Risk assessment of Resseliella citrifrugis for the EU

Abstract

Following a request from the European Commission, the EFSA Panel on Plant Health performed a risk assessment of the citrus fruit midge Resseliella citrifrugis (Diptera: Cecidomyiidae), an oligophagous species, which feeds on fruits of Citrus spp., and is reported from China. The pest was temporarily regulated in October 2022 (Regulation (EU) 2022/1941, under Art. 30 (2016/2031)). The entry risk assessment focused on the citrus fruit pathway. Three scenarios were considered: A0 (current practice, i.e. regulated pest for the EU), A1 (deregulation) and A2 (A0 with additional stand-alone post-harvest cold treatment). Based on the outputs of the entry model, under scenario A0, slightly less than 40 potential founder populations per year are expected (median; 90%-uncertainty interval between about one per 30 years and about 3,000 per year). Under scenario A1, the risk of entry increases by about three times and reaches about 120 potential founder populations per year (median; 90%-uncertainty interval between about one per 10 years and about 9,000 per year). Compared to scenario A0, the risk of entry is orders of magnitude lower for scenario A2 (median = about one potential founder population per 120 years; 90%-uncertainty interval between one per about 600 million years and about two per year). The main uncertainties in the entry assessment are the probability of transfer, the RRO effectiveness (for scenario A2) and the disaggregation of consignments (transport of citrus fruit in boxes or lots to different locations). For all scenarios, the number of established populations is only slightly lower than the number of potential founder populations. Establishment is thus not expected to be a major constraint for this pest to then spread and cause impacts, despite the uncertainty about the pest thermal requirements. The median lag period between establishment and spread is estimated to be about 18 months (90%-uncertainty interval between about 7 and 54 months). After the lag period, the median rate of spread by flying and due to transport of harvested citrus fruit from orchards to packinghouses is estimated at about 100 km/year (90%-range between about 40 and 500 km/year). The main uncertainties in the spread assessment include the level of susceptibility of cultivars of different citrus species in the EU, the spread rate in China and the climate suitability of the initial spread focus in the EU. The median impact of R. citrifrugis in the EU citrus-growing area (proportion of infested citrus fruit out of harvested citrus fruit) is estimated at about 10% (90%-uncertainty interval between about 2% and 25%). Uncertainties affecting the impact assessment include the susceptibility of different citrus cultivars and the effect of the citrus fruit-harvesting season in the EU (mainly winter, the less suitable season for the pest).

Keywords: pathway model; pest prevalence; phytosanitary measures; risk assessment; uncertainty.

Figure 1

Prevalence of Resseliella citrifrugis as the rate (%) of citrus fruit infestation measured in a survey of 29 small orchards in Gangzhou, Jiangxi Province, China. Boxplot reports minimum, 1st quartile, median, 3rd quartile and maximum values. Points indicate individual observations. Data from table 5 of Xia et al. (2021)

Figure 2

Seasonality of import of citrus fruit from China to the EU (2019 (orange) and 2020 (blue), data from EUROSTAT)

Figure 3

Fitted distribution for prevalence at the origin (p_prevalence) defined as the proportion of infested citrus fruit (scenario A1)

Figure 4

Fitted distribution for prevalence at the origin (p_prevalence) defined as the proportion of infested citrus fruit (scenarios A0 and A2)

Figure 5

Trend analysis of the import into the EU of citrus fruit (tons) from China, based on 2011–2020 EUROSTAT data, over the 10 years of the PRA time horizon. Dashed and dotted lines indicate the 98% confidence and prediction intervals, respectively

Figure 6

Fitted distribution for trade flow (N_trade) for citrus fruit as tons per year (all scenarios)

Figure 7

Fitted distribution for sorting (p_sorting) as the proportion of infested fruit (scenario A1)

Figure 8

Fitted distribution for sorting (p_sorting) as the proportion of infested fruit (scenarios A0 and A2)

Figure 9

Fitted distribution for RRO_{effectiveness} as the proportion of infested citrus fruit removed with cold treatment (scenario A2)

Figure 10

Fitted distribution for the disaggregation factor (d) for citrus fruit (number of suitable locations for transfer to which one ton of infested citrus fruit is delivered) (all scenarios A0, A1 and A2)

Figure 11

Fitted distribution for the probability of transfer (p_transfer) for citrus fruit (all scenarios A0, A1 and A2)

Figure 12

Outcome of the model simulations for scenario A0 (regulated pest) showing the relative frequency and cumulative descending probability; log‐scale x‐axis, same x‐axis scale as Figures 13 and 14. The number of potential founder populations of Resseliella citrifrugis in the EU due to import of infested citrus fruit is estimated between about one per 30 years and about 3,000 per year with a 90% probability

Figure 13

Outcome of the model simulations for scenario A1 (deregulation) showing the relative frequency and cumulative descending probability; log‐scale x‐axis, same x‐axis scale as Figures 12 and 14. The number of potential founder populations of Resseliella citrifrugis in the EU due to import of infested citrus fruit is estimated between about one per 10 years and about 9,000 per year with a 90% probability

Figure 14

Outcome of the model simulations for scenario A2 (regulated status with additional RROs) showing the relative frequency and cumulative descending probability; log‐scale x‐axis, same x‐axis scale as Figures 12 and 13. The number of potential founder populations of Resseliella citrifrugis in the EU due to import of infested citrus fruit is estimated between about one per 600 million years and about two per year with a 90% probability

Figure 15

Correlation between the output variable (N_inf) and the parameters of the entry pathway model for scenario A0 (current practice, i.e. regulated status). The parameter p_sorting has a negative correlation coefficient as it is inserted in the model as (1 − p_sorting)

Figure 16

Correlation between the output variable (N_inf) and the parameters of the entry pathway model for scenario A1 (deregulation). The parameter p_sorting has a negative correlation coefficient as it is inserted in the model as (1 − p_sorting)

Figure 17

Correlation between the output variable (N_inf) of the parameters of the entry pathway model for scenario A2 (regulated status with additional RROs). RRO_{effectiveness} and p_sorting have negative correlation coefficients, as they are inserted in the model as (1 − RRO_{effectiveness}) and (1 − p_sorting)

Figure 18

Distribution of Mediterranean climate types in Europe and neighbouring regions. Hot summer‐Csa and warm summer‐Csb occur in citrus‐growing areas of the EU, whereas the cold summer‐Csc Mediterranean climate type is found in scattered high‐altitude locations along the west coasts of North and South America and does not occur in the EU. Note that in the area of current distribution of Resseliella citrifrugis (China, highlighted in red), Mediterranean climate types do not occur (Southern China is subtropical with hot summers with monsoon rains and mild winters)

Figure 19

European citrus‐growing areas based on data of crop area at NUTS 2 level (from EFSA PLH Panel, 2019a). Areas with lines indicate regions with no data. Areas in light grey are neighbouring countries not included in the analysis

Figure 20

Cold‐hardiness zones in Europe and neighbouring regions (Source: Wikimedia Commons)

Figure 21

Comparison of sum of degree days (base temperature = 0°C) between the regions with reports of Resseliella citrifrugis in China (red dots, right‐hand panel) and the EU citrus‐growing area (left‐hand panel). The map was made with CLIMEX which uses a 30‐year period centred around 1995 (from 1981 to 2010). The sum of degree days is calculated as the yearly average of the yearly degree day accumulation over the 30‐year period

Figure 22

Fitted distribution for the probability of establishment (p_estab) (all scenarios)

Figure 23

Outcome of the model simulations for scenario A0 (current practice, i.e. regulated status) showing the relative frequency and cumulative descending probability; log‐scale x‐axis (same x‐scale as in Figures 24–25). The number of established Resseliella citrifrugis populations is estimated between about one every 110 years and about 1,500 per year with a 90% probability

Figure 24

Outcome of the model simulations for scenario A1 (deregulation) showing the relative frequency and cumulative descending probability; log‐scale x‐axis (same x‐scale as in Figures 23 and 25). The number of established Resseliella citrifrugis populations is estimated between about one every 30 years and about 4,400 per year with a 90% probability

Figure 25

Outcome of the model simulations for scenario A2 (regulated status with additional RRO) showing the relative frequency and cumulative descending probability; log‐scale x‐axis (same x‐scale as in Figures 23–24). The number of established Resseliella citrifrugis populations is between one every about two billion years and about seven established populations per year with a 90% probability

Figure 26

Correlation with the output variable (N_est) of the model parameters for scenario A0 (current practice, i.e. regulated status)

Figure 27

Correlation with the output variable (N_est) of the model parameters for scenario A1 (deregulation)

Figure 28

Correlation with the output variable (N_est) of the model parameters for scenario A2 (regulated status with additional RRO)

Figure A.1

Comparison of elicited and fitted values/density function to describe the remaining uncertainties of the parameter

Figure A.2

Cumulative distribution function (CDF) of the parameter

Figure A.3

Comparison of elicited and fitted values/density function to describe the remaining uncertainties of the parameter

Figure A.4

Cumulative distribution function (CDF) of the parameter

Figure A.5

Comparison of elicited and fitted values/density function to describe the remaining uncertainties of the parameter

Figure A.6

Cumulative distribution function (CDF) of the parameter