Biodiversity Data Journal
Pensoft Publishers
Occurrence data for the two cryptic species of (: )
DOI 10.3897/BDJ.9.e68860 , Volume: 9 , Issue: null , Pages: 0-0
Article Type: research-article, Article History


is a psyllid that has been known since 1998 as the vector of the bacterium ‘Candidatus Phytoplasma prunorum’, responsible for the European stone fruit yellows (ESFY), a disease that affects species of . This disease is one of the major limiting factors for the production of stone fruits, most notably apricot () and Japanese plum (), in all EU stone fruit-growing areas. The psyllid vector is widespread in the Western Palearctic and evidence for the presence of the phytoplasma that it transmits to species of has been found in 15 of the 27 EU countries.Recent studies showed that is actually composed of two cryptic species that can be differentiated by molecular markers. A literature review on the distribution of was published in 2012, but it only provided presence or absence information at the country level and without distinction between the two cryptic species.Since 2012, numerous new records of the vector in several European countries have been published. We ourselves have acquired a large amount of data from sampling in France and other European countries. We have also carried out a thorough systematic literature review to find additional records, including all the original sources mentioning (or its synonyms) since the first description by Scopoli in 1763. Our aim was to create an exhaustive georeferenced occurrence catalogue, in particular in countries that are occasionally mentioned in literature with little detail. Finally, for countries that seem suitable for the proliferation of (USA, Canada, Japan, China etc.), we dug deeper into literature and reliable sources (e.g. published checklists) to better substantiate its current absence from those regions.Information on the distribution ranges of these vector psyllids is of crucial interest in order to best predict the vulnerability of stone fruit producing countries to the ESFY threat in the foreseeable future.

New information

We give free access to a unique file of 1975 records of all occurrence data in our possession concerning , that we have gathered through more than twenty years of sampling efforts in Europe or through intensive text mining.We have made every effort to retrieve the source information for the records extracted from literature (1201 records). Thus, we always give the title of the original reference, together with the page(s) citing and, if possible, the year of sampling. To make the results of this survey publicly available, we give a URL to access the literature sources. In most cases, this link allows free downloads of a PDF file.We also give access to information extracted from GBIF (162 exploitable data points on 245 occurrences found in the database), which we thoroughly checked and often supplemented to make the information more easily exploitable.We give access to our own unpublished georeferenced and genotyped records from 612 samples taken over the last 20 years in several European countries (Switzerland, Belgium, Netherlands, Spain etc.). These include two countries (Portugal and North Macedonia), for which the presence of had not been reported before. As our specimens have been genotyped (74 sites with species A solely, 202 with species B solely and 310 with species A+B), our new data enable a better overview of the geographical distribution of the two cryptic species at the Palaearctic scale.



Psyllids (Psylloidea), or jumping plant-lice, are plant sap-sucking hemipterans that could be considered as a minor group in terms of species diversity (3,573 described species according to Ouvrard 2021, compared to 104,165 hemipteran species according to Zhang 2013). However, a few psyllids are amongst the most devastating pests of annual and perennial crops due to their ability to transmit phytopathogenic bacteria causing significant agricultural losses. For example, Bactericera cockerelli (Šulc, 1909) is the vector of a liberibacter responsible for Zebra chips (ZC), a disease that caused millions of dollars in losses to the potato industry in the United States, Mexico, Central America and New Zealand, often leading to the abandonment of entire fields (Munyaneza 2012). The huanglongbing (HLB), the world's most devastating disease of trees of species of Citrus, is associated with two psyllid species, Diaphorina citri (Kuwayama, 1908) and Trioza erytreae (Del Guercio, 1918) (Bové 2006, Gottwald 2010, Khamis et al. 2017, Shimwela et al. 2016, Rwomushana et al. 2017, Ajene et al. 2020). In 2014, T. erytreae was fortuitously discovered in Spain and Portugal in parks and avenues and even in privately owned trees during a survey for other citrus pests (Arenas-Arenas et al. 2018). Although circum-Mediterranean species of Citrus have been, thus far, spared from the disease, the sporadic records of T. erytreae in these regions exposes them to the threat of a potential devastating epidemic (Cocuzza et al. 2017).

Other bacteria transmitted by psyllids to fruit trees have major economic impacts, in Europe in particular (Hadidi et al. 2011). These are phytoplasmas of trees of species of Prunus, as well as apple and pear trees, transmitted by psyllids of the genus Cacopsylla . These, respectively, cause the European stone fruit yellows (ESFY), the Apple Proliferation (AP) and the Pear Decline (PD) (Jarausch et al. 2019). These bacteria and their vectors are native to Europe where they occur widely in orchard as well as wild habitats, preventing the eradication of the vectors and, therefore, containment of the diseases. The psyllid vectors are controlled mainly by insecticides, but the evolution of farming practices (e.g. reduction in the use of pesticides) and European regulations (i.e. pathogens removed from the list of quarantine organisms) could be the source of new emergences in the near future. In spite of great efforts from the European research community to better understand the biology and the ecology of the psyllid vectors of phytoplasmas (COST Action FA0807 2013, MacLeod et al. 2012), the presence of these insects in some parts of Europe and even in other parts of the world impacted by these diseases, remains unclear (Steffek et al. 2012). Resolving this uncertainty would help to assess the risks posed by the fruit tree phytoplasmas (MacLeod et al. 2012) and to make decisions to manage these risks.

Dispersal of psyllid vectors poses a threat to food security across countries, stressing the need to anticipate the risks associated with introductions of new psyllids. Mapping the vector potential distributions under scenarios of introduction is crucial to an efficient pest risk assessment (PRA) framework (Venette et al. 2010). Occurrences representing the extent and variability within the current range of a given species are key to characterise and map its potential distribution under scenarios of introduction or climate change. Species distribution models (SDMs) have become the main predictive tool to achieve this goal (Elith and Leathwick 2009, Guisan et al. 2013). SDMs have proven their usefulness, inter alia, in invasion biology (e.g. Meynard et al. 2017, Syfert et al. 2017) and in conservation biology (e.g. Guisan et al. 2013, Muscatello et al. 2021). In plant pathology, SDMs are also increasingly used to predict the potential distributions of vector-borne plant pathogens (e.g. Benhadi-Marn et al. 2020, Narouei-Khandan et al. 2016, Shimwela et al. 2016). However, the reliability of these models heavily depends on the quality of the occurrence data that are used as input to map species distributions.

At least four criteria should be considered before using occurrence data as input for SDMs (Meynard et al. 2019): geographic and environmental representation and extent, quantity, accuracy of the georeferenced records and accuracy of the taxonomic identification. In short, occurrence data points should represent the full extent of biodiversity within the environments that the species is able to occupy, they should be numerous enough to allow its characterisation and geographic coordinates and taxonomic identification should be accurate, as these may otherwise introduce error in the modelled occurrence-environment correlations. High-quality data to properly map a species’ distribution are often difficult to obtain, especially in insects. Indeed, collecting insects and information on their biology is often a time-consuming process that requires high taxonomic expertise. Insect species identification may necessitate painstaking morphological analyses or even the development of specific tools such as molecular markers (e.g. Peccoud et al. 2013). Recent studies have shown that different populations or genotypes within the same taxon can represent different risks, resulting in strikingly different SDM outputs (Meynard et al. 2017, Chardon et al. 2020). Genotypic information throughout the species range can therefore be crucial in the risk assessment process.

Historical data may also consitute a precious resource to help trace vector dispersion routes or simply to access specimens that can no longer be obtained (e.g. samples from an inaccessible locality). Many museums and academic institutions hold field notebooks and maintain collections that are a rich source of valuable information (e.g. collection date and locality) on insect specimens collected during scientific expeditions (Graham et al. 2004, Lister and Climate Change Research Group 2011, Suarez and Tsutsui 2004). Such data have proven useful in reconstructing the history of human or animal infectious diseases and in identifying their sources or reservoirs, in particular for mosquito-borne pathogens (e.g. West Nile virus, Suarez and Tsutsui 2004). To our knowledge, however, this task has never been undertaken for vector-borne plant diseases and historical records appear underexploited, even if they concern regions where such diseases have been endemic for tens to hundreds of years.

Cacopsylla (Thamnopsylla) pruni (Scopoli, 1763) has been known since 1998 as the vector of a bacterium, ‘Candidatus Phytoplasma prunorum’ responsible for ESFY (Carraro et al. 1998) and is currently listed as a Regulated Non-Quarantine Pest (RNQP) in Annex IV-Part D of the European Council Directive 2019/2072 (EUR-Lex 2020). This psyllid is widespread in the Western Palearctic (Ouvrard 2021) and the phytoplasma it transmits are reported in 15 of the 27 EU countries (Steffek et al. 2012). ESFY is one of the major factors limiting the production of stone fruits, most notably apricot (Prunus armeniaca L.) and Japanese plum (Prunus salicina Lindl.) in all EU stone fruit-growing areas. These areas include the three most important apricot producing countries, Spain, Italy and France, which provided 73% of the EU apricot production in 2012 according to Eurostats. In the last twenty years, great efforts have been made to characterise the biology of the ESFY vector (Peccoud et al. 2013, Peccoud et al. 2018), the life cycle of the transmission (Thébaud et al. 2009), the genetic variability of the pathogen (Danet et al. 2011, Marie-Jeanne et al. 2020) and the risk factors of the disease (Marie-Jeanne et al. 2020, Thébaud et al. 2006). However, despite these efforts and the rigorous sanitary control of fruit trees as part of the certification process, the disease continues to pose great problems to fruit growers in Europe, which raises the question of the origin of contaminations in orchards.

In their review, Steffek et al. (2012) pointed out important uncertainties that could undermine the management of ESFY. The rate of psyllid dispersal at various scales (i.e. a growing region, country, Europe or even larger), by natural means or human transportation and the risk of introduction and establishment in new countries were two of the essential issues that remained unresolved. The presence of the vector in several countries from the southernmost part of Europe (Portugal, southern Spain, Greece etc.) which can be directly impacted by ESFY, as well as neighbouring countries, remains undetermined. At the time of the Steffek et al. (2012) review, preliminary studies had shown that C. pruni was composed of two genetic groups, then called "biotypes" (Sauvion et al. 2007, Sauvion et al. 2009). However, no detailed data were available on the European distribution of these two biotypes, which were analysed jointly in the Steffek et al. review.

Establishing the geographic distribution of C. pruni and possibly for each biotype, was therefore a priority. To this end, we developed molecular markers to easily identify the C. pruni biotypes (Peccoud et al. 2013), which allowed us to establish their species status (Peccoud et al. 2018). Numerous new surveys on the presence of C. pruni in several European countries have been published (e.g. Etropolska et al. 2015, Jarausch et al. 2019, Sabaté et al. 2016, Seljak 2020, Warabieda et al. 2018), sometimes with a distinction between the two species. In our own laboratory at INRAE-Montpellier, we obtained a large collection of samples through twenty years of surveys in France and other European countries (Portugal, Spain, Belgium, Switzerland, Italy etc.). Some of these samples have been used in publications, but the vast majority have not yet been released in a georeferenced format. We were also able to find unpublished and valuable information in GBIF (e.g. metadata from Natural History Museum of London). Recently, we conducted an extensive literature survey for the original sources mentioning C. pruni , as a mean of verification, but more importantly, to precisely locate the source of each specimen. This laborious work often resembled a treasure hunt with its typical pitfalls and puzzles, such as correctly translating Mongolian locality names from a text written in Russian and then georeferencing them (Fig. 1). Sparing others these obstacles was part of our motivation to make the results of this survey publicly available.

Our objective is to give access, through a unique dataset, to all the data we have gathered on the two cryptic species of C. pruni. In this way, we hope to contribute to a better management of ESFY in countries affected by the disease and to a better anticipation of the risk of introduction in countries not yet affected.

General description


This dataset is a compilation that is meant to include all available information (literature, GBIF, INRAE unpublished data) on the geographical distribution of two cryptic species of the psyllid Cacopsylla pruni at the scale of the Palaearctic (Fig. 2). We aimed to publish third-party data that can be otherwise hard to access and first-party data that are not yet published and to ensure free, open access to that information.

Sampling methods

Study extent

The data contained in this dataset have three different origins: a systematic literature review, the Global Biodiversity Information Facility [GBIF] network and field collections by researchers/students from INRAE-Montpellier. They cover several ecoregions of the Palaearctic (Fig. 2): the Euro-Siberian region, the Mediterranean Basin, the Western and East Asia (Northern parts). No data were found for Central Asia nor for the Nearctic, despite the known presence of trees of species of Prunus and conifers on which C. pruni could make its life cycle.

Sampling description

Literature data

In order to extend upon the Steffek et al. (2012) review, we have undertaken a new systematic literature survey for articles/manuscripts/books using the keyword "Cacopsylla pruni", its previous combinations "Chermes pruni" and "Psyllapruni" or its synonym "Psylla fumipennis". To this end, we used the Google Scholar search engine ( and we explored several scientific databases (AGRICOLA, Agris, CAB Abstract, Web of Science), as well as other types of databases more or less specialised on the subject:

  • ISTEX ( a platform offering the French higher education and research community access to more than 23 million articles from all scientific disciplines and which cover a very long period (from ~ 1400 to 2019);

The searches were not restricted by language and were traced back to the first description of C. pruni (1763). Each line of the dataset that we make available (see section 'Data resources') corresponds to a reference. For almost all of them, we have retrieved the PDF file of the orignal publication (including old books) which allowed us to verify the information. The corresponding URL is given for each data in the dataset (DOI link or similar link generally giving direct access to the PDF). We systematically tried to specify the locality where the observation was made (see Quality control section). Whenever the information was available, we specified the cryptic species of C. pruni (A or B, according to Peccoud et al. 2013) and the collection plant. In the end, we were able to exploit 1201 occurrence data from the literature survey (Fig. 3).

GBIF data

A search on the keywords "Cacopsylla pruni" returned 245 occurrences in (14 June 2021). The derived dataset with filtered export of GBIF occurrence data is available at this link: Amongst the 245 occurrences, we were able to extract the names of 45 localities with geographic coordinates. For 87 occurrences, for which only the name of the locality was given, we retrieved the geographic coordinates from Google Earth. The database also provided images of scanned slides from the NHM collection ( from which we retrieved precise information about the sampling (date, location, host plant, collector) (Fig. 4), sometimes redundant with our own information (e.g. data from Iran). Finally, 28 occurrences were derived from information associated with DNA sequences deposited in iBOL (, including 24 sequences deposited by us and already entered in our dataset (e.g. In total, 162 occurrences data have been extracted from GBIF (Fig. 5).

Sampling data

For more than 20 years, researchers (Gérard Labonne, Gaël Thébaud, Jean Peccoud, Christian Cocquempot and Nicolas Sauvion) or students of INRAE-Montpellier have collected C. pruni individuals. Using a beating tray (80 cm x 80 cm), we collected essentially on Prunus spinosa L. (blackthorn) in spring and the rest of the year on Pinus nigra J.F Arnold (Black Pine), Picea abies (L.) H.Karst. (Common Pitch-fir) and Abies alba Mill. (Common Silver Fir). Other congeneric species where sometimes caught, but C. pruni individuals were easily recognised by the colour of the fore wing, which is dark brown at the apex and brown in the remaining part. Soon after identification, samples were conserved in 96% ethanol until DNA extraction and then genotyped (for species determination) according to the protocol described by Peccoud et al. (2013).

We recorded the GPS coordinates of all collected samples in their wild habitat, geolocalising the bush, hedge or shrub sampled. For the few insects sampled in orchards, we attributed a unique GPS coordinate — corresponding to the centre of each plot — to all the corresponding samples. The name of the locality given in the dataset corresponds to the nearest locality to the sampled point. We sampled mainly France, without restriction to apricot-growing regions and focusing on southern regions where species A and B live in sympatry or in strict allopatry. We also collected samples in Spain, Switzerland and Italy. The addition of these 612 new occurrence data improves the picture of the geographical distribution of the two species, hence it should be valuable for risk assessment, phylogeography or population genetics studies (Fig. 2, Fig. 6, Fig. 7)

Quality control

We have a strong expertise in the taxonomy of psyllids (Ouvrard 2021). Over the last few years, we have accumulated a large number of references on these insects in an article database, including references that are old and/or difficult to trace. As we had all these articles in PDF or paper or other metadata (e.g. scanned images), we were able to retrieve and thoroughly verify all information concerning C. pruni or its synonyms and combinations.

All the specimens that we collected in the field were first carefully visually examined and then genotyped according to Peccoud et al. (2013), which effectively eliminates all risks of misidentification.

Wherever possible, geographic coordinates (in WGS-84 coordinate system) refer to specific localities. We used Google Earth to search and reference each locality name found in literature or GBIF, being careful about homonymy and translation of names and possible changes of country names. We consider the precision of these geographical coordinates to be a few kilometres, as authors rarely give very precise coordinates of their sampling points. Conversely, whenever we found geographical coordinates in GBIF, we plotted them on a Google Earth map to identify the closest locality and to check consistency with other information provided (name of the region, country etc.). When no locality name was given, precision may vary from city to province, region or country (e.g. "USSR: South European Part"). In this case, we specified that the “locality is not stated". For data points only specifying countries, we provided the GPS coordinates of the country centres extracted from Google Earth, for lack of a better option. We, therefore, included a column with the estimated precision for each record, stressing that some of these data should be used with caution depending on the level of precision required for analyses. Conversely, GPS coordinates of our own collected samples (see previous section) have an accuracy of a few metres. Each point was first geolocalised with a portable GPS and then checked on Google Maps.

Step description

Most field names of the dataset were chosen according to the Darwin Core format (Wieczorek et al. 2012) and the latest version of the list of core terms as of 28-10-2020 ( “catalogNumber”, “phylum”, “order”, “genus”, “acceptedNameUsage”, “Occurrence”, “country”, “countryCode”, “locationRemarks”, “locality”, “coordinateUncertaintyInMetres”, “decimalLatitude”, “decimalLongitude”, “ownerInstitutionCode”, “locationAccordingTo”, “dateIdentified”, “eventDate”, “associatedReferences”. We have added 11 columns with names not defined by Darwin Core: “suborder”, “superfamily”, “family“, “subfamily”, “speciesA”, “speciesB”, “hostPlantFamily”, “hostPlantLatinName”, “hostPlantVernacularName”, “sourceCategory”, “page”.

Geographic coverage


The database covers the entire known geographic range of the two cryptic species of the psyllid C. pruni, from Morocco to Norway and from Portugal to Mongolia.

We have also extended our search to other countries where either species could potentially be found, in particular countries where different species of Prunus are described in wild or cultivated ecological compartments (e.g. Japan, China, USA, Canada) and where these psyllids could be phytoplasma vectors. Whenever possible, we relied on checklists from recognised taxonomists to ensure the veracity of the information before concluding as "absence" (e.g. Inoue 2010, Maw et al. 2000).


33.815458 and 65.59623333 Latitude; -8.383379 and 112.52588611 Longitude.

Taxonomic coverage


The data paper focuses on two cryptic species of Cacopsylla (Thamnopsylla) pruni (Scopoli, 1763), currently referred to as A and B. Species of Cacopsylla pruni show clear genetic differences despite being morphologically and ecologically indistinguishable (Peccoud et al. 2013, Peccoud et al. 2018). These psyllids are sternorrhynchans of the order Hemiptera, belonging to the superfamily Psylloidea, family Psyllidae and subfamily Psyllinae according to the classification by Burckhardt et al. (2021).

Temporal coverage

Living time period: 1763-2020.


Literature data cover 1763 to 2020.

INRAE data cover 1998 to 2020.

Usage licence

Usage licence

Оpen Data Commons Open Database License (ODbL)

Data resources

Data package title

Compilation of occurrence data for two psyllid species of the Cacopsylla pruni complex (Hemiptera: Psylloidea).

Number of data sets


Data set 1.

Data set name

Cacopsylla pruni_occurrences_v29.csv

Data format

Darwin Core Archive

Number of columns


Character set


Data format version


Data set 1.
Column label Column description
catalogNumber An identifier which assigns a unique code to each of the 1975 records (NS0001 to NS1975).
phylum The full scientific name of the phylum in which the taxon is classified.
class The full scientific name of the class in which the taxon is classified.
order The full scientific name of the order in which the taxon is classified.
suborder The full scientific name of the suborder in which the taxon is classified.
superfamily The full scientific name of the superfamily in which the taxon is classified.
family The full scientific name of the family in which the taxon is classified.
subfamily The full scientific name of the subfamily in which the taxon is classified.
genus The full scientific name of the genus in which the taxon is classified.
acceptedNameUsage The full name, with authorship and date information of the currently valid (zoological) taxon.
Occurrence An existence of an Organism (sensu at a particular place at a particular time. Here, five modalities: "insufficient data" (i.e. insufficient information to determine presence or absence); "probable absence" (i.e. no presence data yet found in records); "probable presence" (i.e. presence very likely, but not yet confirmed); "confirmed presence".
speciesA Information concerning the assignment of the specimens of a population (i.e. caught on the same day in the same locality on the same host plant) to species A of C. pruni. Three modalities: "not genotyped"; "not species A" (i.e. no individual of genotype A was found in the population analysed, but individuals of species B); "species A" (i.e. at least one individual of genotype A found in the population analysed). Genotyping was based on Peccoud et al. (2013).
speciesB Information concerning the assignment of the specimens of a population (i.e. caught on the same day in the same locality on the same host plant) to species B of C. pruni. Three modalities: "not genotyped"; "not species B" (i.e. no individual of genotype B was found in the population analysed, but individuals of species A); "species B" (i.e. at least one individual of genotype B found in the population analysed). Genotyping was based on Peccoud et al. (2013).
country Names of the countries where the individual(s) attributed to C. pruni have been recorded, according the universally applicable code ISO 3166-2:2013.
countryCode Two-letter country codes defined in ISO 3166-1, part of the ISO 3166 standard to represent countries where species have been described.
locationRemarks Comments or notes about the location.
locality The specific description of the place. The locality is given as accurately as possible (precise address, village, town), but may sometimes be imprecise (e.g. mountain, region) or even absent (NA="locality not stated"). See column "coordinateUncertaintyInMetres" for more details on uncertainty.
coordinateUncertaintyInMetres The horizontal distance (in metres) from the given decimalLatitude and decimalLongitude describing the smallest circle containing the whole of the Location. Leave the value empty if the uncertainty is unknown, cannot be estimated or is not applicable (because there are no coordinates). Zero is not a valid value for this term, for example, 30 m = margin of error in the measurement of coordinates using a GPS navigator; 1000 or 10000 m = uncertainty attributed to most locality names in literature, in the absence of more precise information; 50000 m = uncertainty when only the name of the region/province is known.
decimalLatitude The geographic latitude (in decimal degrees according to the geodetic coordinate reference system EPSG 4326) of the geographic centre of a location. Positive values are north of the Equator, negative values are south of it. Legal values lie between -90 and 90, inclusive.
decimalLongitude The geographic longitude (in decimal degrees according to the geodetic coordinate reference system EPSG 4326) of the geographic centre of a location. Positive values are east of the Greenwich Meridian, negative values are west of it. Legal values lie between -180 and 180, inclusive.
hostPlantFamily Six modalities: "Fabaceae"; "Pinaceae"; "Rosaceae"; "Salicaceae"; "unknown" (specimens collected by sweeping or Malaise trap); "unspecified species". Here "host plant" is taken in the broadest sense, i.e. plants on which a psyllid species completes its immature to adult life cycle or shelter plant (plants on which adult psyllids overwinter and on which they may feed) or casual plant (plants on which adult psyllids land, but do not feed).
hostPlantLatinName Latin name of the host plant species (i.e. host plant sensu stricto, shelter plant or casual plant) according to the International Code of Nomenclature for algae, fungi and plants ( For example, Picea abies (L.) H.Karst., Prunus spinosa L. etc.
hostPlantVernacularName Vernacular English name of the host plant species.
sourceCategory The three different sources of information used to compile the dataset: "GBIF" (i.e. data from the Global Biodiversity Information Facility); "literature" (i.e. any data resulting from a text-mining from different sources - manuscript, book, article etc. - accessible or not on the web); "INRAE" (i.e. data from collections by INRAE Montpellier, not published to date).
ownerInstitutionCode The name (or acronym) in use by the institution having ownership of the object(s) or information referred to in the record.
locationAccordingTo Information about the source of this Location information. Could be a publication (gazetteer), institution or team of individuals. Here, detailed title of the original reference associated with the locality; "no data" (i.e. no information found for a particular country, for example, Kyrgyzstan, Malta).
dateIdentified The date on which the subject was determined as representing the Taxon. Here, year of publication of the reference cited in the "locationAccordingTo" column.
page Page where the original information about the locality can be found in the reference cited in the "locationAccordingTo" column.
eventDate The date-time or interval during which an Event occurred. For occurrences, this is the date-time when the event was recorded. Here, year(s) or date of sampling or observation in the locality according to the information in the "locationAccordingTo" column.'1996' (some time in the year 1996). '2010-06' (some time in June 2010). '2010-02-12' (some time during 12 February 2010). '2007/2010' (some time during the interval between the beginning of the year 2007 and the end of the year 2010).
associatedReferences A list (concatenated and separated) of identifiers (publication, bibliographic reference, global unique identifier, URI) of literature associated with the occurrence. Here, URL by which the original information can be retrieved (downloadable PDF file in open access, link to the publisher of a non-open access reference, direct link to the original GBIF occurrence etc.).


We are very grateful to Josiane Peyre for her valuable technical assistance for the genotyping of thousands of psyllids and the following individuals for their contributions to the collection of psyllid or plant samples: N. Courtieu & J.-M. Broquaire (SICA Centrex), N. Galabert & J. Delnatte (SICA L’Edelweiss), B. Rouillé (SRPV-PACA), E. Navarro (Terroir de Crau), P. Delon (CA-Gard), E. Falezan (GIE-Tain l’Hermitage), P. Exbrayat (CA-Drôme), M. Léon-Chapoux and V. Delaunay (SEFRA) and G. Devènes (Agroscope). Many students also took part in the collection of the psyllids, for which they are warmly thanked: Ghislaine Sagna, Léa Merlet, Piroska Czibulyás, Elise Découvreur, Clara Bouchet, Clara Sauvion, Zo-Norosoa Andrianjaka-Camps, Florent Décugis and Olivier Lachenaud.

Part of this work benefitted from a postdoctoral grant to NS funded by an INRA-CIRAD SDIPS grant (Speciation and Molecular Diagnosis of Insect Pest Species Complexes). Field and molecular work for this study were supported by several projects during 15 years:

2005-07: ECOGER "Ecologie et adaptation des insectes phytophages en gestion de leurs populations" founded by le Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation-France;

2007-08: SEE-ERA.NET, network 'Phytoplasma epidemiology', funded by the 6th EU Framework Programme for Research and Development (contract number ERA-CT-2004-515805)

2009-11: SDIPS "Mechanisms of Speciation & Molecular Diagnosis of Insect Pest Species Complexes" founded by INRA-France;

2010-12: SPEED@ID “Accurate SPEciEs Delimitation and IDentification of Eukaryotic biodiversity using DNA markers”. A project proposed by F-BoL, the French Barcode of Life initative - Genoscope Evry-France;

2010-12: PRIMA PHACIE “Pest risk assessment for the European Community plant health: A comparative approach with case studies”, founded by European Food Safety Authority (EFSA), grant agreement CFP⁄EFSA⁄PLH⁄2009⁄01;

2010-12: Bilateral project PIA BOSPHORUS between TUBITAK-Turkey and le Ministère des Affaires étrangères-France "Role of the vectors (psyllids) in the dissemination of the diseases due to phytoplasma on fruit trees";

2011-13: PHYLOPSYL from the project “Bibliothèque du vivant” (BdV) funded by three French institutions (the CNRS, INRA and MNHN);

2015-2018: E-SPACE project number 1504-004, Improving epidemiosurveillance of Mediterranean and tropical plant diseases, French Agropolis Foundation.

2020-21: This data paper was conceived within the stimulating framework of the KIM RIVE (Key Initiative Montpellier: Infectious Risks and Vectors,, supported by MUSE (Montpellier University of Excellence, and the RIVOC key challenge (, supported by the Occitanie Region (France).

Author contributions

NS contributed to text mining, sampling and characterisation of the insects, georeferencing, development of the dataset, map making and writing of the paper; DO provided easier access to scattered taxonomic data through his extensive expertise on psyllids, contributed to species validation and writing of the paper; JP contributed to sampling, molecular characterisation of the insects and writing of the paper; CNM contributed to the development of the dataset and writing of the paper.


    Ajene   I. J. , Khamis   F. , Ballo   S. , Pietersen   G. , van Asch   B. , Seid   N. , Azerefegne   F. , Ekesi   S. , Mohamed   S. 2020. . Detection of Asian citrus psyllid (Hemiptera: Psyllidae) in Ethiopia: a new haplotype and its implication to the proliferation of Huanglongbing. Journal of Economic Entomology 113: 1640-1647 doi: 10.1093/jee/toaa113
    Arenas-Arenas   F. J. , Duran-Vila   N. , Quinto   J. , Hervalejo   A. 2018. . Is the presence of Trioza erytreae, vector of huanglongbing disease, endangering the Mediterranean citrus industry? Survey of its population density and geographical spread over the last years. Journal of Plant Pathology 100: 567-574 doi: 10.1007/s42161-018-0109-8
    Benhadi-Marn   J. , Fereres   A. , Pereira   J. A. 2020. . A model to predict the expansion of Trioza erytreae throughout the Iberian Peninsula using a pest risk analysis approach. Insects 11: 576 doi: 10.3390/insects11090576
    Carraro   L. , Osler   R. , Loi   N. , Ermacora   P. , Refatti   E. 1998. . Transmission of European stone fruit yellows phytoplasma by Cacopsylla pruni. Journal of Plant Pathology 80: 233-239
    Chardon   Nathalie Isabelle , Pironon   Samuel , Peterson   Megan Lynn , Doak   Daniel Forest 2020. . Incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide-spread plant species. Ecography 43: 60-74 doi: 10.1111/ecog.04630
    Cocuzza   G. E.M. , Alberto   U. , Hernandez-Surez   E. , Siverio   F. , Di Silvestro   S. , Tena   A. , Carmelo   R. 2017. . A review on Trioza erytreae (African citrus psyllid), now in mainland Europe, and its potential risk as vector of huanglongbing (HLB) in citrus. Journal of Pest Science 90: 1-17 doi: 10.1007/s10340-016-0804-1
    Danet   J. - L. , Balakishiyeva   G. , Cimerman   A. , Sauvion   N. , Marie-Jeanne   V. , Labonne   G. , Lavin   A. , Batlle   A. , Krizanac   I. , Skoric   D. , Ermacora   P. , Ulubas-Serce   C. , Caglayan   K. , Jarausch   W. , Foissac   X. 2011. . Multilocus sequence analysis reveals the genetic diversity of European fruit tree phytoplasmas and supports the existence of inter-species recombination. Microbiology 157: 438-450 doi: 10.1099/mic.0.043547-0
    Elith   J. , Leathwick   J. R. 2009. . Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40: 677-697 doi: 10.1146/annurev.ecolsys.110308.120159
    Etropolska   A. , Jarausch   W. , Jarausch   B. , Trenchev   G. 2015. . Detection of European fruit tree phytoplasmas and their insect vectors in important fruit-growing regions in Bulgaria. Bulgarian Journal of Agricultural Science 21: 1248-1253
    EUR-Lex   . Commission Implementing Regulation (EU) 2019/2072 of 28 November 2019 establishing uniform conditions for the implementation of Regulation (EU) 2016/2031 of the European Parliament and the Council, as regards protective measures against pests of plants, and repealing Commission Regulation (EC) No 690/2008 and amending Commission Implementing Regulation (EU) 2018/2019. 2020-12-04T00:00:00+02:00
    Gottwald   T. R. 2010. . Current epidemiological understanding of citrus huanglongbing. Annual Review of Phytopathology 48: 119-139 doi: 10.1146/annurev-phyto-073009-114418
    Graham   C. H. , Ferrier   S. , Huettman   F. , Moritz   C. , Peterson   A. T. 2004. . New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology & Evolution 19: 497-503 doi: 10.1016/j.tree.2004.07.006
    Guisan   A. , Tingley   R. , Baumgartner   J. B. , Naujokaitis-Lewis   I. , Sutcliffe   P. R. , Tulloch   A. I.T. , Regan   T. J. , Brotons   L. , McDonald-Madden   E. , Mantyka-Pringle   C. 2013. . Predicting species distributions for conservation decisions. Ecology Letters 16: 1424-142 doi: 10.1111/ele.12189
    Inoue   H. 2010. . The generic affiliation of Japanese species of the subfamily Psyllinae (Hemiptera: Psyllidae) with a revised checklist. Journal of Natural History 44: 5-6 333-360 doi: 10.1080/00222930903437325
    Jarausch   W. , Jarausch   B. , Fritz   M. , Runne   M. , Etropolska   A. , Pfeilstetter   E. 2019. . Epidemiology of European stone fruit yellows in Germany: the role of wild Prunus spinosa. European Journal of Plant Pathology 154: 463-476 doi: 10.1007/s10658-019-01669-3
    Khamis   F. M. , Rwomushana   I. , Ombura   L. O. , Cook   G. , Mohamed   S. A. , Tanga   C. M. , Nderitu   P. W. , Borgemeister   C. , Stamou   M. , Grout   T. G. 2017. . DNA barcode reference library for the African citrus triozid, Trioza erytreae (Hemiptera: Triozidae): vector of African citrus greening. Journal of Economic Entomology 110: 2637-2646 doi: 10.1093/jee/tox283
    Lister   A. M. , Group   Climate Change Research 2011. . Natural history collections as sources of long-term datasets. Trends in Ecology & Evolution 26: 153-154 doi: 10.1016/j.tree.2010.12.009
    Loginova   M. M. 1974. . The psyllids (Psylloidea, Homoptera) of the Mongolian People's Republic II. Nasekomye Mongoli 4: 51-66
    MacLeod   A. , Anderson   H. , Follak   S. , Van Der Gaag   D. J. , Potting   R. , Smith   J. , Steffek   R. , Vloutoglou   I. , Holt   J. , Karadjova   O. , Kehlenbeck   H. , Labonne   G. , Reynaud   P. , Viaene   N. , Anthoine   G. , Holeva   M. , Hostachy   B. , Ilieva   Z. , Karssen   G. , Krumov   V. , Limon   P. , Meffert   J. , Niere   B. , Petrova   E. , Peyre   J. , Pfeilstetter   E. , Roelofs   W. , Rothlisberger   F. , Sauvion   N. , Schank   N. , Schrader   G. , Schroeder   T. , Steinmller   S. , Tjou-Tam-Sin   L. , Ventsislavov   V. , K   Verhoeven , Wesemael   W. 2012. . Pest risk assessment for the European Community plant health: a comparative approach with case studies. EFSA Supporting Publications 9: 319E doi: 10.2903/sp.efsa.2012.EN-319
    Marie-Jeanne   V. , Bonnot   F. , Thébaud   G. , Peccoud   J. , Labonne   G. , Sauvion   N. 2020. . Multi-scale spatial genetic structure of the vector-borne pathogen ‘Candidatus phytoplasma prunorum’ in orchards and in wild habitats. Scientific Reports 10: 5002 doi: 10.1038/s41598-020-61908-0
    Maw   H. E.L. , Foottit   R. G. , Hamilton   K. G.A. , Scudder   G. G.E. 2000. Checklist of the Hemiptera of Canada and Alaska NRC Research Press 220 978-0-660-18165-3
    Meynard   Christine N. , Gay   Pierre-Emmanuel , Lecoq   Michel , Foucart   Antoine , Piou   Cyril , Chapuis   Marie-Pierre 2017. . Climate-driven geographic distribution of the desert locust during recession periods: Subspecies’ niche differentiation and relative risks under scenarios of climate change. Global Change Biology 23: 4739-4749 doi: 10.1111/gcb.13739
    Meynard   Christine N. , Leroy   Boris , Kaplan   David M. 2019. . Testing methods in species distribution modelling using virtual species: what have we learnt and what are we missing?. Ecography 42: 2021-2036 doi: 10.1111/ecog.04385
    Munyaneza   J. E. 2012. . Zebra chip disease of potato: biology, epidemiology, and management. American Journal of Potato Research 89: 329-350 doi: 10.1007/s12230-012-9262-3
    Muscatello   Angela , Elith   Jane , Kujala   Heini 2021. . How decisions about fitting species distribution models affect conservation outcomes. Conservation Biology doi: 10.1111/cobi.13669
    Narouei-Khandan   H. A. , Halbert   S. E. , Worner   S. P. , van Bruggen   A. H.C. 2016. . Global climate suitability of citrus huanglongbing and its vector, the Asian citrus psyllid, using two correlative species distribution modeling approaches, with emphasis on the USA. European Journal of Plant Pathology 144: 655-670 doi: 10.1007/s10658-015-0804-7
    Peccoud   J. , Labonne   G. , Sauvion   N. 2013. . Molecular test to assign individuals within the Cacopsylla pruni complex. PLOS One 8: e72454 doi: 10.1371/journal.pone.0072454
    Peccoud   J. , Pleydell   D. R.J. , Sauvion   N. 2018. . A framework for estimating the effects of sequential reproductive barriers: implementation using Bayesian models with field data from cryptic species. Evolution 72: 2503-2512 doi: 10.1111/evo.13595
    Rwomushana   I. , Khamis   F. M. , Grout   T. G. , Mohamed   S. A. , Stamou   M. , Borgemeister   C. , Heya   H. M. , Tanga   C. M. , Nderitu   P. W. , Seguni   Z. S. 2017. . Detection of Diaphorina citri Kuwayama (Hemiptera: Liviidae) in Kenya and potential implication for the spread of Huanglongbing disease in East Africa. Biological Invasions 19: 2777-2787 doi: 10.1007/s10530-017-1502-5
    Sabaté   J. , Lavina   A. , Batlle   A. 2016. . Incidence and distribution of 'Candidatus Phytoplasma prunorum' and its vector Cacopsylla pruni in Spain: an approach to the epidemiology of the disease and the role of wild Prunus. Plant Pathology 65: 837-846 doi: 10.1111/ppa.12464
    Sauvion   N. , Lachenaud   O. , Mondor-Genson   G. , Rasplus   J. - Y. , Labonne   G. 2009. . Nine polymorphic microsatellite loci from the psyllid Cacopsylla pruni (Scopoli), the vector of European stone fruit yellows. Molecular Ecology Resources 9: 1196-1199 doi: 10.1111/j.1755-0998.2009.02604.x
    Shimwela   M. M. , Narouei-Khandan   H. A. , Halbert   S. E. , Keremane   M. L. , Minsavage   G. V. , Timilsina   S. , Massawe   D. P. , JB   Jones , van Bruggen   A. H.C. 2016. . First occurrence of Diaphorina citri in East Africa, characterization of the Ca. Liberibacter species causing huanglongbing (HLB) in Tanzania, and potential further spread of D. citri and HLB in Africa and Europe. European Journal of Plant Pathology 146: 349-368 doi: 10.1007/s10658-016-0921-y
    Steffek   R. , Swen Follak   S. , Sauvion   N. , Labonne   G. , MacLeod   A. 2012. . Distribution of 'Candidatus Phytoplasma prunorum' and its vector Cacopsylla pruni in European fruit growing areas: a literature survey. EPPO Bulletin 42: 191-202 doi: 10.1111/epp.2567
    Syfert   M. M. , Serbina   L. , Burckhardt   D. , Knapp   S. , Percy   D. M. 2017. . Emerging new crop pests: ecological modelling and analysis of the south American potato psyllid Russelliana solanicola (Hemiptera: Psylloidea) and its wild relatives. PLoS One 12: e0167764 doi: 10.1371/journal.pone.0167764
    Thébaud   G. , Sauvion   N. , Chadoeuf   J. , Dufils   A. , Labonne   G. 2006. . Identifying risk factors for European stone fruit yellows from a survey. Phytopathology 96: 890-899 doi: 10.1094/PHYTO-96-0890
    Thébaud   G. , Yvon   M. , Alary   R. , Sauvion   N. , Labonne   G. 2009. . Efficient Transmission of 'Candidatus Phytoplasma prunorum' is delayed by eight months due to a long latency in its host-alternating vector. Phytopathology 99: 265-273 doi: 10.1094/PHYTO-99-3-0265
    Venette   R. C. , Kriticos   D. J. , Magarey   R. D. , Koch   F. H. , Baker   R. H.A. , Worner   S. P. , Gomez Raboteaux   N. N. , McKenney   D. W. , Dobesberger   E. J. , Yemshanov   D. 2010. . Pest risk maps for invasive alien species: a roadmap for improvement. BioScience 60: 349-362 doi: 10.1525/bio.2010.60.5.5
    Wieczorek   J. , Bloom   D. , Guralnick   R. , Blum   S. , Dring   M. , Giovanni   R. , Robertson   T. , Vieglais   D. 2012. . Darwin Core: An evolving community-developed biodiversity data standard. PLoS One 7: 1 e29715 doi: 10.1371/journal.pone.0029715

Floating objects

Figure 1.
Excerpt from a 1974 article from Loginova referring to Cacopsylla pruni , with translation and information about one of the localities cited, Hamar data. After Loginova (1974).
Figure 2.
Global map of the 1716 occurrence data available in the C. pruni dataset (map generated with QGIS 3.14). The map shows the distribution of cryptic species A (green dots) and B (red dots) according to available data. However, most of the data from the literature (black dots), GBIF (orange dots) or the Psylloidea catalogue of the "Faune de France" (currently being published) do not allow a distinction to be made between cryptic species.
Figure 3.
Occurrence data of Cacopsylla pruni in the Western Palaearctic, obtained from our literature survey (map generated with QGIS 3.14).
Figure 4.
Examples of metadata accessible on the website of the Natural History Museum from links associated with GBIF references (e.g.
Figure 5.
Occurrence data of Cacopsylla pruni in Western Palaearctic from the GBIF database (map generated with QGIS 3.14).
Figure 6.
Occurrence data of species of Cacopsylla pruni A in Western Palaearctic from sampling carried out by INRAE-Montpellier (map generated with QGIS 3.14).
Figure 7.
Occurrence data of species of Cacopsylla pruni B in Western Palaearctic from sampling carried out by INRAE-Montpellier (map generated with QGIS 3.14). is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. data for the two cryptic species of <i/> (: )&author=&keyword= Hemiptera ,psyllid,Cacopsylla pruni,vector-borne plant pathogen,phytoplasma,'Candidatus phytoplasma prunorum',European stone fruit yellows,species distribution,epidemiology,&subject=Data Paper (Biosciences),Insecta,Hemiptera,Psylloidea,Sternorrhyncha,Biogeography,Horticulture,Agricultural ecology,Diseases & Pests,Zoology & Animal Biology,Evolutionary biology,Data analysis & Modelling,Systematics,Ecology & Environmental sciences,Biodiversity & Conservation,Africa,Asia,Europe,Americas,