Amer. Malac. Bull. 31(1): 39–50 (2013) 39 The giant African snail, Achatina fulica (Gastropoda: Achatinidae): Using bioclimatic models to identify South American areas susceptible to invasion Roberto E. Vogler1,2, Ariel A. Beltramino1,3, Mariano M. Sede2,4, Diego E. Gutiérrez Gregoric1,2, Verónica Núñez1,2, and Alejandra Rumi1,2 1 División Zoología Invertebrados, Facultad de Ciencias Naturales y Museo, Universidad Nacional de La Plata, Paseo del Bosque s/n (B1900FWA), La Plata, Argentina 2 Consejo Nacional de Investigaciones Científi cas y Técnicas (CONICET), Argentina 3 Agencia Nacional de Promoción Científi ca y Tecnológica (ANPCyT), Argentina 4 Instituto de Investigaciones Biomédicas en Retrovirus y SIDA, Facultad de Medicina, Universidad de Buenos Aires, Paraguay 2155 (C1121ABG), Buenos Aires, Argentina Correspondence, R.E. Vogler: robertovogler@fcnym.unlp.edu.ar; robertovogler@yahoo.com.ar Abstract. The best way to reduce problems related to invasive species is by preventing introductions into potentially susceptible areas. The purpose of this study was to create distribution models for the invasive gastropod Achatina fulica Bowdich, 1822 in South America in order to evaluate its potential geographic distribution and identify areas at potential risk. This mollusc, considered one of the 100 world’s worst invasive alien species, is the focus of intense concern due to its impact on agriculture, human health, and native fauna. We tested two commonly used ecological niche modeling methods: Genetic Algorithm for Rule-Set Prediction (GARP) and Maximum Entropy (MaxEnt). Models were run with occurrence points obtained from several sources, including the scientifi c literature, international databases, governmental reports and newspapers, WorldClim bioclimatic variables, and altitude. Models were evaluated with the threshold-independent Receiver Operating Characteristic (ROC) and Area Under the Curve (AUC). Both models had consistent performances with similar areas predicted as susceptible, including areas already affected and new potentially susceptible areas in both tropical and temperate regions of South America. Key words: land mollusc, bioinvasion, distribution, GARP, MaxEnt In recent decades, South America has been seriously af- fected by invasive mollusc species (Letelier et al. 2007, Gutiérrez Gregoric and Vogler 2010, Rumi et al. 2010). Some of these introductions have been the result of unintentional events, such as the cases of Theba pisana Müller, 1774 and Melanoi- des tuberculata Müller, 1774 (Rumi et al. 2010, Peso et al. 2011). In contrast, other introductions have been intentional as a consequence of diverse interests, including biological control and ornamental, medicinal, and economic purposes (Cowie and Robinson 2003, Fernandez et al. 2003). The giant African snail Achatina (Lissachatina) fulica Bowdich, 1822, native to eastern Africa, is an example of the latter. Considered one of the 100 world´s worst invasive alien species (Lowe et al. 2000, Raut and Barker 2002), Achatina fulica was fi rst introduced in South America at an agricul- tural fair in Brazil in the late 1980’s as a breeding animal in order to compete with Cornu aspersum (Müller, 1774), the “real” escargot (Teles and Fontes 2002, Thiengo et al. 2007). Due to the lack of snail-eating habits of the Brazilian popula- tion, facilities where the species used to be reared were aban- doned and the animals were released into the wild (Teles and Fontes 2002, Thiengo et al. 2007, Zenni and Ziller 2010). These events were followed by a rapid and expansive dispersal in Brazil with its occurrence confi rmed in 25 of 26 states in the country (Maldonado Júnior et al. 2010, Salgado 2011). Recent reports suggest that Achatina fulica has expanded its range and become widespread throughout several South American countries, including Argentina, Colombia, Ecuador, Paraguay, Peru and Venezuela (Correoso Rodríguez 2006, Martinez-Escarbassiere et al. 2008, Borrero et al. 2009, Correoso and Coello 2009, Paraguay Biodiversidad 2010, Gutiérrez Gregoric et al. 2011). This broad distribution may not only be attributed to active dispersal from established populations (Correoso and Coello 2009) but also to the repeated trans- portation opportunities provided by people, including fi sh- ing (i.e. using the individuals as bait), heliciculture, sold as a “pet”, and the inadvertent transport of specimens (e.g., with waste, building materials, plants). Together these factors have contributed to a rapid geographical range expansion of this invasive species (Borrero et al. 2009, Colley 2010, Gutiérrez Gregoric et al. 2011). In this context, bioclimatic models (also known as species distribution models and ecological niche models; Jeschke and Stryer 2008) constitute important tools to predict future changes in the species geographic ranges, 40 AMERICAN MALACOLOGICAL BULLETIN 31 · 1 · 2013 since they may be used to identify potential areas of suitable habitat where invasive species may actually be present but not yet detected and favorable regions where they may disperse in the future (Nyári et al. 2006, Baldwin 2009, Fukasawa et al. 2009, Robinson et al. 2010). Predictive modeling characterizing the distribution of Achatina fulica in South America, especially in those coun- tries where the species introduction has been recent, repre- sents a valuable tool for developing effective control policy in order to manage the ongoing invasion. An extensive list of methods to generate spatial distribution models of plants, animals, and viruses at different geographical scales, have been described in the literature (see review by Elith and Leathwick 2009) and include GARP (Genetic Algorithm for Rule-Setting Prediction; Stockwell and Peters 1999) and MaxEnt (Maximum Entropy; Phillips et al. 2004, 2006). Using MaxEnt and 13 environmental variables, Borrero et al. (2009) developed a potential distribution model for the giant African snail in South America; however, this model focused on the Andean region. This model indicated low to medium probability that the species would occur in southern South America; however, due to recent fi ndings of the species in subtropical areas of Argentina and Paraguay, we hypothesize that the potential southern distribution area would be larger than previously predicted. In this paper, we generate two high-resolution bioclimatic models using GARP and MaxEnt in order to re-evaluate threat of A. fulica in the southernmost region of South America. The models generated will be useful in planning control strategies, prioritizing areas for surveil- lance, and for taking preventive actions in order to limit the spread of A. fulica. MATERIAL AND METHODS Study area and species records The study area included all South American countries. Presence data (N = 490) for Achatina fulica were obtained from several sources, including the scientifi c literature, inter- national databases, government reports and newspapers (Table 1). For those localities where coordinates were not provided by the source, a georeferenced position was ob- tained from the gazetteer GEOLocate Web Application (http://www.museum.tulane.edu/geolocate/web/webgeoref. aspx). Environmental variables Nineteen bioclimatic variables and a topographic variable (altitude) (Table 2), at a spatial resolution of 30 arc seconds (approximately 1 km2), were used as predic- tors. These data, obtained from WorldClim v. 1.4 (http:// www.worldclim.org), were derived from weather station data spanning 1950–2000 (Hijmans et al. 2005). Given the heterogeneous habitats that Achatina fulica can occupy and its polyphagous diet, we chose to not include vegeta- tion cover in our models, as was done in De Meyer et al. (2008). Modeling of susceptible areas Models of the potential distribution of Achatina fulica were generated by using two algorithms: GARP (DesktopGarp v. 1.1.6; Stockwell and Noble 1992, Stockwell and Peters 1999, Scachetti-Pereira 2002, Scachetti-Pereira and Siqueira 2007) and MaxEnt (MaxEnt v. 3.3.3a; Phillips et al. 2004, 2006). These algorithms model the species’ ecological niche (a set of ecological conditions habitable for a species) by examining the relationship between locations of known species presence and the environmental characteristics of that area and then extrapolating from this the areas where similar conditions oc- cur in the study area (Paredes-García et al. 2011). In both models, 75% of presence records were randomly selected and used in the model training while the remaining 25% were used in the model testing. For GARP, we generated 100 models with a convergence limit of 0.05, a maximum of 100 iterations, and all four rules (atomic, range, negated range, and logistic regression). The 20 best models were selected by hand following the recom- mendations of Anderson et al. (2003). The predictions of the best subset were arithmetically combined in ArcGIS v.9.1 (ESRI, Redlands, California) to create a composite prediction of the species potential distribution. This potential distribu- tion was interpreted as a surface of densities related to the probability of suitable conditions for Achatina fulica. The consensus GARP map was reclassifi ed into four categories: areas highly susceptible (pixels where the 20 models predict- ed presence), areas of susceptibility medium-high (pixels where 19/20 models predicted presence), areas of susceptibil- ity medium-low (pixels where 18/20 models predicted pres- ence), and areas of low susceptibility (pixels which converge up to 16 of the 20 models). For MaxEnt, the model was computed as “logistic”. This output returns a continuous map with an estimated probabil- ity of presence between 0 (no probability of the species pres- ence) and 1 (high probability of presence), which permits fi ne distinctions between the suitability of different areas modeled (Giovanelli et al. 2008). All other parameters were used by default settings. Increased presence probability areas for Acha- tina fulica were considered to be more susceptible to invasion by the species. To determine which variables contributed most to the development of the model, the MaxEnt program was confi g- ured to calculate the significance of variables using the jackknife procedure. This procedure produces three types of models: models created with an omitted variable and all other GIANT AFRICAN SNAIL IN SOUTH-AMERICA 41 Table 1. Sources of Achatina fulica occurrence in South America used in the two bioclimatic models. Country Consulted sources Argentina Gutiérrez Gregoric et al. (2011) Brazil Teles et al. (1997) Vasconcellos and Pile (2001) Teles and Fontes (2002) Carvalho et al. (2003) Barçante et al. (2005) Colley et al. (2005) Eston et al. (2006) Sacramento et al. (2006) Berto and Bogéa (2007) Oliveira et al. (2007) Paula and Lopes (2007) Thiengo et al. (2007) Agudo-Padrón (2008) Schiffl er et al. (2008) Thiengo et al. (2008) Albuquerque et al. (2009) Colley and Fischer (2009) Ohlweiler et al. (2010) Thiengo et al. (2010) Web sites (November 2011): Base de dados sobre especies exóticas invasoras I3N-Brasil: http://www.institutohorus.org.br ClickSergipe (2010): http://www.clicksergipe.com.br Conquiologistas do Brasil (2010): http://www.conchasbrasil.org.br Gazeta do Triângulo (2010): http://www.gazetadotriangulo.com.br/novo/ Injipa (2010): http://injipa.com.br Portal Agora Maranhão (2010): http://www.agoramaranhao.com Portal de São Francisco do Sul (2010): http://www.sfs.com.br Portal O Día (2010): http://www.portalodia.com Prefeitura de Aracruz (2010): http://www.aracruz.es.gov.br Prefeitura de Bela Vista (2010): http://www.belavista.go.gov.br Prefeitura de Jataí (2010): http://www.jatai.go.gov.br Rede Globo (2010): http://www.globo.com Rondônia Digital (2010): http://rondoniadigital.com Bolivia Correoso and Coello (2009) Colombia Borrero et al. (2009) Correoso and Coello (2009) Base de datos sobre especies exóticas invasoras I3N-Colombia (November 2011): http://ef.humboldt.org.co. Ecuador Borrero et al. (2009) Correoso and Coello (2009) Paraguay Paraguay Biodiversidad (2010) Peru Borrero et al. (2009) Correoso and Coello (2009) Venezuela Carvalho et al. (2003) Martínez-Escarbassiere et al. (2008) variables included, models including only one variable, and a model created with all variables. The variables considered most important for model development are those reducing training gain when are not included in the model and show gain when the model is developed involving just the single variable (Colacicco-Mayhugh et al. 2010, Torres and Jayat 2010). Finally, comparison of the resulting models from both algorithms was done through Receiver Operating Characteris- tic (ROC curves analyses; Fielding and Bell 1997) representing 42 AMERICAN MALACOLOGICAL BULLETIN 31 · 1 · 2013 the distribution predictions vs. independent presence and absence data and, by calculating the Area Under the Curve (AUC). The AUC is a threshold independent index com- monly used to assess prediction maps (Pearce and Ferrier 2000, Giovanelli et al. 2008), which can take values between 0.5 (no predictability) and 1 (perfect prediction). According to Loo et al. (2007), a value above 0.8 indicates a strong pre- diction. For GARP it is only possible to calculate a single value of AUC due to the nature of the algorithm (Larson et al. 2010), whereas for MaxEnt two values, one for model training and one for model testing are calculated. However, since the strength of the predictions of both methods can- not be compared directly (Phillips et al. 2006), identifi ca- tion of areas susceptible to invasion by Achatina fulica in South America was interpreted separately but in a compara- tive manner. In addition, to increase the level of detail with- in each country, the primary political division (e.g., state, province) was incorporated into the models for a better in- terpretation of results. RESULTS In general terms, both models had very good perfor- mance and agreed that a large region of South America is highly susceptible to invasion by Achatina fulica (Fig. 1). The AUC calculated from the ROC curve generated for GARP was 0.805. Meanwhile, MaxEnt showed the best performance for the analyzed dataset, with AUC values of 0.944 for training data and 0.921 for test data, with a standard deviation of 0.010. The MaxEnt model showed a larger area of susceptibil- ity to invasion by the giant African snail, showing in all coun- tries a greater number of affected states (also called departments, regions, provinces or districts according to the country) than GARP (Figs. 2–4, Table 3). According to GARP, countries such as Chile and Uruguay, would not be susceptible to inva- sion, but would be according to MaxEnt, although the area affected was small (Fig. 4). The jackknife test of variable importance in MaxEnt showed that temperature seasonality (bio4) and mean tem- perature of coldest quarter (bio11) were the variables that most infl uenced the development of the model when these variables were used alone (Fig. 5). Temperature seasonality (bio4), isothermality (bio3), and altitude (alt) exhibited modest reductions in training gain when they were removed from the model, thus indicating that contain information necessary for the model (Fig. 5). The remaining variables contributed less to model development. DISCUSSION In this study, models generated with GARP and MaxEnt produced similar results and consistent prediction maps. Al- though the predictive power of both algorithms cannot be directly compared (Phillips et al. 2006), parallel interpreta- tion of the graphical output was useful in identifying area sus- ceptible to Achatina fulica invasion. Our results are in agreement with other previous studies, which suggest that even though the species exhibits a wide environmental tolerance, it prefers warm habitats (Raut and Barker 2002, Albuquerque et al. 2009). Specifi cally, tempera- ture seasonality and mean temperature of coldest quarter were identifi ed as the two variables having the most impor- tant effect on the potential distribution of Achatina fulica. These fi ndings would explain the absence of species in south- ern South America, which is characterized by marked season- ality and low temperatures. Our models matched many of the predictions made by Borrero et al. (2009) and Correoso and Coello (2009) with sus- ceptible areas identifi ed in Guyana, French Guiana, Suriname, Peru, Venezuela, Ecuador and Colombia; however, by in- cluding the southernmost records of the species for model training and employing a higher spatial resolution, the poten- tial distribution areas in the South America temperate regions were expanded. According to our results, Chile and Uruguay appear to be the least susceptible countries, with the exception of southern Chile, which might be susceptible to invasion Table 2. Bioclimatic variables used in models development. Tem- peratures are expressed in °C *10, precipitations in mm, and eleva- tion in m above sea level. Variable Description alt Altitude bio1 Annual mean temperature bio2 Mean diurnal range (monthly mean, T° max-T° min) bio3 Isothermality (bio2/bio7) x 100 bio4 Temperature seasonality (standard deviation x 100) bio5 Maximum temperature of warmest month bio6 Minimum temperature of coldest month bio7 Temperature annual range (bio5-bio6) bio8 Mean temperature of wettest quarter bio9 Mean temperature of driest quarter bio10 Mean temperature of the warmest quarter bio11 Mean temperature of coldest quarter bio12 Annual precipitation bio13 Precipitation of wettest month bio14 Precipitation of driest month bio15 Precipitation seasonality (coeffi cient of variation) bio16 Precipitation of wettest quarter bio17 Precipitation of driest quarter bio18 Precipitation of the warmest quarter bio19 Precipitation of the coldest quarter GIANT AFRICAN SNAIL IN SOUTH-AMERICA 43 due to the existence of favorable environmental condi- tions. Specifi cally, this region of Chile contains “Valdivian Temperate Rainforest”, which has been reported to contain another exotic snail, Cornu aspersum, and an exotic slug, Ari- on intermedius Normand, 1852 (Cádiz and Gallardo 2007, Artacho and Nespolo 2009), suggesting that environmental conditions exist to support invasions of other mollusc spe- cies, including Achatina fulica. In Argentina, the northeast region shows varying degrees of susceptibility but the overall trend is towards a medium susceptibility level. Even the north central area could be iden- tifi ed as susceptible, although with a low probability. None- theless, it is important to maintain vigilance of this area because it is comprised of Subtropical Cloudforest (Yungas), which shares some environmental conditions with the Paranaense Rainforest (located in northeastern Argentina, southern Brazil, and East of Paraguay), where the species is already present (Miranda and Cuezzo 2010, Gutiérrez Gregoric et al. 2011). In Paraguay, despite the fact that the majority of the eastern portion of the country can be considered as a highly susceptible area, Achatina fulica has only been reported in the one southern location, the Ayolas (Table 1). Due to the large number of occurrences of A. fulica in Brazil along the Paraguay border (Thiengo et al. 2007, Colley and Fischer 2009), it can be assumed that the species may occur in other locations of Paraguay but its presence has not yet been reported. The majority of Bolivia is highly susceptible to Achatina fulica invasion, with the exception of the southwest region where susceptibility appears to be low. We were unable to lo- cate confi rmed locations of the species presence; however, Correoso and Coello (2009) reported that the species was be- ing commercialized in the city of La Paz. This fact, combined with the country’s shared border with Brazil and Paraguay (Paula and Lopes 2007, Thiengo et al. 2007) suggests that the species is inevitably present in Bolivia but surveys are needed to confi rm its distribution. Figure 1. Bioclimatic models of susceptible areas: A, GARP and B, MaxEnt. Lighter areas indicate low susceptibility regions and darker areas indicate high susceptible regions. Points indicate occurrences records used in study. 44 AMERICAN MALACOLOGICAL BULLETIN 31 · 1 · 2013 Figure 2. Areas susceptible to invasion in northern South America (Venezuela, Guyana, French Guiana and Suriname), according to A, GARP and, B, MaxEnt. GIANT AFRICAN SNAIL IN SOUTH-AMERICA 45 Figure 3. Areas susceptible to invasion in northwestern and central South America (Colombia, Ecuador, Peru, and Brazil), according to A, GARP and, B, MaxEnt. 46 AMERICAN MALACOLOGICAL BULLETIN 31 · 1 · 2013 Figure 4. Areas susceptible to invasion in southern South America (Argentina, Chile, Uruguay, Paraguay and Bolivia), according to A, GARP and, B, MaxEnt. GIANT AFRICAN SNAIL IN SOUTH-AMERICA 47 ACKNOWLEDGMENTS The authors wish to thank Dr. Karen De Matteo for help- ful comments and English editing. LITERATURE CITED Agudo-Padrón, A. I. 2008. Listagem sistemática dos moluscos conti- nentais ocorrentes no Estado de Santa Catarina, Brasil. Comu- nicaciones de la Sociedad Malacológica del Uruguay 9: 147–179. [In Portuguese]. Albuquerque, F. S., M. C. Peso-Aguiar, M. J. T. Assunção- Albuquerque, and L. Gálvez. 2009. Do climate variables and human density affect Achatina fulica (Bowditch) (Gastropoda: Pulmonata) shell length, total weight and condition factor? Brazilian Journal of Biology 69: 879–885. Anderson, R. P., D. Lew, and A. T. Peterson. 2003. Evaluating pre- dictive models of species’ distribution: Criteria for selecting optimal models. Ecological Modelling 162: 211–232. Artacho, P. and R. 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Country Primary political division Total† GARP‡ MaxEnt§ Argentina 23 6 15 Bolivia 9 7 7 Brazil 26 26 26 Chile 15 0 13 Colombia 32 29 31 Ecuador 24 18 23 French Guiana 2 2 2 Guyana 10 10 10 Paraguay 17 16 17 Peru 24 17 24 Suriname 10 10 10 Uruguay 19 1 10 Venezuela 24 24 24 Figure 5. Jackknife test of training gain for the MaxEnt model. Ab- breviations: alt, elevation, bio1 through bio19, bioclimatic variables (Table 2). For Brazil, models indicate the area of highest suscepti- bility to occur between the midwest and Atlantic coast in a southeastward direction. Some areas of low susceptibility are noted in the northwest and northeast portions of the country. In 2005, in response to the invasive behavior of the giant African snail and its implications on health, biodiversity, and agriculture, IBAMA (Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis) forbad the sale and breed- ing of Achatina fulica (Carvalho-Junior and Nunes 2009). Re- markably, Brazil is the only South American country with A. fulica regulatory legislation. We identifi ed a larger area in South America that is cli- matically susceptible to be invaded by the giant African snail than that previously reported by Borrero et al. (2009). A com- parative analysis of our models clearly indicates that several subcontinent temperate regions contain the minimal envi- ronmental conditions able to support an invasion of Achatina fulica. Several newly-predicted susceptible areas involve boundaries with countries where the invasive species is known to occur. This knowledge is especially useful to gov- ernmental authorities in countries where the invasive species was recently confi rmed (e.g., a single location in Argentina that borders Brazil and Paraguay; Gutiérrez Gregoric et al. 2011) or where it is yet unreported (e.g., Chile and Uruguay). 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