COMMENTARY The ethics of incidental findings
Early branch in the animal tree
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LETTERS Health Information and the Like IN THEIR REPORT “SOCIAL INFLUENCE BIAS: A RANDOMIZED EXPERIment” (9 August, p. 647), L. Muchnik et al. explore whether Internet rating dynamics are influenced by the online community. They found that a comment that is initially slightly popular, regardless of credibility or quality, is more likely to garner even more “likes.” The study results “demonstrate that whereas positive social influence accumulates, creating a tendency toward ratings bubbles, negative social influence is neutralized by crowd correction.” This study reveals that social influence substantially biases ratings in systems designed to aggregate online opinions. Social media sites that aggregate online opinions are increasingly popular among the public for dissemination of health information (1, 2). Unfortunately, a study examining the 2009 H1N1 epidemic found that tweets that had a negative sentiment about influenza vaccination were more likely to be retweeted than tweets supporting vaccination (3, 4). Also, unexpectedly, tweets promoting vaccine use increased the likelihood of negative tweets against vaccine use (3). Surprisingly,
CREDIT: ROBERT CHURCHILL/ISTOCKPHOTO
Response REDDI’S THOUGHTFUL COMMENTS HIGHLIGHT the importance to global health policy of understanding the spread of health behaviors in society. Social media sites and online communication platforms are becoming increasingly popular sources for the dissemination of health information about prevention, medical conditions, and treatments. They are therefore central to the promulgation of beliefs about health threats and the efficacy of health interventions. To explain the critical points of divergence between our results and Reddi’s conclusions, we must critically evaluate the scientific evidence. First, ours is a randomized experiment, whereas the studies to which Reddi refers rely on observational data to make claims about information diffusion (the authors of those studies are careful to note that their evidence is predictive rather than causal). Our
in contrast to the findings by Muchnik et al., it appears that “crowd correction” may actually obfuscate social media health communications in certain situations (5). Social influence bias in health communications could potentially be detrimental to public health. Erroneous health information propagated by social media can endanger patient safety (6). Public health entities should consider the effects of social influence bias in health communications. In addition to promoting positive health messages on social media, public health strategies must also limit the spread of “popular” but erroneous health information (4). ANAND REDDI University of Colorado School of Medicine, Anchutz Medical Campus, Aurora, CO 80045, USA. E-mail: [email protected]
References 1. B. W. Hesse et al., Arch. Intern. Med. 165, 2618 (2005). 2. W.-Y. S. Chou, Y. M. Hunt, E. B. Beckjord, R. P. Moser, B. W. Hesse, J. Med. Internet. Res. 11, e48 (2009). 3. M. SalathÈ, D. Q. Vu, S. Khandelwal, D. R. Hunter, EPJ Data Sci. 2, 10.1140/epjds16 (2013). 4. K. S. Yoshida, ARS Technica, “When it comes to vaccination, bad news is contagious” (http:// arstechnica.com/science/2013/04/when-it-comes-to-vaccination-bad-news-is-contagious/). 5. M. SalathÈ, S. Khandelwal, L. A. Meyers, PLOS Comput. Biol. 7, e1002199 (2011). 6. E. Campbell, M. SalathÈ, Sci. Rep. 3, 10.1038/srep01905 (2013).
approach, which randomly manipulates the ratings of online content, holds author and content effects constant, isolating the causal effect of upvoting or downvoting on opinion and behavior change. For example, the studies Reddi refers to analyze whether negative or positive content about vaccinations is more likely to be retweeted. They do not exclude several likely alternative explanations of retweeting behavior that may have nothing to do with whether negative tweets are actually changing people’s opinions about vaccinations. The majority of Twitter users may already have a negative opinion of vaccinations, inspiring more retweets of such content. The evidence Reddi cites shows that negative information spreads farther and faster. Our experiment shows that negative information is less likely to change people’s opinions than positive information. These two claims
www.sciencemag.org SCIENCE VOL 342
are not contradictory. In fact, they are complementary pieces of evidence in a broader story of information diffusion and behavior change. Observational data about and simulations of information diffusion are useful for understanding how information and awareness spread (1, 2), but causal inference is necessary to understand how information diffusion subsequently changes opinions and behavior (3–5). Combining causal analysis of
Letters to the Editor Letters (~300 words) discuss material published in Science in the past 3 months or matters of general interest. Letters are not acknowledged upon receipt. Whether published in full or in part, Letters are subject to editing for clarity and space. Letters submitted, published, or posted elsewhere, in print or online, will be disqualified. To submit a Letter, go to www.submit2science.org.
13 DECEMBER 2013
Published by AAAS
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Downloaded from www.sciencemag.org on November 13, 2015
edited by Jennifer Sills
LETTERS 1
behavior change with predictive analysis of information diffusion creates a powerful lens through which to understand the ebbs and flows of behaviors on our planet. Second, analogies between ratings systems and Twitter will always remain loose at best. Collective opinion rating aggregates and presents the opinion of the community about specific content. Voters are likely motivated by the hope that their vote may influence the opinion of other readers. (Re)tweeting information on Twitter is also likely motivated by the desire to maximize publicity for certain information, the desire to become known as a curator of certain information, and the desire for personal exposure. Ratings are anonymous and do not push information to one’s “followers,” making these motivations less likely in ratings systems than on Twitter. The influence mechanisms in these scenarios differ as well. Twitter users are not privy to collective opinion on a topic when they tweet about it, whereas they are precisely aware of collective opinion when they vote on or rate an item. Thus, the motivations for diffusing information and rating it likely differ. Perhaps more important, differences in the designs of these systems could drive differences between our results and results from studies of Twitter. Whereas the system we studied enables users to either upvote, downvote, or abstain, there is no feature analogous to the downvote on Twitter. The user can only choose to either retweet or not. Facebook operates similarly: One can like content, but not dislike it. Our results generalize to collective opinion aggregation and ratings systems; results from studies of Twitter likely generalize to microblogging and networked information dissemination systems. SINAN ARAL,1* LEV MUCHNIK,2 SEAN TAYLOR3
MIT Sloan School of Management, Cambridge, MA 02142, USA. 2School of Business Administration, The Hebrew University of Jerusalem, Mount Scopus, Jerusalem, 91905, Israel. 3NYU Stern School of Business, New York, NY 10012, USA. *Corresponding author. E-mail: [email protected]
References 1. E. Adar, L. A. Adamic, in The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (Compiégne, France, 2005), pp. 207–214. 2. S. Aral, M. Van Alstyne, Am. J. Soc. 117, 90 (2011). 3. S. Aral, L. Muchnik, A. Sundararajan, Proc. Natl. Acad. Sci. 106, 21544 (2009). 4. C. R. Shalizi, A. C. Thomas, Soc. Meth. Res. 40, 211 (2011). 5. S. Aral, L. Muchnik, A. Sundararajan, Network Sci. 1, (2013).
Setting the Course for a Green Internet IN HIS PERSPECTIVE “TOWARD A GREEN Internet” (29 March, p. 1533), D. Reforgiato Recupero reflects (partially) some of the ideas and even the wording of the ECONET project’s Description of Work (DoW) (1), first published on 6 September 2010. However, the general philosophy of the ECONET Consortium is much broader than described in the Perspective, and we agree with the critical comment (2) that interest in reducing the impact of the Internet on the environment is not a recent development. Apart from Data Centers, which were not part of the ECONET mandate, all network segments ranging from the home network to metro/transport and core networks were considered in the DoW, and optimization methods were considered for each [see (3), which is also the source for the figure shown in the Perspective]. ECONET has recently been working toward implementation of a Network Connectivity Proxy (4), which allows user
CORRECTIONS AND CLARIFICATIONS News & Analysis: “Old dogs teach a new lesson about canine origins” by E. Pennisi (15 November, p. 785). The caption for the photograph of the buried dog in Illinois stated that the dog is 1000 years old; the dog is 8500 years old. The HTML and PDF versions online have been corrected. Association Affairs: “What’s so special about science (and how much should we spend on it?)” by W. H. Press (15 November, p. 817). In Fig. 2, the labels for Germany and Switzerland should be reversed, as well as the labels for Italy and Hungary and for Spain and Czech Republic. The HTML and PDF versions online have been corrected. News of the Week: “Whose brain is it anyway?” (8 November, p. 678). The central brain in the caption was mislabeled as belonging to Conrad Heinrich Fuchs, but it was Carl Friedrich Gauss’s. Also, the son of Rudolf Wagner who likely mixed up the brains 150 years ago was Hermann, not Thomas. The HTML and PDF versions online have been corrected. Reports: “Femtosecond visualization of lattice dynamics in shock-compressed matter” by D. Milathianaki et al. (11 October, p. 220). Two equations were incorrect in the text. The correct equations are Δθ ~ tanθ0sin2θ0 × εen (sixth paragraph p of text) and Δθ ~ –tanθ0 × εt (seventh paragraph of text). The HTML and PDF versions online are correct. Reports: “Functional extinction of birds drives rapid evolutionary changes in seed size” by M. Galetti et al. (31 May, p. 1086). Point 16 in Fig. 1 was slightly misaligned. The PDF and HTML versions online have been corrected. Perspectives: “Toward a green Internet” by D. Reforgiato Recupero (29 March, p. 1533). The figure credit was missing. It should be “The original figure appears in: R. Bolla, R. Bruschi, F. Davoli, F. Cucchietti, IEEE Commun. Surveys Tutorials 13, 223 (2011), as Figure 11 on p. 230.” The HTML and PDF versions online have been corrected.
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devices to be kept in a resting (low-energy) state by maintaining all vital network operations on their behalf. We have always emphasized the tradeoff between energy efficiency and performance: Examples of device-level and network-wide energy-aware optimal management and control strategies can be found in (5) and (6), respectively. Finally, a fundamental aspect to our project that was not discussed in the Perspective is the need to provide standard interfaces between algorithms for management and control and a variety of hardware. The ECONET proposed solution—the Green Abstraction Layer (7)— is currently under consideration for standardization by the European Telecommunications Standards Institute. Although the ECONET project approaches its end after 3 years of activity, network power management technologies are becoming a reality in a number of network devices, as witnessed by industrial prototypes (8), which demonstrate reductions of at least 50% in power consumption. RAFFAELE BOLLA1,2 ROBERTO BRUSCHI,2* FLAVIO CUCCHIETTI,3 FRANCO DAVOLI1,2 1
Department of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, 16126, Genoa, Italy. 2National Inter-University Consortium for Telecommunications (CNIT), University of Genoa Research Unit, 16126, Genoa, Italy. 3Telecom Italia Labs (TILabs), 10148, Torino, Italy. *Corresponding author. E-mail: [email protected]
References 1. ECONET (low-Energy COnsumption NETworks) Project, Grant Agreement no. 258454, Annex I, “Description of Work,” European Commission, 6 September 2010 (available from the authors upon request). 2. Comment on D. Reforgiato Recupero, Science 339, 1533 (2013); http://comments.sciencemag.org/ content/10.1126/science.1235623. 3. R. Bolla, R. Bruschi, F. Davoli, F. Cucchietti, IEEE Commun. Surveys Tutorials 13, 223 (2011). 4. R. Bolla, M. Giribaldi, R. Khan, M. Repetto, “Network connectivity proxy: An optimal strategy for reducing energy waste in network edge devices,” Proc. 24th Tyrrhenian International Workshop on Digital Communications (TIWDC 2013)—Green ICT, Genoa, Italy, 23 to 25 September 2013. 5. R. Bolla, R. Bruschi, A. Carrega, F. Davoli, IEEE/ACM Transactions Networking, 10.1109/TNET.2013.2242485 (2013). 6. E. Niewiadomska-Szynkiewicz, A. Sikora, P. Arabas, J. Kołodziej, Concurrency Comput. Practice Experience 25, 1738 (2013). 7. R. Bolla et al., IEEE Internet Comput. 17, 82 (2013). 8. ECONET Demos (www.econet-project.eu/Public/Demo).
Finding Best Practices for Fossil Fuel Extraction WE WERE HAPPY TO SEE N. BUTT ET AL. DRAW attention to the issue of fossil fuel extraction and its potential effects on biodiversity worldwide in their Policy Forum “Biodiversity risks from fossil fuel extraction” (25 October, p. 425). For years, diverse conservation non-
VOL 342 SCIENCE www.sciencemag.org Published by AAAS
CREDIT: NASA EARTH OBSERVATORY IMAGE BY JESSE ALLEN AND ROBERT SIMMON USING EO-1 ALI DATA COURTESY OF THE NASA EO-1 TEAM
LETTERS governmental organizations and academic institutions have been working with extractive industries to incorporate best practices into their operations (1, 2). Many of these efforts have been focused on conventional petroleum extraction methods, and lessons learned from research done alongside extraction have been incorporated into international standards for best practices (3). Biodiversity can only benefit from increased involvement of biologists in the identification, avoidance, and mitigation of the Athabasca oil sands. effects of fossil fuel exploration and production. Given the progress made thus far toward actively stepping up their efforts to develop understanding the effects of conventional technologically and economically feasible petroleum extraction, we propose that applied extraction of nonconventional reserves (4). ecological research for conservation should We call on ecologists and conservation bioloexpand immediately to areas with proven gists to become actively involved in this trannonconventional fossil fuel reserves (such sition and to increase efforts to work side by as oil sands, oil and gas shale, and coal bed side with national governments, extractive methane). As conventional fossil fuel reserves industries, and other stakeholders. reach their peak, the petroleum industry is In addition, although we agree that north-
www.sciencemag.org SCIENCE VOL 342 Published by AAAS
ern South America and the western Pacific are important areas for biodiversity, we worry that the analysis presented is far too simplistic to be able to make robust recommendations on where efforts for bestpractice enforcement should be focused, particularly when it comes to prioritizing some highly biodiverse areas above others. Any analysis of the pressures of fossil fuel extraction should include analysis of pressures from other sources (such as agricultural activities, legal and illegal extraction of renewable natural resources, lack of rigor in national legislation, and lack of enforcement) that could exacerbate biodiversity loss even when best-practice extraction is used. JESSICA L. DEICHMANN* AND ALFONSO ALONSO Smithsonian Conservation Biology Institute, Washington, DC 20013–7012, USA. *Corresponding author. E-mail: [email protected]
References 1. A. Alonso, F. Dallmeier, G. P. Servat, Eds., Monitoring Biodiversity: Lessons from a Trans-Andean Megaproject (SI Scholarly Press, Washington, DC, 2013). 2. J. Robinson, Conserv. Biol. 26, 1 (2012). 3. International Finance Corporation, Guidance Note 6: Biodiversity Conservation and Sustainable Management of Living Natural Resources (Washington, DC, 2012). 4. R. J. Brecha, Energ. Pol. 51, 586 (2012).
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Early branch in the animal tree
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LETTERS I BOOKS I POLICY FORUM I EDUCATION FORUM I PERSPECTIVES
LETTERS Health Information and the Like IN THEIR REPORT “SOCIAL INFLUENCE BIAS: A RANDOMIZED EXPERIment” (9 August, p. 647), L. Muchnik et al. explore whether Internet rating dynamics are influenced by the online community. They found that a comment that is initially slightly popular, regardless of credibility or quality, is more likely to garner even more “likes.” The study results “demonstrate that whereas positive social influence accumulates, creating a tendency toward ratings bubbles, negative social influence is neutralized by crowd correction.” This study reveals that social influence substantially biases ratings in systems designed to aggregate online opinions. Social media sites that aggregate online opinions are increasingly popular among the public for dissemination of health information (1, 2). Unfortunately, a study examining the 2009 H1N1 epidemic found that tweets that had a negative sentiment about influenza vaccination were more likely to be retweeted than tweets supporting vaccination (3, 4). Also, unexpectedly, tweets promoting vaccine use increased the likelihood of negative tweets against vaccine use (3). Surprisingly,
CREDIT: ROBERT CHURCHILL/ISTOCKPHOTO
Response REDDI’S THOUGHTFUL COMMENTS HIGHLIGHT the importance to global health policy of understanding the spread of health behaviors in society. Social media sites and online communication platforms are becoming increasingly popular sources for the dissemination of health information about prevention, medical conditions, and treatments. They are therefore central to the promulgation of beliefs about health threats and the efficacy of health interventions. To explain the critical points of divergence between our results and Reddi’s conclusions, we must critically evaluate the scientific evidence. First, ours is a randomized experiment, whereas the studies to which Reddi refers rely on observational data to make claims about information diffusion (the authors of those studies are careful to note that their evidence is predictive rather than causal). Our
in contrast to the findings by Muchnik et al., it appears that “crowd correction” may actually obfuscate social media health communications in certain situations (5). Social influence bias in health communications could potentially be detrimental to public health. Erroneous health information propagated by social media can endanger patient safety (6). Public health entities should consider the effects of social influence bias in health communications. In addition to promoting positive health messages on social media, public health strategies must also limit the spread of “popular” but erroneous health information (4). ANAND REDDI University of Colorado School of Medicine, Anchutz Medical Campus, Aurora, CO 80045, USA. E-mail: [email protected]
References 1. B. W. Hesse et al., Arch. Intern. Med. 165, 2618 (2005). 2. W.-Y. S. Chou, Y. M. Hunt, E. B. Beckjord, R. P. Moser, B. W. Hesse, J. Med. Internet. Res. 11, e48 (2009). 3. M. SalathÈ, D. Q. Vu, S. Khandelwal, D. R. Hunter, EPJ Data Sci. 2, 10.1140/epjds16 (2013). 4. K. S. Yoshida, ARS Technica, “When it comes to vaccination, bad news is contagious” (http:// arstechnica.com/science/2013/04/when-it-comes-to-vaccination-bad-news-is-contagious/). 5. M. SalathÈ, S. Khandelwal, L. A. Meyers, PLOS Comput. Biol. 7, e1002199 (2011). 6. E. Campbell, M. SalathÈ, Sci. Rep. 3, 10.1038/srep01905 (2013).
approach, which randomly manipulates the ratings of online content, holds author and content effects constant, isolating the causal effect of upvoting or downvoting on opinion and behavior change. For example, the studies Reddi refers to analyze whether negative or positive content about vaccinations is more likely to be retweeted. They do not exclude several likely alternative explanations of retweeting behavior that may have nothing to do with whether negative tweets are actually changing people’s opinions about vaccinations. The majority of Twitter users may already have a negative opinion of vaccinations, inspiring more retweets of such content. The evidence Reddi cites shows that negative information spreads farther and faster. Our experiment shows that negative information is less likely to change people’s opinions than positive information. These two claims
www.sciencemag.org SCIENCE VOL 342
are not contradictory. In fact, they are complementary pieces of evidence in a broader story of information diffusion and behavior change. Observational data about and simulations of information diffusion are useful for understanding how information and awareness spread (1, 2), but causal inference is necessary to understand how information diffusion subsequently changes opinions and behavior (3–5). Combining causal analysis of
Letters to the Editor Letters (~300 words) discuss material published in Science in the past 3 months or matters of general interest. Letters are not acknowledged upon receipt. Whether published in full or in part, Letters are subject to editing for clarity and space. Letters submitted, published, or posted elsewhere, in print or online, will be disqualified. To submit a Letter, go to www.submit2science.org.
13 DECEMBER 2013
Published by AAAS
1315
Downloaded from www.sciencemag.org on November 13, 2015
edited by Jennifer Sills
LETTERS 1
behavior change with predictive analysis of information diffusion creates a powerful lens through which to understand the ebbs and flows of behaviors on our planet. Second, analogies between ratings systems and Twitter will always remain loose at best. Collective opinion rating aggregates and presents the opinion of the community about specific content. Voters are likely motivated by the hope that their vote may influence the opinion of other readers. (Re)tweeting information on Twitter is also likely motivated by the desire to maximize publicity for certain information, the desire to become known as a curator of certain information, and the desire for personal exposure. Ratings are anonymous and do not push information to one’s “followers,” making these motivations less likely in ratings systems than on Twitter. The influence mechanisms in these scenarios differ as well. Twitter users are not privy to collective opinion on a topic when they tweet about it, whereas they are precisely aware of collective opinion when they vote on or rate an item. Thus, the motivations for diffusing information and rating it likely differ. Perhaps more important, differences in the designs of these systems could drive differences between our results and results from studies of Twitter. Whereas the system we studied enables users to either upvote, downvote, or abstain, there is no feature analogous to the downvote on Twitter. The user can only choose to either retweet or not. Facebook operates similarly: One can like content, but not dislike it. Our results generalize to collective opinion aggregation and ratings systems; results from studies of Twitter likely generalize to microblogging and networked information dissemination systems. SINAN ARAL,1* LEV MUCHNIK,2 SEAN TAYLOR3
MIT Sloan School of Management, Cambridge, MA 02142, USA. 2School of Business Administration, The Hebrew University of Jerusalem, Mount Scopus, Jerusalem, 91905, Israel. 3NYU Stern School of Business, New York, NY 10012, USA. *Corresponding author. E-mail: [email protected]
References 1. E. Adar, L. A. Adamic, in The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (Compiégne, France, 2005), pp. 207–214. 2. S. Aral, M. Van Alstyne, Am. J. Soc. 117, 90 (2011). 3. S. Aral, L. Muchnik, A. Sundararajan, Proc. Natl. Acad. Sci. 106, 21544 (2009). 4. C. R. Shalizi, A. C. Thomas, Soc. Meth. Res. 40, 211 (2011). 5. S. Aral, L. Muchnik, A. Sundararajan, Network Sci. 1, (2013).
Setting the Course for a Green Internet IN HIS PERSPECTIVE “TOWARD A GREEN Internet” (29 March, p. 1533), D. Reforgiato Recupero reflects (partially) some of the ideas and even the wording of the ECONET project’s Description of Work (DoW) (1), first published on 6 September 2010. However, the general philosophy of the ECONET Consortium is much broader than described in the Perspective, and we agree with the critical comment (2) that interest in reducing the impact of the Internet on the environment is not a recent development. Apart from Data Centers, which were not part of the ECONET mandate, all network segments ranging from the home network to metro/transport and core networks were considered in the DoW, and optimization methods were considered for each [see (3), which is also the source for the figure shown in the Perspective]. ECONET has recently been working toward implementation of a Network Connectivity Proxy (4), which allows user
CORRECTIONS AND CLARIFICATIONS News & Analysis: “Old dogs teach a new lesson about canine origins” by E. Pennisi (15 November, p. 785). The caption for the photograph of the buried dog in Illinois stated that the dog is 1000 years old; the dog is 8500 years old. The HTML and PDF versions online have been corrected. Association Affairs: “What’s so special about science (and how much should we spend on it?)” by W. H. Press (15 November, p. 817). In Fig. 2, the labels for Germany and Switzerland should be reversed, as well as the labels for Italy and Hungary and for Spain and Czech Republic. The HTML and PDF versions online have been corrected. News of the Week: “Whose brain is it anyway?” (8 November, p. 678). The central brain in the caption was mislabeled as belonging to Conrad Heinrich Fuchs, but it was Carl Friedrich Gauss’s. Also, the son of Rudolf Wagner who likely mixed up the brains 150 years ago was Hermann, not Thomas. The HTML and PDF versions online have been corrected. Reports: “Femtosecond visualization of lattice dynamics in shock-compressed matter” by D. Milathianaki et al. (11 October, p. 220). Two equations were incorrect in the text. The correct equations are Δθ ~ tanθ0sin2θ0 × εen (sixth paragraph p of text) and Δθ ~ –tanθ0 × εt (seventh paragraph of text). The HTML and PDF versions online are correct. Reports: “Functional extinction of birds drives rapid evolutionary changes in seed size” by M. Galetti et al. (31 May, p. 1086). Point 16 in Fig. 1 was slightly misaligned. The PDF and HTML versions online have been corrected. Perspectives: “Toward a green Internet” by D. Reforgiato Recupero (29 March, p. 1533). The figure credit was missing. It should be “The original figure appears in: R. Bolla, R. Bruschi, F. Davoli, F. Cucchietti, IEEE Commun. Surveys Tutorials 13, 223 (2011), as Figure 11 on p. 230.” The HTML and PDF versions online have been corrected.
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devices to be kept in a resting (low-energy) state by maintaining all vital network operations on their behalf. We have always emphasized the tradeoff between energy efficiency and performance: Examples of device-level and network-wide energy-aware optimal management and control strategies can be found in (5) and (6), respectively. Finally, a fundamental aspect to our project that was not discussed in the Perspective is the need to provide standard interfaces between algorithms for management and control and a variety of hardware. The ECONET proposed solution—the Green Abstraction Layer (7)— is currently under consideration for standardization by the European Telecommunications Standards Institute. Although the ECONET project approaches its end after 3 years of activity, network power management technologies are becoming a reality in a number of network devices, as witnessed by industrial prototypes (8), which demonstrate reductions of at least 50% in power consumption. RAFFAELE BOLLA1,2 ROBERTO BRUSCHI,2* FLAVIO CUCCHIETTI,3 FRANCO DAVOLI1,2 1
Department of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture (DITEN), University of Genoa, 16126, Genoa, Italy. 2National Inter-University Consortium for Telecommunications (CNIT), University of Genoa Research Unit, 16126, Genoa, Italy. 3Telecom Italia Labs (TILabs), 10148, Torino, Italy. *Corresponding author. E-mail: [email protected]
References 1. ECONET (low-Energy COnsumption NETworks) Project, Grant Agreement no. 258454, Annex I, “Description of Work,” European Commission, 6 September 2010 (available from the authors upon request). 2. Comment on D. Reforgiato Recupero, Science 339, 1533 (2013); http://comments.sciencemag.org/ content/10.1126/science.1235623. 3. R. Bolla, R. Bruschi, F. Davoli, F. Cucchietti, IEEE Commun. Surveys Tutorials 13, 223 (2011). 4. R. Bolla, M. Giribaldi, R. Khan, M. Repetto, “Network connectivity proxy: An optimal strategy for reducing energy waste in network edge devices,” Proc. 24th Tyrrhenian International Workshop on Digital Communications (TIWDC 2013)—Green ICT, Genoa, Italy, 23 to 25 September 2013. 5. R. Bolla, R. Bruschi, A. Carrega, F. Davoli, IEEE/ACM Transactions Networking, 10.1109/TNET.2013.2242485 (2013). 6. E. Niewiadomska-Szynkiewicz, A. Sikora, P. Arabas, J. Kołodziej, Concurrency Comput. Practice Experience 25, 1738 (2013). 7. R. Bolla et al., IEEE Internet Comput. 17, 82 (2013). 8. ECONET Demos (www.econet-project.eu/Public/Demo).
Finding Best Practices for Fossil Fuel Extraction WE WERE HAPPY TO SEE N. BUTT ET AL. DRAW attention to the issue of fossil fuel extraction and its potential effects on biodiversity worldwide in their Policy Forum “Biodiversity risks from fossil fuel extraction” (25 October, p. 425). For years, diverse conservation non-
VOL 342 SCIENCE www.sciencemag.org Published by AAAS
CREDIT: NASA EARTH OBSERVATORY IMAGE BY JESSE ALLEN AND ROBERT SIMMON USING EO-1 ALI DATA COURTESY OF THE NASA EO-1 TEAM
LETTERS governmental organizations and academic institutions have been working with extractive industries to incorporate best practices into their operations (1, 2). Many of these efforts have been focused on conventional petroleum extraction methods, and lessons learned from research done alongside extraction have been incorporated into international standards for best practices (3). Biodiversity can only benefit from increased involvement of biologists in the identification, avoidance, and mitigation of the Athabasca oil sands. effects of fossil fuel exploration and production. Given the progress made thus far toward actively stepping up their efforts to develop understanding the effects of conventional technologically and economically feasible petroleum extraction, we propose that applied extraction of nonconventional reserves (4). ecological research for conservation should We call on ecologists and conservation bioloexpand immediately to areas with proven gists to become actively involved in this trannonconventional fossil fuel reserves (such sition and to increase efforts to work side by as oil sands, oil and gas shale, and coal bed side with national governments, extractive methane). As conventional fossil fuel reserves industries, and other stakeholders. reach their peak, the petroleum industry is In addition, although we agree that north-
www.sciencemag.org SCIENCE VOL 342 Published by AAAS
ern South America and the western Pacific are important areas for biodiversity, we worry that the analysis presented is far too simplistic to be able to make robust recommendations on where efforts for bestpractice enforcement should be focused, particularly when it comes to prioritizing some highly biodiverse areas above others. Any analysis of the pressures of fossil fuel extraction should include analysis of pressures from other sources (such as agricultural activities, legal and illegal extraction of renewable natural resources, lack of rigor in national legislation, and lack of enforcement) that could exacerbate biodiversity loss even when best-practice extraction is used. JESSICA L. DEICHMANN* AND ALFONSO ALONSO Smithsonian Conservation Biology Institute, Washington, DC 20013–7012, USA. *Corresponding author. E-mail: [email protected]
References 1. A. Alonso, F. Dallmeier, G. P. Servat, Eds., Monitoring Biodiversity: Lessons from a Trans-Andean Megaproject (SI Scholarly Press, Washington, DC, 2013). 2. J. Robinson, Conserv. Biol. 26, 1 (2012). 3. International Finance Corporation, Guidance Note 6: Biodiversity Conservation and Sustainable Management of Living Natural Resources (Washington, DC, 2012). 4. R. J. Brecha, Energ. Pol. 51, 586 (2012).
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