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Spatial Distribution Model of a Hantavirus Reservoir, the Long-Tailed Colilargo (Oligoryzomys longicaudatus), in Argentina

Aníbal E. Carbajo , Ulyses F. J. Pardiñas
DOI: http://dx.doi.org/10.1644/06-MAMM-A-183R1.1 1555-1568 First published online: 1 December 2007


A 1st step in understanding the ecology of rodents as reservoirs and their relation with the disease they transmit is to determine their geographical distribution. This distribution can be modeled as a function of environmental variables. We georeferenced an extensive database of records of the hantavirus reservoir Oligoryzomys longicaudatus (Cricetidae: Sigmodontinae) in Argentina and used generalized linear models to model the probability of the presence of this reservoir as a function of environmental variables. The variables used in the multiple logistic regression were temperature, precipitation, evapotranspiration, altitude, tree cover, grass cover, bare soil cover, and distance to rivers, to water bodies, and to roads; 2 phytogeographic classifications also were included. Spatial autocorrelation was considered in the model by including a spatial dependence covariate. The best model included temperature and precipitation as explanatory variables. External validation showed that the model without the space covariate correctly classified 95% of the sites with the rodent and 70% of the sites without it; the model including the spatial term correctly classified 100% of the sites with the rodent and 70% of the sites without it. A secondary model included days with frost and percent cover by bare soil as explanatory variables. O. longicaudatus was recorded in 97% of sites in the High Andean-Subantarctic regions, 65% of sites in the Monte-Espinal-Patagonian regions, and 0% of sites in the Pampean region.

Key words
  • Argentina
  • distribution
  • generalized linear model
  • hantavirus reservoir
  • long-tailed colilargo
  • Oligoryzomys longicaudatus
  • Patagonia

A 1st step in understanding the ecology of rodents as reservoirs and their relation with the disease they transmit is to accurately determine their geographical distribution (Mills and Childs 1998). Also, epidemiological analysis and planning of preventive measures require knowledge of the geographic distribution and ecological conditions relevant to the circulation of a pathogen (Kosoy et al. 1997). For many South American rodents involved in zoonoses, basic aspects such as taxonomy and geographic distribution remain poorly known.

The development of predictive habitat distribution models, or niche modeling, has rapidly increased in many fields such as biogeography, evolution, ecology, epidemiology, conservation, and invasive-species management. These models associate species and even community occurrences with environmental variables (biotic or abiotic) to predict their potential geographic distribution (Anderson et al. 2003; Guisan and Zimmermann 2000). The models can rely on presence data alone (Anderson et al. 2003) or include absence data (Guisan and Zimmermann 2000). The latter models comprise regression models, which have been widely used and become popular thanks to generalized linear models and generalized additive models (Guisan et al. 2002). Generalized linear models are parametric in nature and facilitate the development of simple equations relating the environmental variables to species distributions; this helps to understand the association between a species distribution and the environment and also to transfer the results to a geographic information system because the equation can be programmed for each cell of a map. General additive models are better at modeling nonlinear responses, but they give no explicit equation because they are semiparametric.

The long-tailed colilargo (Oligoryzomys longicaudatus Bennet, 1832) belongs to a genus of small-sized mice classified in the New World tribe Oryzomyini (Cricetidae: Sigmodontinae). It is a widespread rodent primarily found in woods and shrublands in Chile and southwestern Argentina (Palma et al. 2005). This sigmodontine rodent is of great importance because of its role as a major reservoir for the Andes Sout genotype of hantavirus, which produces hantavirus pulmonary syndrome in humans in southern South America (López et al. 1996; Toro et al. 1998). Specimens testing positive for hantavirus antibody have been confirmed all along its distributional range, from 38°S to 51°S latitude (Padula et al. 2000; Torres-Perez et al. 2004).

The extensive database on O. longicaudatus and the diversity of environmental conditions present in southern Argentina suggest that the association between the rodent and the environment might provide an informative model for the distribution of this viral reservoir. An occurrence probability distribution would be crucial in mapping risk of hantavirus and an important step forward in knowledge of the distribution of this rodent. The main objective of our study is to build a spatial model for predicting the distribution of hantavirus reservoirs. This model was developed and tested with O. longicaudatus and is the 1st step in a series of distribution models for all species involved in hantavirus transmission in Argentina that will be used as a basis for the study of risk of hantavirus transmission.

Materials and Methods

Geographical database.—An exhaustive database of records of O. longicaudatus in Argentina (Appendix I) was compiled from 3 main sources of information: voucher specimens housed in mammalogical collections; osteological remains recovered from analyses of owl pellets, conducted primarily by 1 of the authors (UFJP; material is housed at the Colección de Material de Egagrópilas y Afines del Centro Nacional Patagónico, Puerto Madryn, Chubut, Argentina); and literature records based on voucher specimens. Collections surveyed included: Colección de Mamíferos y Colección de Material de Egagrópilas y Afines “Elio Massoia” del Centro Nacional Patagónico, Puerto Madryn, Argentina; Colección de Mamíferos del Museo Argentino de Ciencias Naturales “Bernardino Rivadavia,” Buenos Aires, Argentina; Colección de Mamíferos del Museo de La Plata, La Plata, Argentina; and Museum of Vertebrate Zoology, Berkeley, California. Voucher specimens and osteological remains were directly examined to check their taxonomic identity through morphological traits following Carleton and Musser (1989), Gallardo and Palma (1990), and Osgood (1943). Several populations of Oligoryzomys magella-nicus in southernmost Argentina were excluded from the analysis because is considered a full species (Gallardo and Palma 1990; see also Musser and Carleton 2005). In addition, putative records for O. longicaudatus in northwestern Argentina (e.g., Cabrera 1961; Díaz 2000; Díaz and Barquez 2002; Ojeda and Mares 1989) were excluded because examination of a large amount of data indicated that they belong to another species of Oligoryzomys (perhaps O. destructor—see Espinosa and Reig 1991; Gonzalez-Ittig et al. 2002; Musser and Carleton 2005). Procedures followed guidelines approved by the American Society of Mammalogists (Gannon et al. 2007).

Records of O. longicaudatus were incorporated into a geographic information system (GIS) using Arc View 3.2 (Environmental Systems Research Institute 1994). The distribution of O. longicaudatus was represented by a thematic point map with the sites where the rodent was present or absent. Presence was defined by the existence of a voucher specimen or osteological remains. Absence was defined exclusively based on sites where owl pellets with the remains of at least 100 rodents were examined without detecting any sign of O. longicaudatus. This constitutes a large number of pellets without sign, because the number of rodents per pellet is usually 2 with a maximum of 6. A total of 252 sites were examined, and O. longicaudatus was detected at 146 of them. Sites without the rodent that were ∼10 km from a site where it was present were excluded from the analysis (n = 6). Ten randomly selected sites without long-tailed colilargos were separated and used along with 19 records of the rodent (Porcasi et al. 2005) as a validation data set. The analysis included southern Argentina from 33°S to 51°S latitude.

Two phytogeographic classifications were used to characterize the broad climatic and vegetation characteristics of the sites. The Latin American biogeographical provinces (Cabrera and Willink 1973) were used as a broadscale classification. The study area lies in the neotropical region and included 6 phytogeographic provinces encompassed in the Andean-Patagonian, Chaqueño, and Antarctic domains. Each province was considered as a level of the broadscale factor (Table 1). For a detailed classification, we used the classification of the vegetation forms of the Patagonian steppes (Leon et al. 1998). This classification only covers the Monte and Patagonian provinces of the broadscale classification, extending slightly to the west over the Subantarctic and High Andean broadscale provinces. The broadscale classification consisted of 3 provinces (Monte, Patagonian, and Ecotone) subdivided into 10 phytogeographic districts. The detailed classification consisted of 4 broadscale classification levels (Espinal, High Andean, Pampean, and Subantarctic) plus the 10 district subdivisions of the Patagonian and Monte broadscale provinces (Table 1). The Subantarctic and High Andean broadscale provinces were slightly superimposed on the Central, Occidental, and Subandean detailed-scale districts.

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Table 1

Phytogeographic classifications used in the study. Characteristic climatic conditions and vegetation forms are summarized from the Latin American biogeographical provinces (Cabrera and Willink 1973) and the classification of the vegetation forms of the Patagonian steppes (Leon et al. 1998). The percentage of sites with Oligoryzomys longicaudatus (total number of sites within parentheses) are shown for the broad and detailed classifications. The initial factor column shows the starting level for the detailed classification factor (identified by numbers); regions with fewer than 6 sites were grouped together into level 11. The simplified factor column shows the resulting levels (identified with letters) to which regions were assigned after the merging of the initial factor levels.

SourcePhytogeographic provincePhytogeographic districtPrecipitation (mm)/altitude (m)/temperature (°C)Cover (%) by vegetation forms% Sites broad% Sites detailedInitial factorSimplified factor
Leon et al. 1998PatagonianSubandean>300/NA/NAa60% dense grassesb97 (33)7B
Central∼200/NA/NA30-60% shrubs42 (19)8C
Occidental50% tall grasses and shrubs59 (34)9C
PayuniaNA/600-2,000/NAShrubs/scrubland100 (4)11C
San Jorge GulfNA/0-700/NAShrubs and scrubland in valleys, 80% grasses in hilltops25 (4)11C
Magellan300-450/NA/NA50% xeric to 90% humid grasses—(0)
MonteAustral200/NA/NA60% shrubs and scrubland67 (12)10c
Oriental>250/200/NA50—80% shrubs and short trees0(1)11C
EcotoneRío Negro200/300-600/NA30-50% scrubland and cushionlike shrubs100 (3)11C
Península Vaidés>200/NA/NA40–60% shrubs— (0)
Cabrera andPampean600-1,200/N A/13–17Herbaceous steppec0 (44)0 (44)1A
Willink 1973
High AndeanSnow and hail/NA/∼5Xeric grassland92 (24)95 (20)2B
Subantarctic800-5,000/NA/5-9Perennial and deciduous forest100 (41)100 (33)3B
Patagonian100-300/NA/5-13Low cover shrubs and grass steppes70 (96)91 (11)4B
Monte250-800/NA/13-15.5Shrub—grass steppes50 (20)43 (7)5C
Espinal340-600/NA/15-20Scrubland and low trees46 (11)46 (11)6C
  • a NA = not available.

  • b Shrubs grow in areas with higher precipitation and cattle grazing.

  • c Replaced by crops and cattle.

To analyze the relation between the distribution of O. longicaudatus and environmental variables, thematic maps of the continuous variables were built in grid format covering the whole of Argentina. Mean annual temperature (temperature), mean annual cumulative precipitation (precipitation), and mean annual cumulative evapotranspiration (evapotranspiration) were available as isolines (Subsecretaría de Recursos Hídricos 2002); they were transformed to points and interpolated (inverse weighted distance method). Distance to permanent rivers (river distance), to any kind of river or water body (water distance), and to roads (road distance) were calculated from vector-based digital maps (Subsecretaría de Recursos Hídricos 2002) using Arcview 3.2. Other variables were available in grid format directly: annual frost-day frequency (frost days—New et al. 1999), elevation above sea level (United States Geological Survey 2005), and percent cover by trees, grass, and bare soil (Hansen et al. 2003). The spatial resolution of these grids ranged from 0.5 × 0.5 to 45 × 45 km (Table 2). The environmental variable value at each site was obtained with the geographic information system; for vegetation cover variables the modal value in a 5-km-radius circle also was calculated.

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Table 2

Univariate statistics of the explanatory variables used to model distribution of Oligoryzomys longicaudatus in Argentina. The Wilcoxon 2-sample test (column Z) was used to compare the variables between sites grouped according to presence (OP) or absence (OA) of O. longicaudatus. Generalized linear models parameter (B), standard error (SE), and explained deviance (Dev) are given for each significant univariate fit (t-test B/SE and 235 d.f.) for continuous variables; chi-square test on the deviance and 2 d.f. for the phytogeographic broadscale factor (broad factor) and detailed-scale factor (detailed factor). Null model deviance was 313.75.a

VariableDescriptionUnitsCell sideb (km)ZOP median (LQ UQ)OA median (LQ UQ)B SEDevt
AltitudeElevation above seam10−7.121***826 (686-1,075)128 (26-755)0.003 0.00158.35.25**
TemperatureMean annual temperature°C49.379***7.6 (6.5-9.4)14.0 (10.8-15.1)−0.472 0.056109.0−8.50**
PrecipitationMean annual cumulative precipitationmm4−5.489***969 (446-1,726)677 (256-900)0.002 0.00039.45.36**
Frost daysAnnual frost days frequencydays45−9.199***126 (113-136)47 (34-107)0.040 0.005108.38.08**
EvapotranspirationMean annual cumulative evapotranspirationmm42.698**292 (250-324)445 (171-600)−0.006 0.00138.3−5.32**
Tree coverPercentage of surface with tree cover%0.5−5.807***8.5 (3.0-33.5)3.0 (0.0-7,0)0.051 0.01334.54.07**
Grass coverPercentage of surface with grass cover%0.54.114***61.5 (37.3-71.8)70.5 (55.8-91.8)−0.025 0.00717.3−3.84**
Bare soil coverPercentage of surface with bare soil cover%0.50.90317.0 (0.0-33.8)21.5 (0.25-37.0)−1.13
River distanceDistance to nearestkm14.576***1.4 (1.0-3.5)4.2 (1.0-19.6)−0.036 0.00931.8−3.89**
Water distance riverDistance to nearest water body or coursekm14.847***1.0 (0.0-2.0)2.0 (1.0-5.0)−0.189 0.05224.5−3.66**
Road distanceDistance to nearest roadkm1−1.0451.0 (0.0-3.0)1.0 (0.0-2.2)−0.56
Spatial dependenceSpatial dependence covariate9−6.196***0.00 (-0.02-0.03)-0.15 -0.019.380 2.53836.33.70**
Broad factor phytogeographicBroadscale factorVP132.0***
Detailed factorDetailed-scale phytogeographic factorVP156.1***
  • a LQ — lower quartile; UQ = upper quartile; VP = vectorial polygon layers.

  • b Square cells.

  • *** Significant at P ∼ 0.001; ** P ∼ 0.01.

Distribution model.—Preliminary analysis included comparing environmental variables between sites with and without O. longicaudatus with a 2-sample Wilcoxon test. The presence or absence of O. longicaudatus was modeled as a function of environmental variables with generalized linear models (McCullagh and Nelder 1989; Nelder and Wedderburn 1972). Because the model uses maximum-likelihood estimators, the fit is measured by the reduction in deviance instead of variance (typical of least-squares estimation). The explanatory variables (xi, x2, …) are related to the response variable through a linear predictor (LP). LP = a + bx1 + cx2 …where a, b, c, … are parameters to be estimated. We assumed a binomial distribution of errors and applied the logistic function as a link between the response variable and the LP. This link constrains the predicted values to lie between 0 and 1. The response variable used was presence or absence of O. longicaudatus. The probability of a site having O. longicaudatus (p) follows an S-shaped curve when LP is a lst-order polynomial: p = eLP/(l -f eLP). This can be linearized as ln[p/(l — p)] = LP (Crawley 1993); values lower than 0.5 indicate the absence of O. longicaudatus, whereas values equal to or higher than 0.5 indicate its presence. To account for overdispersion, the dispersion parameter was calculated by quasilikelihood methods (McCullagh and Nelder 1989). A manual upward stepwise multiple regression procedure was used with alpha = 0.01 for retention because of the large number of variables considered (Donazar et al. 1993). The significance of continuous variables and each factor level were evaluated with a Mest (parameter/ standard error [SE] with null model degrees of freedom [d.f.]). To deal with collinearity between explanatory variables, we computed a pairwise Pearson correlation coefficient; when it surpassed 0.45 the variable responsible for the greater change in deviance was retained, whereas the other was excluded from further analyses. To simplify the models, when 1 of the levels in a factor was not significant, the level was merged to another with similar parameters (Nicholls 1989). This procedure was stopped when the merging implied a significant decrease in total explained deviance (chi-square test for the change in deviance with 1 d.f.). Vegetation cover variables where used in 2 forms, the exact value at the site and the modal value in a 5-km radius around the site; both forms were tested and the 1 that explained the higher deviance was retained. When a model could not be improved any further, interaction terms between the significant variables were added to check if they contributed to a better fit of the model. Afterward, absolute position (spatial coordinates) were fitted to discard remnant spatial trends (Legendre 1993).

Because it might be possible to have spatial dependence without a trend (autocorrelation), an extra covariate representing the response autocorrelation was added to the logistic model (Augustin et al. 1996). The covariate was built by kriging interpolation with a modification of the method described in Miller and Franklin (2002). This method requires the building of a presence probability surface for O. longicaudatus by indicator kriging, which was impeded by a strong large-scale trend in the response variable. To bypass this problem the trend was removed by regressing the response variable on latitude and longitude (Bailey and Gatrell 1998) with generalized additive models following Kaluzny et al. (1998). To obtain the spatial dependence covariate (SDC), the detrended residuals were analyzed with variograms and interpolated with kriging (Cressie 1993; Jongman et al. 1987) to a 90 × 90 grid covering the whole study area (approximately a 9 × 9-km cell). In the multivariate regression the spatial trends are modeled by the environmental variables and the remnant autocorrelation by the addition of this covariate at the last step.

The standardized residuals were plotted against normal quantiles to check for normality. The explanatory power of the model was estimated with D2, the ratio of the residual to null deviance (equivalent to R2 in least-square models); accuracy and error measures were calculated from the confusion matrix of the predicted and observed values on the validation data set. The kappa index for unbalanced number of positive and negative cases (Titus and Mosher 1984) was used to measure the model improvement over a random classification. The models were resampled by jackknife (refitted excluding 1 observation at a time) to smooth the effect of influential observations; the mean parameter and SE were used to retest significance (t-test as explained above). The potential distribution maps were built by applying the final jackknifed generalized linear model formula pixel to pixel in the geographic information system. S-plus 6.1 with S+ SpatialStats and Arc view GIS add-ons (Insightful Corp.2002) was used for modeling and mapping.


Univariate comparison of sites with and without O. longicaudatus showed significant differences for most of the environmental variables considered, as did the univariate generalized linear models (Table 2). In the latter, phytogeographic regional factors explained the highest deviance, with the detailed classification being slightly better than the broadscale classification. The detailed-scale factor levels were reclassified because there were 2 classes without sites and 4 classes with fewer than 5 sites (Table 1). The number of levels of both phytogeographic factors was reduced to 3 after model simplification. The resulting percentages of sites where O. longicaudatus was present according to the broadscale levels were High Andean-Subantarctic (97%), Pampean (0%), and Monte-Espinal-Patagonian (65%).

The best models described the distribution of O. longicaudatus as a function of temperature and precipitation (model TP [Table 3]), and as a function of frost days and percentage of bare soil (FB [Table 4]). Both models with the spatial dependence covariate (TPS and FBS) explained higher percentages of the deviance than their counterparts without the spatial term (Table 5).

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Table 3

Generalized linear models for the presence or absence of Oligoryzomys longicaudatus in Argentina as a function of mean annual temperature and annual precipitation with (TPS) and without (TP) the fitting of the spatial dependence covariate (SDC). Parameters and SE were obtained by jackknife resampling.

Temperature × precipitation−0.00270.00061−4.789−0.00270.00061−4.345
Residual deviance128.5323296.07231
Null deviance313.75235313.75235
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Table 4

Generalized linear models for the presence or absence of Oligoryzomys longicaudatus as a function of annual frost days and percentage cover by bare soil with (FBS) and without (FB) the fitting of the spatial dependence covariate (SDC). Parameters and SE were obtained by jackknife resampling.

Bare soil−0.1130.0281−4.009−0.2060.0571−3.604
Bare soil20.00070.000312.6150.00140.000513.178
Residual deviance153.523196.1230
Null deviance313.75235313.75235
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Table 5

Characteristics of distribution models for Oligoryzomys longicaudatus and prediction accuracy measured by the external validation data set. The terms included in each model are indicated: temperature (T), precipitation (p), temperature-precipitation interaction (T × p), frost days (F), frost days quadratic term (F2), bare soil (b), and spatial dependence covariate (s).

TermsT + P + T × PT+P+S+T×PF + F2 + B + B2F + F2 + B + B2 + S
% deviance explained59.069.451.070.4
Correct classification0.860.900.860.86
Kappa index0.680.750.660.68

The models TP and TPS are more parsimonious than FB and FBS because they retained more degrees of freedom and had no quadratic terms (Table 5). In the validation analysis, the TPS model presented the highest correct classification rate and kappa index, the highest sensitivity (matched with FB model), and the highest specificity (matched with TP and FBs models [Table 5]). Considering these results, we would choose the TPS model to predict the presence of O. longicaudatus. However, this model only allows interpolation along the sampling area because the spatial dependence covariate cannot be extrapolated. The building of a potential distribution map covering all Argentina requires the exclusion of the spatial term. In this case, the TP model is preferred to FB; it has more degrees of freedom, explains more deviance, and FB shows low specificity. Regarding the grain of the explanatory variables, models TP and TPS present a cell side ranging from 4 to 8 km, whereas FB and FBS present a cell side ranging from 0.5 to 45 km. Similar grains are preferred because dissimilarity would make the grain of the prediction map irregular.

The map of potential distribution of O. longicaudatus according to temperature and precipitation (TP) shows a higher occurrence probability along the western side of the Andean range south of 36°S latitude, narrowing beyond 50°S (Fig. 1). The Patagonian central plateaus show low probability (Chubut and Santa Cruz provinces), whereas the eastern plateaus and the north (Río Negro Province) exceed the probability of 0.5. The probability of occurrence falls toward the northeast in Buenos Aires and Cordoba provinces. The map shows zones of high probability of presence outside the study area, to the north along the Andes (from 33°S latitude to the north) and in Tierra del Fuego. The TPS map shows a similar pattern inside the study area with the addition of 2 high-probability zones (Fig. 2), 1 in the southeast, between 48°S and 50°S latitude, and the other in the northwest, extending north from Neuquén through Mendoza provinces. These latter zones should be regarded with care, because there are few data sites within them.

Fig. 1

Potential distribution of Oligoryzomys longicaudatus in Argentina according to the temperature and precipitation (TP) model. Probability of presence is shown as a function of mean annual temperature and annual precipitation (gray scale). Presence is predicted in areas with probability higher than 0.5. The rectangle approximates the area covered by the study sites. Sites used for model development are indicated by squares and validation sites are indicated by triangles (filled for presence and empty for absence of O. longicaudatus). Hantavirus pulmonary, syndrome occurrences (Andes Sout genotype) are plotted as circles.

Fig. 2

Potential distribution of Oligoryzomys longicaudatus'm Argentina according to the temperature and precipitation model with the spatial dependence covariate fitted (TPS). Probability of presence is shown as a function of mean annual temperature, annual precipitation, and spatial dependence covariate (gray scale). Presence is predicted in areas with probability higher than 0.5. Sites used for model development are indicated by squares and validation sites are indicated by triangles (filled for presence and empty for absence of O. longicaudatus).

The collinearity observed between the explanatory variables retained in the model and the variables that were excluded should be taken into account to interpret the results (Table 6). They show possible secondary variables related to the rodent distribution that were excluded because other covariates were better for modeling that distribution. However, there is no way to distinguish how much effect corresponds to each of the correlated variables. For example, the variables temperature and precipitation in model TP are highly correlated with frost and bare soil (Table 6); these last 2 might also be related to the distribution (as was verified by the selection of the FB and FBS models). Grass cover also was correlated to temperature, frost days, and bare soil, and although it is not a good explanatory variable (it was not retained in any model), it might have some relation with the distribution of the rodent.

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Table 6

Collinearity between explanatory variables and variables retained in the model. Pairwise correlation coefficients higher than 0.45 or lower than ‒0.45 are shown.

TemperaturePrecipitationFrost daysBare soil cover
Frost days‒0.89
Tree cover0.81
Grass cover0.60‒0.610.48


Our distribution map for O. longicaudatus shows the 1st records in the Espinal phytogeographic province and in the political provinces of Mendoza, La Pampa, and Buenos Aires. The distribution of this hantavirus reservoir also extends to the Patagonian central plateaus through Río Negro and Chubut provinces and up to the Atlantic coast (Fig. 1), exceeding the previous known distribution (Redford and Eisenberg 1992).

Oligoryzomys longicaudatus is known to inhabit subantarctic forests, where woods are abundant, and also extend into the steppe along scrublands adjacent to streams and roads (Murua and Gonzalez 1982). In a wood-steppe ecotone, captures were associated with shrub cover and abundance of spiny shrubs (Lozada et al. 2000). The distribution model correctly predicted higher presence probabilities for O. longicaudatus in the west, where woods are found, and also showed a gradient decreasing toward the east, which might follow the decline in abundance of these habitats.

Temperature and precipitation were the variables that best described the distribution of O. longicaudatus at the regional scale; interaction between the variables makes interpretation difficult. At high temperatures, the rodent would probably be associated with lower precipitation (i.e., northeastern Patagonia) and at low temperatures with higher precipitation (i.e., western Andes, close to the Chilean border). The available literature concerns mainly the western part of the study area, which averages lower temperatures than the northeastern pampas. In this area the relation of the distribution of O. longicaudatus to higher precipitation already has been noted (Monjeau 1989), and although some studies disregard its importance (Murua et al. 2003), they have tried to associate precipitation with temporal and not to spatial abundance.

Although the methodology chooses some variables and excludes others because of collinearity and lower explanatory power, we cannot reject a possible secondary relation of the excluded variables to the distribution of O. longicaudatus. The relation to higher precipitation in the TP and TPS models might correspond to higher tree cover (Table 5); in Chile, association to vegetation cover was observed previously, although it was at low rodent densities and a very local scale (Gonzalez et al. 2000). Frost days and bare soil also were found to describe the distribution of O. longicaudatus. In these models, the rodent was associated with higher frost frequency and lower cover by bare soil. The inverse relation of the presence of the long-tailed colilargo to bare soil was previously found in a transition zone between the Andean forests and the Patagonian steppes (Lozada et al. 2000).

The distribution of O. longicaudatus is related primarily to the presence of favorable habitat and not to absolute geographic location (Monjeau et al. 1998). The coarse-grain model presented herein modeled probability of rodent presence as a function of environmental conditions. Phytogeography might be an easily available indicator of these conditions, but its explanatory power was surpassed by combinations of climatic and vegetation cover variables. These latter variables probably provided more detailed information than the former. On the other hand, it is generally climate (together with soil and history, not considered in this study) that determines the distribution of phytogeographic regions.

It would be desirable to predict the distribution of biotic entities on the basis of ecological parameters that are believed to be the causal, driving forces for their distribution and abundance (Guisan and Zimmermann 2000). In this respect, the predictors could be considered as indirect, direct, or resource variables (Austin 1980). Resource variables address matter and energy consumed by the rodents (e.g., food and water). Direct variables are environmental parameters that have physiological importance, but are not consumed (e.g., temperature and humidity). Indirect variables are variables that have no direct physiological relevance for a species' performance (e.g., slope, aspect, elevation, topographic position, and geology). In this regard, our model used resource variables (river distance), direct variables (climatic and vegetation cover), and indirect variables (altitude, phytogeography, and road distance). In our model, the better predictors of the distribution of the long-tailed colilargo might be encompassed as resource or direct variables. This ensures that the model is more general and applicable over larger areas because it is based on what is supposed to be more physiologically “mechanistic” (Guisan and Zimmermann 2000).

The extrapolation of the model to the entire country of Argentina shows that the probability of finding O. longicaudatus extends north along the Andes range. Although the distribution of O. longicaudatus from Tierra del Fuego up to the Argentinean-Bolivian border (Redford and Eisenberg 1992) is repeatedly mentioned in the literature, genetic studies have shown differences between the specimens classified as O. longicaudatus in northern and southern Argentina (Espinosa and Reig 1991; Gonzalez-Ittig et al. 2002). Even though these populations were not a single species, they might both have similar habitat requirements, suggesting recent speciation or weak differentiation. Both models (with and without spatial covariate) failed to predict accurately the presence in the northwest and central-eastern Patagonia. In the northwest, there were few sites, and in the east, the sites with presence and absence of long-tailed colilargos were relatively close to each other, diminishing predictive power at the scale of this study. If favorable habitats were very small and rare in the area, the coarse spatial resolution of the model would not allow them to be detected properly; thus more intensive sampling or higher spatial detail would be required. For example, in 1 of the largest Patagonian plateaus, Meseta de Somuncurá (>25,000 km2 of tableland basalts at an average altitude of 900 m), trapping efforts indicate that O. longicaudatus is restricted to small (∼500-m2) and isolated patches. These patches are composed of dense graminoids (Cortaderia) associated with permanent freshwater springs in or near basalt plateau margins at 600–700 m elevation.

The hantavirus pulmonary syndrome cases recorded in Patagonia are encompassed in the higher presence probability area (Fig. 1). In this area the Andes Sout genotype is responsible for hantavirus cases (southwestern Patagonia). In central Patagonia, no records of hantavirus exist for O. longicaudatus (Cantoni et al. 2001; Piudo et al. 2005). However, a case of hantavirus pulmonary syndrome whose reservoir was not identified occurred near the border between Río Negro and Buenos Aires provinces; according to our maps O. longicaudatus is found in this area suggesting that serological studies should be intensified along all the distribution range. In the northeast, the probability of presence of O. longicaudatus decays toward Buenos Aires Province, where Oligoryzomys flavescens is the reservoir responsible for the hantavirus pulmonary syndrome (Maciel genotype). Similar maps for other reservoirs such as O. flavescens and O. chacoensis in the north and the east might help as preliminary tools to distinguish the relative roles in the transmission of hantavirus and the construction of a map of transmission risk for Argentina. The present model might be a good starting point. It proved useful in Patagonia, with a correct classification higher than 86% and more than 95% of the sites with O. longicaudatus correctly predicted. It uses direct variables such as temperature and precipitation as predictors, which gives a good potential for generalization of the model to test it with other species outside the study area. Actually, the model projected a potential habitat area to the northwest of the country that might be tested for other reservoirs such as O. chacoensis or O. flavescens.

Our results show that the distribution of a hantavirus reservoir can be modeled as a function of environmental variables, obtaining a map of the probability of presence at the regional scale. This kind of approach might also be extended to other regions, because the variables used are easily available, and because the probability of presence of different reservoir species might be related to occurrences of hantavirus pulmonary syndrome. The association between hantavirus pulmonary syndrome cases and the highest rodent probabilities in our study area also suggests that this kind of model might be used as an early tool to estimate transmission risk until more comprehensive models, considering the reservoir, the virus, and the human population, are developed.


We thank J. Polop for facilitating the validation data. This work was partially funded by PICT 2004 no. 20790 from the Agencia Nacional de Promotion Científica y Tecnologica.

Appendix I

View this table:

Localities used in the study. The column O.l. indicates presence (y) or absence (n) of Oligoryzomys longicaudatus. Provinces and coordinates also are provided. PN = Parque Nacional; MN = Monumento Nacional.

CodeLocalityProvinceO.l.West longitudeSouth latitude
1Villa CaciqueBuenos Airesn59°22′58″37°40′1″
2Estancia La CasualidadBuenos Airesn62°27′7″35°30′39″
3Camping Casa AmarillaBuenos Airesn58°1′58″35°37′1″
4Diego GaynorBuenos Airesn59°13′58″34°17′59″
5Cordon LelequeChubuty71°2′59″42°23′59″
6Estancia El MaiténChubuty71°10′1″42°2′59″
7Estancia LelequeChubuty71°4′1″42°23′59″
8Estancia TeckaChubuty71°2′59″43°10′58″
9Punta DelgadaChubutn63°37′58″42°46′1″
10Meseta LehmanChubutn70°0′0″45°0′0″
11Río CorintosChubuty71°31′58″46°6′0″
12Cañadón LargoChubutna67°16′58″42°13′1″
13Estancia San PedroChubutn67°34′1″42°4′1″
14Laguna VerdeChubutn68°17′38″42°30′10″
15Sierra ApasChubutn67°37′58″42°0′0″
16Sierra de TalagapaChubutn68°13′58″42°13′58″
17Sierra de TalagapaChubutn68°13′1″42°12′0″
1830 km E Las ChapasChubutn66°6′50″43°27′10″
1936 km W Los AltaresChubutn68°49′37″43°51′43″
2050 km W Las PlumasChubutn67°47′34″43°50′16″
21Cañadón del LoroChubutn69°49′58″42°32′59″
22Caolinera Dique AmeghinoChubutnb66°25′58″43°40′47″
23Dique AmeghinoChubuty66°27′43″43°42′10″
24Laguna BlancaChubutn69°54′35″42°53′59″
25Lle CulChubuty65°34′58″43°19′58″
26Los AltaresChubutna68°23′52″43°53′31″
27Estancia MoniraChubutn70°49′40″43°42′0″
28Puerto LobosChubutn65°4′15″42°0′0″
29Casa de PiedraLa Pampanb67°12′0″38°12′0″
30Bajo GiulianiLa Pampan64°16′58″36°37′1″
31Junín de los AndesNeuquény70°30′0″40°19′58″
32Junín de los AndesNeuquény71°31′1″40°19′58″
33Junín de los AndesNeuquény70°30′0″39°30′0″
34Paraje La QuerenciaNeuquény70°56′52″39°7′19″
35Estancia CalcatreoRío Negron69°22′1″41°43′58″
36Estancia MaquinchaoRío Negron68°39′0″41°42′0″
37PN Nahuel HuapiRío Negroy71°7′1″41°46′58″
38PN Nahuel HuapiRío Negroy71°12′0″41°7′58″
39Canteras ComalloRío Negron70°10′1″40°46′58″
40Cerro CastilloRío Negroy70°40′29″40°36′28″
41Estancia PilcañeuRío Negron70°40′58″41°7′58″
42Paraje LelequeRío Negron70°37′58″41°8′59″
43Paso de los MollesRío Negron70°43′1′40°55′1″
44Lago CardielSanta Cruzy71°13′1″49°30′0″
45Puerto EnsenadaSanta Cruzn71°10′1″48°40′58″
46Estancia Corcel NegroNeuquény69°47′59″37°7′58″
47Chos MalalNeuquény70°16′1″37°22′58″
48Riscos BayosNeuquénn70°46′58″37°57′0″
49Cerro Casa de PiedraSanta Cruzy67°10′58″38°15′0″
50Gobernador DuvalLa Pampan66°25′58″38°45′0″
51Puente CarreriNeuquény70°25′58″38°52′58″
52Barda NegraNeuquény70°22′58″39°1′58″
53Villa ReginaRío Negroy67°4′58″39°6′0″
54La RinconadaNeuquény70°49′58″40°0′0″
55Cerrito PiñónNeuquény70°37′1″40°13′58″
56Cañadón del TordilloNeuquény70°10′58″40°22′58″
58Estancia El AbraBuenos Airesn63°22′1″40°30′0″
592 km NNW rutas 40 y 237Neuquény70°45′0″40°31′58″
60Cerro Castillo GastreChubutnb70°37′58″40°32′59″
61Cañadón Las ColoradasRío Negroy70°46′1″40°37′1″
6210 km WNW ComalloRío Negroy70°16′1″41°7′1″
63Cerro Corona GrandeRío Negrona66°54′0″41°27′0″
64Riacho San JoséChubutn64°37′1″42°25′1″
65Cañadón de la BuitreraChubutn70°9′0″42°37′58″
66Paso del SapoChubutn69°4′1″42°40′58″
67Pico SalamancaSanta Cruzy67°25′1″45°23′59″
69CatrilóLa Pampan63°25′58″36°24′7″
70Alta ItaliaLa Pampan64°7′1″35°22′8″
71Las GrutasRío Negron65°5′59″40°48′39″
72Junín de los AndesNeuquénnb71°5′2″39°56′9″
74Comodoro RivadaviaChubutn67°29′ 16″45°52′4″
75Río CuartoCordoban64°20′59″33°8′9″
76RamalloBuenos Airesna60°1′1″33°29′9″
77General SarmientoBuenos Airesn58°42′57″34°33′7″
78BerissoBuenos Airesn57°53′59″34°53′9″
79MagdalenaBuenos Airesn57°30′57″35°5′9″
80Carlos TejedorBuenos Airesn62°25′1″35°23′9″
81Mar del TuyuBuenos Airesn56°42′0″36°33′7″
82Mar de AjoBuenos Airesn56°40′58″36°43′8″
83AyacuchoBuenos Airesn58°27′57″37°9′7″
84CarhuéBuenos Airesn62°43′58″37°10′8″
85General LamadridBuenos Airesn61°15′0″37° 15 ′1″
86Villa CaciqueBuenos Airesn59°23′59″37°41′9″
87SaavedraBuenos Airesna62°21′0″37°47′9″
88LoberiaBuenos Airesna58°45′57″38°10′8″
89MiramarBuenos Airesn57°49′58″38°15′7″
90Monte HermosoBuenos Airesn61°17′59″38°59′9″
91Bahía BlancaBuenos Airesn62°15′57″38°43′11″
92General RodríguezBuenos Airesn58°57′0″34°37′8″
93General MadariagaBuenos Airesn57°7′58″37°1′58″
94General RodríguezBuenos Airesn59°1′58″34°40′1″
95Isla Martín GarcíaBuenos Airesn58°15′0″34°10′58″
96Puerto MadrynChubutn65°6′7″42°45′32″
97CastelarBuenos Airesn58°39′39″34°39′28″
98ItuzaingoBuenos Airesn58°40′47″34°39′43″
99Estancia La GloriaChubutn70°39′21″42°10′4″
100Valle HermosoChubut68°30′3″45°44′52″
101Laguna del MateChubutn69°49′11″44°25′44″
102Estancia Laguna GrandeChubutn67°11′56″45°11′45″
103Estancia El DescansoLa Pampan63°30′7″37°42′32″
104Estancia La ElenitaLa Pampan65°36′18″36°5′9″
105PN Lihuel CalelLa Pampanb65°35′38″38°0′39″
106Estancia Arco IrisLa Pampan65°25′15″37°24′14″
107Laguna de la Niña EncantadaMendozan69°52′47″35°9′10″
108Río Seco la HediondaMendozana68°17′20″34°30′7″
109Bardas BlancasMendozan69°48′21″35°51′50″
110Río QuilquihueNeuquény71°5′31″40°3′7″
111La LipelaNeuquény71°9′3″41°0′54″
112Arroyo CovuncoNeuquény70°1′8″38°43′22″
115Cueva de las ManosSanta Cruzna70°30′10″47°2′45″
116PN Perito MorenoSanta Cruznb72°1′37″47°50′24″
117Cerro PampaSanta Cruzn71°32′38″47°56′2″
118MN Bosques PetrificadosSanta Cruzn67°59′16″47°42′54″
119Río La LeonaSanta Cruzy72°0′36″50°7′19″
120Arroyo La TotoraBuenos Airesn57°54′35″38°17′27″
121Arroyo BalleneraBuenos Airesn57°57′3″38°18′43″
122Canal 6 CampanaBuenos Airesna58°55′40″34°9′50″
123Estancia San AlbertoBuenos Airesn62°31′44″35°21′57″
124Monte HermosoBuenos Airesn61°20′49″38°58′40″
125El PorvenirBuenos Airesn62°13′4″34°56′52″
126Playa UlissesBuenos Airesn57°39′10″38°10′29″
127Playa Los LobosBuenos Airesn57°37′30″38°9′25″
128Playa El MarquesadoBuenos Airesn57°36′25″38°7′44″
129Santa EleodoraBuenos Airesn62°40′4″34°41′16″
130Gonzales ChavesBuenos Airesn60°6′14″37°56′52″
131PieresBuenos Airesn58°41′9″38°23′31″
132Santa Clara del MarBuenos Airesn57°28′8″37°47′23″
133Arroyo El PantanosoBuenos Airesn58°53′13″34°42′0″
134Punta NegraBuenos Airesn58°50′16″38°37′1″
135Las GrutasBuenos Airesn58°45′54″38°35′16″
136SaladilloBuenos Airesn59°46′22″35°37′30″
137Cerro Dr. Alberto SerranoBuenos Airesn62°3′7″38°2′59″
138La TomaBuenos Airesn62°3′21″38°3′39″
139TrigalesBuenos Airesn61°57′7″34°32′13″
140Arroyo ChasicóBuenos Airesn62°58′11″38°34′26″
141Mar de las PampasBuenos Airesn56°59′38″37°17′5″
142Punta de IndioBuenos Airesna57°14′5″35°16′4″
143Seccional Glaciar MorenoSanta Cruzy73°0′0″50°28′11″
14452 km WSW El CalafateSanta Cruzy72°50′59″50°22′11″
145Puerto LimonaoChubuty71°37′44″42°51′0″
146Río ArrayanesChubuty72°0′0″43°0′0″
1475 km W LelequeChubuty71°6′32″42°22′48″
148Lago PueloChubuty71°39′32″42°9′32″
149La Catarata El HoyoChubuty71°30′36″42°4′12″
15019 km NNE El BolsonRío Negroy71°25,12″41°47′59″
1513 km NE Río VillegasRío Negroy71°30′0″41°32′23″
15243 km SSW BarilocheRío Negroy71°27′35″41°30′0″
153La VeranadaRío Negroy71°28′48″41°27′0″
15438 km SSE BarilocheRío Negroy71°7′47″41°26′23″
155Lago HessRío Negroy71°43′11″41°22′48″
156Lago MascardiRío Negroy71°39′0″41°15′35″
157Refugio NeumeyerRío Negroy71°18′36″41°15′0″
158Estancia El CóndorRío Negroy71°9′0″41°14′24″
15924 km ESE BarilocheRío Negroy71°2′24″41°12′36″
1602.2 km SE Laguna El TrebolRío Negroy71°28′48″41°11′23″
161Lago GutiérrezRío Negroy71°23′24″41°11′23″
1625 km S BarilocheRío Negroy71°18′36″41°10′12″
163Valle del SolRío Negroy71°28′48″41°9′35″
164Cerro OttoRío Negroy71°20′24″41°8′24″
165Rio Castaño OveroRío Negroy71°49′47″41°7′48″
16615 km W BarilocheRío Negroy71°28′48″41°7′48″
16714 km W BarilocheRío Negroy71°28′11″41°7′48″
168Centro Atómico BarilocheRío Negroy71°25′48″41°7′48″
1699 km W BarilocheRío Negroy71°24′35″41°7′48″
170MelipalRío Negroy71°20′59″41°7′48″
171BarilocheRío Negroy71°18′36″41°7′48″
1724.2 km E BarilocheRío Negroy71°15′0″41°7′48″
17312 km WNW BarilocheRío Negroy71°26′23″41°6′0″
17415 km ENE BarilocheRío Negroy71°8′24″41°4′47″
1755 km ESE Estación Perito MorenoRío Negroy70°57′35″41°4′47″
176Puerto BlestRío Negroy71°52′12″41°4′12″
1773 km SSW Llao Llao HotelRío Negroy71°32′24″41°4′12″
178Punto PanorámicoRío Negroy71°28′48″41°3′36″
17911 km NE BarilocheRío Negroy71°12′35″41°3′36″
180Estación Perito MorenoRío Negroy71°0′36″41°3′36″
1815 km W Llao Llao HotelRío Negroy71°35′24″41°2′59″
182Lago EscondidoRío Negroy71°34′11″41°2′59″
183Península Llao LlaoRío Negroy71°33′36″41°2′59″
1842 km E Aeroclub BarilocheRío Negroy71°12′0″41°2′59″
185Lago Perito MorenoRío Negroy71°32′59″41°1′48″
186Arroyo ChacabucoNeuquény71°12′35″41°1′12″
187Arroyo ChacabucoNeuquény71°11′23″41°0′35″
1886 km N Estación Perito MorenoRío Negroy71°0′0″41°0′0″
189Mallin MulaNeuquény71°8′24″40°58′11″
190Arroyo CorralNeuquény71°4′11″40°54′35″
191Arroyo CarbónNeuquény71°1′48″40°50′24″
192Ruca MalenNeuquény71°37′47″40°49′47″
193N end Lago CorrentosoNeuquény71°36′35″40°49′12″
194Lago Espejo ChicoNeuquény71°39′0″40°47′59″
195Cascada DianaNeuquény71°50′24″40°43′11″
19620 km N Villa La AngosturaNeuquény71°39′35″40°36′0″
197Hua Hum, Lago LacarNeuquény71°39′35″40°6′35″
198Rio Cuyin ManzanoNeuquény70°52′47″40°3′36″
1993 km NW ConfluenciaNeuquény70°50′59″40°2′23″
2002 km SE La RinconadaNeuquény70°39′0″40°0′35″
201E end Laguna VerdeNeuquény71°15′0″39°30′0″
2025 km N Las ColoradasNeuquény70°34′47″39°30′0″
203vicinity of Pampa Hui HuiNeuquény71°20,59″39°21′35″
2043 km W RahueNeuquény70°57′0″39°21′0″
205Lago QuillenNeuquény71°15′36″39°20′24″
20645 km SSE Chos MalalNeuquény70°4′11″37°45′0″
207Lago Ruca ChoroiNeuquény7T10T″39°13′58″
20850 km N de San RafaelMendozay68°40T″34°15′0″
209Chos MalalNeuquény70°17′59″37°22′1″
210Chos MalalNeuquény70°15′0″37°23′59″
211Estancia Los RanquelesLa Pampay65°25′58″37°52′1″
212PN Lihuel CalelLa Pampay65°34′58″38°1′1″
213Estancia Santa ElenaLa Pampay64°4′58″38°22′1″
214Puente CarreriNeuquény70°26′9″38°53′13″
21518 km NW Río ColoradoRío Negroy66°2′59″38°58′58″
216Puesto El CharaBuenos Airesy62°2′59″39°27′0″
217Estación Experimental INTA Hilario AscasubiBuenos Airesy62°38′59″39°22′1″
218Estancia La PetronaBuenos Airesy62°46T39°25′58″
219Cañadón Santo DomingoNeuquény70°10′12″39°2′52″
220ChimpayRío Negroy66°9′0″39°10′1″
221Pampa de Hui HuiNeuquény71°19T″39°22′1″
222Lago CurruhuéNeuquény71°24′0″39°52′58″
223Junín de los AndesNeuquény71°4′58″39°55′58″
224Estancia HuechahueNeuquény70°49′58″39°55′58″
225Potrero QuilquihueNeuquény71°10′58″39°58′58″
226Lago ÑorquincóNeuquény71°15′0″39°8′59″
227Río QuilquihueNeuquény71°4′58″40°4′1″
228Collon CuraNeuquény70°39′0″40°25′58″
229Bahía San BiasBuenos Airesy62°15′54″40°34′51″
230Estancia María SofiaRío Negroy70°9′0″40°37′1″
231Estancia Fortin ChacabucoNeuquény70°58T″40°37′58″
232Lago CorrentosoNeuquény71°40′58″40°43′1″
233Cafiadon arroyo FuquelenRío Negroy70°24′0″40°43′58″
234Río LimayNeuquény71°7T″41°1T″
235Estancia El CondorRío Negroy71°11′52″41°10′15″
236Ingeniero JacobacciRío Negroy69°32′59″41°19′58″
237Villa TaculRío Negroy71°32′59″41°2′59″
238Lago SteffenRío Negroy71°32′59″41°31′1″
239Laguna Los JuncosRío Negroy71°1′58″41°4T″
240Establecimiento San NicolasRío Negroy67°9′50″41°43′51″
241mouth of no NirihuauRío Negroy71°9′0″41°4′58″
242Concon road, 8 km ENE BarilocheRío Negroy71°13′1″41°7′1″
243El RinconRío Negroy66°16′58″41°7′58″
244Puesto BurrosChubuty71°10′58″42°2′59″
246Lago FutalaufquenChubuty71°36′28″42°53′2″
247Estancia Valle HuemulesChubuty71°31T″45°57′0″
249Alero Destacamento GuardaparqueSanta Cruzy72°2′59″47°53′59″
250Río ChicoSanta Cruzy71°31′1″48°17′59″
251Valle Tucu TucuSanta Cruzy71°49′58″48°27′0″
252Estancia La AnitaSanta Cruzy72°31′1″50°27′0″
254El BolsonRío Negroya,c71°31′11″71°31′11″
255El CondorRío Negroya,c71°4′11″71°4′11″
256El HoyoChubutya,c71°26′23″71°26′23″
257El HuecuNeuquénya,c70°35′59″70°35′59″
258Estancia María SRío Negroya,c70°2′59″70°2′59″
259Lago FutalaufquenChubutya,c71°13′11″71°13,11″
260Lago PueloChubutya,c71°2′59″71°2′59″
261Nahuel panChubutya,c71°2′59″71°2′59″
263PN Los AlercesChubutya,c71°46′11″71°46′11″
265Paraje ContraNeuquénya,c71°22′12″71°22′12″
266Las ColoradasNeuquénya,c70°55′12″70°55′12″
268Lago RivadaviaChubutya,c71°2′59″71°2′59″
269Puerto BlestChubutya,c71°17′24″71°17′24″
270Villa la AngosturaNeuquénya,c71°34′47″71°34′47″
271Chos MalalNeuquénya,c70°10′12″70°10′12″
  • a Used for validation.

  • b Excluded (less than 10 km from a positive site).

  • c Data from Porcasi et al. (2005).


  • Associate Editor was John A. Yunger.

Literature Cited

View Abstract