Previous research has shown that modern Eurasians interbred with their Neanderthal and Denisovan predecessors. We show here that hundreds of thousands of years earlier, the ancestors of Neanderthals and Denisovans interbred with their own Eurasian predecessors—members of a “superarchaic” population that separated from other humans about 2 million years ago. The superarchaic population was large, with an effective size between 20 and 50 thousand individuals. We confirm previous findings that (i) Denisovans also interbred with superarchaics, (ii) Neanderthals and Denisovans separated early in the middle Pleistocene, (iii) their ancestors endured a bottleneck of population size, and (iv) the Neanderthal population was large at first but then declined in size. We provide qualified support for the view that (v) Neanderthals interbred with the ancestors of modern humans.
During the past decade, we have learned about interbreeding among hominin populations after 50 thousand years (ka) ago, when modern humans expanded into Eurasia (1–3). Here, we focus farther back in time, on events that occurred more than a half million years ago. In this earlier time period, the ancestors of modern humans separated from those of Neanderthals and Denisovans. Somewhat later, Neanderthals and Denisovans separated from each other. The paleontology and archaeology of this period record important changes, as large-brained hominins appear in Europe and Asia and Acheulean tools appear in Europe (4, 5). It is not clear, however, how these large-brained hominins relate to other populations of archaic or modern humans (6–9). We studied this period using genetic data from modern Africans and Europeans and from two archaic populations, Neanderthals and Denisovans.
Figure 1 illustrates our notation. Uppercase letters refer to populations, and combinations such as XY refer to the population ancestral to X and Y. X represents an African population (the Yorubans), Y is a European population, N is Neanderthals, and D is Denisovans. S is an unsampled “superarchaic” population that is distantly related to other humans. Lowercase letters at the bottom of Fig. 1 label “nucleotide site patterns.” A nucleotide site exhibits site pattern xyn if random nucleotides from populations X, Y, and N carry the derived allele, but those sampled from other populations are ancestral. Site pattern probabilities can be calculated from models of population history, and their frequencies can be estimated from data. Our Legofit (10) software estimates parameters by fitting models to these relative frequencies.
Nucleotide site patterns contain only a portion of the information available in genome sequence data. This portion, however, is of particular relevance to the study of deep population history. Site pattern frequencies are unaffected by recent population history because they ignore the within-population component of variation (10). This reduces the number of parameters we must estimate and allows us to focus on the distant past.
The current data include two high-coverage Neanderthal genomes: one from the Altai Mountains of Siberia and the other from Vindija Cave in Croatia (11). Rather than assigning the two Neanderthal fossils to separate populations, our model assumes that they inhabited the same population at different times. This implies that our estimates of Neanderthal population size will refer to the Neanderthal metapopulation rather than to any individual subpopulation.
The Altai and Vindija Neanderthals appear in site pattern labels as “a” and “v”. Thus, av is the site pattern in which the derived allele appears only in nucleotides sampled from the two Neanderthal genomes. Figure 2 shows the site pattern frequencies studied here. In contrast to our previous analysis (12), the current analysis includes singleton site patterns, x, y, v, a, and d, as advocated by Mafessoni and Prüfer (13). A simpler tabulation, which excludes the Vindija genome, is included as fig. S2.
Greek letters in Fig. 1 label episodes of admixture. We label models by concatenating Greek letters to indicate the episodes of admixture they include. For example, model “αβ” includes only episodes α and β. Our model does not include gene flow from Denisovans into moderns because there is little evidence of such gene flow into Europeans (14, 15). Two years ago, we studied a model that included only one episode of admixture: α, which refers to gene flow from Neanderthals into Europeans (12). The left panel of Fig. 3 shows the residuals from this model, using the new data. Several are far from zero, suggesting that something is missing from the model (16).
Recent literature suggests some of what might be missing. There is evidence for admixture into Denisovans from a superarchaic population, which was distantly related to other humans (2, 11, 17–19), and also for admixture from early moderns into Neanderthals (19). These episodes of admixture appear as β and γ in Fig. 1. Adding β and/or γ to the model improved the fit, yet none of the resulting models were satisfactory. For example, model αβγ implied (implausibly) that superarchaics separated from other hominins 7 million years (Ma) ago.
To understand what might still be missing, consider what we know about the early middle Pleistocene, around 600 ka ago. At this time, large-brained hominins appear in Europe, along with Acheulean stone tools (4, 5). They were probably African immigrants, because similar fossils and tools occur earlier in Africa. According to one hypothesis, these early Europeans were Neanderthal ancestors (6, 7). Somewhat earlier—perhaps 750 ka ago [(8), table S12.2]—the “neandersovan” ancestors of Neanderthals and Denisovans separated from the lineage leading to modern humans. Neandersovans may have separated from an African population and then expanded into Eurasia. If so, then they would not have been expanding into an empty continent, for Eurasia had been inhabited since 1.85 Ma ago (20). Neandersovan immigrants may have met the indigenous superarchaic population of Eurasia. This suggests a fourth episode of admixture, from superarchaics into neandersovans, which appears as δ in Fig. 1.
We considered eight models, all of which include α, and including all combinations of β, γ, and/or δ. In choosing among complex models, it is important to avoid overfitting. Conventional methods such as Akaike’s information criterion (21) are not available because we do not have access to the full likelihood function. Instead, we use the bootstrap estimate of predictive error (bepe) (10, 22, 23). The best model is the one with the lowest value of bepe. When no model is clearly superior, it is better to average across several than to choose just one (24). For this purpose, we used bootstrap model averaging (booma) (10, 24). The booma weight of the ith model is the fraction of datasets (including the real data and 50 bootstrap replicates) in which that model “wins,” i.e., has the lowest value of bepe. The bepe values and booma weights of all models are in Table 1.
The best model is αβγδ, which includes all four episodes of admixture. It has smaller residuals (Fig. 3, right), the lowest bepe value, and the largest booma weight. One other model, αβδ, has a positive booma weight, but all others have zero weight. To understand what this means, recall that bootstrap replicates approximate repeated sampling from the process that generated the data. The models with zero weight lose in all replicates, implying that their disadvantage is large compared with variation in repeated sampling. On this basis, we can reject these models. Neither of the two remaining models can be rejected. These results provide strong support for two episodes of admixture (β and δ) and qualified support for a third (γ). Not only does this support previously reported episodes of gene flow but it also reveals a much older episode, in which neandersovans interbred with superarchaics. Model-averaged parameter estimates, which use the weights in Table 1, are graphed in Fig. 4 and listed in table S1.
Episode δ, which proposes gene flow from superarchaics into neandersovans, is a novel hypothesis. Before accepting it, we should ask whether the evidence in its favor could be artifactual, reflecting a bias in site pattern frequencies caused by sequencing error or somatic mutations. Sequencing error adds a positive bias to the frequency of each singleton site pattern proportional to the per-nucleotide error rate in the corresponding population (see the Supplementary Materials). Somatic mutations have a similar effect. These biases might explain evidence for episode δ, if it were true that larger values of mδ (the fraction of superarchaic admixture in neandersovans) imply larger frequencies of singleton site patterns. However, Table 2 shows that this is not the case. There is no consistent tendency for singleton frequencies to increase with mδ. Indeed, three of them decrease. Consequently, evidence that mδ > 0 cannot be the result of a positive bias in the frequencies of singleton site patterns. The evidence for δ admixture cannot be an artifact of sequencing error or somatic mutations.
The superarchaic separation time, TXYNDS, has a point estimate of 2.3 Ma ago. This estimate may be biased upward because our molecular clock assumes a fairly low mutation rate of 0.38 × 10−9 per nucleotide site per year. Other authors prefer slightly higher rates (25). Although this rate is apparently insensitive to generation time among the great apes, it is sensitive to the age of male puberty. If the average age of puberty during the past 2 Ma were halfway between those of modern humans and chimpanzees, the yearly mutation rate would be close to 0.45 × 10−9 [(26), Fig. 2B], and our estimate of TXYNDS would drop to 1.9 Ma, just at the origin of the genus Homo. Under this clock, the 95% confidence interval is 1.8 to 2.2 Ma.
If superarchaics separated from an African population, then this separation must have preceded the arrival of superarchaics in Eurasia. Nonetheless, our 1.8 to 2.2 Ma interval includes the 1.85 Ma date of the earliest Eurasian archaeological remains at Dmanisi (20). Thus, superarchaics may descend from the earliest human dispersal into Eurasia, as represented by the Dmanisi fossils. On the other hand, some authors prefer a higher mutation rate of 0.5 × 10−9 per year (2). Under this clock, the lower end of our confidence interval would be 1.6 Ma ago. Thus, our results are also consistent with the view that superarchaics entered Eurasia after the earliest remains at Dmanisi.
Parameter NS is the effective size of the superarchaic population. This parameter can be estimated because there are two sources of superarchaic DNA in our sample (β and δ), and this implies that coalescence time within the superarchaic population affects site pattern frequencies. Although this parameter has a broad confidence interval, even the low end implies a fairly large population of about 20,000. This does not require large numbers of superarchaic humans, because effective size can be inflated by geographic population structure (27). Our large estimate may mean that neandersovans and Denisovans received gene flow from two different superarchaic populations.
Parameter TND is the separation time of Neanderthals and Denisovans. Our point estimate, 737 ka ago, is remarkably old. Furthermore, the neandersovan population that preceded this split was remarkably small: NND ≈ 500. This supports our previous results, which indicated an early separation of Neanderthals and Denisovans and a bottleneck among their ancestors (12).
Because our analysis includes two Neanderthal genomes, we can estimate the effective size of the Neanderthal population in two separate epochs. The early epoch extends from TN0 = 455 ka to TND = 737 ka, and within this epoch, the effective size was large: NN0 ≈ 16,000. It was smaller during the later epoch: NN1 ≈ 3400. These results support previous findings that the Neanderthal population was large at first but then declined in size (2, 11).
This project began with a puzzle. We had argued in 2017 that Neanderthals and Denisovans separated early, that their neandersovan ancestors endured a bottleneck of population size, and that the postseparation Neanderthal population was large (12). That analysis omitted singleton site patterns. Mafessoni and Prüfer (13) pointed out that introducing singletons led to different results. In response, Rogers et al. (16) agreed, but also observed that the with-singleton analysis implied that the Denisovan fossil was only 4000 years old—a result that is plainly wrong. Furthermore, a residual analysis showed that neither of the models under discussion in 2017 fit the data very well (16). Something was apparently missing from both models—but what? The present paper provides an answer to that question.
Our results shed light on the early portion of the middle Pleistocene, about 600 ka ago, when large-brained hominins appear in the fossil record of Europe along with Acheulean stone tools. There is disagreement about how these early Europeans should be interpreted. Some see them as the common ancestors of modern humans and Neanderthals (28), others as an evolutionary dead end, later replaced by immigrants from Africa (29, 30), and others as early representatives of the Neanderthal lineage (6, 7). Our estimates are most consistent with the last of these views. They imply that by 600 ka ago, Neanderthals were already a distinct lineage, separate not only from the modern lineage but also from Denisovans.
These results resolve a discrepancy involving human fossils from Sima de los Huesos (SH). Those fossils had been dated to at least 350 ka ago and perhaps 400 to 500 ka ago (31). Genetic evidence showed that they were from a population ancestral to Neanderthals and therefore more recent than the separation of Neanderthals and Denisovans (9). However, genetic evidence also indicated that this split occurred about 381 ka ago [(2), table S12.2]. This was hard to reconcile with the estimated age of the SH fossils. To make matters worse, improved dating methods later showed that the SH fossils are even older, about 600 ka, and much older than the molecular date of the Neanderthal-Denisovan split (32). Our estimates resolve this conflict because they push the date of the split back well beyond the age of the SH fossils.
Our estimate of the Neanderthal-Denisovan separation time conflicts with 381 ka ago estimate discussed above (2, 13). This discrepancy results, in part, from differing calibrations of the molecular clock. Under our clock, the 381-ka date becomes 502 ka (12), but this is still far from our own 737-ka estimate. The remaining discrepancy may reflect differences in our models of history. Misspecified models often generate biased parameter estimates.
Our new results on Neanderthal population size differ from those we published in 2017 (12). At that time, we argued that the Neanderthal population was substantially larger than others had estimated. Our new estimates are more in line with those published by others (2, 11). The difference does not result from our new and more elaborate model because we get similar results from model α, which (as in our 2017 model) allows only one episode of gene flow (table S2). Instead, it was including the Vindija Neanderthal genome that made the difference. Without this genome, we still get a large estimate (NN1 ≈ 11,000), even using model αβγδ (table S3). This implies that the Neanderthals who contributed DNA to modern Europeans were more similar to the Vindija Neanderthal than to the Altai Neanderthal, as others have also shown (11).
Our results revise the date at which superarchaics separated from other humans. One previous estimate put this date between 0.9 and 1.4 Ma [(2), p. 47], which implied that superarchaics arrived well after the initial human dispersal into Eurasia around 1.9 Ma. This required a complex series of population movements between Africa and Eurasia [(33), pp. 66 to 71]. Our new estimates do not refute this reconstruction, but they do allow a simpler one, which involves only three expansions of humans from Africa into Eurasia: an expansion of early Homo at about 1.9 Ma ago, an expansion of neandersovans at about 700 ka ago, and an expansion of modern humans at about 50 ka ago.
Our results indicate that neandersovans interbred with superarchaics early in the middle Pleistocene, shortly after expanding into Eurasia. This is the earliest known admixture between hominin populations. Furthermore, the two populations involved were more distantly related than any pair of human populations previously known to interbreed. According to our estimates, neandersovans and superarchaics had been separate for about 1.2 Ma. Later, when superarchaics exchanged genes with Denisovans, the two populations had been separate even longer. By comparison, the Neanderthals and Denisovans who interbred with modern humans had been separate less than 0.7 Ma.
It seems likely that superarchaics descend from the initial human settlement of Eurasia. As discussed above, the large effective size of the superarchaic population hints that it comprised at least two deeply divided subpopulations, of which one mixed with neandersovans and another with Denisovans. We suggest that around 700 ka ago, neandersovans expanded from Africa into Eurasia, endured a bottleneck of population size, interbred with indigenous Eurasians, largely replaced them, and separated into eastern and western subpopulations—Denisovans and Neanderthals. These same events unfolded once again around 50 ka ago as modern humans expanded out of Africa and into Eurasia, largely replacing the Neanderthals and Denisovans.
MATERIALS AND METHODS
Our sample of modern genomes includes Europeans but not other Eurasians. This allowed us to avoid modeling gene flow from Denisovans because there is no evidence of such gene flow into Europeans. The precision of our estimates depends largely on the number of nucleotides studied. For this reason, we used entire high-coverage genomes. The number of genomes sampled per population has little effect on our analyses, because of our focus on the between-population component of genetic variation, i.e., on site pattern frequencies. Nonetheless, our sample of modern genomes for the Yoruban, French, and English includes all those available from the Simons Genome Diversity Project (SGDP) (14), as detailed in the Supplementary Materials. We also included all available high-coverage archaic genomes (11). These data provide extremely accurate estimates of site pattern frequencies, as indicated by the tiny confidence intervals in Fig. 2. The large confidence intervals for some parameters in Fig. 4 reflect identifiability problems (discussed below) and would not be alleviated by an increase in sample size.
Our quality control (QC) pipeline for the SGDP genomes excludes genotypes at which an FL value equals 0 or N. We also excluded sex chromosomes, normalized all variants at a given nucleotide site using the human reference genome, excluded sites within seven bases of the nearest insertion-deletion, and included sites only if they were monomorphic or were biallelic single-nucleotide polymorphisms. Further details are provided in the Supplementary Materials. All ancient genomes were also filtered against .bed files, which identify bases that pass the Max Planck QC filters. These .bed files are available at http://ftp.eva.mpg.de/neandertal/Vindija/FilterBed.
Molecular clock calibration
We assumed a mutation rate of 1.1 × 10−8 per site per generation (34) and a generation time of 29 years—a yearly rate of 0.38 × 10−9. To calibrate the molecular clock, we assumed that the modern and neandersovan lineages separated TXYND = 25,920 generations before the present (12). This is based on an average of several estimates published by Prüfer et al. [(2), table S12.2]. The average of their estimates is 570.25 ka, assuming a mutation rate 0.5 × 10−9/base pair/year. Under our clock, their separation time becomes 751.69 ka or 25,920 generations.
Because of our focus on deep history, we based statistical analyses on site pattern frequencies, using the Legofit statistical package (10). This method ignores the within-population component of genetic variation and is therefore unaffected by recent changes in population size. For example, the sizes of populations X, Y, and D (Fig. 1) have no effect, so we need not complicate our model with parameters describing the size histories of these populations. This allows us to focus on the distant past.
Nonetheless, our models are quite complex. For example, model αβγδ has 17 free parameters. To choose among models of this complexity, we need methods of residual analysis, model selection, and model averaging. Legofit provides these methods, but alternative methods generally do not. These methods are described in detail elsewhere (10), so we summarize them only briefly here.
We chose among models by minimizing the bepe (22, 23). This approach was needed because we could not use methods, such as Akaike’s information criterion (21), that depend on likelihood. Bepe is analogous to cross validation but uses bootstrap replicates instead of partitions of the data. The model is fit to each bootstrap replicate and then tested against the real data, after applying a correction for bootstrap bias. Bepe estimates the mean squared difference between observed and predicted site pattern frequencies, when the model is fit to one dataset and tested against another.
We also used booma (24), which assigns weights to individual models, based on their bepe values. Parameters are estimated as the weighted average of estimates from individual models. The booma weight of the ith model is the fraction of replicates (including the real data and 50 bootstrap replicates) in which that model wins, i.e., has the lowest value of bepe. Because bootstrap replicates approximate repeated sampling from the process that generated the data, a model will receive zero weight if its disadvantage (as measured by bepe) is large compared with variation in repeated sampling.
Figure S3 illustrates a problem of statistical identifiability. Several parameters are tightly correlated with others, indicating that our problem has fewer dimensions than parameters. This does not lead to incorrect estimates, but it broadens the confidence intervals of the parameters involved. Legofit addresses this problem using principal components analysis to remove dimensions that account for less than a fraction 0.001 of the total variance. This narrows confidence intervals and increases the accuracy of parameter estimates.
Uncertainties are estimated by moving blocks bootstrap (35), using a block size of 500 single-nucleotide polymorphisms. Our statistical pipeline is detailed in the Supplementary Materials.
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/8/eaay5483/DC1
Supplementary Materials and Methods
Fig. S1. Heterozygosity as a function of FL value for genome SS6004468 of the SGDP (14).
Fig. S2. Observed site pattern frequencies excluding the Vindija genome.
Fig. S3. Associations between estimates of several pairs of parameters after second stage in analysis of model αβγδ.
Table S1. Model-averaged parameter estimates.
Table S2. Estimates under model α.
Table S3. Estimates under model αβγδ with a data set that excludes the Vindija Neanderthal genome.
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
REFERENCES AND NOTES
R. G. Klein, The Human Career: Human Biological and Cultural Origins (University of Chicago Press, ed. 3, 2009).
J. J. Hublin, in Neandertals and Modern Humans in Western Asia, T. Akazawa, K. Aoki, O. Bar-Yosef, Eds. (Kluwer, 1998), pp. 295–310.
H. Li, S. Mallick, D. Reich, Population size changes and split times, Supplementary Information 12 of Prüfer et al. (2) (2014).
B. Efron, R. J. Tibshirani, An Introduction to the Bootstrap (Chapman and Hall, 1993).
D. Reich, Who We Are and How We Got Here: Ancient DNA and the New Science of the Human Past (Pantheon Books, 2018).
R. Y. Liu, K. Singh, Moving blocks jackknife and boostrap capture weak dependence, in Exploring the “Limits” of the Bootstrap, R. LePage, L. Billard, Eds. (Wiley, 1992), pp. 225–248.
K. Price, R. M. Storn, J. A. Lampinen, Differential Evolution: A Practical Approach to Global Optimization (Springer Science and Business Media, 2006).
- Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
Russia may sue NASA astronaut over claims of drilling hole in spacecraft – WION
US-Russia spat seems to have reached space now. In a new development, Russian space agency Roscosmos has threatened to sue a NASA astronaut.
The agency claims the astronaut drilled a two-mm hole in a Soyuz MS-09 vehicle, which was docked with the International Space Station (ISS) in 2018.
After completing its investigation recently, the agency deemed the action as a sabotage. It cited Serena Auñón-Chancellor, an ISS crew member during the incident, as the culprit.
As the allegations were handed over to law enforcement of the Russia, Roscosmos announced the possibility of criminal charges.
With the hope of returning home early, Auñón-Chancellor purposefully made the hole, reported the Izvestia newspaper while citing sources on Friday.
Auñón-Chancellor seems to have wanted to leave due to a blood clot or a fight with her boyfriend onboard the ISS, Russian news outlet said citing sources.
When Auñón-Chancellor was in space, she got married to Jeff Chancellor. The couple is still married to this day. It is unclear who is the ‘boyfriend’ as stated by sources.
After a pressure drop was identified due to an air leak, the hole was spotted on August 30, 2018.
(With inputs from agencies)
NASA aims to replace ISS with a commercial space station by 2030 – The Tribune
Washington, Dec 1
The US space agency is planning to replace the International Space Station (ISS) with one or more commercial space stations by 2030.
NASA’s auditing body, the Office of Audits, has produced a report detailing the agency’s commitment to replace the orbiting lab with commercial space stations.
Astronauts have lived and worked onboard the ISS orbiting roughly 250 miles above the Earth’s surface for more than 20 years.
“The ISS costs about $3 billion a year, roughly a third of NASA’s annual human space flight budget, and while current plans call for the Station’s retirement in 2024, an extension to 2030 is likely,” the US space agency said in the audit report.
Anticipating its retirement, NASA has committed to replacing the ISS with one or more commercially owned and operated space destinations.
“In the fiscal year (FY) that ended September 30, 2021, Congress authorised $17 million to that end — a fraction of the $150 million the Agency said it needed. NASA’s plans for long-term, deep space human exploration missions depend on continuous access to a research laboratory in low-Earth orbit,” it added.
The Artemis mission, aimed at returning humans to the Moon and ultimately landing astronauts on Mars, is not feasible without continued human health research and technology demonstrations being conducted on the ISS and its eventual replacement.
“As long as humans intend to travel in space, NASA expects research and testing will be needed in the microgravity environment of low-Earth orbit,” the audio report mentioned.
While overall ISS operations and maintenance costs remained steady at about $1.1 billion a year from FY2016 through FY2020, systems maintenance and upgrade costs trended upward 35 per cent in the same 5-year period, rising to approximately $169 million in FY2020 due primarily to upgrades.
Meanwhile, NASA and Roscosmos are investigating the cause and long-term impacts of cracks and leaks that were recently discovered in the Station’s Service Module Transfer Tunnel, which connects the Service Module to one of eight docking ports on the Station.
“Causes being explored include structural fatigue, internal damage, external damage, and material defects. Notably, based on the models NASA used to assess the structure, the cracks should not have occurred, suggesting the possibility of an earlier-than-projected obsolescence for at least one element of the Station,” the US space agency noted. IANS
Arctic rainfall could dominate snowfall earlier than expected: study – Global Times
A view of Arctic Photo: VCG
Rainfall could start replacing snowfall in the Arctic decades sooner than previously thought, a study found Tuesday, warning the change caused by global warming could have effects beyond the region.
The Arctic is warming much faster than the rest of the planet, melting sea ice and adding moisture to the air that is likely to increase precipitation.
Comparing the latest projections to previous climate models, the study published in the scientific journal Nature Communications estimates the switch from snowfall-dominated annual precipitation to one dominated by rain will come about “one or two decades earlier.”
“Changes are going to be more severe and occur much earlier than projected and so will have huge implications for life in and beyond the Arctic,” the study’s main author Michelle McCrystall told AFP.
“In autumn, for example, when the greatest changes occur, the central Arctic may transition around 2070 in the latest set of models compared to 2090 in the previous set,” added McCrystall, a researcher at Canada’s University of Manitoba.
But everything depends on the degree of global warming.
At the current rate of warming rain could dominate snow in the Arctic before the end of the century, the study says. But it says limiting warming to 1.5 C could mean the Arctic stays dominated by snow.
Gavin Schmidt, director of the NASA Goddard Institute for Space Studies, said the results “imply that the worst impacts can be avoided if countries match their stated intentions to cut emissions in line with the Paris agreement.”
But Schmidt added that he felt the study did not prove the change would come sooner than expected.
Whenever it comes, the switch from snow to rain is likely to have major effects on the Arctic ecosystem.
More rainfall on top of current snow cover could lead to increased surface ice that would make it impossible for caribou and reindeer to forage for food.
Less snow cover also means the Arctic will lose some of its capacity to deflect solar heat and light away from the Earth’s surface and thus contribute to warming.
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