Stable isotope evidence for an amphibious phase in early proboscidean evolution

The order Proboscidea includes extant elephants and their extinct relatives and is closely related to the aquatic sirenians (manatees and dugongs) and terrestrial hyracoids (hyraxes). Some analyses of embryological, morphological, and paleontological data suggest that proboscideans and sirenians shared an aquatic or semiaquatic common ancestor, but independent tests of this hypothesis have proven elusive. Here we test the hypothesis of an aquatic ancestry for advanced proboscideans by measuring delta(18)O in tooth enamel of two late Eocene proboscidean genera, Barytherium and Moeritherium, which are sister taxa of Oligocene-to-Recent proboscideans. The combination of low delta(18)O values and low delta(18)O standard deviations in Barytherium and Moeritherium matches the isotopic pattern seen in aquatic and semiaquatic mammals, and differs from that of terrestrial mammals. delta(13)C values of these early proboscideans suggest that both genera are likely to have consumed freshwater plants, although a component of C(3) terrestrial vegetation cannot be ruled out. The simplest explanation for the combined evidence from isotopes, dental functional morphology, and depositional environments is that Barytherium and Moeritherium were at least semiaquatic and lived in freshwater swamp or riverine environments, where they grazed on freshwater vegetation. These results lend new support to the hypothesis that Oligocene-to-Recent proboscideans are derived from amphibious ancestors.     

Chemical and archaeological evidence for the earliest cacao beverages

Chemical analyses of residues extracted from pottery vessels from Puerto Escondido in what is now Honduras show that cacao beverages were being made there before 1000 B.C., extending the confirmed use of cacao back at least 500 years. The famous chocolate beverage served on special occasions in later times in Mesoamerica, especially by elites, was made from cacao seeds. The earliest cacao beverages consumed at Puerto Escondido were likely produced by fermenting the sweet pulp surrounding the seeds.     

Geophysical, archaeological, and historical evidence support a solar-output model for climate change

Although the processes of climate change are not completely understood, an important causal candidate is variation in total solar output. Reported cycles in various climate-proxy data show a tendency to emulate a fundamental harmonic sequence of a basic solar-cycle length (11 years) multiplied by 2(N) (where N equals a positive or negative integer). A simple additive model for total solar-output variations was developed by superimposing a progression of fundamental harmonic cycles with slightly increasing amplitudes. The timeline of the model was calibrated to the Pleistocene/Holocene boundary at 9,000 years before present. The calibrated model was compared with geophysical, archaeological, and historical evidence of warm or cold climates during the Holocene. The evidence of periods of several centuries of cooler climates worldwide called "little ice ages," similar to the period anno Domini (A.D.) 1280-1860 and reoccurring approximately every 1,300 years, corresponds well with fluctuations in modeled solar output. A more detailed examination of the climate sensitive history of the last 1, 000 years further supports the model. Extrapolation of the model into the future suggests a gradual cooling during the next few centuries with intermittent minor warmups and a return to near little-ice-age conditions within the next 500 years. This cool period then may be followed approximately 1,500 years from now by a return to altithermal conditions similar to the previous Holocene Maximum.     

Global evidence for ultraviolet radiation decreasing COVID-19 growth rates

With nearly every country combating the 2019 novel coronavirus (COVID-19), there is a need to understand how local environmental conditions may modify transmission. To date, quantifying seasonality of the disease has been limited by scarce data and the difficulty of isolating climatological variables from other drivers of transmission in observational studies. We combine a spatially resolved dataset of confirmed COVID-19 cases, composed of 3,235 regions across 173 countries, with local environmental conditions and a statistical approach developed to quantify causal effects of environmental conditions in observational data settings. We find that ultraviolet (UV) radiation has a statistically significant effect on daily COVID-19 growth rates: a SD increase in UV lowers the daily growth rate of COVID-19 cases by ∼1 percentage point over the subsequent 2.5 wk, relative to an average in-sample growth rate of 13.2%. The time pattern of lagged effects peaks 9 to 11 d after UV exposure, consistent with the combined timescale of incubation, testing, and reporting. Cumulative effects of temperature and humidity are not statistically significant. Simulations illustrate how seasonal changes in UV have influenced regional patterns of COVID-19 growth rates from January to June, indicating that UV has a substantially smaller effect on the spread of the disease than social distancing policies. Furthermore, total COVID-19 seasonality has indeterminate sign for most regions during this period due to uncertain effects of other environmental variables. Our findings indicate UV exposure influences COVID-19 cases, but a comprehensive understanding of seasonality awaits further analysis.

In late 2019, a novel virus species from the family Coronaviridae, referred to as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), began spreading throughout China (1). Central among SARS-CoV-2 concerns are its relatively high transmissivity and case fatality rates (2). In the ensuing months, the virus spread globally, prompting the World Health Organization to declare a pandemic on March 11, 2020. At the time of this writing, cases of COVID-19, the disease caused by SARS-CoV-2, have been detected in almost every country (Fig. 1A), with the number of confirmed global cases in the tens of millions.Open in a separate windowFig. 1.Global assemblage of national and subnational COVID-19 records. (A) Total confirmed cases of COVID-19 across 3,235 national and subnational units covering 173 countries from 2 January to 10 April 2020. Darker colors indicate a higher cumulative number of confirmed cases as of 10 April 2020; gray indicates that no data are available. Subnational COVID-19 records were obtained for the United States, Brazil, Chile, Iran, China, South Korea, and 10 European countries. Each box shows within-country heterogeneity in COVID-19 cases for countries with subnational records. (B) For the subset of countries with at least 1,000 reported cumulative cases, the period of available COVID-19 data is shown. Data from countries that are in boldface type are available at the subnational level, with the number of administrative units indicated by the thickness of the time series line. Circles indicate the date when cumulative confirmed cases reach specific thresholds, with larger circles indicating higher case counts.Much remains unknown about COVID-19. An important question concerns how environmental conditions modify COVID-19 transmission. In particular, sensitivity to environmental conditions that vary seasonally may allow prediction of transmission characteristics around the globe over the coming months and have implications for seasonal reemergence of infections (3). Prior evidence from a few other viruses suggests the possibility of COVID-19 seasonality. For instance, H3N2, 2009 H1N1, and other strains of influenza exhibit sensitivity to local temperature and specific humidity (4–8). Furthermore, related strains of coronavirus and influenza are inactivated by ultraviolet (UV) radiation (9–11), and a recent laboratory study suggests a similar role whereby UV of a similar spectral distribution to sunlight inactivates SARS-CoV-2 on surfaces (12). The influence of environmental conditions on population-level COVID-19 transmission, however, remains largely unknown (13, 14). Importantly, population-level effects capture human behavioral responses that are typically omitted from laboratory studies.To estimate the influence of environmental conditions on COVID-19 transmission we first assemble a global dataset of daily confirmed COVID-19 cases. The collated data consist of 1,153,726 COVID-19 cases from 3,235 geospatial units covering 173 countries and five continents (Fig. 1A and SI Appendix, section B, Tables S2 and S3, and Fig. S1), span 1 January 2020 to 10 April 2020, and have nearly global coverage since March 2020 (Fig. 1B). We implement a wide range of data quality control measures, including corrections to the date of reported cases and cross-referencing across multiple sources, to harmonize heterogeneous reporting practices across global sources (SI Appendix, section B). For purposes of testing for heterogeneity in response, these case records are also combined with data on location-specific containment policies and testing regimes (15, 16).We construct our outcome variable as the daily growth rate of confirmed cases, hereafter “daily COVID-19 growth rate,” calculated as the daily change in the logarithm of confirmed cases. Confirmed COVID-19 cases are used because data on recoveries and deaths are not consistently available globally. Growth rates are analyzed because they are a well-established measure for disease spread that reflects changes in transmission characteristics (SI Appendix, section A.1). Daily COVID-19 growth rates are assessed in relation to local population-weighted daily temperature, specific humidity, precipitation, and UV from a 0.25° latitude by 0.25° longitude resolution weather reanalysis dataset (17, 18).We leverage methods from the growing “climate econometrics” literature, which over the last two decades has advanced causal inference techniques to quantify potential impacts of anthropogenic climate change (19–21), including the influence of rising temperatures on civil conflict (22), mortality (23), agricultural yields (24), and human migration (25). The goal of this approach is to mimic controlled experiments by nonparametrically accounting for confounding factors such that the variation in environmental conditions used in the analysis is as good as randomly assigned. Prior work, for example, has used a similar approach to isolate the role of environmental conditions on influenza and provided evidence that low humidity contributes to influenza mortality (26).Although a strictly causal interpretation of results is not possible in any observational study, our research design (detailed in SI Appendix, section A.2) addresses four key issues associated with prior observational analyses. First, a location’s prevailing social and economic characteristics, which likely influence COVID-19 transmission, may also be correlated with its average environmental conditions. For example, countries that are cooler on average tend to have higher income per capita (27), with the latter feature associated with more widespread access to medical care, testing, and reporting. Indeed, a recent review by the National Academies of Sciences, Engineering, and Medicine notes that temperature and humidity effects on COVID-19 remain inconclusive in part because of these cross-sectional differences (13). We address this concern through the use of location-specific “fixed effects,” or dummy variables, which flexibly control for all differences in time-invariant social and economic characteristics and data quality across geospatial units (28). Fig. 2A, Left and Center illustrates how this spatial demeaning affects our data for two sample locations—Santiago, Chile and Paris, France—where average climatological conditions differ strongly and where the timing and intensity of the disease evolved distinctly. Empirical estimation relying on the data shown in Fig. 2A, Left would conflate differences in environmental conditions across these two locations with the many other differences between these cities, such as baseline population density or health (Fig. 2C, discussed in Results).Open in a separate windowFig. 2.Methodological approach to removing spatial and temporal bias in estimating the impact of environmental conditions on the growth rate of confirmed COVID-19 cases. (A) Illustration of, for two example locations, how our empirical strategy isolates idiosyncratic variation in local climatological conditions through the inclusion of semiparametric controls (i.e., “fixed effects”) in a panel regression framework (SI Appendix, Eq. S1). A, Left displays raw time series data from Paris, France (dark color) and Santiago, Chile (light color) for UV exposure (gold), temperature (maroon), specific humidity (green), and daily COVID-19 growth rates (gray). A, Center displays these same time series, after location-specific fixed effects have been removed. A, Right shows the residual variation used in empirical estimation; location-specific averages, day-of-year averages, and country-specific weekly temporal variation are removed using a suite of fixed effects described in SI Appendix, section A.2. The resulting time series no longer display average differences across space or trending behavior within a location, thus removing the possibility that unobserved time-constant or trending variables may confound empirical estimates. (B) Average daily growth rates in confirmed COVID-19 cases for five select countries, indexed against the number of days since the first case was detected. Values shown are unweighted average growth rates computed across all subnational units within each country (Fig. 1). Note that increased variance in the United States average growth rate after approximately 30 d since initial outbreak occurs due to a limited sample of counties for which confirmed cases have been reported for greater than 30 d. (C) Estimates of the cumulative effect of UV exposure on subsequent daily COVID-19 growth rates from three variations of the regression in SI Appendix, Eq. S1. Lines indicate the effect of changing daily average UV from the sample average of 50 kJ⋅m−2⋅h−1 to a given value; shading shows 95% confidence intervals. In gold is the primary specification used throughout our analysis, which includes the full set of semiparametric controls described in SI Appendix, section A.2. In teal, all spatial and temporal controls are removed from the estimation (i.e., data shown in A, Left are used), introducing confounding variation across space and time and leading to substantial bias in the estimated effect of UV on growth rates. In brown, location-specific fixed effects are included, while temporal controls are omitted (i.e., data shown in A, Center are used), introducing confounding low-frequency temporal variation and again leading to a biased estimator.Second, within any given location, there are temporal trends in both daily environmental conditions and the COVID-19 growth rate, with the latter due to anticontagion policies and inherent dynamics of transmission that are unrelated to environmental conditions (SI Appendix, section A.1). We address the concern that such trends may bias causal estimates through the inclusion of flexible location-specific temporal controls that remove low-frequency temporal variation in both COVID-19 and environmental conditions. We additionally employ global-scale, day-of-sample controls to account for any high-frequency common shocks to the evolution of the disease or its reporting across the globe. The resulting location-specific, high-frequency fluctuations in environmental conditions after removal of trends appear to exhibit quasi-random variation (19–21), as illustrated in Fig. 2A, Right.Third, a number of different environmental variables have been postulated to affect transmission, including UV, temperature, humidity, and precipitation (29–33). These atmospheric variables are dynamically linked (SI Appendix, Fig. S3). For instance, solar radiation is correlated with relative humidity and precipitation through cloud formation and convection. Such associations confound causal estimates if key variables are omitted from the analysis (34). We address this concern by simultaneously estimating the effects of UV, temperature, humidity, and precipitation, such that the effect of any single environmental variable is estimated after accounting for correlations with other specified environmental variables.Fourth, any modification of transmission will appear with some delay in observations of confirmed COVID-19 cases. The length of this delay between transmission and case confirmation includes the incubation period as well as time required to diagnose the disease. Prior case studies have identified the incubation period to range between 4 and 7 d (35–37) and the period between symptom onset and case confirmation to range between 2 and 7 d (38), implying a combined delay of 6 to 14 d between transmission and case confirmation. In a population-level study like ours, where individuals reside in diverse testing and reporting regimes, we expect there to be heterogeneity in lag lengths across different individuals and regions of the world. Because the distribution of delays across a population is unknown, estimation of a population-level causal response requires a statistical approach that accounts for the pattern of lagged effects in a data-driven manner. To this end, we employ a temporal distributed lag regression model that enables flexible, data-driven estimates of the effects of environmental conditions on the COVID-19 growth rate up to 2.5 wk later, a period long enough to incorporate the range of delays detected by prior studies. To quantify the total effect of environmental exposure, we sum the estimated effects across all lags for each variable (21, 39). As is standard in investigations of dynamic effects of the environment on socioeconomic conditions (40–45), we treat this “cumulative effect” as our main statistic of interest.Together, inclusion of these four elements in a panel regression model allows us to quantify the impact of quasi-random daily variations in environmental conditions on the subsequent evolution of the COVID-19 caseload (SI Appendix, section A.2 and Eq. S1). We examine the sensitivity of our conclusions to a range of alternative statistical model formulations that, among other things, vary the stringency of the spatiotemporal controls and additionally control for the local timing of COVID-19 outbreaks, testing regimes, and COVID-19 containment policies. The ability of our statistical approach to recover the effects of environmentally driven changes in transmission on the COVID-19 growth rate is confirmed by applying our statistical model to synthetic data simulated by a standard susceptible–exposed–infected–recovered (SEIR) model with an environmentally perturbed transmission parameter (46), as detailed in SI Appendix, section A.1 and Figs. S12 and S13.Finally, we note that several elements of our statistical approach also address concerns regarding systematic reporting biases with COVID-19 case data. First, our use of the growth rate of COVID-19 cases as the outcome variable accounts for location-specific reporting biases in the level of COVID-19 cases. Second, time-invariant reporting biases in COVID-19 growth rates are removed by location-specific fixed effects. Third, inclusion of flexible country-specific time trends accounts for time-varying differences in reporting bias across countries. Fourth, we address remaining differences due to testing regimes by demonstrating that our main result is invariant to controlling for country-level testing policy over time. Remaining challenges associated with identification of environmental effects on COVID-19 transmission are considered in Discussion.     

Global genetic positioning: evidence for early human population centers in coastal habitats

For an alternative perspective on relationships among human populations, we combined genetic and geographic information, using allele frequency gradients to place populations and individuals on the globe. Reanalyzing published data on 51 worldwide populations [Rosenberg, N. A., Pritchard, J. K., Weber, J. L., Cann, H. M., Kidd, K. K., Zhivitovsky, L. A. & Feldman, M. W. (2002) Science 298, 2381-2385] reveals five geographic clusters lying in plausible sites either of early agricultural innovation or on ancient migration routes. Also, the inferred sites show significant association with coastlines, suggesting that most early humans lived near large bodies of water. Our approach is flexible, and developments should prove useful both for exploring historical demography and for the identification of likely origin for unknown forensic samples.     

User-centered design of a virtual reality exhibit for archaeological museums

Nowadays, the adoption of virtual reality (VR) exhibits is increasingly common both in large and small museums because of their capability to enhance the communication of the cultural contents and to provide an engaging and fun experience to its visitors. The paper describes a user-centered design (UCD) approach for the development of a VR exhibit for the interactive exploitation of archaeological artefacts. In particular, this approach has been carried out for the development of a virtual exhibit hosted at the “Museum of the Bruttians and the Sea” of Cetraro (Italy). The main goal was to enrich the museum with a playful and educational VR exhibit able to make the visitors enjoy an immersive and attractive experience, allowing them to observe 3D archaeological artefacts in their original context of finding. The paper deals with several technical issues commonly related to the design of virtual museum exhibits that rely on off-the-shelf technologies. The proposed solutions, based on an UCD approach, can be efficiently adopted as guidelines for the development of similar VR exhibits, especially when very low budget and little free space are unavoidable design requirements.     

From the Cover: Global evidence on the selfish rich inequality hypothesis

We report on a study of whether people believe that the rich are richer than the poor because they have been more selfish in life, using data from more than 26,000 individuals in 60 countries. The findings show a strong belief in the selfish rich inequality hypothesis at the global level; in the majority of countries, the mode is to strongly agree with it. However, we also identify important between- and within-country variation. We find that the belief in selfish rich inequality is much stronger in countries with extensive corruption and weak institutions and less strong among people who are higher in the income distribution in their society. Finally, we show that the belief in selfish rich inequality is predictive of people’s policy views on inequality and redistribution: It is significantly positively associated with agreeing that inequality in their country is unfair, and it is significantly positively associated with agreeing that the government should aim to reduce inequality. These relationships are highly significant both across and within countries and robust to including country-level or individual-level controls and using Lasso-selected regressors. Thus, the data provide compelling evidence of people believing that the rich are richer because they have been more selfish in life and perceiving selfish behavior as creating unfair inequality and justifying equalizing policies.

The idea of the selfish rich has a long history in science, politics, and religion (1). Adam Smith argued that the “natural selfishness and rapacity” of the rich benefits society (2). Others have argued that the selfish rich cause inequality and unfairness, by “pulling the ladder of opportunity away from ordinary people” (3, 4).A growing literature has studied empirically whether the rich are more selfish than the poor, both in behavior and in underlying preferences. The evidence is mixed: Some studies report more selfishness among the rich (5–7), others that the rich are not different from the rest of society or even less selfish (8–11). Previous work has also provided diverse evidence on the causal effect of being rich on selfishness. It has been shown that making people feel richer or think about money causes them to behave more selfishly (6, 12), but at the same time, there is some experimental evidence suggesting that becoming rich makes you behave less selfishly (13–15). The causal link from selfishness to being rich may appear more straightforward. Selfish people are likely more willing to exploit both legal and illegal opportunities to become rich, including, as shown in a laboratory experimental study, to work harder to earn more money (16). Finally, there is recent evidence suggesting that selfish behavior among the rich is contagious and increases selfish behavior among the poor (17).The present study focuses on people’s beliefs about the rich, rather than the actual behavior of the rich. These beliefs are likely to shape inequality acceptance and support for redistribution in society. Evidence from a US sample (students and nonstudents) suggests that perceptions of the rich matter for policy preferences: People who view the rich as selfish are more likely to support taxation of the rich (18). There is also global evidence from 38 countries (students and nonstudents) on whether people have conflicting stereotypes of the rich, focusing on the personality dimensions warmth (friendly, sincere) and competence (capable, skilled) (19). This evidence suggests that people view the rich as cold and competent and shows that there is more ambivalence in how people view others in countries with an intermediate level of conflict or high inequality. It has been shown that there is a close association between a cold personality and selfishness (20), and, thus, the existing global evidence is suggestive of people considering the rich as more selfish than the poor (SI Appendix).We advance the literature in two ways. First, we focus on people’s belief about differences in selfishness as a source of inequality in society. The empirical and experimental literature on the source of inequality has mainly focused on investigating people’s views on the role of luck versus effort in determining income inequality, while the role of selfish behavior has been, in comparison, highly overlooked (21–25). We study whether people believe that selfish behavior among the rich is a source of inequality, which we refer to as the selfish rich inequality hypothesis.Selfish Rich Inequality Hypothesis. The rich are richer than the poor because they have been more selfish in life than the poor.Second, we provide large-scale global data from 60 countries (nationally representative samples) that allow for both between- and within-country analysis of people’s belief in the selfish rich inequality hypothesis.There may be systematic between-country variation in support for the selfish rich inequality hypothesis because countries are likely to differ in the opportunity for and reward from selfish behavior. In particular, it has been argued that nonproductive grabbing behavior may be particularly profitable in countries with poor institutions, where outcomes are more down to personal factors and networks, due to a weak rule of law, malfunctioning bureaucracy, and corruption (26). In contrast, good institutions may attract selfish individuals into productive activities that are beneficial for society. Hence, people in countries with poor institutions may be more likely to believe that the rich have become rich because they have been involved in selfish grabbing activities, while people in countries with good institutions may be more likely to believe that the rich have become rich through activities that have benefited society.There may also be important within-country variation in the belief in the selfish rich inequality hypothesis, since people differ in their experiences and information about the rich, or they may have self-serving beliefs (27, 28). For example, the rich may be less in agreement with the selfish rich inequality hypothesis than the poor because they have more information compared to the poor about the reasons for why they are rich. It may also be favorable for the rich to preserve a positive view of themselves and inequality in society and beneficial for the nonrich to picture the rich in a negative way. Finally, self-selection may affect the association between income rank and the belief in selfish rich inequality, since some people may decide not to pursue wealth because they believe that they have to engage in selfish behavior to become rich.We further consider the relationship between people’s belief in the selfish rich inequality hypothesis and their acceptance of inequality. Are people who believe in the selfish rich inequality hypothesis more likely to consider inequality in their country to be unfair and be more in support of redistribution? The answers to these questions are not straightforward and likely depend on whether selfish behavior of the rich is seen as taking opportunities away from others or as promoting the interests of society.The study shows strong support for the selfish rich inequality hypothesis at the global level, but also substantial between- and within-country variation. Belief in the selfish rich inequality hypothesis is related both to the circumstances in the country, particularly the corruption level, and to people’s position in the income distribution. These beliefs strongly predict people’s inequality acceptance and support for redistribution. Hence, people’s views on the selfish rich inequality hypothesis may play an important role in shaping how societies across the world address inequality.     

Uncovering archaeological landscapes at Angkor using lidar

Previous archaeological mapping work on the successive medieval capitals of the Khmer Empire located at Angkor, in northwest Cambodia (∼9th to 15th centuries in the Common Era, C.E.), has identified it as the largest settlement complex of the preindustrial world, and yet crucial areas have remained unmapped, in particular the ceremonial centers and their surroundings, where dense forest obscures the traces of the civilization that typically remain in evidence in surface topography. Here we describe the use of airborne laser scanning (lidar) technology to create high-precision digital elevation models of the ground surface beneath the vegetation cover. We identify an entire, previously undocumented, formally planned urban landscape into which the major temples such as Angkor Wat were integrated. Beyond these newly identified urban landscapes, the lidar data reveal anthropogenic changes to the landscape on a vast scale and lend further weight to an emerging consensus that infrastructural complexity, unsustainable modes of subsistence, and climate variation were crucial factors in the decline of the classical Khmer civilization.     

The protracted Holocene extinction of California's flightless sea duck (Chendytes lawi) and its implications for the Pleistocene overkill hypothesis

Bones of the flightless sea duck (Chendytes lawi) from 14 archaeological sites along the California coast indicate that humans hunted the species for at least 8,000 years before it was driven to extinction. Direct (14)C dates on Chendytes bones show that the duck was exploited on the southern California islands as early as approximately 11,150-10,280 calendar years B.P., and on the mainland by at least 8,500 calendar years B.P. The youngest direct date of 2,720-2,350 calendar years B.P., combined with the absence of Chendytes bones from hundreds of late Holocene sites, suggests that the species was extinct by approximately 2,400 years ago. Although the extinction of Chendytes clearly resulted from human overhunting, its demise raises questions about the Pleistocene overkill model, which suggests that megafauna were driven to extinction in a blitzkrieg fashion by Native Americans approximately 13,000 years ago. That the extermination of Chendytes was so protracted and archaeologically visible suggests that, if the terminal Pleistocene megafauna extinctions were primarily the result of human exploitation, there should also be a long and readily detectable archaeological record of their demise. The brief window now attributed to the Clovis culture ( approximately 13,300-12,900 B.P.) seems inconsistent with an overhunting event.