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SUPPLEMENTAL METHODS AND RESULTS METHODS Sampling and DNA extraction During two previous studies [1,2] we sampled individual white-footed mice between 2009 and 2013 from 23 separate localities that were used to generate the genomic data used in this study. Sites were chosen to represent a rural to urban gradient (Fig. 1). Rural sites were defined as large tracts of relatively undisturbed natural habitat, and urban sites were fragmented habitat surrounded by urban infrastructure and impervious surface. Urbanization was also quantified by determining the percent impervious surface and human population size in a 2 km buffer surrounding each park (See Table 1 and Figure 1 in [2]). For all sampling locations, we trapped individuals over a period of 1-3 nights each. At each site, we set between one and four 7x7 m transects of Sherman live traps (7.62 cm x 7.62 cm x 22.86 cm), depending on the total area of each sampling site. We weighed, sexed, and took morphological (ear length, tail length, hind foot length, total body length) measurements for all individual mice. At all sites except Central Park, Flushing Meadow, New York Botanical Garden, Brook Haven Park & Wild Wood Park, High Point Park, and Clarence Fahnestock Park, we collected tissue by taking 1 cm tail clips, placing in 80% ethanol, and storing at -20º C in the laboratory. Individual mice were then released. For these other six sites, we used previously-collected liver samples stored in RNAlater (Ambion Inc., Austin, TX) at -80º C. We extracted genomic DNA using standard extraction protocols, quantified the yield, and checked quality before genomic sequencing library preparation. See methods in (Munshi-South et al. 2016) for full details. All animal handling procedures were approved by the Institutional Animal Care and Use Committee at Brooklyn College, CUNY (Protocol Nos. 247 and 266) and by Fordham University’s Institutional Animal Care and Use Committee (Protocol No. JMS-13-03). RAD sequencing and SNP calling We initially sequenced 233 individual white-footed mice but retained 191 P. leucopus individuals from 23 sampling sites for the genome-wide SNP dataset after filtering out close relatives and low-quality samples [2]. Briefly, we followed standard protocols for ddRADseq presented in Peterson et al. (2012), starting with DNA extraction using Qiagen DNEasy kits with an RNAse treatment. Next we used a combination of the enzymes, SphI-HF and MluCI to generate similarly sized DNA fragments. Using two restriction enzymes increases the probability of generating the same RAD loci across all samples. We specifically chose SphI-HF and MluCI to generate ~50,000 RAD loci; this estimate was based on the number of fragments produced using the same REs on a related species (Mus musculus). We cleaned the digested DNA with Ampure XP beads and then ligated unique barcodes to each individual sample. We then pooled samples in groups of 48 and used a Pippin Prep for precise DNA fragment size excision from gels and Phusion high-fidelity PCR to amplify fragments and add Illumina indexes and sequencing primers. The resulting fragments were sent to the NYU Center for Genomics and Systems Biology for 2x100 bp paired-end sequencing in three lanes of an Illumina HiSeq 2000. We checked initial quality of the raw reads using FastQC. Subsequent primer removal, low-quality nucleotide trimming, and de novo SNP calling was conducted using the Stacks 1.21 pipeline [4]. We called and filtered SNPs in Stacks using default setting except for requiring that loci occur in 22 / 23 sampling sites, and within each site, occur in at least 50% of individuals. We chose a random SNP from each RAD tag to avoid linkage between loci. Additionally, we removed individuals if they had too few reads resulting in extremely small SNP datasets or if they showed high levels of relatedness to other white-footed mice sampled. These filters resulted in 14,990 SNPs in the final dataset that we used for demographic modeling. Population structure and migration We investigated observed patterns of genetic diversity to define evolutionary clusters that could be used to inform demographic modeling of P. leucopus populations in the NYC region. We examined population structure and evidence of migration among all 23 sampling sites. The program TreeMix [5] was used to build population trees and identify migration events. TreeMix infers populations splitting and mixing using allele frequencies from large genomic datasets. Using a composite likelihood approach given allele frequency data, TreeMix returns the most likely population tree and admixture events given a user-specified number of admixture events. The number of admixture events tested ranged from 0 - 12 while the rest of the parameters used default settings. P-values were generated for each admixture event and comparisons made between all trees. We confirmed admixture between populations by running f3 three-population analyses in Treemix. These statistics assess admixture between populations by identifying correlations between allele frequencies that do not fit the evolutionary history for that group of three populations. We used 500 bootstrap replicates to assess significance of f3 statistics. We also used sNMF version 0.5 [6] to examine population structure sNMF explores patterns of genetic structure by assigning individual ancestry coefficients using sparse nonnegative matrix factorization. sNMF does not make any model assumptions like requiring populations to be in Hardy-Weinberg and linkage equilibrium [6], as opposed to other likelihood models like STRUCTURE [7]. For the number of putative ancestral populations tested, we chose a range from K = 1 to K = 11 using default parameters, with 10 replicate runs for each value of K. We chose 11 as an initial maximum because there were at least nine urban parks without any vegetated corridors between them (parks in Queens, NY have a small greenway connecting them) plus rural LI and rural Mainland. There was not a pattern supporting a higher number of clusters so we did not analyze K > 11. We ran sNMF on the full 14,990 SNP dataset (≤ 50% of SNPs missing per population) and on a more conservative dataset with only ≤ 15% of SNPs missing per population. sNMF imputes missing genotypes by resampling from the empirical frequency at each locus [6], and using fewer missing data ensured any inferred population structure was not due to incorrectly imputed genotypes (Fig. S1). To infer the most likely number of ancestral populations, each model run generates a cross-entropy estimation based on ancestry assignment error when using masked genotypes. The model with the smallest cross-entropy score implies it is the best prediction of the true number of K ancestral populations [6]. Demographic inference from genome-wide site frequency spectra To reduce model complexity for demographic inference, we grouped individuals into the minimum number of populations representing unique evolutionary clusters. Global analyses in TreeMix and sNMF showed the highest support for two populations split by the East River, and hierarchical analyses using discriminate analysis of principal components [2] showed support for isolated urban populations. Collectively, results suggested a minimum of seven putative populations captured most of the genetic variation between populations (Mainland & Manhattan: MM, Long Island: LI, Central Park: CP, Van Cortlandt Park: VC, Inwood Hill Park: IP, Jamaica Bay: JB, Fort Tilden: FT, Fig. 1). Along with hierarchical population structure results, we chose several of the urban populations to include in demographic modeling based on the size of the park, the relative isolation of the park due to urbanization, and the population density of whitefooted mice in the park. We generated the multi-population site frequency spectrum (MSFS) for subsets of populations to test specific demographic history scenarios. We used the dadi.Spectrum.from_data_dict command implemented in dadi [8] to generate the MSFS. When we created the SNP dataset, we required a SNP to occur in ≥ 50% of individuals from each population, so the MSFS was down-projected to 50% to ensure the same number of individuals for all loci [8]. Once the MSFSs were generated, we used the software program fastsimcoal2 ver 2.5.1 [9] for demographic inference. Fastsimcoal2 (fsc2) uses a composite multinomial likelihood approach to infer demographic histories from the site frequency spectrum generated from genomic scale SNP datasets. The expected SFS under user defined demographic scenarios is obtained using coalescent simulations. Fastsimcoal2 ver. 2.5.1 contained a bug that miscalculated Max Observed Likelihood values if the SFS contained non-integers, leading to Maximum Estimated Likelihoods that are higher than Max Observed Likelihoods. Our data did not display this symptom and when point estimates were re-run using the latest version (fsc25), there was no impact on the values used here and on the final conclusions presented in the main text. We tested demographic histories under a scenario of population isolation with migration (IM). We compared inferred parameters between six hierarchical IM models (Fig. S3) using the same dataset. There was one two-population IM model (seven free parameters) to test older divergence patterns between MM and LI suggested from the geologic record. The remaining five models were three-population IM models (15 free parameters each) testing for recent urban population divergence. We chose to run separate models investigating each urban population separately in order to avoid inconsistencies from over-parameterization. For these remaining models we considered an ancestral population that split at time Tdiv1 and then an urban population that split more recently at time Tdiv2. For Tdiv1 we included a range of divergence times based on the LGM of the Wisconsin glacier, ~18,000 ybp. For Tdiv2 we considered divergence times incorporating the timeframe of urbanization in NYC, ~400 ybp. We allowed for migration between all populations, and tested occurrences of population bottlenecks when urban isolation was incorporated into the model. During likelihood calculation, a conditional maximization algorithm (ECM) is used to maximize the likelihood of each parameter while keeping the others stabilized. This ECM procedure runs through 40 cycles where each composite-likelihood was calculated using 100,000 coalescent simulations. While increasing the number of simulations can increase precision, accuracy does not significantly increase past 100,000 simulations [9]. Additionally, in order to avoid likelihood estimates that oversample parameter values at local maxima across the composite likelihood surface, we ran 50 replicates with each starting from different initial conditions. We chose the replicate with the highest estimated maximum likelihood score for each model. Using parametric bootstrapping, we generated confidence intervals for the most likely inferred demographic parameters generated. The SFS was simulated with the parameter values from the highest likelihood model and then new parameter values reestimated from the simulated SFS. We ran 100 parametric bootstraps. To find consistent signals of divergence that could be attributed to urbanization, we compared parameter values and overlapping confidence intervals between models. DEMOGRAPHIC INFERENCE Parameters were allowed to vary in demographic modeling using fastsimcoal2, but all six models converged on similar parameter values estimated from the observed MSFS. Parameter estimates with the highest likelihood generally fell within the upper and lower bounds generated from parametric bootstrapping (Fig. S2, Table 1). The first two-population model tested divergence time, effective population size, and migration rates between MM and LI populations (LI_MM, Fig. S3). The divergence time for the MM and LI split was inferred to be 13,599 ybp and the effective density of white-footed mice in MM (NE / size, 0.069 mice/ha) 23x larger than LI (NE / size, 0.003 mice/ha) (Table 1). Divergence times are based on a generation time of 0.5 years for Peromyscus leucopus. Migration was also inferred to be low (< 1 individual per generation) between MM and LI (Table 1). The inferred demography for the more complex three-population models generally supported results from the two-population model. The first two complex models both estimated the divergence between MM and LI, but one model tested for divergence of JB and LI after the MM and LI split (LI_JB_MM, Fig. S3) while the other model tested divergence between FT and LI after the MM and LI split (LI_FT_MM, Fig. S3). This model also tested the likelihood of a bottleneck event when FT and JB, both urban populations, diverged. We set up the other three complex models in an identical fashion, except we tested the urban populations of CP (LI_MM_CP, Fig. S3), VC (LI_MM_VC, Fig. S3), or IP (LI_MM_IP, Fig. S3) for divergence from MM after the MM and LI split. Point estimates for demographic parameters converged on similar values and generally fell within the 95% confidence limits from parametric bootstrapping (Fig. S2, Table 1). The average divergence time for MM and LI was 14,679 ybp, SD = 956.19. Similar to the two-population model, the MM NE was larger than the LI NE. While the effective population sizes for the urban parks were smaller than NE for MM or LI, the urban populations contained much higher effective population densities (CP: 28.7 mice / ha, IP: 79.21 mice / ha, JB: 23.8 mice / ha, FT: 24.3 mice / ha, VC: 0.36 mice / ha) than either MM (0.069) or LI (0.003). The individual urban populations all had NE values 10x smaller than MM, but often similar to LI. The divergence time for the five tested urban populations, even with variation in number of generations per year, was consistent with the timeframe of urbanization (mean divergence = 233 ybp; SD = 164.5). Our demographic models proved to be rather robust in returning reasonable parameter values with consistent convergence to similar values across replicates. Although wide confidence intervals on many parameters suggest low resolution in inferring parameter values given the model and data, they are likely a consequence of the complexity of the model given the number of parameters and wide parameter ranges. The narrow confidence intervals on other parameters suggest that these inferences reliably capture important aspects of the true demographic history of white-footed mice in NYC, especially given the often biologically unrealistic parameter search space (See est files). One limitation in using Radseq data for demographic analysis is the effect that allelic dropout has on genetic variation. Mutations can accumulate in RE cut sites causing the loss of loci with potentially higher mutation rates [10]. Thus, ddRADSeq may underestimate genetic diversity [11] and inflate sequence divergence [12,13]. We addressed this limitation by setting a minimum coverage limit and minimizing missing data that might be caused by allelic dropout [11]. We also used multiple other methods including discriminant analysis of principle components, sparse non-negative matrix factorization, classical population genetics statistics, and composite likelihood for population tree inference, to confirm our demographic analysis findings and inform our demographic modeling. REFERENCES 1. Harris, S. E., O’Neill, R. J. & Munshi-South, J. 2015 Transcriptome resources for the white-footed mouse (Peromyscus leucopus): new genomic tools for investigating ecologically divergent urban and rural populations. Mol. Ecol. Resour. 15, 382–394. (doi:10.1111/1755-0998.12301) 2. Munshi-South, J., Zolnik, C. P. & Harris, S. E. 2016 Population genomics of the Anthropocene: urbani zation is negatively associated with genome-wide variation in white -footed mouse populations. Evol. Appl. , doi:10.1111/eva.12357. (doi:10.1111/eva.12357) 3. Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S. & Hoekstra, H. E. 2012 Double Digest RADseq: An Inexpensive Method for De Novo SNP Discovery and Genotyping in Model and Non-Model Species. PLoS One 7, e37135. (doi:10.1371/journal.pone.0037135) 4. Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. 2013 Stacks: an analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140. (doi:10.1111/mec.12354) 5. Pickrell, J. & Pritchard, J. 2012 Inference of population splits and mixtures from genomewide allele frequency data. PLoS Genet. 8, e1002967. (doi:10.1371/journal.pgen.1002967) 6. Frichot, E., Mathieu, F., Trouillon, T., Bouchard, G. & François, O. 2014 Fast and Efficient Estimation of Individual Ancestry Coefficients. Genetics 4, 973–983. (doi:10.1534/genetics.113.160572) 7. Pritchard, J. K., Stephens, M. & Donnelly, P. 2000 Inference of population structure using multilocus genotype data. Genetics 155, 945–959. 8. Gutenkunst, R. N., Hernandez, R. D., Williamson, S. H. & Bustamante, C. D. 2009 Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 5, e1000695. (doi:10.1371/journal.pgen.1000695) 9. Excoffier, L., Dupanloup, I., Huerta-Sanchez, E., Sousa, V. C. & Foll, M. 2013 Robust Demographic Inference from Genomic and SNP Data. PLoS Genet. 9, e1003905. (doi:10.1371/journal.pgen.1003905) 10. Gautier, M., Gharbi, K., Cezard, T., Foucaud, J., Kerdelhué, C., Pudlo, P., Cornuet, J. M. & Estoup, A. 2013 The effect of RAD allele dropout on the estimation of genetic variation within and between populations. Mol. Ecol. 22, 3165–3178. (doi:10.1111/mec.12089) 11. Fraser, B. a., Kunstner, A., Reznick, D. N., Dreyer, C. & Weigel, D. 2015 Population genomics of natural and experimental populations of guppies (Poecilia reticulata). Mol. Ecol. 24, 389–408. (doi:10.1111/mec.13022) 12. Arnold, B., Corbett-Detig, R. B., Hartl, D. & Bomblies, K. 2013 RADseq underestimates diversity and introduces genealogical biases due to nonrandom haplotype sampling. Mol. Ecol. , n/a–n/a. (doi:10.1111/mec.12276) 13. Leach, A. D., Chavez, A. S., Jones, L. N., Grummer, J. a., Gottscho, A. D. & Linkem, C. W. 2015 Phylogenomics of phrynosomatid lizards: Conflicting signals from sequence capture versus restriction site associated DNA sequencing. Genome Biol. Evol. 7, 706– 719. (doi:10.1093/gbe/evv026) TPL file and EST file (input files for fastsimcoal2) showing setup for demographic modeling and parameter size ranges. #Figure S3 #SFS2LINLI.est // Priors and rules file // ********************* [PARAMETERS] //#isInt? #name #dist.#min #max //all N are in number of haploid individuals 1 ANCSIZE unif 100 100000 output 1 NPOP1 unif 100 100000 output 1 NPOP2 unif 100 100000 output 0 N1M1 logunif 1e-2 20 hide 0 N2M2 logunif 1e-2 20 hide 1 TDIV unif 100 40000 output [RULES] [COMPLEX PARAMETERS] 0 0 0 0 0 0 2NM1 = 2*N1M1 hide 2NM2 = 2*N2M2 hide MANGO = NPOP1+NPOP2 hide RESIZE = ANCSIZE/MANGO output MIG1 = 2NM1/NPOP1 output MIG2 = 2NM2/NPOP2 output #SFS2LINLI.tpl //Number of population samples (demes) 2 //Population effective sizes (number of genes) NPOP1 NPOP2 //Samples sizes and samples age 67 126 //Growth rates: negative growth implies population expansion 0 0 //Number of migration matrices : 0 implies no migration between demes 2 //Migration matrix 0 0 MIG1 MIG2 0 //Migration matrix 1 00 00 //historical event: time, source, sink, migrants, new deme size, growth rate, migr mat index 1 historical event 1 historical events TDIV 0 1 1 RESIZE 0 1 //Number of independent loci [chromosome] 10 //Per chromosome: Number of contiguous linkage Block: a block is a set of contiguous loci 1 //per Block:data type, number of loci, per gen recomb and mut rates FREQ 1 0 2.0e-8 #SFS3LIFTNLI.est // Priors and rules file // ********************* [PARAMETERS] //#isInt? #name #dist.#min #max //all N are in number of haploid individuals 1 ANCSIZE unif 100 100000 1 NPOP1 unif 20 100000 1 NPOP2 unif 20 100000 1 NPOP3 unif 20 100000 0 N1M1 logunif 0.01 20 hide 0 N2M2 logunif 0.01 20 hide 0 N3M3 logunif 0.01 20 hide 0 N4M4 logunif 0.01 20 hide 0 N5M5 logunif 0.01 20 hide 0 N6M6 logunif 0.01 20 hide 1 TISLAND1 unif 4 1000 output 1 TISLAND2 unif 4 1000 output 1 TDIV unif 4 30000 output bounded 0 RESIZE1 logunif 1e-10 1 output [RULES] TISLAND2 > TISLAND1 TISLAND2 < TDIV NPOP3 > NPOP2 NPOP3 > NPOP1 NPOP2 > NPOP1 output output output output [COMPLEX PARAMETERS] 0 0 0 0 0 0 0 0 0 0 0 0 0 2NM1 = 2*N1M1 hide 2NM2 = 2*N2M2 hide 2NM3 = 2*N3M3 hide 2NM4 = 2*N4M4 hide 2NM5 = 2*N5M5 hide 2NM6 = 2*N6M6 hide MIG1 = 2NM1/NPOP1 output MIG2 = 2NM2/NPOP1 output MIG3 = 2NM3/NPOP2 output MIG4 = 2NM4/NPOP2 output MIG5 = 2NM5/NPOP3 output MIG6 = 2NM6/NPOP3 output RESIZE2 = ANCSIZE/NPOP3 output #SFS3LIFTNLI.tpl //Number of population samples (demes) 3 //Population effective sizes (number of genes) NPOP1 NPOP2 NPOP3 //Samples sizes and samples age 4 62 125 //Growth rates: negative growth implies population expansion 0 0 0 //Number of migration matrices : 0 implies no migration between demes 3 //Migration matrix 0 0 MIG1 MIG2 MIG3 0 MIG4 MIG5 MIG6 0 //Migration matrix 1 000 0 0 MIG4 0 MIG6 0 //Migration matrix 2 000 000 000 //historical event: time, source, sink, migrants, new deme size, new growth rate, migration matrix index 3 historical events TISLAND1 0 0 0 RESIZE1 0 0 TISLAND2 0 1 1 1 0 1 TDIV 2 1 1 RESIZE2 0 2 //Number of independent loci [chromosome] 10 //Per chromosome: Number of contiguous linkage Block: a block is a set of contiguous loci 1 //per Block:data type, number of loci, per gen recomb and mut rates FREQ 1 0 2.0e-8 #SFS3LIJBNLI.est // Priors and rules file // ********************* [PARAMETERS] //#isInt? #name #dist.#min #max //all N are in number of haploid individuals 1 ANCSIZE unif 100 100000 1 NPOP1 unif 20 100000 1 NPOP2 unif 20 100000 1 NPOP3 unif 20 100000 0 N1M1 logunif 0.01 20 hide 0 N2M2 logunif 0.01 20 hide 0 N3M3 logunif 0.01 20 hide 0 N4M4 logunif 0.01 20 hide 0 N5M5 logunif 0.01 20 hide 0 N6M6 logunif 0.01 20 hide 1 TISLAND1 unif 4 1000 output 1 TISLAND2 unif 4 1000 output 1 TDIV unif 4 30000 output bounded 0 RESIZE1 logunif 1e-10 1 output [RULES] TISLAND2 > TISLAND1 TISLAND2 < TDIV NPOP3 > NPOP2 NPOP3 > NPOP1 NPOP2 > NPOP1 output output output output [COMPLEX PARAMETERS] 0 0 0 0 0 0 0 0 0 0 0 0 0 2NM1 = 2*N1M1 hide 2NM2 = 2*N2M2 hide 2NM3 = 2*N3M3 hide 2NM4 = 2*N4M4 hide 2NM5 = 2*N5M5 hide 2NM6 = 2*N6M6 hide MIG1 = 2NM1/NPOP1 output MIG2 = 2NM2/NPOP1 output MIG3 = 2NM3/NPOP2 output MIG4 = 2NM4/NPOP2 output MIG5 = 2NM5/NPOP3 output MIG6 = 2NM6/NPOP3 output RESIZE2 = ANCSIZE/NPOP3 output #SFS3LIJBNLI.tpl //Number of population samples (demes) 3 //Population effective sizes (number of genes) NPOP1 NPOP2 NPOP3 //Samples sizes and samples age 5 61 125 //Growth rates: negative growth implies population expansion 0 0 0 //Number of migration matrices : 0 implies no migration between demes 3 //Migration matrix 0 0 MIG1 MIG2 MIG3 0 MIG4 MIG5 MIG6 0 //Migration matrix 1 000 0 0 MIG4 0 MIG6 0 //Migration matrix 2 000 000 000 //historical event: time, source, sink, migrants, new deme size, new growth rate, migration matrix index 3 historical events TISLAND1 0 0 0 RESIZE1 0 0 TISLAND2 0 1 1 1 0 1 TDIV 2 1 1 RESIZE2 0 2 //Number of independent loci [chromosome] 10 //Per chromosome: Number of contiguous linkage Block: a block is a set of contiguous loci 1 //per Block:data type, number of loci, per gen recomb and mut rates FREQ 1 0 2.0e-8 #SFS3LINLICP.est // Priors and rules file // ********************* [PARAMETERS] //#isInt? #name #dist.#min #max //all N are in number of haploid individuals 1 ANCSIZE unif 100 100000 1 NPOP1 unif 20 100000 1 NPOP2 unif 20 100000 1 NPOP3 unif 20 100000 0 N1M1 logunif 0.01 20 hide 0 N2M2 logunif 0.01 20 hide 0 N3M3 logunif 0.01 20 hide 0 N4M4 logunif 0.01 20 hide 0 N5M5 logunif 0.01 20 hide 0 N6M6 logunif 0.01 20 hide 1 TISLAND1 unif 4 1000 output 1 TISLAND2 unif 4 1000 output 1 TDIV unif 4 30000 output bounded 0 RESIZE1 logunif 1e-10 1 output [RULES] TISLAND2 > TISLAND1 TISLAND2 < TDIV NPOP3 > NPOP2 NPOP3 > NPOP1 NPOP2 > NPOP1 output output output output [COMPLEX PARAMETERS] 0 0 0 0 0 0 0 0 0 0 0 0 0 2NM1 = 2*N1M1 hide 2NM2 = 2*N2M2 hide 2NM3 = 2*N3M3 hide 2NM4 = 2*N4M4 hide 2NM5 = 2*N5M5 hide 2NM6 = 2*N6M6 hide MIG1 = 2NM1/NPOP1 output MIG2 = 2NM2/NPOP1 output MIG3 = 2NM3/NPOP2 output MIG4 = 2NM4/NPOP2 output MIG5 = 2NM5/NPOP3 output MIG6 = 2NM6/NPOP3 output RESIZE2 = ANCSIZE/NPOP3 output #SFS3LINLICP.tpl //Number of population samples (demes) 3 //Population effective sizes (number of genes) NPOP1 NPOP2 NPOP3 //Samples sizes and samples age 9 66 116 //Growth rates: negative growth implies population expansion 0 0 0 //Number of migration matrices : 0 implies no migration between demes 3 //Migration matrix 0 0 MIG1 MIG2 MIG3 0 MIG4 MIG5 MIG6 0 //Migration matrix 1 000 0 0 MIG4 0 MIG6 0 //Migration matrix 2 000 000 000 //historical event: time, source, sink, migrants, new deme size, new growth rate, migration matrix index 3 historical events TISLAND1 0 0 0 RESIZE1 0 0 TISLAND2 0 2 1 1 0 1 TDIV 2 1 1 RESIZE2 0 2 //Number of independent loci [chromosome] 10 //Per chromosome: Number of contiguous linkage Block: a block is a set of contiguous loci 1 //per Block:data type, number of loci, per gen recomb and mut rates FREQ 1 0 2.0e-8 #SFS3LINLIIP.est // Priors and rules file // ********************* [PARAMETERS] //#isInt? #name #dist.#min #max //all N are in number of haploid individuals 1 ANCSIZE unif 100 100000 1 NPOP1 unif 20 100000 1 NPOP2 unif 20 100000 1 NPOP3 unif 20 100000 0 N1M1 logunif 0.01 20 hide 0 N2M2 logunif 0.01 20 hide 0 N3M3 logunif 0.01 20 hide 0 N4M4 logunif 0.01 20 hide 0 N5M5 logunif 0.01 20 hide 0 N6M6 logunif 0.01 20 hide 1 TISLAND1 unif 4 1000 output 1 TISLAND2 unif 4 1000 output 1 TDIV unif 4 30000 output bounded 0 RESIZE1 logunif 1e-10 1 output [RULES] TISLAND2 > TISLAND1 TISLAND2 < TDIV NPOP3 > NPOP2 NPOP3 > NPOP1 NPOP2 > NPOP1 output output output output [COMPLEX PARAMETERS] 0 0 0 0 0 0 0 0 0 0 0 0 0 2NM1 = 2*N1M1 hide 2NM2 = 2*N2M2 hide 2NM3 = 2*N3M3 hide 2NM4 = 2*N4M4 hide 2NM5 = 2*N5M5 hide 2NM6 = 2*N6M6 hide MIG1 = 2NM1/NPOP1 output MIG2 = 2NM2/NPOP1 output MIG3 = 2NM3/NPOP2 output MIG4 = 2NM4/NPOP2 output MIG5 = 2NM5/NPOP3 output MIG6 = 2NM6/NPOP3 output RESIZE2 = ANCSIZE/NPOP3 output #SFS3LINLIIP.tpl //Number of population samples (demes) 3 //Population effective sizes (number of genes) NPOP1 NPOP2 NPOP3 //Samples sizes and samples age 6 66 119 //Growth rates: negative growth implies population expansion 0 0 0 //Number of migration matrices : 0 implies no migration between demes 3 //Migration matrix 0 0 MIG1 MIG2 MIG3 0 MIG4 MIG5 MIG6 0 //Migration matrix 1 000 0 0 MIG4 0 MIG6 0 //Migration matrix 2 000 000 000 //historical event: time, source, sink, migrants, new deme size, new growth rate, migration matrix index 3 historical events TISLAND1 0 0 0 RESIZE1 0 0 TISLAND2 0 2 1 1 0 1 TDIV 2 1 1 RESIZE2 0 2 //Number of independent loci [chromosome] 10 //Per chromosome: Number of contiguous linkage Block: a block is a set of contiguous loci 1 //per Block:data type, number of loci, per gen recomb and mut rates FREQ 1 0 2.0e-8 #SFS3LINLIVC.est // Priors and rules file // ********************* [PARAMETERS] //#isInt? #name #dist.#min #max //all N are in number of haploid individuals 1 ANCSIZE unif 100 100000 1 NPOP1 unif 20 100000 1 NPOP2 unif 20 100000 1 NPOP3 unif 20 100000 0 N1M1 logunif 0.01 20 hide 0 N2M2 logunif 0.01 20 hide 0 N3M3 logunif 0.01 20 hide 0 N4M4 logunif 0.01 20 hide 0 N5M5 logunif 0.01 20 hide 0 N6M6 logunif 0.01 20 hide 1 TISLAND1 unif 4 1000 output 1 TISLAND2 unif 4 1000 output 1 TDIV unif 4 30000 output bounded 0 RESIZE1 logunif 1e-10 1 output [RULES] TISLAND2 > TISLAND1 TISLAND2 < TDIV NPOP3 > NPOP2 NPOP3 > NPOP1 NPOP2 > NPOP1 output output output output [COMPLEX PARAMETERS] 0 0 0 0 0 0 0 0 0 0 0 0 0 2NM1 = 2*N1M1 hide 2NM2 = 2*N2M2 hide 2NM3 = 2*N3M3 hide 2NM4 = 2*N4M4 hide 2NM5 = 2*N5M5 hide 2NM6 = 2*N6M6 hide MIG1 = 2NM1/NPOP1 output MIG2 = 2NM2/NPOP1 output MIG3 = 2NM3/NPOP2 output MIG4 = 2NM4/NPOP2 output MIG5 = 2NM5/NPOP3 output MIG6 = 2NM6/NPOP3 output RESIZE2 = ANCSIZE/NPOP3 output #SFS3LINLIVC.tpl //Number of population samples (demes) 3 //Population effective sizes (number of genes) NPOP1 NPOP2 NPOP3 //Samples sizes and samples age 6 66 119 //Growth rates: negative growth implies population expansion 0 0 0 //Number of migration matrices : 0 implies no migration between demes 3 //Migration matrix 0 0 MIG1 MIG2 MIG3 0 MIG4 MIG5 MIG6 0 //Migration matrix 1 000 0 0 MIG4 0 MIG6 0 //Migration matrix 2 000 000 000 //historical event: time, source, sink, migrants, new deme size, new growth rate, migration matrix index 3 historical events TISLAND1 0 0 0 RESIZE1 0 0 TISLAND2 0 2 1 1 0 1 TDIV 2 1 1 RESIZE2 0 2 //Number of independent loci [chromosome] 10 //Per chromosome: Number of contiguous linkage Block: a block is a set of contiguous loci 1 //per Block:data type, number of loci, per gen recomb and mut rates FREQ 1 0 2.0e-8 Table S1. Sampling localities and full name descriptions Sampling location code Full name AP Alley Pond & Cunningham WW Wildwood N. Shore Long Island & Brookhaven State Park FT Fort Tilden FM Flushing Meadows FP Forest Park KP Kissena Park RR Ridgewood Reservoir NYBG NY Botanical Garden VC Van Cortlandt Park IP Inwood Hill Park SW Saxon Woods LCC Louis Calder Center MRG Mianus River Gorge CFP Clarence Fahnestock Park CIE Cary Institute of Ecosystem Studies CPV Carter Road, Pleasant Valley, NY MH Cabin, Cornwall CT HIP High Point State Park HP Harriman State Park MR Minnewaska SP Reserve CP Central Park JB Jamaica Bay PB Pelham Bay Park Table S2. Inferred demographic parameters with 95% confidence values from parametric bootstrapping for remaining three fastsimcoal2 models not displayed in main manuscript. LI_MM_VC Parameters Ancestral Ne Site(s) (X) - Long island Ne (LI) Mainland & Manhattan Ne (MM) Local park Ne Van,Cortlandt Time of divergence (LI_MM) (LI_MM) Time of divergence (Urban_LI or MM) (VC_MM) Ancestral resize factor (LI_MM) Urban pop resize factor (VC) Time of urban pop resize (VC) Mig_(X)_to_MM (LI) Mig_(X)_to_(LI) (MM) LI_MM_IP (Point Estimate) (95 % CI) 90482 (71280,312469) 8991 (8180,74287) 16416 (13984,93388) 156 (27,36482) 29669 (17400,29663) 373 (459,8586) 5.5 (2.9,7.0) 0.6 ,9 (3.8x10 ,0.66) 182 (251,3071) 2.4x10,6 ,7 ,5 (7.0x10 ,1.2x10 ) Site(s) (X) (LI) (MM) Inwood Hill (LI_MM) (IP_MM) (Point Estimate) (95 % CI) 73627 (53734,283269) 8723 (6639,70959) 15138 (12371,80804) 6886 (32,41092) 28662 (23600,29647) 462 (545,14379) 4.8 (LI_MM) (IP) (IP) (LI) 1.4x10,6 (7.9x10,7,6.5x10,5) LI_JB_MM (2.5,7.2) 4.8x10 ,8 (LI) (MM) Jamaica Bay (LI_MM) (JB_LI) (85828,269490) 7354 (9849,74872) 17186 (18832,76281) 6279 (37,54167) 29666 (18312,29636) 327 (667,7749) 6.7 (LI_MM) ,1 455 (326,4058) 2.4x10,6 ,6 (6.3x10 ,6.5x10 ) (JB) (JB) (LI) 2.1x10,6 (MM) (Point Estimate) (95 % CI) 114700 (3.2,7.2) ,5 (2.2x10 ,7.8x10 ) ,7 Site(s) (X) (7.9x10,7,3.7x10,5) 1.86 ,8 (1.8x10 ,0.83) 55 (331,3148) 2.9x10,6 7 (7.6x10 ,1.4x10,5) 1.3x10,6 (MM) (7.8x10,7,2.9x10,4) Mig_(X)_to LI (VC) Mig_(X)_to_MM (VC) Mig_LI_to_(X) (VC) Mig_MM_to_(X) (VC) 0.16 (6.5x10,4,0.39) 0.07 (3.5x10,5,2.3x10,1) 1.4x10,3 (1.5x10,4,3.1x10,3) 5.1x10,3 (7.5x10,5,3.5x10,3) (IP) (IP) (IP) (IP) 5.1x10,3 (4.4x10,4,0.22) 1.4x10,5 (5.6x10,6,1.9x10,1) 5.4x10,4 (8.6x10,5,2.7x10,3) 5.9x10,3 (5.0x10,5,4.5x10,3) (JB) (JB) (JB) (JB) 3.8x10,3 (2.5x10,5,1.9x10,1) 1.2x10,3 (1.9x10,4,5.9x10,2) 5.3x10,3 (5.9x10,4,4.8x10,3) 4.4x10,3 (1.7x10,5,2.4x10,3) Ne = effective population size. Time of divergence is in generations. Migration is reported as the coalescent m, proportion of individuals that move from one population to another per generation. 0.6 0.4 0.0 0.2 Ancestry K=2 0.8 1.0 A AP CN FM FP FT JB LI 0.6 0.4 .2 Ancestry K=2 0.8 1.0 B KP RR WWP CFP CIE CP CPV HIP HP IP LCC MH MM MR MRG NYBG PB SW VC 0.0 AP CN FM FP FT JB KP RR WWP CFP CIE CP CPV HIP HP IP LI MH MR MRG NYBG PB MR PB SW VC MM 0.6 0.4 0.0 0.2 Ancestry K=2 0.8 1.0 B LCC AP CN FM FP FT JB KP RR WWP CFP CIE CP CPV HIP HP LI IP LCC MH MRG NYBG SW VC MM Figure S1 (A) Population structure analysis of 14,990 SNPs analyzed in sNMF. Vertical bars represent individuals and are assigned to populations by color. Results are shown for K=2 (highest support). Red = assigned to Long Island. Blue = assigned to Mainland & Manhattan. Mixed colors represent admixture. (B) sNMF results for K=2 (highest support) of ≤ 15% missing SNP dataset (3,664 SNPs). Red = assigned to Long Island. Blue = assigned to Mainland & Manhattan. JB 0 1000 40000 FT E VC 6000 D IP CP 7000 LI_CP 5000 LI_IP 4000 LI_VC 3000 50000 0 2e+04 40000 6e+04 MM NE 4e+04 20000 LI NE 8e+04 60000 1e+05 B 2000 Urban divergence 30000 LI_FT 40000 20000 LI_JB 30000 10000 C 0 Urban pop NE A LI MM_JB TDIV2_JB MM_FT MM_VC TDIV2_FT MM_IP TDIV2_VC MM_CP TDIV2_IP MM TDIV2_CP 2000 200 1000 10000 0 0 TDIV2_FT TDIV2_VC TDIV2_IP TDIV2_CP 40000 TDIV2_JB 30000 MM & LI divergence E CP IP VC 20000 FT 10000 JB F TDIV1_FT TDIV1_VC TDIV1_IP TDIV1_CP TDIV1_LI_MM Migration (m) 0 20 40 60 80 100 TDIV1_JB JB_into_MM Figure S2 MM_into_JB FT_into_MM MM_into_FT VC_into_MM MM_into_VC IP_into_MM MM_into_IP CP_into_MM MM_into_CP Confidence Intervals from 100 parametric bootstraps for inferred demographic parameters. Red dots represent point estimates from replicates with the highest likelihood. Results are from all six tested models. (A) Ne of LI inferred from each model. X-axis labeled with the urban population being tested for divergence. (B) Ne of MM inferred from each model. X-axis labeled with the urban population being tested for divergence. (C) Ne for each diverged urban population. (D) Time of divergence for urban populations from LI or MM. (E) Time of divergence for LI and MM inferred from each model. (F) # of migrants per generation from urban populations into MM and MM into urban populations. 1 A B Na C Na TDIV1 TDIV1 M23 N1 N2 M12 M12 M21 MM N2 M12 TDIV2 N3 M13 N1 M21 M21 M32 TDIV2 2 Na TDIV1 N1 M31 LI CP, VC, IP MM LI MM M13 M13 N2 M23 M32 N3 LI JB, FT 3 4 5 Figure S3 6 fastsimcoal2 models. MM = Mainland & Manhattan. LI = Long Island. CP = Central Park. VC = Van Cortlandt Park. IP = Inwood 7 Hill Park. JB = Jamaica Bay. FT = Fort Tilden. TDIV = Time of Divergence. N = Effective Population Size. M = migration rate. (A) 8 Model 1 tests divergence between MM and LI. (B) Models 2, 3, and 4 test divergence of CP, VC, and IP from MM after the split. (C) 9 Models 5 and 6 test for divergence of JB and FT from LI after split.