LANDSCAPE GENETICS OF RHINELLA SPINULOSA
REVISTA CHILENA DE HISTORIA NATURAL
Revista Chilena de Historia Natural 84: 391-406, 2011
391
© Sociedad de Biología de Chile
RESEARCH ARTICLE
Relationship between the genetic structure of the Andean toad Rhinella
spinulosa (Anura: Bufonidae) and the northern Chile landscape (21°- 24° S)
Relación entre la estructura genética del sapo andino Rhinella spinulosa
(Anura: Bufonidae) y el paisaje del norte de Chile (21°- 24° S)
CAROLINA E. GALLARDO1, HÉCTOR J. HERNÁNDEZ2, JOSÉ A. F. DINIZ-FILHO3, R. EDUARDO PALMA4 &
MARCO A. MÉNDEZ1, *
1 Laboratorio
de Genética y Evolución, Departamento de Ciencias Ecológicas, Facultad de Ciencias, Universidad de Chile,
P.O. Box 653. Las Palmeras 3425, Ñuñoa, Santiago, Chile
2 Laboratorio de Geomática y Ecología del Paisaje, Facultad de Ciencias Forestales, Universidad de Chile, P.O. Box 1004,
Santa Rosa 11315, La Pintana, Santiago, Chile
3 Departamento de Biologia Geral, ICB, Universidade Federal de Goiás, Cx.P. 131 Campus II 74001970, Goiânia, GO - Brasil
4 Laboratorio de Biología Evolutiva, Departamento de Ecología y Centro de Estudios Avanzados en Ecología y Biodiversidad
(CASEB), Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago 6513677, Chile
*Corresponding author: mmendez@uchile.cl
ABSTRACT
We analyzed the relationship of landscape and environmental features on the genetic differentiation of Rhinella
spinulosa (Wiegmann, 1834) in the Altiplano of Antofagasta (Chile). We performed three types of analyses at different
spatial scales: (1) Considering all populations; (2) Grouping populations by watershed and by sub-watershed; and (3)
Using the results of a spatial analysis of molecular variation (SAMOVA). Landscape features were incorporated using
Geographic Information Systems, with three hypothetical dispersal models: (1) Euclidean distance (null model);
(2) Least cost based on wetland locations; and (3) Least cost based on least slopes. We also included differences in
temperature, precipitation and altitude among localities. The Akaike information criterion was used to select the best
model and the relative importance of each variable in the model was estimated with partial regressions. We found a
high genetic differentiation among populations (Fst = 0.693) and isolation by distance (r = 0.767). AMOVA showed
that the watersheds explained 8.67 % of the genetic variance and sub-watersheds 35.99 %. At the largest spatial scale,
considering all populations, the model that best explained genetic differentiation included Euclidean distance, altitude
and annual precipitation. At a smaller scale, in two of three sub-watersheds (Río San Pedro and Salar de Atacama) the
genetic differentiation was best explained by landscape variables (principally temperature and altitude). At the smallest
scale, considering those populations that have diverged recently detected by SAMOVA, the genetic differentiation
was best explained by the wetland-based route and annual precipitation. This approach revealed the importance of
landscape features in the colonization of R. spinulosa in this zone.
Key words: altiplano, amphibian, landscape genetics, least cost models, mtDNA.
RESUMEN
Se evaluó la relación entre las características del paisaje y ambientales y la diferenciación genética de Rhinella
spinulosa en el altiplano de la Región de Antofagasta (Chile). Para esto se realizaron tres tipos de análisis a diferentes
escalas espaciales: (1) considerando todas las poblaciones; (2) agrupando las poblaciones por cuencas y por
subcuencas; y (3) utilizando los resultados del análisis espacial de variación molecular (SAMOVA). Las características
del paisaje se incorporaron diseñando tres modelos hipotéticos de dispersión con los Sistemas de Información
Geográfico: (1) distancia euclidiana (modelo nulo); (2) de menor costo basado en la localización de los humedales;
y (3) de menor costo basado en las pendientes menores. Además, se incluyeron las diferencias en temperatura,
precipitación y altitud entre localidades. Para seleccionar el modelo que mejor explicara la diferenciación genética
se utilizó el Criterio de Información de Akaike y se estimó la importancia relativa de cada variable del modelo
seleccionado utilizando regresiones parciales. Se encontró una alta diferenciación genética entre las poblaciones
(Fst = 0.693) y un patrón claro de aislamiento por distancia (r = 0.767). El análisis AMOVA mostró que las cuencas
explicaron un 8.67 % de la varianza genética y las subcuencas un 35.99 %. A mayor escala espacial, considerando todas
las poblaciones, el mejor modelo que explicó la diferenciación genética incluyó las variables distancia euclidiana,
altitud y precipitación anual. A menor escala, en dos de las tres subcuencas (Río San Pedro y Salar de Atacama) la
diferenciación genética fue mejor explicada por variables del paisaje (temperatura y altitud, principalmente). A menor
escala, considerando las poblaciones que han divergido recientemente detectadas por SAMOVA, la diferenciación
genética fue mejor explicada por la ruta basada en humedales y la precipitación anual. Esta aproximación muestra la
importancia de las características del paisaje en la colonización de R. spinulosa en esta zona.
Palabras clave: altiplano, anfibios, genética del paisaje, modelos de menor costo, mtDNA.
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GALLARDO ET AL.
INTRODUCTION
The interaction between landscape (e.g., slope)
and environmental (e.g., temperature) features
may strongly af fect the dispersal and gene
flow of organisms; it is essential to ide ntify
the biotic and/or abiotic factors involved in
the differentiation of populations in order to
model and predict the evolution of genetic
diversity (Wiens 2001, Guillot et al. 2005). These
factors may be quantified using Landscape
Genetics, a research area that integrates
population genetics, landscape ecology and
spatial statistics. They also allow to describe
spatial genetic patterns and the processes that
can originate those patterns (Manel et al. 2003).
Landscape genetics is studied in a spatially
explicit metapopulation context, allowing to
investigate the interaction between landscape
features and spatial dispersal (Michels et al.
2001, Storfer et al. 2007, Diniz-Filho et al. 2008).
Analyses that incorporate landscape variables
in studies of population genetic str ucture
represent a new approximation to the ecological
causes of evolutionar y patterns (Kozak et al.
2008). Therefore, Landscape Genetics has a
promising potential in evolution, ecology and
conservation biology studies (Manel et al. 2003).
To include the landscape component in
landscape genetic studies, spatial analysis
techniques from Geographic Infor mation
Systems have been used in order to model
least-cost dispersal scenarios. A scenario is
constructed by determining the accumulated
movement cost of a species from one point
to another through landscape features (i.e.
topography, habitat type, cover type, etc.) that
have an assigned value, creating a least-cost
route. After the least-cost route is obtained a
modified geographic distance is calculated,
which is then cor related with the genetic
distance found between studied populations
(Spear et al. 2005, Storfer et al. 2007).
In landscape genetic analysis, the process
of isolation by distance should be considered as
the null model (Broquet et al. 2006). Therefore,
deviation from the null model implies that
other factors may be involved in the processes
of genetic dif ferentiation (Spear et al. 2005,
Telles et al. 2007). For example, Michels et al.
(2001) implemented a least-cost distance based
on dispersal rates of zooplankton, and Clark
et al. (2008) proposed a model of least cost for
the rattlesnake Crotalus horridus based on the
number of thermoregulation sites between
hibernacula. In both cases, rather than simple
Euclidian distance it was the least cost distance
that showed a greater correlation with genetic
distance. This can be explained by the fact
that there is no available habitat between two
sites in a straight line; therefore, the genetic
differentiation cannot be explained by isolation
by distance only (Koscinski et al. 2009). Likewise,
the modeling of animal movement through a
landscape can facilitate the interpretation of
contemporar y and historic genetic variation
patterns (Koscinski et al. 2009).
Amphibians, due to their limited vagility,
high philopatr y (Seppä & Laurila 1999) and
spatially separated reproductive sites, constitute
a good model for landscape genetic studies,
since these factors may affect their dispersal
and gene flow (Funk et al. 2005). In a study
of the tiger salamander (Ambystoma tigrinum
melanostictum), Spear et al. (2005) found that
differences in altitude and distance between
populations were the main factors that favored
genetic dif ferentiation, acting as barriers to
dispersal and gene flow. On the other hand,
Funk et al. (2005) found that mountains
ridges and elevation differences between sites
were associated with high levels of genetic
dif ferentiation between populations of Rana
luteiventris. At the same time, high levels of
gene flow were obser ved among low-altitude
sites separated by large geographic distances.
Genetic studies on Rhinella spinulosa in
northern Chile have shown two highly divergent
lineages, which differed in their phylogeographic
str ucture (Cor rea et al. 2010). Within the
southern lineage, a group of populations located
in the southeast part of the Salar de Atacama,
showed the highest genetic divergence. Correa
et al (2010) explained this pattern proposing
a model of peripatric differentiation for these
populations. Therefore, it would be interesting
to evaluate the population genetic structure of
this lineage, considering whether habitat features
can shed light on the conditions that molded the
genetic structure of these amphibian populations.
The Andean landscape of northern Chile consists
of arid and mountainous environments. In
this type of landscape, streams are formed
between steep slopes, creating potential
sites for amphibian reproduction, which are
distributed as small patches in landscape terms,
LANDSCAPE GENETICS OF RHINELLA SPINULOSA
generating substantial population str ucture
between amphibian populations (Dayton &
Fitzgerald 2006). Additionally, in ectotherms
it has been described that environmental
variables (e.g., temperature, precipitation)
may affect the evolution of many fundamental
biological traits (i.e life history traits, dispersion
and sur vival), promoting genetic divergence
between populations (Kozak et al. 2008, Méndez
& Correa-Solis 2009). Therefore, our study
addressed the following questions: what is the
relative impact of landscape and environmental
factors on the distribution of genetic diversity
in R. spinulosa?. Which landscape features can
affect dispersal and gene flow in this species?.
Do the differentiation patterns detected depend
upon the spatial analysis used?. To address these
questions, we analyzed 13 sampling sites from
the Antofagasta region in northern Chile.
393
METHODS
Study area
The study area was located in the foothills of the Andes
of nor thern Chile, characterized by a high-altitude
deser t climate (Castillo et al. 1997). Precipitation
gradually increases with altitude being higher during
summer seasons (December to March; Ar royo et
al. 1988). Water reaches lower altitudes watersheds
through runoff and percolation, sustaining low altitude
regions with extreme aridity and permanent water
deficit (Salazar 1997). The specific geographic location
of the study sites were the foothill zones and the Puna
area of Atacama in Antofagasta region (21-24º S; Fig.
1). These populations cor respond to the souther n
lineage described in Correa et al. (2010). We sampled
203 individuals from 13 sampling sites (Table 1, Fig.
1). Each locality was geo-referenced using the WGS84
datum, UTM zone 19S. The specimens used in this
study were stored in the Herpetological Collection of
the Departamento de Biología Celular y Genética of the
Universidad de Chile (DBGUCH), and were previously
collected as part of other studies.
Fig.1: Geographic location of the sampling areas within watersheds (solid lines) and within sub-watersheds (dotted lines) in the Antofagasta Region of Chile.
Ubicación geográfica de las áreas de muestreo dentro de cuencas (línea sólida) y dentro de subcuencas (línea punteada) en la
Región de Antofagasta en Chile.
394
GALLARDO ET AL.
Study species
R. spinulosa is an anuran whose distributional range
extends from the Peruvian and the Bolivian Altiplano, to
the southern Andes of Chile and Argentina. They inhabit
zones near streams, lakes and high altitude wetlands,
between 1000 and 4600 m (Cei 1962, Veloso & Navarro
1988). Currently, there are few studies on the dispersal
capacity or habitat use of this species. It is known that
lar vae and postmetamorphics are diurnal, whereas
adults are nocturnal, resting under rocks during the day
(Cei 1962, Lambrinos & Kleier 2003). It has also been
documented that adults are more terrestrial than aquatic
forms (Cei 1962). Espinoza & Quinteros (2008) reported
that postmetamorphics are found always near water,
moving a maximum of 1 m away from a pond, while Vidal
(2009) established that they could move up to 15 m.
Phylogeographic studies performed on the distributional
range of R. spinulosa in nor thern Chile, based on
mitochondrial (Correa et al. 2010) and nuclear markers
(Méndez et al. 2004), found a northern (18-20º S) and a
southern lineage (21-24º S). The southern lineage had a
greater structure, mainly due to the high divergence of
the eastern springs populations of the Salar de Atacama
(Correa et al. 2010).
DNA extraction and sequencing
We per for med the extraction, amplification and
sequencing of mitochondrial DNA (control region) using
the procedures described by Correa et al. (2010). The
sequences were edited and aligned using the BioEdit
7.0.9.0 software (Hall 1999). For multiple alignment of
the edited sequences we used the Clustal W 1.4 software
(Thompson et al. 1994) with default parameters, after
which alignments were inspected visually.
Genetic differentiation and structure
Genetic differentiation was evaluated using pairwise Fst
distance values between all pairs of localities, using the
Arlequin 3.11 software (Excoffier et al. 2005) with 10000
permutations. The existence of isolation by distance was
evaluated by means of a Mantel test, which estimates
the statistical significance between correlation matrices
(Manly 1985). This test was performed with the F st
and geographic distance matrices, using XLSTAT 2.03
software (Addinsoft 2009), with 10000 permutations.
Indices of haplotype and nucleotide diversity were
calculated using DNAsp 4.5 software (Rozas et al.
2003). Haplotype networks were obtained using the
median joining network method (Bandelt et al. 1999),
with the Network 4.5.1.0 software. The haplotypes for
this analysis included the sites with indels which were
informative, in order to separate populations.
To determine which of variables best explain
the genetic dif ferentiation at dif ferent spatial scales
we performed three types of analyses: (1) At a large
spatial scale, using all localities; (2) At a small spatial
scale, grouping populations by watershed and by sub-
TABLE 1
Sampling localities, number of Rhinella spinulosa specimens utilized (n) in this study, altitude
(meters above sea level), precipitation (mm), temperature (°C), UTM coordinates (Datum WGS84
19S) and watershed and sub-watershed names.
Localidades de muestreo, número de especímenes utilizados de Rhinella spinulosa (n) en el estudio, altitud (metros
sobre el nivel del mar), precipitación (mm), temperatura (°C), coordenadas UTM (Datum WGS84 19S) y nombres de
las cuencas y subcuencas.
Watershed
Subwatershed
Locality
n
Altitude
Annual mean
temperature
Annual
precipitation
UTM E
UTM S
Salar de Ollagüe
Carcote
13
3688
6.7
60
570111
7661446
Río Loa
Río Loa Alto
Caspana
13
3245
9.5
34
581365
7545277
Río Loa
Río Loa Alto
El Tatio
19
4264
4.8
42
601129
7544953
Río Loa
Río Loa Alto
Chita
15
3741
7
36
584997
7536439
Fronterizas Salar
Michincha-Río Loa
Río Loa
Río Loa Alto
Vado Putana
15
4286
4.7
41
598267
7523790
Salar de Atacama
Río San Pedro
Machuca
15
3979
6.1
38
596010
7516227
Salar de Atacama
Río San Pedro
Río Grande
10
3045
10.4
34
579237
7508817
Salar de Atacama
Río San Pedro
Katarpe
17
2460
12.8
40
582276
7490638
Salar de Atacama
Río San Pedro
Vilama
15
2579
12.9
42
583916
7486983
Salar de Atacama
Salar de Atacama
Jere
23
2513
12.6
46
603327
7451780
Salar de Atacama
Salar de Atacama
Camar
15
2727
12.4
44
606481
7427442
Salar de Atacama
Salar de Atacama
Peine
16
2440
13.7
44
596023
7397016
Salar de Atacama
Salar de Atacama
Tilomonte
17
2365
13.9
43
590772
7385283
LANDSCAPE GENETICS OF RHINELLA SPINULOSA
watershed, performing for this two Analyses of Molecular
Variance (AMOVA) using Arlequin 3.11 software, with
10000 permutations, determining the genetic variance
explained at the watershed and sub-watershed levels; and
(3) Using the results of a spatial analysis of molecular
variation (SAMOVA), without a priori structure (i.e.
watershed, sub-watershed). For the SAMOVA analysis,
which finds groups of contiguous localities that maximize
the between-group variance determining the most
probable genetic structure according to the number of
groups entered by the user, we used the SAMOVA 1.0
software (Dupanloup et al. 2002).
Landscape and environment variables
Wetland presence and slopes (dispersal routes)
To incorporate landscape variables into the study
(wetland presence and slopes) we created three types
of probable dispersal routes between localities (Fig.
2) using the ArcGIS 9.2 software (ESRI 2006). These
variables were represented in raster format. The first
route was Euclidean distance (the null model). The
second route used a least cost model based on high
altitude wetlands, which in this area correspond to water
courses and springs that depend upon the precipitation
regime (Castillo et al. 1997). In the latter route, the cost
of movement within wetlands was set to 0, and outside
of them it was set to 100; for this we used the wetlands
vegetation cover created by Faúndez & Escobar (2006).
The value of 100 was chosen due to the habitats of
R. spinulosa are distributed in patches, with no water
sources among them, which would presumably inhibit
their dispersal. The value 0 was chosen for dispersion
inside wetlands, where it is more probable to find this
species, taking also into consideration that there are no
predators that could limit the movement of R. spinulosa
inside the vegetation patches. The third type of route
was also a least cost model, but based on slopes; the
cost of movement from one pixel to another (friction)
increased linearly with slope increase up to 45º. We used
395
the data of Shuttle Radar Topography Mission 90 m,
available at http://srtm.csi.cgiar.org/.
Altitude, temperature and precipitation
The altitude of localities was obtained with GPS that
included a barometric altimeter to measure altitude
differences. This variable has been positively related
with the genetic differentiation of amphibians (Funk
et al. 2005, Spear et al. 2005). Temperature and
precipitation data for the sites were obtained from the
Worldclim dataset that includes climatic variables at ~1
km2 spatial resolution (Hijmans et al. 2005). Precipitation
was included since its spatial distribution in the
Altiplano is heterogeneous, with a latitudinal gradient
decreasing from north to south (Aceituno 1997, Salazar
1997), and an altitudinal gradient that increases with
altitude (Arroyo et al. 1988). Temperature was included
because it has an important altitudinal and seasonal
variation in the study area, with a mean that is relatively
low, decreasing with altitude (Aceituno 1997). With
the climatic variables we created Euclidean distance
matrices between localities, using the NTSYSpc 2.10
software (Rohlf 2000), in order to include them in the
evaluation and selection of the model that best explains
the genetic differentiation.
Criteria for selection of the best model
To select the model which best explained the genetic
dif ferentiation among all possible combinations of
variables, we used the Akaike Information Criterion
(AIC). The AIC considers the fit of each model to
an obser ved series as a function of the number of
parameters utilized. Thus, it determines the combination
of variables that best explains the observed data. The
AIC of each model is transformed to a AIC, which is the
difference between the AIC of a model and the minimum
AIC value found in the set of models compared. A value
of AIC greater than 7 indicates that the model has a
relatively poor fit compared to the best model; a value
less than 2 indicates that the model is equivalent to the
minimum AIC (Burnham & Anderson 2002). In this way,
we were able to reduce the possible models to three
candidate models, one for each route type, and then
choose the best model among these three. Thus, we
determined which route and which variables were most
strongly related to the genetic data. The variables and
routes that were not included in the selected models
were excluded from the rest of the analyses. The
analyses just described were performed with the SAM
3.0 software (Rangel et al. 2006).
Best model variance partition
Fig. 2: Example of the three types of hypothetical dispersal routes created for Rhinella spinulosa between
all localities. Routes between El Tatio and Chita localities are shown.
Ejemplo de los tres tipos de rutas hipotéticas de dispersión
creadas para Rhinella spinulosa entre todas las localidades. Se
muestran las rutas entre las localidades de El Tatio y Chita.
Once the best model was selected, the amount of genetic
differentiation explained by each variable was estimated
using partial regressions. These analyses were performed
with Statistica 6.0 (StatSoft Inc. 2001) and SAM 3.0
packages. Different studies have used regression analyses
to evaluate hypotheses about the ef fects of spatial,
temporal and environmental components on the genetic
differentiation (Spear et al. 2005, Telles & Diniz-Filho 2005,
Broquet et al. 2006, Hull et al. 2008).
In a multiple regression analysis, variance partition
is analogous to the cor relation analyses between
explanatory variables (X1, X2, X3, etc.) and the response
variable (Y). These analyses can be conceptually
represented by a Venn diagram, as is commonly used
396
GALLARDO ET AL.
in set theory, where the components are displayed as
overlapping circles that correspond to the influence
areas of the explanatory variables (Anderson & Gribble
1998). This methodology allows evaluation of different
groups of explanator y variables, considering their
capacity to explain the obser ved patterns (Legendre
& Legendre 1998). Since the explanator y variables
are usually not independent, this procedure can help
to identify the influence of each component and their
overlap effects on the dependent variable, clarifying the
influence of the variables considered in the proposed
model (Anderson & Gribble 1998). However, this
approach does not necessarily identify the causal factors;
it simply facilitates the formulation of hypotheses about
the processes which may have generated the observed
patterns (Legendre & Legendre 1998)
Carcote; (2) Jere; (3) Peine and Tilomonte; (4)
Caspana, El Tatio, Chita, Vado Putana, Machuca,
Río Grande, Katarpe, Vilama and Camar (Table
3). This str ucture and relationship among
haplotypes was also detected in the haplotype
network, in which the Carcote locality had a
unique haplotype; the Jere and Peine localities
shared a different unique haplotype, but also
had haplotypes from group 4 (Fig. 3, Table 5).
Analyses at a large spatial scale (using all
localities)
Best model selection
RESULTS
Analysis of sequences, genetic differentiation and
genetic structure
We sequenced 203 specimens, obtaining
863 nucleotide sites, of which 31 sites were
polymorphic and four had indels. We found
25 haplotypes including the sites with indels.
Haplotype and nucleotide diversity per
site were 0.85 and 0.00589, respectively.
All sequences were deposited in GenBank
with accession numbers AY663485-AY663519;
FJ643165-FJ643276 and FJ790426-FJ790434.
The genetic dif ferentiation between all
localities was high (global F st = 0.693; P =
0.001), with a wide variation range among
populations (Table 2). The Mantel test revealed
a significant pattern of isolation by distance
(r = 0.767, P < 0.001). The most differentiated
localities were Carcote, Peine and Tilomonte
(Table 2). These localities are in the northern
and southern ends of the study area. Carcote
is located at the nor th in a dif ferent subwatershed from the rest of the localities, 116
km from the closest other locality. Peine and
Tilomonte are located in the south, in the
Salar de Atacama sub-watershed. The genetic
differentiation in the Río Loa Alto (Fst = 0.146;
P = 0.0005), and Río San Pedro (Fst = 0.120; P
= 0.0145) sub-watersheds were much lower
than the value for all populations. By contrast,
differentiation within the Salar de Atacama subwatershed was high (Fst = 0.630; P = 0.0001).
T h e A M O VA s h o w e d t h a t g r o u p i n g
populations by sub-watersheds explained more
variation than grouping them by watersheds
(Table 3). SAMOVA showed that maximum
variance was obtained with four groups: (1)
The best model chosen by AIC using all
localities was the one that included the
variables Euclidean distance, altitude and
annual precipitation (Table 4A). This model
explained 64.6 % of the genetic differentiation,
5.8 % more than the Euclidean distance (null
model). The variables altitude and annual
precipitation were chosen in all candidate
models for each route type. The wetland-based
route had the greatest AIC value and explained
the least amount of genetic variation.
Best model variance partition
According to the AIC, the best model included
the predictive variables annual precipitation,
altitude and Euclidean distance. We determined
the contribution of each variable by relating
the response variable (Fst) to the predictive
variables in a linear regression analysis; altitude,
annual precipitation and distance had values
of 0.09 %, 17.64 % and 58.82 %, respectively
(Fig. 4A). Partial regressions showed that the
greatest proportion of the differentiation was
explained by the Euclidean distance alone (r2
= 46.5 %) and the overlap of Euclidean distance
and precipitation (r2 = 16.6 %). The overlap of
the three variables of the model had a low value
(g = 0.88 %). The overlaps of precipitation and
altitude, and distance and altitude had low and
negative values, probably due to the variables
have opposite effects on genetic differentiation.
It is important to indicate that, although altitude
was one of the chosen variables, the regression
analysis showed that there was no significant
relation with genetic differentiation; therefore
in this case it may not be a good predictive
variable.
TABLE 2
Genetic differentiation of Rhinella spinulosa populations using mtDNA. Pairwise Fst values between populations (below the diagonal). Significant
values are indicated by asterisk (P < 0.05). Euclidean distances in kilometers (above the diagonal). Numbers in superscript indicate subwatershed: 1.
Salar de Ollagüe, 2. Río Loa Alto, 3. Río San Pedro, 4. Salar de Atacama.
Locality
Carcote1 Caspana2 El Tatio2
Chita2
V. Putana2
Machuca3
Río Grande3
Katarpe3
Vilama3
Jere4
Camar4
Peine4
Tilomonte4
Carcote1
-
117
121
126
141
148
153
171
175
212
237
266
277
Caspana2
0.914*
-
20
10
27
33
37
55
58
96
120
149
160
El Tatio2
0.684*
0.195*
-
18
21
29
42
57
60
93
118
148
160
Chita2
0.848*
0.003
0.213*
-
18
23
28
46
49
87
111
140
151
V. Putana2
0.724*
0.061
0.132*
0.094*
-
8
24
37
40
72
97
127
139
Machuca3
0.817*
0.242*
0.307*
0.231*
0.081
-
18
29
32
65
89
119
131
0.871*
0
0.183*
0.031
0
0.043
-
18
22
62
86
113
124
Katarpe3
0.822*
0.168*
0.186*
0.197*
0.001
0.24*
0.094
-
4
44
68
95
106
Vilama3
0.829*
0.116
0.169*
0.151*
0
0.216*
0.051
0
-
40
64
91
102
Jere4
Río
Grande3
0.39*
0.372*
0.4*
0.386*
0.35*
0.368*
0.33*
0.374*
0.362*
-
25
55
68
Camar4
1*
0.765*
0.339*
0.657*
0.349*
0.702*
0.702*
0.289*
0.343*
0.41*
-
32
45
Peine4
0.821*
0.83*
0.808*
0.83*
0.8*
0.815*
0.804*
0.828*
0.822*
0.385*
0.859*
-
13
Tilomonte4
0.984*
0.975*
0.925*
0.966*
0.938*
0.956*
0.967*
0.957*
0.959*
0.515*
0.988*
0.305*
-
LANDSCAPE GENETICS OF RHINELLA SPINULOSA
Diferenciación genética de poblaciones de Rhinella spinulosa utilizando mtDNA. Valores pares de Fst entre las poblaciones (bajo la diagonal). Valores significativos se indican con
asterisco (P < 0.05). Las distancias euclidianas en kilómetros (sobre la diagonal). En superíndice se indica la subcuenca: 1. Salar de Ollagüe, 2. Río Loa Alto, 3. Río San Pedro, 4.
Salar de Atacama.
397
398
GALLARDO ET AL.
Fig. 3: Haplotype network constructed using the median-joining method for the 25 mitochondrial haplotypes
found in 203 individuals of Rhinella spinulosa from Antofagasta Region. Colors of the localities (circles in the
map) indicate the four divergent haplogroups corresponding to the structure found in the SAMOVA analysis.
The size of each circle in the haplotype network is proportional to its frequency. The short transverse lines on
the branches indicate the mutational steps between haplotypes.
Red de haplotipos construida utilizando el método median-joining para los 25 haplotipos mitocondriales encontrados en 203
individuos de Rhinella spinulosa de la Región de Antofagasta. Los colores de las localidades (círculos en el mapa) muestran
los cuatro haplogrupos divergentes, los cuales corresponden a la estructura encontrada en el análisis SAMOVA. El tamaño de
cada círculo en la red de haplotipos corresponde a la frecuencia haplotípica. Las líneas cortas sobre las ramas indican los pasos
mutacionales inferidos entre haplotipos.
TABLE 3
Results for the components of the spatial analysis of molecular variance (SAMOVA, see Fig. 3) and
molecular analysis of variance (AMOVA) with 10000 permutations, separating Rhinella spinulosa
populations into watersheds and sub-watersheds. *P < 0.05.
Resultados de los componentes del análisis espacial de varianza molecular (SAMOVA, ver Fig. 3) y el análisis de
varianza molecular (AMOVA) con 10000 permutaciones, separando a las poblaciones de Rhinella spinulosa según
cuenca y subcuenca. *P < 0.05.
AMOVA
Source of variation
SAMOVA
Watershed
(three groups)
Sub-watershed
(four groups)
(four groups)
Among groups
8.67*
35.99*
76.5*
Among populations between groups
61.87*
35.97*
3.02*
Within population
29.46
28.04*
20.48*
399
LANDSCAPE GENETICS OF RHINELLA SPINULOSA
TABLE 4
Best model selection to explain the genetic differentiation of Rhinella spinulosa at different scales.
ΔAICc is the difference between the AICc value of the model and the minimum AICc value found
among all the models. r² is the proportion of the genetic differentiation explained by each model.
The best model for each route type and the Euclidean distance without the environmental variables
are shown. (A) Between all localities. (B) For each sub-watershed. (C) Between the group 4
localities found with SAMOVA.
Selección del mejor modelo que explica la diferenciación genética de Rhinella spinulosa a diferentes escalas. ΔAICc
corresponde a la diferencia entre el valor AICc de cada modelo y el valor mínimo de AICc encontrado entre todos los
modelos. r² es la proporción de la diferenciación genética explicada por cada modelo. Se muestran el mejor modelo
para cada tipo de ruta y la distancia euclidiana sin las variables ambientales. (A) Entre todas las localidades. (B) Para
cada subcuenca. (C) Entre las localidades del grupo 4 del análisis SAMOVA.
(A)
Routes
Variables
AICc
ΔAICc
r²
Euclidean distance
Annual precipitation, altitude
-17.351
0
0.646 (P < 0.0001)
Least slope
Annual precipitation, altitude
-12.364
4.987
0.622 (P < 0.0001)
Based wetlands
Annual precipitation, altitude
-7.161
10.19
0.596 (P < 0.0001)
-10.032
25.343
0.588 (P < 0.0001)
Variables
AICc
ΔAICc
r²
Least slope route
Annual precipitation,
annual mean temperature, altitude
-123.619
0
0.998 (P < 0.001)
Euclidean Distance
Annual precipitation,
annual mean temperature, altitude
-122.494
1.125
0.998 (P < 0.001)
Wetland-based route
Annual precipitation,
annual mean temperature, altitude
-122.071
1.547
0.997 (P < 0.001)
Euclidean Distance
3.168
126.786
0.079 (P = 0.59)
Río San Pedro
AICc
ΔAICc
r²
Euclidean distance
(B)
Routes
Río Loa Alto
Wetland route
Annual precipitation,
annual mean temperature, altitude
-111.198
0
0.989 (P = 0.003)
Euclidean distance
Annual precipitation,
annual mean temperature, altitude
-110.854
0.344
0.989 (P = 0.003)
Least slope route
Annual precipitation,
annual mean temperature, altitude
-105.831
5.367
0.974 (P = 0.01)
Euclidean Distance
-2.183
109.015
0.749 (P = 0.026)
Salar de Atacama
AICc
ΔAICc
r²
Least slope route
Annual precipitation,
annual mean temperature, altitude
-156.934
0
1 (P < 0.001)
Euclidean Distance
Annual precipitation,
annual mean temperature, altitude
-141.578
15.357
1 (P < 0.001)
Wetland-based route
Annual precipitation,
annual mean temperature, altitude
-110.856
46.079
0.999 (P < 0.001)
18.455
175.39
0.031 (P = 0.73)
AICc
ΔAICc
r²
Euclidean Distance
(C)
Routes
Variables
Wetland-based route
Annual precipitation, altitude
-49.93
0
0.749 (P < 0.0001)
Wetland-based route
Annual precipitation, temperature
-49.788
0.142
0.748 (P < 0.0001)
Annual precipitation, altitude
-48.421
1.509
0.738 (P < 0.0001)
-40.81
9.12
0.625 (P < 0.0001)
Least slope route
Euclidean Distance
400
GALLARDO ET AL.
Analysis at a small spatial scale, grouping populations by sub-watershed
We performed the small scale spatial analysis
at the sub-watershed level because the AMOVA
showed that sub-watersheds explained a greater
amount of variance than watersheds (Table 3).
Therefore, we analyzed the routes and variables
that best explained the genetic differentiation
within each sub-watershed. The Carcote locality
was not included in these analyses, since it was
the only locality in its sub-watershed.
The AIC showed that the best model in
the Río Loa Alto sub-watershed included
the variables: precipitation, temperature,
altitude and least-slope route. The genetic
dif ferentiation explained for this model was
high (99.8 %; P < 0.001) (Table 4B, Fig. 5A).
However, separately these variables were not
significant. The Euclidean distance had a low
value and not significant (Table 4B).
The AIC showe d that the best model in
the Río San Pedro sub-watershed included
the variables: precipitation, temperature,
altitude and wetland based route. The genetic
dif fer entiation explained for this model
was high (98.9 %; P = 0.003). The variables
temperature, altitude and wetland based route
had values high and significant (80.4 %, 83.4
% and 74.3% respectively), and are highly
correlated, in contrast to precipitation (Fig.
5B). This model explains 28 % more than the
Euclidean distance (Table 4B).
The AIC showed that the best model in
the Salar de Atacama sub-watershed included
the variables: precipitation, temperature,
altitude and least-slope route. The genetic
dif ferentiation explained for this model was
100 % (P < 0.001). The variables temperature
and altitude had values high and significant (55
% and 84.4 %, respectively) and also are highly
correlated, unlike precipitation and least-slope
Fig. 4: The diagram shows the variation in Fst (dependent variable) as a function of three explanatory variables.
The combined effects of variables are shown in d, e, and f. The individual effects of variables are shown in a, b,
and c. The variation using the three variables is shown in g. The decomposition of the variation was performed
with partial regressions, using linear models and considering the proportion of the variation explained. (A)
Between all populations, explanatory variables: Euclidean distance, altitude and precipitation. The model explained 63 % of the total variation. (B) Between SAMOVA localities (group 4), explanatory variables: wetland-based
route, altitude and precipitation. The model explained 74.9 % of total variation.
El diagrama muestra la variación en Fst (variable dependiente) como una función de tres variables explicativas. Los efectos
combinados de las variables se muestran en d, e y f. Los efectos individuales de las variables se muestran en a, b y c. La variación utilizando las tres variables se muestra en g. La descomposición de las varianza fue realizada con regresiones parciales,
utilizando modelos lineales y considerando la proporción de la varianza explicada. (A) Entre todas las poblaciones, variables explicativas: distancia euclidiana, altitud y precipitación. El modelo explicó un 63 % de la varianza total. (B) Entre las localidades
del grupo 4 de SAMOVA, variables explicativas: ruta basada en humedales, altitud y precipitación. El modelo explicó un 74.9 %
de la varianza total.
LANDSCAPE GENETICS OF RHINELLA SPINULOSA
route (Fig. 5C). The Euclidean distance had a
low value and not significant (Table 4B).
In contrast to the results obtained using all
populations, within sub-watersheds the variable
Euclidean distance was not selected in the
best explanatory model. Only in Río San Pedro
sub-watershed this variable was important in
explaining the genetic differentiation.
Analysis using the results of a spatial analysis of
molecular variation
The SAMOVA showed that maximum variance
was obtained with four groups: (1) Carcote;
(2) Jere; (3) Peine and Tilomonte; (4) Caspana,
Tatio, Chita, Vado Putana, Machuca, Río
Grande, Katarpe, Vilama and Camar. Analyses
that included landscape variables wer e
per formed using localities from group 4,
because the other groups were formed by only
401
one or two localities. The genetic differentiation
(Fst) in this group was 0.219 (P = 0.0001). The
model with the lowest AIC value for the group 4
localities included wetland-based route, annual
precipitation and altitude; it explained 74.9 % of
the genetic differentiation. However, according
to AIC all the selected models were equivalent;
hence it was not possible to determine the best
model. Despite this, the annual precipitation
variable was present in all the chosen models,
whereas wetland-based route was present in
the first two models (Table 4C). We determined
the contribution of each variable by relating
the response variable (Fst) to the predictive
variables; altitude, annual precipitation and
wetland-based route which had values of 0.72
%, 20.97 % and 64.50 %, respectively (Fig. 4B).
Partial regressions showed that the greatest
propor tion of dif ferentiation was explained
by the wetland-based route alone (r2 = 53.9)
Fig. 5: Similar procedures described in the legend of
Fig. 4, however the diagram shows the variation in Fst
(dependent variable) as a function of four explanatory
variables (A) in the Río Loa Alto, (B), Río San Pedro
and (C) Salar de Atacama subwatersheds.
Procedimientos similares a los descritos en la leyenda de la
Fig. 4, sin embargo, el diagrama muestra la variación en Fst
(variable dependiente) como una función de cuatro variables
explicativas en las subcuencas (A) Río Loa Alto, (B) Río San
Pedro y (C) Salar de Atacama.
402
GALLARDO ET AL.
and the overlap of wetland-based route and
precipitation. The Euclidean distance had a
high and significant (Table 4C).
DISCUSSION
Geographic distance may be a factor of little
impor tance in maintaining genetic structure
compared to other features of the environment
which may limit dispersal, such as climatic
gradients and topography changes (Kozak et al.
2008). Landscape analyses provide a powerful
framework for directly analyzing relationships
between population processes and landscape
structure at relevant spatial and temporal scales
(Segelbacher et al. 2010). We found that at a
large spatial scale, the genetic differentiation
of R. spinulosa has been strongly influenced
by isolation by distance. The isolation by
distance pattern was expected, as patterns
related to landscape features would dilute in
a biogeographic or phylogeographic context
(over 100 km). Thus, distance is the only
relevant factor in genetic differentiation at a
large scale; however, the isolation by distance
model does not fit fine-scale genetic patterns
(Stepien et al. 2007, Koscinski et al. 2009).
Accor ding to our r esults, landscape
features only explained 5.8 % more of the total
genetic dif ferentiation at a large scale (all
population considered). We obser ved that
annual precipitation is an impor tant factor
explaining population genetic dif ferentiation,
although the overlap detected between this
variable and Euclidean distance did not allow us
to clarify the effects of each of them on genetic
dif ferentiation. This overlap may be caused
because localities with similar precipitation
levels are closer. The precipitation alone only
explains 1.28 % of genetic differentiation, in
comparison to Euclidean distance that explains
46.5 % (Fig. 4A). Thus at a large scale, the
pattern predominant found, was isolation by
distance.
At a smaller scale, sub-watersheds explained
a greater percentage of the genetic variance
than watersheds did, suggesting a strong
association between local geographic structure
and genetic str ucture. At sub-watersheds
level, the genetic differentiation was explained
by environmental and landscape variables.
In two of three sub-watersheds (Río San
Pedro and Salar de Atacama) the altitude
and temperature explains a high degree
of genetic dif ferentiation. It is dif ficult to
know the importance of each on the genetic
dif ferentiation due to the high cor relation
between them in both sub-watersheds.
Interestingly, altitude was also chosen in the
large scale model; however, it did not have
as much impor tance as it did at the subwatersheds level.
In Río San Pedr o sub-watershed, we
obser ved low genetic dif ferentiation among
localities, in presence of remarkable differences
in altitude. This result supports the idea that
sporadic fl oods could allow contact between
nearby localities (Méndez et al. 2004, Correa
et al. 2010). The Chilean Altiplano has been
in a period of drought since the late Holocene
(Latorre et al. 2003), although occasional rains
have been repor ted driving to catastrophic
floods allowing the mixing of populations
(Niemeyer & Cereceda 1984). In our model, the
wetland-based route, was a model component
prefer red over Euclidian distances model
(Table 4B, Fig. 5B). Thus, in this sub-watershed
floods along to altitude dif ferences among
localities could explain the connectivity
obser ved among populations across wetland
route.
In the Salar de Atacama sub-watershed, we
found a high level of genetic dif ferentiation
among populations with low temperature and
altitude dif ferences (< 300 m). In this subwatershed the populations are located at minor
differences of altitude in comparison to other
sub-watershed; therefore flood events could not
promote the connectivity among populations.
According with this idea, we did not fi nd a
relationship between genetic dif ferentiation
and probable routes of dispersal (i.e. wetland,
Euclidian and least slope routes). Thus,
considering the low vagility of R. spinulosa,
we do not expect contact among populations.
This pattern could explain the high divergence
obser ved between close populations (i.e.
Peine and Tilomonte) and support a model of
peripatric differentiation as proposed by Correa
et al. (2010).
In the Río Loa Alto sub-watershed the model
that considered all variables was significant
(Table 4B), however, when each variable was
considered individually, we did not obser ved
signifi cance in any variable. However, when
TABLE 5
Haplotypes found in each population included in this study. The last row indicates the frequency of each haplotype in relation to all individuals.
Haplotipos encontrados en cada población incluida en este estudio. La última fila muestra la frecuencia de cada haplotipo en relación a todos los individuos.
H1
H2
H3
H4
H5
H6
H7
H8
H9
H10
H11
H12
H13
H14
H15
H16
H17
H18
H19
H20
H21
H22
H23
H24
H25
Carcote
13
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Caspana
0
10
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
El Tatio
0
4
0
6
5
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Chita
0
12
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Vado Putana
0
2
4
0
2
0
0
0
0
0
2
4
1
0
0
0
0
0
0
0
0
0
0
0
0
Machuca
0
5
1
0
0
0
0
0
0
0
0
7
2
0
0
0
0
0
0
0
0
0
0
0
0
Río Grande
0
6
2
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
Katarpe
0
3
10
0
0
0
0
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
Vilama
0
4
8
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
Jere
0
0
11
0
0
0
0
0
0
0
0
0
0
4
1
1
1
1
2
1
1
0
0
0
0
Camar
0
0
15
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Peine
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
14
0
0
0
Tilomonte
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
14
2
Haplotype
frequency
0.064 0.227 0.276
0.03
0.034 0.005 0.005 0.005 0.005 0.015
0.01
0.099 0.015
0.02
0.005 0.005 0.005 0.005
0.01
0.005 0.005 0.069 0.005 0.069
LANDSCAPE GENETICS OF RHINELLA SPINULOSA
Locality
0.01
403
404
GALLARDO ET AL.
these populations are incorporated in the
SAMOVA analysis, it was possible to fi nd a
relationship among these populations with
landscape variables, being concordant with the
model that considered all variables together.
The group analyzed using the results of
SAMOVA showed that annual precipitation
explained the genetic dif ferentiation, in
agreement with the analysis of all populations.
However, the wetland-based route was selected
instead of the Euclidean distance. We suggest
that the wetland-based route is related to a
recent diversification pattern, as is shown by
localities with little genetic dif ferentiation.
This patter n was also detected by Cor rea
et al. (2010), suggesting that it could be
related to an early divergence among these
populations. The wetland-based route is based
on a stepping stone model. This pattern may
be related to changes in the precipitation
regime in the Altiplano zone, which have had
ef fects on the expansion and contraction of
the vegetation since the end of the Pleistocene
and during all of the Holocene (Betancourt et
al. 2000, Latorre et al. 2006, Quade et al. 2008).
Wetlands are the only appropriate habitat for
R. spinulosa; the desert is much more hostile
for this species. For this reason, we expected
this route to be selected in our model, because
the ability of amphibians to move between
isolated populations is largely dependent on the
suitability of habitat among populations (Marsh
et al. 2001).
Our study shows that incorporating
landscape features in a biogeographic and small
scale context produces a greater understanding
of the processes occurring at different levels.
While between all localities Euclidean distance
and annual precipitation were related to genetic
differentiation at a larger spatial scale, altitude,
temperature and wetland based route, were
related at a smaller spatial scale. Probably
the same variables appear to be related to
events that occurred in a more recent time.
The altitude dif ference has been related
with genetic dif ferentiation in amphibians
inhabiting mountain regions, showing high
levels of population differentiation (Funk et al.
2005, Spear et al. 2005). In this case, it would
also be a relevant factor, that does not allow
the dispersal of R. spinulosa, as is shown in
nearby populations (Peine and Tilomonte). This
effect will be reduced when other factors can
encourage the dispersion. The temperature,
in R. spinulosa, could act as a potential barrier
to dispersal, determining, along with altitude,
local genetic differentiation. According to this
idea, Méndez & Correa-Solis (2009) reported
local adaptation to temperature in R. spinulosa,
which ultimately could lead to population
differentiation.
The landscape genetic approach allowed
us to understand the importance of landscape
and environmental features in the histor y of
diversifi cation of populations of R. spinulosa
in the Altiplano. Although R. spinulosa is
considered a species of least concern by the
International Union for the Conser vation of
Nature (Angulo 2004), we believe that it is a
challenge to understand the histor y of this
species in the high altitude wetlands. Recently,
wetlands in arid and semiarid zones are
endangered by water extraction and pollution
due mainly to mining activity. Understanding
the dynamics of this ecosystem can help us to
appreciate the importance of conserving these
unique places.
ACKNOWLEDGEMENTS: This work was supported by
grants FONDECYT 1061256, 1100558, and DOMEYKO
Biodiversidad Iniciativa Transversal 1, Universidad
de Chile. The Ser vicio Agrícola y Ganadero supplied
collecting permits; Resolutions number 3085/2000,
2105/2004, and 13/2006. We thank Lorenzo Campos,
Luis Pastenes, Pamela Morales, Daniel Montaner,
Claudio Cor rea, Cristián Estades, Rodolfo Gajardo
and people of Departamento de Biologia Geral of the
Universidad Federal de Goiás. Gallardo C.E. thanks
Becas de Estadías Cor tas de Investigación de la
Vicerrectoría de Asuntos Académicos de la Universidad
de Chile.
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