Download Electronic supplementary material: supplementary methods and

Electronic supplementary material: supplementary methods and results
Loltún cave description
The Loltún Cave is located in the westernmost portion of the state of Yucatán, México, about 110
km S Mérida and approximately 7 km SW Oxkutzcab (20º 15' N, 89º 28' W), at an elevation of 40
m. The site is found in a karstic region that has several kinds of geological formations, including
caves, formed in the exposed Miocene rocks, on the foothills of the Sierra de Ticul. The cave
has an area of 0.5 km2 and is formed by tunnels and chambers with an east-west orientation. It
has two entrances, one for tourists (Nahkab) and one with a vertical drop of about 10 m (Loltún).
The ceiling has collapsed in some of the chambers, in one of which, the Huechil room that is a
semi-circular opening between 15-20 m diameter, two units were excavated: El Toro and El
Túnel. These units were controlled and studied stratigraphically, resulting in a large collection of
findings, including layers with ceramics and lithics, and layers only with lithics in association with
extinct animals [1]. The predominantly subterranean drainage of karst terrains like in this cave
allows the development of closed depressions and the concentration of vertebrate remains [2].
None of the rodent remains have been radiocarbon dated. There is only a horse bone from
stratum VII dated at 12,720 years ago [3].
Ancient DNA protocol
DNA extraction was performed at the McMaster University, Hamilton, Canada, in a physically
isolated area of the Ancient DNA Centre, where no modern Ototylomys or related species were
ever stored or processed. DNA extraction, PCR amplifications and all molecular procedures
were performed in different laboratories following strict procedures specifically designed for fossil
DNA manipulation. Importantly, no modern Ototylomys or related species were ever stored or
processed in the laboratories we used, a fact that protects against any potential contamination
routes ([4] Gilbert et al. 2005). Since obtaining ancient DNA is a destructive procedure, and
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considering we only had 0.1-0.16 g of mandible sample and that only approximately one third of
this was used for DNA extraction (see electronic supplementary material Fig. 1), standard
morphometric measures (ESM Table 4) and 3D images using structured light methods [5] were
taken for each sample in order to thoroughly document morphology prior to destructive analysis.
To ensure that different individuals from the different layers were used, our sampling was
based on both the availability of complete mandibles (all three molars and incisor) and selection
of hemimandibles with the same laterality. Accordingly, we selected only right mandibles (layers
1 to 5; see ESM Table 4), however this was not possible in two cases (layers 6 and 12) where
right mandibles were fragmented and incomplete. In order to ensure they came from different
individuals, we thoroughly examined that these samples differed in morphometric measures (see
ESM Table 5), molar wear and incisor enamel; these samples also were selected from the most
distant quadrants between layers. The selected samples included all of the molars and the
alveolae that encapsulate the cementum and the root tip, which have proven to be an effective
source of DNA [6]. The remaining fragments of all samples were preserved for future direct
dating analyses. The DNA extraction protocol followed was that of Schwarz et al. [7], with
demineralization and digestion supernatants processed separately. Negative buffer controls
were included for each set. After DNA extraction, we measured PCR inhibition following King et
al. [8], targeting a synthetic spike of the cytochrome b mitochondrial gene of Mammuthus, using
primers cytB F111-131 and cytB R171-194 (see results on ESM table 5).
Cytochrome b amplification
All quantitative PCRs were performed with the Mx3000P Quantitative PCR platform (Stratagene
Inc.) or the CFX Connect Real-Time PCR Detection System (BioRad Inc.). Extraction blanks and
PCR negative controls were included at all times. Positive controls were not added because of
the additional contamination risks [9, 10]. Amplifications were done performing three different
PCRs for each 100 bp segment per sample to ensure reproducibility. PCR product size was
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confirmed on 2.5% agarose gels stained with ethidium bromide. No products larger than 200 bp
sizes could be amplified and a lower number of amplifiable copies were obtained from the older
(deeper) layers than from more recent ones. Cytochrome b amplicons were successfully
obtained for 16 out of the 28 selected jawbone fossil samples (57% success), whereas we
completed a 666 bp gene fragment for a total of 12 samples. PCR product purification and
sequencing were performed by Macrogen Inc. (3730xl DNA analyser). The forward and reverse
strands of positive amplifications were directly sequenced after purification. For the final data,
we corroborated that all polymorphisms for each 100 bp segment were identical in at least two
amplicons –and in some cases we were able to use the three amplicons. Assembly and edition
of sequences was performed in Geneious version 5.0 (Biomatters Ltd.). In order to ensure the
quality of our sequences, we used at least two of three independent amplicons to call sites and
checked for stop codons by translating to amino acid sequences. In addition, each position in
the 666bp obtained was covered independently by at least two amplicons; also, complete
sequences and independent amplicons were compared to the GenBank database using a
nucleotide Blast in http://blast.ncbi.nlm.nih.gov/Blast.cgi. Results for amplicons showed 94-100%
identity with Ototylomys phyllotys, while complete sequences showed divergence values of 3353% with respect to species that could potentially carry contamination (ESM Table 6).
Phylogenetic analyses and time of divergence estimation
We used the Ototylomys phyllotis sequences from the phylogeographic study of Gutiérrez-García
& Vázquez-Domínguez’s [11], in combination with the 12 fossil samples from which the complete
666 bp target could be reconstructed, for the phylogenetic analyses. The selection of outgroup
species for the phylogenetic tree was made based on the fact that the jawbone fossil samples
were morphologically identified as Ototylomys, hence its sister clades should be members of the
Tylomyinae and/or Neotominae families (Nyctomys and Otonyctomys), as recognized by different
authors [11, 12, 13, 14]. In addition, we also selected species from the genus Muridae in order to
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provide nodal calibration points. Accordingly, we had a final total of eight genera as outgroups:
Calomyscus (Accession number EU135583), Clethrionomys (AY309435), Microtus (AF119279),
Neotoma (AF294344), Peromyscus (AF131926), Reithrodontomys (AF176257), Otonyctomys
(JQ183060), Nyctomys (AY195801). The entire data set included 96 sequences. The estimation
of the best-fit substitution model under the Akaike Information Criterion (AIC) was calculated
using jModelTest v.0.1.1 [15], which selected as the most appropriate model: TIM2+I+G (-lnL =
6565.370 p<0.001, AIC = 13526.741); with base frequencies A = 0.2847, C = 0.3028, G = 0.1489
and T = 0.2636; proportion of invariant sites I = 0.2640; gamma distribution γ = 0.8450 and
substitution rates A-C = 1.9620, A-G = 6.3582, A-T = 1.9620, C-G = 1.0000, C-T = 7.5988, G-T =
1.0000. The substitution model was used to perform a maximum likelihood analysis with PhyML
v.3.1 [16], using BioNJ [17] to construct the starting tree, and with the best NNI and SPR as
searching mode. Branch support was calculated using the fast approximate likelihood ratio test
(aLRT) based on a Shimodaira-Hasegawa-like procedure [18]. The estimation of times of
divergence was performed with the Bayesian MCMC phylogenetic software BEAST 1.7.5 [19].
We used the GTR+I+G model of evolution across all gene and codon positions, priors included
empirical base frequencies, four gamma categories and an uncorrelated lognormal relaxed
molecular clock method. Time of divergence was estimated with an uncorrelated lognormal tree
prior, with a constant population size prior and lognormal calibration dates. For the relaxed
method, we provided calibration points and error estimates derived from a lognormal distribution,
using the following calibration points: split of Avicolinae-Cricetinae from Neotominae [13] at 18.719.6 million years ago (mya), Microtus-Cleithronomys split at 5.5 to 6.6 mya [13], Neotominae
radiation 7.7-11 mya [20] and the appearance in the fossil record of the Neotoma ancestor
Paraneotoma at 2.5 mya [21]. These analyses estimated tree shape and divergence dates for all
nodes and were sampled every 1,000th iteration for 500,000,000 generations with 10% of the
initial samples discarded as burn-in. We tested several trials in BEAST and the stability of runs
was reviewed with Tracer v.1.5. [22].
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Genetic diversity and demographic history
We estimated genetic diversity as the number of haplotypes, number of segregating sites (S),
nucleotide diversity (π), haplotype diversity (h) and mean number of pairwise differences (k)
using DnaSP v.5 [23] for the ancient samples that were grouped as an independent clade.
Neutrality of sequences was tested with Tajima’s D [24] and Fu and Li’s D [25]. Levels of genetic
differentiation were evaluated by estimating p-uncorrected genetic distances and net nucleotide
divergence values (Da) with DnaSP v.5. Genetic differentiation comparisons were estimated
both between the different layers (within the ancient samples) and between the ancient samples
(Loltún clade) and modern Ototylomys and sister species. In addition, the relationship between
haplotypes was estimated by a median-joining method [26] implemented in Network v.4.5.1.6 (by
http://www.fluxus-engineering.com). To evaluate if a signal of demographic expansion or
population bottleneck was evident in the set of ancient sequences, we calculated the Fu’s Fs
index, together with the Harpending’s raggedness index r [27] and the R2 statistic of RamosOnsins & Rozas’s [28], all with DnaSP v.5. Finally, we constructed Bayesian Skyline Plots with
BEAST [29], to infer population fluctuations over time by estimating the posterior distribution of the
effective population size at specified intervals along a phylogeny [30]. We used the coalescentbased prior that does not depend on a pre-specified parametric model of demographic history,
with minimal assumptions as recommended when the number of sequences is low [29].
Genealogies and model parameters were sampled every 1,000th iteration for 1x107 generations
under a relaxed lognormal molecular clock, with uniformly distributed priors and a burn-in of 100
iterations, using coalescent intervals (m) of 10. Demographic plots for each analysis were
visualized using TRACER.
References
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1.
Arroyo-Cabrales, J., Alvarez, T. 2003 A preliminary report of the Late Quaternary mammal
fauna from Loltún cave, Yucatán, México. In Ice Age Cave Faunas of North America (eds.
B. W. Shubert, J. I. Mead & R. Wm. Graham), pp. 262-272. United States of America:
Indiana University Press and Denver Museum of Nature & Science.
2.
Simms, M. J. 1994 Emplacement and preservation of vertebrates in caves and fissures.
Zoological Journal of the Linnean Society 112, 261-283.
3.
Morales-Mejía, F. M., Arroyo-Cabrales, J., Polaco, O. J. 2009 New records for the
Pleistocene mammal fauna from Loltún Cave, Yucatán, México. Current Research in the
Pleistocene 26, 166-168.
4.
Gutiérrez-García, J. C., Mosiño, J. F., Martínez, A., Gutiérrez-García, T. A., VázquezDomínguez, E., Arroyo-Cabrales, J. 2013 Practical eight frame algorithms for fringe
projection profilometry. Optics Express 21, 903-917.
5.
Gilbert, M. T. P., Bandelt, H.-G., Hofreiter, M., Barnes, I. 2005 Assessing ancient DNA
studies. Trends in Ecology & Evolution 20, 541-544.
6.
Adler, C. J., Haak, W., Donlon, D., Cooper, A. 2011 Survival and recovery of DNA from
ancient teeth and bones. Journal of Archaeological Science 38, 956-964.
7.
Schwarz, C., Debruyne, R., Kuch, M., McNally, E., Schwarz, H., Aubrey, A. D., Bada, J.,
Poinar, H. 2009 New insights from old bones: DNA preservation and degradation in
permafrost preserved mammoth remains. Nucleic Acids Research 37, 3215-3229.
8.
King, C. E., Debruyne, R., Kuch, M., Schwarz, C., Poinar, H. N. 2009 A quantitative
approach to detect and overcome PCR inhibition in ancient DNA extracts. BioTechniques
47, 941-949.
9.
Cooper, A., Poinar, H. N. 2000 Ancient DNA: do it right or not at all. Science 289,1139.
10.
Kemp, B. M., Smith, D. G. 2010 Ancient DNA Methodology: thoughts from Brian M. Kemp
and David Glenn Smith on “Mitochondrial DNA of Protohistoric remains of an Arikara
population from South Dakota”. Human Biology 82, 227-238.
6
11.
Gutiérrez-García T. A. & Vázquez-Domínguez E. 2012 Biogeographically dynamic genetic
structure bridging two continents in the monotypic Central American rodent Ototylomys
phyllotis. Biological Journal of the Linnean Society 107, 593-610.
12.
Reeder, S. A. & Bradley, R. D. 2004 Molecular systematics of Neotomine-Peromyscine
rodents based on the Dentin Matrix Protein 1 gene. Journal of Mammalogy 85, 1194-1200.
13.
Steppan, S. J., Adkins, R. M., Anderson, J. 2004 Phylogeny and divergence-date estimates
of rapid radiations in Muroid rodents based on multiple nuclear genes. Systematic Biology
53, 533-553.
14.
Matocq, M. D., Shurtliff, Q. R., Feldman, C. R. 2007 Phylogenetics of the woodrat genus
Neotoma (Rodentia: Muridae): implications for the evolution of phenotypic variation in male
external genitalia. Molecular Phylogenetics and Evolution 42, 637-652.
15.
Guindon, S. & Gascuel, O. 2003 A simple, fast, and accurate algorithm to estimate large
phylogenies by maximum likelihood. Systematic Biology 52, 696-704.
16.
Guindon, S., Dufayard, J. F., Lefort, V., Anisimova, M., Hodrijk, W., Gascuel, O. 2010 New
algorithms and methods to estimate Maximum-Likelihood phylogenies: assessing the
performance of PhyML 3.0. Systematic Biology 59, 307-321.
17.
Gascuel, O. 1997 BIONJ: an improved version of the NJ algorithm based on a simple
model of sequence data. Molecular Biology and Evolution 14, 685-695.
18.
Shimodaira, H., Hasegawa, M. 1999 Multiple comparisons of log-likelihoods with
applications to phylogenetic inference. Molecular Biology and Evolution 16, 1114-1116.
19.
Drummond, A. J., Suchard, M. A., Dong, X., Rambaut, A. 2012 Bayesian phylogenetics
with BEAUti and the BEAST 1.7. Molecular Biology and Evolution 29, 1969-1973.
20.
León-Paniagua, L., Navarro-Sigüenza, A. G., Hernández-Baños, B. E., Morales, J. C. 2007
Diversification of the arboreal mice of the genus Habromys (Rodentia: Cricetidae:
Neotominae) in the Mesoamerican highlands. Molecular Phylogenetics and Evolution 42,
653-664.
7
21.
Hibbard, C. W. 1967 New rodents from the Late Cenozoic of Kansas. Papers of the
Michigan Academy of Science, Arts and, Letters 52, 115-131.
22.
Rambaut, A., Drummond, A. J. 2003-2009 MCMC Trace Analysis Tool Version v1.5.0.
Available at: http://tree.bio.ed.ac.uk/software/tracer/
23.
Librado, P., Rozas, J. 2009 DnaSP v5: a software for comprehensive analysis of DNA
polymorphism data. Bioinformatics 25, 1451-1452.
24.
Tajima, F. 1989 Statistical method for testing the neutral mutation hypotheses by DNA
polymorphism. Genetics 123, 585-595.
25.
Fu, Y. X. 1997. Statistical tests of neutrality of mutations against population growth,
hitchhiking and background selection. Genetics 147, 915-925.
26.
Bandelt, H.-J., Forster, P., Röhl, A. 1999 Median-joining networks for inferring intraspecific
phylogenies. Molecular Biology and Evolution 16, 37-48.
27.
Harpending, H. 1994 Signature of ancient population growth in a low-resolution
mitochondrial DNA mismatch distribution. Human Biology 66, 591-600.
28.
Ramos-Onsins S. E. & Rozas, J. 2002 Statistical properties of new neutrality test against
population growth. Molecular Biology and Evolution 19, 2092-2100.
29.
Drummond, A. J., Rambaut, A., Shapiro, B., Pybus, O. G. 2005 Bayesian coalescent
inference of past population dynamics from molecular sequences. Molecular Biology and
Evolution 22, 1185-1192.
30.
Drummond, A. J. & Rambaut, A. 2007 BEAST: Bayesian evolutionary analysis by sampling
trees. BMC Evolutionary Biology 7, 214.
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