Review: Kennedy D, Norman C (2005). What don't we know? Science, Special Supplement, 309: 75-102. DOI:10.1126/science.309.5731.75

Feature Paper: Kennedy D, Norman C (2005). What don't we know? Science, Special Supplement, 309: 75-102. DOI:10.1126/science.309.5731.75

Author Abstract: At Science, we tend to get excited about new discoveries that lift the veil a little on how things work, from cells to the universe. That puts our focus firmly on what has been added to our stock of knowledge. For this anniversary issue, we decided to shift our frame of reference, to look instead at what we don’t know: the scientific puzzles that are driving basic scientific research.

 
We began by asking Science’s Senior Editorial Board, our Board of Reviewing Editors, and our own editors and writers to suggest questions that point to critical knowledge gaps. The ground rules: Scientists should have a good shot at answering the questions over the next 25 years, or they should at least know how to go about answering them. We intended simply to choose 25 of these suggestions and turn them into a survey of the big questions facing science. But when a group of editors and writers sat down to select those big questions, we quickly realized that 25 simply wouldn’t convey the grand sweep of cutting-edge research that lies behind the responses we received. So we have ended up with 125 questions, a fitting number for Science’s 125th anniversary.
 
First, a note on what this special issue is not: It is not a survey of the big societal challenges that science can help solve, nor is it a forecast of what science might achieve. Think of it instead as a survey of our scientific ignorance, a broad swath of questions that scientists themselves are asking. As Tom Siegfried puts it in his introductory essay, they are “opportunities to be exploited.”


We selected 25 of the 125 questions to highlight based on several criteria: how fundamental they are, how broad-ranging, and whether their solutions will impact other scientific disciplines. Some have few immediate practical implications—the composition of the universe, for example. Others we chose because the answers will have enormous feasible, or how much the carbon dioxide we are pumping into the atmosphere will warm our planet, for example. Some, such as the nature of dark energy, have come to prominence only recently; others, such as the mechanism behind limb regeneration in amphibians, have intrigued scientists for more than a century. We listed the 25 highlighted questions in no special order, but we did group the 100 additional questions roughly by discipline.
 
Our sister online publications are also devoting special issues to Science’s 125th anniversary. The Science of Aging Knowledge Environment, SAGE KE (www.sageke.org), is surveying several big questions confronting researchers on aging. The Signal Transduction Knowledge Environment, STKE (www.stke.org), has selected classic Science articles that have had a high impact in the field of cell signaling and is highlighting them in an editorial guide. And Science’s Next Wave (www.nextwave.org) is looking at the careers of scientists grappling with some of the questions Science has identified.

 
We are acutely aware that even 125 un- knowns encompass only a partial answer to the question that heads this special section: What Don’t We Know? So we invite you to participate in a special forum on Science’s Web site (www.sciencemag.org/sciext/eletters/125th), in which you can comment on our 125 questions or nominate topics we missed—and we apol- ogize if they are the very questions you are working on.From the nature of the cosmos to the nature of societies, the following 100 questions span the sciences. Some are pieces of questions discussed above; others are big questions in their own right. Some will drive scientific inquiry for the next century; others may soon be answered. Many will undoubtedly spawn new questions.

Note to Readers: Follow links above for author email, full article text, or the publishing scientific journal. Author notes in my review are in quotes.

Review: The paper we'll review this week is a special supplement published by the eminent scientific journal "Science," which tries to outline 125 of the most important questions still remaining in science across a variety of fields. Since only the top questions have a dedicated single-page review written up for them (while remaining questions are only given a few sentences), I think the best way to approach a review this week is to merely list all of the questions mentioned and provide a copy of the special supplement so those interested can read further.

I would point out that the below list is not reflective of my personal views, but those of the Science journal staff, which seem predisposed to physics. Also remember that below is a list of scientific questions so questions such as "Does God exist?" will not be addressed since they can't be addressed scientific.

1. What is the universe made of?
2. Is ours the only universe?
3. What drove cosmic inflation?
4. What is the biological basis of consciousness?
5. When and how did the first stars and galaxies form?
6. Where do ultra-high-energy cosmic rays come from?
7. What powers quasars?
8. What is the nature of black holes?
9. Why do humans have so few genes?
10. Why is there more matter than antimatter?
11. Does the proton decay?
12. What is the nature of gravity?
13. Why is time different from other dimensions?
14. To what extent are genetic variation and personal health linked?
15. Are there smaller building blocks than quarks?
16. Are neutrinos their own antiparticles?
17. Is there a unified theory explaining all correlated electron systems?
18. What is the most powerful laser researchers can build?
19. Can the laws of physics be unified?
20. Can researchers make a perfect optical lens?
21. Is it possible to create magnetic semiconductors that work at room temperature?
22. What is the pairing mechanism behind high-temperature superconductivity?
23. Can we develop a general theory of the dynamics of turbulent flows and the motion of granular materials?
24. How much can human life span be extended?
25. Are there stable high-atomic-number elements?
26. Is superfluidity possible in a solid? If so, how?
27. What is the structure of water?
28. What is the nature of the glassy state?
29. Are there limits to rational chemical synthesis?
30. What controls organ regeneration?
31. What is the ultimate efficiency of photovoltaic cells?
32. Will fusion always be the energy source of the future?
33. What drives the solar magnetic cycle?
34. How do planets form?
35. How can a skin cell become a nerve cell?
36. What causes ice ages?
37. What causes reversals in Earth's magnetic field?
38. Are there earthquake precursors that can lead to useful predictions?
39. Is there -- or was there -- life elsewhere in the solar system?
40. How does a single somatic cell become a whole plant?
41. What is the origin of homochirality in nature?
42. Can we predict how proteins will fold?
43. How many proteins are there in humans?
44. How do proteins find their partners?
45. How does Earth's interior work?
46. How many forms of cell death are there?
47. What keeps intracellular traffic running smoothly?
48. What enables cellular components to copy themselves independent of DNA?
49. What roles to different forms of RNA play in genome function?
50. Are we alone in the universe?
51. What role do telomeres and centromeres play in genome function?
52. Why are some genomes really big and others quite compact?
53. What is all that "junk" doing in our genomes?
54. How much will new technologies lower the cost of sequencing?
55. How and where did life on Earth arise?
56. How do organs and whole organisms know when to stop growing?
57. How can genome changes other than mutations be inherited?
58. How is asymmetry determined in the embryo?
59. How do limbs, fins, and faces develop and evolve?
60. What determines species diversity?
61. What triggers puberty?
62. Are stem cells at the heart of all cancers?
63. Is cancer susceptible to immune control?
64. Can cancers be controlled rather than cured?
65. What genetic changes made us uniquely human?
66. Is inflammation a major factor in all chronic diseases?
67. How do prion diseases work?
68. How much do vertebrates depend on the innate immune system to fight infection?
69. Does immunologic memory require chronic exposure to antigens?
70. How are memories stored and retrieved?
71. Why doesn't a pregnant woman reject her fetus?
72. What synchronizes an organism's circadian clocks?
73. How do migrating organisms find their way?
74. Why do we sleep?
75. How did cooperative behavior evolve?
76. Why do we dream?
77. Why are there critical periods for language learning?
78. Do pheromones influence human behavior?
79. How do general anesthetics work?
80. How will big pictures emerge from a sea of biological data?
81. What causes schizophrenia?
82. What causes autism?
83. To what extent can we stave off Alzheimer's?
84. What is the biological basis of addiction?
85. How far can we push chemical self-assembly?
86. Is morality hard-wired into the brain?
87. What are the limits of learning by machines?
88. How much of personality is genetic?
89. What is the biological root of sexual orientation?
90. What are the limits of conventional computing?
91. Will there ever be a tree of life that systematists can agree on?
92. How many species are there on Earth?
93. What is a species?
94. Why does lateral transfer occur in so many species and how?
95. Who was LUCA (the last universal common ancestor)?
96. Can we selectively shut off immune responses?
97. How did flowers evolve?
98. How do plants make cell walls?
99. How is plant growth controlled?
100. Why aren't all plants immune to all diseases?
101. What is the basis of variation in stress tolerance in plants?
102. Do deeper principles underlie quantum uncertainty and nonlocality?
103. What caused mass extinctions?
104. Can we prevent extinction?
105. Why were some dinosaurs so large?
106. How will ecosystems respond to global warming?
107. Is an effective HIV vaccine feasible?
108. How many kinds of humans coexisted in the recent past, and how did they relate?
109. What gave rise to modern human behavior?
110. What are the roots of human culture?
111. What are the evolutionary roots of language and music?
112. How hot will the Greenhouse World be?
113. What are human races, and how did they develop?
114. Why do some countries grow and others stagnate?
115. What impact do large government deficits have on a country's interest rates and economic growth rate?
116. Are political and economic freedom closely tied?
117. Why has poverty increased and life expectancy declined in sub-Saharan Africa?
118. What can replace cheap oil -- and when?
119. Is there a simple test for determining whether an elliptic curve has an infinite number of rational solutions?
120. Can a Hodge cycle be written as a sum of algebraic cycles?
121. Will Malthus continue to be wrong?
122. Will mathematicians unleash the power of the Navier-Stokes equations?
123. Does Poincaré's test identify spheres in four-dimensional space?
124. Do mathematically interesting zero-value solutions of the Riemann zeta function all have the form of a + bi?
125. Does the Standard Model of particle physics rest on solid mathematical foundations?

Review: Vince G (2009) How to survive the coming century. New Scientist, February 25, 2009 (Issue 2697): 6pp.

Feature Paper: Vince G (2009) How to survive the coming century. New Scientist, February 25, 2009 (Issue 2697): 6pp.

Author Abstract: Alligators basking off the English coast; a vast Brazilian desert; the mythical lost cities of Saigon, New Orleans, Venice and Mumbai; and 90 per cent of humanity vanished. Welcome to the world warmed by 4ºC.

Note to Readers: Follow links above for author email, full article text, or the publishing scientific journal. Author notes in my review are in quotes.

Review: This week I will change tactics a little bit. Usually I either review a scientific journal article or I discuss a topic in science that usually has implications for coral reefs and the marine environment. This week we'll look at a "popular magazine article" (popular not in the sense that the article reviewed this week has been read by many people, but rather in the sense of a journal intended for consumption by lay readers (usually called a magazine). I have avoided such articles until now just as I would avoid reviewing a Wikipedia article: any university student in the hard sciences (hard not in difficulty but to separate from the social sciences) will have been told by their professors that any articles reviewed must be peer reviewed. Scientific journals articles are first submitted and then reviewed by a committee of the authors' peers (selected by the journal of submission) to consider whether the scientific methods and discoveries are worthy not only of publication but that they meet certain scientific standards. Books and popular mass media articles (either in newspapers, magazines, or the internet), while expected to uphold certain ethical standards (not misstating facts and performing due diligence in research) are not often reviewed as intricately as scientific journal articles (often due to either publication guidelines or the anonymous nature of self-publishing as in internet articles). 

However, public perception is often just as important as scientific fact. I would go so far as to say that it doesn't really matter if something is true or not, only whether people believe it is true or not, since people act on their beliefs rather than their knowledge. To put in another way, often we can't know for certain whether something is true so we have to make educated guesses. The same is true of any scientific survey. Since scientists can't possibly know _everything_ about a certain area or a subject, they take samples and assume (with varying degrees of statistical confidence that are blatantly stated) that their subsamples reflect reality on a larger scale. Of course, as discussed last week, the larger a geographic area being extrapolated (often through mathematical models), the greater the chance that patterns at local levels will not be reflected at regional or metacommunity scales.

I remember reading an article published a few years ago (I believe in Science, a preeminent scientific journal) that reviewed more than 600 scientific journal articles about global climate change and couldn't find a _single_ paper where scientists stated that "global warming" didn't exist, yet a similarly extensive review of popular literature found a 50% dissent rate in opinions about global warming. In other words, articles read by "the public" said half of the time that global warming does not exist or is exaggerated (and made claims of dissent among the scientific community) yet such dissent is not actually found among scientists. Rather, scientists argue or discuss differences in models that are aimed to predict the effect of increasing greenhouse gas levels (well-known and accepted as at their highest concentrations in the last 600,000 years through analysis of Arctic and Antarctic ice core samples -- through analyzing gas bubbles trapped in glacial ices that have not been exposed to current atmospheric regimes and thus are uncontaminated). However, while there is a difference of opinion over how global warming will manifest in large-scale weather systems and ocean currents, there is no dissent (at least in the scientific literature) over whether global warming is actually occurring. Scientists agree that _it is_ occurring.

But as scientists, we must accept that "the public" at large isn't reading scientific literature and thus are being exposed to popular media accounts that are interpreted, in most cases, by people who are _not_ scientific experts. 

Therefore, I thought we would look at one such article. I chose this particular article because the author, while a journalist, was the former scientific media editor for the scientific journal Nature (along with Science and the Proceedings of the National Academy of Sciences, it is one of the top three most influential scientific journals -- see my review in Science Corner paper 16). Therefore, the author is a rare breed of journalist so I hope that this article proves acceptable for this week's review.

The article discusses what may happen if the world's global mean temperature rises by 4ºC (in the middle of predicted temperature increases by the end of the 21st century, as reported in a "2007 report of the Intergovernmental Panel on Climate Change"). One point I'd like to make clear (and I've mentioned this a few times in the past) is that when we (as scientists) talk about "global warming" (or global climate change), we are talking about temperature increases for the entire planet as a whole. However, the planet will not (and does not) heat evenly, so some locations will experience higher temperature increases while others may experience less. Also, as the poles of the Earth heat up, ice melts, which causes a mass of cold air to move away from the poles (both through ocean convection and through air masses), which may cause temporary _cooling_ of high latitudes (temperate regions). However, as heating in the poles continues, eventually regulation of the climate will result in overall heating of all areas. 

Note the figure below (from NOAA data; left shows temperature change from 1901 to 2005 on a "per century" basis while the right shows just temperature shifting from 1979 to 2005 on a decadal basis), which shows how temperature has shifted on average in all parts of the world over the last century. Note that not all areas are the same color, meaning that not all areas of the Earth have heated equally. Note on the righthand side of the figure the area north of Antarctica that is blue, showing an overall temperature _decrease_ while most other areas show a temperature increase (and vast areas of the ocean show no temperature change, in white), which I described above.

The problem that scientists face is in predicting how such simple facts (heating of the poles) manifest in reality to change very complex (and not completely understood) weather patterns on a global scale. Everything in nature is seeking an equilibrium (often through the effects of entropy). However, when we look at weather systems that have been established over thousands of years (the global ocean "conveyor belt" that transports water from the poles to the equator and back takes about 1000 years to complete a single cycle), it has proven very difficult to accurately predict _how_ changes will manifest. For example, while scientists know that more tropical storms and hurricanes are occurring in the Atlantic Ocean than ever before, they cannot yet predict how many storms will occur each season or exactly where because there aren't enough data available for the global climate system.

Given this uncertainty, the author of this week's review article discusses what various scientists and governmental groups think about the survival of the "world as we know it." The author points out that "the good news is that the survival of humankind itself is not at stake: the species could continue if only a couple of hundred individuals remained. But maintaining the current global population of nearly 7 billion, or more, is going to require serious planning."

The author is also correct to point out the difference between a localized difference in 4ºC versus "an average warming of the entire globe." In the figure above, just in the 25 years from 1979 to 2005, the highest noted temperature increase was only about 1ºC. Imagine the _entire_ globe, therefore, increasing by 4ºC and it becomes clear that the unpredictable and "extreme" weather we've seen in the last few decades is _nothing_ compared to what is in store.

Often, to predict the future, we look to the past. Scientists are the same. I've already noted how global carbon dioxide levels can be measured accurately until about 600,000 years ago, but the fossil record can also help infer certain levels. The author notes that "the last time the world experienced temperature rises of this magnitude [4ºC over the entire globe] was 55 million years ago, after the so-called Palaeocene-Eocene Thermal Maximum event." During that time in the Earth's history (shortly after, in a geologic sense, the reign of the dinosaurs) there were no polar ice caps. Sea levels were 100 meters higher than today (the highest they can go given a complete melting of all ice on Earth, unlike the predictions of some movies like 2012 or Waterworld), over 90% of ocean life died off through the release of vast methane stores at the bottom of the ocean, and the atmosphere of the Earth was "filled with around 5 gigatonnes of carbon." 

Should just all the ice on Greenland melt, for instance, sea levels would rise by about 10 meters, not to mention the 100 meters (333 feet) higher sea levels should all ice melt. While 33 feet doesn't sound like a lot for most places, the reality is that many coastal regions would be submerged (including nearly all of Florida, Bangladesh, the Maldives, many Pacific Islands, and a vast sea would bisect the United States through flooding huge parts of Texas and extending north). Something like 90% of the people on the planet live within 150km of a coastline, meaning that most coastal cities would also be affected and many agricultural regions (often in low-lying basins adjacent to rivers, which tend to deposit fertile soils) would be destroyed through saltwater inundation.

The author points out that fully "half of the world's surface lies in the tropics, between 30º and -30º latitude, and these areas are particularly vulnerable to climate change." Also, since the equator is warmer, sea levels would rise faster there than in higher latitudes through expansion of already hot water masses.

The author discusses various dissenting views of predictions on how humanity will survive in a "doomsday" scenario as the Earth's global climate system adjusts to regulate itself. While such predictions are purely guesswork (based on expectations from large-scale changes), the reality is that change IS coming and that since the atmosphere is nearly the same over the entire world in terms of carbon dioxide levels (though localized pollution levels certainly occur and there are differences in moisture levels and pressure levels, which help to drive the global ocean conveyor belt mentioned earlier), we can expect at the very least that as global atmospheric carbon dioxide levels increase, environmental changes will also be on a global level.

Turning to the sociological, the author notes that "in order to survive, humans may need to do something radical: rethink our society not along geopolitical lines but in terms of resource distribution. [However] taking politics out of the equation may seem unrealistic: conflict over resources will likely increase significantly as the climate changes, and political leaders are not going to give up their power just like that. Nevertheless, overcoming political hurdles may be our only chance [since] we will need to abandon huge areas and move people to where the water is."

All of the above may seem more like science _fiction_ than science but the unfortunate reality is that science only goes so far. Science can work to explain current events and patterns and try to predict (with varying degrees of statistical confidence) environmental regimes in the future, but it is up to politicians to decide how to use such knowledge. Unfortunately, with some of the most powerful (politically and militarily) nations of the world (United States of America, Australia, China) continuing to pollute and contributing greatly to atmospheric climate change on a global level, and with increasing cattle production (cows alone account for nearly 25% of all greenhouse gas emissions globally compared to only 2% for all airline traffic and 4% for all shipping traffic) and deforestation and increasing ocean acidification (discussed in several past Science Corner articles), it remains unclear whether humanity will act in time to stave off a "doomsday" and rise to meet the challenge facing future generations.

Only time will tell...

Review: Pascual M (2005). Computational Ecology: From the complex to the simple and back. PLoS Computational Biology, 1(2):e18. doi:10.1371/journal.pcbi.0010018

Feature Paper: Pascual M (2005). Computational Ecology: From the complex to the simple and back. PLoS Computational Biology, 1(2):e18. doi:10.1371/journal.pcbi.0010018

Author Abstract: In 1958, when ecology was a young science and mathematical models for ecological systems were in their infancy, Elton  wrote of the ‘‘neolithic days of animal ecology, that is to say about twenty-five years ago.’’ Acknowledging the influence of Lotka and Volterra, he noted, ‘‘Being mathematicians, they did not attempt to contemplate a whole food-chain with all the complications of five stages. They took two: a predator and its prey.’’


Note to Readers: Follow links above for author email, full article text, or the publishing scientific journal. Author notes in my review are in quotes.


Review: Sorry for being a day late with this week's review article. This article goes through the history of how the science of ecology developed to include mathematics from the classic days of Lotka and Volterra and the progress since then. For those who've taken a basic university ecology class, the Lotka-Volterra Predator-Prey model is key. Today, from the basic interactions outlined by those pioneering ecologists (not too long ago, by the way, to put the development of ecology and many other sciences in perspective), ecologists are using mathematical models to describe even complex "transmission of infectious diseases often [using] spatial or social networks [that] can span from local to global distances." 

The author notes though that instead of getting caught up on how complex nature can truly be, "an alternative and more useful role of computation is to address questions on the relationship between dynamics at different temporal, spatial, and organizational scales." 

The author notes how effects at local scales can be used to infer results at the global level. Local populations can either be well-mixed, in which case "mean-field equations" are appropriate, or else "stochastic models such as interacting particle systems" can be used for more complex local population dynamics. 

The author mentions how a difficulty with large-scale or global models is that they often rely on assumptions about knowledge at "microscopic" or local levels. One must be careful with models to ensure that they can account for local effects as well as large-scale effects.

The author then describes how ecology has expanded into the field of "global change ecology." As more data are collected from more locations worldwide, scientists are now able to track changes on large (or global) scales over time. A specific example given by the author is the case of phytoplankton, which are now monitored through remote sensing globally in terms of net primary productivity. 

The author further discusses how the larger a network becomes, the more non-linear the dynamics of interactions within the network become. In ecology, the classic example of "food webs" is receiving renewed interest as scientists are able to collect and analyze data at higher resolutions than ever before. 

Ecologists now also recognize that besides predator and prey interactions, other interactions need to be accounted for, such as "mutualism and parasitism, which can play an important role in ecosystem persistence and bioenergetics." 

The author concludes the article with the take-home message that "stochastic assembly models are prehaps the best candidates to develop a general dynamic theory not only to address open questions on the relationship between structure and dynamics, but also to generate the macroscopic community patterns that ecologists observe in nature and characterize diversity (such as species-area curves and species-rank abundance curves)."

In the last 25 years, ecology has come a long way and now as we enter the era of macroecology, where now scientists are trying to "explore the relationship between dynamics across scales," particularly those at regional and global levels. These interactions become increasingly vital to ascertain as the threat of various kinds of ecosystem collapse (or significant changes in interactions as species extinctions occur more frequently) from human-induced climate change may very well occur in the coming 25 years.

Review: Zhang DD, Brecke P, Lee HF, He Yuan-Qing, Zhang J (2007) Global climate change, war, and population decline in recent human history. Proceedings of the National Academy of Sciences, 104(49):19214-19219.

Feature Paper: Zhang DD, Brecke P, Lee HF, He Yuan-Qing, Zhang J (2007) Global climate change, war, and population decline in recent human history. Proceedings of the National Academy of Sciences, 104(49):19214-19219.  

Author Abstract: Although scientists have warned of possible social perils resulting from climate change, the impacts of long-term climate change on social unrest and population collapse have not been quantitatively investigated. In this study, high-resolution paleo-climatic data have been used to explore at a macroscale the effects of climate change on the outbreak of war and population decline in the preindustrial era. We show that long-term fluctuations of war frequency and population changes followed the cycles of temperature change. Further analyses show that cooling impeded agricultural production, which brought about a series of serious social problems, including price inflation, then successively war outbreak, famine, and population decline successively. The findings suggest that worldwide and synchronistic war–peace, population, and price cycles in recent centuries have been driven mainly by long-term climate change. The findings also imply that social mechanisms that might mitigate the impact of climate change were not significantly effective during the study period. Climate change may thus have played a more important role and imposed a wider ranging effect on human civilization than has so far been suggested. Findings of this research may lend an additional dimension to the classic concepts of Malthusianism and Darwinism.  

Note to Readers: Follow links above for author email, full article text, or the publishing scientific journal. Author notes in my review are in quotes.  

Review: After a couple of weeks discussing modern science, we return this week to a review of a scientific paper. We'll discuss this paper out of the dozens on climate change because it also addresses how war versus peacetime can be affected by climate change. In essence, all conflict in the world is over environmental resources, even religious or ideological conflicts. People generally fight over who should have the right to certain resources and whether the excuse is ideological or not, the result is the same: some people are in power over certain lands and resources while others are not.  

The paper discusses how climate change can lead to social unrest and population collapse. Whereas many people assume that population will continue to increase, many scientists believe that the Earth's current population level is unsustainable for the resources that are being used. What is interesting about this paper is that the authors found that war frequency and population changes followed periods of climate change. The authors further found that "worldwide war-peace, population, and price cycles in recent centuries have been driven mainly by long-term climate change."  

The authors performed a review of past studies that addressed the concept of environmental conflict and how in middle-latitude regions long-term climate change acted as a control on population size. The authors discussed how past studies have lacked large-scale quantitative data to make significant conclusions towards clearly linking climate change with wars and conflicts. The authors were able to make their conclusions by seeing the effects of climate change on agricultural production. During periods of environmental cooling between AD 1400 - 1900, agricultural production in many areas of the world decreased. The authors then determined how as the "carrying capacity" of land goes down, food resources grow short and the incidences of "armed conflicts, famines, and epidemics" increased, leading to reductions in population size. As population decreased historically as a result of the above stressors, food resources increased and population increased. In essence, it is similar to a "boom and bust" in predator-prey models, only instead of predators and prey the metrics are agricultural production and climate change. The authors then linked agricultural production levels with human conflicts and population size.  

A good paragraph of the paper that sums up several of the authors' key tenets is: "Under ecological stress, adaptive choices for animal species are the reduction of population size, migration, and dietary change. Depopulation typically takes place through starvation and cannibalism. Humans have more pathways, social mechanisms, to adapt to climate change and mitigate ecological stress. Besides migration, they include warfare, economic change, innovation, trade, and peaceful resource redistribution. We believe that in late agrarian society established political boundaries in populated areas limited mass migration; the result of such mass migration, when it occurred, often was war. Economic change was a costly and slow process that involved changing cultures, technologies, and habits. When the speed of human innovation and its transfer were not fast enough to keep pace with rapid ecological change, famine and disease became difficult to avoid. Trade and redistribution under the condition of shrinking resources would not help much because the ecological stress was at a global or very large regional scale. Finally, human social development in the form of international and national institutions was not strong enough to buffer the tensions caused by food resource scarcity. Therefore, war and population decline became common consequences of climate-induced ecological stress in the late preindustrial era."  

The authors quantitatively showed how their conjectures were supported by data from several databases that had not previously been linked, summarized in their figure below. 

The only period of time within the last 600 years of the study where the patterns of temperature, agricultural production, warfare, and population decrease did not follow predicted patterns was during "the late 19th century when the temperature of the Southern Hemisphere was its coldest in the last millennium and a great number of wars occurred in the southern part of Africa." Based on the above figure, the authors were able to conclude that "synchronous periods of relative peace and turbulence during [the 500 years from 1400 - 1900 AD] were a global phenomenon seemingly linked to temperature change."  

And while the authors only had high-resolution temperature data for the last 600 years, the authors were able to link temperature and warfare in China back to 1000 AD as a result of climate change causing certain tribal groups under temperature stress "to enter central China" with warfare aimed at securing resources that were lost through climate change in the north of China.

The authors also found that after 1900 AD (as a result of changes in society to industrialized regimes) climate change only had a limited affect on agricultural production, the great conflicts of the 20th century, and changes in population densities. Therefore, technology may have some ability to stave off certain pressures on human population decline following environmental stress. What is unclear is how the current increasing temperature as a result of human-induced global climate change and increasing greenhouse gas production may complicate historical models of climate, population decline, and warfare. It is also unclear whether technology will enable societies to stave off population decline should the new "model" of increasing greenhouse gas production cause crop failures and lower levels of agricultural production. Also, there are now pesticides and fertilizers that have increased agricultural production levels higher than at any time in history.

In essence, we (as a society) are entering a grey area. On the one hand, technology has enabled us to better quantify effects that humans have made to the environment, but on the other hand as those effects become larger, the chances of humans having to deal with environmental conditions unlike anything else in recorded history for the Earth also increase. Furthermore, many scientists have stated that as global temperature increases with increasing carbon dioxide production (as a result of industrialization), droughts will also increase and potentially lead to water shortages and conflicts (e.g., wars) over control of those resources. It seems fairly certain that the Earth is not going to enter a period of lower temperature soon but it remains unclear whether the cycle of war and population pressure will ever end.

I firmly believe that all of the Earth's environmental problems can be solved using already-developed technologies but that such technologies are not being implemented through a lack of will. Individuals and corporations generally act in their own interests, whereas the stresses that future generations will face through global climate change must involve a global response, which only governments can  deliver.

Review: Allesina & Pascual (2009) Googling food webs: Can an Eigenvector measure species' importance for coextinctions? PLoS Computational Biology, 5(9):e1000494 (6pp). doi:10.1371/journal.pcbi.1000494

Feature Paper: Allesina & Pascual (2009) Googling food webs: Can an Eigenvector measure species' importance for coextinctions? PLoS Computational Biology, 5(9):e1000494 (6pp). doi:10.1371/journal.pcbi.1000494


Author Abstract: A major challenge in ecology is forecasting the effects of species’ extinctions, a pressing problem given current human impacts on the planet. Consequences of species losses such as secondary extinctions are difficult to forecast because species are not isolated, but interact instead in a complex network of ecological relationships. Because of their mutual dependence, the loss of a single species can cascade in multiple coextinctions. Here we show that an algorithm adapted from the one Google uses to rank web-pages can order species according to their importance for coextinctions, providing the sequence of losses that results in the fastest collapse of the network. Moreover, we use the algorithm to bridge the gap between qualitative (who eats whom) and quantitative (at what rate) descriptions of food webs. We show that our simple algorithm finds the best possible solution for the problem of assigning importance from the perspective of secondary extinctions in all analyzed networks. Our approach relies on network structure, but applies regardless of the specific dynamical model of species’ interactions, because it identifies the subset of coextinctions common to all possible models, those that will happen with certainty given the complete loss of prey of a given predator. Results show that previous measures of importance based on the concept of ‘‘hubs’’ or number of connections, as well as centrality measures, do not identify the most effective extinction sequence. The proposed algorithm provides a basis for further developments in the analysis of extinction risk in ecosystems.


Note to Readers: Follow links above for author email, full article text, or the publishing scientific journal. Author notes in my review are in quotes.


Review: What is particularly interesting about this paper is that it uses a technique from computer science (specifically, the algorithm Google uses to establish page ranks) to help predict species extinctions. Taking the place of a computer network, the authors substitute the network of a food web.
Crucial to the author's definition of species extinction is the concept of keystone species, where other species rely on one species for their survival. When such keystone species go extinct, there is the potential for ecosystem collapse to occur. The authors note that "a species is important if important species rely on it for their survival," with the most important keystone species having the highest "rank" much as Google might rank a website as the highest based on how many people or links connect or reference to it.


The authors use a food web approach because "species are not isolated, but connected to each other in tangled networks of relationships known as food webs." The authors use the Google approach because they recognize that the more species there are in an ecosystem, the more complex a ranking system becomes. Therefore, taking a cue from computer science, the authors "reverse engineer" the problem of determining how to "make biodiversity collapse in the most efficient way in order to investigate which species cause the most damage if removed." The authors found that the Google "Page Rank" algorithm (adapted for food webs) "always solves this seemingly intractable problem, finding the most efficient route to collapse. The algorithm works in this sense better than all the others previously proposed and lays the foundation for a complete analysis of extinction risk in ecosystems."


Using knowledge of food web connections the authors ran their algorithm by removing one species at a time and recording the number of secondary extinctions. The more secondary extinctions, the more important a given primary species is. Species with particular importance form nodes, with the largest nodes being keystone species.


Through using techniques in the mathematical branch of topology and a computer science approach, the authors were able to solve a problem in ecology that has been around for 50 years: how can scientists predict extinction rates. Besides helping with better understanding and protecting modern ecosystems, the results of this paper can also be used to look at extinction coefficients that are used to reconstruct mutation rates and speciation events in historical ecology.

Review: Fersht A (2009) The most influential journals: Impact Factor and Eigenfactor. Proceedings of the National Academy of Sciences (PNAS), 106(17):6883-6884. DOI: www.pnas.org/cgi/doi/10.1073/pnas.0903307106

Feature Paper: Fersht A (2009) The most influential journals: Impact Factor and Eigenfactor. Proceedings of the National Academy of Sciences (PNAS), 106(17):6883-6884. DOI: www.pnas.org/cgi/doi/10.1073/pnas.0903307106

Author Abstract: Progress in science is driven by the publication of novel ideas and experiments, most usually in peer-reviewed journals, but nowadays increasingly just on the internet. We all have our own ideas of which are the most influential journals, but is there a simple statistical metric of the influence of a journal?

Note to Readers: Follow links above for author email, full article text, or the publishing scientific journal. Author notes in my review are in quotes.

Review: This week's review tackles the topic of influential scientific journals, and as the opening lines of this paper correctly asserts, scientific progress is driven by the publication of ideas and discoveries. Performing science alone is not important enough. If no one can read or hear about your discoveries or ideas, then it is just as if those ideas or discoveries were never known. This paper is quite short (just over 1 page in length) and can be accessed through a free online scientific journal that also happens to be one of the more influential journals (by the metrics in the journal, the second-most influential journal).

The author discusses the two most common scientific metrics to determine influence of scientific journals since in theory, if an idea or discovery is published in a journal with a wide audience and a journal that is well-respected, then the idea or discovery will reach the largest scientific audience possible. The author states that there are at least "39 scales" used to measure impact of scientific ideas, but two are most commonly used.

The first metric to determine scientific influence for journals is "Impact Factor" (IF; methods can be found at www.thomsonreuters.com/products_services/scientific/Journal_Citation_Reports). Impact Factor represents "the average number of citations in a year given to those papers in a journal published in the previous 2 years." Part of the problem of that method is that a popular paper is more likely to become even more popular as time goes on because when someone wants to cite or reference a topic in their own paper as background, they are more likely to pick an influential paper to cite if given a choice. Nevertheless, it is true that a paper cannot artificially get to a high number of citations: scientific peers must decide that the paper is influential. If a paper is published in an influential journal it is more likely to be cited as some will almost by default consider the paper "good" but still, to be cited by many is a democratic process.

The second metric that can determine influence is "Eigenfactor" (methods at www.eigenfactor.org/methods.htm). Instead of concentrating on individual articles or authors, Eigenfactor ranks journals by their influence. The author shows that Eigenfactor heavily correlates to total citations for a journal. By the Eigenfactor method, four journals have much more scientific influence than any other journals: Nature, Proceedings of the National Academy of Sciences, Science, and the Journal of Biological Chemistry.

The author points out that "all journals have a spread of citations, and even the best have some papers that are never cited plus some fraudulent papers and some excruciatingly bad ones. So, it is ludicrous to judge an individual paper solely on the IF of the journal in which it is published." This is an important point because all institutions use publication record to evaluate scientific employees and those who publish in the big four journals are likely to be viewed much more favorably just by nature of that publication than other similarly qualified peers.

The author points out a third metric that he describes as "the least evil," the h-index, "which ranks the influence of a scientist by the number of citations to a significant number of his or her papers; an h of 100 would mean that 100 of their publications have been cited at least 100 times each."

The author finishes the article with the caution that "it is important not to rely solely on one standard," important words to live by in any profession.

Next paper we'll examine a scientific effort to implement the an "Eigen" metric similar to that mentioned in this article for determining the impact of extinctions on ecosystem collapse.

Review: Alon U (2009) How to give a good talk. Molecular Cell, 36:165-167. DOI 10.1016/j.molcel.2009.10.007

Feature Paper: Alon U (2009) How to give a good talk. Molecular Cell, 36:165-167. DOI 10.1016/j.molcel.2009.10.007


Author Abstract: We depend on talks to communicate our work, and we spendmuch of our time as audience members in talks. However, few scientists are taught the well-established principles of giving good talks. Here, I describe how to prepare, present, and answer questions in a scientific talk. We will see how a talk prepared with a single premise and delivered with good eye contact is clear and enjoyable.


Note to Readers: Follow links above for author email, full article text, or the publishing scientific journal. Author notes in my review are in quotes.


Review: The way I see it, if there are any people reading regularly this Science Corner, we've gone through a semester's worth of scientific papers, with more than half about coral reefs. Essentially, we've covered a seminar in marine biology with a few primers on topics aimed at making students eventual professionals and other interested readers in gaining knowledge similar to a graduate-level marine biology seminar series. Sorry I can't give university credits out ;)


So this week we will look at a paper on giving a public talk, since most seminars involve a final presentation at the end. Also, I read somewhere that public speaking is the greatest fear of Americans and I am guessing it is a similar fear in other cultures and countries. I have over 300 hours of public speaking experience to various groups: school children (grade school through high school), university settings, the public, at scientific conferences, to journalists (newspaper, magazine, radio, television), for an educational promotional environmental documentary, etc., so hopefully I can put in some tips after reviewing this week's paper.


The three main stages of a talk that Alon discusses are: 1) Prepare; 2) Presentation; 3) Questions


Within the preparation stage, Alon mentions that you should "choose a premise for the entire talk and title each slide with its own premise." 


Alon states that each slide should be titled with a complete sentence rather than fragments or questions. The sentence should "convey the idea you want to get across."


Within the presentation stage, Alon points out that it is key to "make eye contact with audience." 


Alon states that it is important to avoid a lot of experimental data within a talk so that the audience does not think you are trying to impress them with the amount of work you completed. Remember, the greatest speakers and teachers can take complex ideas and present them clearly and in layman's terms so that anyone can understand what they are talking about. Think about a great nature documentary. You don't see the months of waiting and the hours of unused footage. You see the end product and you are still impressed. The purpose of a talk is not to convey how much work you did but rather to convey an idea or concept that you discovered through your research.


One great piece of advice I was given was that for presentations involving slides or powerpoint, only have a maximum of one slide for every 3 minutes of speaking, including your introductory slide and your closing side (with an upper maximum of about 15 slides). Also, make sure those slides don't have a lot of writing on them. You don't want your audience reading every little detail on your slides. You want to convey information so that you can talk people through your slide, which could be a complex image, rather than essentially reading your slide out loud. Alon states that you should plan for a slide every 90 - 120 seconds, but I think that shorter is better. However, Alon also states that you should plan for a talk by preparing a third shorter than your allotted timeframe, so in actuality, he advocates a slide every 2 - 3 minutes, which is about in-line with what I was taught as well. The main point is that you want to avoid a rapid succession of slides or slides without substance.


Now, you may have seen a talk that impressed you that breaks the above rules, but also remember that it is good to learn the "rules" of public speaking and get comfortable before you start breaking convention. You should understand why you want to break a certain rule and the impact that your rule-breaking will have on your talk.


But always remember, you should know your slides but be speaking to the audience when they are on the screen. The only exception that I was taught was that if you have a graph, you should highlight (say with a laser pointer) the axes and describe them, though you can do the same by stating to the audience, "On the X axis we have..." or something similar.


For the questions stage, be sure to "listen and repeat the question and answer its content, not its tone."


By repeating the question you ensure that for those audience members who didn't hear the question, that you can give background for your answer. I can concur with Alon that there is little more frustrating as an audience member to listen to a great talk but then have someone ask a question that you couldn't hear, only to become confused by the answer rather than enlightened.


Alon also points out that for complex questions or areas where you truly don't understand, it is better to admit that you don't know or that you'd like to discuss the question further with the audience member after the talk.


Also, to deal with "aggressive questions, separate between the dramatic action -- the music of the question, and the text -- the content of the question." Be sure not to get defensive during questions. It is better to respond with "that is a very important criticism" rather than lose your cool.


And while I believe that practice helps with getting better at public speaking, Alon also gives this advice for those with stage fright: "One of the best ways to overcome stage fright is to tell yourself an empowering store: instead of coming before a pack of wolves, you are a shepherd leading the audience, holding each member's hand through a fascinating story." Despite the religious overtones of that advice, it is an apt analogy. You are there to tell a story, so keep that story simple with fewer slides than you think you need so that you do not get more stressed during your talk by having to end in the middle because you are rambling on and then quickly parading through dozens of superfluous slides to try and finish your main conclusions in time. And if you are really worried about reaching your conclusions, then give them first. Tell the audience what you discovered and then use the rest of the talk as a vehicle for taking the audience on a journey with you as you describe how you came to your conclusions.


But most importantly, good luck!


After you've given a few talks you may be at the stage of writing your first scientific paper, but where should you submit? Next week's paper review may help you.