DOI: 10.5061/dryad.9p8cz8ws7
title: Infestation rates of winter fleshy-fruited plants by the invasive fruit fly Drosophila suzukii in Amiens, France, 2022

description: This study aimed to disentangle the relative roles of climatic, landscape
 and local factors affecting fruit infestation rates of winter and spring
 host plants by the invasive fruit fly Drosophila suzukii. We assessed
 infestation in Aucuba japonica, Elaeagnus ×submacrophylla (syn. Elaeagnus
 ×ebbingei), Mahonia aquifolium, M. japonica and Viscum album fruit in the
 north of France, between January and July 2022.
description: Fruit infestation rates of four fleshy-fruited plant species by
 the invasive fly Drosophila suzukii were measured near Amiens city in
 northern France between January and July 2022. Each sample was accompanied
 by measurements of climatic and local and landscape variables in order to
 identify the environmental drivers of host fruit infestations.
 <strong>Materials and Methods</strong>
 <em>Study area</em> The study was
 conducted between January and July 2022 in the region of Amiens (49°53′40″
 N, 2°18′07″ E) in northern France. The region’s climate is oceanic with a
 mean annual temperature of 10.7°C and an average annual rainfall of 691.9
 mm (data from meteorological station Dury-les-Amiens, StatIC network). The
 landscape of this region is generally characterised by agricultural
 production and consists of a mosaic of open fields cultivated for cereals,
 rapeseed and sugar beet, interspersed with orchards, grasslands, woodland
 patches and rivers. <em>Sampling
 design</em> <em>Aucuba japonica</em>
 var. <em>variegata</em>, <em>Elaeagnus
 ×submacrophylla</em>, <em>Mahonia aquifolium</em>,
 <em>Mahonia japonica</em> agg. (including a complex of related
 cultivars and hybrids) and <em>V. album</em> fruit were
 sampled every month, with individual plants of each species randomly
 sampled within a landscape area of 35 <em>×</em> 45 km. The
 number of individuals sampled per species and per month varied depending
 on fruit availability. When possible, a minimum of 100 fruit were randomly
 sampled from each plant individual, with some variation depending on
 seasonal availability of fruit. <em>Traits of
 host plant species and fruit</em> Collected fruit
 were separated into three subsets to monitor
 <em>Drosophila</em> emergence: undamaged (‘healthy’) and
 damaged fruit collected on the plant (‘damaged’) and on the ground
 (‘ground’; if present). The fruit were categorized as ‘damaged’ when they
 were opened / injured (incised skin) and/or rotten (brown spots). Several
 traits of the sampled plants were measured to characterise the local
 resources available for the flies and the local microhabitat. For each
 sampled plant, five berries were randomly selected to measure length and
 width and calculate volume (4/3 × π × mean radius<sup>3</sup>)
 and fruit skin area (4 × π × mean radius<sup>2</sup>). Five
 leaves were also taken, their length and width measured and the leaf
 surface index (length × width; Ulmer et al. 2022) calculated. Individual
 morphology of each plant species was characterised by measuring the
 minimum and maximum plant canopy diameter, the circumference of the
 largest trunk (except for <em>V. album</em>) and the total
 number of fruit present on the plant. Mistletoe being a parasitic shrub,
 we also recorded the host tree species and measured the height of the
 mistletoe individual on the tree (from the ground), the tree height, the
 crown diameter, the trunk circumference, and the number of mistletoe
 individuals present on the host tree and in a 20 m
 radius around it. <em>Environmental
 variables </em> Local, landscape and climatic
 variables were measured at each sampling site or extracted from online
 databases to examine the influence of regional and local environmental
 conditions on infestation rates. Local environmental
 conditions were described as follows. First, within a 5 m radius plot
 centred on the sampled plant, the cover and height of the tree, shrub and
 herbaceous layers were estimated, as well as soil litter thickness .
 Second, within a 20 m radius, the percentage of local habitats surrounding
 the host was recorded (e.g., orchard, woodland, grassland, swamp, crop,
 garden, shrub, building, hedgerow, river, pond, poplar plantation, park,
 road), as well as the percentage of other plant species with maturing
 fruit. The landscape composition around each sampled
 plant was then characterised. A geographic database was created using a
 Geographic Information System (GIS; ArcGIS Pro v.2.5, ESRI). The sampled
 plants were positioned in the GIS and buffers of 50, 100, 250, 500, 750,
 1000, 1250, 1500, 1750, 2000, 2500 and 3000 m radii around each host tree
 were created for subsequent analyses of landscape composition. Landscape
 elements (crop, water, woodland, shrubland, grassland, road, urban area,
 orchard, industrial zone) were extracted from the OSO 2022 database (THEIA
 2023) and updated using aerial photographs and, in buffers &lt;100 m,
 field observations. Macroclimatic conditions were
 characterised for each sampling site using regional measurements. Daily
 meteorological data were retrieved from the three meteorological stations
 closest to each site, from 1 January 2022 to each sampling date
 (https://www.historique-meteo.net/france/). Daily minimum, mean and
 maximum temperatures, rainfall and snowfall were calculated for all sites
 using inverse-distance weighting (IDW) interpolation (Willmott et al.
 1985) from the data from the three nearest weather stations. Accumulated
 degree-days (“Growing Degree Days”, GDD) were calculated using a lower
 threshold of 0°C between 1 January 2022 and the sampling date (Baskerville
 and Emin 1969). The baseline value of 0°C is a standard threshold commonly
 used to calculate GDD in insect and plant studies (White et al. 2012;
 McNeil et al. 2020). It is particularly suitable to study the temporal
 synchrony between insects and plant resources (Iler et al. 2013; Ulmer et
 al. 2022). It was also chosen because (i) active <em>D. suzukii
 </em>can be observed even at very low positive temperature (&lt;
 5°C) during winter, including during periods of snowfall (Ulmer et al.
 2024), (ii) flies are able to recover from chill coma after exposure to
 –1°C (Wallingford et al. 2016) and (iii) mistletoe fruit can undergo
 freeze-thaw cycles before ripening ends (Thomas et al. 2023). From daily
 precipitation values, we also calculated mean daily and cumulative
 precipitation between 1 January 2022 and each sampling date, and within
 the 7- or 14-days periods preceding each sampling date.
 Microclimate temperatures were recorded at each sampling site
 using Hobo loggers (TIDBIT data logger V2 TEMP TBI-001, ONSET Company,
 Bourne MA, USA), recording every 60 min. In each sampling site a logger
 was suspended 1.5 m above the ground in the plant canopy, under the shade
 of a branch to avoid direct exposure to solar radiation and oriented
 northward. The minimum, mean and maximum air temperatures were extracted
 every day to compute the mean daily minimum, mean and maximum
 temperatures. As described for macroclimate data, GDD were calculated
 using Hobo logger data to characterise the local microclimate under the
 canopy of the sampled plants. <em>Emergence of
 </em>Drosophila<em> species</em>
 After collection, the fruit sampled from each plant were placed
 on wet cotton wool in cylindrical plastic transparent containers (diameter
 = 118 mm, height = 135 mm, volume = 1,476 cm<sup>3</sup>),
 covered with a nylon mesh, and maintained in a temperature-controlled room
 at 20°C under a 16:8 L:D regime. Adult flies emerging from the fruit were
 placed in 70% ethanol. They were identified to species level using Bächli
 et al. (2004) and specific criteria published for <em>D.
 suzukii</em> (Withers and Allemand 2012). Individuals of each
 species were sexed and counted using a Leica M205C stereomicroscope
 equipped with a Leica MC170 HD camera and the Leica Application Suite
 software. <em>Infestation
 variables</em> We examined the relationships
 between environmental variables and two common infestation variables that
 were either centred on the fruit (Fruit Infestation Rate for a given
 <em>Drosophila</em> species: FIR = 100 × number of emerged
 individuals from fruit collected from a given plant individual / total
 number of fruit collected from the same plant individual) or on the plant
 species (Plant Infestation Rate: PIR = 100 × number of infested plant
 individuals of a species in a month / total number of plant individuals of
 this same species sampled in a month). These variables can be interpreted
 as follows: FIR reflects the plant auto-contamination by the flies while
 PIR reflects fly dispersal between host plant individuals (e.g., when the
 FIR and PIR are both high, both auto-contamination within the plant
 individual and dispersal of the flies between plant individuals take
 place; when the FIR is high and the PIR is low, there is mostly plant
 auto-contamination; when the FIR is low and the PIR high, there is mostly
 fly dispersal; when both FIR and PIR are low, there is an absence of both
 auto-contamination and dispersal). These infestation variables were
 calculated for each fruit category (healthy or damaged fruit on the plant
 and fallen fruit on the ground) following Deconninck et al.
 (2024).
description: # Infestation rates of winter fleshy-fruited plants by the invasive fruit
 fly Drosophila suzukii in Amiens, France, 2022
 [https://doi.org/10.5061/dryad.9p8cz8ws7](https://doi.org/10.5061/dryad.9p8cz8ws7) ## Description of the data and file structure The dataset contains fruit infestation variables (fruit infestation rates, number of Drosophila individuals emerged from different categories of fruit) and environmental variables (local, landscape, meteorological variables). The dataset is an Excel file (XLSX) containing 3 spreadsheets: a spreadsheet with the description of variables and their codes, a spreadsheet with the data matrix and another sheet with the reference of the publication. ### Files and variables #### File: DATA\_Ecological\_Entomology.xlsx **Speadsheet 1: Variables description. This spreadsheet contains the description of variables and their codes;** **Speadsheet 2: DATA matrix. This spreadsheet contains the data matrix with the values of measured and calculated variables;** **Speadsheet 3: Reference. This spreadsheet contains the reference of the related manuscript.** ## Code/software The data can be viewed by any software able to use XLSX files. ## Access information Meteorological station data and landscape data were derived from the following publicly accessible open sources: * [https://www.historique-meteo.net/france/](https://www.historique-meteo.net/france/) * THEIA (2023) OSO 2022 database: product “OSO_20220101_VECTOR_departement_80”, Somme department, France, 1/01/2022, Centre d’Expertise Scientifique (CES) “Occupation des sols”, published: 2023-07-06; [https://www.theia-land.fr/product/carte-doccupation-des-sols-de-la-france-metropolitaine/](https://www.theia-land.fr/product/carte-doccupation-des-sols-de-la-france-metropolitaine/)