer3,4, Jens M. Olesen 5, Thomas Tscheulin1 &
Theodora Petanidou 1
0():,; Despiteprogressinunderstandingpollinationnetworkstructure,thefunctionalrolesoffloral
123456789 sceonmsmoruynitsytimcuolnite(xvtis,ueavl,enolftahcotourgyh) shuacvhe tnreavitesr abreeenknaodwdnrestsoedmecdoimatperehpelannsti–vpeolyllininatoar
interactions. Here, we use a comprehensive dataset of floral traits and a novel dynamic
data-poolingmethodologytoexploretheimpactsoffloralsensorydiversityonthestructure
ofapollinationnetworkinaMediterraneanscrubland.Ourapproachtrackstransitionsinthe
network behaviour of each plant species throughout its flowering period and, despite
dynamism in visitor composition, reveals significant links to floral scent, and/or colour as
perceived by pollinators. Having accounted for floral phenology, abundance and phylogeny,
the persistent association between floral sensory traits and visitor guilds supports a deeper
role for sensory bias and diffuse coevolution in structuring plant–pollinator networks. This
knowledge of floral sensory diversity, by identifying the most influential phenotypes, could
help prioritize efforts for plant–pollinator community restoration.
1LaboratoryofBiogeographyandEcology,DepartmentofGeography,UniversityoftheAegean,81100Mytilene,Greece.2DepartmentofNeurobiologyand
Behavior,CornellUniversity,Ithaca,NY14853,USA.3DepartmentofMediaandCommunication,RoyalMelbourneInstituteofTechnology,Melbourne,
3000VIC,Australia.4DepartmentofPhysiology,MonashUniversity,Melbourne,3800VIC,Australia.5DepartmentofBioscience,AarhusUniversity,Ny
Munkegade116,8000Aarhus,Denmark.6Presentaddress:DepartmentofEnvironmentalSystemsScience,ETHZürich,Schmelzbergstrasse9,8092Zürich,
Switzerland.CorrespondenceandrequestsformaterialsshouldbeaddressedtoA.K.(email:[email protected])
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E
arly in the twentieth century, biologists adopted network theflowercolouroftemporallyoverlappingplantsandthevisual
theoryinordertoinvestigatecomplexsystemssuchasfood systems of pollinators was shown to influence insect attraction
webs1. Networks now constitute a powerful analytical tool and the plants’ ecological specialization in one community34.
usedinthestudyofmutualisticinteractions,suchaspollination2, However,pollinatorslikelyexperiencefloraltraitsasmultimodal
seed dispersal3, plant–mycorrhiza associations4 and sensoryinformation,ratherthansolelyvisualorolfactorycues35.
cleaner–client reef fish relationships5. In particular, network Pollination network studies have not yet considered the full
analysis has transformed the conceptualization of pollination complement of floral stimuli; hence, it remains unclear whether
fromabiologicalfunctiontraditionallystudiedinisolatedpairsor communityvisitationpatternshaveasensorybasisor,asinferred
smallgroupsofspecies,toakeyecosystemfunctionthatsustains inmoststudiestodate,theyrepresentafunctionoffloraldensity
primary productivity and the stability of communities1. At pre- and/or phenological matching. Answering this question requires
sent, given that all the major worldwide threats to biodiversity acomprehensivedatasetonthesensoryecologyofanentirefloral
affectplant–pollinator(p–p)interactionsaswell6,intensiveglobal community, including human-unbiased parameterizations of
research efforts focus on understanding the structure and floral scent and colour28, and an evaluation of specialization
dynamics of p–p networks, so that conservation and restoration through a balanced design that directly compares the relative
strategies can be effectively employed7,8. impacts of phylogeny, floral traits, phenology and density.
During the last twenty years, there has been a lively debate Species must co-occur in time in order to interact; therefore,
regarding the interplay of ecological and evolutionary speciali- flowering phenology is the first factor filtering p–p
zation in p–p interactions. The assessment of large interaction interactions2,13,16,32,36 in a community. Yet, by assessing the
datasets revealed that symmetrical specialization in communities overall (static) p–p network of the entire flowering/sampling
israrerthanexpectedfromthepollination‘syndrome’paradigm9, season,oneconsidersputativeinteractionsthatarenotpossiblein
the applicability and influence of which remain contentious time,therebyinflatingthecalculatedmeasuresofspecialization37.
subjects10,11. Tight pairwise or 1:1 species relationships strictly The resulting inflation can be considerable, given that most
matching the ‘syndrome’ concept (according to which specific speciesinmanycommunities,includingtheMediterraneanones,
sets of plant traits provide some predictive power in the identi- tend to have short flowering phenophases2,13,38. Recent studies
ficationofevolutionarilyimportantpollinatorgroups)areindeed have addressed this issue by partitioning the total network into
rare in natural communities12. Thus, studies of p–p networks smaller regular time-sequential networks16,39. However, even
have tended to de-emphasize specialization and, as a con- networkstudieswithaday-scaleresolutioninevitablyemploythe
sequence, the importance of floral phenotypes as adaptations in static version of plants’ ecological specialization and centrality
ordering network structure. Long-term observations of p–p net- (i.e. the degree of the influence that a given species has on the
works indicate that they are characterized by a great temporal network’s structure)39, simply because this type of data aggrega-
plasticity of interactions2,13. Furthermore, the opportunistic tionprovidesthetotalnumberoflinksamongthenodes,against
foraging behaviour of flower-visiting insects has been attributed which the realized interactions will be compared.
to extreme seasonality in certain biomes2, whereas theoretical Toovercomethismethodologicalbias,weintroduceadynamic
models have inferred that, in mutualistic networks, linkage rules approach to data pooling in bipartite ecological networks, the
may largely be explained by phenology and/or species’ abun- ‘phenonet’ structure, which partitions the total cumulative net-
dance14–16. work into a set of networks equal in number to the interacting
However,weknowthat,inpollinationnetworks,nearlyhalfof speciesinthecommunity.Here,aphenonetbecomesasnapshot
the visitor species interact with only one or very few plant of the entire p–p network of the community, encompassing the
species13,17. Moreover, mutualistic interactions entail at least interaction spectrum occurring within each species’ phenophase.
somedegreeofphenotypiccomplementarity18,exemplifiedbythe Our method is based on the simple facts that (i) not all species
simplematchingbetweenproboscislengthandcorolladepth19,20, interactdirectlywitheachother,butonlywiththetemporallyco-
to the more sophisticated interplay of floral stimuli (e.g. scent, occurring species and (ii) species interact directly with other
colour) and the sensory systems of pollinators21,22. The shared trophic levels only during their phenophase. Thus, the actual
evolutionary history of plants and pollinators is linked to their ecological or evolutionary specialization may vary according to
interaction patterns23–25, is considered a major driver of floral timeanddependingontheecologicalcontext,i.e.theantagonistic
diversification and, although it may not act alone26, has been or facilitative interactions with co-occurring species.
shown to operate within the almost generic asymmetrical struc- The second key element known to determine interaction pat-
ture of mutualistic networks18,27. Recently, our study revealed a terns and the structure of networks is floral density19,34. In
phenotypic integration between floral colour (as perceived by general, floral abundance is used in p−p network studies as an
pollinators)andscentatacommunitylevelamongtheflowering independent factor; however, from a functional ecological
plants28; this finding suggests a coordinated adaptation of plants standpoint, the number of flowers represents the relative abun-
tothesensorysystemsofpollinatinginsects.Webuilduponthis danceofspecific(e.g.visualandolfactory)phenotypictraitsthat
finding by asking whether floral phenotypes that match visitors’ constitute the floral sensory landscape in a community of
physical and sensory biases in a community context represent blooming plants. We thusadopt theconcept of ‘apparency’used
evolutionary vestigial traits or relics with no extant function, or in chemical ecology of herbivory40 to describe the multiple ways
alternatively, whether they are correlated with the realized thataplantcanpresentolfactorilyorvisuallydistinguishable(and
pollinator-niches of the plants. thus more apparent) flowers across the community.
Distinctsensorybiasesandcognitiveabilitiesofpollinatorsare Here we investigate the role of floral sensory stimuli on the
expectedtofiltertheinformationavailableinflorallandscapes28, structure of a p–p network in the phrygana, a natural Medi-
and to shape foraging behaviours29. In this context, insect terraneanscrubland,byemployingtheabove-mentioneddynamic
responsestonaturalfloralvolatileblendswerefoundtocorrelate datapooling,andbyaccountingforspecies’functionalabundance
with visitation patterns30, and experimental manipulation of and phylogeny. We specifically ask (i) whether plant centrality
those blends was shownto reversibly affect visitation patterns in and generalization are significantly associated with floral pheno-
two keystone plant species studied simultaneously in a commu- type,(ii)whetherfloralphenotypepredictsvisitationratesbythe
nity31. Similarly, floral reflectance has been connected to visita- different pollinator groups and (iii) which phenotypic elements
tionpatterns32,33,andrecently,thephenotypicmatchingbetween areimportantforthestructureofthep–pnetwork.Ourfindings
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a b c
Plants Insects
250
**
200
ncy 150
e
u
q
e 100
Fr
50
0
16 18 20 22 24
Transitions in randomizations Coleoptera
Coleoptera
Bees Asteraceae
Wasps
Lepidoptera
Diptera
NA
Fig.1Thephylogeneticstructureofplant–pollinatorinteractions.aTheprevailingpollinatorgroupmapped,asacategoricalvariable,withinthephylogeny
oftheplants.NA:≥2groupswiththehighestvisitationrate.bThehistogramofthenullmodelofevolutionarytransitionsaccordingtothealgorithm‘phylo.
signal.disc’fordiscretetraits.Thearrowshowsthenumberofobservedtransitionsofthetrait,whichissignificantlylowerthantherandomizedmedian
(**P=0.008).cThetwocladesshowingthelowestresiduals(highestphylogeneticcongruence)intheProcrusteancophylogeneticanalysis.Fordetails,
seeMethods
revealthatfloralscentandcolourasperceivedbypollinatorsare cladescomparedwiththerestofthenetwork(Fig.1c).Giventhat
related to plant behaviour in the community; species that are the Procrustean analysis employs the binary version of the
critical for holding together the entire network are characterized network (i.e. not including the number of visits for each link)42,
by specific floral traits. Moreover, the visitation rates by the dif- wecouldsuggestthatitactuallymeasuresthephylogeneticsignal
ferent pollinator groups are correlated with different floral sti- oftheinitialattractionbetweenplantsandinsect,exactlybecause
muli. Overall, our approach, which can be applied in different itequallyvaluesalllinks:somep−plinksoccurredrepeatedly(i.e.
bipartite networks, disentangles the significance of floral pheno- many visitation events), yet some others only occurred once
type in the interacting community, and helps to define the most because although the insect was attracted, it decided not to visit
influential plant species. another flower of the same plant species. Thus, this approach
revealed that the “first contacts” between plants and pollinators
are related to p−p coevolution.
Results Moreover, we found that the modular structure of the
The static p–p network of the community. The total p–p net- network43 (consisting of nine modules, Supplementary Table 2)
work (Supplementary Data 1)41 sampled in two consecutive is significantly associated with the insect phylogeny (Rezende’s
floweringperiods(seeMethods)consistedof41plantspecies(18 algorithm,P<0.001),andnotwiththatoftheplants(P=0.355).
families) and 168 insect species (44 families), including 403 The fact that the insect (instead of the plant) phylogeny shapes
plant–insect species links and 7955 interaction events, i.e. insect module composition could imply convergence of floral traits of
visits to individual flower units. Most of these events were made unrelated plant lineages as a response to selection pressure by
by bees (57.9%), followed by beetles (Coleoptera; 33.8%), flies specificcladesofpollinators28,ortheexploitationofspecificfloral
(Diptera;5.2%),butterflies(Lepidoptera;2.2%)andwasps(0.7%) resources by the different taxonomic groups of pollinators from
(Supplementary Data 1)41. The bees were also the most species- randomly selected plant species (refs. 24,44 and refs. therein).
rich group (66 taxa), followed by flies (37), beetles (26), wasps Finally, the phylogenetic relatedness of plants was a source of
(19) and butterflies (17) (Supplementary Table 1). Connectance variation for the prevailing group of visitors. When visitation by
(i.e. the number of realized interactions as a proportion of all each major pollinator group was treated as a categorical plant
possible interactions) was 5.9%, which is an intermediate value variable, we found thatfor each plantspecies inthe community,
for Mediterranean scrubland networks2,19. Nestedness (i.e. the the predominant group (as to no. of visits), showed a significant
asymmetrical pattern in which interactions of specialist species phylogeneticsignal,i.e.phylogeneticallyrelatedplantspecieswere
are subsets of the interactions of the generalist ones, with the mainlyvisitedbythesamepollinatorgroup(Rezende’salgorithm,
latterrepresentingthecoreofinteractingspeciesinacommunity) P=0.008; Fig. 1a, b). Furthermore, significant phylogenetic
was 95.5%, a value typical for mutualistic networks17. signals were found for the visitation rates by flies, beetles, and
The coupled evolutionary history of plants and insects was wasps (Supplementary Fig. 1). These associations align with the
associated with the structure of the pollination network in the view that the phylogenetic congruence of interacting plants and
community. The cophylogenetic signal of the binary network pollinators occurs at high taxonomic levels (orders, rather than
(including only the presence/absence of p−p interactions) was species)24, and suggest that grouping the flower visitors of the
significant (Procrustes sum of squares, m2=0.97, P=0.001), Mediterranean scrubland studied according to their taxonomic
indicating that the backbone of interactions was correlated with order was ecologically meaningful.
the shared evolutionary history of plants and pollinators. For
example, interactions between the sunflower family (Asteraceae)
andbeetles(Coleoptera)showedthelowestProcrusteanresiduals, Phenonets reveal the temporal plasticity of interactions. A
implying particularly strong cophylogenetic signal between these phenonet is the interaction network covering each plant species’
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April May June July
RAR ANE ASP GEN SED TAR TRG TRC ORC SEV SCA TUB SEC HED CIS MOE LAV ANA CIC FUM ECH LIN ORN TRA BLA CRE ANT TOL PRA TRV GER PAP LAG MED TEU ANL CEP ONO CES CUS HEL
C
N
P × A
Anagallis arvensis B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
Anemone pavonina C C C C C C C C C C C C C C C C C C C C C C B-C B-C B-C B-C C B-C B-C
Anthemis auriculata C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C
Anthyllis hermanniae B B B B B B B B B B B B B B B B B B B B B B B
Asphodelus ramosus C-D D D C-D C-D C-D C-D D D C-D D C-D C-D C-D D C-D C-D C D C-D C C B-C B B B-C D B-C
Blackstonia perfoliata B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
Centaurea solstitialis B B B B B B B B B B B B-D B-D B B-D B-D B B-D B
Cistus creticus B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
Cistus salvifolius C C C C C C C C C C C C C C C C C C C C C C C C C C C C C B C C C C-D C C-D
Crepis neglecta C C C C C B-C B B B B B B B B B B B B B B B C B B B B B B B B B B B B B B-C B B B
Cuscuta epithymum D B-D D D D D D C B-D D D-W B-D D B-C-D-W D B-C-D-WB-C-D-W D D D
Echium plantagineum B-C B-C B-C B-C B-C B B B B B B B B B B B B B B B B C B B B B B B B B-C B-C B-C B-C C B-C C B-C
Fumana arabica C C C C C C C C C B C C C C C B-C B-C B-C B-C B-C B B C B B B B-C B B B B B B B B B B B B B
Genista acanthoclada B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
Hedypnois cretica C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C B C B-C B B-C B-C
Lagoecia cuminoides C C C C C C C C-D C-D C-D C-D C-D C-D C-D C C C-D C-D C-D C C-D C-D C C-D C C-D C-D C-D C-D C-D C-D
Lavandula stoechas B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
Linum trigynum C-D D D D D D D D D D D B B-D B-D B-D D B-D B-D D D D B-D C-D B B D D B B-C-D D D B D B D D D D D D
Medicago minima B B B B B B B B B B B B B B B B B B B-C
Moenchia mantica D D D D D D D D D C-D D D D D D D D D D D D D B-C-DB-C-DB-C-D D D D B-C B-C-DB-C-D B-C C-D C-D C-D C-D C-D C-D C-D C-D
Onopordum tauricum B B-C B B B B B B B B B B B B B B B B B B
Orchis sancta B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
Ornithogalum sp. C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C
Prasium majus B B B B B B B B B B B B B B B B B B B B B B B B B B B B
Ranunculus paludosus B-C B-C B B-C B-C B-C B-C B B-C C B-C B-C B-C B-C B-C B-C B-C B-C B-C B-C B-C C C
Scandix sp. D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D
Sedum confertiflorum W W W W W W W W W W W W W W W W W W W W W W W W W
Serapias cordigera B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
Serapias vomeracea B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
Taraxacum sp. C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C B
Teucrium divaricatum B B B B B B B B B B B B B B B B B B B B B B B B
Tolpis barbata C C C C C C C C C C C C C C C-D C-D C C-D C-D C-D C-D C C C D C-D D C D D C D D D D D D-L D-L
Trifolium angustifolium L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L L
Trifolium arvense B B B B B B B B B B B B B B B B B B-C-D B B B B B B B B B B B B B B-D B-D B-D B-D
Trifolium campestre B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B
Trifolium glanduliferum B-C B-C B-C B-C B-C B B B B B B B B B B B B B B B B B B B B B B B B B B
Tuberaria guttata C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C C
Network hub (c > 0.62, z > 2.5) Connector (c > 0.62, z ≤ 2.5) Module hub (c < 0.62, z > 2.5) Peripheral (c ≤ 0.62, z ≤ 2.5)
Fig.2Thefunctionalrolesoftheplantspeciesinthephenonetsofthecommunity.Plantphenonetsareshowninthetoprow(shadedinblack)andare
sortedtemporally,sothatplantswithearlierstartandshorterdurationareorderedfirst,fromlefttoright.Monthscorrespondtothestartoffloweringonly.
Foreachphenonet,thevaluesofthreenetworkpropertiesareprovidedinagradientcolourscale:Connectance(C),Nestedness(N),andNetworksize(P×
A).Foreachplantspecies(rows),thepollinatorgroupwiththehighestnumberofspeciesvisitingtheplantineachphenonetisalsoprovided:Bees(B),
Coleoptera(C),Diptera(D),Lepidoptera(L)andWasps(W).Whitecellsdenoteabsencefromtherespectivephenonet.Forthephenonetname
abbreviations,seeSupplementaryTable6
flowering period. The plant phenonets in the study community ND is the size of the pollinator niche of the plants in the
(Supplementary Table 3) had various sizes depending on the community; BC is the extent to which a plant species connects
phenophase length of each plant species. The smallest phenonet parts of the network that would otherwise be isolated; CC
consisted of 42 insect and 15 plant species (phenonet of Cen- measureshowcloseoneplantspeciesistotheotherco-flowering
tauriumpulchellum),whereasthelargestphenonetcontained165 onesviasharedpollinators47.Themeanvaluesofthethreenode
insect and 36 plant species (Anthemis auriculata). One caveat is properties examined were all significantly higher in the species’
that this data-pooling method generates binary networks, and phenonets than in the static network (Fig. 3; Supplementary
hence the metrics and algorithms used in the analysis should be Table 4). Specifically, mean ND was 68.8% higher in the
appropriately selected (see Methods). phenonets than in the static network, mean BC was 37.0%
Nestednesswasnegativelycorrelatedwithplants’phenonetsize higher,andmeanCCwas33.3%higher(N=37).Thefunctional
(Pearson’s r=−0.82, P<0.001, N=40) (Fig. 2), and ranged or topological roles of the plants (i.e. peripherals, module hubs,
from78.7%(Ornithogalumsp.)to95.8%(Echium plantagineum, connectors and network hubs)43 also differed between the static
Linumtrigynum).Forplantspeciesthatfloweredatthetemporal networkvs.thephenonets(SupplementaryTable4;Fisher’sexact
extremes of the flowering season, phenonets became smaller in test for count data, P<0.001, N=37). Specifically, six species
size, more connected and more nested compared with the mid- acquired more generalist topological roles (three peripheral
season ones, when the interaction patterns were more random species became connectors, two peripherals became module
(Fig. 2). Nestedness in mutualistic systems is directly linked to hubs,onemodulehubbecameaconnector),andconversely,three
community dynamics. Specifically, it has been shown to reduce species acquired more specialist roles (two connectors became
effective competition and enhance the number of coexisting peripherals, and one module hub became a peripheral; Supple-
species45, and to be positively associated with community mentary Table 4).
persistence in seasonal or unpredictable environments46. The The series of phenonets allowed for a detailed observation of
connectance of plant phenonets ranged from 6.6% (Anthemis the p–p network’s temporal dynamics. During the entire
auriculata) to 19.8% (Centaurium pulchellum), and also was sampling season,the aforementioned functional rolesof plants43
negativelycorrelatedwiththesizeofthephenonet(Pearson’sr= were generally far from static: species considered peripheral (i.e.
−0.88, P<0.001, N=40). extreme specialists) in the static network temporarily become
The phenonet approach yields significantly higher ecological generalists (e.g. Anemone pavonina, Ranunculus paludosus;
generalization and centrality values of the plant species within a Fig. 2), in the phenonets of other species. Apart from Cistus
community, compared with the static p–p network. We creticus, which was the only network hub in the static network
compared three fundamental node properties, describing the (Supplementary Table 4), other species also served as network
centralityandgeneralizationlevelofplantspeciesinthetotalp–p hubs for shorter periods, e.g. Cistus salviifolius, Crepis neglecta,
network and in the phenonets: (i) Normalized Degree (ND), (ii) Onopordum tauricum and Taraxacum sp. (Fig. 2). On the other
Betweenness Centrality(BC) and (iii)Closeness Centrality(CC). hand, there were specific plants that maintained their role as
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0.07
0.20
0.4
0.06
V=0 V=74 V=0
P<0.0001 P=0.002 P<0.0001
0.15 0.05
0.3
y
malized degree 0.2 enness centralit 0.10 eness centrality 00..0034
Nor Betwe Clos
0.02
0.05
0.1
0.01
0.00 0.00
0.0
Static network Phenonet Static network Phenonet Static network Phenonet
Fig.3Plantspecies’behaviourasestimatedintheirphenonetsvs.thestaticnetwork.Thethreespecies-levelnetworkpropertiestestedaretheNormalized
Degree(ND),BetweennessCentrality(BC)andClosenessCentrality(CC)ofthestudyplants(fordetails,seeMethods).Statisticalsignificancewas
assessedwithpairedWilcoxonsigned-ranktests(N=37)
module hubs, thus their pollinator “clientele” persisted in the Thelevelofgeneralization(ND)wassignificantlyrelatedtovisual
communityoveralongtime,acrossseveralplantphenonets(e.g. (viz.trichromaticfloralcolour,horizontalandverticalapparency)
Anthyllis hermanniae, Crepis neglecta, Lavandula stoechas and and chemical floral traits (viz. apparency of sesquiterpene and
Scandix sp.). Interestingly, the above-mentioned hubs mainly aliphatic volatiles). BC was significantly related to trichromatic
attractbeespecies(Fig.2).‘Connectors’(i.e.speciesthatconnect colour, sesquiterpene apparency, proportion of aliphatics in the
different modules) show also a general constancy regarding the emissions and surface apparency. Thus, the trichromatic floral
most species-rich order of visitors they receive. Thus, even colour, scent and surface apparency predicted the importance of
generalistplantsorthosewithshiftingrolesduringtheflowering plantspeciesforthecohesionor,inversely,thefragmentednature
seasonaremainlyassociatedwithspeciesofspecificinsectorders. of the p–p network. Interestingly, CC, the rate of shared polli-
In sum, our approach reveals (i) the multidimensional temporal natorspecieswithotherplantsinthecommunity,wascorrelated
mosaic of the functional diversity of insect-pollinated plants, (ii) only with floral scent properties (Table 1). CC values increased
species that are highly influential in the community during their withthenumberofpollinatorspeciesthataplantspeciesshared
phenophases and (iii) those plants that maintain in time certain with other plants, i.e. with the pollinator niche overlap48. Thus,
modulesofthep–pnetwork,beingthuspotentiallyimportantfor pollinatornichesimilaritywassignificantlyassociatedwithfloral
the conservation of their module partners, both plants and scent in our focal community. This result expands upon recent
animals. findingsabouttheimportanceoffloralscentasakeypartitioning
factor for two major plant nodes within a p–p network31. Since
high values of BC and CC identify species with the greatest
The role of floral phenotype. After evaluating the analytical influence on network topology48, we may conclude that the
adequacyofthephenonets,andrevealingthetemporaldynamics diversity of floral sensory stimuli, i.e. the relative variability of
of the interactions, we tested the three network node properties colour descriptors and scent properties of the flowers, was a
that describe the plant species’ behaviour within them (ND, BC driver of plant functional complexity in the assembly.
andCC)againstthecomponentsoffloralphenotype.Forthis,we Then, we tested the relationship between the different
applied generalized least-squares models corrected for statistical components of floral phenotype and the overall structure of the
dependence due to phylogenetic proximity (PGLS in Table 1). static p–p network. For this, we applied a multivariate-response
Apart from the standard floral traits (viz. scent variables, col- generalized linear model designed for compositional data
orimetric properties, symmetry, nectar presence and corolla (MGLMs in Table 2), using the weighted (quantitative) static
depth—seeMethods),wecalculatedthechemicalandthephysical network interaction matrix as a multidimensional response
floral apparency for each plant species (Fig. 4). These values variable(seeMethods).Wefoundthatvisualandolfactoryfloral
describe the degree to which a plant species has more or less stimuli (viz. trichromatic colour, emissions of sesquiterpene
noticeableflowerscomparedtoitsco-floweringspecies,regarding volatiles and horizontal apparency) contributed significantly to
its inflorescence emissions or its physical appearance, given its the network link structure, implying that interactions were not
floral abundance in the community (Supplementary Fig. 2). We randomly assembled (Table 2). Repeating the same analysis for
found that the behaviour of the plant species in their phenonets every subset of the p–p network including a major pollinator
was significantly associated with floral sensory stimuli (Table 1). taxonomic group (viz. bees, wasps, Coleoptera, Diptera and
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Table1Floral traits associated withthe threenode properties of plantsinthenetwork studied
Nodeproperty AIC AIC Predictor d.f. F P-value Slope
null best
NormalizedDegree −4.1 −48.1 Trichromaticcolour(r ×θ ) 6,30 8.11 0.008 +
tri tri
%Aliphatics 41.85 <0.001 –
Sesquiterpeneapparency 16.10 <0.001 +
Heightapparency 23.02 <0.001 +
Surfaceapparency 12.93 0.001 +
Normalboilingpointofscent 4.06 0.053 +
blend
BetweennessCentrality −5.0 −36.3 Trichromaticcolour(r ×θ ) 4,32 6.70 0.014 +
tri tri
%Aliphatics 17.02 <0.001 –
Sesquiterpeneapparency 24.66 <0.001 +
Surfaceapparency 12.33 0.001 +
ClosenessCentrality −119.7 −123.6 %Aliphatics 2,34 5.52 0.025 –
Al_app×Be_app×Mo_app× 2.62 0.115 +
Se_app
Thebestphylogeneticallyinformedgeneralizedleast-squaresmodelsaccordingtoAICarepresented.TheAICvalueofthenullmodel(~1)isprovidedforcomparison.SignificantP-values(>0.050)are
indicatedinbold.Positiveornegativerelationshipsaregiven(slope+:>0,–:<0).Fordetailsonthepredictors,seeMethodsandtheSupplementaryInformation
Alaliphatics,Bebenzenoids,Momonoterpenes,Sesesquiterpenes,appapparency
Lepidoptera) and their host plants revealed that sesquiterpene or indirect effects (e.g. pleiotropic interactions with other traits)
emissionswereassociatedwiththelinkstructureinthenetworks of aliphatic compounds in such interactions.
of bees, whereas physical apparency was important for the Visitation rates by theother pollinatorgroupswere associated
networks of almost all groups of insects (Table 2). The withfloralsensorystimuli(Table3)withtheexceptionofwasps,
importance of physical apparency (see also ref. 32) might reflect which are known for their limited reliability as pollinators (but
a general attractiveness response or opportunism by insects, see ref. 54). The visitation rate by beetles, which are believed to
relatedeithertooptimalforagingstrategies(e.g.inhoneybees)or have a poor affinity for subtle floral phenotypic components10,
to limited flight capabilities (e.g. in beetles). was correlated withfloral scent and nectar presence. Specifically,
Regardingthechemicalphenotypictraits,wefoundthathigher species that offered no nectar showed higher visitation rates by
sesquiterpene emissions characterized plants with broader beetles, implying a possibility of niche partitioning with the
pollinator-niches and stronger impact on the cohesion of the nectar-seeking species in the community. Visitation rate by flies
network. These semi-volatile compounds are typical of the so- was correlated with aliphatic emissions, but little information is
called “aromatic plants”, which release high amounts of VOCs available about volatile preferences by different fly lineages55.
from glandular trichomes on their flower and leaf surfaces49, in Butterflies are not major pollinators in Mediterranean scrub-
theMediterranean-typescrublands.Atthesametime,suchplants lands56, appearing mostly towards the end of the flowering
represented central nodes in the study network: Lavandula season. Foraging by butterflies has been associated with both
stoechas, Teucrium divaricatum and Cistus creticus (Fig. 4) were visual and olfactory floral stimuli, depending upon the species
abundant perennials with high visual and chemical floral studied57. In addition, adult butterflies can be diverse both in
apparency, emitting high amounts of terpenes (Figs. 3–4; visual physiology and in foraging preferences for different floral
Supplementary Data 2 (ref. 41); Supplementary Table 5). Given traits57,58. In our study, tetrachromatic colour as perceived by
the risk of low efficacy of volatile emissions due to the strong swallowtailbutterflies(viz.saturationandhuecomponentφ )58,
tet
windsandhightemperaturesinthephrygana28,50,thefunctional as well as aliphatic emissions, were associated with butterfly
roles of the aromatic Mediterranean plants deserve further visitation rates. The fact that visitation at the level of order is
scrutiny. Compared with other classes of floral volatiles, associatedwiththefloralsensoryphenotypesuggeststhat,atleast
sesquiterpenes are larger and less volatile molecules51, allowing inphrygana,thetaxonomicorderscanrepresentfunctionalguilds
ustosuggestthattheheavyemissionsofthesecompoundscreate of pollinators.
a compact olfactory environment that is sufficiently resistant to
dilution by wind forces and to rapid volatilization50,52. Con-
sideringsesquiterpenoidemissionsasanadaptationtotheabiotic Discussion
environment28,35remainstobetestedintheMediterranean-type In this study, we conducted a trait-based analysis to investigate
biomes of the world, characterized by the abundance of such therolesofsensoryphenotypiccharacteristicsofinsect-pollinated
plants(e.g.CalifornianchaparralsandSouthAfricanfynbos).The flowers in structuring the p–p network of a natural community,
emissionofaliphaticcompounds(i.e.non-terpenoidcompounds and in shaping plant behaviour in the network. In order to
that lack ringed C-skeletons but otherwise vary substantively in achieve the latter, we (i) removed the effects of phenology by
chain length and degree of saturation) demonstrated unique applyinganewdynamicdata-poolingmethodology,(ii)weuseda
associations. Although they were negatively correlated with BC functional approach that incorporated floral abundance into the
and ND (Table 1), implying that they characterize specialist phenotypic apparency of the species and (iii) we accounted for
plants in the network, they were positively associated with bee the effects of phylogenetic affinity between plant species. Thus,
andbutterflyvisitationrates(Table3).Giventhatmanyof these whatremainedtobetestedwasmerelythefunctionalrelationship
compounds can show behaviour-eliciting functions and are between floral phenotypic traits and species’ centrality or spe-
highly attractive to certain insects (e.g. male andrenid bees53), cialization. This work represents a step toward the unbiased
the observed pattern in the phrygana probably indicates that assessment of multimodal floral stimuli and the role of sensory
aliphatic volatiles mediate specialized relationships with bees or landscapes in community-wide pollination studies. Despite the
butterflies. Manipulative experiments will be necessary to limited knowledge of the enormous phenotypic diversity of
distinguish between direct functions (e.g. pollinator attractants) flowers, and of the innate preferences and sensory systems of
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100 400
24
3 ONO
–2
80 ΔERf
ncy 60 ASP
e
ar
pp 40 PRA
a
ht
g
Hei 20 GEN TEU LAV
CIS CIC
0 CER SCEEDNRMAENDTRNGEOORRRCNCBULASPAPHECEALHLNASHLGEEVDTAUNBTAALRINATNRECSACNAMFTOUEMSECTOLTTRRRGCRE
–20
–2 0 2 4
log(surface apparency + 2)
Fig.4Floralapparencyinthestudycommunity.Apparencyvalueshavebeencalculatedbytakingintoconsiderationonlytheco-floweringspecies(see
Methods).Discradiiarescaledversionsoftheapparencyofthetotalvolatileemissions(ΔER).Disccoloursapproximatethehuman-perceivedcoloursof
f
theflowersintheRGBsystem.FlowersofTeucriumdivaricatum(TEU),Lavandulastoechassubsp.stoechas(LAV)andCistuscreticus(CIC)thatexhibithigh
terpenoidemissionsareshown.Fortheco-floweringspeciesandfortheabbreviationsofscientificnames,seeSupplementaryTable6.PhotosbyA.Kantsa
their visitors, we found significant relationships within a single the same group of floral visitors (Fig. 2). Under this view, see-
network,suggestingthatourapproachcouldbefruitfullyapplied mingly generalized behaviours of pollinators might include a
to p–p networks elsewhere. somewhat cryptic specialization for floral sensory stimuli29.
Theplantphenonetstructureintroducedhereiscentredonthe Defining functional specialization of species in interaction data-
temporally experienced ‘flowering lifetime’ of each community setsisalreadyacomplicatedtask28,29,andgiventherelationships
member with the subset of species that co-flower or are active observed here, the sensory systems of animals as well as the
during that lifetime. Consequently, it is possible to track down corresponding phenotypes of plants deserve more attention, as
every species’ behaviour during the active lifetime of every other the subjects of natural selection. For example, there is evidence
speciesinthecommunity. Thestaticpoolingwould insteadonly that floral colour in several community or regional datasets
describespecies’behaviourduringtheentireflowerseasonofthe represents an adaptation to the ancient, well-conserved visual
area studied or during (biologically) arbitrary time intervals, system of the Hymenoptera28,60,61. Thus, it is worth examining
resulting, inevitably, in inflated specialization or underestimated whethersensorysystemsthatcharacterizeentirecladesofinsects
centrality for the nodes. Different types of mutualistic networks (e.g. the trichromatic vision of bees or the tetrachromatic vision
have been shown to exhibit invariant properties, leading to a of butterflies) make them ecologically significant functional
predictablecommunitystructure5,27.Atthesametime,theextent guilds, and perhaps attempting to reinvent the ‘syndrome’ con-
ofspatio-temporalscaledependencyofmutualisticnetworksand cept60.Studiesthatrevealsensorybiasesinpollinatorperception
theiranalysisisstilllargelyunresolved,althoughthisiscrucialto of flowers, combined with consideration of abiotic (signal effi-
ourcomprehensionofnaturalcomplexityingeneral(e.g.ref.59). cacy) and biotic (selection from florivores, larcenists and patho-
Thus, any approach that disentangles the drivers that shape gens) effects on floral signal evolution, should lead us to a more
networktopologyanddynamicsmaybeapplicabletoavarietyof nuanced and less typological concept of floral phenotypic
ecological systems. Phenological constraints may operate in any ‘syndromes’.
interactionnetwork:e.g.fruitingphenologyisaknownconstraint Terpenoids are primarily considered as defence compounds62
in plant–disperser networks14. The phenonet methodology and their role in p–p interactions has remained largely unex-
offered here (i) is fully compatible with the temporally dynamic plored in a community context63, despite the fact that Schiestl’s
nature of any interaction network, (ii) allows a phenology- meta-analysis64 showed a 90% overlap between the presence of
unbiased approach and (iii) can be used to more accurately sesquiterpenes as insect pheromones and as floral volatiles. Our
describe the species’ roles, specialization and centrality in any results suggest that sesquiterpenes are likely to play multiple
network dataset that has been sampled with regular intervals ecologicalrolesinplantsofthephryganacommunity,ashasbeen
during their periods of activity. In this manner, trait-based ana- demonstrated for other terpenoid compounds in thyme Thymus
lyses may focus on the actual role of phenotypes in shaping vulgaris (Lamiaceae) across France65. In particular, we suspect
interaction patterns, beyond phenology. that sesquiterpenes function as innate or learned attractants for
Our phenonet results highlight an interesting, and perhaps bees in the phrygana, because (i) bees are by far the most com-
challenging to interpret, interplay between the ecological and mon and diverse pollinators in the region56, (ii) bee-pollinated
evolutionarytrendsthatshapep–pinteractions.Forseveralplant plants commonly emit terpenoid scents55, (iii) sesquiterpene
species, ecological roles in the community are plastic in time compounds have been shown to attract naïve honeybees66 and,
(Fig.2)indicating shiftinginteractionswithfloralvisitorsduring most importantly, (iv) in our community, sesquiterpene emis-
theirphenophases.Givensuchtransitionsinvisitationdynamics, sions were correlated with floral colorimetric properties as per-
one might predict a secondary role for floral traits. Instead, our ceived by the trichromatic visual system of the Hymenoptera28.
findings show that floral sensory phenotype (visual or chemical) Here we found that the quantitative distribution of bee visits
is significantly associated with plant species’ behaviour in the among the plants was related to sesquiterpene apparency
phenonet,andthatshiftingpatternsofinteractionsremainwithin (Table 2). In addition, the significance of trichromatic floral
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Table2 Floral traitsassociated with the quantitativedistribution of linksinthe staticnetworkand itssub-networks
(Sub-)network AICsum AICsum Predictor Deviance P-value
null best
Total 5166 4843 Trichromaticcolour(r ×θ ) 343.60 0.027
tri tri
%Sesquiterpenes 352.80 0.038
Phenology(start×duration) 343.60 0.030
Surfaceapparency 551.10 0.004
Coleoptera 1188 1015 Phenology(start×duration) 79.94 0.009
Symmetry 90.78 0.003
Surfaceapparency 158.75 0.001
Diptera 1054 1021 Tetrachromaticcolour(rtet × θtet × φtet) 76.78 0.069
Phenology(start×duration) 88.11 0.024
Apparencyoftotalemissions 90.09 0.025
Hymenoptera—Bees 2035 1897 Phenology(start×duration) 103.80 0.143
Sesquiterpeneapparency 163.60 0.011
Heightapparency 192.70 0.005
Surfaceapparency 206.00 0.055
Hymenoptera—Wasps 353 310 Trichromaticcolour(r × θ ) 25.87 0.128
tri tri
Surfaceapparency 63.17 0.012
Phenology(start×duration) 68.16 0.001
Lepidoptera 462 374 Surfaceapparency 63.09 0.005
Phenology(start×duration) 93.36 0.001
Eachsubnetworkincludesoneofthemajorpollinatorgroupsinthestudycommunity.Thebestmultivariate-responsegeneralizedlinearmodels(family:negativebinomial)accordingtoAICare
presented.TheAICvalueofthenullmodel(~1)isprovidedforcomparison.SignificantP-values(>0.050)areindicatedinbold.%Sesquiterpenes:thepercentageofsesquiterpenecompoundsinthetotal
floralemissions.Fordetailsonthepredictors,seeMethods,andtheSupplementaryInformation
colour to the topologically important plants in the network focusing on sensory stimuli perceived by floral visitors, remain
(Table1)corroboratesthefundamentalroleofbeesasimportant overlooked or omitted from such studies. Despite the methodo-
pollinators and selective agents in this habitat type. Accordingly, logical challenges, trait-based analyses of interaction webs
thePGLSmodelsshowedthatthevariationinbeevisitationrates improve our understanding of the complexity of natural
relates to the trichromatic floral hue and increases with sesqui- communities.
terpene emissions (Table 3). Hence, beyond the signal efficacy
hypothesis, it is possible that some groups of bees may have
Methods
sensory biases to C15 compounds deserving further exploration.
Studyarea.Samplingwascarriedoutinacoastalthermo-Mediterraneanscler-
Interestingly, semi-volatile sesquiterpenes have been shown to
ophyllouscommunity(EastMediterraneanlowscrub,a.k.a.phrygana)inAghios
mediate positive plant−plant associations in natural commu- Stefanos,LesvosIsland,Greece(39°18′.00N,26°23′.40E;whatthreewordsgeocode
nities, through diverse mechanisms of conspecific eavesdrop- reviewers.gladness.hesitantly).Themostdominantfloweringplantsofthecom-
ping67, as well as adsorption and re-emission by (heterospecific) munityareLavandulastoechas(Frenchlavender),Cistuscreticus(pinkrock-rose)
neighbouring plants, to defend against herbivores68. Given the Sarcopoteriumspinosum(thornyburnet),andtheautumn-floweringErica
manipuliflora(autumnheather),withsporadicpresenceofQuercuscoccifera
profusionofsesquiterpenesinMediterraneanscrublandsandthe
(kermesoak),Pistacialentiscus(lentisc)andOleaeuropaea(wildolivetree).The
associationofnativeplants’emissionswiththesensorysystemsof climateisMediterraneanwithhotdrysummersandmildwinters.
bees, future research should focus on their broader functional
roles across the spectrum of plant–plant interactions, from alle-
Flowervisitation.Visitationcensuseswereconductedduringthespringflowering
lopathy and associational herbivore resistance to facilitation of periods(April–July)intwosuccessiveyears(2011–2012).Therepetitionof
sharedbeepollinators.Specifically, itisworthexploringwhether observationswasimportantinordertoaccount,tothefullestpossibleextent,for
floralscentmediatesfacilitativeinteractionsinp−passemblages, theabove-mentionedhightemporalplasticityofspeciesandinteractions.We
as compared with floral morphological traits28. haphazardlyestablishedsixpermanentobservationplotswithdimensions3×25=
75m2inthestudyarea.Duringbothyearsofsampling,thesameobserver(AK)
Rapid progress has been made towards the development of visitedthestudysitein10day-intervalsandrecordedallplant–insectinteractions
toolsforthefunctionalrestorationofpollinationnetworks7,8,and duringthree15mindiurnalsessions(quarters),distributedfrom9amto3pm.
the identification of plant species with high conservation prior- Duringeachquarterofanhour,theobservermovedatasteadypacewithinthe
ity69. Given that floral traits relate to plant population vulner- plot,recordinginteractions,andcollectinginsectspecimenswithahandnet,in
ability70, our approach indicates that sensory-targeted functional casetheinstanttaxonomicidentificationwasnotpossibleatthesite.Plantand
insectspecimenswereidentifiedtospeciesortothelowesttaxonomiclevelpos-
restoration and conservation schemes would increase our
sible.Aninteractionwasrecordedonlywhenaninsecttouchedthereproductive
understanding of and ability to maintain network dynamics and organsoftheflowerformorethantwoseconds.Followingaconventioninthis
possibly facilitative effects in natural communities. For example, typeofstudies2,8,16,43,thesevisitorshavebeenusedasaproxyforpollinators.In
it has been shown that selecting plants for restoration only total,46.7hwerespentobservingfloralvisitation(23.2hin2011and23.5hin
2012).Toevaluatesamplingcompleteness,weusedtheapproachofChacoffetal.
according to the amount of rewards they offer may undermine (basedontheChao2estimator)73,whichshowedthatwedetected73.0%ofthe
facilitativeeffectsandresultinundesirableeffectsduetoelevated totalvisitorspeciesrichnessinthecommunity(SupplementaryFig.3),whichis
competition (e.g. ref. 7). We provide evidence that influential typicalinsimilarstudiesofp–pnetworks73,74.
plantsinthecommunitymayexhibitvisualand/orolfactorytraits Duringeverysamplingday,allopenflowerunitswerecountedinthe
associated with specific pollinator groups, such as bees. Per- observationplots.Asflowerunitswedefined(i)allinflorescences(e.g.compact
spikesinLavandula,headsinAsteraceae,denseumbelsinApiaceae),wherethe
spectivesexpandifweconsiderthatfloraltraitsarealsoinvolved distancebetweenindividualflowerswassoshortastoallowsmallinsectstowalk
in antagonistic interactions71, and the spread of pathogens72. onthesurfaceoftheinflorescenceratherthanflyinsearchofthenextsourceof
Overall,thenumerouswell-resolvedp–pdatasetscollectedacross rewardor(ii)theindividualflowersthateitherweresolitarilyorplacedinless
compactinflorescencesthanin(i).Flowerdensitywascalculatedasflowerunitsper
theworld areinvaluableecologicalresources; yettheir predictive m2,bydividingthetotalnumberofunitscountedinallcensusesbythetotalarea
capacitywillremainincompleteaslongasfloraltraitassessments, observed(250m2).Noneoftheplantsinthiscommunityhasanexclusively
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Table3 Floral traitsassociated with visitationrates bythe major pollinator groupsinthe community
Insectgroup AICc AICc Predictor d.f. F P-value Slope
null best
Coleoptera 53.5 45.0 Nectar 3,34 6.98 0.012 –
Al_app×Be_app×Mo_app× 7.92 0.004 +
Se_app
Diptera 9.0 2.4 Symmetry 4,33 3.38 0.075 –
Heightapparency 3.77 0.061 –
%Aliphatics 8.34 0.007 –
Hymenoptera—Bees 121.5 97.6 Phenology(start×duration) 7,30 0.00 0.928 –
Trichromaticcolour(r ×θ ) 4.35 0.045 –
tri tri
Sesquiterpeneapparency 4.92 0.034 +
%Aliphatics 8.92 0.006 +
Surfaceapparency 9.34 0.005 +
Tetrachromaticsaturation(r ) 15.29 <0.001 +
tet
Hymenoptera—Wasps −13.9 −14.3 Trichromaticcolour(r × θ ) 2,35, 2.66 0.112 +
tri tri
Lepidoptera 119.8 90.4 %Al×%Be×%Mo×%Se 6,31 3.83 0.059 –
Surfaceapparency 7.53 0.010 –
Tetrachromatichue(φ ) 16.98 0.003 –
tet
Tetrachromaticsaturation(r ) 10.09 <0.001 –
tet
%Aliphatics 18.14 <0.001 +
Thebestphylogeneticallyinformedgeneralizedleast-squaresmodelsaccordingtoAICarepresented.TheAICvalueofthenullmodel(~1)isprovidedforcomparison.%Compoundclass:thepercentage
ofcompoundsofthisclassinthetotalfloralemissions.SignificantP-values(>0.050)areindicatedinbold.Positiveornegativerelationshipsaregiven(slope+:>0,–:<0).Fordetailsonthepredictors,
seeMethodsandtheSupplementaryInformation
Alaliphatics,Bebenzenoids,Momonoterpenes,Sesesquiterpenes,appapparency
nocturnalanthesis,andnonightsurveyswerecarriedout.Specimensaredeposited specieswhoseremovalfromthenetworkwilldisruptthep–pinteractionsin
attheMelissothequeoftheAegean,UniversityoftheAegean,Mytilene,Greece. theentirecommunityorrewiretheorganizationofthepaths47,48.
3. ClosenessCentrality(CC).Itisconsideredasthemostinformativemeasureof
Plantphenonets.Thephenophaseofaspeciesrepresentsthetimeperiodbetween centrality, reflecting the mean distance of a node to other nodes47. It is
theobservationdaysofthefirstandthelastfloweringindividualsinthecom- computedusingthebinaryunipartitenetworksofeachtrophiclevel.Higher
munity(daynumbersoftheJuliancalendar;SupplementaryTables1and2). CCofaspeciesindicatesthatthespeciesinteractwithmanyspecieswitha
Becausewehavetwoyearsofobservations,phenophasesconsistoftheaveragefirst high ND. It thus reflects how close one plant is to the other co-flowering
andtheaveragelastdaysoffloweringforeveryplantofthetwoyears,exceptfor plantsviasharedpollinators(pollinatornicheofplants).
GeraniumrobertianumandHeliotropiumeuropaeum,whichwereonlypresentin Allthethreemetricswerecalculatedintheplantphenonets,withtheRpackage
thecommunityonlyinthesecondyear.Accordingly,thephenophaseofaninsect bipartitev.2.06.
isdefinedasthetimeperiodbetweenthefirstandthelastdayitwasrecordedin Inadditiontotheabove,wecalculatedthefunctionalrole(i.e.networkhub,
thecommunityirrespectiveofwhetheritsactivitywentbeyondthetimelimitsof modulehub,connectororperipheral)ofeveryplantinthephenonetsinwhichit
thestudy.Forthoseinsectsoccurringonlyinoneyear,weusedthesingleday participates,bycomputingtheamong-moduleconnectivitycandthewithin-
numbersoftheJuliancalendar(SupplementaryTable1). moduleconnectivityz(thecoordinatesinthefunctionalnetworktopography43)
Inordertoconstructthephenonetforagivenplantspecies,weextractedall usingtheNetCartosoftware75.Thesamesoftwarewasusedforacquiringthe
othertaxathatwerepresentinthecommunity(i.e.‘inflower’forplants,and compositionofthemodulesofthestaticnetwork(SupplementaryTable2).Note
‘active’forinsects)duringitsfloweringphenophase.Thissortingrequiresdetailed thatthreeplantsthatreceivednoinsectvisitsduringtheobservations,aswellas
dataonthephenologyofthecommunity.Theaforementionedsamplingdesign, one,whichhadnoco-floweringspecies(SupplementaryData1)41,werenot
whichmonitorsthevisitationnetworkandfloralabundanceinthecommunity assignedanyphenonetnodeproperties.Wecomparedthemeanvalueofeachnode
withinregulartemporalintervals,providedthisinformation.Next,weassembled propertycalculatedfortheentirestaticnetworkvs.thephenonetnodeproperties
therealizedspecies–specieslinkswithinthephenophase(accumulatedforthetwo forallplants(SupplementaryTable4),byemployingapairedWilcoxonsigned-
yearsofsampling)into41newbinarynetworks(SupplementaryTable3).Inthis ranktestswiththefunction‘wilcox.test’intheRpackageMASS.Network
way,allnodepropertiesattributedtoeachfocalplantcorrespondtothevalues nestednesswascalculatedusingthefunction‘nestedness’intheRpackagebipartite
obtainedfromtheanalysisperformedintheownplant’sphenonet.Thisrationale v.2.06.
makesuseofthedynamicnatureoftheinteractionnetworks,andassignscorrected
rolestothemembersofthecommunity,bytakingintoaccountthephenologyof
eachspeciesandthefactsthat(i)notallinsectspeciesareactiveduringthe Floraltraits.Thefloraltraitsrecordedineachoneofthe41insect-pollinatedplant
floweringperiodofaplantand(ii)notallplantspeciesarefloweringduringan speciesofthecommunityare:
insect’sactivityperiod.
Inourstudy,phenonetdataaggregationinevitablyassumesthatspecies,even 1. Inflorescencescentcompositionsampledinvivoandinsitu,expressedasthe
thoughnotobservedinoneyear,maybepresentinbothyears.Thiscompromiseis relative proportions of each chemical class (viz. aliphatics, benzenoids and
necessaryinordertoacquirethewidestpossiblefloralnichesofforaginginsects. phenylpropanoids, monoterpenes, and sesquiterpenes) in the total species
Besides,itdistinguishesthephenologicallyoverlappingandthuspotentially blend.
interactingspecieswithinthefloweringseason. 2. Colorimetric properties (viz. key descriptors of saturation and hue) as
perceivedbyeitherthetrichromaticvisualsystemofbeesorthepotentially
higherdimensiontetrachromaticvisualsystemofswallowtailbutterflies.
Specializationandcentralitymetricsofspecies.Weaimedtoinvestigatethe
roleofthevisualandchemicalfloralphenotypeinthedegreeofspecializationof 3. Presence/absenceofnectar.
4. Floralsymmetry.
theplantspecies,intheabilityofplantspeciestomaintainnetworkcohesion,and
tomediatetheflowofinformationwithinthenetwork.Thesethreequestionswere 56.. FFlroowntearlhseuirgfhact.eareaoftheflowerunit.
addressedbythreewidelyusedmetricsthatdescribethebehaviourandthe
importanceoftheplantspeciesinthep–pcommunity,andaredefinedbelow: 7. Corolladepth.
8. Floweringphenology.
1. NormalizedDegree(ND),i.e.thenumberofinteractingpartnersofaspecies 9. Floraldensity.
as a proportion of the maximum possible number of interactions in the Scentcollectionswerecarriedoutinvivoandinsituwithdynamicheadspace
phenonet.Thisisthesimplestindexofcentralityforthenodesinanetwork, sampling,exceptforBlackstoniaperfoliata,whichwassampledinvivointhelab1
andafirstmeasureoftheirgeneralizedorspecializedbehaviour48. hafterthetestplantwascollectedinthefieldandtransferredtothelabinapot
2. BetweennessCentrality(BC).Itisadirectmeasureoftheconnectivityofa alongwiththeoriginalsoil.Allcollectionswereperformedonceduringthepeakof
node,proportionaltothenumberofallpossiblepathsconnectingallpairsof thefloweringperiodofaspecies,ondayswithclearandcalmweather,andatthe
nodes passing through the focal node. It is computed using the binary peakofthepollinators’activity(9am–1pm).Scents(SupplementaryData2)41
unipartite networks of each trophic level. High BC of a species indicates werecollectedfromApriltoJuly2012,exceptforCistuscreticusandTeucrium
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NATURECOMMUNICATIONS|DOI:10.1038/s41467-018-03448-w
divaricatumfromwhichscentswerecollectedinMay–June2011.Onaverage,we Data1)41.Weincludedthismodelinordertodescribetheflower-foraging
collectedfourreplicatesamplesoffloralheadspaceforeachplantspeciesinthe behaviourofnon-beevisitors,sincethereisreasonableevidencethatvisionmight
community.Duringvolatilecollectionsessions,themeanambienttemperature betrichromaticortetrachromaticinsomecasesfordifferentinsects,butdefinitive
(±SD)was25.7±1.4°C,andthemeanambienthumidity(±SD)was55.5±2.8%, evidenceforspecificspeciesisoftenabsentinmanycases.Thepolarexpressionof
measuredonthespot.ForthedynamicheadspacesamplingweusedaPAS-500 thelociinthetetrahedralcolourspacerepresentingsaturation(r ),andthetwo
personalairsampler(Supelco,Bellefonte,PA,USA)setata200mLmin−1flow anglesofhue(φ ,θ )78intheswallowtailvisionwerecalculatedtetusingfunction
tet tet
rate.Onlyherbivore-free,healthy-looking,freshinflorescenceswereselectedand ‘tcs’inthetheRpackagepavov.0.5–1.Reflectancedataandthecolorimetric
enclosedinPETovenroastingbagswithathicknessof12μm(SANITAS,Sarantis propertiesareincludedinref.28.
Group,Maroussi,Greece)10minpriortosampling.Thebaggedinflorescences Foreachinsect-pollinatedplantspeciesinthecommunity,weassessedfloral
werelightlycoveredwithaluminiumfoilforshadingagainstsunlighttoavoid symmetrybydistinguishingbetweenactinomorphicandzygomorphicflowers,and
sweating. corolladepth,bydistinguishingshallow(<3mm)fromdeepcorollas(≥3mm)
®
Adsorbenttrapscontained10mgofPorapak Q(80/100mesh,Supelco), (SupplementaryTable6).Thelatterassessmentwasbasedonmeasuringthelength
packedbetweentwoplugsofsilane-treatedglasswool(Supelco)inaborosilicate oftheactualdepthafterinsertingDrummondmicrocaps®intoflowers79.
glassPasteurpipette(ø7mm).Thecollectionperiodwas90minforthestrongly Allplantswerescoredfornectarpresence/absence28withfieldobservationsand
scentedplants(e.g.Lavandulastoechas,Prasiummajus,Teucriumdivaricatum), byusingpublisheddata79.
and300minfortheothers,asshortersamplingswerefoundinsufficientfor Forflowerheight,i.e.theaveragedistanceoftheflowerunitfromtheground
capturingthevolatileprofilesofplantswithloweremissions.Additionally,during foragivenplantspecies,wetookthedistancefromthegroundofthehighestand
eachsamplingsession,twoambientsampleswereadditionallycollected.Samples thelowestflowerunitperindividual(measurementoffiveindividualsforraretaxa,
fromgreenplantpartswerecollectedifpossible,inordertodetectanycompounds andupto20individualsforcommonones).Measurementswerecarriedoutinthe
fromvegetativeparts.However,thiswasnotalwaysfeasibleduetothesmallsizeof fieldatthepeakofeachplant’sfloweringperiodusingatapemeasure
someplants(e.g.Anagallisarvensis,Sedumconfertiflorum),whichwouldcause (SupplementaryTable6).
extensivetissuedamages.Ingeneral,wewantedtotrapallthecompoundsemitted Forfloralsurfacearea,weestimatedtheareaoftheflowerunitasobservedfrom
naturallybytheinflorescenceincludingitsgreenparts(bracts,calyx),andwhich afrontalview.Foractinomorphicorgloboseflowersandflowerunits(e.g.Lagoecia
significantlycontribute(especiallyinLamiaceae,Apiaceaeetc.)tothestrong cuminoides,Scandixsp.),wemeasuredthediameterofthecircularcontourand
chemosensoryenvironmentofpollinatorsintheMediterraneanshrublands49. calculatedthefrontalareausingtheformulaAc¼π´radius2.Forzygomorphic
Immediatelyafterscentcollectionandyetonthespot,theadsorbenttrapswere flowersanddensespike-likeflowerunits(e.g.Lavandulastoechas)wemeasuredthe
elutedwith300μLofa10:1solutionofhexane(puriss.p.a.—Merck,Hohenbrunn, twoperpendiculardimensionsofthefrontalviewandcalculatedtherectangular
Germany)andacetone(CHROMASOLV®forHPLC—Sigma-Aldrich,Bellefonte, areausingtheformulaAr¼length´width.Allmeasurementswereperformedon
PA,USA).Theeluateswerestoredinafreezer(−20°C)untilchemicalanalysis. 5(forraretaxa)to20flowerspertaxon,usingadigitalcalliper(Supplementary
Beforeanalysis,thescentsampleswereconcentrateddownto50mLwithgaseous Table6).
N,and1ngoftoluene(Fluka,Bellefonte,PA,USA)wasaddedasaninternal
2
standardinordertoestimateemissionrates(ER)intolueneequivalentsperfresh Floralapparency.Weusetheterm‘apparency’inordertodescribethedegreeto
massofplanttissue.ForthecalculationoftheERweusedthefollowingformula: whichaplantspeciesinthecommunityhasnoticeableflowerscomparedtoitsco-
ðPpeakareaofVOCiÞ´amountoftolueneðngÞ floweringinsect-pollinatedspecies(seeanalogyinref.80),andwedistinguish
Emissionrate¼ peakareaoftoluene ´concentratedvolumeðμLÞ betweenvisualandchemicalflowerapparency(SupplementaryFig.2).Forthefirst
freshhobuiorsmoafsssasmamplpinlegdðgÞ one,weusedtwometricsbasedon(i)floralheight(verticalapparency)and
(ii)flowerdisplayarea(horizontalapparency).Wemustunderlinethatfloral
TheERofacompoundisexpressedinng(compound,intolueneequivalents)g−1 apparencyisirrelevanttoplantgrowthform,i.e.specieslargerinsizeorperennials
(biomass)h−1.Foreachplantsampled,theaverageERforeachcompoundfrom arenotnecessarilymoreapparentthantheotheronesinalldimensionsexamined
thedifferentsampleswascalculated(SupplementaryData2)41. here(SupplementaryTable6).
AllscentanalyseswereperformedonanAgilent7890A/5975CGC/MSsystem Theflowersofaplantareapparentunlesstheco-floweringspecieshavemore
(AgilentTechnologies,PaloAlto,CA,USA)usingsplitlessinjectionsat240°Cona highly-positionedflowers(comparetoref.81);therefore,wecalculatedflower
polarGCcolumn(AgilentJ&WDB-WAX,length30m,ø0.25mm,filmthickness heightapparencyasthedifferenceofthemedianfloralheightofaplantfromthe
0.25µm)andHeasacarriergaswithaflowrateof1mLmin−1.TheGCovenwas medianflowerheightoftheco-floweringspeciesinthecommunity,duringits
heldinitiallyat40°Cfor3minandthetemperaturewasincreasedat10°Cmin−1 floweringperiod.Verticalapparencyforafocalplant(ΔHf)isdefinedasthe
to250°Cfor5min.Thetwoeluents(hexaneandacetone)weretestedfor differenceofitsfloralheightfromthemedianfloralheightofitsco-flowering
contaminantsusingthesamemethod;apartfromsomeothertraces,diacetone plantsinthecommunity:
alcoWhoelu(CseAdSA:g1i2le3n-4t2M-2S)DwaPsrotdhuecotinvliytyabCuhnedmaSnttatcioonntasmofitnwaanrte.v.E.02.01(Agilent ΔHf ¼Hf(cid:2)H~
Technologies)toretrievetheGC/MSdataandAMDISv.2.62softwareforpeak
deconvolutioncombinedwithNIST05MassSpectralLibraryv.2.0d(NISTMass Forthecalculationofthemedian,onlytheco-floweringplantsofthefocalspecies
SpectrometryDataCenter,Gaithersburg,MD,USA)toidentifyVOCs.Kovats weretakenintoconsideration,inaccordancetothephenonetconcept
retentionindexwascalculatedforalltheVOCsafteranalysisofanauthenticalkane (SupplementaryTable3).Thevaluesarenegativeincasethefocalplant’sflowers
mix(C10-C40;Sigma-Aldrich)undertheabove-mentionedchromatographic arelower-positionedandthereforelessapparentthantheco-floweringplants.
conditions.Publisheddataonmassspectraandretentiontimes,andauthentic Toestimatehorizontalapparency,wefirstweightedfloralsurfacevaluesfor
standardswereadditionallyused.Wheneverpossible,wecomparedVOCretention eachplantbyitsmeanfloraldensity(flowerunitsperm2)measuredin2011and
timesandmassspectratothoseofauthenticstandards28. 2012.Inthisway,weobtainedthetotalfloralareaofeachplantspeciesperm2of
Ineachspecies’floralscentemissions,wecalculatedtheproportionofeachof flowercover.Floralsurfaceapparencyofafocalplant(ΔAf)wascalculatedin
thefourmainchemicalclasses,i.e.aliphatics,benzenoidsandphenylpropanoids, accordancewithheightapparency(cf.above),anditrepresentsthedifferenceofthe
monoterpenesandsesquiterpenes.Finally,weestimatedthemeannormalboiling weightedflowerareaofthefocalplant(Af)andthemedianweightedfloralareaof
point(nBP),i.e.themeanboilingpointatanatmosphericpressureof760mmHg theco-floweringplantsinthecommunity:
voofleaaticlhityp,lafonltloswpiencgiesK’aflnotrsaalevtoalal.t5i1le(Sbulepnpdl,emaseant‘arreyveTrsaebliend6i)c.ator’ofaVOC’s ΔAf ¼Af(cid:2)A~
Toacquirethereflectancespectraoftheinsect-pollinatedflowersofthe
communityweusedaportableJazspectrometerequippedwithaPremium600μm Again,forthecalculationofthemedian,onlytheco-floweringplantsofthefocal
reflectanceprobe(OceanOpticsInc.,Dunedin,FL,USA)withUVtransmitting oneweretakenintoconsideration,inaccordancewiththephenonetconcept.
optics.Measurementsweretakenfromthepetalsorflowerunits(e.g.entire Negativevaluesindicatelowerhorizontalapparencycomparedwiththeotherco-
umbels)andfromdifferentindividuals;inthecaseofrareplantswecollectedas floweringplants.
manyflowerunitsasavailable28.Wemeasuredseparatelyeachofthedifferently ChemicalapparencyisdefinedasthedifferenceoftheERofscentperm2
colouredareaspresentonaflowerunit(e.g.theyellowtubularandwhiteligulate occupiedbyagivenplant(ΔERf)fromthemedianERoftheco-floweringplant
floretsinAnthemisheads),butweincludedintheanalysisonlythespectrum taxa.Themedianwasusedasameasureofcentraltendencybecauseoftheright-
(wavelengths:300–650nm)ofthecolourofthelargestarea28.ForPrasiummajus skeweddistributionoftheERdataowingtothelargedifferencesofscentamong
weusedspectraextractedfromtheFloralReflectanceDatabase(FReD)76.Spectral theplantsinthecommunity:
dataWweefirersptrcoaclceusslaetdedwtithhetphoelaRrlpoaccikoafgethpeapvloanvt.s0.i5n–t1h.ehexagonalcolourspaceof ΔERf ¼ERf(cid:2)EfR
Hymenoptera77,wheretheradiant(r )representssaturation,andtheangle(θ )
tri tri
representsthehueasperceivedbytheinsect61.Apartfromthetrichromaticvision, ChemicalapparencywascalculatedforthesumofERineachplant,aswellasfor
wealsoemployedamodelrepresentingtetrachromaticinformationprocesses(i.e. theERofeachoneofthefourmainVOCclasses(viz.aliphatics,benzenoids,
sensitivetoUV,blue,greenandredregionsofthespectrum)thatcorrespondsto monoterpenesandsesquiterpenes)(SupplementaryTable5).
thevisionoftheswallowtailbutterflyPapilioxuthus(Papilionidae)58.The Floralapparencyisafunctionofthemaximalfloraldensityofeach
genusPapiliooccursinthestudycommunity(Papiliomachaon,Supplementary entomophilousplantspeciesinthecommunity.Thisapproachdoesnotintendto
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