Predicting the potential invasion suitability of regions to cassava lacebug pests (Heteroptera: Tingidae: Vatiga spp.) S.I. Montemayor1,2*, P.M. Dellapé1,2 and M.C. Melo1,2 1División Entomología, Museo de La Plata, Universidad Nacional de La Plata, Paseo del Bosque s/n, B1900FWA, La Plata, Argentina: 2Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina (CONICET) Abstract Cassava (Manihot esculenta Crantz) is one of the most important staple crops for small farmers in the tropics, feeding about 800 million people worldwide. It is cur- rently cultivated in South and Central America, Africa and Asia. The genus Vatiga is widespread throughout the Neotropical region. Its species are sympatric and feed exclusively on cassava. The main objectives of this paper are: (1) to assess the potential distribution ofVatiga, one of themost relevant pests of cassava; (2) to project the resulting models onto the world; (3) to recognize areas with suitable and optimal climates (and thus, high probability) for future colonization, and (4) to compare this model with the harvested area of cassava analyzing the climatic variables required by both the host and the pest species. Species distribution models were built using Maxent (v3.3.3k) with bioclimatic variables from the WorldClim database in 2.5 arc min resolution across the globe. Our model shows that Vatiga has the potential to expand its current distribution into other suitable areas, and could invade other regions where cassava is already cultivated, e.g., Central Africa and Asia. Considering the results and the high host specificity of Vatiga, its recent appearance in Réunion Island (Africa) poses a serious threat, as nearby areas are potentially suit- able for invasion and could serve as dispersal routes enablingVatiga to reach the con- tinent. The present work may help prevention or early detection of Vatiga spp. in areas where cassava is grown. Keywords: pest, Manihot esculenta, SDM, bioclimatic profiles, Vatiga (Accepted 9 November 2014) Introduction Cassava (Manihot esculenta Crantz) is one of the most important staple crops for small farmers in the tropics. Recent estimates suggest that 800 million people worldwide consume cassava on a regular basis (FAO, 2013). Originally from Amazonia, it is currently grown in South and Central America, Africa and Asia (Monfreda et al., 2008; Léotard et al., 2009). Although it was long considered as unsuitable for intensification, since the year 2000, the world’s annual cas- sava production has increased by an estimated 100 million tons (FAO, 2013). According to this report, cassava will see a shift to monocropping, higher-yielding genotypes and greater use of irrigation and agrochemicals. But intensification carries great risks, such as the outbreak of pests and plant disease (FAO, 2013). Several arthropods are pests of cassava, including lace bugs (Heteroptera: Tingidae), with Vatiga species being the most relevant (Froeschner, 1993; Neal & Schaefer, 2000). Vatiga is a Neotropical genus that is widespread throughout the tropical and subtropical areas of the region. Its five species are sympatric and feed exclusively on cassava (Froeschner, 1993). The species most frequently recorded as cassava pests *Author for correspondence Phone: 54 221 4257744 E-mail: smontemay@fcnym.unlp.edu.ar Bulletin of Entomological Research, Page 1 of 9 doi:10.1017/S0007485314000856 © Cambridge University Press 2014 mailto:smontemay@fcnym.unlp.edu.ar http://crossmark.crossref.org/dialog/?doi=10.1017/S0007485314000856&domain=pdf http://crossmark.crossref.org/dialog/?doi=10.1017/S0007485314000856&domain=pdf areVatiga illudens (Drake) andVatiga manihotae (Drake), which are exhaustively mentioned in the literature (Oliveira et al., 2001; Moreira et al., 2006; De Paula-Moraes et al., 2007; Fialho et al., 2009; Halbert, 2010; Alves et al., 2012; Bellon et al., 2012; Streito et al., 2012). However, misidentifications are frequent because of the intraspecific variability within the genus (Froeschner, 1993), so that the number of species in- volved could be different. The symptoms of Vatiga spp. infestation are evident. Leaves have yellowish stains, which later become reddish- brown. Sucking of the sap by the bugs weakens the plant and reduces its photosynthetic capacity, favoring the prema- ture fall of basal leaves (Oliveira et al., 2001; Moreira et al., 2006). Few studies have evaluated the consequences of Vatiga spp. infestations, indicating a yield loss of 21% (Fialho et al., 1994), 39% (Bellotti et al., 1999), 35% (Moreira et al., 2006) and 48–55% (Fialho et al., 2009). Even though other arthropods (e.g., whiteflies, mealybugs andmites) are considered to be the main pests of cassava crops, there has been increasing inci- dence of Vatiga spp., which has become a serious concern to cassava farmers (Bellon et al., 2012). Recently, well-established populations of V. illudens have been reported outside their native range, i.e., in Florida, USA (Halbert, 2010) and in the Réunion Island, Africa (Streito et al., 2012). In both cases, the most plausible means of entry is by accidental human action. The spread of Vatiga spp. outside its native range suggests its invasive nature. Streito et al. (2012) have warned that in the absence of phytosanitary con- trol measures, it will quickly spread to the whole area of cas- sava crops. The main objectives of this paper are: (1) to assess the potential distribution ofVatiga spp. through a species distribu- tional model (SDM); (2) to project the resulting models to the world; (3) to recognize areas with suitable and optimal cli- mates (and thus a high probability) for future colonization; and (4) to compare this model to the areawhere cassava is har- vested, analyzing the climatic variables required by both the host and the pest species. This information may help preven- tion or early detection of Vatiga spp. in areas where cassava is grown. Material and methods Occurrence data An occurrence database for the Vatiga species was com- piled from the literature and from material studied from Museum collections (California Academy of Sciences, USA (CAS); the Instituto Oswaldo Cruz, Rio de Janeiro, Brazil (IOC); the Institut Royal des Sciences Naturelles de Belgique, Belgium (IRSNB); the Museo de La Plata, Argentina (MLP); the Museo Argentino de Ciencias Naturales ‘Bernardino Rivadavia’, Buenos Aires, Argentina (MACN); and the National Museum of Natural History, USA (USNM)). This database comprises 92 localities: 82 from the Neotropics (native range), 1 from the Nearctic (Florida, USA), and 9 from the Ethiopian Region (Réunion Island). One of the main problems of most SDMs is the failure to account for spatial dependence of occurrence data (Gelfand et al., 2006; Bahn & McGill, 2007; Dormann, 2007; Elith et al., 2010; Record et al., 2013). Spatial autocorrelation arises in eco- logical data because the nearby points tend to be more similar in physical characteristics and/or species occurrences or abundances than are pairs of locations that are farther apart (Legendre, 1993). In order to avoid spatial autocorrelation, a modelwith the 82 records from the native areawas developed, and the spatial autocorrelation was measured among pseudo-residuals (1 – probability of occurrence generated by model) by calculating Moran’s I at multiple distance classes using SAM v4.0 (Rangel et al., 2010). Significance was deter- mined using permutation tests. A minimum distance of 352- km was detected so a grid of cell of those dimensions was created and the occurrence point closest to the centroid of each cell was selected. As a result, the dataset was reduced from 82 to 44 occurrence points from the native range, which we used for model calibration. Selection of variables Climate match, although it has limitations, has been asso- ciated with the successful establishment of a range of non- indigenous organisms, and can be a useful predictor of risk (Floerl et al., 2013). Moreover, climate sensitivity has been re- cognized as the main characteristic that is significantly asso- ciated with invasive species across biological groups (Hayes & Barry, 2008; Bomford et al., 2009; Elith et al., 2010). To train themodel, data fromWorldClim databasewere used in 2.5 arc min resolution across the globe (Hijmans et al., 2005). To exclude correlated variables used for modeling, Pearson’s cor- relation coefficient (r) was calculated between each pair of the 19 WorldClim variables for the 44 points from the native range. For each comparison with r≥ 0.90, one variable was se- lected for modeling. The discarded variables were those corre- lated with more than one variable, or if the pair of variables corresponded to monthly or quarterly information, the one corresponding to monthly information was discarded. SDM and model evaluation SDMswere built usingMaxent v3.3.3k (Phillips et al., 2006), which was developed to model species distributions using only presence data. Default settings were used to run themod- els, which were built through cross-validation. We excluded 10% of the occurrence data and then tested the accuracy of the model to predict the excluded data points. This was repeated 10 times for each model, and the mean output was used to determine distribution probabilities and overall model performance. The accuracy of each model was Fig. 1. Model performance is measured as the AUC of receiver operating characteristic (ROC) of model testing. Star, average AUC of real model; squares, AUC of null models. S.I. Montemayor et al.2 determined using the area under the curve (AUC). The null model approachwas used to test whether the resultingmodels provide a better fit than chance. Ninety-nine null models were built by drawing occurrence points at random without replacement. Each null model was based on an equal number of occurrence points as the real model and modeled under the same conditions. The AUCs of the null models were used to test the significance of the real model. If the AUC of the real model fell in or above the highest 5% of the null models’ AUCs, the real model was considered statistically significantly better than random Raes & Ter Steege (2007). Recognition of susceptible areas to infestation In order to recognize areas susceptible to infestation, the Maxent logistic output was converted to two binary pres- ence/absence climate suitability maps, one with suitable and the other with optimal climatic conditions. These maps were Fig. 2. Suitability map from a SDM for Vatiga spp. based on 44 presence records (empty circles) and 12 bioclimatic variables projected worldwide using Maxent. Filled circles represent all presence records of Vatiga spp. Fig. 3. Suitable area for Vatiga spp. based on the ‘minimum training presence logistic threshold’ provided by Maxent, and superimposed with the harvested area of cassava. Potential invasion suitability of regions to cassava lacebug pests (Tingidae) 3 based on two thresholds provided by Maxent, the ‘minimum training presence logistic threshold’ that indicates values abovewhich the climatic conditions are suitable, and the ‘max- imum test sensitivity plus specificity logistic threshold’ that indicates values above which the climatic conditions are opti- mal (Kebede et al., 2014). Both binary maps were superim- posed with a map of the actual harvested regions of cassava downloaded from Monfreda et al. (2008). Bioclimatic profiles With the goal of comparing the climatic variables from the harvested area of cassava and the area modeled with suitable climatic conditions for Vatiga spp., we created box plots. We used the Maxent logistic output converted to binary pres- ence/absence data (values above the ‘minimum training pres- ence logistic threshold’) and the raw environmental data from the crop-layer of cassava (Monfreda et al., 2008). The cassava and Vatiga spp. rasters were converted to point shapefiles using ArcGIS 10 and the values of the environmental data at each point were extracted using DIVA-GIS (Hijmans et al., 2005). With this information, box plots were built for each of the variables used to construct the model. In order to determine whether there is a niche shift when Vatiga spp. invades new areas and which variables it involves, we explored the bioclimatic profiles ofVatiga spp. in native and invaded areas using DIVA-GIS. The bioclimatic profiles were built considering all the distributional information for Vatiga spp. andallWorldClimbioclimatic variables. Values of the bio- climatic variableswere extracted from the recorded localities of Vatiga spp. and arranged in a cumulative frequency distribu- tion where native and invasive records were identified. Results Pearson results The exploratory analysis of the climatic variables (Supplementary material S1) led to a combination of 12 minimally correlated variables. From the 19 climatic variables considered, we recorded the following: mean monthly tem- perature range (Bio2), isothermality (Bio3) [(BIO2/BIO7) (*100)], temperature annual range (Bio7), mean temperature of wettest quarter (Bio8), mean temperature of driest quarter (Bio9), mean temperature of warmest quarter (Bio10), annual precipitation (Bio12), precipitation seasonality (Bio15), pre- cipitation of wettest quarter (Bio16), precipitation of driest quarter (Bio17), precipitation of warmest quarter (Bio18) and precipitation of coldest quarter (Bio19). Predicted range of suitable climates for Vatiga spp. Our model provides a significantly better fit than expected by chance alone (fig. 1), with high predictive performance. The average AUC of the 99 null models was 0.733, standard devi- ation was 0.029, and the AUC range was 0.647–0.800, in con- trast to our model, for which average AUC was 0.940, standard deviation was 0.026 and the AUC range was 0.903– 0.984. The pixels with highest probabilities are concentrated around the Equator; and the predicted climatically suitable range is between approximately 25°S and 25°N (fig. 2). InAmerica, we found suitable climatic conditions in almost all north-central South America, not extending west of the Andes. Suitable conditions are observed throughout continen- tal Central America, in addition to the known distribution in the Caribbean Islands. In North America, conditions are suit- able on the eastern coast of Mexico and a narrow strip along the west coast of USA. In Africa, conditions are favorable in lowland Central, southern West and coastal East Africa, and in Madagascar and neighboring islands. We observed the most suitable conditions (>50%) mainly around the Equator. In Asia, we found suitable climatic conditions in the south- east, with most favorable conditions (>50%) around the Equator between 10°S and 10°N. We also found suitable con- ditions (>30%) in Oceania, mainly on the southern and eastern coast of Australia, north West of New Zealand and almost all the territories of Papua, New Guinea and the Pacific Islands. Fig. 4. Optimal area for Vatiga spp. based on the ‘maximum test sensitivity plus specificity logistic threshold’ provided by Maxent, and superimposed with the harvested area of cassava. S.I. Montemayor et al.4 The main areas with highly favorable climatic conditions (>50%) correspond to Papua, New Guinea and the Pacific Islands. Comparison with the actual area of cassava cultivation The overlapping of the suitable climatic map with the harvested area of cassava (fig. 3), shows the areas where Vatiga spp. could actually be found, as they feed exclusively on this crop. According to our results, areas close to the Equator are the most susceptible of being invaded. The same pattern is observed in the optimal climatic map but the area covered is much smaller, forming small interconnected patches (fig. 4). Bioclimatic conditions and profiles of Vatiga spp. and cassava Climatic variable scores for the cassava harvested area and the potential distribution above the ‘minimum training pres- ence logistic threshold’ of Vatiga spp. are shown as boxplots in fig. 5. The visual comparison of the boxplots of the 12 vari- ables shows that the ranges of almost all the variables of cas- sava exceed those observed for Vatiga spp. Hence, the climatic niche of Vatiga spp. is, as expected, clearly narrower than that of cassava. The presence of cassava in areas where there is a remarkable difference in the ranges of the same variables, such as Bio7, Bio12, Bio15, Bio16, Bio17, Bio18 and Bio19, sug- gests that these variables could constrain the distribution of Vatiga spp. Importantly, all of these (except for Bio7) are related to precipitations. Fig. 5. Direct comparison of cassava harvested records and Vatiga spp. records extracted from the cassava harvested area and the Maxent logistic output at a threshold value above the ‘minimum training presence logistic threshold’. Potential invasion suitability of regions to cassava lacebug pests (Tingidae) 5 The only variable whose upper range is wider inVatiga pp. than in cassava is Bio9 (mean temperature in driest quarter), which indicates that there could be populations of Vatiga spp. in areas where the values of Bio9 are higher than in those where cassava is cultivated. A possible explanation could be that these higher values correspond to areas with populations of Vatiga spp. that feed on wild varieties of cassava adapted to this kind of climatic conditions. The bioclimatic profiles of Vatiga spp. (figs 6 and 7) inva- sive records are all within the ranges of the native records so no niche shift was observed, though the case of three variables related to temperature (mean monthly temperature range Fig. 6. Bioclimatic profile of Vatiga spp. using the climatic variables (Bio1–Bio12); cumulative relative frequencies (0–100) are displayed for the full data set. Smaller diamonds indicate native records; white circles represent African records from the Réunion Island and the black circle represents the Nearctic record from Florida. S.I. Montemayor et al.6 (Bio2), isothermality (Bio3), and maximum temperature of warmest month (Bio5)) the invasive records are among the lowest values of the range. This situation is observed in the isothermality for the African and Nearctic records, and in the mean monthly temperature range and the maximum temperature of warmest months for the African records. Discussion Climatic model predictions are an important tool and a valuable first approach to the potential magnitude and dis- tributional pattern of future impact of species, but they should be interpreted carefully as they do not consider important factors other than climate, such as biotic interactions (Montemayor et al., 2014). Thus, an area predicted as suitable does not mean that populations of the species will necessarily become successfully established there, but it is useful informa- tion for identifying areas of potential invasion and spread. Models can also underestimate areas of potential invasion as it has been suggested that a shift in the species’ climate niche during a comparatively short time frame is possible during biological invasions (Broennimann et al., 2007; Fitzpatrick et al., 2007; Steiner et al., 2008; Alexander & Edwards, 2010). Niches may even be conserved along some environmental axes but not along others (Fitzpatrick et al., 2008). Ourmodel shows that themost suitable conditions inAfrica includemany countries in centralAfrica, formost ofwhich cas- sava is among the three most important crops in terms of pro- duction for the year 2012 (FAO, 2013). Considering the results obtained and the invasive nature and high host specificity of Vatiga spp., its recent appearance in Réunion Island represents a serious threat, as nearby areas are potentially suitable for invasion and could serve as dispersal routes throughout the continent. The optimal climatic map shows that even though areas with suitable climatic conditions for Vatiga spp. are not large, there exists a connection between them forming poten- tial pathways for invasion. This is valid not only for Africa but also for South America, Asia and Oceania. Fig. 7. Bioclimatic profile ofVatiga spp. using the climatic variables (Bio13–Bio19); cumulative relative frequencies (0–100) are displayed for the full data set. Smaller diamonds indicate native records; white circles represent African records from the Réunion Island and the black circle represents the Nearctic record from Florida. Potential invasion suitability of regions to cassava lacebug pests (Tingidae) 7 As weak fliers, the dispersal of Vatiga spp. is limited and their entry into new environments depends on human actions. As a result of superimposing the optimal areas of Vatiga spp. with the harvested area of cassava we have been able to iden- tify where it is important to take phytosanitary control mea- sures, and in the case that species of Vatiga are detected in any of these areas, which of the neighboring areas are also at risk as the potential pathways for invasion have also being identified (fig. 4). It must be considered that to analyze the potential distribu- tion ranges ofVatiga spp., we assumed that the known range is in equilibrium with environmental parameters (Araújo & Pearson, 2005), and that the niche is conservative across space and time (Wiens & Graham, 2005). There are three vari- ables (mean monthly temperature range, isothermality and maximum temperature of warmest month) where the invasive records are among the extreme lowest values of the range (figs 6 and 7) showing a tendency towards a climatic shift. However, because of lack of information regarding Vatiga spp. outside their native range any conclusion is speculative. A further important step for future research would be to place more emphasis on the evaluation of the ecological and physiological characteristics of Vatiga spp. to identify other important environmental parameters, to evaluate if climatic shift should be expected in Vatiga spp. and how climatic change could affect its distribution. Similar approaches to the one used here may be of general application to other pests of crops for which maps of the cultivated areas are avail- able. As noted by Neal & Schaefer (2000), it is likely that in absence of phytosanitary control measures, the economic importance of lace bugs will increase. It is to be expected that the range of the various Vatiga species will expand, fol- lowing thewider cultivation and increasing importance of cas- sava to feed a growing world population. Supplementary Material The supplementary material for this article can be found at http://www.journals.cambridge.org/BER Acknowledgements This study was supported by the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina; and the following grants: PIP CONICET 0255 (2010–2012), and PICT 2010 1778 to senior author. References Alexander, J.M. & Edwards, P.J. (2010) Limits to the niche and range margins of alien species. Oikos 119, 1377–1386. 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