Predicting the potential global distribution of an invasive alien pest Trioza erytreae (Del Guercio) (Hemiptera: Triozidae)

doi:10.21203/rs.3.rs-1341844/v1

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Research Article

Predicting the potential global distribution of an invasive alien pest Trioza erytreae (Del Guercio) (Hemiptera: Triozidae)

https://doi.org/10.21203/rs.3.rs-1341844/v1

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The impact of invasive alien pests on agriculture, food security, and biodiversity conservation has been worsened by climate change caused by the rising earth’s atmospheric greenhouse gases. The African citrus triozid, Trioza erytreae (Del Guercio; Hemiptera: Triozidae), is an invasive alien pest of all citrus species. It vectors the phloem-limited bacteria “Candidatus Liberibacter africanus” and “Ca. L. asiaticus”, the causal agents of citrus greening disease or huanglongbing (HLB). Understanding the global distribution of T. erytreae is critical for surveillance, monitoring, and eradication programs. Therefore, we combined geospatial and physiological data of T. erytreae to predict its global distribution using the CLIMEX model. The model’s prediction matches T. erytreae present-day distribution and shows that large areas in the Mediterranean region are suitable for the pest. The model predicts habitat suitability in the major citrus-producing countries, such as Brazil, China, India, and the USA. In the SRES A1B and A2 scenarios, the model predicts a reduction in habitat suitability from the current time to 2070. The findings show that global citrus production will continue to be threatened by T. erytreae. However, our study provides relevant information for biosecurity and risk assessment.

African citrus triozid

citrus greening disease

CLIMEX model

climate change

Biological invasion of agricultural ecosystems has increased due to global factors, such as weak economies, international trade, climate change, poor regulatory regimes, and environmental concerns ^1–4. These factors have compounded the impact of invasive species on sustainable agriculture production, food security, and biodiversity conservation^5–7. The African citrus triozid (ACT), Trioza erytreae (Del Guercio; Hemiptera: Triozidae), is an invasive alien pest of all citrus species. Trioza erytreae feeds on about 18 other non-citrus host plants, all belonging to the family Rutaceae ⁸. The pest spreads locally through natural dispersal up to about 1.5km per week⁹, but the biological invasion has been mainly through anthropogenic activities across regions. It is listed as an A1 quarantine pest by the European, Mediterranean Plant Protection Organization, the Caribbean Plant Protection Commission and Organismo Internacional Regional de Sanidad Agropecuaria¹⁰. Trioza erytreae was first reported on citrus in 1922 in the South African regions of the Eastern Cape and Stellenbosch ¹¹. Over 28 countries from Europe, Asia, and Africa, have reported Trioza erytreae ¹⁰. Several million citrus trees have been killed by T. erytreae and the disease it transmits¹², posing a threat to others.

Studies have shown that T. erytreae prefer cool and moist climates because extreme temperatures are detrimental to all stages ^13–14. Trioza erytreae induces direct damage to hosts through its feeding activities. The symptomatic shoots show pit-like galls and secretion of honeydew, which has been associated with a reduction of yield and productivity of the affected plants ¹⁵. Direct damage to hosts is important, but of more significant concern is its ability to vector the phloem-limited bacterium “Candidatus Liberibacter africanus”, implicated in incurable huanglongbing or citrus greening disease, which represents a substantial setback to the citrus industry worldwide¹⁶. A recent study showed that, within five to ten years, the estimated yearly value of lost productivity in four East African nations ranged from US$ 21.3 to US$ 63.8 million due to HLB ¹². The authors further indicated that annual output losses for these countries were predicted to vary from US$ 63.8 to 127.6 over a longer timeframe of ten to fifteen years.

Climate is one of the most important factors influencing the occurrence, geographical distribution, population dynamics, and natural enemies of pests on a global scale ^17–19. Warmer temperatures cause insect species to grow faster, leading to a greater reproductive capacity, which can increase the number of generations produced in a season and the rate of population growth²⁰. Global warming will worsen insect pest problems by increasing their incidence and severity ²¹. High temperatures can increase pest outbreaks ²². Understanding the areas at risk of invasion will be helpful for policy formulation, prevention measures, and regulatory plans. Insect pests respond differently to climate change, demonstrating their uniqueness and variances in environmental adaptation ^23–24. Species distribution modeling (SDM) [also known as ecological niche modeling, habitat modeling, predictive habitat distribution modeling, and range mapping] employs presence records and environmental variables, builds the model using a machine learning algorithm, forecasts species ecological demands, and then projects the analyzed results over time and space to estimate possible potential distribution of the species ^25–26. Species distribution modeling is an important tool in quantitative ecology ²⁷, and has been used to predict suitable areas for several invasive pests, including Drosophila suzukii (Diptera: Drosophilidae) ²⁸, Acrosternum spp. (Hemiptera: Pentatomidae) ²⁹, Helicoverpa armigera (Hübner, 1808) (Lepidoptera: Noctuidae).³⁰

Species distribution modeling is achieved using either correlative SDMs or mechanistic SDMs. Correlative SDMs use multiple regression techniques to simulate the observed distribution of a species as a function of regionally referenced climatic predictor variables, whereas mechanistic SDMs use physiological data about a species to identify the range of environmental conditions in which the species can survive ^31–32 Species distribution modeling employs either correlative or mechanistic methods for predicting the geographical distribution of species. In correlative SDMs, several models, including bioclimatic modeling (BIOCLIM), Boosted Regression Trees (BRT), generalized linear models (GLM), DOMAIN, and maximum entropy (MaxEnt). In contrast, the climate change experiment (CLIMEX) is a semi-mechanistic model, and has been used to predict suitable areas of invasive pests^33–34.

Recent research on T. erytreae has mainly focused on its biology ^{13–14, 35–43}, management strategies ^44–46, host range ³⁸, morphometry ⁴⁷, distribution ⁴⁸ and citrus greening disease^{12, 43, 49}. Predicting the potential geographical distribution of T. erytreae has been limited to a local sale^50–51, whereas global forecasting of risk areas under current and future climate change scenarios, is generally lacking. Yet, knowledge of the global regions at risk of invasion is required for international collaboration in developing biosecurity and preventive measures to slow down the spread of the pest. In the present study, we combined geospatial and physiological data of T. erytreae to predict the global distribution using the CLIMEX model.

Occurrence records of Trioza erytreae

We obtained the presence records of T. erytreae through field surveys in Kenya, Tanzania, and Uganda, from March 2016 to February 2018. The sampling methods involved the collection of infested shoots, sampling of development stages and adults, deployment of yellow sticky card traps, and sampling of symptomatic shoots. We obtained the GPS coordinates using Garmin eTrex® 32x. Data were also obtained from scientific literature ^{13− 15, 35, 38− 39, 40− 41, 43− 45, 47− 48, 52− 57}), utilizing online databases, such as Web of Science, Science Direct, Google, Google Scholar, PubMed, and MEDLINE. The keywords for global data search on the pest included Trioza erytreae, African citrus triozid, African citrus psyllid, ACT, first report, citrus greening vectors, and HLB visitors biology, citrus pests, psyllids, and distribution. We also downloaded additional T. erytreae data from the Global Biodiversity Information Facility (GBIF, http://www.gbif.org), Centre for Agriculture and Bioscience International (CABI, https://www.cabi.org/), and European and Mediterranean Plant Protection Organization (EPPO, https://gd.eppo.int/). The localities where a village was mentioned, we used Google Earth Pro to obtain the latitudes and longitudes. Due to CLIMEX requirements, we cleaned the data by removing duplicates, fuzzy, and neighboring records.

The duplicate records from the datasets were removed, leaving just one occurrence per grid cell (5 km). The occurrence datasets of Trioza erytreae were organized and saved in Microsoft Excel as a CSV (.csv). Overall, we used 286 documented presence records for the global prediction of T. erytreae. The occurrence records used for the model are included in the supplemental material Table S1 and illustrated in Fig. 1.

CLIMEX model

The potential distribution of a species has been achieved using ecological niche models through semi-mechanistic software such as CLIMEX ⁵⁸. CLIMEX is a bioclimatic niche model that forecasts suitable locations based on species traits and climate variables^59–60. It has been used to predict the potential distribution of various species ⁶¹. CLIMEX is built on the idea that if you know where a species lives, you can estimate its tolerated climatic conditions⁶⁰. As a result, it maximizes the favorable growing season for the species while minimizing the unfavorable growth season ^{60, 62}. This software generates forecasts by combining specific climatic criteria generated from biological data (e.g. heat and moisture requirements) with known species distributions^58–59. Thus, we created the potential distribution model for T. erytreae using CLIMEX. This software provides information on the climate suitability of species, based on stress and growth indices through the Ecoclimatic Index (IE), which is organized on a scale from 0 to 100, where 0 indicates inadequate areas and 100 ideal areas with high suitability for the occurrence of the species ⁶⁰. ArcGIS software extracted the CLIMEX outputs and projected the suitable areas for T. erytreae in the largest citrus-producing countries.

Parameter assembly

The parameters used for the CLIMEX model are presented in Table 1. We adjusted the growth and stress indices in CLIMEX based on scientific research related to T. erytreae available in the literature. Based on the best agreement between the observed distribution and the tolerance for climatic variables, the stress indices could be adjusted and classified as satisfactory ⁶³.

Moisture Index

Trioza erytreae can develop in hot and dry climates like semi-arid areas ⁶⁴, thus considering CLIMEX template data of “Semi-arid template,” the Soil moisture indices were defined at a lower soil moisture threshold (SM0) 0.1 and lower optimum soil moisture (SM1) 0.3 ⁶⁵. The upper optimum soil moisture (SM2) 0.7 and the upper soil moisture threshold (SM3)1.5 were adjusted to agree with the known occurrence of T. erytreae and indicate areas with excess soil moisture.

Temperature Index

Eggs and the first instars of T. erytreae are highly vulnerable to desiccation and the lower temperature threshold for the development of the juvenile stages is about 10ºC ⁶⁴. Furthermore, temperatures between 15 and 24°C allowed the development of T. erytreae immature stages¹⁴. Thus the lower temperature threshold (DV0) was set at 10°C, the Lower optimum temperature (DV1) at 15°C. The Upper optimum temperature (DV2) at 30°C and the Upper temperature threshold (DV3) were set respectively at 38°C. These indices were adjusted to consider CLIMEX template data of “Semi-arid template” and allow agreement with the high occurrence of T. erytreae in semi-arid and arid regions.

Stress index

Cold Stress

The temperature significantly affects the development of all the immature stages with the lowest developmental temperature threshold ranging between 3 and 15°C ¹⁴. However, the temperature threshold for cold stress (TTCS) was defined as 1°C, and the accumulation of cold stress (THCS) was set at -0.002 week-1 to fit the pest distribution in the areas of occurrences.

Dry Stress

Considering the occurrence of T. erytreae in many arid and semi-arid regions, we adjusted the dry stress limit (SMDS) to 0.05, and the dry stress accumulation rate (HDS) was set at -0.005 week-1 like the CLIMEX template data of “Semi-arid template”.

Wet Stress

Although T. erytreae populations grow with difficulty, they can also develop in climates characterized by high temperatures and frequent rain, like tropical regions ⁶⁴. Therefore, the wet stress parameter (SMWS) was set to 2.5, and the stress accumulation rate (HWS) was set to 0.002 week-1 based on CLIMEX Wet Tropical Template.

Heat Stress

Temperatures of 32ºC or more combined with 30% relative humidity or less kill all stages of the pest ^{64, 65}. Thus, considering the CLIMEX semi-arid template and ensuring the best fit for the T. erytreae occurrence, we set the temperature threshold for heat stress (TTHS) to 44°C and the heat stress accumulation rate (THHS) to 0.002 week − 1.

Table 1

CLIMEX parameter values are used for *Trioza erytreae.*
Index	Parameter	Mnemonic	Values	References
Temperature	Lower temperature threshold	DV0	10 ºC	⁶⁴
	Lower optimum temperature	DV1	15 ºC	¹⁴
	Upper optimum temperature	DV2	32 ºC	⁵⁹
	Upper temperature threshold	DV3	38 ºC	⁵⁹
Moisture	Lower soil moisture threshold	SM0	0.1	⁵⁹
	Lower optimum soil moisture	SM1	0.3	⁵⁹
	Upper optimum soil moisture	SM2	0.7	Fit to data
	Upper soil moisture threshold	SM3	1.5	Fit to data
Cold stress	Temperature threshold	TTCS	1 ºC	Fit to data
	Stress accumulation rate	THCS	-0.002 week^− 1	Fit to data
Heat stress	Temperature threshold	TTHS	44 ºC	⁵⁹
	Stress accumulation rate	THHS	0.002 week^− 1	⁵⁹
Dry Stress	Soil moisture threshold	SMDS	0.05	⁵⁹
	Stress accumulation rate	HDS	-0.005 week^− 1	⁵⁹
Wet stress	Soil moisture threshold	SMWS	2.5	^64,59
	Stress accumulation rate	HWS	0.002 week^− 1	^64,59

Future Climatic Scenarios

We used the CliMond 10′ gridded climate data to model CLIMEX since, it provides a good spatial resolution. The CliMond 10′ consists of long-term values of monthly average minimum and maximum temperature (Tmin and Tmax), precipitation (Ptotal), and relative humidity at 09:00 h (RH 09:00) and 15:00 h (RH15:00) ⁶³. The A1B and A2 SRES scenario and the global climate model (GCM) CSIRO-Mk3.0 (CS) of the Centre for Climate Research, Australia, were used to perform the modelling procedure for T. erytreae in the climate changes predicted for 2050 and 2070. The CS climate system model contains a comprehensive representation of the four major components of the climate system (atmosphere, land surface, oceans, and sea-ice), and in its current form is as comprehensive as any of the global coupled models available worldwide⁶⁶.

Our study focuses on two different scenarios, a mitigation scenario (A1B) and a no mitigation scenario (A2). The CliMond database currently does not have data for RCP scenarios, much less the scenarios are foreseen for the 6th IPCC report that will be released now in 2021. Thus, we used the A1B and A2 SRES latest scenarios present in CliMond. According to Van Vuuren & Carter ⁶⁷, the key factor differentiating RCPs from the SRES is their CO₂ concentration. The A2 SRES assumes an increase in CO₂ concentrations by the end of the century to 846 ppm. Its best RCP equivalent is the RCP 8.5, which assumes a concentration of 936 ppm. Another difference between these two equivalent scenarios is related to the temperature increase. The predicted temperature increase for the A2 SRES is approximately 6°C, while for the RCP 8.5 is 7°C. Therefore, due to its best equivalence to the RCP 8.5, in this study, the A2 SRES scenario was used to model the impact of climate change on T. erytreae distribution. Likewise, SRES A1B was used in this study as a scenario more positive, with mitigation actions, equivalence to RCP 2.6.

Model verification and validation

The model was validated by comparing the output to known distributions in the South African regions of the Eastern Cape, the first report of T. erytreae on citrus ¹¹ (Fig. 2). These observations were used to evaluate our model’s reliability. Thus, through the Ecoclimatic Index (EI), the suitable areas for T. erytreae were classified as unsuitable (EI < 0), moderate suitability (0 < EI < 30), and high suitability (EI > 30) ⁵⁸.

Current global potential distribution of Trioza erytreae

The potential geographical distribution of T. erytreae, is primarily found in the west, central, east, and south of Africa; south, east, and west of Asia; north, south, and east of South America, and east of Australia. Under the current climate, The Ecoclimatic Index (EI) for T. erytreae modelled using the CLIMEX model for the current climate is shown in Fig. 3. The global geographical distribution of T. erytreae is consistent with its present-day distributions. However, the model predicts new areas, such as Ghana, Cote D’Ivoire, Benin, Togo, Somalia in Africa; the USA, Mexico, Honduras, Brazil, Bolivia, Paraguay, Uruguay, and Argentina in the Americas; Papua New Guinea and Australia in Oceania; Indonesia, China, India, Thailand, Vietnam, Cambodia, and Myanmar in Asia; Italy in Europe as highly suitable for the pest.

Fugue global potential distribution of Trioza erytreae

In the SRES A1B and A2 scenarios, the changes in habitat suitability of the pest from the current time to 2070 are shown in Figs. 4 and 5. The EI for T. erytreae in the A2 scenario for the current time to 2070, in the largest citrus-producing countries (Brazil, China, and the USA), showed a suitable growth area for the pest (Fig. 6). The EI for T. erytreae in the A2 scenario for the current time to 2070, in the largest citrus-producing countries (Brazil, China, and the USA) (Fig. 7), shows a contraction of suitable areas. The model predicts a shift of suitable areas to its east and south coasts in Brazil. In China, the most appropriate regions will shift to the southeast coasts. In South America, there will change of habitat suitable to the east coast of the USA.

In the present study, the physiological data and some of the presence points of ACT were sourced from the scientific literature. This encourages data reuse for future research, allowing future research work to progress more efficiently and effectively ^68–69. Unlike the mechanistic models, the CLIMEX model uses the occurrence records of a species and its physiological stress factors to establish the potential distribution of species. The predictions are consistent with the known global T. erytreae distribution. The model predicted suitable areas in regions that were earlier predicted using different machine learning algorithms, such as MaxEnt ⁵⁰. Again, the prediction covered suitable areas, including the validation area, in which the data were not used to estimate the geographical distribution of the pest. Overall, of the 301 occurrence points, only one occurrence record was found in areas deemed unsuitable by the model fit, thus most of the occurrence records inside the validation area matched the CLIMEX-estimated suitable areas for T. erytreae invasion and spread.

The geographical distribution of invasive species like T. erytreae has been evaluated through SDMs, but most studies were conducted locally. For example, Kyalo et al. ⁵⁰, combined MaxEnt with remotely-sensed vegetation variables to predict the spatial distribution of T. erytreae in Kenya. A study by Aidoo et al. ⁷⁰, indicated the potential distribution of T. erytreae using bioclimatic variables and elevation datasets in Kenya using MaxEnt. A recent study predicted the expansion of T. erytreae in the Iberian Peninsula using a pest risk analysis approach ⁵¹. A more recent study modeled the potentially suitable areas of T. erytreae in two European countries, Portugal and Spain using MaxEnt ¹⁵. Finally, Espinosa-Zaragoza et al. ⁴⁴, predicted the suitable areas of T. erytreae in Mexico based on MaxEnt and only bioclimatic variables. However, none of these studies evaluated the impact of climate change on the global geographical distribution of T. erytreae, and under current and future scenarios.

Prevention of invasive alien pests is the surest way of reducing or avoiding their impact on sustainable agriculture production and food security thus prevention of biological invasion is less expensive and more manageable than post-entry management ⁷¹. Therefore, it is essential to understand and assess the impact of climate change on the potential distribution and range shifts of invasive species like T. erytreae for a better biosecurity plan. The study's information may be used to develop a proactive, adaptive, and integrated invasive species management approach to curtail and prevent further spread.

In the present study, we used the CLIMEX model to predict habitat suitability for T. erytreae, and the suitable areas ranged from unsuitable (EI < 0) to high suitability (EI < 0 to EI > 30) is concentrated in the north-south temperate zone around the world. The model predicts an expansion of suitable areas outside reported countries in Europe (i.e., Spain and Portugal). These areas were predicted to have moderate (0 < EI < 30) to high suitability (EI > 30) for T. erytreae. The new moderately suitable regions identified by the model include Cyprus, Croatia, Greece, France, Albania, and these countries produce citrus in Europe; thus the introduction of T. erytreae into these zones could threaten their citrus industry. To date, T. erytreae is still restricted to Span and Portugal ⁷². However, the pest can invade and establish in other European countries where citrus is cultivated ⁷³. However, suggests that there is a need for early detection and eradication measures to avoid the spread of this invasive alien pest across Europe. In Oceania, T. erytreae has not been reported, but our study predicts highly suitable areas, particularly in the east coast of Australia. If T. erytreae invade Australia, its establishment will be facilitated by the suitable temperate climate, especially its mild winter temperatures ^{74, 75}. Therefore, continuous strict compliance of the continent biosecurity measures is required at the entry points (i.e., railway stations, harbors, airports, and lorry parks) to intercept host materials. Apart from citrus, alternative host plants belonging to the family Rutaceae, such as the curry tree, Murraya koenigii (Bergera koenigii), and Clausena anisata (Willd.) Hook. f. ex Benth., ⁷⁰ should be regularly checked for all stages of T. erytreae at the Australian entry points. According to Australian Plant Biosecurity report ⁷⁶, there is a need to pay attention to additional types of invasion pathways, including natural spread, human travelers and their luggage, and infested machinery.

Detection and monitoring of T. erytreae using sticky traps can be of great importance to where the pest exists or is at risk of invasion. In Kenya, Aidoo et al. ⁴⁵, demonstrated that sticky card traps, especially the yellow ones, effectively detected field populations of T. erytreae even at low densities. Other sampling methods, such as visual observation of adults immature and collection of symptomatic leaves, could facilitate early detection of the pest, particularly after it has invaded new locations. In most areas, anthropogenic activities have been strongly associated with the spread of T. erytreae through the alternate hosts' movement. Therefore, regular visits to areas where these alternate hosts are present may help early detection and monitoring.

We demonstrated that the overall potential habitat suitability for T. erytreae will decline in the future under SRES A1B and A2 scenarios compared to the current climate. The A1B and A2 SRES scenarios and the global climate model (GCM) CSIRO-Mk3.0 datasets predict a temperature increase of 2.11°C and a precipitation loss of 14% by 2100 ⁷⁷. Previous studies have shown that temperatures above 32°C coupled with 30% relative humidity degrees are detrimental to T. erytreae development^64–65. As a result, in regions near the equator where the annual mean temperature is around 31°C, a rise in 2.11°C may increase T. erytreae mortality due to its climatic requirements^77–78. In contrast, areas with high elevations, such as Kenya, Tanzania in East Africa, will remain suitable because the changing climate may favor the survival and distribution of the pest in those areas. The areas that will reduce suitable areas are primarily concentrated in countries, such as India and Mexico. Because it has been demonstrated that T. erytreae prefers cool and moist climatic conditions⁷⁹, the extreme temperature may cause high mortalities¹⁴. On the other hand, the pest has adapted to and settled in a wide range of ecological conditions, including equatorial, arid, and warm temperate climates with varying temperatures and rainfall¹³. In the future, our model predicts more areas of moderate suitability (0 < EI < 30) than high (EI > 30). However, parts of the five major citrus-producing countries in the world (i.e., China, Brazil, Mexico, India, and the USA) will continue to remain highly suitable for the pest until 2070. None of these countries has reported T. erytreae⁷². Nevertheless, our study will guide policymakers and plant protection and regulatory services to implement quarantine and preventive measures to avoid future invasion by the pest.

Trioza erytreae and the agent it transmits (CLaf), are heat-sensitive ⁶⁴, and the development of symptomatic leaves varies based on temperature ¹⁶. In addition, detecting the disease in leaves is problematic because ‘Ca. L. species’ are of low concentration and unevenly distributed within their host tissues ^{80, 81}. The difficulty in detection may facilitate the spread of CLaf in new areas when T. erytreae is introduced through pathways, such as citrus fruit⁸², nursery stock (live plants) ⁸³, budwood ⁸⁴, fresh leaves ⁸², and infected Psyllid ⁸⁵. Ajene et al. ⁸⁶ predicted habitat suitability for CLaf in several regions, including Western, Eastern, and sub-Saharan Africa in Africa; South and Central America in the Americas; the United Kingdom and the Iberian Peninsula in Europe, Australia, and Asia. Similarly, these areas were predicted to be suitable for T. erytreae, the disease's primary vector. Because these regions are suitable for both T. erytreae and CLaf, there is a need to develop proactive measures against the pest and the disease to safeguard the citrus industry ⁷⁶.

It is important to note that there are limiting abiotic and biotic factors, such as natural enemies; entomopathogens, predators, parasitoids; human factors, urban accessibility; and host plant availability; geographical factors; vegetation, land cover, and elevation, that may limit the distribution of the pest, but were not included in the current model. Other factors that were not considered, include competition with native species, propagule pressure, gene editing, sterilized inset technology, farm-level management strategies, policies, and quarantine measures. All these factors should be considered when interpreting the current study results. Richard et al. ⁵⁰ found that including remotely-sensed vegetation in a MaxEnt model improved the prediction of suitable areas of T. erytreae. Therefore, we recommend that future studies include these factors in the CLIMEX model when predicting the suitable areas of T. erytreae.

As predicted in the current study, the suitable areas in Ghana, Swaziland, Zimbabwe, the Democratic Republic of the Congo, and the Madagascar Mediterranean coast of North Africa, where citrus is cultivated, require plant protection and regulatory plans. Moreover, these areas need regular inspection or testing for pests, prohibition of portions of the host, a pre-entry or post-entry quarantine system, stated conditions on consignment preparation, specified treatment of the consignment, restrictions on end-use, distribution, and entry periods of the commodity are among the measures that can be implemented ⁷⁶.

The CLIMEX model predictions are consistent with the known historical records of T. erytreae, but suitable areas will increase from the current time to 2050 then decline until 2050. However, T. erytreae suitable areas in some regions will remain highly suitable in the future, implying that major citrus growing countries will continue to be threatened by T. erytreae in the future. Therefore, the findings from the present study may help develop preventative and control measures against the pest, especially in regions where T. erytreae is absent.

Acknowledgement

GIZ financially supported this research through the project on “Strengthening Citrus Production Systems through the Introduction of Integrated Pest Management (IPM) Measures for Pests and Diseases in Kenya and Tanzania (SCIPM)” (Project no.: 14.1432.5‐001.00/Contract no.: SCIPM 81180346) through the International Centre of Insect Physiology and Ecology (icipe). We also gratefully acknowledge the icipe core funding provided by UK Aid from the Government of the United Kingdom; Swedish International Development Cooperation Agency (Sida); the Swiss Agency for Development and Cooperation (SDC) and the Kenyan Government. We also thank the Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM) and the University of Environment and Sustainable Development (UESD) for access to instruments.

Contribution

O.F.A conceived the study and drafted the manuscript. P.G.C.S., R.S.S., P.A.S.J., and M. C.P. analysed the data. O.F.A., C.B., M.S., S.E. and R.K. collected the field data. O.F.A., P.G.C.S and C.B. wrote the manuscript. All authors reviewed the manuscript.

Data availability

All data generated or analysed during this study are included in this published article.

Competing interests

The authors declare no competing interests.

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Predicting the potential global distribution of an invasive alien pest Trioza erytreae (Del Guercio) (Hemiptera: Triozidae)

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