BRIEF COMMUNI CATION

American bullfrog invasion in Argentina: where shouldwe take urgent measures?

Javier Nori • Mauricio S. Akmentins • Romina Ghirardi •

Nicolas Frutos • Gerardo C. Leynaud

Received: 14 November 2010 / Accepted: 3 February 2011 / Published online: 16 February 2011� Springer Science+Business Media B.V. 2011

Abstract Argentina is the country with the most geographically extended biological

invasion of the American bullfrog (Lithobates catesbeianus) in South America after Brazil.

Here, we used a maximum entropy ecological niche modeling algorithm (using records of

the native range of American bullfrog) to project the model onto the whole of Argentina.

We determined the most suitable habitats for this invasive alien species and where we

consider urgent measures should be taken. Our projections showed good agreement with

known feral populations of American bullfrog in Argentina. By implementing the

J. Nori (&) � G. C. LeynaudLaboratorio de Herpetologıa y Animales Venenosos, Centro de Zoologıa Aplicada,Facultad de Ciencias Exactas, Fısicas y Naturales, Universidad Nacional de Cordoba,Rondeau 798, 5000 Cordoba, Argentinae-mail: javiernori@gmail.com

J. NoriMuseo Patagonico de Ciencias Naturales, Av. Roca 1250, General Roca,Rıo Negro 8332, Argentina

M. S. Akmentins � R. Ghirardi � N. FrutosCONICET, Buenos Aires, Argentina

M. S. AkmentinsCentro de Investigaciones Basicas y Aplicadas (CIBA), Universidad Nacional de Jujuy,Gorriti 237, 4600 S. S. Jujuy, Argentina

M. S. AkmentinsInstituto de Bio y Geociencias del NOA (IBIGEO), Universidad Nacional de Salta, Mendoza 2,4400 Salta, Argentina

R. GhirardiInstituto Nacional de Limnologıa (CONICET_UNL), Ciudad Universitaria, Paraje El Pozo,3000 Santa Fe, Argentina

N. FrutosInstituto de Altos Estudios Espaciales Mario Gulich, Comision Nacional de Actividades Espaciales(CONAE), Universidad Nacional de Cordoba (UNC), Ruta C45 km 8, Falda de Canete,5187 Cordoba, Argentina

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Biodivers Conserv (2011) 20:1125–1132DOI 10.1007/s10531-011-0014-3

‘‘Multivariate Environmental Similarity Surface’’ analysis, we be able to determine that

factors such as low precipitations or highest altitudes could be limiting the species’ ability

to invade the west and south of the country. We suggest that strategies should focus on

detecting established feral populations of the American bullfrog and preventing further

introductions or range expansion of feral populations in the northeast portion of the

country. Lastly, we report a new feral population of bullfrogs in Argentina.

Keywords Argentina � Ecological modeling � Invasion � Lithobates catesbeianus �MAXENT � Multivariate Environmental Similarity Surface

Introduction

Over the past three decades, the North American-native American bullfrog, Lithobatescatesbeianus, has become a part of an increasingly centralized and expanded trade of live

animals for food within South America, and between South America and the United States

of America (Mazzoni et al. 2003; Hanselmann et al. 2004). As a consequence of this trade,

there are currently feral populations of the American bullfrog in the majority of South

American countries. Brazil and Argentina have reported the most extensive biological

invasion of the American bullfrog, with feral populations reported in a large extent of their

territories (Giovanelli et al. 2008; Akmentins and Cardozo 2010). In Argentina, the biggest

expansion of bullfrog captive-breeding facilities started in 1983 and remained without

survey or control by provincial or national government agencies up to 1993. In 1994, the

Argentine Fish and Agriculture Agency (Direccion Nacional de Pesca y Acuicultura)

began a regulation program on the introduction of exotic aquatic organisms. This year, 69

bullfrog farming facilities were reported in nine of the 23 provinces of the country (Luchini

1995). At present, only a few frog farms are still in production since a great majority where

closed and dismantled (Akmentins per. obs.). Akmentins and Cardozo (2010) suggest that

the lack of effective control over captive-breeding programs of exotic animals is of serious

concern and the main source of feral populations of the American bullfrog in Argentina.

They described the need to develop a predictive ecological niche model to access the

outcome of this biological invasion.

Recently, researchers have developed some techniques to allow modelling species

distribution based on ecological niche dimensions (Peterson 2001). One of the applications

is to predict ecologically suitable areas for the establishment of invasive species in regions

where the species is not found yet (Peterson 2003; Giovanelli et al. 2008; Elith et al. 2010).

This methodology builds an ecological niche model based on the values of environmental

variables (niche dimensions) at know localities for the target species (Ron 2005). Some

global predictive models for the potential distribution of invasive American bullfrog

populations have been proposed (e.g. Ficetola et al. 2007; Giovanelli et al. 2008), however,

these works were mostly aimed at predicting bullfrog invasions on Brasil and Europe,

without details of Argentina.

Any attempt to limit further spread of the bullfrog requires extensive knowledge of their

current distribution. Considering the increasing number of feral populations reported in

Argentina (Akmentins and Cardozo 2010), there is an urgent need to identify candidate

habitats for future monitoring (Rodder et al. 2009). In this study, we used the ecological

niche model based on distributional data from the native range of the bullfrog, to determine

the most suitable areas for this species in Argentina and compared our results with the

reported feral populations of American bullfrog in this country. Additionally, we

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determined the most limiting variables for the species in relation to their native range of

distribution. Lastly, we reported a new feral population of American bullfrog from central

Argentina.

Materials and methods

We performed field sampling in an area of Central Argentina (Los Sauces River, 3 km

north of Villa Dolores, Cordoba province, Argentina). The specimens were collected by

hand or fishing net and euthanized with an overdose of Lidocaine 2%. They were then fixed

with formalin 10%, transferred to 70% ethanol after 3 days and deposited as part of the

herpetological collection in the Centro de Zoologıa Aplicada (Cordoba, Argentina).

In order to obtain an ecological niche model for the native distribution of American

bullfrog, we used 897 geo-referenced individual records from North America, obtained

from the Global Biodiversity Information Facility (2010) and HerpNet (2010) databases,

including occurrences in Mexico, USA, and Canada (Fig. 1a). Georeferencing was con-

ducted when necessary with the Alexandria Digital Library Gazetteer (http://middleware.

alexandria.ucsb.edu/client/gaz/adl/index.jsp). All data were checked in the DIVA-GIS

software for bias and errors (Hijmans et al. 2002).

We used 19 bioclimatic layers and 1 topographic layer as predictors. The bioclimatic

variables resulted from global land area interpolation of climate point data (1950–2000) at a

spatial resolution of 10 arc-min (Hijmans et al. 2005; available at http://www.worldclim.

org). The altitude variable was derived from USGS, at the same spatial resolution (available at

http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/GTOPO30).

The realized ecological niche was modeled using MAXENT 3.3.3a (Phillips et al. 2006;

http://www.cs.princeton.edu/*shapire/maxent). MAXENT uses the maximum entropy

principle: in the estimation of an unknown probability distribution, the least biased solution

is the one that maximizes its entropy, subject to some constraints that reflect available

information. In the case of ecological niche modeling, the target is to calculate a proba-

bility distribution (the niche model) for a species over the given geographical space, using

information from the observed association of the species’ localities with environmental

variables. MAXENT calculates this maximum entropy distribution using the observed

association between the species’ and environmental layers to set the following constraint:

the expected value (expectation) of each ‘‘feature’’ (which is either an independent var-

iable itself or one derived from it) under the estimated distribution must be similar to its

observed average over sample locations (Phillips et al. 2006).

Occurrence data were divided into training data (75% of occurrence point data used for

model prediction) and test data (25% of occurrence point data used for model validation).

Several runs were performed and ten replicates were used as the input parameter and were

‘‘cross-validated’’ as replicated run type. The resulting average model was evaluated with

the Receiver Operating Characteristics Curve (ROC), calculating the area under the curve

(AUC), a threshold independent index widely used in ecological studies. A ROC plot was

created by plotting the sensitivity values, the true-positive fraction against 1-specificity,

and the false positive fraction for all available probability thresholds (further reading:

Fielding and Bell 1997; Manel et al. 2001). The AUC is a measure of the area under the

ROC ranging from 0.5 (random accuracy) to a maximum value of 1.0 (perfect

discrimination).

The cumulative output of MAXENT is a continuous map which allows fine distinctions

to be made between the modeled suitability of different areas (scale range: 0–100). To

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generate a prediction map, we used a cumulative threshold value that balances training

omission, predicted area, and threshold value, i.e., balancing commission and omission

errors (Phillips et al. 2006). The resulting model was projected onto all of Argentina to

assess the potential geographic distribution of the species. Then, we plotted the actual

species occurrence points in Argentina on the predicted distribution map. To avoid spu-

rious projections, we calibrated the models using the native range of the species (repre-

sented by the inset, Fig. 1a). By implementing the ‘‘multivariate environmental similarity

surface’’ (MESS) (implemented in MAXENT 3.3.3a) across this analysis, we can know

how similar a point is to a reference set of points (native range), with respect to a set of

predictor variables. Negative values discriminate areas where at least one variable has a

value that is outside the range of environments of the reference set. Information on which

variable is driving the MESS value at points outside the calibration area were extracted and

mapped with the aim to know the most dissimilar (or limiting) variables (MoD) for the

species (Fig. 1c) (For details see Elith et al. 2010).

Results and discussion

Climate matching and numerous introduction attempts are the basis for a successful bio-

logical invasion (Kolar and Lodge 2001; Bomford et al. 2009). This is clearly the case of

the recent American bullfrog invasion in Argentina. In fact, Argentina is the country with

the most geographically extended biological invasion of the American bullfrog in South

America after Brazil (Akmentins and Cardozo 2010) (Fig. 1b). Although there are less than

20 known bullfrog populations in Argentina (Table 1), all were reported over the past

5 years, adding to the number of frogs’ farms installed in the country, which leads us to

think there are several unreported feral populations. In this study, we report a new feral

population of American bullfrog, located at Los Sauces river, 3 km North of Villa Dolores,

Cordoba province, Argentina (between 31�5503100S, 65�0803800W and 31�5404700S,

65�0706100W). This is the second feral population reported for this province, 158 km

southwestern from the first one. We documented a well established feral population

(adults, tadpoles and metamorphs) of American bullfrog in a lotic environment and col-

lected 5 of them deposited at herpetological collection of Centro de Zoologıa Aplicada:

CZA-H 150-154.

In the MAXENT model, using 25% random test points out of the data set for testing

(224 for testing and 663 for training), we received very good AUC values (Test: 0.821;

Training: 0.901) suggesting high predictive power. Model probabilities below the threshold

value of 1.26 were classified as predicted absence and transformed to null values.

The projection of the native distribution model onto Argentina showed that the pre-

dicted occurrence included all but one (in northwestern Argentina, at San Juan province) of

the feral populations of the species in the country. Based on our model, we consider that

this is a clear example of human assisted introduction of an alien species in a poorly

suitable environment. The future of this feral population is unclear because it is located in

Fig. 1 a Predicted potential geographic distribution for native range of Lithobates catesbeianus. Whitecircles: localities within the specie native range (obtained from HerpNet and Global BiodiversityInformation Facility). Inset: area of environmental calibration for the purpose of projecting the habitatsuitability model. b Projection map for the potential distribution of Lithobates catesbeianus in Argentina andzoom of the area with highest probability values showing the river system. Black star: New reported locality.White cruxes: real occurrence localities in Argentina. Black lines: rivers. c Limiting factors based on MESSanalysis showing the most dissimilar variables (MoD) in each pixel outside the calibration area

b

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an arreic basin and may provide a clear chance to begin an effective eradication program

(Adams and Pearl 2007).

Our results suggest that all areas in the northeastern portion and eastern coastal of

Argentina are suitable areas for American bullfrog (Fig. 1b). The predicted areas of the

Atlantic Forest (northeastern portion of Argentina) are in total agreement with the pre-

dictive distribution map of Giovanelli et al. (2008) for SE Brazil. Similarly, we conclude

that the presence of American bullfrogs in this biome is of special concern because the

Atlantic Forest is a biodiversity hotspot with a large number of endemic species which can

be severely affected though predation and competition. Additionally, detected feral pop-

ulations and the projected most suitable habitats are connected by Argentina’s mayor river

systems belonging to the Parana and Uruguay rivers basins with a large latitudinal

extension. This, added to the American bullfrogs’ capacity to successfully colonize lotic

systems (Clarkson and De Vos 1986; Kupferberg 1997), raises concern for the possible

rapid range expansion of the species.

Most of the Patagonia and High Andean biomes at south and west of Argentina were

outside the model’s calibrated range for the species native distribution. An examination of

the MoD in these areas shows that the main differences between these areas and the native

range of the species are associated with precipitations and altitude (Fig. 1c). The same

variables were the most influential in the model predictions (results not shown), so we can

affirm that factors such as low precipitations, or highest altitudes could be limiting the

species’ ability to invade the west and south of the country.

In agreement with our results, the areas with high values of suitability for American

bullfrog include the zoogeographics regions of Eastern Subtropical and Littoral-Mesopo-

tamian batrachofaunas, which are the most anuran diverse regions in Argentina (Cei 1980).

Thus, we consider that urgent measures need to be taken in order to detect new feral

Table 1 Real occurrence localities of Lithobates catesbeianus in Argentina

Province Locality Longitude Latitude Source

BuenosAires

Bahıa Blanca -62.3319 -38.2408 IABIN (2010)

BuenosAires

9 de Julio district—permanentsemi-natural pond

-60.9001 -35.4869 Barraso et al. (2009)

BuenosAires

9 de Julio district—irrigation channelsystem

-60.8658 -35.4533 Barraso et al. (2009)

BuenosAires

9 de Julio district—rural zone -60.9548 -35.5027 Barraso et al. (2009)

Cordoba Aguas de las Piedras -64.2160 -30.8128 Akmentins et al. (2009)

Cordoba Villa Dolores -65.1440 -31.9300 Present study

Misiones Guaranı—El Soberbio -54.1950 -27.4595 Pereyra et al. (2006)

Misiones San Pedro—RB Yabotı: Saltos delMocona

-53.8905 -27.1558 Pereyra et al. (2006)

Misiones San Pedro—RB Yabotı: MoconaProvincial Park

-53.9014 -27.1531 Pereyra et al. (2006)

Misiones San Pedro—Tobunas -53.9275 -26.4680 Pereyra et al. (2006)

Salta La Candelaria -65.0918 -26.1279 Akmentins & Cardozo(2010)

San Juan Calingasta departament -69.4150 -31.3383 Sanabria et al. (2005)

Bold letters indicate new reported locality

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populations in these areas. Also, control and eradication programs should be set up

focusing on the four reported feral populations of Misiones province (Table 1) (reported

populations in the areas with highest values of suitability) to prevent further range

expansions. Lastly, the use of American bullfrog for economical activities, in particular in

the center-eastern and northeastern portion of the country, should be strictly regulated and

monitored by government entities with the aim of avoiding future accidental invasions.

Acknowledgements We are grateful to Marıa Eugenia Periago who provided invaluable support on fieldwork and improved the English style. Also Ernesto Verga and Marıa Lucrecia Herrero for collaboration inthe field and Gerardo Coria for providing important information. Lastly, we thank Julian Lescano, EduardoSanabria, Nicolas Urbina-Cardona and Enrique Martinez Meyer for suggestions on an earlier version of themanuscript.

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