R ES E A RC H R E PO R TS in mortality explained by time exposed on the ground was 22.5%, whereas body size [snoutvent length (SVL)] accounted for 9.8%. These findings suggest that although both behavior and morphology can simultaneously contribute to survival, their importance is context-dependent and varies under different selective regimes. Although behavior largely defines how animals interact with the environment, the evolutionary consequences of interindividual variation in behavior remain largely unknown (7, 8). Our replicated field study provides evidence that natural selection operates differently on interindividual variation in behavior under different, experimentally manipulated selective pressures. Moreover, our results indicate that differences in habitat use between sexes likely influence the strength of natural selection on behavioral traits. By showing that selection can simultaneously and independently operate on behavior and morphology, we demonstrate that rapid environmental changes can shape different phenotypic dimensions at the same time; the evolutionary outcome of such selection will depend on the genetic basis of these traits and the extent to which they are correlated. Our results thus underscore the need to explicitly integrate interindividual variation in behavior as a relevant phenotypic dimension in studies of adaptation (7, 8, 35). Moreover, we found that under increased predation pressure, behavior is a more important factor explaining survival than the morphological traits that have been the subject of previous investigation (22); the extent to which these results can be generalized across species remains to be determined. Our results demonstrate that consistent behavioral variation among individuals can be an important focus of selection when populations experience novel environmental conditions—an increasingly common situation in the current context of global change. 17. M. Wolf, G. S. van Doorn, O. Leimar, F. J. Weissing, Nature 447, 581–584 (2007). 18. N. J. Dingemanse, M. Wolf, Philos. Trans. R. Soc. B 365, 3947–3958 (2010). 19. D. Réale, N. J. Dingemanse, A. J. N. Kazem, J. Wright, Philos. Trans. R. Soc. B 365, 3937–3946 (2010). 20. S. R. X. Dall, A. M. Bell, D. I. Bolnick, F. L. W. Ratnieks, Ecol. Lett. 15, 1189–1198 (2012). 21. J. A. Endler, Natural Selection in the Wild (Princeton Univ. Press, 1986). 22. J. B. Losos, T. W. Schoener, D. A. Spiller, Nature 432, 505–508 (2004). 23. J. B. Losos, Lizards in an Evolutionary Tree: Ecology and Adaptive Radiation of Anoles (Univ. of California Press, 2009). 24. T. W. Schoener, D. A. Spiller, J. B. Losos, Nature 412, 183–186 (2001). 25. O. Lapiedra, Z. Chejanovski, J. J. Kolbe, Glob. Change Biol. 23, 592–603 (2016). 26. See supplementary materials. 27. P. A. Bednekoff, S. L. Lima, Proc. R. Soc. B 271, 1491–1496 (2004). 28. D. S. Wilson, A. B. Clark, K. Coleman, T. Dearstyne, Trends Ecol. Evol. 9, 442–446 (1994). 29. M. Drakeley, O. Lapiedra, J. J. Kolbe, PLOS ONE 10, e0138016 (2015). 30. M. López-Darias, T. W. Schoener, D. A. Spiller, J. B. Losos, Ecology 93, 2512–2518 (2012). 31. D. S. Steinberg et al., Proc. Natl. Acad. Sci. U.S.A. 111, 9187–9192 (2014). 32. J. J. Kolbe, M. Leal, T. W. Schoener, D. A. Spiller, J. B. Losos, Science 335, 1086–1089 (2012). 33. T. W. Schoener, Ecology 49, 704–726 (1968). 34. J. S. Wyles, J. G. Kunkel, A. C. Wilson, Proc. Natl. Acad. Sci. U.S.A. 80, 4394–4397 (1983). 35. A. Sih, M. C. O. Ferrari, D. J. Harris, Evol. Appl. 4, 367–387 (2011). AC KNOWLED GME NTS We thank D. Fernández-Bellon and Q. Quach for field assistance; personnel from Friends of the Environment at Marsh Harbour; M. Melé, D. Spiller, and members of the Losos lab at Harvard University who provided valuable comments to improve the manuscript; personnel from the Museum of Comparative Zoology who helped to accession specimens; and the Bahamas Ministry of Agriculture and the Bahamas Environment, Science and Technology (BEST) Commission of the Ministry of the Environment for permission to conduct this research. Funding: Supported by the AGAUR in the form of Beatriu de Pinós postdoctoral fellowship 2014 BP-A 00116 (O.L.). Fieldwork was also funded with a Putnam Expedition Grant from the Museum of Comparative Zoology and a National Geographic Explorer Grant (O.L.) and funds from the University of Rhode Island. Author contributions: O.L. conceived the study; O.L., J.J.K., J.B.L., M.L., and T.W.S. designed the study; O.L. and J.J.K. collected the data; O.L. analyzed the data; and all authors extensively discussed results and contributed to manuscript preparation. Competing interests: The authors declare no competing interests. Data and materials availability: Data are available from the Dryad Digital Repository (doi:10.5061/dryad.9hn3dg7). SUPPLEMENTARY MATERIALS www.sciencemag.org/content/360/6392/1017/suppl/DC1 Materials and Methods Figs. S1 to S10 Tables S1 to S5 References (36–42) 12 September 2017; accepted 23 April 2018 10.1126/science.aap9289 POLITICAL SCIENCE The effect of partisanship and political advertising on close family ties M. Keith Chen1*† and Ryne Rohla2* RE FE RENCES AND N OT ES 1. E. Mayr, Animal Species and Evolution (Harvard Univ. Press, 1963). 2. C. M. Bogert, Evolution 3, 195–211 (1949). 3. R. B. Huey, P. E. Hertz, B. Sinervo, Am. Nat. 161, 357–366 (2003). 4. D. Sol, D. G. Stirling, L. Lefebvre, Evolution 59, 2669–2677 (2005). 5. O. Lapiedra, D. Sol, S. Carranza, J. M. Beaulieu, Proc. R. Soc. B 280, 20122893 (2013). 6. M. M. Muñoz, J. B. Losos, Am. Nat. 191, E15–E26 (2018). 7. S. R. X. Dall, S. C. Griffith, Front. Ecol. Evol. 2, 1–7 (2014). 8. M. Wolf, F. J. Weissing, Trends Ecol. Evol. 27, 452–461 (2012). 9. S. R. X. Dall, A. I. Houston, J. M. McNamara, Ecol. Lett. 7, 734–739 (2004). 10. A. Sih, A. Bell, J. C. Johnson, Trends Ecol. Evol. 19, 372–378 (2004). 11. D. Réale, S. M. Reader, D. Sol, P. T. McDougall, N. J. Dingemanse, Biol. Rev. Camb. Philos. Soc. 82, 291–318 (2007). 12. A. M. Bell, S. J. Hankison, K. L. Laskowski, Anim. Behav. 77, 771–783 (2009). 13. N. J. Dingemanse, C. Both, P. J. Drent, J. M. Tinbergen, Proc. R. Soc. B 271, 847–852 (2004). 14. J. N. Pruitt, J. J. Stachowicz, A. Sih, Am. Nat. 179, 217–227 (2012). 15. C. D. Santos et al., Sci. Rep. 5, 15490 (2015). 16. N. G. Ballew, G. G. Mittelbach, K. T. Scribner, Am. Nat. 189, 396–406 (2017). 1020 Research on growing American political polarization and antipathy primarily studies public institutions and political processes, ignoring private effects, including strained family ties. Using anonymized smartphone-location data and precinct-level voting, we show that Thanksgiving dinners attended by residents from opposing-party precincts were 30 to 50 minutes shorter than same-party dinners. This decline from a mean of 257 minutes survives extensive spatial and demographic controls. Reductions in the duration of Thanksgiving dinner in 2016 tripled for travelers from media markets with heavy political advertising—an effect not observed in 2015—implying a relationship to election-related behavior. Effects appear asymmetric: Although fewer Democratic-precinct residents traveled in 2016 than in 2015, Republican-precinct residents shortened their Thanksgiving dinners by more minutes in response to political differences. Nationwide, 34 million hours of cross-partisan Thanksgiving dinner discourse were lost in 2016 owing to partisan effects. A merican political partisanship has risen sharply over the past 25 years. More than 55% of Democrats and Republicans described “very unfavorable” feelings toward the opposing party in 2016, up from 17 to 21% in the mid-1990s; growing numbers of Independents express disfavor with both parties, and rising party defections increase polarization (1). Spatial partisan sorting produces increasingly homogeneous electoral “bubbles” at both state and local levels (2), and political minorities within these bubbles show reticence to participate in or reveal their party affiliation (3). 1 Anderson School of Management, University of California, Los Angeles, Los Angeles, CA 90095, USA. 2School of Economic Sciences, Washington State University, Pullman, WA 99164, USA. *These authors contributed equally to this work. †Corresponding author. Email: keith.chen@anderson.ucla.edu EMBARGOED UNTIL 2PM U.S. EASTERN TIME ON THE THURSDAY BEFORE THIS DATE: 1 JUNE 2018 • VOL 360 ISSUE 6392 sciencemag.org SCIENCE RE S EAR CH R E P O R T S Fig. 1. Sampling and imputation validation. (A) Results of the 2016 U.S. presidential election by precinct (excludes unpopulated census blocks). (B) Home locations of smartphone users in the 2016 sample. (C) Correlation between actual two-party vote share by state and the District of Columbia (DC) (x axis) and predicted vote share (y axis) using each smartphone user’s home precinct. Nationally, this predicts a 0.516 Clinton vote share, compared to an actual vote share of 0.511. Highlighted are the two most Democratic-leaning states (California and Massachusetts) and two most Republican-leaning states (Wyoming and West Virginia), as well as the states with the largest prediction error. Animosity toward political rivals is not limited to the ballot box; implicit partisan biases manifest in discriminatory decisions even more frequently than racial or gender biases (4). Parents express intolerance of their children dating and marrying across partisan lines (5), and observed dating and marital choices segregate more strongly on politics than on physical attributes or personality characteristics (6). Political polarization affects decisions, such as where to work and shop, at higher rates than race, ethnicity, or religion (7). We study whether politics strain close family ties by measuring family-gathering durations. After the historically divisive 2016 presidential election, 39% of American families avoided political conversations during the holidays, an aversion that spanned both party and socioeconomic lines (8). We examine Thanksgiving, which, in U.S. election years, may bring together family members with differing political views just weeks after votes are cast. Anecdotal evidence suggests that, in the wake of the 2016 election, many families canceled or otherwise cut short Thanksgiving plans with their most politically problematic relatives (9). Several cognitive biases in social and political psychology explain why individuals might limit such interactions. A “partisan selective exposure” motivation occurs when individuals avoid counterattitudinal political information that might engender cognitive dissonance or harm relationships (10). Numerous studies find “belief polarization,” whereby individuals gravitate toward more extreme versions of their own initial positions during discussion of political issues (11). Exacerbating this effect, individuals also incorrectly expect others to respond to discussion and debate in the same direction as their own response, anticipating belief convergence rather than polarization (12), and attribute a lack of convergence to the bias and irrationality of others, while viewing themselves and copartisans as less ideological than cross-partisans (13). Our study examines whether these effects, which are well-studied in experimental settings among strangers, extend to close family gatherings. We analyze how political differences affect the duration of Thanksgiving dinner by merging two datasets. Anonymized smartphone-location data from more than 10 million Americans allow observation of actual travel at extremely precise spatial and temporal levels. We combine this with a precinct-level database for the 2016 election to impute presidential voting at the finest spatial resolution possible. By comparing vote shares in an individual’s home and Thanksgiving destination precincts, we test the relationship between political disagreement and time expenditure. To isolate the particular effect of election-year political partisanship from a multitude of demographic and spatial confounds, we construct comparison sets of smartphone users that share the same home-destination pairs. Our measured effects are neither eliminated nor attenuated by comparing only matched users, suggesting that the measured time loss is not an artifact of politically correlated demographics or spatial sorting. Furthermore, because political advertising polarizes opinions (14) and heightens dislike for opposing parties (15), we compare partisan rifts between comparable users who fall just on opposite sides of media-market boundaries. Accounting for political advertising more than tripled our measured “Thanksgiving effect” in 2016, but not in 2015, before ads were run. This noneffect of yet-to-be-run ads acts as a political placebo EMBARGOED UNTIL 2PM U.S. EASTERN TIME ON THE THURSDAY BEFORE THIS DATE: SCIENCE sciencemag.org 1 JUNE 2018 • VOL 360 ISSUE 6392 1021 R ES E A RC H R E PO R TS Table 1. Effect of political mismatch on Thanksgiving dinner duration. Each regression (column) estimates the effect of voting disagreement on 2016 Thanksgiving dinner duration. All results use linear regressions with fixed effects controlling for an individual’s home location–cross–Thanksgiving destination. Stepwise regressions control for progressively finer pairs, culminating in a five-digit geohash, a square grid about 3 miles by 3 miles in size. The mean duration of Thanksgiving dinner was 257 min (SD = 162 min). The average probability of voting mismatch was 0.44 (SD = 0.10). Standard errors are reported in parentheses and clustered at the precinct-cross-precinct level. R2, coefficient of determination. ***P < 0.001; **P < 0.01. Independent variable 1 2 3 Dependent variable: Duration of Thanksgiving dinner (min) –38.04*** –45.23** 4 ............................................................................................................................................................................................................................................................................................................................................ Probability of political mismatch –21.58*** –56.26** ................................................................................................................................................................................................................................................................... (2.226) (2.952) (8.696) (14.55) ............................................................................................................................................................................................................................................................................................................................................ Observations 642,962 642,962 642,962 642,962 ............................................................................................................................................................................................................................................................................................................................................ 2 R 0.0003 0.0660 0.458 0.661 ............................................................................................................................................................................................................................................................................................................................................ Fixed effects None County pairs ZIP code pairs Geohash-5 pairs Number of fixed-effects groups 0 35,507 302,716 414,950 ............................................................................................................................................................................................................................................................................................................................................ ............................................................................................................................................................................................................................................................................................................................................ test, further bolstering the argument that our measured Thanksgiving losses stem from political partisanship rather than from preexisting demographic or spatial confounds. We collect precinct-level results for the 2016 presidential election through internet scraping and by contacting secretaries of state, boards of election, and individual county clerks via email, phone, or fax or in person. Finally, we match vote totals to precinct polygonal shapefiles using Geographic Information Systems (GIS) software. The resulting dataset covers 172,098 precincts across 99.9% of counties nationally (Fig. 1A). Political advertising data are from Kantar Media’s Campaign Media Analysis Group (16) and count every U.S. presidential television ad aired in all 210 Nielsen Designated Market Areas after 12 June 2016, including ads purchased directly by campaigns or outside groups such as political action committees. Data from the 2010 Decennial Census and the Census Bureau’s 2012– 2015 American Community Survey form the basis of demographic controls. Location data rely on numerous smartphone apps and were aggregated by SafeGraph, a company that builds and maintains anonymized geospatial datasets for more than 10 million U.S. smartphones. These data consist of “pings,” each identifying the coordinates of a particular smartphone at a moment in time. Our primary analysis includes 21 billion pings from November 2016 and 4.5 billion from November 2015. To merge datasets, we infer the precinct and census block of each smartphone user’s “home” on the basis of that user’s pings between 1:00 a.m. and 4:00 a.m. over the 3 weeks before Thanksgiving. This procedure identifies more than 6 million approximate home locations in November 2016 (Fig. 1B), which we then link with precinctlevel two-party vote shares and census demographics. Similarly, a user’s Thanksgiving location is based on their modal location between 1:00 p.m. and 5:00 p.m. (24 November 2016 and 26 November 2015). By construction, this sample is representative of the 77% of Americans who own smartphones, 1022 raising the question of whether our sample is politically representative of the American electorate as a whole. We test this by assigning to each resident a vote ratio proportional to the 2016 two-party vote share of their home precinct. A resident of a precinct that recorded 150 Clinton and 50 Trump votes, for example, would be assigned 0.75 Clinton and 0.25 Trump votes. Figure 1C compares these votes against actual 2016 two-party vote shares for each state and Washington, D.C. The 45° line represents where states would lie if the SafeGraph sample politically matched the distribution of American voters. Our imputed votes are accurate to within 1 percentage point in 33 states and within 5 percentage points in all states. Nationally, the data suggest a two-party Democratic vote share of 0.516, compared to the actual share of 0.511. We first examine whether, conditional on traveling for Thanksgiving dinner, the partisan distance between a home and destination affects that dinner’s duration. We restrict our sample to residents who were home both in the morning and during the night of Thanksgiving, but who traveled for Thanksgiving dinner, to focus our analysis on travelers who could control the duration of their visits. In Table 1, we estimate the following equation: durationij = a + b mismatchij + gFij + eij where mismatchij = Pi(1 – Pj) + (1 – Pi)Pj In this specification, durationij is the number of minutes traveler i spent with host j on Thanksgiving, Fij is a set of fixed effects that form groups of people defined by pairs of home (i) and destination (j) locations, and b is the coefficient of interest. Pi and Pj are the two-party vote shares associated with home precincts for i and j, where Pi = democratici /(democratici + republicani). By using Pi and Pj , mismatchij is the imputed probability that persons i and j voted for different candidates in 2016. In all tables, regressions control for progressively finer (i, j) location pairs, culminating in five-digit geohash (geohash-5) boxes, a global grid of rectangular areas, each about 3 miles by 3 miles in size. To control for confounds including demographics, distance, and travel time, our regressions compare Thanksgiving dinner durations between travelers with the same home and destination areas. For example, regression 3 compares two travelers if and only if they both live in ZIP code X and visit ZIP code Y. The coefficient of interest b measures the reduction in Thanksgiving dinner duration between travelers within the same Fij comparison groups but who likely voted differently than their Thanksgiving hosts. Standard errors are clustered at the home precinct–cross–destination precinct level. We use progressively tighter spatial controls to control for both demographics and travel distance simultaneously. The results in Table 1 indicate that families that were likely to have voted for different presidential candidates spent about 30 to 50 fewer minutes together—subtracted from an average Thanksgiving dinner time of 4.2 hours—after controlling for both travel distance and locationcorrelated demographics. As we add finer spatial controls, our estimate of b remains fairly stable, with a point estimate of 56.3 ± 14.6 min under our tightest geohash-5 controls. In table S4, we report qualitatively identical results when demographics such as race, age, education, income, and employment are controlled for separately. We examine the two components of mismatchij, Pi (1 – Pj) and (1 – Pi)Pj, to separately measure the effect of voting disagreement among Democraticprecinct residents (DPRs) visiting Republicanprecinct residents (RPRs) and vice versa. Table 2 demonstrates that, conditional on traveling, DPRs shortened their visits to RPR hosts by about 20 to 40 min, whereas RPRs shortened their visits to DPRs by about 50 to 70 min. F-test results indicate that these estimates are statistically different (P < 0.0001 in four of five specifications), with RPRs shortening their cross-party stays by more minutes than DPRs. When investigating whether these effects interact with local political advertising, we find EMBARGOED UNTIL 2PM U.S. EASTERN TIME ON THE THURSDAY BEFORE THIS DATE: 1 JUNE 2018 • VOL 360 ISSUE 6392 sciencemag.org SCIENCE RE S EAR CH R E P O R T S Table 2. Asymmetric effects of political mismatch. Each regression (column) estimates the effect of voting disagreement between travelers and hosts [DPR traveler to RPR host (DPR→RPR) and RPR traveler to DPR host (RPR→DPR)] on 2016 Thanksgiving dinner duration. The F-test P value tests for equality between coefficients, to test for an asymmetric mismatch effect. The average probability of DPRs attending a RPR-hosted dinner and vice versa was 0.221 and 0.215, respectively (for both, SD = 0.10). Standard errors are reported in parentheses and clustered at the precinctcross-precinct level. ***P < 0.001; **P < 0.01; *P < 0.05. Independent variable 1 2 3 4 Dependent variable: Duration of Thanksgiving dinner (min) ..................................................................................................................................................................................................................... –5.60* –23.44*** –30.16** –44.53** (2.454) (3.207) (9.358) (15.73) ..................................................................................................................................................................................................................... Probability DPR→RPR .............................................................................................................................................. –38.74*** –53.47*** –60.23*** –69.20*** (2.555) (3.314) (9.455) (16.14) ..................................................................................................................................................................................................................... Probability RPR→DPR F-test (RPR→DPR ≠ DPR→RPR) .............................................................................................................................................. 0.0001 0.0001 0.0001 0.0572 ..................................................................................................................................................................................................................... Observations 642,962 642,962 642,962 642,962 ..................................................................................................................................................................................................................... 2 R 0.0003 0.0662 0.458 0.661 ..................................................................................................................................................................................................................... Fixed effects None County pairs ZIP code pairs Geohash-5 pairs ..................................................................................................................................................................................................................... Number of fixed-effects groups 0 35,507 302,716 414,950 ..................................................................................................................................................................................................................... Table 3. Political advertising heightens partisan effects. Each regression (column) estimates the effect of voting disagreement between travelers and hosts on 2016 Thanksgiving dinner duration. The second and fourth regressions explore whether political advertising heightens these effects. Media markets in swing states like Florida saw more than 26,000 ads in 2016. Standard errors are reported in parentheses and clustered at the precinct-cross-precinct level. A blank cell indicates that the variable was not included in this regression. ***P < 0.001; *P < 0.05. Independent variable 1 2 3 4 Dependent variable: Duration of Thanksgiving dinner (min) ..................................................................................................................................................................................................................... –21.58*** –14.40*** .......................................................... Probability of political mismatch (2.226) (2.588) ..................................................................................................................................................................................................................... –5.604* 4.117 .......................................................... Probability DPR→RPR (2.454) (2.879) ..................................................................................................................................................................................................................... –38.74*** –33.68*** .......................................................... Probability RPR→DPR (2.555) (2.978) ..................................................................................................................................................................................................................... Number of political ads 1.334*** 1.349*** ...................... (1000 ads per market) Probability of political mismatch (0.185) –2.645*** ...................... (0.185) ..................................................................................................................................................................................................................... times number of political ads Probability DPR→RPR times number (0.393) ..................................................................................................................................................................................................................... of political ads Probability RPR→DPR times number –3.237*** ...................... (0.417) –2.122*** ..................................................................................................................................................................................................................... of political ads ...................... (0.439) ..................................................................................................................................................................................................................... Observations 642,962 642,962 642,962 642,962 2 R 0.0003 0.0004 0.0003 0.0004 ..................................................................................................................................................................................................................... ..................................................................................................................................................................................................................... that cross-partisan Thanksgiving dinners are further shortened by around 2.6 min on average for every 1000 political advertisements aired in the traveler’s home media market (Table 3). Some media markets in swing states saw more than 26,000 ads over the course of the campaign, implying a 69-min-shorter Thanksgiving dinner for vote-mismatched families in Orlando, for example, compared to those in markets without advertising. Although this effect may not be solely due to advertising, which may be correlated with other campaign activities such as rallies, cam- paign visits, and fundraising efforts, these results bolster the conclusion that measured effects on Thanksgiving dinner duration likely stem from an increased intensity and salience of partisan differences. The results in table S1 support this finding and report the results of a placebo test concerning whether advertisements in 2016 affected Thanksgiving dinner behavior the year before airing. Regardless of whether we pool smartphone users or split the sample into DPRs and RPRs, we find no evidence of preexisting partisan effects in regions that witnessed high advertising levels. Although our empirical results estimate briefer Thanksgiving dinners among cross-partisan gatherings in both years, the ad-related amplification of this effect is present only in 2016, both in statistical significance and magnitude, supporting our conjecture that the main effect is most likely political in nature. An examination of destination choices suggests that travelers did not change plans to reduce political divisions from 2015 to 2016. Among travelers who traveled in both years— the strongest possible control for demographic and spatial confounds—we observe no appreciable difference in the distribution of likely political mismatch (fig. S1). This finding suggests that travelers were more likely to change the duration of Thanksgiving gatherings than to change the destination. Finally, tables S2 and S3 estimate linear probability models for the choice of whether to travel for Thanksgiving, in both 2015 and 2016. When matched residents living within 1.5 miles of each other are compared, DPRs reduced their likelihood of travel between 2015 and 2016 by 2 percentage points more than comparable RPRs, an effect that increases substantially in areas with heavy political advertising. Examining only those residents included in both the 2015 and 2016 data yields qualitatively similar results. Among residents at home on Thanksgiving morning in both years, 56.4% traveled for Thanksgiving in 2015, whereas 51.9% traveled in 2016 (n = 28,890; Fisher’s exact text, P < 0.0005). Accompanying this difference is a reduction in Thanksgiving dinner duration for those cross-partisan dinners that still occurred. By comparing travelers who went to the same location both years (n = 1271), we estimate that politically mismatched gatherings declined by 42.1 ± 41.4 min. Although this small sample size precludes statistical significance, this estimate is very close to our findings shown in Table 1. By aggregating across the 77% of American adults who own smartphones (17), our results suggest that partisan differences cost Americans 73.6 million hours of Thanksgiving time with others in 2016, 47.8% from DPRs and 52.2% from RPRs. Political advertising–related partisanship comprised 15.9 million of lost personhours, 46.3% from DPRs and 53.7% from RPRs. Altogether, an estimated 33.9 million personhours of cross-partisan discourse were eliminated, perhaps creating a feedback mechanism by which partisan segregation reduces opportunities for close cross-party conversations. Our findings have several implications, both for the literature and for campaign policy. After the 2016 election, anecdotal media reports and online social-media behavior (18) demonstrated an avoidance of personal confrontations over political issues among Democratic voters, findings our study corroborates. RPRs, however, were more sensitive to partisan differences at Thanksgiving dinners, an effect that supports findings of greater partisan-selective exposure among Republicans in news-media consumption EMBARGOED UNTIL 2PM U.S. EASTERN TIME ON THE THURSDAY BEFORE THIS DATE: SCIENCE sciencemag.org 1 JUNE 2018 • VOL 360 ISSUE 6392 1023 R ES E A RC H R E PO R TS (19). Our results suggest that partisan polarization extends in quantitatively meaningful ways to close family settings and that political advertising and related campaign efforts can exacerbate these fissures. As abbreviated Thanksgiving gatherings tend to accumulate in regions with greater campaign activity, policies designed to shorten campaigns may reduce the private costs of political polarization. RE FE RENCES AND N OT ES 1. Pew Research Center, “Partisanship and political animosity in 2016,” 22 June 2016; http://people-press.org/2016/06/22/ partisanship-and-political-animosity-in-2016. 2. M. Motyl, R. Iyer, S. Oishi, S. Trawalter, B. Nosek, J. Exp. Soc. Psychol. 51, 1–14 (2014). 3. R. Perez-Truglia, G. Cruces, J. Polit. Econ. 125, 1208–1243 (2017). 4. S. Iyengar, S. Westwood, Am. J. Pol. Sci. 59, 690–707 (2015). 5. S. Iyengar, G. Sood, Y. Lelkes, Public Opin. Q. 76, 405–431 (2012). 6. J. Alford, P. Hatemi, J. Hibbing, N. Martin, L. Eaves, J. Polit. 73, 362–379 (2011). 7. C. McConnell, Y. Margalit, N. Malhotra, M. Levendusky, Am. J. Pol. Sci. 61, 5–18 (2017). 8. B. Oliphant, S. Smith, “How Americans are talking about Trump’s election in 6 charts,” Pew Research Center, 22 December 2016; www.pewresearch.org/fact-tank/2016/ 12/22/how-americans-are-talking-about-trumps-election-in6-charts/. 9. S. Tavernise, K. Seelye, “Political divide splits relationships—and Thanksgiving, too,” The New York Times, 15 November 2016; www.nytimes.com/2016/11/16/us/political-divide-splitsrelationships-and-thanksgiving-too.html. 10. J. Frimer, L. Skitka, M. Motyl, J. Exp. Soc. Psychol. 72, 1–12 (2017). 11. C. Sunstein, J. Polit. Philos. 10, 175–195 (2002). 12. L. Ross, D. Greene, P. House, J. Exp. Soc. Psychol. 13, 279–301 (1977). 13. R. Robinson, D. Keltner, A. Ward, L. Ross, J. Pers. Soc. Psychol. 68, 404–417 (1995). 14. S. Ansolabenhere, S. Iyengar, Going Negative: How Political Advertisements Shrink and Polarize the Electorate (Free Press, 1996). 15. M. Levendusky, N. Malhotra, Polit. Commun. 33, 283–301 (2016). 16. C. Zubak-Skees, “Tracking TV ads in the 2016 presidential race,” The Center for Public Integrity, 25 October 2016; http://publicintegrity.org/2016/01/21/19164/tracking-tv-ads2016-presidential-race. 17. L. Rainie, A. Perrin, “10 facts about smartphones as the iPhone turns 10,” Pew Research Center, 28 June 2017; www.pewresearch.org/fact-tank/2017/06/28/10-factsabout-smartphones/. 18. E. Bakshy, S. Messing, L. A. Adamic, Science 348, 1130–1132 (2015). 19. C. Rodriguez, J. Moskowitz, R. Salem, P. Ditto, Transl. Issues Psychol. Sci. 3, 254–271 (2017). HUMAN EVOLUTION Ancient human parallel lineages within North America contributed to a coastal expansion C. L. Scheib,1,2* Hongjie Li,3 Tariq Desai,4 Vivian Link,5 Christopher Kendall,6 Genevieve Dewar,6 Peter William Griffith,1 Alexander Mörseburg,1 John R. Johnson,7 Amiee Potter,8,9 Susan L. Kerr,10 Phillip Endicott,11 John Lindo,12 Marc Haber,13 Yali Xue,13 Chris Tyler-Smith,13 Manjinder S. Sandhu,13 Joseph G. Lorenz,14 Tori D. Randall,15 Zuzana Faltyskova,1 Luca Pagani,2,16 Petr Danecek,13 Tamsin C. O’Connell,1 Patricia Martz,17 Alan S. Boraas,18 Brian F. Byrd,19 Alan Leventhal,20,21 Rosemary Cambra,20 Ronald Williamson,22 Louis Lesage,23 Brian Holguin,24 Ernestine Ygnacio-De Soto,25 JohnTommy Rosas,26 Metspalu Mait,2 Jay T. Stock,1,27 Andrea Manica,28 Aylwyn Scally,4 Daniel Wegmann,5 Ripan S. Malhi,3* Toomas Kivisild1,2* Little is known regarding the first people to enter the Americas and their genetic legacy. Genomic analysis of the oldest human remains from the Americas showed a direct relationship between a Clovis-related ancestral population and all modern Central and South Americans as well as a deep split separating them from North Americans in Canada. We present 91 ancient human genomes from California and Southwestern Ontario and demonstrate the existence of two distinct ancestries in North America, which possibly split south of the ice sheets. A contribution from both of these ancestral populations is found in all modern Central and South Americans. The proportions of these two ancestries in ancient and modern populations are consistent with a coastal dispersal and multiple admixture events. A n increasing body of archaeological (1–3) evidence shows that the initial peopling of the Americas occurred at least a few thousand years prior to the spread of the Clovis cultural complex ~13,000 years ago (all dates are calibrated) (4), with a majority of well-supported Pre-Clovis sites clustered in coastal areas and around glacial edges (1, 3, 5). Studies of ancient and modern genomes have uncovered four distinct ancestry components within the Americas arriving in three hypothesized waves: the most recent Thule-related NeoEskimo ~2000 years ago, the Saqqaq/Dorset Paleo-Eskimo ~4500 years ago (both restricted to the Arctic region), and a “First American” dis- persal prior to 13,000 years ago that split within North America into a northern and a southern branch (6–10). The northern branch is ancestral to populations including Algonquian, Na-Dené, Salishan, and Tsimshian speakers from Canada (NAM), whereas the southern branch includes the ancestors of the Clovis individual (Anzick-1) and all Mexicans, Central Americans (CAM), and South Americans (SAM) (9–12). Within the southern branch there is some localized evidence of early population structure, as a few modern Amazonian populations show an excess genetic affinity to Australasians (13, 14). The second oldest North American genome, The Ancient One (Kennewick Man, 8700 to 8400 years ago), is ACKN OW LEDG MEN TS The authors thank seminar participants at UCLA, Washington State, Northwestern, Simon Fraser, and Stanford Universities, as well as E. Long, for helpful comments and A. Hoffman, R. Squire, and N. Yonack at SafeGraph for data access and technical assistance. Funding: There are no funding sources related to this study. Author contributions: M.K.C. and R.R. designed and implemented the study, acquired the data, and drafted and revised the manuscript. Competing interests: The authors declare no competing interests. Data and materials availability: All data and code are available at www.anderson.ucla.edu/faculty/keith.chen/ datafilm.htm. SUPPLEMENTARY MATERIALS www.sciencemag.org/content/360/6392/1020/suppl/DC1 Fig. S1 Tables S1 to S4 References 5 October 2017; accepted 23 April 2018 10.1126/science.aaq1433 1024 1 Department of Archaeology, University of Cambridge, Cambridge CB2 3DZ, UK. 2Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia. 3Department of Anthropology and Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. 4Department of Genetics, University of Cambridge, Cambridge CB2 3EH, UK. 5Department of Biology, Université de Fribourg, Fribourg, Switzerland. 6Department of Anthropology, University of Toronto, Toronto, Ontario M5S 2S2, Canada. 7Santa Barbara Museum of Natural History, Santa Barbara, CA 93105, USA. 8Department of Anthropology, Portland State University, Portland, OR 97232, USA. 9Knight Diagnostics Laboratory, Oregon Health & Science University, Portland, OR 97239, USA. 10Department of Anthropology, Modesto Junior College, Modesto, CA 95350, USA. 11Department Hommes Natures Societies, Musée de l’Homme, Paris 75016, France. 12Department of Anthropology, Emory University, Atlanta, GA 30322, USA. 13Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK. 14Department of Anthropology and Museum Studies, Central Washington University, Ellensburg, WA 98926, USA. 15Department of Anthropology, San Diego City College, San Diego, CA 92101, USA. 16APE Lab, Department of Biology, University of Padova, Padova, Italy. 17Department of Anthropology, California State University, Los Angeles, CA 90032, USA. 18 Kenai Peninsula College, Soldotna, AK 99669, USA. 19Far Western Anthropological Research Group Inc., Davis, CA 95618, USA. 20Muwekma Ohlone Tribe of the San Francisco Bay Area, P.O. Box 360791, Milpitas, CA 95036, USA. 21Department of Anthropology, San Jose State University, San Jose, CA 95192, USA. 22Archaeological Services Inc., Toronto, Canada. 23HuronWendat Nation, Canada. 24Department of Anthropology, University of California, Los Angeles, CA 90095, USA. 25Barbareño Chumash, California Indian Advisory Committee, Santa Barbara Museum of Natural History, Santa Barbara, CA 93105, USA. 26 Tongva Nation, CA, USA. 27Department of Anthropology, University of Western Ontario, London, Ontario N6A 3K7, Canada. 28 Department of Zoology, University of Cambridge, Cambridge CB2 3EJ, UK. *Corresponding author. Email: cls83@ut.ee (C.L.S.); tk331@cam.ac.uk (T.K.); malhi@illinois.edu (R.S.M.) EMBARGOED UNTIL 2PM U.S. EASTERN TIME ON THE THURSDAY BEFORE THIS DATE: 1 JUNE 2018 • VOL 360 ISSUE 6392 sciencemag.org SCIENCE