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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 1 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 2 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 3 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]. 4 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 5 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. 8