Journal of Mammalogy, 88(6):1555–1568, 2007
SPATIAL DISTRIBUTION MODEL OF A HANTAVIRUS
RESERVOIR, THE LONG-TAILED COLILARGO
(OLIGORYZOMYS LONGICAUDATUS), IN ARGENTINA
ANÍBAL E. CARBAJO*
AND
ULYSES F. J. PARDIÑAS
Unidad de Ecologı́a de Reservorios y Vectores de Parásitos, Departamento Ecologı́a, Genética y Evolución,
Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Pab. II, C1428EHA - CONICET,
Buenos Aires, Argentina (AEC)
Centro Nacional Patagónico, Casilla de Correo 128, 9120 Puerto Madryn, Chubut, Argentina - CONICET (UFJP)
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
* Correspondent: [email protected]
Ó 2007 American Society of Mammalogists
www.mammalogy.org
1555
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
1556
JOURNAL OF MAMMALOGY
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 (Lopez et al. 1996; Toro
et al. 1998). Specimens testing positive for hantavirus antibody have been confirmed all along its distributional range,
from 388S to 518S latitude (Padula et al. 2000; Torres-Pérez
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 Mamı́feros y Colección de Material de Egagrópilas y Afines ‘‘Elio Massoia’’ del Centro Nacional Patagónico,
Puerto Madryn, Argentina; Colección de Mamı́feros del Museo
Argentino de Ciencias Naturales ‘‘Bernardino Rivadavia,’’
Buenos Aires, Argentina; Colección de Mamı́feros 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 magellanicus 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; Dı́az 2000; Dı́az 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).
Vol. 88, No. 6
Records of O. longicaudatus were incorporated into a
geographic information system (GIS) using ArcView 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 ¼ 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 338S to 518S 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.
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 (Subsecretarı́a de Recursos Hı́dricos 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 (Subsecretarı́a de Recursos Hı́dricos 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
December 2007
CARBAJO AND PARDIÑAS—SPATIAL DISTRIBUTION OF O. LONGICAUDATUS
1557
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.
Precipitation
(mm)/altitude
(m)/temperature (8C)
Source
Phytogeographic
province
Phytogeographic
district
Leon et al. 1998
Patagonian
Subandean
Central
Occidental
Payunia
San Jorge Gulf
NA/6002,000/NA
NA/0700/NA
Magellan
Austral
Oriental
Rı́o Negro
300450/NA/NA
200/NA/NA
.250/200/NA
200/300600/NA
Penı́nsula Valdés
Pampean
.200/NA/NA
6001,200/NA/1317
High Andean
Subantarctic
Patagonian
Snow and hail/NA/,5
8005,000/NA/59
100300/NA/513
Monte
Espinal
250800/NA/1315.5
340600/NA/1520
Monte
Ecotone
Cabrera and
Willink 1973
a
b
c
.300/NA/NAa
,200/NA/NA
Cover (%) by
vegetation forms
60% dense grassesb
3060% shrubs
50% tall grasses and shrubs
Shrubs/scrubland
Shrubs and scrubland in valleys,
80% grasses in hilltops
50% xeric to 90% humid grasses
60% shrubs and scrubland
5080% shrubs and short trees
3050% scrubland and
cushionlike shrubs
4060% shrubs
Herbaceous steppec
Xeric grassland
Perennial and deciduous forest
Low cover shrubs and grass
steppes
Shrubgrass steppes
Scrubland and low trees
% Sites
broad
% Sites
detailed
Initial
factor
Simplified
factor
97
42
59
100
25
(33)
(19)
(34)
(4)
(4)
7
8
9
11
11
B
C
C
C
C
—
67
00
100
(0)
(12)
(1)
(3)
—
10
11
11
—
C
C
C
00 (44)
— (0)
00 (44)
—
1
—
A
92 (24)
100 (41)
70 (96)
95 (20)
100 (33)
91 (11)
2
3
4
B
B
B
50 (20)
46 (11)
43 (7)
46 (11)
5
6
C
C
NA ¼ not available.
Shrubs grow in areas with higher precipitation and cattle grazing.
Replaced by crops and cattle.
(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.
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
(x1, x2, . . .) are related to the response variable through a linear
predictor (LP). LP ¼ a þ bx1 þ 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 1st-order polynomial: p ¼
eLP/(1 þ eLP). This can be linearized as ln[p/(1 p)] ¼ 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 ¼ 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 t-test (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
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JOURNAL OF MAMMALOGY
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
Variable
Altitude
Description
Elevation above sea
level
Temperature
Mean annual temperature
Precipitation
Mean annual cumulative
precipitation
Frost days
Annual frost days
frequency
Evapotranspiration Mean annual cumulative
evapotranspiration
Tree cover
Percentage of surface
with tree cover
Grass cover
Percentage of surface
with grass cover
Bare soil cover
Percentage of surface
with bare soil cover
River distance
Distance to nearest
river
Water distance
Distance to nearest
water body or course
Road distance
Distance to nearest
road
Spatial dependence Spatial dependence
covariate
Broad factor
Broadscale
phytogeographic
factor
Detailed factor
Detailed-scale
phytogeographic
factor
Cell
Units sideb (km)
Z
OP median
(LQ UQ)
OA median
(LQ UQ)
B SE
Dev
t
m
10
7.121***
826 (6861,075)
128 (26755)
58.3
5.25**
8C
mm
4
4
9.379***
5.489***
7.6 (6.59.4)
969 (4461,726)
14.0 (10.815.1) 0.472 0.056 109.0
677 (256900)
0.002 0.000 39.4
8.50**
5.36**
days
45
9.199***
126 (113136)
47 (34107)
mm
4
2.698**
292 (250324)
445 (171600)
%
0.5
5.807***
8.5 (3.033.5)
3.0 (0.07.0)
%
0.5
%
0.003 0.001
0.040 0.005 108.3
8.08**
0.006 0.001
38.3
5.32**
0.051 0.013
34.5
4.07**
4.114*** 61.5 (37.371.8)
70.5 (55.891.8) 0.025 0.007
17.3
3.84**
0.5
0.903
17.0 (0.033.8)
21.5 (0.2537.0)
—
km
1
4.576***
1.4 (1.03.5)
4.2 (1.019.6)
0.036 0.009
31.8
3.89**
km
1
4.847***
1.0 (0.02.0)
2.0 (1.05.0)
0.189 0.052
24.5
3.66**
km
1
1.045
1.0 (0.03.0)
1.0 (0.02.2)
—
—
9
6.196*** 0.00 (0.020.03)
—
VP
132.0***
—
VP
156.1***
0.15 0.01
9.380 2.538
—
—
36.3
1.13
0.56
3.70**
a
LQ ¼ lower quartile; UQ ¼ upper quartile; VP ¼ vectorial polygon layers.
Square cells.
*** Significant at P , 0.001; ** P , 0.01.
b
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 largescale 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 Sþ SpatialStats
and Arcview GIS add-ons (Insightful Corp.2002) was used for
modeling and mapping.
RESULTS
Univariate comparison of sites with and without O.
longicaudatus showed significant differences for most of the
environmental variables considered, as did the univariate
December 2007
CARBAJO AND PARDIÑAS—SPATIAL DISTRIBUTION OF O. LONGICAUDATUS
1559
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.
TP
Model
Parameter
SE
Intercept
Temperature
Precipitation
Temperature precipitation
SDC
Residual deviance
Null deviance
10.932
0.905
0.033
0.0027
2.895
0.232
0.008
0.0006
TPS
d.f.
1
1
1
128.53
313.75
t
Parameter
SE
3.777
3.903
4.318
4.789
9.894
0.763
0.033
0.0027
9.481
96.07
313.75
3.315
0.253
0.008
0.0006
3.552
231
235
232
235
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).
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 extrapo-
d.f.
t
1
1
1
1
2.985
3.010
3.964
4.345
2.669
lated. 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 368S latitude, narrowing beyond 508S (Fig. 1).
The Patagonian central plateaus show low probability (Chubut
and Santa Cruz provinces), whereas the eastern plateaus and
the north (Rı́o 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 338S 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 488S and 508S 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.
The collinearity observed between the explanatory variables
retained in the model and the variables that were excluded
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.
FB
Model
Parameter
SE
Intercept
Frost
Frost2
Bare soil
Bare soil2
SDC
Residual deviance
Null deviance
12.159
0.313
0.0014
0.113
0.0007
2.385
0.061
0.0003
0.028
0.0003
153.5
313.75
FBS
d.f.
1
1
1
1
231
235
t
Parameter
SE
5.098
5.140
4.521
4.009
2.615
16.356
0.404
0.0017
0.206
0.0014
16.079
96.1
313.75
3.962
0.110
0.0006
0.057
0.0005
4.433
d.f.
1
1
1
1
1
230
235
t
4.128
3.673
2.991
3.604
3.178
3.627
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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).
Model
Terms
d.f.
% deviance explained
Validation
Sensitivity
Specificity
Correct classification
Kappa index
TP
TþPþTP
232
59.0
0.95
0.70
0.86
0.68
TPS
FB
TþPþSþTP
231
69.4
1.00
0.70
0.90
0.75
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.
DISCUSSION
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 Rı́o 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
2
FBS
2
FþF þBþB
231
51.0
1.00
0.60
0.86
0.66
2
F þ F þ B þ B2 þ S
230
70.4
0.95
0.70
0.86
0.68
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
December 2007
CARBAJO AND PARDIÑAS—SPATIAL DISTRIBUTION OF O. LONGICAUDATUS
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.
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
1561
FIG. 2.—Potential distribution of Oligoryzomys longicaudatusin
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).
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
1562
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.
Temperature
Evapotranspiration
Precipitation
Elevation
Frost days
Tree cover
Grass cover
Vol. 88, No. 6
JOURNAL OF MAMMALOGY
Precipitation
Frost
days
0.53
0.65
0.69
0.89
0.81
—
Bare soil
cover
ACKNOWLEDGMENTS
0.56
0.59
We thank J. Polop for facilitating the validation data. This work was
partially funded by PICT 2004 no. 20790 from the Agencia Nacional
de Promoción Cientı́fica y Tecnológica.
LITERATURE CITED
0.81
0.60
0.61
used as an early tool to estimate transmission risk until more
comprehensive models, considering the reservoir, the virus,
and the human population, are developed.
0.48
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 Rı́o 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
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Submitted 16 June 2006. Accepted 6 March 2007.
Associate Editor was John A. Yunger.
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JOURNAL OF MAMMALOGY
APPENDIX I
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 ¼ Parque Nacional; MN ¼ Monumento Nacional.
Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
Locality
Province
Villa Cacique
Estancia La Casualidad
Camping Casa Amarilla
Diego Gaynor
Cordón Leleque
Estancia El Maitén
Estancia Leleque
Estancia Tecka
Punta Delgada
Meseta Lehman
Rı́o Corintos
Cañadón Largo
Estancia San Pedro
Laguna Verde
Sierra Apas
Sierra de Talagapa
Sierra de Talagapa
30 km E Las Chapas
36 km W Los Altares
50 km W Las Plumas
Cañadón del Loro
Caolinera Dique Ameghino
Dique Ameghino
Laguna Blanca
Lle Cul
Los Altares
Estancia Monira
Puerto Lobos
Casa de Piedra
Bajo Giuliani
Junı́n de los Andes
Junı́n de los Andes
Junı́n de los Andes
Paraje La Querencia
Estancia Calcatreo
Estancia Maquinchao
PN Nahuel Huapi
PN Nahuel Huapi
Canteras Comallo
Cerro Castillo
Estancia Pilcañeu
Paraje Leleque
Paso de los Molles
Lago Cardiel
Puerto Ensenada
Estancia Corcel Negro
Chos Malal
Riscos Bayos
Cerro Casa de Piedra
Gobernador Duval
Puente Carreri
Barda Negra
Villa Regina
La Rinconada
Cerrito Piñón
Cañadón del Tordillo
Confluencia
Estancia El Abra
2 km NNW rutas 40 y 237
Cerro Castillo Gastre
Cañadón Las Coloradas
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
Chubut
La Pampa
La Pampa
Neuquén
Neuquén
Neuquén
Neuquén
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Santa Cruz
Santa Cruz
Neuquén
Neuquén
Neuquén
Santa Cruz
La Pampa
Neuquén
Neuquén
Rı́o Negro
Neuquén
Neuquén
Neuquén
Neuquén
Buenos Aires
Neuquén
Chubut
Rı́o Negro
O.l.
West longitude
South latitude
n
n
n
n
y
y
y
y
n
n
y
na
n
n
n
n
n
n
n
n
n
nb
y
n
y
na
n
n
nb
n
y
y
y
y
n
n
y
y
n
y
n
n
n
y
n
y
y
n
y
n
y
y
y
y
y
y
y
n
y
nb
y
598229580
62827970
58819580
598139580
71829590
71810910
7184910
71829590
638379580
7080900
718319580
678169580
67834910
688179380
678379580
688139580
68813910
66869500
688499370
678479340
698499580
668259580
668279430
698549350
658349580
688239520
708499400
65849150
67812900
648169580
70830900
71831910
70830900
708569520
69822910
68839900
7187910
71812900
70810910
708409290
708409580
708379580
70843910
71813910
71810910
698479590
70816910
708469580
678109580
668259580
708259580
708229580
67849580
708499580
70837910
708109580
708319580
63822910
70845900
708379580
70846910
37840910
358309390
35837910
348179590
428239590
42829590
428239590
438109580
42846910
4580900
4686900
42813910
4284910
428309100
4280900
428139580
42812900
438279100
438519430
438509160
428329590
438409470
438429100
428539590
438199580
438539310
43842900
4280900
38812900
36837910
408199580
408199580
39830900
39879190
418439580
41842900
418469580
41879580
408469580
408369280
41879580
41889590
40855910
49830900
488409580
37879580
378229580
37857900
38815900
38845900
388529580
39819580
3986900
4080900
408139580
408229580
40830900
40830900
408319580
408329590
40837910
December 2007
CARBAJO AND PARDIÑAS—SPATIAL DISTRIBUTION OF O. LONGICAUDATUS
1565
APPENDIX I.—Continued.
Code
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
Locality
Province
10 km WNW Comallo
Cerro Corona Grande
Riacho San José
Cañadón de la Buitrera
Paso del Sapo
Pico Salamanca
Astra
Catriló
Alta Italia
Las Grutas
Junı́n de los Andes
Trevelin
Comodoro Rivadavia
Rı́o Cuarto
Ramallo
General Sarmiento
Berisso
Magdalena
Carlos Tejedor
Mar del Tuyú
Mar de Ajó
Ayacucho
Carhué
General Lamadrid
Villa Cacique
Saavedra
Loberı́a
Miramar
Monte Hermoso
Bahı́a Blanca
General Rodrı́guez
General Madariaga
General Rodrı́guez
Isla Martı́n Garcı́a
Puerto Madryn
Castelar
Ituzaingo
Estancia La Gloria
Valle Hermoso
Laguna del Mate
Estancia Laguna Grande
Estancia El Descanso
Estancia La Elenita
PN Lihuel Calel
Estancia Arco Iris
Laguna de la Niña Encantada
Rı́o Seco la Hedionda
Bardas Blancas
Rı́o Quilquihue
La Lipela
Arroyo Covunco
Pilolil
Rahue
Cueva de las Manos
PN Perito Moreno
Cerro Pampa
MN Bosques Petrificados
Rı́o La Leona
Arroyo La Totora
Arroyo Ballenera
Canal 6 Campana
Estancia San Alberto
Monte Hermoso
Rı́o Negro
Rı́o Negro
Chubut
Chubut
Chubut
Santa Cruz
Chubut
La Pampa
La Pampa
Rı́o Negro
Neuquén
Chubut
Chubut
Córdoba
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Chubut
Buenos Aires
Buenos Aires
Chubut
Chubut
Chubut
Chubut
La Pampa
La Pampa
La Pampa
La Pampa
Mendoza
Mendoza
Mendoza
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Santa Cruz
Santa Cruz
Santa Cruz
Santa Cruz
Santa Cruz
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
O.l.
West longitude
South latitude
y
na
n
n
n
y
n
n
n
n
nb
y
n
n
na
n
n
n
n
n
n
n
n
n
n
na
na
n
n
n
n
n
n
n
n
n
n
n
70816910
66854900
64837910
7089900
69843910
67825910
678289580
638259580
6487910
65859590
7185920
71829920
678299160
648209590
6081910
588429570
578539590
578309570
62825910
56842900
568409580
588279570
628439580
61815900
598239590
62821900
588459570
578499580
618179590
628159570
58857900
57879580
59819580
58815900
6586970
588399390
588409470
708399210
68830930
698499110
678119560
63830970
658369180
658359380
658259150
698529470
688179200
698489210
71859310
7189930
7081980
708569450
70855980
708309100
72819370
718329380
678599160
72809360
578549350
57857930
588559400
628319440
618209490
4187910
41827900
42825910
428379580
428409580
458239590
458439580
36824970
35822980
408489390
39856990
4384980
45852940
3388990
33829990
34833970
34853990
3585990
35823990
36833970
36843980
3789970
37810980
37815970
37841990
37847990
38810980
38815970
38859990
388439110
34837980
37819580
34840910
348109580
428459320
348399280
348399430
42810940
458449520
448259440
458119450
378429320
3685990
38809390
378249140
35899100
34830970
358519500
4083970
41809540
388439220
398399250
398199580
47829450
478509240
47856920
478429540
50879190
388179270
388189430
34899500
358219570
388589400
n
n
n
n
nb
n
n
na
n
y
y
y
y
y
na
nb
n
n
y
n
n
na
n
n
1566
Vol. 88, No. 6
JOURNAL OF MAMMALOGY
APPENDIX I.—Continued.
Code
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
Locality
Province
El Porvenir
Playa Ulisses
Playa Los Lobos
Playa El Marquesado
Santa Eleodora
Gonzales Chaves
Pieres
Santa Clara del Mar
Arroyo El Pantanoso
Punta Negra
Las Grutas
Saladillo
Cerro Dr. Alberto Serrano
La Toma
Trigales
Arroyo Chasicó
Mar de las Pampas
Punta de Indio
Seccional Glaciar Moreno
52 km WSW El Calafate
Puerto Limonao
Rı́o Arrayanes
5 km W Leleque
Lago Puelo
La Catarata El Hoyo
19 km NNE El Bolson
3 km NE Rı́o Villegas
43 km SSW Bariloche
La Veranada
38 km SSE Bariloche
Lago Hess
Lago Mascardi
Refugio Neumeyer
Estancia El Cóndor
24 km ESE Bariloche
2.2 km SE Laguna El Trebol
Lago Gutierrez
5 km S Bariloche
Valle del Sol
Cerro Otto
Rio Castaño Overo
15 km W Bariloche
14 km W Bariloche
Centro Atómico Bariloche
9 km W Bariloche
Melipal
Bariloche
4.2 km E Bariloche
12 km WNW Bariloche
15 km ENE Bariloche
5 km ESE Estación Perito Moreno
Puerto Blest
3 km SSW Llao Llao Hotel
Punto Panorámico
11 km NE Bariloche
Estación Perito Moreno
5 km W Llao Llao Hotel
Lago Escondido
Penı́nsula Llao Llao
2 km E Aeroclub Bariloche
Lago Perito Moreno
Arroyo Chacabuco
Arroyo Chacabuco
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Buenos Aires
Santa Cruz
Santa Cruz
Chubut
Chubut
Chubut
Chubut
Chubut
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Neuquén
Neuquén
O.l.
West longitude
South latitude
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
na
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
62813940
578399100
578379300
578369250
62840940
60869140
58841990
57828980
588539130
588509160
588459540
598469220
6283970
62839210
61857970
628589110
568599380
57814950
7380900
728509590
718379440
7280900
71869320
718399320
718309360
718259120
71830900
718279350
718289480
71879470
718439110
71839900
718189360
7189900
71829240
718289480
718239240
718189360
718289480
718209240
718499470
718289480
718289110
718259480
718249350
718209590
718189360
71815900
718269230
71889240
708579350
718529120
718329240
718289480
718129350
71809360
718359240
718349110
718339360
71812900
718329590
718129350
718119230
348569520
388109290
38899250
38879440
348419160
378569520
388239310
378479230
34842900
38837910
388359160
358379300
38829590
38839390
348329130
388349260
37817950
35816940
508289110
508229110
42851900
4380900
428229480
42899320
42849120
418479590
418329230
41830900
41827900
418269230
418229480
418159350
41815900
418149240
418129360
418119230
418119230
418109120
41899350
41889240
41879480
41879480
41879480
41879480
41879480
41879480
41879480
41879480
4186900
41849470
41849470
41849120
41849120
41839360
41839360
41839360
41829590
41829590
41829590
41829590
41819480
41819120
41809350
December 2007
CARBAJO AND PARDIÑAS—SPATIAL DISTRIBUTION OF O. LONGICAUDATUS
1567
APPENDIX I.—Continued.
Code
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
Locality
Province
6 km N Estación Perito Moreno
Mallin Mula
Arroyo Corral
Arroyo Carbón
Ruca Malen
N end Lago Correntoso
Lago Espejo Chico
Cascada Diana
20 km N Villa La Angostura
Hua Hum, Lago Lacar
Rio Cuyin Manzano
3 km NW Confluencia
2 km SE La Rinconada
E end Laguna Verde
5 km N Las Coloradas
vicinity of Pampa Hui Hui
3 km W Rahue
Lago Quillen
45 km SSE Chos Malal
Lago Ruca Choroi
50 km N de San Rafael
Chos Malal
Chos Malal
Estancia Los Ranqueles
PN Lihuel Calel
Estancia Santa Elena
Puente Carreri
18 km NW Rı́o Colorado
Puesto El Chara
Estación Experimental INTA Hilario Ascasubi
Estancia La Petrona
Cañadón Santo Domingo
Chimpay
Pampa de Hui Hui
Lago Curruhué
Junı́n de los Andes
Estancia Huechahue
Potrero Quilquihue
Lago Ñorquincó
Rı́o Quilquihue
Collón Cura
Bahı́a San Blas
Estancia Marı́a Sofı́a
Estancia Fortı́n Chacabuco
Lago Correntoso
Cañadón arroyo Fuquelen
Rı́o Limay
Estancia El Cóndor
Ingeniero Jacobacci
Villa Tacul
Lago Steffen
Laguna Los Juncos
Establecimiento San Nicolás
mouth of rı́o Ñirihuau
Concon road, 8 km ENE Bariloche
El Rincón
Puesto Burros
Epuyén
Lago Futalaufquen
Estancia Valle Huemules
Trevelin
Alero Destacamento Guardaparque
Rı́o Chico
Rı́o Negro
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Mendoza
Neuquén
Neuquén
La Pampa
La Pampa
La Pampa
Neuquén
Rı́o Negro
Buenos Aires
Buenos Aires
Buenos Aires
Neuquén
Rı́o Negro
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Neuquén
Buenos Aires
Rı́o Negro
Neuquén
Neuquén
Rı́o Negro
Neuquén
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Rı́o Negro
Chubut
Chubut
Chubut
Chubut
Chubut
Santa Cruz
Santa Cruz
O.l.
West longitude
South latitude
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
7180900
71889240
71849110
71819480
718379470
718369350
71839900
718509240
718399350
718399350
708529470
708509590
70839900
71815900
708349470
718209590
70857900
718159360
70849110
71810910
68840910
708179590
70815900
658259580
658349580
64849580
70826990
66829590
62829590
628389590
62846910
708109120
6689900
71819910
71824900
71849580
708499580
718109580
71815900
71849580
70839900
628159540
7089900
70858910
718409580
70824900
7187910
718119520
698329590
718329590
718329590
71819580
67899500
7189900
71813910
668169580
718109580
718229580
718369280
71831910
71830900
72829590
71831910
4180900
408589110
408549350
408509240
408499470
408499120
408479590
408439110
40836900
40869350
40839360
40829230
40809350
39830900
39830900
398219350
39821900
398209240
37845900
398139580
34815900
37822910
378239590
37852910
3881910
38822910
388539130
388589580
39827900
39822910
398259580
39829520
39810910
39822910
398529580
398559580
398559580
398589580
39889590
4084910
408259580
408349510
40837910
408379580
40843910
408439580
4181910
418109150
418199580
41829590
41831910
4184910
418439510
41849580
4187910
41879580
42829590
428139580
42853920
45857900
4686900
478539590
488179590
1568
Vol. 88, No. 6
JOURNAL OF MAMMALOGY
APPENDIX I.—Continued.
Code
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
a
b
c
Locality
Province
O.l.
West longitude
South latitude
Valle Tucu Tucu
Estancia La Anita
Cholila
El Bolsón
El Cóndor
El Hoyo
El Huecu
Estancia Marı́a S
Lago Futalaufquen
Lago Puelo
Nahuel pan
Neuquén
PN Los Alerces
Rinquilon
Paraje Contra
Las Coloradas
Esquel
Lago Rivadavia
Puerto Blest
Villa la Angostura
Chos Malal
Santa Cruz
Santa Cruz
Chubut
Rı́o Negro
Rı́o Negro
Chubut
Neuquén
Rı́o Negro
Chubut
Chubut
Chubut
Neuquén
Chubut
Neuquén
Neuquén
Neuquén
Chubut
Chubut
Chubut
Neuquén
Neuquén
y
y
ya,c
ya,c
ya,c
ya,c
ya,c
ya,c
ya,c
ya,c
ya,c
ya,c
ya,c
ya,c
ya,c
ya,c
ya,c
ya,c
ya,c
ya,c
ya,c
718499580
72831910
718279350
718319110
71849110
718269230
708359590
70829590
718139110
71829590
71829590
68839360
718469110
708359590
718229120
708559120
718199110
71829590
718179240
718349470
708109120
48827900
50827900
718279350
718319110
71849110
718269230
708359590
70829590
718139110
71829590
71829590
68839360
718469110
708359590
718229120
708559120
718199110
71829590
718179240
718349470
708109120
Used for validation.
Excluded (less than 10 km from a positive site).
Data from Porcasi et al. (2005).