This article isprotected rights by All copyright. reserved. 10.1111/1365 doi: lead todifferences version between this andthe ofPleasec Version Record. been and throughtypesetting, proofreadingwhich may thecopyediting, process, pagination This article undergone has for and buthas accepted been not review publication fullpeer Summary Running MaudEditor: Ferrari Section: Plant Article type Article :Research PIERREMRS. a
Département biologiques, desduQuébecMontréal, sciences Université à Canada Montréal,
Accepted Articleb Trait 1.
Canada Research Chair in IntegrativeCanada Researchin Chair Ecology.
Pierre prey interactions. such asproxies traits and functional phylogeny models could predator/ helpinfer into unable between topredictco interactions newly interactions species between numerous modelsare simultaneously, butdescriptive ability tounderstand ecosystem level new ofthese consequences globalWith change oursuccess assemblages, modifyingspecies predicting in - Head: Trait - matching and phylogeny as predictors of predator predictors of as phylogeny matching and
* Correspondingbrousseau.pierre author: - - Animal InteractionsAnimal - Marc Brousseau MARC BROUSSEAUID :0000 (Orcid MARC - 2435.12943 interactions involving ground beetl ground involving interactions
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interactions. Current food webinteractions.considers food theory Current , Dominique Gravel ke, Sherbrooke, Canadake, Sherbrooke,
communities will depend, will inpart,communities onour Département biologie, de - - [email protected] occurring -
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and I. Tanya Handa - 0699
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This article isprotected rights by All copyright. reserved. Accepted Article 5. 4. 3. 2.
resource descriptive approaches and novelboth functional modelphyl combining traits and better The to >75 eachwithin trophiclevelincreased theability topredict interactions) interactions) trait biting force/cuticular prey prey toughness ratio.best andpredator/ body size The unrealized interactions, phylogenetic using and thetrait information The parsimonious model accurately most predicted81 functional traits, and information. phylogenetic ecological approach based functional traits wereThen, measureda directlyusing onspecimens. ofto determine specie which pair A feedingconducted ground involving experiment was 20 beetleand species 115prey organisms. difficult document to traits, along withphylogeny as (used apr Herewe used trait - matching biting ofpredator force andtoughness a cuticular prey demonstrated based models predicted correctlybased >80 modelspredicted
predictivepredator/ power thecommonly ratio. than body size Our prey used
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Adding
tions of newly introduced species and ofnewly toresolve webs.tions cryptic species food introduced on thematching - matching between predator feeding traits and vulnerability prey ), toinferground usingas predatory model interactions beetles
a phylogenetic termtheevolutionary representing distance <58 % of
could represent predict toolto avaluable - centrality formalism, wecentrality evaluatedformalism, 511predictive s did or did not interact. Eight predator and four prey ordidinteract.predatorand s did Eight not which species did not interactdid not which species ombinations ofallombinations predatorand prey oxy for chemical traitsoxy forand defence other
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This article isprotected rights by All copyright. reserved. Accepted2002) is used asgroups aproxy intrait related ofsimilarity values of between species five dimensional space, traits related presumably tofunctional Previous studiesdemonstrate ina thatnetworks ecological three most can represented to be instrumental tod the individual level influencing an thefitness organism of 2004; Bersier &Kehrli 2008) information; functional traits develop inference tools techniques (direc not feasibleall potentialinteractions todocument between a withtraditional region of species further thatfoodweb is by interactions.It problem modelsare limited of thedocumentation is occurring a resulting species recent from inva Articleknown interacting unable and topredict isthus species interactions newlyco between food webs species toanticipate anduseful istheconsequences ofspeci Currentconsiders foodweb simultaneous interactions theory number betweenof a high ability tounderstand interactions between species globalPredictingof novel arising change communities with thedynamics Introduction formalism, networks Key -
words and interactions an accordingly conservatism based in indicates evolutionary ofclose Recent modelsoffood descriptive web structure rely ontwoimportant of sources (Dunne, Williams & Martinez 2002) (Dunne, Williams & Martinez
Foodfunctional web, traits,
etermine foragingability andconsumer ofathe thevulnerability resource. of t observation, DNA t observation, analysisgutcontent,important to it etc.), of making
(Morales (Petchey . Functional representcharacteristics measurable any traits at - Castilla
et a
et al.2015) l. 2008; Allesina 2011) l. 2008; trophic interactions sion or a range shift sion . Its. predictive ability to limited ishowever (Van Macel Putten, & der Visser 2010) .
(Violle es extinction onextinction thestructurees of , matching (Eklöf
and phylogeny et al.2007) (Gravel
et al. 2013) et al. - centrality
will dependwill onour et al.2013) et
and could be (Webb et al. . (Cattin Phylogeny - . A
et al. . This article isprotected rights by All copyright. reserved. Acceptedcharacterizedmatching and of centrality by a traits set traits; theseevaluated couldas be of interactions species; are other in words, sharingexpected a number species traits torealize similar similar traits ofthe resource. centrality The determines component generality thespecificity/ a of quantifies thecompatibility thevulnerability ofthe between theforagingconsumerand traits betweenco ornewly rare network and has formalism, simultaneously whichconsiders componentand thematchingcentrality Brind'Amour 2014) contentlipid can be toprey matched traits 2012) the matching pollinator tongue traitscorolla flower depth of with suchas tube length ArticleBascompte Albouy& 2014;Gravel, Thuiller 2016 interactionstraits thetraits ofthe betweenofthe consumerand the resource Rohr few existing predictive are based models still uniquely ontrait only interactions digestive of enzymes predators are as tomeasure, chemical such hard relatives
(Rezende et al.2016) . Similarly, marine traitsmammal of predatorssuch as swimming and speed muscle The trait Although bothtraits and mixing phylogenybe efficient couldtopredict ecologi (Bersier & Kehrli 2008) &(Bersier Kehrli (Morales
(Rohr & Bascompte&(Rohr 2014) et al. 2009; Rohr et al.2009; - matching approach has been extended through the matching matching has extended approach through been the . The trait the added advantage beingbutexisting topredict able links unobserved, of .
- Castilla - occurring species - matching account into takes approach the probability of
et al.2015) (Feyereisen 1999)
et al.2010;RaffertyIves& Krasnov 2013; . Thisaspectbe pa could defence . Eachspecies (consumer a , foodmodelsincluding are web them descriptive
such as andcaloric lipid such content (Rohr
of prey .
a et al.2016) ) . Anexa (Eisner, Eisner &Eisner(Eisner, Siegler 2005)
rticularly for traits that important mple oftrait . The matching component - ma tching nd resource) is nd (Gravel - matching would be matching (Spitz, Ridoux &(Spitz,
et al.2016) (Rohr &(Rohr - centrality
et al.2013; (Ibanez
s or cal . The
of a
This article isprotected rights by All copyright. reserved. Accepted ofthephylogeny most observedexplain feeding interactions.Ourwere would predictions documentedhard orthat tomeasure. were Our traits working thatboth an hypothesis was withphylogeneticcomplemented represent thatwere ourto analysis information traits not as themain groundusingmodel organisms. used We functionalas beetles(Coleoptera: Carabidae) traits they abilitywere predictive quantifiedany for system. these traits predatorcould larger interact soft hard with and prey smaller Such matchingcould c armor weakerfrom protecting thempredators matchedtraits. torelevant For prey thecuticular instance, toughness ofprey asan act can handling ability Articleof predators as biting such force through thematching of trait mechanismspossible aswhen measuring traits. functional true the measurement ofsometraits, traits intermediate since isessentially variableaimed a it latent torepresent unmeasured functional of adepth). position species The phylogenetic in latent variables measured ordirectlyusing traits (e.g. tongue bodylength, size, nectar holder (Cadotte - Our objective apredictive infer todevelop to was predator/ model prey interactions Incase ofpredator/in terrestrial thespecific prey theuse interactions environments, matches to predict interactionsmatches has topredict
could help to refinecould helpto trait predictors of interactions,predictors matching based onthe of
et al.2009) (Brodie - centrality formalism centrality formalism reate aprey such inthethat shift predator/ relationship ratio size
& Formanowicz 1983;Cunha& Planas 1999) . It circumvent to . elegant an solution provides but theisthatonedoes drawback acquire not insight into (Wheater & Evans 1989; ChristiansenWroe 1989; & 2007) (Wheater& Evans - matching sofar, models,but neither descri their been limited to the size ratio tothesize ofpredatorbeenand prey limited (Gravel (Broeckhoven, Diedericks & Mouton 2015) (Broeckhoven,& Mouton Diedericks a communitya couldas beused an
et al.2013)
- centrality formalism, andwecentrality formalism,
. Other known important traits known . Other (Enders 1975) (Enders
have yet have be to
problems related to . Including. ptive nor ptive nor
and d . This article isprotected rights by All copyright. reserved. Accepted Likewise,on live specimens. all ground were70 % preserved alcohol. beetlepredators in when measureone we more specimen thatcouldnotbeassessed had thanto traits onhand) We kept atleastimpossible. ones as(hereafter morphospecies referred toasspecies) Preywere identifiedlevel tothe lowest we taxonomic reach were able to and landplanarians (TableS2).Theseall lif included species of (woodlice,millipedes,caterpillars, arthropods spiders, etc),earthworms, mollus and four sub we collected20species specimensgeground beetlesrepresenting of 13 southern Quebec, phylogenetic Canada,tomaximize and morphological diversity.Overall, Groundpreyand beetles their were collectedat habitat and several types sites invarious in ArticleCollection ofspecimens Material and methods feedingground beetles interactionsof matching based onthe different beetlesground ordidsuccessfully did not of we species interact. Then, predicted the withafeedingones. tested We these experiment predictions beetles clade wouldbelikely belonging tothedistantly same more than toshareprey related respectively body body tothetraits prey width andcuticular size, and ground ii) toughness, force that i)
(Evans & Forsythe&1985; Wheater1989;Cohen (Evans & Evans the most importantthe most groundgape wouldbe bodyand beetletraits mandibular size, - families 1and (Table S1
pecimen of each speciespecimeneach in70 of
in Supporting Informationin Supporting ,
when specieswas identification level e stages from specimens. eggs todead
to determine - centrality formalism. centralityformalism.
et al.1993)
% alcohol when% (i.e. alcohol possible ) and a total of115preya) and total ,
prey withwhich or wereclassifiedor nera, nine tribes nera, ninetribes
combined
biting
k s This article isprotected rights by All copyright. reserved. toughness head size and mandibular interactions ( their potential interactions(Table included prey 2). 1)predator/ limitations These ratio size Ground beetlea Functional traits interactionconsidered wasas unrealized. two occasions,ground beetle the the the successfullykilled prey,it; notconsume butdid unreal realizednoted if theprey interaction partially. and killed at was consumed least An were was 24and recorded withoneprey atconsumption 48h.A provided atime.after Prey changed a twice week. lined withamoistened filter paper at Filter the bottom. was paper dailyand moistened S2) (Appendix variation intraits and phylogenetic diversity ofpr every interactions was the tested.Nonetheless, were interaction tomaximize chosen possible keep tothe were themaliveexperiment, pairwisegenerally prior andtests not notreplicated, effort (e.g. twicea once collectorganisms week to for id or twomonths), environment at24 conductedWe 475 pairwise a total thelaboratory offeeding experiments in i Feeding experimen Acceptedused measured o proxy whichas for isacommonly bodylength) limitation physical Article ized interaction was recordedground interactionized beetle ifthe the prey was kill didnot after 48h. On Ground beetles we (Wheater & Broeckhoven 1989; (Wheater Evans (Cohen . Ground beetles 11 were kept inplasticof containers separately nd matched prey traits were selected to represent hypothesized limitations prey limitations nd matched werein torepresent traits hypothesized selected
°C, 70 °C, t
et al. 1993) et
% humidityand aGiven day/ themajor cycle of16/ night 8hours. re starved for 24 h prior to the feeding experiment, after which for starved theyfeedingre experiment, after 24hprior tothe (Wheater& Evans 1989) ; 2)predator
biting force
eyforground beetlespecies each et al. ) thatcuticular was tomatchprey
2015)
(estimated from allometries with (estimated allometries from ; 3) predator mandibu ; 3)predator entify them and n a regulated
×
11
lar gape,
× f
4.5 cm This article isprotected rights by All copyright. reserved. Accepted(PCoA) wereon both ground prey determine beetle and performed distance t matricesto wereconsideredas belonging todifferent sub morphospe was characterizedso that incremented byit one only thetopologyof tree.For level.taxonomic Forlevels ofthe 15taxonomic (species each tosuper The phylogen Phylogeny S2. measure inAppendix canfound traits be value for per Further modelconstruction. used species was onthe methodologyused to detail wassix measured onone to groundwereindividual and prey every beetle measuredCutic specimen. on addedentomological of0.45diameter. an pin mm All traits, except for cuticular toughness, prey was m Articleon livingmeasuredand specimens. widthofprey possible when Cuticular were toughness of preservedgraduated specimens a witha under dissectioneyepiece. microscope Body length for many ofour prey ty prey predators, before to offering them andto metrics instead for ofbodyselectedwas it masswas practical impossible for reasons: weigh usto absorption section, length ofcutting ( section that hypothesized to relatetoprey handling Kredler 1993) 1985) which isrelated and tomatchprey ability tohandling was body width
were tomatch toany were difficult preylength These mandibular traits. length, ofliquid ; and4) thatwas predator eyesize tomatchthe prey speed of ofmovement
cies, the distance was assigned as the next taxonomiccies,assigned level; the e.g. was distance asall Noctuidae thenext sp. easured on dead specimens pressureeasured a with (Medio ondead Pesola® set eticground matricesbeetle distance andbased ofpreyspecieswere on . We also included four predator traits associated also. We includedfour toma predator traits pes. All measurements All groundpes. were of made beetletraits onalcohol
specimens per species (depending onavailability); a trait mean (Evans & Forsythe 1985; Acorn & BallForsythe&&(Evans 1991) 1985;Acorn
terebra) andBody ofapicalterebra) tooth. length length - families. Principal coordinates analysesfamilies. estimate
body were mass notavailable ndibular characteristics - (Evans & Forsythe(Evans phylum), thedistance - ular tough Line) towhich we (Bauer &(Bauer , but ness ness he - This article isprotected rights by All copyright. reserved. Accepted andmm) themillipede and larger prey,ground as beetle between such the added 74 interactions from were included therarely literature have as also We these been documented. species), trait were prey values attributed of measured Nounrealized equivalent specimens. from citedreferences were interactionsLarochelleLarivièreand reported in their own (e.g. stages stages, immobile vs.mobile a larva stages awere species same of different, highly they aswere twodifferent included species interactions were as coded recordedWe each for pair of tested ( species analysis Statistical Articleactive stages. distinguish over the previous speci for prey.three six ground for beetlesand T force of the model thehypothesized with genera) levels betweenthe species onpermittingfiner lower scores aresolution taxa(ord transformed phylogenetic distancetoreduce theimportanceof matrix superior of eachposition speciesinthephylogenetic Thewere PCoA space. ona performed / prey cuticular toughness and predator mandibular g andpredator toughness mandibular cuticular / prey mens of prey a same particular corresponding specieswas valueto settothe (Pagel 1997) forbidden interactions forbidden trait values.included Overall,151prey thematrix types.88realized added We
interactions
stage
. We selected. We the Narceus americanus group onbo of groundwith inactive beetlesof stages ofprey
L included (Table 1). For theseFor included 1). (Table 88cases 13prey (representing ij
= 1and unrealized interactions (Morales
th axes. Byth axes. correctly sodoing, was it to possible trait δ
- value for each forgroup goodnessvalue giving thebest matches predatorpredatormatches prey size/ size, - Castilla he phylogenetiche score ofall
i (de Beauvois)(de , j nd adult ofholometabolous insects)nd ) if they) if interacted or (2003)
et al.2015) Elaphropus anceps ; only interactionswe could confirm
ape/ prey body width; it was it setto ape/ body width; prey (size =(size 70 L
between the smallest predatorsbetween the ij
= development 0.When
not (noted not (noted
mm). mm). from
(LeConte) eggs, pupa andeggs, dead interact
taxonomic taxonomic L ers, families,ers, ij
(size =(size 2.1 50 points 50 points ). Realized biting biting , each with ions δ
-
of with - fit
This article isprotected rights by All copyright. reserved. Accepted Morales conservatism resulting evolutionary ofinteractions from scoresPCoA a which twoaxes), to latent isequivalent along thefirst trait representing the accountedfor. also We considered thephylog speedForunmatched (Table ofmovement. predatorcentrality traits 2), the only was cuticulargape/ mandibular toughness,widthandpredator prey predator eye body prey size/ matches thatincluded predator bodybody length/prey observed we traits, while considered here themdirectly. & Bascompte 2014) vulnerabilitywhile the prey, ofthe the "centrality" prey( of wh Article generalfollows a log model (GLM) linear number of associated predatorand prey,accounts for generality i.e. theirspecificity/ respective tothe relative traits interact."centrality" the direct The traits alone component ofthe represented for effect component represented combined the effectofpredat Each byrepresented wasa species "matching""centrality"Theand "matching" component. Interactions werefollowinganalyzed the Matchi ere λ ng logit , - Castilla δ - 1 centrality formalism
and (P(L δ
ij 2 et al.
= are parameters "matching"( describingtheimportance ofthe . Rohr et al. . Rohr 1 )) preyThe or probability predators. ofinteraction 2015) = v i * ) and predators ( λ(v .
i
(2016) f f j
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evaluated latent δ matching represents theforagingrepresents abilit - i linear model oflinear theform v f i i * ). Ecologically, the + enetic position of a determinedenetic position species by (as δ 2 f - j centrality formalism
or (
length, predator
traits after and relatedthem to We tested four hypothesized traitfour testedhypothesized We f processes processes
j ) and ( prey v
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- . This article isprotected rights by All copyright. reserved. speci best The fit. goodness match, thephylogenetic unmatched determine and butalso to model hadthe trait term, which wantedWe todetermi Goodness predatorsaxisprey and on where component, given is as: for thematching to interact another with position onlyonits in phylogenetic based new The equation space. axes ofgroundPCoA the probability beetlesandexplains prey.thus Thismodel a species of hypothetical relationshipS3). was obtained (Appendix match tofindthe lowe meaningless missing data highly curve influenceand theshapeof make can ecologically it each High however,thecurve can, force term. smoothing toover functionsmooth determining thenumber models (GLM) usedWe models(GAM) general ahigher additive which flexibility permit than general linear General Additive(GAM) Models s Ε 1
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are smooth functions and are functions smooth 2 - 1 of fi ) , - PCoA (Wood fit = true skill
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This article isprotected rights by All copyright. reserved. Acceptedthe interactions model between null theobserved and in matrix The prey. predators second independently oftraits, species.scenarioby phylogeny or computed This was randomizing models.Thetwo null fir testedWe thestatistical significance thepredictivecomparing modelswith abilityof the by modelNull predicted ofthespecies withindividuals tointeractvalues. only withextremetrait interactions. approach This allowed elimi usto trait valuepredict of themodel at theprey. Second, needed to least 25 the predictedof interaction probabilityof ≥0.5for the needed atonecombination tobe least interactionsground between beetle aa prey and were species determ cuticularspeed toughness.was for Novariation allowed ofmovement. realized Predicted trait and stepof valuesfor2g width,anda 0.5mm consideringa length stepof mm variability, theprobabilityof calculated interactionwas for all potentialcombination Articleintraspecific that predator/ variation.Given wouldbeinfluenced interactions prey by this termscombinations ofthe nine four thephylogenetic unmatched and termpossible traits a (representing total of511 The goodness incorrectvaries predictions and between prediction) and relation correct 1(perfectlyrepresents in predicted). predictions to TSS unrealizedAll threea interactions. valuegood measuresbetween have 0(absenceof of well percentagewell of - Forwe observedrange a manyoftraitreflecting species, prey wide values predicted realizedinteractions and isthepercentag specificity
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Table S4). - 1 (incorrectly1 pred
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sensitivityis thepercentage ined in two steps. First, ined in e of welle predicted of
% of realized % of - matches,
- 2 s ofprey
for This article isprotected rights by All copyright. reserved. 'gam'regression'mgcv' thefunction package using inthe R preventto 1.4 Regression with shrinkage" (TPRS) Splines approach at degree fixed 24.All smoothing was of forrandomize which a therangewhich within themean models, i.e. was thehigher TSS ofthe smoothing null value 500 iterations for each degree kept We ofsmoothing. the upper for degreeofsmoothing of smoothing ( based onequation the only 2including of groundand beetles preyofa Then,TSS weremodel calculatedspecies wethe randomized. relationship beetles betweenground and species. prey as for thetrait ( mean the value usedtotest was model null ob standardized withthe formula effect=( SES (SES) size models specificity for and TSS each randomized matrix. thousandrun for iterations calculated weremodels. We bothnull sensitivity, accuracy, andbeetle unrealized thesame numberrealized interactions. species always of had Ten wereinteractions matrix of the randomized observed onlythat each between so prey, groundspecific for thattraits beetles,but andIn phylogeny involved. were not scenario, this hypothesized th P < Accepted Articles erved value, 0.05) The over (accuracy, and specificity TSS) sensitivity, indicated indicated . The was SES calculatedfor models bothnull - s matches. Thus, wematches.a createdmodelassuming phylogenetic null a random 1 at ofspecialization the(number level ofinteractions observed) was species - and ) from 10 to30w ) from 10 over fitting ofthe phylogeneticc term theGAM by a better goodness a better
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This article isprotected rights by All copyright. reserved. 0.013 = 0.26 predatorgape/ prey mandibular body width(accuracy = 67 (accuracy = 7 to0.63. and TSS match goodness models fit null thanfor of both and of0. a TSS + predator models null goodness fit sensitivity, (accuracy, were TSS) of and specificity significantly higher than both length of predator trait highest percentage (86 any the prey 14offered of species. interactionsprey, offered with including 17 ground 10observations.with ≥ Only species beetlespecies had four < per species B Results
Accepted Article etween 4and 71 - matches prey size/ (predator predator size, )
; TSS = 0.23 ; TSS predator ( The best single trait The best modelhad overall an accuracy of8 SES= eye size
cutting biting force (T
( SES>3.9, SES>3.9,
1.5 able 1). Realized interactionsobserved Realized able were feeding testsfor 1). in54%of the the 1.94,
61 mandibular gape/mandibular body width prey
/ prey section) and (App thephylogeneticsection) term %
. Thismodel alsohad significantlyfour aspects higherthe scores ofthe for observations weregroundwith onaverageeach forobservations 23 made beetlespecies
( P ( SES= SES=3.17, SES=3.17, > / prey cut/ prey
%) ofobservations).(12 interactions interactions%) realized 14 over P speed 0.05 <0.001
1.67, - ) match modelwasmatch predator ) and prey predator size/ (accuracy= size 66 ), two ) icular toughness + phylogeny, toughnessicular + accuracy of withan P P . The 'best parsimonious' model was predator size/ prey'best size/ modelwas. The parsimonious' predator size <0.001) >
Pterostichus caudicalis unmatched predator traitsmandible (length predator unmatched ofthe
0.05) Myas cyanescens ) (Table3). The modelwith ( ; TSS = 0.37 = ; TSS SES>3.7, biting force biting slightly% theaccuracy increased to82.5 3.4 P <
( biting force
%, a TSS of0.6 a TSS %, SES=2.92, SES=2.92, 0.001)
endix S4).Allfour aspectsendix ofthe
Dejean .5 Say / prey cuticular toughness cuticular and/ prey
%
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This article isprotected rights by All copyright. reserved. resistance arthropodecologists ispossibly touse measure it due of tothe difficulty to the vertebrates arthropodal. 2015for examples) herbivorous considered studying when interactionsof ratiosize alone. predator/ While prey ra size interactions and invertebrates soil considering amongbetter arthropod than predator/ prey demonstrated increase could thatabilityinfer these combining trophic threeterms our to the variation predator of ground(Table beetles3). The phylogenetic trait combination of the term with foundWe that Discussion P phy increased by 2 for all models by S4) (Appendix were(Figure S8 predicted response around a toughness cuticular of40 interactions pre between hardened strong predators and notspecificity.models, but increased predictedan Theprobability realized model of preyhad a toughness cuticular accuracy, significantlythannull higher TSS and sensitivity
Accepted<0.001 Article logenetic information (accuracy information logenetic = 74 Adding increased phylogeneticinformation ofall theaccuracy models TSS and the ) biting force biting ) had a better goodness offit thanany a) had better trait single (e.g.McHenr Wroe,
of 9 . the matching
predator/ preypredator/ interactions It a strong alsohad onthe specificity impact positive onaverage increasing it % onaveragewith the added alone, term. phylogenetic taken When 24
/ prey predator/ toughnesscuticular ratio and size captured prey of most %. trait For individual A). - centrality formalism
y& Christiansen& Thomason Wroe 2005; 2007)
% arthropods arthropods observed -
matches andmatches unmatched thespecificity traits,
( tio is of common isof use,tio g mm SES=3.61, SES=3.61, . It however,commonly with . is, used accurately predict - 2 , following intera which norealized in o (but see Ibanez(but see y,an butwethreshold abrupt found P ur experiments. This
<0.001 - match 3). model(Table ) ; TSS = 0.46 ; TSS biting force ed
et al. 2013; Deraisonet al.2013;
predatory success
has rarely beenhas ( SES=3.72, SES=3.72,
- interactions matches . The
ctions
et
This article isprotected rights by All copyright. reserved. (Morales predator/ interactionsthat prey cannot for beaccounted easily withavailabletrait data (Eisner to include ina trait H springtails strategies, secretion such as mucus u unrealizedmaking interactions, themharder Furthermore, topredict. probable ishighly that it for many thefeeding interactionsin experiment mayartificially thenumber increased of have to predict thanunrealizedinteractions based equal thenullmodels). Thisdifference to interactionswere that demonstrates realized easier % ofrealized interactions,none predicted5 than more all theinformation. higher While than TSS modelswith thenullmodel No modeltheinclusion accurately without unrealized ofphylogenetic predicted interactions Phylogeny angoodness overall offit thanpredator/ better size prey arthropods. expectation, Contrary predator toour least andcan asa between comparative tosuccessfully be tool, used interactions predict vertebraextinct force could arthropod this becircumvented bites, but byallometries theuse of as of usedwith nrealized interac Acceptedowever, chemical specialized andother Article
et al. - Castilla
(Bauer &(Bauer Kredler 1993)
2005) tes
tions could be could tions et al.2015 - (Wroe , phylogenetic information can be useful asses, phylogeneticinformation to can matching model. As these traits are generally are closematching relatives model.Asthese shared with traits
et al. ) .
2005) better predicted
by slugs by slugs and deterrent chemicaland deterrent compounds . Our study shows thatthese arereliable,at allometries . Our shows study defence (Pakarinen 1994) on trait
by
biting force strategies toquantify can hard and be thus including traitsincluding thatreflect - matching only. 8
% ofunrealized interactions (i.e. ratio
/ prey cuticular/ prey toughne , fast jumping ability jumping of , fast (Table 3).(Table s several aspectss several of The lack ofreplicationThe lack s predicted at least (Eisner
prey et al. defence
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This article isprotected rights by All copyright. reserved. the eye and ofthe tobe themovement size predator ofprey speed was hypothesized (Bremigan & 1994) Stein morematch thatswallow isexpectedtobeimportant their for resources predators a whole as cuticular prey ratio phylogenetic toughness, size and predator/ the term. However, this trait prey bring didnot tot information significantly more our original thetrait hypothesis, et al.2016) (David 2014) Lundgren&2007) Rosentrater (Verwaijen, &Damme Van Herrel 2002) groundpredictor with beetles, The trait Trait collection of 1999) the mandibles were unrelatedtotheir phylogeneticposition wasexample, it shownthatintiger and beetles Cicindelinae),size length (Carabidae: body of Also, a phylogenetic analysis relevantvariation could miss amongFo close relatives. useful understand web to food t inprey involved and does bring not anyonthe mechanisms information selection. phylogenygenera) species(or experiment, includedfor isrelevant onlymaybe inthepresent whichu could suggest that Accepted Articlehe informa - . We see. We as auseful phylogeny helping tool tocircumvent problem matching Phylogeny by capturesexplained initself muchof thevariation traits 3) (Table - match
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This article isprotected rights by All copyright. reserved. Accepteddetermine aquatic interactionsin Srygley 1990, wing probabilitymatchedcould be loading)todetermine ofinteractions their of flying related predators, traits m toflying predators,web use traps asspiders, to adapted interactionswith different ofpredatorsground than behaviour foraging beetles. 2003; Deines, & Matz 2009) Jürgens ingestion by prey anddigestionby length defence chemical ground beetles For revealed onrotifers example,studies trait similar identified inonesystemmight another in alsoprovetounderstand one. useful theinteractions our approach integrates nowformally the Articleintuitively theoccurrence,consideredthe past explain to of in absence,interactions orthe but ability tocatch, ingest handle, array organismsall predatory determined as of interactionsare Our resolve to studya cryptic newgeneralized webs brings tool food and couldtoalarge be A novelpredator quantify to tool consume our springtailsexperiment. in ofinterest(Herbst)). is Despite that that,it possibly byexplained of inclusion only the one truly big layerlitter predictimportant to of predation ground springtails beetlesco on thatoften Nevertheless, ( Bauer& 1993) Kredler : catching ability by was : catching limited defence, handling preyspeed, by physical Combes et al. 2013) et Combes other trait combinations undoubteother will trait and digestpreyand - environments prey interactions . Similarly, be performance important to swimming could .
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McPeek, . In. thecase Passive ssfully ssfully
This article isprotected rights by All copyright. reserved. Accepted scavenging notinfluence will ofprey populationdynamics bewill used todistinguish scavengingclassic given more predatory from that interactions allowimportant their to prediction naturalwebs in food web analysis webs, donoteasily but scavenging predation errorsfrom discriminate food and include can in habits (Table techniques many 1). havepermitted Molecular inunderstanding advances food at leastrevealing occasionally, scav that identified as necrophagous +2400 feedingexperiment, species.all14ground (only our During already beetlespecies one report for necrophagy ofground only beetle observations 37species frequentlyUnfortunately,Larochelle is information this Larivière overlooked. and Articlepredator species acquiredatcost alowenergy for thepredatorcould andbefavorablemaintena tothe absence). interactions suchextend an approach the morphology flowers of corolla networks, thetrait proboscis one consider match length/ might depthof ofpollinator the SchrotBrown & 1996) Finally, model incorporates scavenging our (Ibanez 2012)
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) that were a offered ate) thatprey dead it .
that
(Wilson & Wolkovich 2011) Wolkovich &(Wilson
restrict predator/prey s in North America outof s inNorth (2003) nce of nce of
. This article isprotected rights by All copyright. reserved. be 2008; Petcheyet al.2008) energyand predator/ buttraits intakehandlingsuchas time, (MacArthur & 1966) Pianka aspects ability sucha as predator the of tooptimize energy handling time per intake Integrating interactionstrength frontier thenext is al. for co integrateinteraction other of drivers strength.theretoaccount While aremethods promising exclusively ontrait the behavioror prey parasi of the occurrence and of therealization interactions, predator for withanother influencing instance and theforaging thecouldalsoabilitycontext influence predator. The of biotic the the encounter ofthe species, probability the matching considered depend will interaction ina on The naturalenvironment separately. of twospecies Gravel& Thuiller 2012) modeledabundance distinctively becauserespond they to different to driv Gravel 2015) availability, higher realized niche inatruly from naturalenvironmentvary thepotentialdue will niche t ourmodelWhile identifies thepotentialground feedingthelaboratory, of niche beetlesin the P erspectives
Accepted Article important. 2015; Bartomeus et al. 2016) 2015; Bartomeuset al. - occ urrence
. Akin tospecies modeling distribution . where presence
(Poisot - - order interactionsorder and matching model, and attemptto future phylogeny inour efforts should , potentialinteractions thestrength be, and ofinteractions should
et al.
andresource biting force/ toughness . It. unclearwhich still traits correctly (ifany) is will approximate 2015; Gravel et al. 2016 2015; Gravelet al. ,
tes reducing its ability to escape predation. While we abilitytes reducing While its focus toescape predation. integrating varying abioticvarying conditions
foraging behavior mayforaging behavior bechallenging. more and doing require us so,will
of their traits of their traits b )
and relative abundance
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et al. (Boulangeat,
to consider et al.2016)
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This article isprotected rights by All copyright. reserved. AcceptedOur research wasby and financed Engineering theNatural Sciences Resear Mont wouldlikeWe tothank Donald Rivard Rodrigue, and Nathalie Parcat staff the national du Acknowledgme authors contributed critically tothe writing. collectedand by analyzedPMB PMBand ledthe manuscript. DG. by PMBwritingof All the The ideas Data methodologywereall jointlythreeauthors. anddeveloped were the by Author's Contribution web interactions. foragingencounter optimal and are probability future predict suggested stepstobetter food commensalism,mutualism, etc. herbivory.approachcoul The proposed Article ismoreit generalizable infer body antagonistic than matching to interactions size including preycan toughness cuticular help trait unraveltheir interactions.This demonstrateWe suchas that the traits andaccurately infer for that predatory species aredifficult observe interactions to inthe field. approach phylogenetic offers traits and a mixing information new opportunity tounderstand andconsequences causedloss speciesglobal drivers. ofspeciesinvasion by change Our et al.2016 Roze Species interactions influence Co nclusion nfeld 2014) - Saint ) . Thus,interactionnetworks needwell understoodthe tobe predict - Bruno their support,for andThéo help Payfor
which inturnhavefor local consequences ecological processes nts
species distributions atand small large distributions scalespecies spatial (Morales biting force d also bebeneficialotheras toinfer interactions such - Castilla
et al.2015) of arthropod
with . Adding data about. Adding data species predators and correspondingand predators
field andlaboratory work. - match isinterestingmatch as ch of Council (Bartomeus (Bartomeus (Araújo &(Araújo This article isprotected rights by All copyright. reserved. Accepted &Barraclough, Vogler, Hogan,J.E. Testing A.P.whether (1999) ecological T.G., factors Araújo,& A.(2014) M.B.geographic Rozenfeld, scaling The biotic interactions. of &Allouche, A. O.,Tsoar, Kadmon, Allesina, S.(2011)relations Predicting inecological trophicoftheallometric a test networks: &Acorn,Ball,(1991) The G.E.someadult J.H. ground mandiblesof structure, beetles: ArticleI.,Abbott, L.K. Abbott, References https://doi:10.5061/dryad.7tn01(Brousseau, andHanda2017). Gravel All data files are availableRepository from theDryad Digital AccessibilityData Nature (FRQNT). et technologies Canada (NSERC) 1061 Proceedings B: Biological Society Sciences, Series ofthe Royal ofLondon. promote cladogenesis inagroup of tiger beetles (Coleoptera: Cicindelidae). Ecography, Ecology, prevalence,models: and kappa thetrue(TSS). statistic skill diet breadth model. of Zoology, function, of herbivory and theevolution (Coleopte species of Darwin's finches. - 1067.
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This article isprotected rights by All copyright. reserved. Accepted Appendix S2 S2 Table S1 Table Appendix S1 Additional supporting the onlinever may information found in be Supporting Information McHenry,Wroe,& (2005) S., club: Bite comparative Thomason, J. C. bite (2015) S. Mixed VehicleWood, GAM Computation withGCV/AIC/REML Smoothness (2006 S. Wood, Article E.E.&Wilson, Wolkovich,carrion E.M.(2011)and howcarnivores Scavenging: structure Wheater, & C.P. Ackerly, C.O., Webb, McPeek,& (2002) D.D., M.A.Donoghue, Phylogenies and M.J. I. Hummel, Vile, D.,Violle,Fortunel, C.,Navas, C., Garnier, Kazakou,& M.L.,E.,(2007) E. Verwaijen, R.&Damme, D.,Van Herrel, A. Royal Society B: Sciences, ofLondon Biological mammals and thepredictionofpredatory taxa. infossil behaviour Estimation. BocaHall/CRC, Raton communities. predacious Coleoptera. community ecology. Let the Ecology, force, lacertid handling prey efficiency lizards. and sympatric dietintwo
Prey values trait Ground beetle trait values
Additional information onmethodology Species trait values concept functional! oftrait be )
16, Evans Generalized Additive R Introduction Models: with An
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842 Trends in Ecology & Trends in Ecology Evolution, , M.E.G. (1989) The, mandibul - 850.
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This article isprotected rights by All copyright. reserved. † interactions included intheanalysis (Total) (Lit.) ( 1 Table Appendix S4 Appendix S3
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Trechinae Nebriinae Harpalinae Carabinae Subfamily Species for which were necrophagous observedfeeding habits experiment. during the Accepted Article) interactions observedinthe feeding derived from experiment, theliterature (only realized) (LarochelleLarivière& 2003
List ofgr
Smooth Smooth term of GAM models Bembidiini Notiophilini Nebriini Sphodrini Pterostichini Platynini Perigonini Harpalini Carabini Tribe List ofall models ound beetle species studied and summary and theound beetlespeciesstudied (+) realized of and unrealized
Nebria lacustris Synuchus impunctatus Pterostichus tristis (Newman)† Pterostichus rostratus Pterostichus mutus Newman Pterostichus lachrymosus (Chaudoir) Pterostichus diligendus (Newman) Pterostichus coracinus Pterostichus caudicalis Myas cyan Platynus tenuicollis Platynus opaculus Agonum retractum Perigona nigriceps Har Anisodactylus harrisii nemoralisCarabus Species Total Elaphropus anceps Bembidion chalceum aeneus Notiophilus
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This article isprotected rights by All copyright. reserved. 2 Table Prey traits Ground beetle traits Traits mandibles edge ofmandibles) Movement speed Body width Cuticular toug Body length Terebra length (cutting Mandible length Eye size gapeMandibular Biting force Body length Length apical of tooth of Length absorption of Acceptedsection of mandibles Article
Traits considered inthe
hness
(0 to5) Categories mm g mm mm mm mm mm mm mm mm N mm Unit
studygroundbeetle/ of based prey onthematching interactions
- 2
Fast moving difficult catch are prey more to body thin Animals with beeasierbycould predators tohandle Hard cuticleanacts against armor predators(Broeckhoven as et al.2015) groundAs for beetles Ball related 1991); assumedtobe handling toprey Handlingability; maintains theprey inplacewhileslicing withthe terebra it (Acorn and handling Longer(Evans feedersForsythe onliquid toprey assumedtobe and 1985); related be related handling toprey Longer Assumed tobe relatedprey handling to 1993) Related tothe visualacuity ofused speed andKredler (Bauerand prey themovement Forsythe 1985) Determinesability; thehandling(width) of themaximumsize used i.e. Related (Wheater1989). tothe abilitystrongandfood hardened and touse Evans and physiological BodyIt lengthusedasmeasure is acommonly a size. ofbody of is used physical proxy Function
for strict predators; shorter assumed for and(Evans Forsytheto predators; for 1985); omnivores strict
constraints ofinterspecificconstraints interactions (Cohen et al.1993).
- centrality formalism.
prey (Evansprey and
This article isprotected rights by All copyright. reserved. ‡ size † * standardized effect (SES). sizes 'best' model. "Best modelwiththe lowest parsimonious" numberof tothea is the TSS terms similar and for"Best" 10,000iterations. isthemodelwith highest limit without ofterms while TSS Values modelsrepresent upper fornull limit and the lower the whilenullmodel the observedmatrix 2 isarandomization of prey interaction speciesonly. interactions observed inafeeding model1is experiment. complete Null randomization of matches, traits and foura phylogeneticterm) unmatched ground toinfer bee (best and overall of all parsimonious) used and terms best testedindividually(four trait Tab **
Unmatched predator traits Trait Phylogeny Best models Null models Model Phylogeny+ predato Phylogeny+ predator prey body + size/ body size
Accepted Article Mandible Terebra Absorption Apical tooth Eye movement speed size/ Body body size/ size gape/Mandibular width body Biting force Best parsimonious B Null 2 Null 1 SES > SES / le 3 est speed - matches (predator/ prey)(predator/matches †
Goodness offit oftwomodels,the null twobestof511 testedmodels modelsout
3.09
+ length ofthemandible (predator)+ length ofthe
Significance ofeachgoodnessSignificance
/ cuticular / (
P <0.001 ‡ r body prey + size/ body size
)
toughness **
SES >2.32 SES
(
P Accuracy 51 40 <0.01 - of 6 6 64 64 60 66 67 7 74 81 8 - - -
4 3 1 3 62 63 fit aspect was determined by calculatingfit aspect was by determined * * * * * * ) * * ** ** ** **
*
SES >1.96 SES biting force biting force
Sensitivity 53 43
81 83 94 82 84 92 8 87 78 87 87 - -
9
76 73 * * * * * * * * * * * * * ** * * ** ** ** * ** ** * *
terebra of a 95% confidence interval (
/ cuticular toughnes/ cuticular + toughness/ cuticular P
<0.05
Specificity 28 24
(predator) ) 39 3 22 38 25 30 3 49 68 74 7 - -
6 7 8 59 60 * * * * ** **
tle/ prey
- - 0.13 0.24
s 0.2 0.1 0.16 0.2 0. 0.23 0.26 0.37 0.46 0. 0.6 TSS
- - 1 61
eye - 8 5 0.29 0.24 * * * *
* ** ** **