Methods Ecol Evol. 2019;10:2129–2140. wileyonlinelibrary.com/journal/mee3  | 2129© 2019 The Authors. Methods in Ecology and Evolution © 2019 British Ecological Society

Received:28November2018  |  Accepted:16August2019DOI: 10.1111/2041-210X.13292

R E S E A R C H A R T I C L E

Monitoring insect pollinators and flower visitation: The effectiveness and feasibility of different survey methods

Rory S. O'Connor1,2  | William E. Kunin2  | Michael P. D. Garratt1  | Simon G. Potts1  | Helen E. Roy3  | Christopher Andrews4  | Catherine M. Jones2,5 | Jodey M. Peyton3  | Joanna Savage3 | Martin C. Harvey3  | Roger K. A. Morris6 | Stuart P. M. Roberts1 | Ivan Wright7 | Adam J. Vanbergen4,8  | Claire Carvell3

1CentreforAgri‐EnvironmentalResearch,SchoolofAgriculture,PolicyandDevelopment,UniversityofReading,Reading,UK;2TheFacultyofBiologicalSciences,UniversityofLeeds,Leeds,UK;3CentreforEcology&Hydrology,Wallingford,UK;4CentreforEcology&Hydrology,Penicuik,UK;5Buglife–TheInvertebrateConservationTrust,Peterborough,UK;6CommonsideEast,Surrey,UK;7ShotoverWildlife,Oxford,UKand8Agroécologie,AgroSupDijon,INRA,Univ.BourgogneFranche‐Comté,Dijon,France

CorrespondenceRoryS.O'ConnorEmail:r.oconnor@reading.ac.uk

Funding informationBiotechnologyandBiologicalSciencesResearchCouncil,Grant/AwardNumber:BB/I000348/1;WellcomeTrust,Grant/AwardNumber:BB/I000348/1;DepartmentforEnvironment,FoodandRuralAffairs,Grant/AwardNumber:BB/I000348/1andWC1101;ScottishGovernment,Grant/AwardNumber:BB/I000348/1andWC1101;NaturalEnvironmentResearchCouncil,Grant/AwardNumber:NE/R016429/1

HandlingEditor:LuisaCarvalheiro

Abstract1. Thestatusofpollinatinginsectsisofinternationalconcern,butknowledgeofthemagnitudeandextentofdeclines is limitedbya lackof systematicmonitoring.Standardizedprotocolsareurgentlyneeded,alongsideabetterunderstandingofhowdifferentmethodsandrecorders(datacollectors)influenceestimatesofpol-linatorabundanceanddiversity.

2. Wecomparedtwocommonmethodsforsamplingwildpollinating insects (soli-tarybees,bumblebeesandhoverflies),pantrapsandtransects,insurveysof1kmcountryside squares (agricultural and semi‐natural habitats) and flowering cropfieldsacrossGreatBritain,includingtheinfluenceoflocalfloralresources(nectarsugaravailabilityorcropflowerdensity)ontheinsectssampled.Further,wecom-pared theperformanceof recorderswithdifferingexpertise (non‐specialist re-searchstaff,taxonomicexpertsandnon‐expertvolunteers)inapplyingmethods.

3. Pantrapsandtransectsproducedcompositionallydistinctsamplesofpollinatorcommunities.Inthewidercountryside,pantrapssampledmorespeciesofsolitarybeeandhoverfly.Infloweringcrops,transectsrecordedagreaternumberofindi-vidualbumblebees,butfewerspecies.

4. Across all taxonomic groups and countryside and crop samples, transects gen-erallyhadlowerratesofspeciesaccumulationperindividualcollectedthanpantraps.Thisdemonstrates thatdifferencesbetweenmethods inestimating rich-nessarenotduetosamplingeffortalone.However,recorderspossessinggreatertaxonomicexpertisecanproducespeciesaccumulationdatafromtransectsthatarealmostcommensuratewithpantrapping.

5. Theabundanceandspeciesrichnessofpollinators(exceptsolitarybees)ontran-sects in thewider countrysidewaspositively related to theavailabilityofesti-matednectarsugar.Incrops,pollinatorabundanceresponsestoflowerdensities

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1  | INTRODUC TION

There is international concern about declines in the diversity anddistributionofinsectpollinatorsandtheconsequencesforpollina-tion services (Potts et al., 2016). Research is increasingly demon-stratinghow land‐use change, pesticides, climate change, invasivenon‐nativespecies,pestsanddiseasemayact,andinteract,tocausedeclines in pollinating insects (Vanbergen et al., 2013). However,evidenceis incompleteandimportantgapsremainwithrespecttothemagnitude,geographicandtaxonomicextentofthesedeclines(Pottsetal.,2016).Forexample,ourunderstandingofthepopulationstatusandtrendsinabundanceanddiversityofpollinatinginsectsisseverelylimitedbyaworldwidelackofstandardized,long‐termandlarge‐scaledata(Lebuhnetal.,2013).Thiscreatesanurgentneedformonitoring,andprotocolsthataccommodatebroadtaxonomicandgeographic coverage, account for potential biases in the data andgenerateadequatesamplesizes;allwhileremainingcosteffective.

The most important providers of pollination services globallyare insects, particularly bees and some flies (e.g. hoverflies) (Pottsetal.,2016).Currentbestevidenceforthestatusofwildbeesandhoverflies comes from records of species occurrence collected innationalandglobalbiodiversitydatabases.InGreatBritain(GB),re-cordscollatedbytheBees,WaspsandAntsRecordingSocietyandtheHoverflyRecordingSchemehaveallowedunparalleled insightsintothestatusanddistributionalchangesofbeesandhoverflies inGB(Carvalheiroetal.,2013;Powneyetal.,2019).Toourknowledge,suchverifiedlong‐termoccurrencedataforwildbeesandhoverfliesexistonlyforGB,theNetherlands,Belgium(Carvalheiroetal.,2013)andbumblebees intheUSA(Cameronetal.,2011).Thesedataarecollectedusingunstandardizedorsemi‐standardizedprotocols(Isaac&Pocock,2015)andchangesinrecordingintensity,taxonomicabilityandsamplingstrategiesmeansourcesofbiashavenotbeenconsis-tentovertime.Critically,occurrencerecordsprovidenostandardizedestimates of abundance, which are fundamental to understandingchangesinpopulationsizeandthelinksbetweenpollinatorsandpol-linationservices(Pottsetal.,2016).Identifyingthebestapproachesforpollinatormonitoringiscrucialtoreducetheselimitations.

Different methods for sampling pollinating insects are associ-atedwithdifferentoutputsandchallengeswithregardtotaxonomiccoverage and implementation. Direct observations (transects and

observation plots) and pan traps (sampling within painted water‐filledbowls)arethemostcommonlyusedmethods(Westphaletal.,2008).Transectsandtimedfocalfloralobservationsarestraightfor-wardtoconductandcangeneratedataoninsect–plantinteractionsbut depend on the expertise of the observer (Sutherland, Roy, &Amano,2015)andmaybebiasedtowardsmoreconspicuousspecies(Dennisetal.,2006).Pantrapstendtosamplemorespeciesofbeethanotherstandardizedmethods(Westphaletal.,2008),areinde-pendentofobserverexpertiseandarerecommendedbytheFoodandAgricultureOrganisation (FAO)formonitoringbees inagricul-tural habitats (LeBuhn,Droege, Connor, Gemmill‐Herren, &Azzu,2016).However, pan trap efficacymaybebiasedbecause certaintaxa (e.g. social bees)may be less likely to be caught and effectsoflocalfloralresourcedensityoncatchesarenotwellunderstood(Cane,Minckley,&Kervin,2000;butseeWood,Holland,&Goulson,2015).Similarly,usingnon‐expertvolunteers,or ‘citizenscientists’,presentsanopportunitytocollectlargeamountsofdataandengageawiderangeofindividualsinwildliferecording.However,theseben-efitspotentiallytrade‐offagainstthereducedtaxonomicresolutionthat thesevolunteerscantypicallygatheranddataaccuracy (Roy,Baxter,Saunders,&Pocock,2016),whichisrequiredtoaddresseco-logicalquestionsconcerningthediversityofwildpollinators.

Wecomparedthepotentialofpantrapsandtransectsforsur-veyingpollinatinginsectsin(a)thewidercountrysideand(b)flow-ering crop fields in 38 sites acrossGB. Furthermore, in thewidercountryside, we explored the effect of recorder expertise on thenatureandaccuracyofdatacollectedusingtransectsandfloralob-servationplots.Thereafter,weoutlineoptionsforthedevelopmentof protocols formonitoring pollinator abundance and diversity tofacilitatetheproductionoflong‐term,standardizednationalandin-ternationaldatasetsinaccordwithinternationalscienceandpolicyneedsidentifiedbytheIntergovernmentalScience‐PolicyPlatformonBiodiversityandEcosystemServices(Pottsetal.,2016).

2  | MATERIAL S AND METHODS

2.1 | Wider countryside surveys

We tested three commonly usedmethods for sampling bees andhoverflies(O'Connoretal.,2016;Westphaletal.,2008);

wereidiosyncraticaccordingtocroptype,butoveralltheresponsewaspositiveandnegativefortransectsandpantraps,respectively.

6. Giventhesetaxonomicandcontext‐specificdifferencesinmethodperformance,weassesstheirsuitabilityformonitoringpollinatinginsectcommunitiesandpol-linationservices.Wediscusstherelevanceofthesefindingswithinthecontextofachievingstandardized,large‐scalemonitoringofpollinatinginsects.

K E Y W O R D S

abundance,bees,diversity,expertise,hoverflies,pantraps,pollinatormonitoring,transects

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(i) Pan traps: a triplet of plastic bowls (350 ml capacity; Salbert,Item Number: 92012A500) sprayed with UV fluorescent paint(1 × white, 1 × yellow, 1 × blue; Sparvar “Leuchtfarbe”) witheachbowl containing100mlofwaterplusadropofunscenteddetergenttobreaksurfacetension.Eachtriplet(hereafterstation)was fixed toawoodenstakeusingwiresupportsandsetat theaverage height of flowers or other surrounding vegetation orsecured to the ground in very short vegetation or bare ground.

(ii) Insectvisitationtransects:Fivetransectsections,each200minlengthandfollowingalinearroute,werewalkedataslowpaceforbetween12and15minallowingforvariationintransectter-rain.Allinsectsseenvisitingflowerswererecordedwithina1m3 samplingboxaheadandtothesideoftherecorderandassignedtooneofthefollowingtaxonomicgroups:bumblebees,honey-bees, solitary bees (including primitively eusocial species) andhoverflies. Individual insectswere recordedonlyonce.Wherespecieslevelidentificationswererequired(seebelow),individu-alswere netted, placed in a labelled tube and frozen for lateridentification,unlesstheycouldbereadilyidentifiedinsitu.Timespenthandlinginsectsforidentificationwasnotincludedinthetransecttime.

(iii) Floralobservationplots:adefinedareaobservedforasettimetorecordinsectflowervisitors.Plotsof50×50cm2 were ob-served for 10min for insect flower visitation on a focal plantspecies,insectswereobservedandrecordedonceandclassifiedinto taxonomic groups, as described above (without specimenidentification).Focalplantspeciesonasitewereselectedfromalistof25nationallycommonfloweringplants(TableS1)or,ifnotpresent,thenalocallyabundantplantspecies.Theplantspeciesandnumberoffloralunitswithineachplotwererecorded.

Thewidercountrysidesurveysusedaone‐dayprotocoltosamplewithina1km2,compatiblewithexistingbiodiversitymonitoringschemesinGB

(e.g.Pescottetal.,2015).Fourteen1kmgridsquares(Brtishnationalgrid)were sampled acrossGB (Figure1a; England=6; Scotland=6;Wales = 2) with half the squares dominated (>50%) by semi‐naturallandcoverandhalfdominatedbyagriculturallandcover(arable,horti-cultureorimprovedgrasslandcollectively).Ineachsquare,wesituatedfive200mtransectsandfivepantrapstationsatapproximately200mintervalsonadiagonallinebisectingthesquare(Figure1b),typicallyfol-lowingboundaryfeaturesor,whereaccessible, followingtractor lineswithincroppedfieldsoredgesofgrassfieldswithlivestock.

Pan trap stationswere deployed at the start of each transect(Figure1b) and left exposed for6–7hr (dependingon terrain andtime taken to complete the other methods) between 10:00 and16:00.Afterpantrapdeployment,each200mtransectsectionwaswalkedtorecord insect flowervisitors.Foreachsection,availablefloralresourceswerequantified.Thenumberoffloralunits(flowerheads,umbelsorspikes)of≥5mostcommonfloweringplantspe-cieswasalsorecordedona5‐pointordinalscale:(1)1–2,(2)2–30,(3)31–300,(4)301–3,000, (5)>3,000.Tostandardizenectaravail-abilitypertransect,thetotalamountofavailablenectarsugarwasestimated for each recorded flowering plant species as µg sugarproducedin24hrperfloralunit(followingBaudeetal.,2016);seeSupplementaryMaterial).Wemultiplied this value by themediancoverageofeachspecies forcategories1–4andby3,001forcat-egory5andconverteditintoanestimateofnectaravailabilityperm2foreachtransect(bydividingthisproductby200).Duetosomeextremeestimatesofflowerdensity,weimposedamaximumlimitof20,000µgsugarperm2per24hr.Two10‐minfocalfloralobser-vationspersitewerealsoconductedduringeachsamplingday.Eachsitewassampledonceduringfoursamplingroundsin2015:(a)27April–10May,(b)1–14June,(c)6–19July,(d)17–30August.

Toexploretheeffectofrecorderexpertiseonthedatacollected,we classified recorders according to their degree of expertise infield surveys and recognizing pollinating insects: (a) non‐specialist

F I G U R E 1   (a)Distributionofstudysites,showingtheagriculturalwidercountrysidesites(browncircles).Semi‐naturalwidercountrysidesites(yellowcircles),strawberrysites(redstars),fieldbeansites(redsquares)andapplesites(redtriangles);(b)Thelayoutofpantrapsandtransectsforthewidercountryside‘one‐day’protocolata1kmsamplingsquare;(c)Thelayoutofpantrapsandtransectsinasamplingplotforfloweringcrops

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researchstaff–employeesofuniversitiesorresearchinstituteswithpriorexperienceofsurveyingandidentifyinginsectsandplantstoatleastbroadgrouplevels;(b)taxonomicexperts–volunteerorpro-fessional entomologists who submit records to existing biologicalrecordingschemespossessingahighlevelofexpertiseincollectingandidentifyingatleastonebroadtaxonomicgrouptospecieslevel;(c)non‐expertvolunteers–membersofthepublicwhopartake incitizenscienceprojectspossessingvaryinglevelsoffamiliaritywithpollinator identification or ecological surveys. All recorders con-ducted transects, volunteers and researchers conducted focalob-servations,butonlyresearchersconductedpantraps.Allrecordersfollowedthesameprotocolforeachmethodandwereprovidedwithidentificationguidesforbroadinsectgroupsandfocalplantspecies.Researchstaffandexpertscollecteddatatospeciesresolutionasfaraspossible,whereasnon‐expertsonlyclassifiedinsectsintobroadgroups.

Allsitesweresurveyedbyresearchstaff;taxonomicexpertsvis-itedonlythesitesinEnglandandWalesandnon‐expertvolunteerswererestrictedtoroundsthreeandfour,surveyingonthesamedaysastheresearchstaff.Researchstaffandvolunteersundertooktran-sectswithin15minofeachotherandfocalobservationsinparallelonthesamepatchesofflowers.Here,55sitevisitswereachievedbyresearchstaff,25bytaxonomicexperts,and17byvolunteernon‐experts(TableS2).

2.2 | Flowering crop surveys

Tocomparepollinator surveymethods in crops,pan trappingandtransectswerecarriedoutsimultaneouslyindessertapples(Malus domestica, variety Cox's Orange Pippin), strawberries (Fragaria X ananassa,mixedvarieties)andfieldbeans(Vicia faba,varietyWizard)inthespringandsummerof2011(Garratt&Potts,2011).WeusedeightappleorchardsinKent,eightstrawberryfieldsinYorkshireandeightfieldbeanfieldsinOxfordshireandBerkshire(Figure1a),withthreesamplingroundscarriedoutduringstrawberryandfieldbeanflowering and two during apple bloom. Sampling plots containedtwo150msamplingtransects,dividedintothree50msectionsandapantrapstationwasplacedattheendofeachsection,givingsixpseudo‐replicates of eachmethod per field (Figure 1c). Transectswereatleast25mapartandfromthefieldedge(Figure1c)andeach50msectionwaswalkedfor10minatasteadypace.Pantrapswereasspecifiedaboveforwidercountryside,butused460mlbowls,leftoutfor24hrinapplesandstrawberries,and7–10hrinfieldbeans.Appleflowerdensitieswerecountedwithin1×1m2quadratsheldagainst treesatheadheight,whereas forstrawberriesa1×2m2 areawasassessed.Fieldbeanfloweringstemswerecountedwithina1×2m2area,andmultipliedbythemeanflowercountsonfiverandomlychosenstems.

2.3 | Survey conditions and identification

All surveys were carried out between 10:00 and 16:00 in dryweather, with light winds (<29 km/hr, Beaufort 5), and where

minimumtemperaturesexceeded13°Cif<50%cloudcover,or15°Cif>50%cloudcover(although11°Cor13°CwasallowedforsomeuplandlocationsorvisitsinApril).Collectedbeeandhoverflyspeci-menswerestoredin70%ethanolforidentificationtospecieslevelbyexperttaxonomistsandarchivedin99%ethanol.

2.4 | Analysis

All analyseswere performed usingR version 3.3.2 (RCore Team,2016).

2.5 | Similarity of pan trap and transect samples of pollinator communities

Dataweresummarizedatthesite (1kmsquareorcropfield) leveltodemonstratethetypicalsamplesizesachievedbythetwometh-odsandbythedifferentrecordergroupsacrossthefourfocalinsectgroups(Tables1and2;TablesS3andS4).

We assessed the degree of dissimilarity (Morisita–Horn abun-dance‐based dissimilarity index) between the pollinator (bees andhoverflies identified to species) communities sampledby researchstaffusingpantrapsandtransectsinthewidercountrysidedatasetandeach floweringcropdataset (apple, strawberryand fieldbeanseparately).Todetermineifthepantrapandtransectmethodspro-duced significantly dissimilar assemblages,we used permutationalANOVAs (r: vegan: adonis) against random permutations of theoriginal data (countryside=999; FC=255 for each cropdataset)(Oksanenet al., 2015).Data for thewider countryside semi‐natu-raldominatedsiteinWaleswereexcludedduetotoofewrecords.Non‐metricmultidimensionalscaling(NMDS)wasusedtovisualizedissimilarity between sampling methods based onMortista–Horndissimilarity(r:vegan:MetaNMDS;Oksanenetal.,2015).

2.6 | The effects of sampling effort and recorder expertise on estimates of species richness

We used species accumulation curves to understand the influ-enceof samplingefforton theefficacyofmethodsand recorderstoproducespecies richnessestimatesgiven theirdifferentmodesofactionandinherentbiases.Thenumberofindividualssampledisthebasiccurrencywithwhichspeciesrichnessestimatesbetweensamples or datasets can be compared.Using the inext package inr (Hsieh,Ma, & Chao, 2019), we plotted individual‐based speciesaccumulation curves that show interpolated species richness (percumulativeindividualsampled)uptothetotalsamplesizeandthere-after extrapolated species richness. Curves were plotted for pantrapsandtransects,usingsamplesamalgamatedacrossthedatasetforeachbroadtaxonomicgroup in thewidercountrysidedataset,forsolitarybeesinapples,bumblebeesinstrawberriesandbumble-bees and solitary bees in field beans. Further, for a subset of thewidercountrysidedatacoveringsevensites(fourwithsamplesforallfoursamplingrounds,oneforthe2nd,3rdand4thsamplingroundsandtwoforthefirsttwosamplingrounds,totally23samplingvisits)

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individual‐basedspeciesaccumulationcurveswereplottedforbum-blebees,solitarybeesandhoverfliestocomparepantrapswithtran-sectsconductedbyeitherresearchersortaxonomicexperts.

Correlationanalyses(Spearman'sorKendall'srank)wereusedtocompareestimatesofbumblebee,solitarybee,hoverflyandhoney-bee abundance from transectswalkedby research staff andnon‐expertvolunteers(17sitevisitswithcorrespondingdata)andfromparallelfloralobservationplots.

2.7 | Per sampling unit differences between pan traps and transects

Generalizedlinearmixedmodels(GLMMs)wereusedtotestfordif-ferencesbetweenpantrapsandtransectsatthesamplingunitlevel(individualpantrapstationorcorrespondingtransectsection),alongwiththeeffectsoflocalfloralresourcesandothercovariates,usingthedatasetsforbumblebees,solitarybeesandhoverfliesgeneratedbyresearchstaff(honeybeenumberswereinsufficient).ModelswerefittedandselectedusingtheglmmADMBpackage(Skaug,Fournier,Bolker, Magnusson, & Nielsen, 2015), which allows zero‐inflatedmodels,althoughpoissonornegativebinomialerrorswereappropri-ateforallmodels.Finalmodelswereselectedbystepwiseeliminationof non‐significant variables using log‐likelihood tests (Zuur, Hilbe,& Ieno, 2013). Final models were also run with the lme4 package(Pinheiro,Bates,DebRoy,&Sarkar,2015)tochecktheagreementofmodelfitsbetweenpackages. Inevery instance,theywerecompa-rable,givingthesamequalitativeresultswithonlyslightdifferencesinparameterestimates.Thelsmeanspackage(Lenth,2016)wasusedtocalculateleastsquaremeansandmarginaleffectsplotsfromlme4outputwereproducedusingtheSJPlotpackage(Lüdecke,2017).

Fortheabundanceandspeciesrichnessofbumblebees,solitarybeesandhoverfliessampledonthewidercountrysidesurveys,ini-tial model predictors included sampling method, sampling round,

country (EnglandandWaleswereamalgamated intoone levelduetolowreplicationforWales),logestimatednectarsugaravailabilitypertransect(µgper24hr),maximumdaytimetemperature(°C)fromthenearestUKMETofficerecordingstationanddominantland‐useofthesiteasfixedeffects.Two‐wayinteractionswereincludedbe-tweenmethodandlognectar,methodandsamplinground,lognec-tarandsamplinground,andcountryandsamplinground.Allmodelsincludedan intercept level randomeffectof sample location (1–5)nestedwithinsite(1–14).

ForeachFCdataset,estimatesofabundanceforthedominantinsectpollinatorvisitorgroupweremodelled;solitarybeesforap-ples, bumblebees for strawberries and fieldbeans.Datawerenotsufficient to model the abundance of all groups individually, butmodelsofthetotalabundanceofallbeesandhoverflieswererunforcomparison.Speciesrichnessofallbeesandhoverflieswasalsomodelled.Initialmodelsincludedsamplingmethod,thenaturallogofflowerdensityandtheirinteractionasfixedeffectsandaninterceptlevelrandomeffectofthesamplingsection(1–6)nestedwithinthesite.

3  | RESULTS

Pan traps and transects implemented by research staff on thewider countryside surveys across 14 1 km2 sampled a total of110species (16bumblebee,38solitarybee,55hoverflyspeciesand thehoneybeeApis mellifera)with variations in species rich-nessandabundance foreachmethod (Table1,TableS3). In thewider countryside, 65% of solitary bees, 19% of hoverflies and14%ofbumblebeesrecordedbyresearchstaffwereidentifiedtothegrouplevelonly,becausespecimenswerenotnettedforiden-tification.Taxonomicexpertsrecorded10speciesofbumblebee,21speciesofsolitarybeeand34speciesofhoverflyontransects,

TA B L E 1  Mean±SEabundanceandspeciesrichnesspersamplingsite(n=14)sampledbyresearchstaffacrossthewidercountryside

Method

Abundance Species richness

Bumblebee Solitary bee Honeybee Hoverfly Bumblebee Solitary bee Hoverfly

PanTrap 12.14±3.17 18.36±5.77 3.00±1.03 32.07±70.53 2.36±0.59 2.43±0.74 9.43±1.28

Transect 17.86±3.18 5.86±2.35 4.36±1.39 39.79±16.93 2.64±0.42 0.5±0.24 3.64±0.75

TA B L E 2  Meanabundance±SEandspeciespersamplingsiteforapples,strawberryandfieldbeansites

Crop Method

Abundance Species

Bumblebee Solitary bee Honeybee Hoverfly Bumblebee Solitary bee Hoverfly

Apple Pantrap 2.63±0.46 148.88±53.82 0.88±0.35 0.13±0.13 2.25±0.53 16.88±2.22 0.13±0.13

Transect 4.38±0.98 14.00±3.49 5.88±1.64 1.38±1.10 2.13±0.40 2.00±0.38 0.00±0.00

Strawb Pantrap 15.75±6.01 11.13±2.75 5.25±2.02 3.75±1.29 3.75±0.53 4.13±0.81 0.88±0.23

Transect 147.25±32.28 1.75±0.65 121.00±34.55 40.00±12.30 3.88±0.35 0.38±0.26 0.25±0.16

FieldB Pantrap 16.50±6.35 33.75±4.55 3.50±1.58 2.38±0.46 4.63±0.84 12.25±0.88 1.63±0.26

Transect 65.38±9.43 1.88±0.58 8.75±1.96 1.25±0.45 5.63±0.38 0.88±0.30 0.13±0.13

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whilstforthesamenumberofsamplingvisitstothesametransectlocations (25, thoughondifferentdays) research staff recorded11,9and18speciesofeach,respectively.Forcrops,werecordeda total of54 species in apples (8bumblebee,44 solitarybee,1hoverflyandthehoneybee),32speciesinstrawberries(12bum-blebee,14solitarybee,5hoverflyandthehoneybee)and55 infield beans (14 bumblebee, 31 solitary bee, 9 hoverfly and thehoneybee)(Table2,TableS4fortotalspeciesrichnessandabun-dancepercrop).

3.1 | Community dissimilarity

Overall,therewasasignificantdissimilaritybetweenthepollinatorcommunities sampled using pan traps and transects in the wider

countryside(R2=0.121,F1,24=3.312,p<.001)drivenbymoresoli-tarybeeandhoverflyspeciesdetectedbypantrapsthantransects,butmore individuals of common bumblebee species on transects(Figure2,TableS3,FigureS1a).Therewasa significantdissimilar-itybetween thepollinatorcommunities sampledbypan trapsandtransectsinallcroptypes;apples(R2=0.51,F1,14=14.309,p=.008);strawberries(R2=0.29,F1,14=5.744,p=.008);fieldbeans(R

2=0.41,F1,14 = 9.58,p = .008). (Figure 3). Transects sampledmuch highernumbersofbumblebee individuals in strawberriesand fieldbeansthandidpan traps (around10and5 times, respectively,TableS4)with samplesmoredominatedby commonspecies thanpan traps(FigureS1c,d).Inappleswerepantrapssamplednearly10timesthenumberofsolitarybees(TableS4).

3.2 | Species accumulation and recorder effects

For bumblebees in the wider countryside, there was a close cor-respondence between the species accumulation rates for eachmethod;although theoverallpan trapsaccumulatedmorespeciesand transects sampledmore individuals (Figure 4a). In crops, thispatternwasaccentuated,withthetransectmethodshowinglowerratesofbumblebeespeciesaccumulationperindividualsampledandreaching an asymptote,whereas the steeper accumulation curvesforpantrapsarepredictedtocontinue(Figure4b). Ingeneral, thespecies accumulation curves for bumblebeeswere broadly similarbetweenpantraps,transectsbyresearchersandtransectsbytaxo-nomicexperts(Figure5a).

Forsolitarybees,thesamegeneralpatternofspeciesaccumu-lationbetweenpantrapsandtransectswasobservedinthewidercountrysideandinapplesandfieldbeans.Itwasdifficulttoconstructmeaningfulspeciesaccumulationcurvesfortransects(Figure4candFigureS2)becausealargeproportionofindividualswasnotidenti-fiedtospeciesresolution(TableS4).However,whilethenumberofindividualsrecordedbytaxonomicexpertsontransectswaslowerthan those sampled in pan traps, species accumulation curves fortransects completed by experts suggest that, per individual, this

F I G U R E 2  Non‐metricmulti‐dimensionalscaling(NMDS)plotofpantraps(largerdarkgreycircles)andtransects(largerlightgreycircles)forallspeciesofbeeandhoverflydetectedinthewidercountrysidebynon‐expertresearchers.Bumblebeesareshownbystars,Apis melliferaasquare,solitarybeesbytrianglesandhoverfliesbycircles.Circleswiththesamenumberareforthesamesiteandthepolygonsconnectingsitesindicatetheoverlapbetweensamples

F I G U R E 3  Non‐metricmulti‐dimensionalscaling(NMDS)plotsofpantraps(largerdarkgreycircles)andtransects(largerlightgreycircles)forallspeciesofbeeandhoverflydetectedin(a)apples,(b)strawberriesand(c)fieldbeans.Bumblebeesareshownbystars,Apis melliferaasquare,solitarybeesbytrianglesandhoverfliesbycircles.Circleswiththesamenumberareforthesamesiteandthepolygonsconnectingsitesindicatetheoverlapbetweensamples

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wouldachievecomparableorbetterspeciescoveragewithgreatersamplingofindividuals(Figure5b).

Hoverflieswerenotsampledincropsinhighenoughnumbers,butforthewidercountryside,therateofspeciesaccumulationper

individualforpantrapswasarounddoublefortransects(Figure4d).However, it is notable that two species (E. balteatus and S. ribesi)comprised 84% of individual hoverflies sampled on transects andidentifiabletospeciesresolution.Removingthesetwospeciesleadstogreatercorrespondencebetweenpantapsandtransectsinspe-ciesaccumulation (FigureS3a).Correspondencebetweenhoverflyspecies accumulation curves for pan traps and taxonomic expertssuggestthattheyperformcomparablyintermsofsamplingspecies(Figure5c).Removing thehighly abundantE. balteatus and S. ribe-sisimprovedthecorrespondenceofresearchertransectstoexperttransectsandpantraps(FigureS3b).

Estimates of abundance for all taxonomic groupswere signifi-cantly,positivelycorrelatedbetweenresearchstaffandvolunteers,usingtransectandfocalobservations(seeSupplementaryMaterialandFiguresS4andS5forfullresults).

3.3 | Sampling unit level analyses

There were significant differences between sampling methods inboththeabundanceandspeciesrichnessofsolitarybeespersam-plingunit(pantrapstationor200mtransectsection).Pantrapssam-pledgreaternumbersofsolitarybeeindividuals(β=−1.27±0.22,z=−5.77,p<.001;Figure6b)andspecies(β=−2.38±0.27,z=−8.87,p<.001;FigureS7b)thantransects.However,forbumblebeesandhoverflies, significant interactions suggest that the effects of thesamplingmethodonabundanceandspeciesrichnessweredepend-entonboththeestimatednectarsugaravailabilityalongthe200mtransectand,forhoverflies,thetimingofthesamplinground(TablesS5andS6).Ontransects,theincreasingnectaravailabilityhadasig-nificant,positiveeffectcomparedtopantrapsforbumblebeeabun-dance (β =0.28±0.07, z =4.12,p < .001;Figure6a) and speciesrichness(β=2.09±0.34,z=6.09,p<.001;FigureS7a),andhoverflyabundance(β=0.16±0.06,z=2.59,p=.010;Figure6c)andspeciesrichness(β=0.16±0.06,z=2.74,p=.006;FigureS7c).Theeffectsofcountry,samplingroundandmaxtemperature inthemodelsofabundanceandrichnessarereportedintheSupplementaryMaterial(TablesS5andS6).

Inapplesa significant interactionbetweenmethodand flowerdensityshowedanegativeeffectofincreasedflowerdensityonsol-itarybeeabundanceinpantrapsbutapositiveeffectontransects(β=0.87±0.18,z=4.99,p<.001;Figure7a).Themodelforabun-danceofallpollinatinginsectswasqualitativelythesame(TableS7),aswasforspeciesrichness(β=0.51±0.13,z=3.92,p<.001;FigureS7a,TableS8).

In strawberries, bumblebee abundance on transects was sig-nificantly higher than in pan traps regardless of flower density(β=2.27±0.13,z=17.00,p<.001;Figure7b).However,fortheabun-dance of all pollinating insects, estimates from transects increasedsignificantly with flower density compared to those of pan traps(β=0.52±0.13,z=4.10,p<.001;TableS7),asdidthenumberofspe-ciessampled(β=0.38±0.12,z=3.32,p=.001;FigureS7b,TableS8).

In field beans, a significant interaction between method andflowerdensityshowedbumblebeeabundanceincreasedwithflower

F I G U R E 4   Individual‐basedspeciesaccumulationcurvesacrossthewholedatasetspooledfor(a)bumblebeesinthewidercountryside(b)bumblebeesinfieldbeansandstrawberries(c)solitarybeesinthewidercountrysideand(d)hoverfliesinthewidercountryside.Curveswereplottedbasedondatagroupedacrossallsites,usingtheinextpackageinr.Thesolidlineshowspredictionsbasedoninterpolationandthedashedpartshowspredictionsbasedonextrapolation.95%confidenceintervalsareshownasshadedareas

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F I G U R E 5   Individual‐basedspeciesaccumulationcurvesfromasubsetofdatafromacrosssevenofthewidercountrysitesprovidingcorrespondingdatafrompantraps,transectsconductedbyresearcherandtransectsconductedbyprofessionalexpertsfor(a)bumblebees,(b)solitarybeesand(c)hoverflies.Thesolidlineshowspredictionsbasedoninterpolationdashedlinethepredictionsbasedonextrapolation,95%confidenceintervalsareshownasshadedareas

F I G U R E 6  Plotsforthewidercountrysideof(a)predictionsofthemarginaleffectsofsamplingmethodandnectarsugaravailabilityonbumblebeeabundance(b)theleastsquaremeanpermethodforsolitarybeeabundanceand(c)predictionsofthemarginaleffectsofsamplingmethodandnectarsugaravailabilityonhoverflyabundance.Unbrokenlinesshowpredictedvaluesforpantrapsandbrokenfortransects.95%confidenceintervalsareshowningrey.Errorbarsonpointsshow±SE.Thesamplingunitforpantrapsisatrappingstation(tripletofbowls)andfortransectsisa200msection(Figure1b).ModelresultsarepresentedinTableS4.ModelsforspeciesrichnessarepresentedinFigure4sandTableS5

F I G U R E 7  Plotsshowing(a)predictionsformarginaleffectsofsamplingmethodandflowerdensityonsolitarybeeabundanceinapplecrops(b)meanabundancebumblebeespersamplingmethodinstrawberrycropsand(c)predictionsformarginaleffectsofsamplingmethodandflowerdensityonbumblebeeabundanceinfieldbeancrops.Unbrokenlinesshowpredictedvaluesforpantrapsandbrokenfortransects.95%confidenceintervalsareshowningrey.Errorbarsonpointsshow±SE.Samplingunitforpantrapsisatrappingstation(tripletofbowls)andfortransectsisa50msection(Figure1c).ModelresultsarepresentedinTablesS7.ModelsforthespeciesrichnessofallbeesandhoverfliesareshowninFigureS5andTableS8

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densityontransects,butdeclinedwithflowerdensityinpantraps(β=0.38±0.12,z=3.32,p=.001;Figure7c).Resultsfortotalpol-linator abundance were qualitatively the same (β = 0.35 ± 0.16,z=2.15,p=.032;TableS7),aswerethoseforthenumberofspeciessampled(β=0.42±0.15,z=2.88,p=.004;FigureS7c,TableS8).

4  | DISCUSSION

Understanding the status and trends of pollinators is an urgentglobal priority requiringdevelopmentof national scalemonitoringusing repeatable and standardized survey methods (Dicks et al.,2016).Ourstudycomparedtheperformanceofdifferentpollinatorsurveymethods insamplingdifferenttaxonomicgroupsandwhenimplementedbydifferentrecordersvaryinginexperience.Wedis-cussour findingswithin the contextof the logistical and financialconstraintspresentedbylarge‐scalebiologicalmonitoring.

Pantrapsandtransectsprovidedadifferentpictureofthepol-linatinginsectcommunity.Overall,theassemblagessampledbythetwomethodswere significantly dissimilar compositionally in boththewider countryside and crop fields. This differencewas drivenbytransectssamplingfewerspecies,particularlyofsolitarybeeandhoverfly,butmorebumblebeeindividuals,particularlyincrops.

Sampling effort dictates the relative performance of methods(Rhoadesetal.,2017),forexample,increasingthedurationofexperttransectsmayresultindatathatconvergesontherichnessestimatesproduced by pan‐traps. Fundamentally different modes of actionmakeitimpossibletoproperlystandardizethesamplingeffort(e.g.samplingduration)betweenpantrapsandtransects.However,usingspecies accumulation curves, we were able to compare estimatesof species richnessproducedby thedifferentmethods andactorstounderstandtheextentthatsamplingeffort(i.e.numbersofindi-vidualscollected)contributestotheobserveddifferentialpatterns.Accumulationofspeciesoccurringatasimilarrateindicatesthatdif-ferencesinrelativesamplingeffortaredrivingdifferencesinspeciesrichness.Wefoundhigherspeciesaccumulationratesforpantraps,exceptforbumblebeesinthewidercountryside,suggestingfactorsotherthansamplesizearedrivingdifferencesbetweenmethods.

In all datasets, transects sampledmore individual bumblebeesthanpantraps,probablydueinparttothestrongpositiveassocia-tionbetweenfloral resourcesandbumblebeecountson transectsandtothebias inpantrapsagainstsampling largerbodiedinsects(Caneetal.,2000).Thatthisdifferencewasofagreatermagnitudeinstrawberryandfieldbeanfieldscomparedtothewidercountry-sidemaybebecausethesecropsarepredominantlybumblebeepol-linated(Kleijnetal.,2015)andduetothecompetitionforbumblebeevisitsfromtheabundantfloraldisplaysofthesecropmonocultures,loweringpantrapcatches.However,pantrapsshowedhigherratesof species accumulation and generally sampled more species ofbumblebee.Oneexplanationisthatthetransectprotocolwascon-strainedtorecordflowervisitorsonly,sospeciesforagingspecialismwillreducethepoolofspeciesbeingsampled,particularlyincrops(whereonlyoneflowertypewassurveyed).

Forsolitarybees,pantrapscollectedmorespeciesandindividu-alsthantransects,andinapplesthelargermagnitudeofdifferenceinnumberscollectedmayrelatetothe24‐hrpantrappingused(asopposedto6–7hr).Projectingspeciesaccumulationwasdifficultfortransectsduetolowratesofspecieslevelidentification.However,whenexpertsundertooktransectsinthewidercountryside,thoughthenumberofsolitarybeesrecordedwasstilllowerthanpantraps,species accumulation rate per individual became higher for tran-sects. These findings highlight a limitationwhen using such ‘real‐time’methods tocollectdataonsolitarybees thataredifficult todetect,identifyorcapture,particularlyforlessexperiencedrecord-ers.Forhoverflies,pantrapsshowedsimilarlyhigherratesofspe-ciesaccumulationperindividualsampledthantransects,butagain,expert recordersmitigated this by providing a convergent rate ofspeciesaccumulationbetweenmethods.

While expertise seems necessary to collect species resolutiondatafromtransects,ourresultssuggesttransectscouldbesuitablefor novices to collect group level abundance data of bumblebeesandpossiblyhoverflies,withbasicinstructions.However,wefoundthe potential for miscounts or misclassifications, particularly forhoverflies.Kremen,Ullman,andThorp(2011),similarlyfoundesti-matesofbeeabundancewerecorrelatedbetweenvolunteerswithfivehourstrainingandexperts.Atransect‐based(1–2km)approachin 373 sites, ‘BeeWalks’, has been developed by the BumblebeeConservationTrust in theUK and is generating data on trends inabundance for bumblebee species (Comont & Dickinson, 2017).However, training, assessment and data validation processes areneededbeforemassparticipationobservationalmethodsarewidelyadoptedformonitoring.

Across all surveys, per sampling unit, estimates of abundanceandspeciesrichnessontransectsincreasedwithestimatednectaravailability or floral density. This effect is intrinsic to themethod(transectsrecordedflowervisitors),butthestrengthofresponsefordifferenttaxonomicgroupstofloralresourcesmayreflecttheirdif-ferentecologies.Socialbumblebeesincreasecolonyforagingactiv-ityinresponsetonectaravailability(Dornhaus&Chittka,2001)andover larger ranges thansmaller, solitarybeespecies (Gathmann&Tscharntke,2012;Osborneetal.,1999).Thismayexplainthestrongresponse of bumblebees to transect floral resources in thewidercountrysidecomparedwithsolitarybeesthatpossesssmaller for-agingrangesandalackofsocialrecruitmentbehaviour.Hoverfliesalsodonot recruit,butarenot restrictedto foragingaroundnestsites,andsoindividualsmayfreelyaggregatearoundhighfloralre-sources.Thisisconsistentwithourresultsshowingapositiverela-tionbetweenhoverflyabundanceandnectaravailability.

Fortransects,abundancerecordsmayreflectpopulationdensitiesinalocationbutalsotheredistributionofindividualsacrossaland-scapeinresponsetotemporaryincreasesinfloralresources(Carvell,Bourke,Osborne,&Heard,2015);however,methodsarenowavail-abletoaddressthis(Kleijnetal.,2018).Thenegativerelationshipbe-tweenlocalfloraldensityandthenumberofindividuals(andspecies)caughtinpantrapsinfloweringcropfieldssuggestthatcropflowerswere‘competing’withpantrapsbydrawingawayinsects(e.g.Caneet

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al.,2000).Ifpantrappingisconfoundedbyfloraldensities,thiscouldaffect theiruse inmonitoringschemesas itmay leadtoerroneousdetectionofdeclinesifanarea’sfloralresourcesincreaseovertime.However, this inverserelationshipbetweenpantrapcatchandflo-raldensitywasparticulartocrops,likelyduetotheveryhighflowerdensitiesinthesecropmonocultures.Themagnitudeoffloral‘com-petition’withpantrapswillbelowerinflorallyheterogeneouswidercountryside environments. Moreover, our results reflect a seriesofsnapshotsamplesofthedifferentmethods inspace.Structured,longitudinalmonitoringorexperimentsmanipulatingfloraldensitiesareneededtodemonstratehowpantrapcatchesmightrespondtoannualandmultiannualchangesinfloralresourcesatagivensite.Itmustbenotedthatournectarestimatesandpantrapstationswerenotpreciselyspatialcoincidentandquantifyingfloralresourcesinafixedareasurroundingthepantraps(inthewidercountrysidesetting)mayhavegivendifferentresults(Carvelletal.,2016).Previousfind-ingsontheimpactsoffloralresourcesonpantrapcatcheshavealsobeenmixed;withnegativeeffectsonabundance(Roulston,Smith,&Brewster,2007)andspeciesrichness (Baum&Wallen,2011),posi-tiveeffectsonabundance(e.g.Woodetal.,2015),andnoeffect(e.g.Rhoadeset al., 2017).Overall,measuresaccounting for local floralresourceswillbeavitalcovariateforcollectionwithanymethodusedinpollinatorsurveyprotocolsformonitoring.

Pan traps and transects have different utility and efficacy formonitoringdifferentaspectsofpollinatorbiodiversity.Identifyingtheobjectiveofthemonitoringandwhatmetricsofthepollinatorcom-munityare required isessential todeterminingwhichmethodsareemployed.Characterizingplant–pollinatorinteractionsoridentifyingwhichspeciesofinsectaredeliveringpollinationservicetocropsandwildflowersrequiretransects(orotherobservationalmethods)aspantrapsdonotreflectthis(Gibbsetal.,2017;Kleijnetal.,2015).Whilepan trapshave limitations andbiases, theyprovide species resolu-tiondataindependentofexpertiseandrequirelesspersonefforttoachieveequivalentsamplesizeswhencomparedtotransects.Theycouldalsominimizenoiseinthedatafromdifferentlevelsofrecorderknowledgeorchangesinrecordersovertime.Ourresultsshowthat,independentofdifferencesinsamplingeffort,transectsconductedbypeoplewithoutalargedegreeoftaxonomicexpertisewillnotsam-plethesamenumberofspeciesaspantraps,andforsolitarybeesthey require considerablymore sampling effort to detect asmanyindividuals. This could be particularly important when recorderswithappropriateexpertisearealimitingfactor,alongwithlogisticalandresourcingimplications.Forexample,ifspecies‐levelabundanceanddiversityofsolitarybeesweretargeted,ourresultssuggestfivetransectswould require sampling for36–45minby someonewithextensiveexperienceandtaxonomicexpertisetoachieveequivalentsamplesizesandspeciescoverageasfive6–7hrofpantraps.Ifstaffavailabilityorresourcesarelimiting,pantrapsusingnon‐expertre-corderscoupledwithspecies identificationbyexpertscanbeused(LeFéonetal.,2016)andmolecularmethodsmaysoonbeanoption(Creedyetal.,2019).Thoughlethal,pantrapsareunlikelytoreducepollinatinginsectpopulationsatthesamplingintensitiestestedhere(Gezon,Wyman,Ascher,Inouye,&Irwin,2015).

Noonesamplingmethodcanfullycharacterizethepollinatingin-sectcommunityatagivenlocation,butsamplingshouldaimtoprovidenecessarytaxonomiccoverageandkeepbiasasconsistentaspossi-bleover time.Furthermore,combiningdata fromdifferent locationsrequires methods that ensure datasets are at least comparable attheirmostbasic resolution.Anationalpollinatormonitoringschemecouldemploypantrapsandobservationalmethodstoallowthecom-plimentaryrecordingofdifferentfacetsofthepollinatorcommunityincludingabundance,speciesrichness,functionalrolesandpollinationservicepotential.Acrucialcaveat,however, isthedifferentialeffectoflocalfloralresourceavailabilityontheefficacyofthepantrapsandobservationalmethodsandhowthismayinfluencethedataobtainedand the conclusions drawn. This potential complementarity and ca-veatshouldbothbeconsideredcarefullyduringmethod(s) selectionalongsidemonitoringobjectives,desiredmetricsandtheavailabilityoffinancialorhuman resources.Only throughsuchstandardardizationcan monitoring efforts become internationally cohesive. The valueof obtaining standardized datasets on pollinating insects cannot beoverstatedinprovidingrobustevidenceonlong‐termandlarge‐scalepatternsandtrendstoinformnationalandinternationalpolicyneeds.

ACKNOWLEDG EMENTS

TheUKDepartment for theEnvironment,FoodandRuralAffairs,the ScottishGovernment and theWelshGovernment funded thewidercountrysidesurveyunderprojectWC1101.Thecropssurveyswere funded jointly by grant BB/I000348/1 from BBSRC, Defra,NERC,theScottishGovernmentandtheWellcomeTrust,undertheInsectPollinatorsInitiative.ThisworkwassupportedbytheNaturalEnvironmentResearchCouncilawardnumberNE/R016429/1,partoftheUK‐SCAPEprogrammedeliveringNationalCapability.Thankstothefarmers,landownersandlandmanagerswhoallowedusac-cesstotheir land.ThankstoA.Perry,D.Chapman,N.Majlessi,A.Turner,D.Coston,C.Dodson,R.Evans,L.TrusloveandM.Lappagefor undertaking fieldwork and to all the non‐expert volunteers.ThankstoS.Freemanforstatisticaladvice.Thankstothreereview-erswhosinsightsandsuggestionsimprovedthemanuscript.

AUTHORS' CONTRIBUTIONS

R.S.O.–H.E.R,A.J.V.andC.C.concievedanddesignedtheproject.R.S.O., C.A.–M.H. and S.P.M.R.–C.C. collected and collated thewidercountrysidedata,andM.H.–I.W.providedspecimenidenti-fications.M.P.D.Gcoordinatedthecollectionofandprovidedthefloweringcropdata.R.S.O.analyzedthedata.R.S.O.–H.E.R,A.J.V.andC.C.ledthewritingofthemanuscript.Allauthorscontributedcriticallytodraftsandgavefinalapprovalforpublication.

DATA AVAIL ABILIT Y S TATEMENT

Data for the wider countryside surveys are available from theNERC Environmental Information Data Centre: https://doi.org/10.5285/69a0d888‐9f6b‐4e67‐8d29‐402af1412d8e. Data

     |  2139Methods in Ecology and Evolu onO'CONNOR et al.

for the flowering crops surveys are available from Data DryadRepository; https://datadryad.org/stash/dataset/doi:10.5061/dryad.31f7ph7ht tps://datadr yad.org/resource/10.5061/dryad.31f7ph7(Garratt&Potts,2011).

ORCID

Rory S. O'Connor https://orcid.org/0000‐0001‐7633‐4304

William E. Kunin https://orcid.org/0000‐0002‐9812‐2326

Michael P. D. Garratt https://orcid.org/0000‐0002‐0196‐6013

Simon G. Potts https://orcid.org/0000‐0002‐2045‐980X

Helen E. Roy https://orcid.org/0000‐0001‐6050‐679X

Christopher Andrews https://orcid.org/0000‐0003‐2428‐272X

Jodey M. Peyton https://orcid.org/0000‐0002‐8313‐6194

Martin C. Harvey https://orcid.org/0000‐0001‐7512‐2449

Adam J. Vanbergen https://orcid.org/0000‐0001‐8320‐5535

Claire Carvell https://orcid.org/0000‐0002‐6784‐3593

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Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle.

How to cite this article:O'ConnorRS,KuninWE,GarrattMPD,etal.Monitoringinsectpollinatorsandflowervisitation:Theeffectivenessandfeasibilityofdifferentsurveymethods.Methods Ecol Evol. 2019;10:2129–2140. https://doi.org/10.1111/2041‐210X.13292