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1 December 2017 Potential Distribution of Drosophila suzukii (Diptera: Drosophilidae) in Relation to Alternate Hosts in Mexico
Rigoberto Castro-Sosa, María del R. Castillo-Peralta, Alejandro I. Monterroso-Rivas, Jesús D. Gomez-Díaz, Erick Flores-González, Ángel Rebollar-Alviter
Author Affiliations +

The spotted wing drosophila, Drosophila suzukii (Matsumura) (Diptera: Drosophilidae), is one of the most important pests of berry crop production in Mexico. The purpose of this research was to model the potential distribution of D. suzukii in the Mexico relative to 4 non-crop hosts using Maximum Entropy Ecological Niche Modeling. Spotted wing drosophila records were collected from a survey conducted in commercial blackberry plots and non-cultivated areas between 2013–2015. The data for the presence of non-crop hosts in the country and the bioclimatic variables used in the modeling were obtained from the Global Biodiversity Information Facility and WorldClim websites, respectively. For climatic variable selection, a principal component analysis on climatic variables was conducted prior to the MaxEnt modeling. The results demonstrate that the potential distribution of spotted wing drosophila was primarily in central Mexico. However, other suitable locations in the southeastern portion of the county were identified, which were not previously known. Likewise, the joint modeling depicted areas of coincidence between the spotted wing drosophila distribution and 4 alternating non-crop hosts commonly distributed in the berry-producing region, which includes the states of Michoacán, Jalisco, Guanajuato, and Mexico. This joint modeling of the potential distribution of spotted wing drosophila and non-crop hosts partly explains how the populations of the pest sustain themselves during seasons of low or no commercial berry production in Mexico.

Drosophila suzukii (Matsumura) (Diptera: Drosophilidae), commonly known as the spotted wing drosophila, is one of the most important invasive pests in the world because of its wide range of hosts, mostly soft-skinned fruit crops (Asplen et al. 2015). The ability of D. suzukii to adapt to different environmental conditions and hosts has enabled it to invade tropical and subtropical areas in both hemispheres as has been recently reported (dos Santos et al. 2017).

The presence of D. suzukii in Mexico was first reported in 2011 (NAPPO 2011), and it is currently considered a quarantine pest because of its potential to cause a severe economic loss in host crops, which have great economic importance. These crops include blackberries, raspberries, strawberries, and blueberries. Therefore, this pest is under surveillance by the Mexican government (SENASICA 2015).

Several cultivated and non-cultivated hosts of D. suzukii have been previously reported (Berry 2012; Lee et al. 2015; Poyet et al. 2014; Poyet et al. 2015; CABI 2016; Kenis et al. 2016). Recent studies under laboratory conditions in Mexico (unpublished) indicate that black cherry (Prunus serotina subsp. capuli (Cav.) McVaugh [Rosaceae]), yellow mombin (Spondias mombin L. [Anacardiacea]), wild blackberry (Rubus adenotrichos Schltd [Rosaceae]), and cultivated and non-cultivated guava (Psidium guajava L. [Myrtaceae]) represent alternate hosts for D. suzukii. These studies have shown that in hosts such as guava and yellow mombin the insect can complete its life cycle in 15 d in both hosts in laboratory conditions (23 ± 2) (Rebollar-Alviter, unpublished data) which indicates that they favor the sustainability of the populations during the seasons where commercial berry crops are not in production (Rebollar-Alviter et al. 2015). These non-crop species are widely distributed in the tropical and subtropical regions of Mexico (Rzedowski & Calderón de Rzedwski 1999; Rzedowski & Calderón de Rzedowski 2005; Jaiswal & Jaiswal 2005; Cruz & Gutiérrez 2010). Additionally, the species inhabit the same geographical space and are frequently associated with the commercial production areas of berry crops.

Obtaining knowledge of the potential distribution of an invasive species is useful for planning and decision-making for public plant health policies. In this regard, different algorithms for modeling ecological niches have been published that use presence and absence data or only presence in conjunction with environmental variables in a particular zone (Phillips et al. 2006; Franklin 2009; Peterson et al. 2011). Maximum Entropy Ecological niche modeling with the MaxEnt algorithm is one of the most popular methodologies for modeling species distribution (Phillips et al. 2006; Booth et al. 2014).

The MaxEnt algorithm estimates a target probability distribution by searching for the distribution of the probability of maximum entropy (close to uniform distribution) subject to a set of constraints that represents incomplete information regarding the target distribution (Phillips et al. 2006). The available information regarding this distribution is the set of environmental variables known as characteristics. Additionally, expected constraints of each characteristic must correspond to their mean values of the sample (the sample mean for a set of sampling points has been extracted from the destination distribution) (Phillips et al. 2006; Cruz-Cárdenas et al. 2014a).

The ecological niche modeling with the MaxEnt algorithm has been used to estimate the potential distribution of different invasive species (Václavík & Meentemeyer 2009; Elith 2014; Hill & Thomson 2015; Jarnevich & Young 2015). Additionally, the potential distribution of insect species of economic importance, such as Lobesia botrana Den. and Schiff. (Lepidoptera: Tortricidae), beneficial and harmful insects for grapes (Fiaboe et al. 2012), and Rhynchophorus ferrugineus (Olivier) (Coleoptera: Rhynchophoridae) in palm trees (Lv et al. 2011; Hoffmann & Thomson 2013), has previously been reported. In addition, Phenacoccus solenopsis (Tinsley) (Hemiptera: Pseudococcidae) in cotton (Fand et al. 2014) and Diaphorina citri Kuwayama (Hemiptera: Liviidae) in citrus (López-Collado et al. 2013) have been studied. Recently, Biber-Freudenberger et al. (2016) estimated the potential distribution of Bactrocera invadens Drew, Tsuruta & White (Diptera: Tephritidae), Ceratitis cosyra (Diptera: Tephritidae), and Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) in Africa. A common objective for these studies was the ability to generate scenarios for the purpose of planning and designing more efficient strategies for the management of these pests at different spatial scales.

In addition to modeling the distribution of invasive species, it is important also to consider their relationship with their potential hosts. Barredo et al. (2015) determined the potential distribution of Hylobius abietis L. (Coleoptera: Curculionidae) and Cameraria ohridella Deschka & Dimić (Lepidoptera: Gracilariidae) in 5 species of trees. They considered the vulnerable areas as those that could be occupied by both the insect pests and the host. The shared areas were divided into 4 categories: (a) habitat not suitable for the insect, (b) suitable habitat for the insect and no host presence, (c) adequate habitat for the insect and presence of the host, and (d) habitat not suitable for the insect but with host presence. This categorization allowed the precise identification of changes in the habitat suitable for insects for each host studied.

Damus (2009) estimated the potential distribution of D. suzukii in North America with MaxEnt modeling by using the invaded area and the native area in southeastern Asia. In this context, the European and Mediterranean Plant Protection Organization (EPPO 2010) estimated the potential distribution of D. suzukii at a global scale with an emphasis on Europe based on the parameters published by Damus (2009). More recently dos Santos et al (2017) also modeled the global distribution of D. suzukii using MaxEnt and GARP (Genetic Algorithm for Ruleset). However, modeling the potential distribution of D. suzukii with the presence of alternate hosts has not been published.

Mexico has an annual production of 596,592 tons of berries (blackberry, raspberry, blueberry, and strawberry) in an area of 29,721 ha (SIAP 2016). This production results in an income greater than 805 million dollars with an annual growth rate of approximately 29.8%. Given the accelerated growth and the opening of new berry production areas in Mexico, it is useful to model the potential distribution of D. suzukii in the presence of non-host crops. The purpose of this research was to determine the potential distribution of D. suzukii in Mexico, given scenarios that include the presence of wild blackberry, yellow mombin, black cherry, and guava using Ecological Niche Modeling with the MaxEnt algorithm.

Materials and Methods


Records of D. suzukii were obtained from a trap route defined mainly by commercial blackberry production areas and neighboring sites with presence of non-crop hosts, such as wild grape, wild blackberries, nance (Byrsonima crassifolia (L.) Kunth [Malpighiaceae]), yellow mombin, and guava. The data were collected from 3 sampling routes (Los Reyes, Ziracuaretiro, and Tacámbaro) in the state of Michoacán between 2013 and 2015. A total of 150 traps was placed along these routes in the main blackberryproducing zone. Each trap consisted of a 1 L clear plastic cup with 0.5 cm diam side holes (Lee et al. 2012) containing 200 mL of apple cider vinegar (Clemente Jacques®) and a few drops of dishwashing soap as an attractant and drowning solution, which was changed every 14 d. To extend the sampling points of D. suzukii beyond the target zone in state of Michoacán, additional data were provided by the Plant Health Committees for the states of Michoacán, Jalisco, Colima, and Guanajuato. These data are the result of D. suzukii sampling conducted during an official program on “Phytosanitary Management of Spotted Wing Drosophila” in 2015 and 2016 (Table 1).

Table 1.

Number of records documenting the presence of Drosophila suzukii and non-crop hosts used in MaxEnt modeling potential distribution.


The data for the presence of wild blackberry, yellow mombin, black cherry, and guava were obtained from the Global Biodiversity Information Facility ( Additionally, data from field sampling during 2013 to 2015 in the areas surrounding commercial blackberry production were included in the analyses.


Worldclim's bioclimatic variables ( were used in the analysis (Hijmans et al. 2005). WorldClim consists of a set of data layers generated from the interpolations of monthly average weather data from meteorological stations around the world with a 30-s arc of grid resolution (often referred to as 1 km2). The variables included total monthly and average monthly precipitation, and minimum and maximum temperatures, to generate 19 bioclimatic variables (O'Donnell & Ignizio 2012).

A principal component analysis was performed with the 19 World-Clim variables for points that contained records of the presence of D. suzukii in order to reduce variable redundancy and the existence of multicollinearity (Graham 2003; Cruz-Cárdenas et al. 2014b). A data matrix of 19 columns (values of the environmental variables) and N rows was created, which corresponds to the observations or locations with the presence of D. suzukii and the non-crop hosts under study. The proportion of variance explained by each component in the principal component analysis, eigenvalues, and eigenvectors were obtained with Minitab (Ver. 17 Minitab Inc. State College, Pennsylvania, USA). The number of components was determined by using the Cliff criterion, retaining components with eigenvalues accounting for 70% or more of the total variance (Cliff 1987). The correlation between the original variables and the selected components was estimated according to Pla (1986). The discrimination of the climatic variables was completed by quantifying the proportion of the variance explained by each original variable on the components selected by the sum of squares of the correlation between original variables and the selected components. The variables that contributed less than 80% to the variance were excluded from the modeling. Given the large number of D. suzukii records in some of the sampled areas, a grid of 6 × 6 km was generated, which considered only 1 record within each grid. For the non-crop hosts under study, the spatial distribution of the records was wider, and no modifications were made.


Modeling of the potential distribution of D. suzukii and non-crop hosts (R. adenotrichos, S. mombin, P. serotina var. capuli and P. guajava) was performed with the MaxEnt algorithm (Phillips et al. 2006). Previously, the environmental variables were selected as indicated above, and the records for presence of non-crop hosts were obtained.

The settings used for the MaxEnt modeling for D. suzukii and noncrop hosts were set to default (Phillips & Dudík 2008; Cruz-Cárdenas et al. 2014a; Jarnevich & Young 2015). The cumulative output format was used as a prediction for conditions suitable for the species above a threshold (Phillips 2009). This output was obtained in the ASCII format, which was later input in ArcGIS ver. 10.3 (Esri Inc. Redlands, California, USA) for editing and processing the resulting maps that display the potential distribution of each species. The criterion used as a threshold from which D. suzukii was considered present was the maximum test sensitivity plus specificity (Liu et al. 2005) which was also applied by Barredo et al. (2015). For the non-crop host species, the ten percentile training presence was used, i.e., ten percent of the records fell outside of the potential area and were those with an atypical environment, not included in the niche (Scheldeman & van Zonneveld 2011) and which 90% of the points of presence were within the potential range. In order to evaluate the performance of the 5 models that were generated in the MaxEnt algorithm, we used the receiver operating characteristic analysis. The receiver operating characteristic curve evaluates the configuration of a model regarding omission and commission errors through a single number independent of any threshold choice (Phillips et al. 2006). The shared common area between the pest and the noncrop hosts under study was estimated according to the presence or absence categorization proposed by Barredo et al. (2015).



Table 2 displays the environmental variables identified by principal component analysis for each species that was analyzed. According to the contribution to the variance in the selected components, the average temperature in the coldest quarter (bio11) was the variable with the highest contribution to the presence of D. suzukii. For wild blackberry, yellow mombin, and guava, mean annual temperature (bio1) was the variable with the highest contribution to the variance, and for black cherry the average temperature in the hottest quarter (bio10) was the variable that contributed the most to the variance presence data.

The reduction in the number of records of presence using a 6 × 6 km grid enabled us to obtain a model for the potential distribution of D. suzukii. This approach permitted a better model fit than with the original data given its spatial aggregation for the main berry-producing states of central and western Mexico (Michoacán, Jalisco, and Colima). Of the 1,680 records initially obtained, 135 were used in the final Max-Ent modeling.

Based on the receiving operator characteristic curve, the performance of the models was better than the random distribution. In all cases, the area values under the curve were higher than 0.9. These results indicate that the area predicted by the resulting models for the species have a higher sensitivity and a lower specificity, which classifies them as excellent (Swets 1988).


Figure 1 depicts the potential distribution of D. suzukii in Mexico. The state of Jalisco had the highest probability of favorable environmental conditions for the pest (Table 3) followed by the states of Michoacán and Oaxaca. For wild blackberry, the potential distribution indicated that this host is distributed mainly in the mountainous zone of Mexico, in the Sierra Madre Occidental, the Transverse Volcanic Belt, part of the Gulf Coastal Plain, and in a portion of the Sierra Madre del Sur. In this latter region, the state of Oaxaca contained the greatest area, followed by the states of Michoacán and Jalisco. Yellow mombin was associated more with tropical environments in Mexico, such as the Yucatán Peninsula and the Gulf coast of Mexico, and it was associated less with areas in the Pacific coast. Black cherry was distributed in the mountainous areas, similar to wild blackberry, and it extended more northward, but covered a considerable area in the central portion of the country. Finally, guava was distributed mainly in the tropical areas of the Gulf and the Pacific coasts, but it also occupied areas of temperate zones, although to a lesser extent (Fig. 2).

Table 2.

Selected environmental variables by principal component analysis. Values correspond to variance contribution of each variable to the selected component.


Fig. 1.

Potential distribution Drosophila suzukii in México based on MaxEnt modeling.


Table 3.

Potential distribution of Drosophila suzukii and non-crop host species per state of Mexico (km2) estimated with MaxEnt modeling.



The presence probability thresholds selected for each species allowed calculation of estimates of the potential distribution of D. suzukii in relation to non-crop hosts (Table 4). The area shared by D. suzukii and non-crop host species, based on the categorization by Barredo et al. (2015) is presented in Figure 2. Of the total area predicted for potential distribution of D. suzukii, 72% coincided with the presence of P. guajava, 59% with P. serotina var. capuli, 35% with R. adenotrichos, and 17% with S. mombin. The results also indicated that of the area predicted for R. adenotrichos, 69% was shared with the area predicted for D. suzukii. Likewise, the corresponding values for P. serotina subsp. capuli, P. guajava, and S. mombin were 54, 36, and 14%, respectively.


Drosophila suzukii has been reported to attack a large number of cultivated and non-cultivated hosts (Berry 2012; Lee et al. 2015; Poyet et al. 2014; CABI 2016). This study demonstrates that the suitable habitat for D. suzukii distribution in Mexico is concentrated mainly in the central and western region of the country, including certain areas of the states of Oaxaca, Chiapas, and Baja California (Ensenada Municipality). Previously, preliminary modeling demonstrated a similar distribution of D. suzukii in the northwestern and central portions of Mexico (SENASICA 2013; LaNGIF 2014), but the parameters and data sources for the modeling were not reported.

The principal component analysis allowed a priori reduction for the number of environmental variables to be used in the MaxEnt modeling for the potential distribution of the species studied, which facilitated the reduction of overfitting the models (Cruz-Cárdenas et al. 2014a). In this regard, the temperature of the coldest month was the variable that explained the most variance for the D. suzukii distribution data. Wiman et al. (2014) and Tochen et al. (2015) indicated that temperature and relative humidity were the most influential factors that affected physiology, survival, fecundity, reproduction, behavior, and the population dynamics of D. suzukii. Adult mortality rate was reported to increase when temperatures were below 10 °C (Dalton et al. 2011).

The determination of the potential distribution of D. suzukii in Mexico are consistent with the known distribution of the pest, especially in the central and western regions. Additionally, the results indicate additional favorable conditions for the development of this invasive species in areas in the southeastern portion of Mexico, such as certain zones in the states of Chiapas and Oaxaca in which the production of berries is nonexistent or incipient. The MaxEnt modeling for 4 non-crop hosts showed the areas potentially suitable for the development of D. suzukii. The potential distribution of wild blackberry is consistent with that described by Rzedowski and Calderón de Rzedowski (2005), which reported that the presence of this host was mainly in the temperate zones of the Pacific coast. Yellow mombin distribution was similar to that previously reported (Avitia et al. 2003; Cruz & Rodriguez 2010; Arce-Romero et al. 2017), and its distribution is mainly in the coastal zones of the Yucatan peninsula and the state of Veracruz, although it has been found to a lesser extent on the Pacific coast of Mexico. Additionally, potential distribution of black cherry was similar to that previously reported (Fresnedo-Ramirez et al. 2011). Our results on the potential distribution of guava were generally similar to that previously described by Jaiswal and Jaiswal (2005), but the distribution was different in locations with temperate conditions. This difference could be because during the construction of the model we included records of the host presence above 2,000 m above sea level, generally considered as temperate climate compared to places with similar latitude but with low elevations.

Fig. 2.

Potential distribution of Drosophila suzukii in relation to four noncrop hosts; in white, non-suitable habitat and non-crop host presence; in blue suitable habitat for the non-crop host but non-suitable habitat D. suzukii; in yellow suitable habitat D. suzukii but not for the non-crop host and in red color suitable habitat for D. suzukii and for the non-crop host.


Taken together, these results indicate that wild blackberry, yellow mombin, black cherry, and guava have a wide distribution in Mexico. Interestingly, the areas with a greater probability of distribution of these hosts coincided with the highest probability of D. suzukii distribution in the central and western zones of Mexico. This is also the region with the highest production of berry crops in the country. This coincidence of the presence of alternate hosts in the areas most favorable for D. suzukii development could partly explain the maintenance of the population of this pest during the seasons when commercial production is limited or non-existent (Jul to mid-Sep). During these summer months, yellow mombin, black cherry, wild blackberry, and guava are in full production, and they provide food, shelter, and suitable hosts for reproduction of D. suzukii in the absence of commercial berry production.

In summary, our results indicate that D. suzukii has a wide potential distribution in Mexico, confirming reports from the field, and our results add new potential zones of occurrence not previously reported. Likewise, our results depict the area with the greatest potential distribution in relation to 4 non-crop hosts, which partly explains the capacity for the survival and sustainability of the D. suzukii populations during the periods free from commercial production of berries (during the summer). These results will be useful for decision-making regarding the expansion of sampling and pest monitoring areas in the current and potential new areas for berry production in Mexico.

Table 4.

Potential distribution of Drosophila suzukii in relation to 4 non-crop host species per state (Km2) based on MaxEnt modeling.



The authors are grateful to the Plant Health Committees of Michoacán, Jalisco, Guanajuato, and Colima states for generously providing their data for D. suzukii presence from their monitoring regions. Financial support was provided by the Fundación Produce Michoacán, Coordinadora Nacional de Fundaciones Produce (COFUPRO) and SAGARPA-Mexico. Salaries were provided by the Universidad Autónoma Chapingo.

References Cited


Arce-Romero AR, Monterroso-Rivas AI, Gómez-Díaz JD, Cruz-León A. 2017. Mexican plums (Spondias spp.): their current distribution and potential distribution under climate change scenarios for Mexico. Revista Chapingo Serie Horticultura 23: 5–19. Google Scholar


Asplen MK, Anfora G, Biondi A, Choi DS, Chu D, Daane KM, Gibert P, Gutierrez AP, Hoelmer KA, Hutchison WD, Isaacs R, Jiang ZL, Kárpáti Z, Pascual M, Philips CR, Plantamp C, Ponti L, Vétek G, Vogt H, Walton VM, Yu Y, Zappalà L, Desneux N. 2015. Invasion biology of spotted wing drosophila (Drosophila suzukii): a global perspective and future priorities. Journal of Pest Science 88: 469–494. Google Scholar


Avitia GE, Castillo GAM, Pimienta BE. 2003. Ciruela mexicana y otras especies del género Spondias L. Universidad Autónoma Chapingo, Texcoco, México. Google Scholar


Barredo JI, Strona G, de Rigo D, Caudullo G, Stancanelli G, San-Miguel-Ayanz J. 2015. Assessing the potential distribution of insect pests: case studies on large pine weevil (Hylobius abietis L.) and horse-chestnut leaf miner (Cameraria ohridella) under present and future climate conditions in European forests. EPPO Bulletin 45: 273–281. Google Scholar


Berry JA. 2012. Pest Risk Assessment : Drosophila suzukii: spotted wing drosophila (Diptera: Drosophilidae) on fresh fruit from the USA. Ministry for Primary Industries. MPI Technical Paper No: 2012/05. New Zealand Government, Ministry for Primary Industries. Google Scholar


Biber-Freudenberger L, Ziemacki J, Tonnang HEZ, Borgemeister C. 2016. Future risks of pest species under changing climatic conditions. PLoS ONE 11: 1–17. Google Scholar


Booth TH, Nix HA, Busby JR, Hutchinson MF. 2014. Bioclim: the first species distribution modeling package, its early applications and relevance to most current Maxent studies. Diversity and Distributions 20: 1–9. Google Scholar


CABI. 2016. Drosophila suzukii (spotted wing drosophila). Retrieved 26 Mar 2016 from Google Scholar


Cliff N. 1987. Analyzing Multivariate Data. New York, New York. Harcourt Brace Jovanovich, San Diego, California, USA. Google Scholar


Cruz LA, Gutiérrez JAG. 2010. Distribución geográfica del género Spondias en México, pp. 31–38 In Cruz LA, Pita DÁ, Rodriguez HB [Eds], Jocotes, Jobos Abales o Ciruelas Mexicanas. Universidad Autónoma Chapingo, Texoco, Mexico. Google Scholar


Cruz LA, Rodriguez HB. 2010. Cultivo, pp. 77–101 In Cruz LA, Pita DÁ, Rodriguez HB [Eds], Jocotes, Jobos Abales o Ciruelas Mexicanas. Universidad Autónoma Chapingo, Texcoco, Mexico. Google Scholar


Cruz-Cárdenas G, López-Mata L, Villaseñor JL, Ortiz E. 2014a. Potential species distribution modeling and the use of principal component analysis as predictor variables. Revista Mexicana de Biodiversidad 85: 189–199. Google Scholar


Cruz-Cárdenas G, Villaseñor JL, López-Mata L, Martínez-Meyer E, Ortiz E. 2014b. Selección de predictores ambientales para el modelado de la distribución de especies en Maxent. Revista Chapingo, Serie Ciencias Forestales y del Ambiente 20: 187–201. Google Scholar


Dalton DT, Walton VM, Shearer PW, Walsh DB, Caprile J, Isaacs R. 2011. Laboratory survival of Drosophila suzukii under simulated winter conditions of the Pacific Northwest and seasonal field trapping in five primary regions of small and stone fruit production in the United States. Pest Management Science 67: 1368–1374. Google Scholar


Damus M. 2009. Some preliminary results from Climex and Maxent distribution modeling of Drosophila suzukii. Version 2. CFIA Plant Health Risk Assessment, Ottawa, Ontario, Canada. Google Scholar


dos Santos LA, Mendes MF, Kruger AP, Blauth ML, Gottschalk MS, Garcia FRM. 2017. Global potential distribution of Drosophila suzukii (Diptera, Drosophilidae). PLoS One 12: e0174318. Google Scholar


Elith J. 2014. Predicting distributions of invasive species. arXiv:1312.0851 [QBio]: 1–28. Retrieved from Google Scholar


Fand BB, Kumar M, Kamble AL. 2014. Predicting the potential geographic distribution of cotton mealybug Phenacoccus solenopsis in India based on MAXENT ecological niche model. Journal of Environmental Biology 35: 973–982. Google Scholar


Fiaboe KKM, Peterson AT, Kairo MTK, Roda AL. 2012. Predicting the potential worldwide distribution of the red palm weevil Rynchophorus ferrugineus (Olivier) (Coleoptera: Curculinoidae) using ecological niche modeling. Florida Entomologist 95: 659–673. Google Scholar


Franklin J. 2009. Mapping Species Distributions: Spatial Inference and Prediction. New York: Cambridge University Press . Cambridge, United Kingdom. Google Scholar


Fresnedo-Ramírez J, Segura S, Muratalla-Lúa A. 2011. Morphovariability of capulín (Prunus serotina Ehrh.) in the central-western region of Mexico from a plant genetic resources perspective. Genetic Resources and Crop Evolution 58: 481–495. Google Scholar


Graham MH. 2003. Confronting multicollinearity in ecological multiple regression. Ecology 84: 2809–2815. Google Scholar


Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965–1978. Google Scholar


Hill MP, Thomson LJ. 2015. Species distribution modelling in predicting response to climate change, pp. 16–37 In Björkman C, Niemelä P [Eds], Climate Change and Insect Pests. CAB International, Wallingford, Oxfordshire, United Kingdom. Google Scholar


Hoffmann AA, Thomson LJ. 2013. Developing tools for predicting responses of viticultural pests and their natural enemies under climate change: modelling, management and extension. The University of Melbourne, Victoria, Australia. Google Scholar


Kenis M, Tonina L, Eschen R, van der Sluis B, Sancassani M, Mori N, Haye T, Helsen H. 2016. Non-crop plants used as a host by Drosophila suzukii in Europe. Journal of Pest Science 89: 735–748. Google Scholar


Jaiswal U, Jaiswal VS. 2005. Psidium guajava guava , pp. 394–401 In Litz RE [Ed], Biotechnology of Fruit and Nut Crops. CABI eBooks, Wallingford, Oxfordshire, United Kingdom. Google Scholar


Jarnevich CS, Young N. 2015. Using the MAXENT program for species distribution modelling to assess invasion risk, pp. 65–81 In Venete RC [Ed], Pest Risk Modelling and Mapping for Invasive Alien Species. CAB International, Wallingford, Oxfordshire, United Kingdom. Google Scholar


LaNGIF. 2014. Posible dispersión de Drosophila suzukii. Retrieved 10 Jan 2017 from Scholar


Lee JC, Burrack HJ, Barrantes LD, Beers EH, Dreves AJ, Hamby KA, Haviland DR, Isaacs R, Richardson TA, Shearer PW, Stanley CA, Walsh DB, Walton VM, Zalom FG, Bruck DJ. 2012. Evaluation of monitoring traps for Drosophila suzukii (Diptera: Drosophilidae) in North America. Journal of Economic Entomology 105: 1350–1357. Google Scholar


Lee JC, Dreves AJ, Cave AM, Kawai S, Isaacs R, Miller JC, van Timmeren S, Bruck DJ. 2015. Infestation of wild and ornamental noncrop fruits by Drosophila suzukii (Diptera: Drosophilidae). Annals of the Entomological Society of America 108: 117–129. Google Scholar


Liu C, Berry PM, Dawson TP, Pearson RG. 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28: 385–393. Google Scholar


López-Collado J, Lopez-Arroyo JI, Robles-Garcia PL, Marquez-Santos M. 2013. Geographic distribution of habitat, development, and population growth rates of the Asian citrus psyllid, Diaphorina citri, in Mexico. Journal of Insect Science 13: 1–17. Google Scholar


Lv W, Li Z, Wu X, Ni W, Qv W. 2011. Maximum entropy niche-based modeling (Maxent) of potential geographical distribution of Lobesia botrana (Lepidoptera: Tortricidae) in China, pp. 239–246 In Li D, Chen Y [Eds], Computer and Computing Technologies in Agriculture V. Springer, Dordrecht, The Netherlands. Google Scholar


NAPPO. 2011. Detection of spotted-wing drosophila (Drosophila suzukii Matsumura) in the Municipality of Los Reyes, State of Michoacan, Mexico. Retrieved15 Mar 2016 from Scholar


O'Donnell MS, Ignizio DA. 2012. Bioclimatic Predictors for Supporting Ecological Applications in the Conterminous United States. US Geological Survey Data Series 691. Google Scholar


Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, Araujo M. 2011. Ecological Niches and Geographic Distributions. Princeton University Press. Princeton, New Jersey, USA. Google Scholar


Phillips SJ. 2009. A brief tutorial on Maxent. Network of Conservation Educators and Practicioners. Center for Biodiversity and Conservation, American Museum of Natural History. Google Scholar


Phillips SJ, Anderson RP, Schapire RE. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231–259. Google Scholar


Phillips SJ, Dudík M. 2008. Modeling of species distribution with Maxent: new extensions and a comprehensive evaluation. Ecography 31: 161–175. Google Scholar


Pla EL. 1986. Análisis Multivariado: Método de Componentes Principales. Secretaría General de la Organización de los Estados Americanos (OEA). Washington, DC, USA. Google Scholar


Poyet M, Eslin P, Héraude M, Le Roux V, Prévost G, Gibert P, Chabrerie O. 2014. Invasive host for invasive pest: when the Asiatic cherry fly (Drosophila suzukii) meets the American black cherry (Prunus serotina) in Europe. Agricultural and Forest Entomology 16: 251–259. afe.12052 Google Scholar


Poyet M, Le Roux V, Gibert P, Meirland A, Prévost G, Eslin P, Chabrerie O. 2015. The wide potential trophic niche of the Asiatic fruit fly Drosophila suzukii: The key of its invasion success in temperate Europe? PLoS ONE 10: 1–26. Google Scholar


Rebollar-Alviter Á, Monserrat PP, Erick FG, Juárez-Guillermo AC, Pineda-Guillermo S, Segura-Ledesma S. 2015. Seasonal dynamics of Drosophila suzukii on cultivated and non-cultivated areas of blackberry (Rubus sp.) and preference of wild fruits, in Michoacan, Mexico, p. 75 In XVIII International Plant Protection Congress. Berlin, Germany. Google Scholar


Rzedowski J, Calderón de Rzedowski G. 1999. Flora del Bajío y de Regiones adyacentes: Anacardiaceae. Fascículo 78. Instituto de ecologia, A.C. Centro Regional del Bajío. Pátzcuaro, Michoacán, México. Google Scholar


Rzedowski J, Calderón de Rzedowski G. 2005. Flora del Bajío y de Regiones adyacentes: Rosaceae. Fascículo 135. Instituto de ecologia, A.C. y Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, Pátzcuaro, Michoacán, México. Google Scholar


Scheldeman X, van Zonneveld M. 2011. Modelación y análisis de distribución de especies, pp. 139–158 In Scheldeman X, van Zonneveld M [Eds], Manual de Capacitación en Análisis Espacial de Diversidad y Distribución de Plantas. Bioversity International, Rome, Italy. Google Scholar


SENASICA. 2013. Mosca del vinagre de alas manchadas Drosophila suzukii Matsumura. Dirección General de Sanidad Vegetal - Sistema Nacional de Vigilancia Epidemiológica Fitosanitaria. México City, Mexico. D.F. Ficha Técnica No. 7. Google Scholar


SENASICA. 2015. Aviso público del riesgo y situación actual de la: mosca del vinagre de las alas manchadas Drosophila suzukii (Matsumura, 1931) (Diptera: Drosophilidae). Dirección General de Sanidad Vegetal-Servicio Nacional de Sanidad, Inocuidad y Calidad Agroalimentaria, Mexico City, Mexico. Google Scholar


SIAP. 2016. Cierre de la producción agrícola por estado. Retrieved 26 Dec 2016 from Scholar


Swets JA. 1988. Measuring the accuracy of diagnostic systems. Science 240: 1285–1293. Google Scholar


Tochen S, Woltz JM, Dalton DT, Lee JC, Wiman NG, Walton VM. 2015. Humidity affects populations of Drosophila suzukii (Diptera: Drosophilidae) in blueberry. Journal of Applied Entomology 140: 47–57. Google Scholar


Václavík T, Meentemeyer RK. 2009. Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions? Ecological Modelling 220: 3248–3258. Google Scholar


Wiman NG, Walton VM, Dalton DT, Anfora G, Burrack HJ, Chiu JC, Daane KM, Grassi A, Miller B, Tochen S, Wang X, Ioriatti C. 2014. Integrating temperature-dependent life table data into a matrix projection model for Drosophila suzukii population estimation. PLoS ONE 9: e106909. Google Scholar
Rigoberto Castro-Sosa, María del R. Castillo-Peralta, Alejandro I. Monterroso-Rivas, Jesús D. Gomez-Díaz, Erick Flores-González, and Ángel Rebollar-Alviter "Potential Distribution of Drosophila suzukii (Diptera: Drosophilidae) in Relation to Alternate Hosts in Mexico," Florida Entomologist 100(4), 787-794, (1 December 2017).
Published: 1 December 2017

distribución espacial
invasive pests
plaga invasora
spatial distribution
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