LOTVS: A Global Collection of Permanent Vegetation Plots
Abstract
Analysing temporal patterns in plant communities is extremely important to quantify the extent and the consequences of ecological changes, especially considering the current biodiversity crisis. Long-term data collected through the regular sampling of permanent plots represent the most accurate resource to study ecological succession, analyse the stability of a community over time and understand the mechanisms driving vegetation change. We hereby present the LOng-Term Vegetation Sampling (LOTVS) initiative, a global collection of vegetation time-series derived from the regular monitoring of plant species in permanent plots. With 79 data sets from five continents and 7,789 vegetation time-series monitored for at least 6 years and mostly on an annual basis, LOTVS possibly represents the largest collection of temporally fine-grained vegetation time-series derived from permanent plots and made accessible to the research community. As such, it has an outstanding potential to support innovative research in the fields of vegetation science, plant ecology and temporal ecology.
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J Veg Sci. 2022;33:e13115. | 1 of 11 https://doi.org/10.1111/jvs.13115 Journal of Vegetation Science wileyonlinelibrary.com/journal/jvs Received:27July2021 | Revised:8January2022 | Accepted:11January2022 DOI: 10.1111/jvs.13115 REPORT LOTVS: A global collection of permanent vegetation plots Marta Gaia Sperandii1 | Francesco de Bello1,2 | Enrique Valencia3 | Lars Götzenberger2,4,5 | Manuele Bazzichetto1 | Thomas Galland2 | Anna EVojtkó2,5 | Luisa Conti5,6 | Peter B. Adler7 | Hannah Buckley8 | JiříDanihelka4,9 |NicolaJ.Day10 |JürgenDengler11,12,13 |DavidJ.Eldridge14 | Marc Estiarte15,16 |RicardoGarcía-González17 | Eric Garnier18 | DanielGómez-García17 | Lauren Hallett19 | Susan Harrison20 | Tomas Herben4,21 |RicardoIbáñez22 | Anke Jentsch23 | Norbert Juergens24 | Miklós Kertész25 |DuncanM.Kimuyu26,27 | Katja Klumpp28 |MikeLeDuc29 | Frédérique Louault28 | Rob H. Marrs29 |GáborÓnodi25 | Robin J. Pakeman30 | Meelis Pärtel31 |BegoñaPeco32 |JosepPeñuelas15,16 | Marta Rueda33 | Wolfgang Schmidt34 | Ute Schmiedel24 | Martin Schuetz35 | Hana Skalova4 | Petr Šmilauer36 |MarieŠmilauerová2 | Christian Smit37 | MingHua Song38 | Martin Stock39 | James Val14 | Vigdis Vandvik40 | Karsten Wesche13,41,42 | Susan K. Wiser43 | Ben A. Woodcock44 | Truman P. Young27,45 | FeiHai Yu46 | Amelia A. Wolf27,47 | Martin Zobel31 | Jan Lepš2 1Centro de Investigaciones sobre Desertificación (CSICUVGV), Valencia, Spain 2DepartmentofBotany,UniversityofSouthBohemia,ČeskéBudějovice,CzechRepublic 3Departamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos, Móstoles, Spain 4InstituteofBotanyoftheCzechAcademyofSciences,Průhonice,CzechRepublic 5InstituteofBotanyoftheCzechAcademyofSciences,Třeboň,CzechRepublic 6FacultyofEnvironmentalSciences,CzechUniversityofLifeSciencesPrague,Praha-Suchdol,CzechRepublic 7DepartmentofWildlandResourcesandtheEcologyCenter,UtahStateUniversity,Logan,Utah,USA 8SchoolofScience,AucklandUniversityofTechnology,Auckland,NewZealand 9DepartmentofBotanyandZoology,MasarykUniversity,Brno,CzechRepublic 10SchoolofBiologicalSciences,VictoriaUniversityofWellington,Wellington,NewZealand 11VegetationEcologyGroup,InstituteofNaturalResourceSciences(IUNR),ZurichUniversityofAppliedSciences(ZHAW),Wädenswil,Switzerland 12PlantEcologyGroup,BayreuthCenterforEcologyandEnvironmentalResearch(BayCEER),UniversityofBayreuth,Bayreuth,Germany 13GermanCentreforIntegrativeBiodiversityResearch(iDiv)Halle-Jena-Leipzig,Leipzig,Germany 14Biological,EarthandEnvironmentalSciences,UniversityofNewSouthWales,Sydney,Australia 15CentreforEcologicalResearchandForestryApplications(CREAF),CerdanyoladelVallès,Spain 16CSIC,GlobalEcologyUnitCREAF-CSIC-UAB,Bellaterra,Spain 17InstitutoPirenaicodeEcología(IPE-CSIC),Jaca-Zaragoza,Spain 18CenterinEcologyandEvolutionaryEcology(CEFE),FrenchNationalCentreforScientificResearch(CNRS),UnivMontpellier,ÉcolepratiquedesHautes Études(EPHE),ResearchInstituteforDevelopment(IRD),Montpellier,France 19EnvironmentalStudiesProgramandDepartmentofBiology,UniversityofOregon,Eugene,Oregon,USA 20DepartmentofEnvironmentalScienceandPolicy,UniversityofCalifornia,Davis,California,USA ©2022InternationalAssociationforVegetationScience. 16541103, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/jvs.13115 by Universidad De Sevilla, Wiley Online Library on [18/01/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
2of11 | Journal of Vegetation Science SPERANDII Et Al. 21DepartmentofBotany,CharlesUniversity,Prague,CzechRepublic 22DepartmentofEnvironmentalBiology,UniversityofNavarra,Pamplona,Spain 23Department of Disturbance Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany 24ResearchUnitBiodiversity,Evolution&Ecology(BEE)ofPlants,InstituteofPlantScienceandMicrobiology,UniversityofHamburg,Hamburg,Germany 25InstituteofEcologyandBotany,CentreforEcologicalResearch,HungarianAcademyofSciences,Vácrátót,Hungary 26DepartmentofNaturalResources,KaratinaUniversity,Karatina,Kenya 27MpalaResearchCentre,Nanyuki,Kenya 28UniversitéClermontAuvergne,FrenchNationalInstituteforAgriculture,Food,andEnvironment(INRAE),VetAgroSup,UMREcosystèmePrairial,Clermont- Ferrand, France 29SchoolofEnvironmentalSciences,UniversityofLiverpool,Liverpool,UK 30TheJamesHuttonInstitute,Aberdeen,UK 31DepartmentofBotany,InstituteofEcologyandEarthSciences,UniversityofTartu,Tartu,Estonia 32TerrestrialEcologyGroup(TEG),DepartmentofEcology,InstituteforBiodiversityandGlobalChange,AutonomousUniversityofMadrid,Madrid,Spain 33DepartmentofPlantBiologyandEcology,UniversidaddeSevilla,Seville,Spain 34DepartmentofSilvicultureandForestEcologyoftheTemperateZones,UniversityofGöttingen,Göttingen,Germany 35CommunityEcology,SwissFederalInstituteforForest,SnowandLandscapeResearch(WSL),Birmensdorf,Switzerland 36DepartmentofEcosystemBiology,UniversityofSouthBohemia,ČeskéBudějovice,CzechRepublic 37ConservationEcologyGroup,GroningenInstituteforEvolutionaryLifeSciences,Groningen,TheNetherlands 38LaboratoryofEcosystemNetworkObservationandModelling,InstituteofGeographicSciencesandNaturalResourcesResearch,ChineseAcademyof Sciences, Beijing, China 39WaddenSeaNationalParkofSchleswig-Holstein,Tönning,Germany 40DepartmentofBiologicalSciencesandBjerknesCentreforClimateResearch,UniversityofBergen,Bergen,Norway 41BotanyDepartment,SenckenbergNaturalHistoryMuseumGörlitz,Görlitz,Germany 42InternationalInstituteZittau,TechnischeUniversitätDresden,Dresden,Germany 43ManaakiWhenua–LandcareResearch,Lincoln,NewZealand 44UKCentreforEcology&Hydrology,Wallingford,UK 45DepartmentofPlantSciences,UniversityofCalifornia,Davis,California,USA 46InstituteofWetlandEcology&CloneEcology/ZhejiangProvincialKeyLaboratoryofPlantEvolutionaryEcologyandConservation,TaizhouUniversity, Taizhou,China 47DepartmentofIntegrativeBiology,UniversityofTexas,Austin,Texas,USA Correspondence Marta Gaia Sperandii, Centro de Investigaciones sobre Desertificación (CSICUVGV), 46113, Valencia, Spain. Email:[email protected] Funding information NERCandBBSRC,Grant/Award Number:NE/N018125/1;2017 program for attracting and retaining talent of Comunidad de Madrid, Grant/ AwardNumber:2017-T2/AMB-5406; European Research Council, Grant/ AwardNumber:ERC-SyG-2013-610028; German Federal Ministry of Education andResearch(BMBF),Grant/Award Number:01LG1201N(SASSCALABC); NewZealandMinistryforBusiness, Innovation and Employment; Community of Madrid and Rey Juan Carlos University; AnaEE-France,Grant/AwardNumber: ANR-11-INBS-0001;FundaciónRamon Areces;GAČR,Grant/AwardNumber: 20-02901Sand19-28491X;Agencia EstataldeInvestigación-SpanishPlan NacionaldeI+D+i,Grant/AwardNumber: P G C 2 0 1 8 - 0 9 9 0 2 7 - B - I 0 0 ; S p a n i s h Government,Grant/AwardNumber: PID2019-110521GB-I00;European Abstract Analysingtemporalpatternsinplantcommunitiesisextremelyimportanttoquantify theextentandtheconsequencesofecologicalchanges,especiallyconsideringthe currentbiodiversitycrisis.Long-termdatacollectedthroughtheregularsamplingof permanent plots represent the most accurate resource to study ecological succession, analyse the stability of a community over time and understand the mechanisms drivingvegetationchange.WeherebypresenttheLOng-TermVegetationSampling (LOTVS)initiative,aglobalcollectionofvegetationtime-seriesderivedfromtheregular monitoring of plant species in permanent plots. With 79 data sets from five continentsand7,789vegetationtime-seriesmonitoredforatleast6yearsandmostlyon anannualbasis,LOTVSpossiblyrepresentsthelargestcollectionoftemporallyfine- grained vegetation timeseries derived from permanent plots and made accessible to theresearchcommunity.Assuch,ithasanoutstandingpotentialtosupportinnovative research in the fields of vegetation science, plant ecology and temporal ecology. KEYWORDS ecoinformatics, ecological succession, ecosystem stability, global scale, permanent plots, plant communities, plant diversity, temporal analysis, timeseries, vegetation 16541103, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/jvs.13115 by Universidad De Sevilla, Wiley Online Library on [18/01/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
| 3of11 Journal of Vegetation Science SPERANDII Et Al. 1 | BACKGROUND Anthropogenicchangesareseverelyimpactingecosystems(Bradshaw etal.,2021).Therateofspecieslosshasnowexceededbackground extinctionrates(Pimmetal.,2014),leadingmanyscientiststoclaima sixthmassextinction(Ceballosetal.,2015;Pereiraetal.,2012).Atthe same time, a considerable proportion of natural habitats has been lost (Convention on Biological Diversity, 2020) and a number of ecosystemfunctionsandservicesareseriouslyatrisk(IPBES,2019). Theanalysisoftime-basedpatternsinbiologicalcommunities, especially when focused on primary producers like plants, representsanopportunitytoquantifytheextentandtheconsequences ofsuchchangesinbiodiversity(Dornelasetal.,2014;Gonzalezetal., 2016;Blowesetal.,2019).Thisresearchfieldhaspotentialto:(a) unravel the mechanisms that drive and maintain biodiversity over time(Hillebrandetal.,2018;Jonesetal.,2017);(b)shedlightonhow externaldrivers(e.g.globalchanges)affectcommunitydynamicsin naturalhabitats(Bernhardt-Römermannetal.,2015;Newboldetal., 2015); and (c) assess relationships between community stability overtimeandthedeliveryofecosystemservices(Isbelletal.,2018). Reliableanswerstothesequestionscanonlybeprovidedbydrawing upon ecological data collected at several points in time using consistent sampling procedures. For plant communities, longterm data collected by regularly sampling permanent plots probably constitute the most precise approach to detect temporal changes at the local scale(Bakkeretal.,1996;deBelloetal.,2020;Damgaard,2019). First,owingtotheirgeographicalpositionbeingkept“fixed”inthe field, permanent plots prevent relocation bias, i.e. the error derived fromtryingtofindtheoriginalplotlocation.Thisbiasisinherentin vegetationresurveys(Verheyenetal.,2018).Second,therepeated collection of vegetation data from permanent plots provides broad benefits to our understanding of vegetation change, including the meanstotrackdetailedsuccessionaltrajectories,monitorspecies interactions over time and assess the stability of the community as a whole. For this reason, permanent plots have been listed among thesixmostimportantdevelopmentsinvegetationscienceoverthe past three decades (Chytrý et al., 2019). In recent decades, vegetation science has benefited from the development and maintenance of large vegetation databases (Dengler etal.,2011).Historicalvegetationrelevésperformedbyearlyvegetation ecologists, together with recent vegetationplot data stemming from regional, but also national or continental research and survey projects, have been carefully assembled and digitally archived in thecontextofcentralizedinitiatives(Bruelheideetal.,2019;Chytrý et al., 2016; Sabatini et al., 2021; Wiser, 2016). Such global collections of vegetationplot data are essential to investigate macroecological patterns and provide spatially meaningful answers to global issues, i.e. to effectively perform globalscale biodiversity research. Inthiscontext,acomparableeffortspecificallyaimedatassembling and maintaining global databases built on timeseries of vegetation data is urgently needed to lay a common ground for future studies focusing on: (a) providing global estimates of changes in plant diversity trends over time; (b) monitoring the conservation status of natural habitats over time; or (c) assessing the stability of ecosystem functionsandservices.Tothebestofourknowledge,theBioTIME initiative (Dornelas et al., 2018) represents the most important global collection of biodiversity timeseries to date, including abundance records measured in species assemblages belonging to the marine, freshwater and terrestrial environments. Yet, the powerful spatialrepresentationofBioTIMEhaslimitationsthatinclude:(a)an often limited length and/or periodicity of the timeseries, which particularlyaffectsvegetationdata(29datasetswithatleastsixdata points); and (b) a poor focus on vegetation and terrestrial plant biodiversity (96 data sets, corresponding to about 27% of the whole database). Given that a high number of ecosystem functions and services stronglydependonplants(Maestreetal.,2012;vanderPlas,2019), we deem it crucial for the fields of vegetation science and ecology tobeabletorelyonaconsistentandstandardizedcollectionofdata setsincludinghigh-qualitytime-seriesmeasuredatregularintervals and specific to plant communities. Regional Development Fund (Centre of ExcellenceEcolChange);GermanFederal Ministry of Education and Research, Grant/AwardNumber:01LC0024, 01LC0024Aand01LC0624A2;Te ApārangiRoyalSocietyofNewZealand; EstonianResearchCouncil,Grant/Award Number:PRG609;Oberfrankenstiftung, Grant/AwardNumber:OFSFP00237;La FageINRAexperimentalstation;Catalan Government,Grant/AwardNumber: SGR 20171005; Rural and Environment ScienceandAnalyticalServicesDivision; German Federal Ministry of Education and Research(BMBF),Grant/AwardNumber: 031B0516C(SUSALPS);NationalScience Foundation,Grant/AwardNumber:LTREB DEB 1931224; International Institute Zittau,TechnischeUniversitätDresden Coordinating Editor: Milan Chytrý 16541103, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/jvs.13115 by Universidad De Sevilla, Wiley Online Library on [18/01/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
4of11 | Journal of Vegetation Science SPERANDII Et Al. Basedonthesepremises,weherebypresenttheLOng-Term VegetationSampling(LOTVS)initiative,agrowingglobalcollection of vegetation timeseries derived from the regular (mostly, annual) monitoring of plant species in permanent plots. By promotingtheuse,andsupportingthevisibilityofhigh-qualitytemporaldatacollectedusingpermanentplots,LOTVSultimatelyaims toprovidethetoolstoaskrelevantecologicalquestionsacrossa numberoftaxa, ecosystems andregions.TheLOTVScollection provides a platform for aggregating the currently disconnected data sets sampled around the world based on permanent plots. Assuch,researchersarewelcometocontributetoand,basedon a scientific proposal (see Section 3.2), use the available collection of data. 2 | DESCRIPTIONOFLOTVS AsofDecember2021,LOTVSencompasses79datasets(Figure1) foratotalof7,789vegetationtime-series,collectedusingpermanent plotsthatweremonitoredforaminimumofsix,andamaximumof 99 years (first quartile: 10; mean: 17.5; median: 16; third quartile: 23years;seeFigure2).ThevastmajorityofLOTVStime-serieshavea finegrained temporal resolution: measurements in permanent plots weretakenon10%–100%ofthetemporalinterval(with100%meaningthatplotsweresampledatleastannually;firstquartile:82.8%; mean:87.4%;median:100%;thirdquartile:100%).Adescriptionof the single data sets can be found in Valencia et al. (2020; supplementarymaterial).ThisisalsosupportedbyonlinemetadataonZenodo FIGURE 1 Mapshowingthegeographicallocationofthe79datasetsincludedinLOTVS.Amoredetailedviewisgivenforareas featuringahighdensityofdatasets:(a)Europe,(b)NorthAmerica.ESRIWorldSatelliteImagerywasusedasthebasemap.Forasubsetof sites,representativevegetationtypesareshown.Photosweretakenat:(left,startingfromthetop):Soebatsfontein(SouthAfrica),Bayreuth (Germany),Roquefort-sur-Soulzon(France),McLaughlinNaturalReserve(California),SantaRitaExperimentalRange(Arizona);(right,starting fromthetop):Laikipia(Kenya),GöttingerWald(Germany),KrkonošeMountains(CzechRepublic),Ohrazení(CzechRepublic),Hays(Kansas) (a) (b) 16541103, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/jvs.13115 by Universidad De Sevilla, Wiley Online Library on [18/01/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
| 5of11 Journal of Vegetation Science SPERANDII Et Al. (https://doi.org/10.5281/zenodo.5807378;seeSection3.3).Atpresent,LOTVSincludesvegetationtime-seriesspecificallyfocusedon herbaceous species and shrubs mostly belonging to grassland habitats,followedbymixedvegetationtypes(i.e.savannas,shrubsteppes, degraded stages of heathlands), shrublands (including heathlands), forest understoreys and wetlands (mostly salt marshes; Figure 3; see Box1).Hereweconsidermixedvegetationtypestoconsistofcommunitiesinwhichgrassesandshrubscoexistinamosaiclandscape (naturally, or as the result of anthropogenic disturbance processes) or are co-dominant. Forest plots exclusively monitoring long-term changesintreespeciesare,atthemoment,excludedfromLOTVS. TheLOTVScollectioncontainsdatafromfivecontinents(Figure1;an interactive map can be also accessed here and through the proposal templateavailable at https://doi.org/10.5281/zenodo.5807378),althoughEuropeandNorthAmericaarethemostrepresentedareasto date.AlthoughEuropeistheleadingcontinentintermsofdatasets (38of79),NorthAmericahoststhemajorityofplots(almost70%, distributed across 30 data sets). DatasetsincludedinLOTVSspanawideclimaticgradient,their meanannualtemperaturerangingbetween−11.5°Cand20.1°C,and their mean annual precipitation between 140 and 2592 mm (source: WordClim 2; Fick& Hijmans,2017). As such, theyaremostlyincluded in the temperate seasonal forest, temperate grassland/desert andinthewoodland/shrublandbiomes(sensuWhittaker,1975;see Figure 4a). InalmosthalfofthepermanentplotsincludedinLOTVS(48.5%), vegetationhasbeensubjectedtoexperimentaltreatmentsmanipulating abiotic or biotic conditions. The most frequent treatment typesareherbivoreexclusion,fertilizerapplicationandgrazingintensification (applied to ~35%,18%and17%ofthetreatedplots, respectively; Figure 5). Yet, even in the absence of such treatments, LOTVS includes plots subjected to regular management regimes, suchasmowingorgrazing,thatarenecessarytomaintaintraditional landuse in given habitats. PermanentplotsintheLOTVScollectionaresurveyedusingdifferenttechniques.Thevastmajority(~85%)arequadratplots,but linetransectsandquadratplotsarrangedalongatransectarealso present.Plotsizerangesfrom0.04to400m2; ~80%oftheplots range from 0.04 to 1.25 m2, with 1 m2beingthemostfrequent(49%) plotsizeinthecollection.Informationonplotsizeismissingfor90 plots,correspondingto0.8%ofthewholeLOTVScollection.The methodusedtoquantifyspeciesabundancealsovariesamongthe 79datasets(Figure3b).Themostfrequentapproachusesvisualestimationofspeciescover(75%),followedbyrecordingthefrequency of individual species across a given number of subplots (17%), and third by the collection of aboveground biomass for each species in the plot (9%). In most cases, biomass clipping is intended to mimic mowing.AllplotsincludedinLOTVSwerepermanentlymarkedin the field; geographic coordinates are available for either specific plots or foruniquelocalities ofeachdataset.Besidesestimating plantspeciesabundance,someofthedatasetswithinLOTVSalso include information about bare ground cover or the abundance of othertaxa(e.g.bryophytes,lichens). Taxonomic standardization is fundamental when compiling vegetationdatabases.Thisisanecessarystep:(a)whenaddressing the issue of nomenclature redundancy, caused by the multitude of synonyms characterizing botanical literature (Kalwij, 2012); and (b) to the ultimate goal of performing comparative analyses across data sets. It also allows a later integration of data with ancillary information linked to taxonomic entities, e.g. species functionaltraits.OriginaldatasetsincludedinLOTVSshowconsiderablevariationinthechosentaxonomicalreferencesreflecting FIGURE 2 Distributionoftheduration (in years) of the timeseries included in LOTVS.Thenumberoftime-serieswithin each class is reported above the bars 16541103, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/jvs.13115 by Universidad De Sevilla, Wiley Online Library on [18/01/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
6of11 | Journal of Vegetation Science SPERANDII Et Al. different regional and national traditions as well as the time when theworkwasundertaken.Assuch,ataxonomicstandardization wasdeemednecessary.Todothis,westandardizedthenomenclatureofplantspeciesfollowingThePlantList,currentlythemost widelyusedglobalreferencelist(Kalwij,2012).ThiswasdoneinR (RCoreTeam,2019)usingthepackageTaxonstand (Cayuela et al., 2019),whichallowstheautomaticstandardizationofplantnames byrunninganinternalquerytoThePlantList(http://www.thepl antlist.org)andreturningstandardizedspeciesnames,eventually resolving synonyms and homogenizing intraspecific taxonomic entitiestothelevelofspecies.Nevertheless,anon-standardized versionoftheLOTVScollection,including(foreachdataset)the original nomenclature of the species, is also available for potential users (see Section 3.2). 3 | DATAUSAGE Asanunprecedentedcollectionofvegetationtime-series,LOTVS has huge potential to support timely and innovative research in the fields of vegetation science, plant ecology and temporal ecology. It should be noted, however, that installing and maintaining permanentplotsisaverytime-andresource-consumingtask,andthusa powerfulcollectionofvegetationplotssuchasLOTVScanonlyarise from collaborative efforts that stem from an impressive amount of workcarriedoutbymanydatacontributors,whoseeffortmustbe acknowledged.Tothisend,anyonecancontributetoLOTVSwith originaldata,aslongasthedatafullycomplywithLOTVSrequirements(seeSection3.2).Atthesametime,dataincludedinLOTVS canberequestedand,basedontheaccessibilitylevel(seeSection 3.2), used following a simple procedure that is intended to support wellgrounded research projects. 3.1 | Contributingdata DetailedinformationonhowtocontributetoLOTVS,aswellas specificdatarequirementscanbefoundonthededicatedwebsite (https://lotvs.csic.es/contribute/). LOTVS welcomes data sets including vegetation timeseries collected from permanent plotswithafixed(i.e.permanentlymarked)geographicalposition in the field, possibly replicated in space, maintained for a minimum of 6 years and sampled at annual intervals. In principle, the timeseries should be continuous, i.e. no gaps should be present. However, exceptions are allowed, provided that observations for some years are only missing for a reduced number of plots. In cases of missing years for all of the permanent plots, the new data set can only be incorporated to LOTVS if: (a) only a very FIGURE 3 Habitattypes(a)andsamplingmethods/approaches(b)coveredinLOTVS.Thenumberoftime-serieswithineachclassis reportedontopofthebars.SeeBox1forworkingdefinitionsofhabitattypeandsamplingmethods 16541103, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/jvs.13115 by Universidad De Sevilla, Wiley Online Library on [18/01/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
| 7of11 Journal of Vegetation Science SPERANDII Et Al. limited number of years is missing; and (b) their distribution within thetime-seriesisirregular,i.e.LOTVSisnotintendedtoaccept permanent plots that, according to their original scope, are only sampled every nyears.Followingtheserequirements,wearealso notlookingtoincludedatacollectedinthecontextofso-called resurveying studies, where historic vegetation plots are revisited after a longer period and rerecorded. In fact, these are the subject of other databases (e.g. the ReSurveyEurope initiative, http:// e u r o v e g . o r g / e v a - d a t a b a s e - r e - s u r v e y - e u r o p e ) . A l s o , a l t h o u g h data collected using different sampling approaches are welcomed (e.g.visualestimationofspeciescover,biomass,frequency,number of individuals), the sampling approach should be consistent overtime.LOTVSdoesnotplan,atpresent,toincorporatepermanent plots that only record species occurrence (i.e. presence/ absencedata).TobeincludedinLOTVS,permanentplotsshould be preferably representative of natural or seminatural vegetation.Theformercanbedefinedasvegetationthatdevelopedin the absence of human influence and/or has long been left undisturbedbyhumans;astothelatter,itsexistenceandmaintenance dependonhumanpractices(e.g.grazingormowing)carriedout foreitherproduction,conservationoramixofthetwopurposes. Assuch,time-seriesdatarecordedfromartificialseedmixtures suchasthosesowninbiodiversityexperimentsarenotcurrently accepted. 3.2 | Requestingdata Because of the effort needed to maintain permanent plots in time and collect temporal vegetation data (Mills et al., 2015), access to individualtime-seriesordatasetsincludedinLOTVSisgoverned by a data policy that allows data owners and contributors to remain in full control of their data if they so choose (for detailed information, see https://lotvs.csic.es/contribute/). The availability and access of single data sets depend on the choice of individual data owners and contributors, who decide the accessibility level oftheirdata(from“restricteddata”,i.e.dataareonlyusableupon consentfromdataowners/contributors,thatshouldbeexpressed eachtimetheirdatasetisrequested;to“freedata”,i.e.datathatare freelyavailabletousethroughtheLOTVSplatform).Wenotethat about65%ofLOTVSdatasetsarepubliclyavailable,eitherbecause theybelongtolong-termecologicalresearch(LTER)programs,or because they were archived and published by their data owners. Also,severaloftheLOTVSdatasetsarepubliclyavailableintheir own right, via contact with their owners. Depending on the accessibility level specified, data owners and contributors hold (or not, in caseoffreelyusabledata)therighttorequestauthorshiponeventual publications based on the proposal submitted by the applicants whentheyrequestthedata.Inallcases,torequestdataincluded inLOTVS,ashortandsoundscientificproposaldescribingtheaims BOX 1 Workingdefinitionsofthemainhabitattypesandsamplingmethodsmentioned.Habitatdefinitionswere adaptedfromtheConventionofBiologicalDiversitywebsite(https://www.cbd.int/forest/definitions.shtmland https://www.cbd.int/drylands/definitions.shtml),fromGoldsteinandDellaSala(2020)andfromtheRamsar Conventionwebsite(https://www.ramsar.org/about/the-convention-on-wetlands-and-its-mission). Habitattype Forest Vegetated land with a tree canopy cover >10% and covering an area of more than 0.5 ha. Forest understorey Thevegetativelayerofaforest,consistingoftreeseedlings,shrubsandherbaceousvegetationgrowing below the forest canopy. Grassland Openvegetationdominatedbygraminoidsandforbs,characterizedbyalowcoverofwoodyspecies(i.e.trees and shrubs). Heathland Evergreen formation mostly composed of heathers, i.e. dwarf shrubs of the Ericaceae family Salt marsh Coastal wetlands dominated by saltand floodtolerant vegetation (mostly grasses, rushes and sedges). Savanna Vegetationtypicaloftropicalandsubtropicalseasonallydryclimates,normallycharacterizedbyanopen canopy of scattered trees and an understorey dominated by grasses, often intermingled with large grassland patches. Shrubland Open vegetation dominated by shrubs or small (<5mtall)trees,oftencharacterizedbyasinglecanopylayer. Wetland Permanentortemporaryareasofmarsh,fen,peatlandorwaterwhereopenbodiesofwatermaybestaticor flowing,fresh,brackishorsalt. Sampling method Biomass Clipping, drying and weighing the aboveground phytomass present in a sampling unit (either as a whole or separately for each species). Cover Visual estimation of plant species cover in percentage (%) or through the use of coverabundance classes. Frequency Estimation of species abundance based on recording the percentage of subunits occupied by each species in acertainsamplingunit.Thiscanbedone,forexample,bycountingthenumberofcontactsbetweeneach species and a pin. 16541103, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/jvs.13115 by Universidad De Sevilla, Wiley Online Library on [18/01/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
8of11 | Journal of Vegetation Science SPERANDII Et Al. oftheprojectandthetypeofdatarequiredshouldbeprepared andsubmittedtotheLOTVSsupervisingcommittee.Thisprocessis intendedto:(a)minimizeconceptualoverlapofproposalsaddressinghighlysimilarresearchquestions;and(b)makesurethatalldata owners are informed about the possible use of their data and are freetodeclineitiftheywishso.Tohelppotentialusersbecome familiarwiththedataandfacilitatedatarequests,weprovideboth ametadatasheetdescribingtheLOTVSdatasetsandaproposal template (both available on Zenodo: https://doi.org/10.5281/zenodo.5807378).Thiswillbeupdatedeverytimenewdatasetsare addedtotheLOTVScollection(orcurrentdatasetsareupdated). Thismetadatasheetprovidesabriefdescriptionofeachdataset and contains information on several features (e.g. accessibility level, number of plots, length of the timeseries, habitat type, number of surveyed years, presence and type of treatments, data type), which will support interpretation and accessibility for all users in theirdatarequests.Theproposaltemplatealsoincludesalinkto an interactive map displaying the geographical location of sampling siteswithintheLOTVScollection,alongwithkeydatasetfeatures. 4 | PERSPECTIVES Initspresentform,LOTVSincludesvegetationtime-seriesforalmost8,000permanentplotsinstalledandmaintainedinnaturaland semi-naturalplantcommunitiesworldwide.However,asexplained in Section 2, the geographical representation of both individual data sets and permanent plots is not homogeneous; it is biased towardsEuropeandNorthAmerica,andmanyhabitats,suchasforest understoreys, are strongly under-represented. To promote a moreequalrepresentationintermsofgeographicalareasandhabitats, one of the goals of this article is to encourage new data sets, FIGURE 4 ClimaticsummaryofthedatasetsincludedinLOTVS.Meanannualtemperatureandmeanannualprecipitationareplottedon the x and yaxes,respectively.Eachdotrepresentsmeanclimaticconditionscharacterizingsiteswithineachdataset.Dotsaresuperimposed onWhittakerbiomes(i.e.indicatingpotentialvegetation;Whittaker,1975),asredrawnfromRicklefs(2008).Theplotwascreatedusingthe Rpackageplotbiomes(version0.0.0.9001,RCoreTeam,RFoundationforStatisticalComputing,Vienna,AT) 16541103, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/jvs.13115 by Universidad De Sevilla, Wiley Online Library on [18/01/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
| 9of11 Journal of Vegetation Science SPERANDII Et Al. including timeseries recorded using permanent plots located in currentlyunder-representedcontinentssuchasAfrica,Asia,Australia andSouthAmerica.Similarly,time-seriesrecordedinforestunderstoreys, tundra and coastal areas would be particularly welcome. Furthermore, we are very interested in data sets featuring spatial as well as temporal replication (minimum 6 years as mentioned above) to disentangle the differences between temporal and spatial changes. Finally, to broaden the range of potential applications of LOTVS(andinlinewithwhathasbeendonebyotherglobalinitiatives, see Bruelheide et al., 2019), we are planning to integrate it with information on environmental variables (climate, microclimate) and species functional traits. Such integration will eventually allow users complementaryaccesstoancillarydatacrucialtoexploretheevolution of different facets of diversity over time (Monnet et al., 2014; Sperandii et al., 2021). 5 | CONCLUSIONS LOTVSpossiblyrepresentsthelargestcollectionoftemporallyfine- grained vegetation timeseries made accessible to the research community derived from permanent plots addressing the study of plant communitiesthroughtime.Assuch,LOTVScanbehighlyusefulto perform timely research on a wide range of topics in the field of vegetation science: investigating patterns and drivers of ecologicalsuccessioninnaturalplantcommunities,quantifyingvegetation changes through time, as well as assessing community stability and identifying its driving mechanisms. At the same time, because it includes a considerable proportion of permanent plots subjected tosomekindoftreatment (e.g.grazing, fertilization,etc.),LOTVS can also support the development of largescale studies aiming to understand how temporal dynamics are affected by different treatmentsinthecontextofglobalchanges.Lastbutnotleast,webelieve LOTVScouldalsoserveasavaluableresourcetoconductmethodologicalresearchaddressingtopicsrelatedto,forexample,methods toquantifydissimilaritythroughtimeandtheirpartitioning(Baselga, 2010;Legendre&Condit,2019)orquantitativeapproachestoinvestigate community dynamics and more specifically, stability. ACKNOWLEDGEMENTS The authors acknowledge institutional support as follows. Nicola J. Day: Te Apārangi Royal Society of New Zealand (Rutherford PostdoctoralFellowship).JiříDanihelka:CzechScienceFoundation (projectno. 19-28491X)and Czech Academy of Sciences (project no. RVO 67985939). Francesco de Bello: Spanish Plan Nacional de I+D+i(projectPGC2018-099027-B-I00).EricGarnier:LaFage INRAexperimentalstation.TomášHerben:GAČRgrant20-02901S. AnkeJentsch:GermanFederalMinistryofEducationandResearch (grant031B0516C-SUSALPS)andOberfrankenstiftung(grantOFS FP00237).NorbertJuergens:GermanFederalMinistryofEducation and Research (grant 01LG1201N - SASSCAL ABC). Frédérique Louault and Katja Klumpp: AnaEE-France (ANR-11-INBS-0001). Robin J. Pakeman: Strategic Research Programme of the Scottish Government's Rural and Environment Science and Analytical Services Division. Meelis Pärtel: Estonian Research Council (PRG609) and European Regional Development Fund (Centre of FIGURE 5 (Left)Piechartdescribingthedistributionoftime-seriesincludedintheLOTVScollectionaccordingtotheabsence(“control”) orpresence(“treatment”)oftreatment.(Right)DistributionofthetypesoftreatmentspresentinLOTVS.Thenumberoftime-serieswithin eachtreatmentisreportedonthetopofthebars.Namesforthetreatmentsareabbreviated.Ungrazed:grazingexclosure;Fert:fertilization; Grazed:grazing;Dist:disturbance;Rem:removalofplantspecies;Dist_Gr:disturbance+grazing;Dist_Rem:disturbance+ removal; CC: climatechange;Fert_Gr:fertilization+grazing;Rem_Fert:removal+fertilization;Rem_Fert_Gr:removal+fertilization+grazing;Dist_Fert: disturbance +fertilization;Fert-:decreaseofproductivity;Rem_Gr:removal+grazing;Fert_Ungr:fertilization+grazingexclosures. 16541103, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/jvs.13115 by Universidad De Sevilla, Wiley Online Library on [18/01/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License