# VectorNet Data Series 3: Culicoides Abundance Distribution Models for Europe and Surrounding Regions

Thomas Balenghien, Neil Alexander, Auður Lilja Arnþórsdóttir, Marina Bisia, Alison Blackwell, René Bødker, Maria Bourquia, Sofia Boutsini, Simon Carpenter, Claire Colenutt, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Thomas Balenghien, Neil Alexander, Auður Lilja Arnþórsdóttir, Marina Bisia, Alison Blackwell, et al.. VectorNet Data Series 3: Culicoides Abundance Distribution Models for Europe and Surrounding Regions. Open Health Data, 2020, 7, 10.5334/ohd.33. [https://hal.inrae.fr/hal-03897222](https://hal.inrae.fr/hal-03897222)

HAL Id: hal-03897222

https://hal.inrae.fr/hal-03897222

Submitted on 15 Dec 2022

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DATA PAPER

# VectorNet Data Series 3: *Culicoides* Abundance Distribution Models for Europe and Surrounding Regions

Thomas Balenghien<sup>1,2,3</sup>, Neil Alexander<sup>4</sup>, Auður Lilja Arnþórsdóttir<sup>5</sup>, Marina Bisia<sup>6</sup>, Alison Blackwell<sup>7</sup>, René Bødker<sup>8</sup>, Maria Bourquia<sup>9</sup>, Sofia Boutsini<sup>4</sup>, Simon Carpenter<sup>10</sup>, Claire Colenutt<sup>10</sup>, Lorna Culverwell<sup>11</sup>, Aleksandar Cvetkovik<sup>12</sup>, Lenuťa Dascălu<sup>13</sup>, Nick De Regge<sup>14</sup>, Sofie Dhollander<sup>15</sup>, Armin Elbers<sup>16</sup>, Marion England<sup>10</sup>, Serhii Filatov<sup>17</sup>, Claire Garros<sup>18,2</sup>, Maria Goffredo<sup>19</sup>, Nabil Haddad<sup>20</sup>, Toke Thomas Høye<sup>21</sup>, Doru Hristescu<sup>13</sup>, Khalid Khallaayoune<sup>9</sup>, Alica Kočišová<sup>22</sup>, Magdalena Larska<sup>23</sup>, Javier Lucientes<sup>24</sup>, Bruno Mathieu<sup>25</sup>, Miguel Angel Miranda<sup>26</sup>, Archie Murchie<sup>27</sup>, Cristina Nițescu<sup>13</sup>, Zanda Ozoliņa<sup>28</sup>, Isabel Pereira da Fonseca<sup>29</sup>, Dušan Petrić<sup>30</sup>, Dubravka Pudar<sup>30</sup>, David Ramilo<sup>29</sup>, Jane Richardson<sup>15</sup>, Zanda Seglina<sup>28</sup>, Soufien Sghaier<sup>31</sup>, Jovana Stefanovska<sup>12</sup>, Despoina Stougiou<sup>6</sup>, Ståle Sviland<sup>32</sup>, Simona Tchakarova<sup>33</sup>, Wim Van Bortel<sup>34,35</sup>, Marta Verdun Castello<sup>36</sup>, Eva Veronesi<sup>37</sup>, Veerle Versteirt<sup>38</sup> and William G.R. Wint<sup>4</sup>

<sup>1</sup> CIRAD, UMR ASTRE, M-10100 Rabat, MA

<sup>2</sup> ASTRE, CIRAD, INRA, Univ Montpellier, Montpellier, FR

<sup>3</sup> MIMC, IAV Hassan II, 10100 Rabat-Instituts, MA

<sup>4</sup> Environmental Research Group Oxford (ERGO), Department of Zoology, Oxford, OX1 3PS, UK

<sup>5</sup> Icelandic Food And Veterinary Authority, Selfoss, IS

<sup>6</sup> Veterinary Centre of Athens, Athens, GR

<sup>7</sup> APS Biocontrol Ltd, Dundee, GB

<sup>8</sup> University of Copenhagen, Copenhagen, DK

<sup>9</sup> Institut Agronomique et Vétérinaire Hassan II, Rabat, MA

<sup>10</sup> The Pirbright Institute, Pirbright, UK

<sup>11</sup> University of Helsinki, Medicum, Department of Virology, Haartmaninkatu 3, Helsinki 00014, FI

<sup>12</sup> Faculty of veterinary medicine, Skopje, MK

<sup>13</sup> Institute for Diagnosis and Animal Health, București, RO

<sup>14</sup> Sciensano, Brussels, BE

<sup>15</sup> European Food Safety Agency, Parma, IT

<sup>16</sup> Wageningen Bioveterinary Research, Lelystad, NL

<sup>17</sup> Institute of Experimental and Clinical Veterinary Medicine, Kharkiv, UA

<sup>18</sup> CIRAD, UMR ASTRE, 97490 Ste Clotilde, La Réunion, FR

<sup>19</sup> Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise 'G. Caporale', Teramo, IT

<sup>20</sup> Lebanese University - Faculty of Public Health, Beirut, LB

<sup>21</sup> Department of Bioscience and Arctic Research Centre, Aarhus University, Aarhus, DK

<sup>22</sup> University of Veterinary Medicine and Pharmacy, Košice, SK

<sup>23</sup> National Veterinary Research Institute, Puławy, PL

<sup>24</sup> Facultad de Veterinaria. IUI Mixto Agroalimentario de Aragón IA2, Zaragoza, ES

<sup>25</sup> Institut de Parasitologie et de Pathologie Tropicale, EA7292, Strasbourg, FR

<sup>26</sup> University of the Balearic Islands, Majorca, ES

<sup>27</sup> Agri-Food and Biosciences Institute, Northern Ireland, UK

<sup>28</sup> Institute of Food safety, Animal Health and Environment 'BIOR', Riga, LV

<sup>29</sup> CIISA - Centro de Investigação Interdisciplinar em Sanidade Animal, Faculdade de Medicina Veterinária, Universidade de Lisboa, Lisboa, PT

<sup>30</sup> University of Novi Sad, Faculty of Agriculture, Novi Sad, RS

<sup>31</sup> Institut de la Recherche Vétérinaire de Tunisie, Tunis, TN

<sup>32</sup> Norwegian Veterinary Institute, Oslo, NO

<sup>33</sup> National Diagnostic and Research Veterinary Medical Institute, Sofia, BG

<sup>34</sup> European Center for Disease Control, Stockholm, SE
This is the third in a planned series of data papers presenting modelled vector distributions produced during the ECDC and EFSA funded VectorNet project. The data package presented here includes those *Culicoides* vectors species first modelled in 2015 as part of the VectorNet gap analysis work namely *C. imicola*, *C. obsoletus*, *C. scoticus*, *C. dewulfi*, *C. chiopterus*, *C. pulicaris*, *C. lupicaris*, *C. punctatus*, and *C. newsteadi*. The known distributions of these species within the Project area (Europe, the Mediterranean Basin, North Africa, and Eurasia) are currently incomplete to a greater or lesser degree. The models are designed to fill the gaps with predicted distributions, to provide a) first indication of vector species distributions across the project geographical extent, and b) assistance in targeting surveys to collect distribution data for those areas with no field validated information. The models are based on input data from light trap surveillance of adult *Culicoides* across continental Europe and surrounding regions (71.8°N -33.5°S, -11.2°W - 62°E), concentrated in Western countries, supplemented by transect samples in eastern and northern Europe. Data from central EU are relatively sparse.

**Keywords:** VECTORNET; Midges; *Culicoides*; species; distribution; Boosted Regression Trees; Random Forest; Model ensembles

**Funding statement:** This work was carried out with support from the VectorNet framework contract OC/EFSA/AHAW/2013/02-FWC1 funded by the European Centre for Disease prevention and Control (ECDC) and the European Food Safety Agency (EFSA) and the PALE-Blu H2020 Project ID: 727393.

# 1. Overview

## Introduction/Study Description

VectorNet [1] is a joint initiative of the European Food Safety Authority (EFSA) and the European Centre for Disease Prevention and Control (ECDC), which started in May 2014. The project supports the collection of distribution data on tick, sandfly, mosquito and *Culicoides* midge vectors, related to both animal and human health.

While VectorNet and its predecessor VBORNET [2] have made substantial progress collating European data on key vector species, the coverage is still incomplete. The 'Gap Analysis' work within these projects aims to identify those areas of likely species distribution within the project extent where there are no current data. These estimates were produced throughout the project and were intended to meet two objectives: firstly to help direct extensive VectorNet sampling efforts in the field, and secondly to provide first indications of the current likely extent and distribution of key vector species within continental Europe and its surrounding regions. The models provided here are the latest iteration using the distribution data available at the end of 2018. It is hoped that publishing these models will aid experts to engage the more extensive research and professional community in the drive to expand and validate the VectorNet database, and will also contribute to the veterinary and public health planning for Europe and its neighbouring countries. Readers are encouraged to contact the authors or visit the VectorNet website [1] for further details of the project, and to view distribution maps of arthropod disease vectors of midges, ticks, mosquitos, and sandflies.

For each model, abundance maps with a resolution of 1 km were generated using both Boosted regression trees and Random Forest spatial modelling techniques available through the VECMAP [3] system. The outputs from each technique were ensembled to create a 'consensus' output of Ln Maximum Annual number per trap per day.

# 2. Context

## Spatial coverage

Description: Continental Europe and surrounding regions

Northern boundary: 71.8

Southern boundary: 33.5

Eastern boundary: 62.3

Western boundary: -11.2

## Temporal coverage

(01/04/2014 – 01/05/2018).

## Species

*Culicoides imicola* Kieffer, *Culicoides obsoletus* (Meigen), *Culicoides scoticus* Downes and Kettle, *Culicoides dewulfi* Goetghebuer, *Culicoides chiopterus* (Meigen), *Culicoides pulicaris* (Linnaeus), *Culicoides lupicaris* Downes and Kettle, *Culicoides punctatus* (Meigen) and *Culicoides newsteadi* Austen.

*Culicoides imicola* is a proven bluetongue virus (BTV) vector species as a livestock-associated species, as numerous isolations of the virus have been made from field-collected individuals, and as the entire transmission cycle was reproduced experimentally for this species [4, 5]. The other listed species belonging to the *Avaritia* and *Culicoides* subgenera

<footnote n="35">³⁵ Institute of Tropical Medicine, Antwerp, BE</footnote>

<footnote n="36">³⁶ CReSA-IRTA, Universitat Autònoma de Barcelona, Bellaterra, ES</footnote>

<footnote n="37">³⁷ National Centre for Vector Entomology, Institute of Parasitology, University of Zürich, Zürich, CH</footnote>

<footnote n="38">³⁸ Avia-GIS, Zoersel, Belgium; Flemish Agency for Nature and Forest, Brussels, BE</footnote>

<footnote>Corresponding author: Neil Alexander (neil.alexander@zoo.ox.ac.uk)</footnote>
are considered probable vectors based on their ecological
habits, on virus isolation or viral genome detections from
field-collected individuals and on experimental infections.
BTV was isolated from field-collected *C. obsoletus* [6–8]
and *C. pulicaris* [9] – it was however not clear if these taxa
referred to species or group of species. BTV-8 genome from
*C. dewulfi* and *C. chiopterus* field individuals has been iden-
tified by real-time RT-PCR in the Netherlands [10, 11] and
in France [12]. In the Basque country, BTV-1 genome was
detected by real-time RT-PCR from *C. obsoletus*/*C. scoti-
cus*, *C. pulicaris* and *C. lupicaris* parous females [13].
*Culicoides* *obsoletus* and *C. scoticus* from the United
Kingdom have been experimentally infected by BTV-8 and
BTV-9, *C. scoticus* showing higher viral titers [14]. Pools of
*C. pulicaris* were found infected with BTV-2 in Sicily [15],
and BTV genome was detected in *C. punctatus* and *C. new-
steadi* field-collected specimens in Italy [16].

**3. Methods**

Steps

The series of procedures followed to produce the data-
set. This should include any source data used, as well as
software and instrumentation involved.

Model training data

The reported distributions of each vector species held in
the VectorNet archive on May 2018 were used as the basis
for species present training data for the analysis. They
were formally released to the authors on request to ECDC
(reference number 18-1421).

The raw input data was provided by light trap surveil-
lance of adult *Culicoides* set up mostly in ruminant farms
across continental Europe and surrounding regions
(72N-33.5S, -11.2W – 62E), concentrated in Western
countries, supplemented by transect samples in eastern
and northern Europe. Data from central EU are relatively
sparse (see maps Appendix 1). These data were obtained
either from National surveillance systems or from surveys
carried out by the VectorNet project. Species were iden-
tified using a morphological identification key [17] from
field collections or, in some case, retrospectively from
stored collections from National surveillance systems.

Midge abundance varies throughout the year, and sev-
eral metrics may be used to represent abundance. The
one used here for every species is the mean annual maxi-
mum number per trap per day. Data was used only from
locations that were sampled with at least one collection
per month throughout the season of the peak of abun-
dance. If data from more than a single year was available,
the annual average was used. For each species zero values
from the abundance datasets were included in the input
data, but were not supplemented by zero values for which
only presence/absence data were available. These values
represent a standardised measure of abundance at the
annual resolution, and so represent one aspect of abso-
lute abundance. They are not, however comparable with
traditional absolute abundance measures as they are not
associated with a specific date.

Maps of the recorded distributions at that time are pre-
sented as overlays to the model outputs, in Appendix 1
available within this data package.

Modelling procedure

A range of modelling techniques are available in the VECMAP [3] system, of which Boosted Regression Trees (BRT) and Random Forest (RF) [18], using 10–25 repeated bootstraps per replicate, were used. Five replicates were implemented for each method. Each model was run using a 25% holdback for validation, but which also ensured variability between replicates. BRT model parameters were adjusted to result in 1000 trees; the RF parameters were set to the system defaults = namely 100 trees, the best 15% of the available covariates, and each tree using approximately 70% of available sample data with replacement. An ensembled average (and an associated standard deviation image) was then produced from the ten replicates. The standard deviation maps provide useful indicators of uncertainty in the model outputs.

The covariates offered to the modelling procedures
were drawn from a standardised set of environmental
parameters, and in particular a suite of Fourier processed
MODIS satellite imagery [19] which provides a range of
biologically interpretable variables related to levels and
seasonality of temperature and vegetation related factors
during the period 2001–2015. These are summarised in
**Table 1** and are all available to registered members of
the PALE-Blu Data Website [20]. Each BRT model was run
with the top ten predictors identified in the trial model
runs for each species, which are listed at the end of
Appendix 1.

Quality Control

As indicated above, only raw data with sufficient samples
per site to ensure reliability were used as model inputs.
The model outputs were evaluated using the standard,
and very extensive, accuracy metrics (e.g. R-squared, AIC,
Kappa, Confusion matrices) provided by the VECMAP [3]
software. Providing the accuracy metrics indicated suf-
ficient statistical reliability, the outputs were ensembled
as described above. AUCs for the training sets for all the
models exceed 0.85.

Sampling strategy

The abundance data used to train the maps were collected
by longitudinal UV-light trap collections, a method com-
monly used to survey adult *Culicoides* populations at a
wide scale. The reliability of UV-light trap collections to
assess the 'aggressive density' on animals (which is the
abundance parameter related to the risk of transmis-
sion) is still under debate and may be species dependent
[24-28]. However, it is worth highlighting that abun-
dances assessed by UV-light traps have been used for more
than a decade to manage animal movements under EU
regulations, and that this system has demonstrated its
utility.

Constraints

There were no constraints in data production.

Privacy

Not applicable. No human data were used in the analyses
or are provided in these datasets.
<table><caption>**Table 1:** Covariates offered to modelling procedures.</caption><tr><td>1</td><td>ER011503A0: Middle infra-red mean</td><td>38</td><td>ER011514P2: NDVI phase 2</td></tr><tr><td>2</td><td>ER011503A1: Middle infra-red amplitude 1</td><td>39</td><td>ER011514P3: NDVI phase 3</td></tr><tr><td>3</td><td>ER011503A2: Middle infra-red amplitude 2</td><td>40</td><td>ER011514VR: NDVI variance</td></tr><tr><td>4</td><td>ER011503A3: Middle infra-red amplitude 3</td><td>41</td><td>ER011515A0: EVI mean</td></tr><tr><td>5</td><td>ER011503MN: Middle infra-red minimum</td><td>42</td><td>ER011515A1: EVI amplitude 1</td></tr><tr><td>6</td><td>ER011503MX: Middle infra-red maximum</td><td>43</td><td>ER011515A2: EVI amplitude 2</td></tr><tr><td>7</td><td>ER011503P1: Middle infra-red phase 1</td><td>44</td><td>ER011515A3: EVI amplitude 3</td></tr><tr><td>8</td><td>ER011503P2: Middle infra-red phase 2</td><td>45</td><td>ER011515MN: EVI minimum</td></tr><tr><td>9</td><td>ER011503P3: Middle infra-red phase 3</td><td>46</td><td>ER011515MX: EVI maximum</td></tr><tr><td>10</td><td>ER011503VR: Middle infra-red variance</td><td>47</td><td>ER011515P1: EVI phase 1</td></tr><tr><td>11</td><td>ER011507A0: Daytime LST mean</td><td>48</td><td>ER011515P2: EVI phase 2</td></tr><tr><td>12</td><td>ER011507A1: Daytime LST amplitude 1</td><td>49</td><td>ER011515P3: EVI phase 3</td></tr><tr><td>13</td><td>ER011507A2: Daytime LST amplitude 2</td><td>50</td><td>ER011515VR: EVI variance</td></tr><tr><td>14</td><td>ER011507A3: Daytime LST amplitude 3</td><td>51</td><td>EDV590EL: DEM (Elevation)</td></tr><tr><td>15</td><td>ER011507MN: Daytime LST minimum</td><td>52</td><td>EDV590RG: DEM (Ruggedness)</td></tr><tr><td>16</td><td>ER011507MX: Daytime LST maximum</td><td>53</td><td>ERPRECA0: WORLDCLIM precipitation mean</td></tr><tr><td>17</td><td>ER011507P1: Daytime LST phase 1</td><td>54</td><td>ERPRECA1: WORLDCLIM precipitation amplitude 1</td></tr><tr><td>18</td><td>ER011507P2: Daytime LST phase 2</td><td>55</td><td>ERPRECA2: WORLDCLIM precipitation amplitude 2</td></tr><tr><td>19</td><td>ER011507P3: Daytime LST phase 3</td><td>56</td><td>ERPRECA3: WORLDCLIM precipitation amplitude 3</td></tr><tr><td>20</td><td>ER011507VR: Daytime LST variance</td><td>57</td><td>ERPRECMN: WORLDCLIM precipitation minimum</td></tr><tr><td>21</td><td>ER011508A0: Nighttime LST mean</td><td>58</td><td>ERPRECMX: WORLDCLIM precipitation maximum</td></tr><tr><td>22</td><td>ER011508A1: Nighttime LST amplitude 1</td><td>59</td><td>ERPRECP1: WORLDCLIM precipitation phase 1</td></tr><tr><td>23</td><td>ER011508A2: Nighttime LST amplitude 2</td><td>60</td><td>ERPRECP2: WORLDCLIM precipitation phase 2</td></tr><tr><td>24</td><td>ER011508A3: Nighttime LST amplitude 3</td><td>61</td><td>ERPRECP3: WORLDCLIM precipitation phase 3</td></tr><tr><td>25</td><td>ER011508MN: Nighttime LST minimum</td><td>62</td><td>ERPRECVR: WORLDCLIM precipitation variance</td></tr><tr><td>26</td><td>ER011508MX: Nighttime LST maximum</td><td>63</td><td>ERXXGRPD: GRUMP Human Population density</td></tr><tr><td>27</td><td>ER011508P1: Nighttime LST phase 1</td><td>64</td><td>ERV59EL500: SRTM Elevation</td></tr><tr><td>28</td><td>ER011508P2: Nighttime LST phase 2</td><td>65</td><td>EREELCBARE: consensus % bare ground</td></tr><tr><td>29</td><td>ER011508P3: Nighttime LST phase 3</td><td>66</td><td>EREELCDCBD: consensus % deciduous broadleaved forest</td></tr><tr><td>30</td><td>ER011508VR: Nighttime LST variance</td><td>67</td><td>EREELCEVBD: consensus % evergreen broadleaved forest</td></tr><tr><td>31</td><td>ER011514A0: NDVI mean</td><td>68</td><td>EREELCEVBD: consensus % evergreen needleleaved forest</td></tr><tr><td>32</td><td>ER011514A1: NDVI amplitude 1</td><td>69</td><td>EREELCFLD: consensus % flooded</td></tr><tr><td>33</td><td>ER011514A2: NDVI amplitude 2</td><td>70</td><td>EREELCHERB: consensus % herbaceous cover</td></tr><tr><td>34</td><td>ER011514A3: NDVI amplitude 3</td><td>71</td><td>EREELCMANG: consensus % managed land</td></tr><tr><td>35</td><td>ER011514MN: NDVI minimum</td><td>72</td><td>EREELCOTR: consensus % other land cover</td></tr><tr><td>36</td><td>ER011514MX: NDVI maximum</td><td>73</td><td>EREELCSHR: consensus % shrub cover</td></tr><tr><td>37</td><td>ER011514P1: NDVI phase 1</td><td>74</td><td>EREELCURB: consensus % urban</td></tr><tr><td></td><td></td><td>75</td><td>EREELCSNOW: consensus % snow</td></tr><tr><td></td><td></td><td>76</td><td>EREELCWAT: consensus % water</td></tr></table>

LST = Land Surface Temperature. NDVI Normalised Difference vegetation Index; EVI Enhanced Vegetation Index. DEM Digital Elevation. All files starting with ER0115 are Fourier processed MODIS Satellite Imagery produced by the Environmental Research Group Oxford [19].

Files with Worrclim in filename derived from WORLCLIM datasets [21].

GRUMP derived from population layers produced by [22].

All Files with EREELC in file name were derived from the Earthenv consensus land cover data product [23].

All layers extracted and standardised by ERGO for PALEBLU (www.palebludata.com) [20].
**Ethics**

Not Applicable – no personal data has been provided, and no animal welfare constraints apply to entomological sampling.

**4. Dataset description**

**Object name**

VectorNet/PALE-Blu Midge Abundance Models

**Data type**

Processed data; Interpretation of data

**Ontologies**

N/A.

**Format names and versions**

JPG, TIF, TFW, DOCX

**Creation dates**

The start and end dates of when the data was created
01052018 – 01042019.

**Dataset creators**

The modelling work was led by William Wint (ERGO, the Environmental Research Group Oxford) using data assembled and processed by Thomas Balenghien (CIRAD) and provided by the authors listed above together with additional collaborators of the VectorNet project as listed, with literature sources in the table in Appendix 2.

**Language**

English

**Programming language**

N/A.

**Licence**

The open licence under which the data has been deposited CC-BY 4.

**Accessibility criteria**

The data are distributed as GIS raster GeoTIFF formats, which is a standard proprietary GIS raster format. To access and analyze the raster data directly GeoTIFFs can be read by most GIS software and some other software packages. These formats are compatible with proprietary (ESRI ArcGIS) and open source Quantum GIS (QGIS) or (R-project raster package). If the user has no suitable software already installed, the authors suggest downloading the open source QGIS software free of charge from http://www.qgis.org to view these data.

A simple schematic of the data layers and directories found within this data package is shown below with descriptions where filenames are not self-explanatory:

* **Appendices** – Zipfile containing the appendices for this document.

* **ohd_VNMIDGESV1Appendix1.Pdf:** document with quick looks of ensemble models with and without training data, and a summary of best covariate predictors

* **ohd_VNMIDGESV2Appendix2.Pdf.** Full list of training data sources

* **Model output ZIPS** – Each zip contains 1) geotiffs of ensemble model mean, standard deviation, for display and interrogation within GIS and geostatistical software*; and 2) the quicklook jpg format graphics for display in word processors and the like. Zip file names as follows:

* **chiopterusensemblemay18.zip.** Files for *Culicoides chiopterus*

* **obsoletusandscoticusensemblemay18.zip.** Files for *Culicoides obsoletus/Culicoides scoticus*

* **dewulfiensemblemay18.zip.** Files for model of *Culicoides dewulfi*

* **imicolaensemblemay18.zip.** Files for model of *Culicoides imicola*

* **pulicarisensemblemay18.zip.** Files for model of *Culicoides pulicaris*

* **lupicarisensemblemay18.zip.** Files for model of *Culicoides lupicaris*

* **pulicarisandlupicarisensemblemay18.zip.** Files for model of *Culicoides pulicaris/lupicaris*

* **punctatusensemblemay18.zip.** Files for model of *Culicoides punctatus*

* **newsteadiensemblemay18.zip.** Files for model of *Culicoides newsteadi*

*Only the .tif files within this directory are listed. Other file formats of the same name within the directory (e.g. .tfw) are ancillary files that provide additional data to the GIS software and as a rule should be copied along with the TIFF file of the same name if you are moving the data between directories

**Repository location**

https://doi.org/10.6084/m9 figshare.12932844

**Publication date**

09/09/2020

**5. Reuse potential**

Please briefly (approx. 50–200 words) describe the ways in which your data could be reused by other researchers both within and outside of your field. This might for example include aggregation, further analysis, reference, validation, teaching or collaboration.

These layers have been created in an attempt to identify probable areas of species distribution where there are currently no sample data. These maps, therefore, attempt to identify the actual distribution of each species and so could be useful in identifying areas at risk from the disease for which each species is a vector and to identify suitable areas for further sampling. The VectorNet project plans to utilise these datasets in such a way.

The covariates of the models are also mainly climate orientated. A possible avenue of further work, therefore, could be to use the models to assess the potential change in distribution after a shift in climate parameters.

**Acknowledgements**

The authors extend their thanks to the VectorNet management team at AVIA-GIS – especially Guy Hendrickx and Els
Ducheyne. This work would have been impossible without the information supplied so generously by the data providers and producers listed in Appendix 2. The authors would like to thank them once again for their support.

## Competing Interests

The authors have no competing interests to declare.

## Author Contributions

Thomas Balenghien and William G.R. Wint prepared this manuscript. Thomas Balenghien, Auður Lilja Arnþórsdóttir, Marina Bisia, Alison Blackwell, René Bodker, Maria Bourquia, Sofia Boutsini, Simon Carpenter, Claire Colenutt, Lorna Culverwell, Aleksandar Cvetkovikj, Lenuťa Dascălu, Nick De Regge, Armin Elbers, Marion England, Serhii Filatov, Claire Garros, Maria Goffredo, Nabil Haddad, Toke Thomas Høye, Doru Hristescu, Khalid Khallaayoune, Alica Kočišová, Magdalena Larska, Javier Lucientes, Bruno Mathieu, Miguel Angel Miranda, Archie Murchie, Cristina Nițescu, Zanda Ozoliņa, Isabel Pereira da Fonseca, Dušan Petrić, Dubravka Pudar, David Ramilo, Zanda Seglina, Soufien Sghaier, Jovana Stefanovska, Despoina Stougiou, Ståle Sviland, Simona Tchakarova, Marta Verdun Castello and Eva Veronesi provided distribution data. Sofie Dhollander, Jane Richardson, Wim Van Bortel and Veerle Versteirt manged the VectorNet project which funded the data collections. Thomas Balenghien processed and filtered these data to generate the training set for the distribution modelling. Neil Alexander and William G.R. Wint collated the covariate data. William G.R. Wint implemented the modelling and prepared this manuscript.

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**How to cite this article:** Balenghien, T, Alexander, N, Arnþórsdóttir, AL, Bisia, M, Blackwell, A, Bødker, R, Bourquia, M, Boutsini, S, Carpenter, S, Colenutt, C, Culverwell, L, Cvetkovikj, A, Dascălu, L, De Regge, N, Dhollander, S, Elbers, A, England, M, Filatov, S, Garros, C, Goffredo, M, Haddad, N, Høye, TT, Hristescu, D, Khallaayoune, K, Kočičová, A, Larska, M, Lucientes, J, Mathieu, B, Miranda, MA, Murchie, A, Niţescu, C, Ozoliņa, Z, da Fonseca, IP, Petrić, D, Pudar, D, Ramilo, D, Richardson, J, Seglina, Z, Sghaier, S, Stefanovska, J, Stougiou, D, Sviland, S, Tchakarova, S, Van Bortel, W, Castello, MV, Veronesi, E, Versteirt, V and Wint, WGR. 2020. VectorNet Data Series 3: *Culicoides* Abundance Distribution Models for Europe and Surrounding Regions. *Open Health Data*, 7: 2. DOI: https://doi.org/10.5334/ohd.33

**Published:** 16 September 2020

**Copyright:** © 2020 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.

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