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Potential Distribution of the American Bullfrog (Lithobates Catesbeianus) inEcuadorAuthor(s): Carlos A. Iñiguez and Felipe J. MorejónSource: South American Journal of Herpetology, 7(2):85-90.Published By: Brazilian Society of HerpetologyDOI: http://dx.doi.org/10.2994/057.007.0211URL: http://www.bioone.org/doi/full/10.2994/057.007.0211

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POTENTIAL DISTRIBUTION OF THE AMERICAN BULLFROG (LITHOBATES CATESBEIANUS) IN ECUADOR

CARLOS A. IÑIGUEZ1,2 AND FELIPE J. MOREJÓN1

1 Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja, San Cayetano Alto s/n, Loja 1101608, Ecuador.2 E-mail corresponding author: cainiguez@utpl.edu.ec

ABSTRACT. The American bullfrog (Lithobates catesbeianus) is a native species from eastern North America that was introduced to Ecuador in 1985. We built two models in Maxent, (1) one model with native records and, (2) one model with native and invasive records, to provide an approximate map of the potential geographical distribution for this species in Ecuador. Both models showed significant differences in the prediction of suitable areas, model 2 being the most consistent in relation to occurrence records. Here, we present the invasive potential of the American bullfrog to occupy a wide variety of environments such as Amazonia, if human activities lead to an accidental or induced introduction. Furthermore, this study is the first survey about the distribution of the American bullfrog in Ecuador, thus identifying some susceptible areas where conservation efforts should be focused to prevent new settlements and uncontrolled breeding of this species.

KEYWORDS. Potential distribution modeling; Lithobates catesbeianus; Invasive species; Invasive potential; Ecuador.

INTRODUCTION

Alien invasive species are negatively affecting na-tive biota in almost all ecosystems and regions of the world. One of these invasive species is the American bullfrog (Lithobates catesbeianus, Shaw 1802), which has been introduced globally over the past two cen-turies (Lever, 2003). Its native range covers eastern North America, from southern USA to southern Cana-da. It has been introduced into Europe, Asia and South America (Ficetola et al., 2007). In South America, populations of the bullfrog have been reported in Ar-gentina, Colombia, Brazil, Ecuador, Venezuela, Uru-guay, Chile, Guyana, Paraguay, and Peru (Akmen-tins and Cardozo, 2009). Since 1985 the bullfrog has been introduced in Ecuador both legally and illegally for the establishment of bullfrog farms, which occur along the western and eastern lowlands (Cisneros-Heredia, 2004; Gallardo, 2004), although most farm-ing activity is concentrated in southeastern Ecuador-ian Amazonia (Zamora Chinchipe province). There is little data about population losses in some Ecuadorian native amphibians, but it is assumed that some losses are due to this invasive species (Young et al., 2001).

Several studies suggest that established bullfrog populations are either difficult or impossible to eradi-cate (Adams and Pearl, 2007) and can have a negative impact on native amphibians through competition and predation (Kats and Ferrer, 2003; Laufer et al., 2008). For this reason, the bullfrog is considered to be one of the most harmful invasive species (Beebee and Griffiths, 2005; Kats and Ferrer, 2003). More-over, bullfrogs can be healthy carriers of the fungus Batrachochytrium dendrobatidis (Garner et al., 2006,

Schloegel et al., 2010). This fungus is the agent of chytridiomycosis, an emergent amphibian disease that causes the extinction of amphibian populations on a global scale (Berger et al., 1998; Lips et al., 2006; Pounds et al., 2006). Therefore, knowledge of the bullfrog’s potential distribution is extremely im-portant in planning conservation strategies aiming to control and prevent invasion by bullfrogs.

Ecological niche modeling is a useful tool to predict the invasive potential of non-native species (Rodriguez et al., 2007). However, to succeed, it is necessary to know the ecological requirements of the invader species and use predictors that influence the likelihood of establishment in other regions (Ficetola et al., 2007). Recently, a few studies were carried out on the distributional range of L. catesbeianus. Fic-etola et al. (2007) predicted the bullfrog’s potential global distribution, but their work was mostly aimed at evaluating the outcomes of introductions in Euro-pean non-native populations. Giovanelli et al. (2007) generated a predictive map for the distribution of the bullfrog in Brazil, using native occurrence data to do a projection. Additionally, IUCN (2008) has created global geographic distribution maps of the extent of occurrence for mostly amphibian species, including the bullfrog.

The aim of this study was to generate a predictive map for the distribution of L. catesbeianus in Ecuador from native and invasive records, in order to identify which areas are more susceptible to bullfrog invasion. To develop this research we considered some steps of a similar study based on the prediction of the po-tential distribution of the American bullfrog in Brazil (Giovanelli et al., 2007).

South American Journal of Herpetology, 7(2), 2012, 85-90© 2012 Brazilian Society of Herpetology

MATERIALS AND METHODS

Data Sources

Native occurrence data were compiled from the Global Biodiversity Information Facility (2009) da-taset, obtaining 1489 georeferenced presence records (Figure 1A). Additionally, we created a second occur-rence dataset based on invasion records in Ecuador compiled from recent surveys (Cisneros-Heredia, 2004; Ortega, 2007; Rodríguez et al., 2003) (Fig-ure 2B). Occurrence data were checked in the DI-VA-GIS software (version 5.2; Hijmans et al., 2002) to delete bias and errors by identifying outliers.

We used a set of 21variables represented in ras-ter format with 30 arcsecond resolution grid (1 km approximately). The set was composed of 19 bio-climatic variables from the Worldclim database de-veloped by Hijmans et al. (2005) (version 1.4), one topographic variable derived from the U.S. Geologi-cal Survey Hydro-1K data set (USGS, 2009). These

variables were used successfully to generate a predic-tive map of bullfrog distribution in Brazil (Giovanelli et al., 2007). Finally, we used as a variable the human footprint which is a measure of human influence on global surfaces (see details in Sanderson et al., 2002). This variable had a positive contribution in the pre-diction of the global distribution of bullfrog by Fice-tola et al. (2007), because some human modifications of land may positively influence bullfrog distribution.

In order to limit the environmental data, we only used the variables that contributed most to the model. These variables were selected after a first model run in Maxent (Phillips et al., 2006) due to the fact that this software provides a table that gives a heuristic estimate of the relative contributions of the envi-ronmental variables to the model. To determine the estimate in each iteration of the training algorithm, the increase in regularized gain is added to the con-tribution of the corresponding variable, or subtracted from it if the change to the absolute value of lambda is negative.

FIGURE 1. Point locations for (A) native and (B) invaded range. Potential distribution models based on native range for (C) eastern North America and (D) Ecuador. Green polygons represent IUCN distributional maps for the bullfrog in Ecuador.

Distribution of Lithobates catesbeianus in Ecuador 86

Niche Modeling

We built two models, (1) with native records and, (2) with native and invasive records. The modeling process was developed in Maxent (Phillips et al., 2006) since it had been applied for modeling the po-tential distribution of the American bullfrog at global (Ficetola et al., 2007) and regional (Ficetola et al., 2009; Giovanelli et al., 2007) scales. Maxent (Phil-lips et al., 2006) is a machine-learning method that applies the maximum entropy principle to calculate the probability distribution of a species. The result-ing model expresses the habitat suitability value for a species as a function of the predictor variables. These values can be interpreted as a relative index of envi-ronmental suitability, where higher values represent a prediction of better conditions for the species. Fur-thermore, a comparison of several modeling methods that was recently done by Elith et al. (2006) showed the predictive capacity of this approach to modeling from incomplete occurrence data (i.e., presence-only data).

Models were evaluated with the receiver operating characteristic curve (ROC) by calculating the area un-der the curve (AUC) (Fielding and Bell, 1997). AUC values range from 0 to 1, where 0.5 indicates that the model has no predictive power, 1 signifies a perfect model and values below 0.5 would indicate a rela-tionship worse than expected (Guisan et al., 2007). Generally, the ROC analysis is accepted as a standard method to evaluate the accuracy of the potential dis-tribution models and it enables us to determine their applicability (Elith et al., 2006). Consequently, we evaluated the performance of the models according to the parameters found in Phillips et al. (2006). For both models, we made 10 random partitions of the occurrence localities. Each partition was created by randomly selecting 75% of the occurrence localities as training data, the remaining 25% was reserved for testing the resulting models. Then, we ran a model for each partition. We set Maxent based on the regular-izations given in Phillips and Dudík (2008) and fitted according to our number of occurrence records and the scale.

Both output models were projected onto North America and Ecuador (Figure 1C, D) to determine the most suitable areas for bullfrog distribution applying a threshold value that balances commission and omis-sion errors. Finally, both models were overlapped with the distributional polygons of the American bullfrog from the IUCN Amphibian database (IUCN et al., 2008) in order to observe any relationships.

RESULTS AND DISCUSSION

Maxent modeling within the bullfrog native range (model 1) suggested high predictive power of the model, yielding AUC values > 0.959 and 0.926 for training and testing processes, respectively. The output model of the potential geographical distri-bution for L. catesbeianus in North America using occurrence records within the bullfrog native range (Figure 1A, C), showed a similarity with other mod-els (see Ficetola et al., 2007 and Giovanelli et al., 2007).

When a similar model was projected on Brazil (Giovanelli et al., 2007), the results were consistent since there was concordance between the predicted distribution and the actual distribution of L. catesbe-ianus in Brazil. However, our model showed singu-lar results for Ecuador (Figure 1D). The projection identified the principal distribution range in three areas of the Coastal region, for instance there were a few areas near Guayaquil with a high suitability for the bullfrog and other areas with medium to low suitability values. Most of Amazonia was consid-ered unsuitable with only a few small areas with low suitability. The Andean region (Central Ecua-dor) had no suitable areas for this species, because this region has different environmental conditions than other Ecuadorian regions and lacks appropri-ate habitat for bullfrogs. In contrast, the records of L. catesbeianus are located mainly in Amazonia (blue triangles in Figure 1B), with a few localities in western Ecuador, but no records were found in the predicted distribution. Within the IUCN distri-butional polygons the proportion of potential distri-bution is not representative. Beaumont et al. (2009) state that an invasive species can occupy different climatic niches from its native range and it is crucial to include the entire range data (native and invasive) in order to obtain a useful model that does not mis-represent the potential for invasion of a species. Al-though some authors (Bradley et al., 2010), consider that these predictive models are most useful under a climate change scenario, since they only project the fundamental niche of a species. We believe that our model shows all the possible sites that the bullfrog may invade in a country without a legal framework to support the monitoring and control of the bullfrog farms.

The accuracy of model 2, built from native and invasive records, (Figure 2) was consistently high as with model 1, i.e., with AUC values > 0.956 and 0.912 for training and testing, respectively. In this case, the

Iñiguez, C. A. and Morejón, F. J. 87

predicted distributional range is greater than model 1 and the current distribution is within the prediction. There are several areas with significant suitability val-ues both in the Coastal region and in Amazonia. Ad-ditionally, model 2 predicted areas in Central Ecuador but mostly with low suitability values. IUCN distri-butional polygons contain a considerable proportion of medium to high suitability, except for polygon 4 which presents fewer areas with those values. There-fore, to avoid underestimating the potential range of an invasive species when projecting distributions into new regions, it is very helpful to consider the entire data range before modeling (Beaumont et al., 2009). In addition, it is useful to implement a management plan for the American bullfrog in Ecuador, because our results take into account areas where the species has not been recorded.

Several studies report the negative impacts that established bullfrog populations have on native am-phibians (Kats and Ferrer, 2003; Laufer et al., 2008; Young et al. 2001). However, in Ecuador there is no research about the population dynamics and biotic in-teractions in areas where the bullfrog has been intro-duced. If we consider Fig. 2 as an approximate map of potential geographical distribution of L. catesbe-ianus in Ecuador, the invasion risk of this species is high since it may inhabit several areas with suit-able environments, such as Amazonia. For example, in Brazil (Giovanelli et al., 2007) L. catesbeianus has not been recorded in Amazonia and most of the environments in the Brazilian Amazon were outside the species’ native range, as well as in model 1 in this study (Figure 1D). However, in Ecuador there are records of the species in the Amazon region. We

FIGURE 2. Potential distribution model of L. catesbeianus based on native and invasive records. Current distribution (blue triangles) and IUCN distributional maps of the bullfrog (green polygons) for Ecuador.

Distribution of Lithobates catesbeianus in Ecuador 88

believe that the invasive potential of L. catesbeianus may be underestimated due to the fact that the spe-cies can occupy a wide variety of environments dif-ferent to its native range, including the Amazon re-gion (Figure 2). Furthermore, the positive influence of human activities on bullfrog distribution (Ficetola et al., 2007) may encourage colonization in the Ama-zon region via accidental or designed introductions.

Finally, according to IUCN, Ecuador is the third country with the greatest diversity of amphibians in the world. It is crucial that further investigations fo-cus on measuring the interactions with other species and population dynamics of L. catesbeianus in Ecua-dor in order to detect threats. Also, it is important to implement a monitoring program of bullfrog popu-lations in the Zamora Chinchipe province (southern Amazonia) where most farming activity is concen-trated and where the species has been observed in the rainforest, in streams and in ponds (F. Morejón field interviews). To reduce the risk of invasion, the National wildlife agencies (e.g., Agencia Ecu-atoriana de Aseguramiento de la Calidad del Agro) should focus on controlling new introductions of the species and create a national plan for the detection and management of the American bullfrog in Ecua-dor considering some success strategies (Doubledee et al., 2003) developed to control this alien invasive species.

RESUMEN

La rana toro (Lithobates catesbeianus) es nati-va del este de Norteamérica y fue introducida en el Ecuador en 1985. Se elaboraron dos modelos en Maxent, (1) un modelo con los registros nativos y (2) otro modelo con los registros nativos e introduci-dos con el fin de proporcionar un mapa aproximado de la distribución geográfica potencial de esta especie en el Ecuador. Ambos modelos mostraron diferen-cias significativas en la predicción de áreas idóneas, siendo el modelo 2 más consistente en relación a los registros de ocurrencia. Aquí, se presenta el potencial invasivo de la rana toro para ocupar una amplia va-riedad de ambientes como la Amazonia si las activi-dades humanas facilitan introducciones accidentales o inducidas. Además, este estudio es el primer trabajo sobre la distribución de la rana toro en el Ecuador, identificando así algunas áreas susceptibles donde los esfuerzos de conservación deben ser enfocados para prevenir nuevos asentamientos y una reproducción incontrolada de esta especie.

ACKNOWLEDGMENTS

We would like to thank Universidad Técnica Particular de Loja (UTPL) for their financial support and staff from the Insti-tuto de Ecología at UTPL for their assistance in the field. Special thanks go to David Draper Munt for comments and suggestions to previous versions of this manuscript. Finally, we wish to thank Paul Cahen for helping us with the editing of this manuscript.

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Submitted 21 January 2012 Accepted 28 April 2012

Distribution of Lithobates catesbeianus in Ecuador 90