supplement

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supplement

www.sciencemag.org/cgi/content/full/323/5915/785/DC1
Supporting Online Material for
Stability Predicts Genetic Diversity in the Brazilian Atlantic Forest
Hotspot
Ana Carolina Carnaval,* Michael J. Hickerson, Célio F. B. Haddad, Miguel T.
Rodrigues, Craig Moritz
*To whom correspondence should be addressed. E-mail: [email protected]
Published 6 February 2009, Science 323, 785 (2008)
DOI: 10.1126/science.1166955
This PDF file includes
Materials and Methods
Figs. S1 toS6
Tables S1 and S2
References
Supporting Online Material.
Materials and methods.
Overview. Based on locality data from seven herpetological collections, we used
MAXENT (S1) to map the expected distribution of the target species under current
environmental conditions. Following published protocols (S2), we ensured that model
performance was satisfactory under current climatic conditions and projected the
distribution into 6 kybp and 21 kybp climatic scenarios, to generate a map of areas of
stability for each taxon. Refugia or stable areas were defined as those grid cells for which
species occurrence was inferred in all time projections.
A portion of the mitochondrial NADH-dehydrogenase subunit 2 (ND2) gene was
sequenced from multiple individuals per locality, covering the entire range of the target
species (Fig. S6), and the molecular data were analyzed in three different ways. A
Bayesian phylogenetic analysis (S3) was used to describe phylogeographic structure.
Population genetic statistics assessed levels of diversity (S4), spatial structure (S5, S6),
and signature of population expansion (S7) in refugial vs. unstable areas. Finally, a
Hierarchical Approximate Bayesian framework (S8) was applied to the comparative
phylogeographic data to test whether the historical scenario suggested by the
palaeoclimatic model had empirical support across all three taxa.
Species distribution records. Species data used to generate climatic niche models were
obtained from the following collections: Museu Nacional do Rio de Janeiro (MNRJ);
Museu de Zoologia da Universidade de São Paulo (MZUSP); Departamento de Zoologia,
Universidade Federal do Rio de Janeiro (ZUFRJ), Coleção Célio F. B. Haddad,
Universidade Estadual de São Paulo, Campus Rio Claro (CFBH); Museum of
Comparative Zoology, Harvard University (MCZ); Museum of Vertebrate Zoology,
University of California (MVZ); University of Kansas Biodiversity Research Center
Herpetological Collection (KUNH; the last three accessed through GBIF Data Portal).
With the exception of the records available through GBIF, locality data were georeferenced with the help of the Global Gazetteer (http://www.fallingrain.com/world/, last
accessed Nov 14, 2007) or Brazil’s digital map of municipalities (S9), from which we
extracted and employed the centroid of the sampled township as a geo-referencing point.
The broad distribution of the target species, their occurrence in primary and secondary
forest, and their tolerance to edge habitats reduce model biases and potential errors
related to the use of centroids as proxies for actual localities.
Climate niche models and stability surfaces. To train the distribution models under
current climatic conditions, we used 85, 133, and 50 unique geo-referenced points for
Hypsiboas albomarginatus, H. faber, and H. semilineatus, respectively (Fig. S2; Table
S1). As per published protocols (2), distribution models under current climate were tested
with precise locality data collected through Geographical Positioning System (GPS)
technology (30, 22, and 13 presence points for H. albomarginatus, H. faber, and H.
semilineatus, respectively; Fig. S2; Table S1) and 100 absence points randomly sampled
from neighboring biomes in which the species do not occur (such as the Cerrado and the
Caatinga). MAXENT’s continuous distribution outputs were then converted into
presence/absence maps (S2).
DNA sequencing protocols. We sampled and sequenced mtDNA data from 71, 66, and
47 individuals of H. albomarginatus, H. faber, and H. semilineatus, representing 26, 27,
and 16 localities from throughout the Atlantic rainforest, respectively (Fig. 2, Figs. S1,
S3–S5). DNA collection, transportation, extraction, amplification and sequencing
followed published protocols (S10). Primers ND2-B1 (5’GCTAACAAAGCTATCGGGCCCAT-3’) and MVZ38 (5’TTCTTAGGGCTTTGAAGGCTC-3’) were used. Sequences were obtained with an ABI
3730 capillary sequencer and deposited in GenBank (accession numbers FJ502639FJ502822).
Phylogenetic analyses. With MrModeltest (S11), we used the Akaike Information
Criterion (AIC) to select the best-fitting substitution model for the data, which were
partitioned by codon position. Bayesian phylogenetic inference was performed with
MrBayes v.3.0b4 (S3), with four incrementally heated Metropolis-coupled Monte Carlo
Markov chains. Chains were started from random trees and run for 20 million generations
each, being sampled every 1,000 generations. Number of generations required for
convergence was determined via inspection of the likelihood scores of model parameters,
and all trees obtained prior to convergence were discarded.
Population genetic summary metrics. We estimated the diversity parameter Theta ()
from (S4, S12) based on a finite site model. Pairwise net divergences among
populations within and outside refugia were evaluated with Nei’s Da values (S5), using
Tamura- Nei corrected distances (S13). Population expansion was estimated with Fay and
Wu’s Hs test, which has been claimed appropriate for cases of small numbers of
segregating sites (S7). Other commonly used metrics were also employed to detect past
population expansion in unstable areas, and many indeed had less power than Fay and
Wu’s Hs test (Table S2) (S14–S16). Significance was tested with 10,000 coalescence
simulations (S12). A Mantel test (S6) was implemented to evaluate the occurrence of
isolation by distance, searching for a correlation between the straight-line distance that
unites each pair of localities and their Tamura-Nei corrected Fst value (S17). Significance
was tested with 10,000 permutations (S17). Fst values (for use in Mantel test) and Da
values were estimated for localities with two or more sampled individuals. The single
exception was the inclusion of H. faber from Feira de Santana (N=1), given its unique
genetic composition relative to the remaining haplotypes in the Bahia refugium.
Overview of Hierarchical approximate Bayesian computation. HABC enables the
analysis of multiple phylogeographic datasets at once in order to make across-species
(i.e., across taxon-pair) inferences, while explicitly allowing for uncertainty in the
demographic differences within each taxon (S8). To that end, it uses hyper-parameters
that describe processes across the co-distributed species, as well as sub-parameters that
allow variation in the demographic history of each taxon. By using a probabilistic
simulation model to create datasets that are compared against the empirical data, HABC
sidesteps the requirement of an explicit likelihood function (S18). The simulated and
empirical data are contrasted through the use of summary statistics, and the parameters of
the simulation model are easily estimated from an approximate sample of the posterior
distribution.
The HABC procedure implemented here was based on the background provided
by (S8, S19) and detailed in (S18). In HABC, sub-parameters (; or within taxon-pair
parameters) are conditional on “hyper-parameters” (), that quantify the variability of among the Y taxon-pairs. Instead of explicitly calculating the likelihood expression
P(Data | ,) to obtain a posterior distribution, we sample from the posterior distribution
P((,) | Data) by simulating the data K times under a three taxon-pair colonization
history using a coalescent model. Each simulation uses candidate parameters randomly
drawn from the joint hyper-prior and sub-prior distribution P(,). A
rejection/acceptance algorithm follows, involving the calculation of a summary statistic
vector from the observed data and each of the K simulated data sets. As per (S20), K
Euclidian distances between the normalized observed summary statistic vector D* and
each of the K normalized summary statistic vectors Di are calculated (||Di – D* || = d). An
arbitrary proportion (tolerance) of the K simulations with the lowest d values are then
used to obtain an approximate sample from the joint posterior. We used a tolerance of
0.001 such that only the 2,000 closest simulations were accepted out of K = 2,000,000
random draws from the hyper-prior. Subsequently, these 2,000 closest values are
weighted by an Epanechnikov kernel that has a maximum when D= D*.
Following these steps, we transform the accepted values to obtain an improved sample of
the posterior (S8, S18, S20).Because the hyper-parameter we wish to estimate is discrete
and categorical (Z2), we use a transformation procedure that preserves these accepted
parameter values as discrete integers. To that end, we use a weighted multinomial logistic
regression procedure (S21, S22). Because Z2 is a categorical model indicator with a
sample space of 0, 1, 2, and 3, that corresponds to categorical models M0, M1, M2, and
M3, we can sample it from the hyper-prior P() and treat it as a categorical random
variable in the HABC simulations. We then apply categorical regression to estimate P(Z2
= z|D= D*), where z = 0, 1, 2, 3 is the indicator for model Mz. Under multinomial logistic
regression, we can estimate the coefficient in
3
P(Z 2 = z | D) = exp( zT D)( exp( zT D))1
using the VGAM package in R.
i= 0
Hierarchical Approximate Bayesian Computation implementation. HABC was
implemented with a modified version of the MSBAYES comparative phylogeographic
software pipeline (S19) consisting of several C and R programs run with a Perl “frontend” and utilizing a finite sites version of Hudson’s classic coalescent simulator (S23).
For the purposes of the current paper, we used HABC to quantify the probability
of two historical models (Fig. 3A; H1 and H2) across three frog population pairs: i) H1,
where both populations have been persistent (each in a refugium) through at least the last
two glacial maxima; ii) H2, where one population has been colonized by migrants from a
another (refugial) population after the LGM. Under both models, each population pair
was modeled as two contemporary populations with mutation-drift parameters 1 and 2,
that split from an ancestral population at a time in the past. Each taxon-pair’s is 2Nμ (N
is the summed haploid effective population size of each population-pair with daughter
populations N1 + N2, and μ is the per gene, per generation mutation rate). Under H1, the
split was modeled as occurring 120,000 – 1.2 Mybp (1). Under H2, population 2 was
modeled as being colonized from population 1 at 0 – 20,000 years ago (2). In both
models, population 2 was modeled as being founded by a colonizing population of size
( ) 2 that comes from population 1 of size ( )1 at times 1 or 2. Under H2, ( ) 2 was
constrained to be < 0.05 the size of the contemporary population size 2, whereas under
H1 the colonizing population size was allowed to vary freely between 0 – 1.0 of the size
of the contemporary southern population 2. The population ( )1 was modeled as
relatively constant under H2 (0.9 – 1.0 the size of its contemporary size 1), whereas free
to vary under H1 (0 – 1.0 the size of the contemporary population 1). After 1 or 2, both
( )1 and ( ) 2 were modeled to grow exponentially until they reached contemporary
sizes 1 and 2. To explore the sensitivity of using different prior assumptions, we ran the
analysis with and without post-isolation migration between the two populations. Time of
vicariance, population sizes and migration rates were free to vary across the three taxonpairs, according to their prior distributions. Generation time was modeled as one year,
and the ND2 gene was assumed to change at a rate of 0.957% per lineage per million
years (S24).
To estimate how prevalent H1 and H2 are across the three taxon-pairs, we used
HABC to estimate the Z2 hyper-parameter (Z2 representing the number of taxon-pairs that
evolved under H2). The categorical hyper-parameter Z2 signifies how many of the three
taxon-pairs evolved under H2 and can range from 0 to 3, whereas Z1 signifies how many
of the three taxon-pairs evolved under H1, such that Z1 + Z2 = 3.
Each of the Yi taxon-pairs (Y1, Y2, and Y3) are therefore assigned the subparameter zi such that zi = 0 under H1, and zi = 1 under H2, and
Y
Z 2 = zi .
i=1
Hyper-parameters used in the HABC models were drawn from their respective
hyper-prior distributions, including: 1) Z2, the number of population-pairs that arise from
recent colonization at times T2 = { 12 ,…, 2Z 2 }, wherei is given in units of AVE /μ
generations, and AVE is the parametric expectation of across Y taxon-pairs given the
v
1
prior. 2) 2, the number of different colonization times 2 = { t 2 , …, t 2 2 } across Z2
actual colonization times T2 = { 12 ,…, 2Z 2 }, under H2; and 3.) 1, the number of different
v
1
colonization times 1 = { t1 , …, t1 1 } across Z1 actual colonization times T1 =
{ 11,…, 1Z1 }, under H1. Both 1 and 2 were drawn from the discrete uniform prior (1,
Z2), and (1, 3-Z2). Only 1 was drawn if Z2 = 0, whereas only 2 was drawn if Z2 = 3.
Each of the three
were assigned a global population mutation-drift subv population-pairs
1
3
parameters ( = { , …, } sampled from the uniform prior (0.0, 75.0). The priors for
v
v
each population-pair’s sub-population mutation-drift parameters 1 = { 11, …, 13 }, and 2
= { 12 , …, 23 }) were drawn from uniform sub-priors (0.0, 2 i ) and (0.0, (2 i - 1i )).
Subsequently, each of the three taxon-pairs drew their relative size of the colonized
population at the isolation/colonization time ( ) 2 from the uniform sub-prior (0.0, 0.05)
under H2 and sub-prior (0.0, 1.0) under H1. On the other hand, the size of the population 1
at the time of isolation/colonization ( )1 was drawn from the sub-prior (0.0, 1.0) under
H1, whereas under H2 it was drawn from the sub-prior (0.9, 1.0). v
Under the assumption of
post-isolation migration, each population-pair’s migration rate ( M = { M 1 , …, M 3 } was
independently drawn from the arbitrary uniform prior (0.0, 10.0). If Z1 1, then the 1, 2,
v
or 3 (1) different colonization times within ( 1) were drawn from the uniform prior
(0.0, 5.0). Each of these 1 different colonization times were then sequentially assigned
to the Z1 actual colonization times (T1 = ( 11,…, 1Z1 )) under H1. If Z1 = 3 and 1 = 2, the
third actual colonization time was assigned randomly from the first two. If Z2 1, then
the 2 different colonization times and the Z2 actual colonization times were drawn in the
same way as 1 and Z1. Both sets of actual isolation/colonization times (T1 and T2) are in
units of i /μ generations, where i is each taxon-pair’s population mutation-drift
parameter and μ is the per gene per generation mutation rate.
To compare the relative hyper-posterior support of H2 being dominant across all
three population-pairs, we calculated a Bayes factor that compares the relative hyperposterior support for Z2 = 3 over all other histories (Z2 < 3), while accounting for the
relative hyper-prior support for these two scenarios (S25), as given
by B(Z 2 = 3,Z 2 < 3) = (P(Z 2 = 3D = D* ) P(Z 2 < 3D = D* )) (P(Z 2 = 3) P(Z 2 < 3)).
For the HABC procedure, each species was split into a population pair. In the
models that aimed at testing the hypothesis of post-LGM colonization of unstable areas
by refugial populations (results shown in Fig. 3B, C), the pair corresponded to a northern
group (occupying a putative refugium, representing population 1) and a southern group
(occupying the predicted unstable or recently colonized area, representing population 2).
The former modeled only a subset of the demes from within the Bahia or São Paulo
refugium (depending on the species, see below) which were most closely related to the
southern lineages, to prevent confounding older isolation events between north refugia
populations with inflated ancestral population sizes. For H. albomarginatus, the modeled
southern population was contrasted to empirical data from all localities south of the
predicted refugium (green diamonds in Fig. 2A: Bertioga, Corupá, Cubatão, Eldorado,
Itariri, Santo André, Porto Belo, São Bernardo do Campo, Ubatuba). Individuals pooled
from the southernmost refuge sites of Angra dos Reis, Magé, and Rio de Janeiro (yellow
right triangles in Fig. 2A) were analyzed as the northern population. Similarly, for H.
semilineatus, models of the southern population were contrasted with data from Bertioga,
Eldorado, Iguape, Itariri, Ubatuba (green diamonds in Fig. 2B), whereas individuals from
the southernmost refugial sites of Magé and Casimiro de Abreu (yellow right triangles in
Fig. 2B) were modeled as the northern population. In H. faber, three data configurations
were modeled. The first pooled all haplotypes from the predicted unstable southern
region into the southern population (Buri, Corupá, Iguape, Morretes, Ortigueira,
Sapiranga, Treviso, Wenceslau Braz; green diamonds in Fig. 2C), and all southernmost
refuge sites (representing all localities within the middle-sized refugium: Itariri, Santo
André, Ubatuba, Salesópolis, São Bernardo do Campo, Natividade da Serra; blue crosses
in Fig. 2C) as the northern population. The second configuration used solely the localities
represented in the most basal clade of haplotypes found in the predicted unstable area and
southern refuge sites (Fig. 2, Fig. S5), thus pooling haplotypes of Itariri and Santo André
as the northern population, and those from Corupá, Iguape, Morretes, Ortigueira,
Sapiranga, and Treviso, as the southern population. Conversely, the third configuration
used only localities represented in the most derived clade of haplotypes found in recently
colonized areas and southern refuge sites (Fig. 2, Fig. S5), hence combining those of
Natividade da Serra, Salesópolis, São Bernardo do Campo and Ubatuba as the northern
population, and Buri and Wensceslau Braz as the southern population. Because all three
configurations provided identical qualitative results, we here report the posterior values
for the second configuration only.
In the models that aimed at testing the hypothesis of long-term persistence of
populations in distinct refugia (results shown in Fig. 3D, E), the HABC population pair
comprised of northern group (population 1, the putative Pernambuco refugium) and a
central group (population 2, the putative Bahia refugium). Empirical data compared
against simulated results for population 1 corresponded to all haplotypes from sites
located within 20 km of the predicted Pernambuco refugium (pink squares, Fig. 2: Cabo
de Santo Agostinho, Ibateguara, and Jaqueira in Hypsiboas albomarginatus; Cabo de
Santo Agostinho in H. semilineatus; Jaqueira in H. faber). Such distance was needed
given that the size of the predicted refugium was too small in some of the target taxa.
Empirical data compared against simulated results for population 2 consisted of
haplotypes of all northernmost sites within the putative Bahia refugium (yellow
rectangles and circles, Fig. 2: Feira de Santana, Salvador, Uruçuca, and Caraíva in H.
albomarginatus; Una in H. semilineatus; Feira de Santana, Uruçuca, and Jussari in H.
faber). Models with a reverse order of migration (i.e., modeling the Bahia refugium as
population 1, and Pernambuco refugium as population 2) provided similar results, and are
not shown here.
Summary Statistic Vector for HABC Acceptance/Rejection Algorithm. In order to
implement the HABC procedure, we used a modified version of the summary statistic
vector D used in (S8). We calculated ten summary statistic classes from each of the three
anuran population-pairs (30 total). This included b (average pair-wise differences
between a pair of southern and northern populations), (average pair-wise differences
among all individuals within a population-pair), 1 and 2 (average pair-wise differences
within individuals sampled in southern and northern populations respectively), W
(Watterson’s estimator of calculated from all sampled individuals of a population-pair),
(W)1 and (W)2 (W calculated from individuals sampled in southern and northern
populations respectively), Var( - W), Var( - W)1 and Var( - W)2. Under this scheme,
the vector D was
1 1 1 1
1
2
W
2
2
2
2
D = 1 2 W
3
3
3
3
1 2 W
(W )11
(W )12
Var( W )1
Var( W )11
Var( W )12
(W )12 (W ) 22 Var( W ) 2 Var( W )12 Var( W ) 22
(W )13 (W ) 32 Var( W ) 3 Var( W )13 Var( W ) 32
b1 b 2 ,
b 3 where each of the three rows correspond to the three population-pairs and the ten
columns correspond to the ten summary statistic classes.
Supporting Figures
42
41
40
Atlantic forest vegetation map
39
38
37
36
Ecotone
Deciduous forest
Semideciduous forest
Open evergreen forest
Dense evergreen forest
Mixed evergreen forest
35
33
34
32
31
30
25
22
18 19
17
16
20 21
7b
6
7a
29
28
27
23 24 26
8
5
4
3
14
13 15
11
12
9 10
2
1
Fig. S1. Vegetation map of the Brazilian Atlantic Forest and numbers for localities
sampled in molecular study. Localitiy names provided in Figs S3-S5.
A
B
C
Fig. S2. Atlantic rainforest biome (grey background) with modeled distribution of H.
albomarginatus (A), H. semilineatus (B), and H. faber (C) under current climatic
conditions (shown in black). Circles represent point locality data used in model
training (yellow) and testing (green). Scale = 400 km. Models for all species
overpredict into interior Brazil, towards the Cerrado biome.
40. Caruaru, PE (BC510)
37. Ibateguara, AL (SG266)
38. Jaqueira, PE (CO1057)
42. Timbauba, PE (AA938)
39. Cabo de Santo Agostinho, PE (GU1148)
42. Timbauba, PE (AA903)
40. Caruaru, PE (BC780)
36. Feira de Santana, BA (3463)
35. Salvador, BA (3455)
34. Uruçuca, BA (3449)
31. Caraíva, BA (3446)
28. Santa Teresa, ES (Ster119)
30. Poté, MG (TOt9)
26. Anchieta, ES (3439)
24. Mimoso do Sul, ES (MimS80)
27. Domingos Martins, ES (DomM42)
21. Magé, RJ (Mage41)
19. Rio de Janeiro, RJ (Barra2)
18. Angra dos Reis, RJ (ariro7)
18. Angra dos Reis, RJ (ariro8)
4. Corupá, SC (Cor69)
3. Porto Belo, SC (PB80)
17. Ubatuba, SP (Itam106)
17. Ubatuba (Itam105)
17. Ubatuba (Itam103)
11. São Bernardo do Campo, SP (Sbern11)
14. Bertioga, SP (SLour2)
13. Santo André, SP (10877)
12. Cubatão, SP (Cub44)
12. Cubatão (Cub42)
11. São Bernardo do Campo, SP (SBern12)
8. Eldorado, SP (Eld155)
10. Itariri, SP (Itar128)
10. Itariti, SP (Itar127)
Fig. S3. Majority rule consensus Bayesian phylogenetic tree of H. albomarginatus,
rooted with sequences from the other two congeneric species studied (root not
shown). Thick internodes denote clades with posterior probability greater than 90%.
For localities, see Fig. S1.
32. Una, BA (Una3485)
32. Una, BA (Una3484)
32. Una, BA (Una3467)
32. Una, BA (Una3481)
32. Una, BA (Una3468)
32. Una, BA (Una3483)
32. Una, BA (Una3482)
32. Una, BA (Una3471)
29. Linhares, ES (Lin76)
32. Una, BA (Una3465)
23. São José do Calçado, ES (MRT1239)
24. Mimoso do Sul, ES (MSul92)
25. Mariana , MG (JC757)
25. Mariana, MG (JC762)
22. Casimiro de Abreu, RJ (CasA147)
22. Casimiro de Abreu, RJ (CasA149)
21. Magé, RJ (Magé37)
9. Iguape, SP (USNM303256)
Fig. S4. Majority rule consensus Bayesian phylogenetic tree of H. semilineatus, rooted with
sequences from the other two congeneric species studied (root not shown). Thick internodes
denote clades with posterior probability greater than 90%. For localities, see Fig. S1.
38. Jaqueira, PE (CO1041)
41. Brejo da Madre de Deus (MD397)
33. Jussari, BA (mrt5790)
25. Mariana (jc799)
25. Mariana (jc794)
20. Petrópolis, RJ (Petr92)
28. Santa Teresa (Ster136)
Petrópolis, RJ (Petr90)
25. Mariana, MG (MG750)
20. Petrópolis, RJ (Petr91)
16. Natividade da Serra, SP (BAlt67)
15. Salesópolis, SP (USNM303034)
11. São Bernardo do Campo, SP (IT-H05)
7a. Wenceslau Braz, PR (IIH185)
11. São Bernardo do Campo, SP (IT-H06)
7a. Wenceslau Braz, PR (IIH179)
7b. Buri, SP (IT-H621)
1. Sapiranga, RS (Sap44)
9. Iguape, SP (Ruth5)
13. Santo André, SP (MTR10399)
13. Santo André, SP (MTR10394)
2. Treviso, SC (Trev21)
6. Ortigueira, PR (II-H72)
6. Ortigueira, PR (II-H71)
5. Morretes, PR (tadpoleA1)
5. Morretes, PR (tadpoleA2)
Fig. S5. Majority rule consensus Bayesian phylogenetic tree of H. faber, rooted with
sequences from the other two congeneric species studied (root not shown). Thick
internodes denote clades with posterior probability greater than 90%. For localities,
see Fig. S1.
A
B
C
Fig. S6. Localities sampled for genetic study (yellow triangles) and known
localities of occurrence of H. albomarginatus (A), H. semilineatus (B), and H. faber
(C), as per collection records (black circles).
Supporting tables.
Table S1. Locality data used to generate climatic niche models of the target species.
“Data usage” indicates whereas the locality was used in model training or model
testing.
Species
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
Data
usage
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
State
RJ
PR
SC
SP
SP
MG
ES
SC
BA
SE
PR
RJ
RJ
MG
RJ
MG
RJ
RJ
MG
MG
RJ
RJ
RJ
RJ
ES
ES
ES
ES
ES
BA
BA
BA
BA
BA
SE
SE
AL
SP
Municiality
Magé
Guaratuba
Itapema
Mongaguá
Pindamonhangaba
Mariana
São José do Calçado
[Rio Humboldt]
Itabuna
Santa Luzia do Itanhy
Morretes
Barra Mansa
Engenheiro Paulo de Frontin
Belmiro Braga
Miguel Pereira
Juiz de Fora
Petrópolis
Niterói
São João Nepomuceno
Viçosa
Maricá
Tanguá
Saquarema
Iguaba [Grande]
Marataízes
Santa Teresa
Cariacica
Linhares
Conceição da Barra
Wenceslau Guimarães
Porto Seguro
Urutuca
Valença
Una
Estância
Itaporanga D'Ajuda
Coruripe
Iguape
S
-43.1132
-48.7687
-48.6329
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-43.0035
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-39.29
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-36.2227
-47.25
E
-22.6121
-25.8172
-27.1077
-24.0674
-22.8792
-20.3292
-20.9836
-26.43
-14.8
-11.3613
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-22.5059
-21.745
-22.4017
-22.9159
-21.5831
-20.7401
-22.9151
-22.7813
-22.8786
-22.8373
-21.0989
-19.8768
-20.2899
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-18.4686
-13.6301
-16.6194
-14.5155
-13.3597
-15.2225
-11.2486
-11.0504
-10.0792
-24.347
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. semilineatus
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
train
train
train
train
train
train
train
train
train
train
train
train
test
test
test
test
test
test
test
test
test
test
test
test
test
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
SC
PR
SP
SP
SP
SP
SP
SP
RJ
BA
SE
AL
PE
ES
ES
ES
ES
MG
SP
SP
RJ
SP
SP
BA
SP
SP
SP
SP
SP
SP
SC
SP
SP
PR
SC
SC
SC
PR
PR
SP
SP
Paraguay
Paraguay
SC
SC
SP
Corupá
Antonina
Juquiá
Iguape
Campinas
São Paulo
Praia Grande
Santo André
Angra dos Reis
São José da Vitória
Areia Branca
Rio Largo
Cabo de Santo Agostinho
Santa Teresa
Linhares
Domingos Martins
Mimoso do Sul
Teófilo Otoni
Itariri
Eldorado
Casimiro de Abreu
Bertioga
Ubatuba
Ilhéus
Pariquerá-Açu
Analândia
Americana
Mogi das Cruzes
Rio Claro
Pilar do Sul
São Bento do Sul
Queluz
Ribeirão Branco
Tijucas do Sul
Mafra
São Domingos
Angelina
Ortigueira
Wenceslau Braz
São Bernardo do Campo
Buri
[not provided]
Vera, Yatai
Brusque
Corupá
Salesopolis
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-23.854
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-23.53
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
BA
MG
RJ
RJ
BA
SC
RS
RS
PR
SP
RS
RJ
ES
PB
MG
MG
RJ
MG
BA
RJ
MG
MG
MG
RJ
RJ
MG
RJ
MG
RJ
MG
RJ
SE
BA
SP
MG
MG
RJ
MG
RJ
RJ
RJ
RJ
MG
BA
ES
SC
Bom Jesus da Lapa
Além Paraíba
Itaboraí
[Duque de] Caxias
Caetité
Florianópolis
Porto Alegre
Farroupilha
Bituruna
Pedro de Toledo
Santo Augusto
Nova Iguaçu
Aracruz
Mamanguape
Juiz de Fora
Barão de Cocais
Santa Maria Madalena
Itabira
Itapebi
Saquarema
Manga
Grão Mogol
Rio Novo
Miguel Pereira
Marangatiba
Turmalina
Parati
Barra Mansa
Chiador
Rio das Ostras
Santa Lúcia [Luzia] do Itanhy
Ilhéus
Ribeirão Branco
Viçosa
Lagoa Santa
Itatiaia
São Gonçalo do Rio Abaixo
Angra dos Reis
Niterói
Petrópolis
Belmiro Braga
Jussari
Conceição da Barra
Joinville
-43.2897
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H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
PR
ES
RJ
RJ
RJ
SC
MG
MG
RJ
MG
ES
BA
BA
RJ
MG
BA
MG
SP
MG
SP
RJ
MG
SP
SP
AL
RJ
SP
RJ
SP
SP
PR
RS
SP
PR
BA
MG
SP
SE
SC
SP
BA
SC
SP
SE
SP
BA
Guaraqueçaba
Cariacica
Rio de Janeiro
Tanguá
Três Rios
Porto Belo
Minas Novas
Espera Feliz
Teresópolis
Antônio Dias
Domingos Martins
Prado
Itabuna
Campos [dos Goytacazes]
Marlieria
Maracás
Simonésia
Iguape
Vargem Alegre
Piracicaba
[Macaé]
Mariana
São Vicente
Franco da Rocha
Palmeira dos Índios
[Itatiaia]
Caieiras
Valença
Cotia
Amparo
Curitiba
Gramado
Cerqueira Cesar
[Matinhos]
Uruçuca
Paraopeba
Biritiba-Mirim
Areia Branca
Timbé do Sul
Piracaia
São José [da Vitória]
Timbó
Embu
[Itabaiana]
Miracatu
Salvador
-48.367
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H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. faber
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
SP
SP
SP
SP
SP
SP
SP
SC
MG
SP
SP
MG
SC
BA
BA
SP
SP
SP
SP
SP
RS
SC
SC
SC
SC
PR
SC
PR
SP
SP
SP
SP
RJ
MG
ES
ES
ES
ES
BA
BA
PE
PE
AL
SE
RJ
SP
Itapecerica da Serra
São Paulo
Ferraz de Vasconcelos
Piquete
São Sebastião
Boracéia
[Santo André]
Corupá
Caeté
Barueri
Iporanga
Sabará
Novo Horizonte
Itiuba
[Una]
Ubatuba
[Cananéia]
Botucatu
Buritizal
[Ribeirão Grande]
Sapiranga
Mafra
Treviso
Corupá
Corupá
Tijucas do Sul
Angelina
Morretes
Itariri
Bairro Alto
Ubatuba
Ubatuba
Petrópolis
Teófilo Otoni
Mimoso do Sul
Domingos Martins
Santa Teresa
Linhares
Uruçuca
Feira de Santana
Brejo da Madre de Deus
Jaqueira
São Miguel dos Campos
São Cristóvão
Niterói
Ubatuba
-46.8584
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-14.5881
-12.1689
-8.0762
-8.71315
-9.7386
-10.8581
-22.9281
-23.3586
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
test
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
SP
SC
AL
RJ
ES
PE
AL
PE
PE
PE
BA
BA
BA
ES
BA
ES
MG
ES
ES
RJ
SP
SP
SP
SC
SP
SE
SC
SC
SC
SC
SC
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
SP
H. albomarginatus
train
SP
Pariquerá-Açu
Itajaí
Nova Iguaçu
Santa Teresa
Caruaru
Ibateguara
Jaqueira
Cabo de Santo Agostinho
Timaúba
Uruçuca
Salvador
Feira de Santana
Anchieta
Caraíva
Santa Teresa
Teófilo Otoni
Domingos Martins
Mimoso do Sul
Rio de Janeiro
Bertioga
Ubatuba
Ubatuba
Corupá
Itariri
Eldorado
Corupá
Guaramirim
Pirabeiraba [Joinville]
Joinville
Porto Belo
Iporanga
Itapetininga
Sete Barras
Buritizal
Juquiá
Iguape
Miracatu
Itanhaem
São Paulo
Cubatão
Praia Grande
Ferraz de Vasconcelos
Santos
Paranapiacaba [Santo
André]
-47.88
-48.7586
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-43.4119
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-49
-48.9333
-48.8333
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-48.5833
-48.05
-47.9167
-47.7333
-47.6333
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-47.4667
-46.7833
-46.6167
-46.4078
-46.4
-46.3667
-46.3333
-24.5969
-26.9456
-9.6853
-22.5853
-19.9528
-8.3736
-8.9822
-8.71315
-8.23682
-7.60513
-14.5881
-12.8736
-12.1689
-20.7967
-16.7997
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-17.7451
-20.2888
-21.0471
-22.9956
-23.7934
-23.3952
-23.4023
-26.4405
-24.2997
-24.5336
-26.4333
-26.45
-26.2
-26.3
-27.1731
-24.5833
-23.6
-24.3833
-20.1833
-24.3167
-24.7167
-24.2833
-24.1833
-23.5333
-23.8649
-24
-23.5333
-23.95
-46.3167
-23.7833
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
train
train
train
train
train
train
train
train
train
train
train
SP
SP
SP
RJ
RJ
RJ
RJ
RJ
RJ
RJ
RJ
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
RJ
RJ
RJ
RJ
RJ
RJ
MG
RJ
RJ
RJ
RJ
RJ
RJ
RJ
RJ
RJ
RJ
BA
RJ
RJ
ES
ES
ES
ES
BA
BA
BA
BA
BA
BA
BA
BA
SE
BA
Caraguatatuba
Ubatuba
Picinguaba [Ubatuba]
Parati
Resende
Ariró [Angra dos Reis]
Angra dos Reis
Mangaratiba
Itaguaí
Tijuca [Rio de Janeiro]
Jacarepaguá [Rio de
Janeiro]
Duque de Caxias
Ramos [Rio de Janeiro]
Bonsucesso [Rio de Janeiro]
Rio de Janeiro
Niterói
Chiador
Magé
Teresópolis
Maricá
Tanguá
Saquarema
Araruama
Cabo Frio
Santa MAria Madalena,
Armação de Búzios
Rio das Ostras
Santa Cruz Cabrália
Campos [dos Goytacazes]
São João da Barra
Santa Teresa
Santa Leopoldina
Cariacica
Linhares
Jussari
Itabuna
Uruçuca
Porto Seguro
Valença
Ilhéus
Salvador
Entre Rios
Cristinápolis
Conde
-45.4167
-45.0667
-44.8333
-44.7167
-44.45
-44.3263
-44.3
-44.0333
-43.7789
-43.6839
-43.4331
-23.6167
-23.4333
-23.3667
-23.2167
-22.4667
-22.9294
-23.00
-22.95
-22.8669
-22.5494
-22.8775
-43.3581
-43.3083
-43.2631
-43.2525
-43.2333
-43.0944
-43.0589
-43.0153
-42.9833
-42.8167
-42.7167
-42.5
-42.3333
-42.0167
-42.0167
-42
-41.95
-41.8
-41.3
-41.05
-40.6
-40.5333
-40.4167
-40.0667
-39.5333
-39.2667
-39.2667
-39.0833
-39.0833
-39.0333
-38.5167
-38.0833
-37.7667
-37.6167
-22.9428
-22.7867
-22.8547
-22.8625
-22.9
-22.8844
-22.0417
-22.6556
-22.4333
-22.9167
-22.7333
-22.9333
-22.8833
-22.8833
-21.95
-22.8333
-22.5333
-12.8167
-21.75
-21.6333
-19.9167
-20.1
-20.2667
-19.4167
-15.2
-14.8
-14.5833
-16.4333
-13.3667
14.81667
-12.9833
-11.9333
-11.4833
-11.8167
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
H. albomarginatus
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
train
SE
SE
SE
SE
AL
AL
PE
AL
AL
AL
AL
PE
PE
PE
PR
SP
SP
SP
MG
BA
BA
Santa Luzia do Itanhy
Areia Branca
Aracaju
Santo Amaro das Brotas
Quebrangulo
Serra dos Cavalos [Caruaru]
Murici
Rio Largo
Maceió
Passo de Camaragibe
Timbaúba
Recife
Igarassu
Guaraqueçaba
Eldoraldo
Jacupiranga
Cubatão
Maracás
Itagibá
-37.25
-37.1333
-37.0667
-37.0667
-36.4833
-36.0833
-35.9667
-35.9333
-35.85
-35.7167
-35.4333
-35.3167
-34.9
-34.9
-48.3258
-48.1075
-48.0389
-46.4078
-43.0167
-40.45
-39.85
-11.15
-11.0667
-10.9167
-10.7833
-9.33333
-9.78333
-8.28333
-9.31667
-9.48333
-9.66667
-9.25
-7.51667
-8.05
-7.83333
-25.2983
-24.5222
-24.6986
-23.8649
-21.55
-13.4333
-14.2833
Table S2. Genetic metrics of population expansion in predicted unstable areas.
Notice that only Fay and Wu’s Hs test is able to detect significance in H. faber.
Fu and Li's D*
Fu and Li's F*
Fu's Fs
Tajima's D
Fay and Wu's Hs
H. albomarginatus
-2.78730, p < 0.05*
-2.86956, p< 0.05*
-4.754, p < 0.05*
-1.69816, p < 0.05*
-11.49858, p < 0.05*
H. semilineatus
-0.01614, p> 0.10
-0.08811, p> 0.10
0.875, p> 0.10
-0.24036, p> 0.10
0.11429, p> 0.10
H. faber
0.99556, p> 0.10
0.80625, p> 0.10
2.391, p> 0.10
-0.03737, p> 0.10
-13.2549, p < 0.05*
Supporting references and notes.
S1. S. J. Phillips, R. P. Anderson, R. E. Schapire, Ecol. Modell. 190, 231-259 (2006).
S2. A. C. Carnaval, C. Moritz, J Biogeogr. 35, 1187-1201 (2008).
S3. J. P. Huelsenbeck, F. Ronquist, Bioinformatics 17, 754-755 (2001).
S4. F. Tajima, Genetics 143, 1457-1465 (1996).
S5. M. Nei, Molecular Evolutionary Genetics (Columbia Univ. Press, New York, NY,
1987).
S6. N. Mantel, Cancer Res. 27, 209-220 (1967).
S7. J. C. Fay, C. I. Wu, Genetics 155, 1405-1413 (2000).
S8. M. J. Hickerson, E. Stahl, H. A. Lessios, Evolution 60, 2435-2453 (2006).
S9. Instituto Socioambiental, Dossiê Mata Atlântica (Ipsis Gráfica e Editora, Brasília,
Brazil, 1991).
S10. A. C. O. Q. Carnaval, Integr. Comp. Biol. 42, 913-921 (2002).
S11. J. A. A. Nylander, MrModeltest v2. (Program distributed by the author,
Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden, 2004).
S12. J. Rozas, J. C. Sanchez-Delbarrio, X. Messeguer, R. Rozas, Bioinformatics 19,
2496-2497 (2003).
S13. K. Tamura, M. Nei, Mol. Biol. Evol. 9, 678-687 (1993).
S14. F. Tajima, Genetics 123, 585-595 (1989).
S15. Y.-X. Fu, W.-H. Li, Genetics 133, 693-709 (1993).
S16. Y.-X. Fu, Genetics 147, 915-925 (1997).
S17. L. Excoffier, G. Laval, S. Schneider, Evolutionary Bioinformatics Online 1, 47-50
(2005).
S18. M. J. Hickerson, C. P. Meyer, BMC Evol. Biol. 8, 322 (2008).
S19. M. J. Hickerson, E. Stahl, N. Takebayashi, BMC Bioinformatics 8, 268 (2007).
S20. M. A. Beaumont, W. Zhang, D. J. Balding, Genetics 162, 2025 (2002).
S21. N. J. R. Fagundes et al., Proc. Natl. Acad. Sci. USA 104, 17614-17619 (2007).
S22. M. A. Beaumont in Simulations, Genetics and Human Prehistory, S. Matsumura, P.
Forster, C. Renfrew, Eds. (McDonald Institute for Archaeological Research, Cambridge,
2008), pp. 135-154.
S23. R. R. Hudson, Bioinformatics 18, 337 (2002).
S24. A. J. Crawford, Mol. Ecol. 12, 2525-2540 (2003).
S25. R. E. Kass, A. Raftery, J. Am. Stat. Assoc. 90, 773-795 (1995).

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