galaz et al 2009

8/14/2019 Galaz et al 2009

1/8

Frontiersin

Ecologyand theEnvironment

Can web crawlers revolutionizeecological monitoring?Victor Galaz, Beatrice Crona, Tim Daw, rjan Bodin, Magnus Nystrm, and Per Olsson

Front Ecol Environ 2009; doi:10.1890/070204

This article is citable (as shown above) and is released from embargo once it is posted to the

Frontiers e-View site (www.frontiersinecology.org).

The Ecological Society of America www.frontiersinecology.org

Please note: This article was downloaded fromFrontiers e-View, a service that publishes fully editedand formatted manuscripts before they appear in print in Frontiers in Ecology and the Environment.Readers are strongly advised to check the final print version in case any changes have been made.

esaesa

8/14/2019 Galaz et al 2009

2/8


The combined impacts of global environmentalchange and the complex behavior of ecological sys-tems, create opportunities for major ecological sur-prises at various spatial scales (Schneider and Root1995; Gunderson 2003; Gordon et al. 2008). Ecosystemsprovide many vital ecosystem services (ES), such aswater purification and food production, but rapidchanges due to, for instance, climate change and shiftingglobal markets, present serious challenges to their futureability to deliver these life supporting services (MA2005). Examples of such changes include collapsing fish-

eries at national and global scales (Berkes et al. 2006),irreversible degradation of freshwater ecosystems andcoral reefs, and decreasing soil productivity (Scheffer et

al. 2001; MA 2005).The situation is exacerbated bynational and international responses to such changesthat are either insufficient or non-existent. Restorationmay be difficult, because feedbacks in the system can actto stabilize these new, undesirable ecosystem states(Scheffer et al. 2001; Gordon et al. 2008). It is thereforeof primary importance to try and avoid crossing thethresholds that lead to these outcomes.

Despite advances in monitoring technology (Clark etal. 2001), it is evident that existing information onchanges in ES tends to be poor and contains serious gaps.

Furthermore, existing monitoring systems are unable tocapture the impacts of rapid demographic, economic,and sociopolitical changes that result from economicdevelopment and increasing global flows of information,trade, and technology (MA 2005; Berkes et al. 2006;Carpenter et al. 2006). The difficulties in quantifyingsocial and ecological uncertainty, the lack of expertagreement on what indicators to monitor, poor-qualityexisting data, and the costs associated with setting uplong-term monitoring programs (Walters 2007) all ham-per our ability to steer away from, or to prepare for,abrupt changes to ecosystems and the loss of related ES.This is particularly true for countries that suffer from

poor governance and weak environmental institutions(Danielsen et al. 2003; UNEP 2007).

Information and communication technologies

The role of information and communication technology(ICT) for economic growth, education, and humandevelopment has been discussed elsewhere (Leach andScoones 2006). Meanwhile, the evolution of web 2.0permits more interactive use of the internet and allowsusers to post, edit, comment on, and provide information

CONCEPTS ANDQUESTIONS

Can web crawlers revolutionize ecologicalmonitoring?Victor Galaz1*, Beatrice Crona1,5, Tim Daw3, rjan Bodin1,4, Magnus Nystrm1,2, and Per Olsson1

Despite recent advances, ecosystem service monitoring is limited by insufficient data, the complexity of

socialecological systems, and poor integration of information that tracks changes in ecosystems and eco-

nomic activities. However, new information and communication technologies are revolutionizing the genera-

tion of, and access to, such data. Can researchers who are interested in ecological monitoring tap into these

increased flows of information by mining the internet to detect early-warning signs that may signal

abrupt ecological changes? Here, we explore the possibility of using web crawlers and internet-based informa-

tion to complement conventional ecological monitoring, with a special emphasis on the prospects for avoid-

ing late warnings, that is, when ecosystems have already shifted to less desirable states. Using examples from

coral reef ecosystems, we explore the untapped potential, as well as the limitations, of relying on web-based

information to monitor ecosystem services and forewarn us of negative ecological shifts.

Front Ecol Environ 2009; doi:10.1890/070204

1Stockholm Resilience Centre, Stockholm University, Stockholm,

Sweden *([email protected]); 2Department of

Systems Ecology, Stockholm University, Stockholm, Sweden;3School of Development Studies, University of East Anglia,

Norwich, UK; 4Department of Government, Uppsala University,

Uppsala, Sweden; 5The Centre for the Study of Institutional

Diversity, Arizona State University, Tempe, AZ

In a nutshell: Steering away from catastrophic shifts in ecosystems is of prime

concern in an era of global environmental change Existing monitoring of ecosystem services is poor and frag-

mented, especially in developing countries Information and communications technology is revolutioniz-

ing the generation of, and access to, social, ecological, and eco-nomic information

Systematic data mining of such information through the

internet can provide important early warnings about possiblepending abrupt losses of ecosystem services

8/14/2019 Galaz et al 2009

3/8

Web crawlers and ecological monitoring V Galaz et a

www.frontiersinecology.org The Ecological Society of Americ

in blogs and wikis or via podcasting, videoblogs, andother networking tools. Globally, access to informationtechnology is very unequally distributed (IER 2005;Leach and Scoones 2006), but access to, and use of, theinternet is increasing rapidly in all regions of the world.For example, between 2000 and 2004, the number ofinternet users in the developing world tripled, from 96million to almost 333 million; in Africa alone, the num-ber of users increased more than five-fold, from 4.3 to21.8 million, during that same period (IER 2005).

The rapid development of ICT has not only led toincreased flows of information at a global scale, but alsosets the stage for innovative uses of internet-based infor-mation ranging from e-mail lists and local newspaperarticles to preprints of peer-reviewed journal articles asan important complement to conventional ecologicalmonitoring. The potential of ICT is currently beingexplored in a number of contexts for ecology. Exampleshere range from the Resilience Assessment wiki(http://wiki.resalliance.org), to online datasets such as

those posted by the US National Center for Ecology andAnalysis and Synthesis (www.nceas.ucsb.edu), to the useof the internet to coordinate citizen-science projects(Levitt 2002). In addition, Crowl et al. (2008) suggestthe creation of a coordinated cyber-infrastructure tofacilitate prompt warnings of invasive alien species andinfectious diseases.

Here, we explore the possibilities and limitations ofmore systematic data mining of the internet, and thepotential for obtaining complementary information andearly warnings not only about discrete ecological events(eg a disease outbreak caused by invasive species), butalso changes in ecological drivers, and the impacts of

ecosystem change to forewarn us of ES losses.

Ecology on the internet

One example of how informal ICT informa-tion can support ecological monitoring is theuse of electronic mailing lists to disseminateand compile field observations trackingglobal-scale coral bleaching during the19971998 El Nio event. The existence ofan electronic mailing list for coral reef-asso-ciated news proved invaluable for promptassessments of the mass-bleaching event

(Hoegh-Guldberg 1999), with reports rang-ing from detailed accounts with accuratemeasures of bleaching and mortality, to briefanecdotal reports obtained during a rapidsite visit (Wilkinson 1999; see WebPanel1). Information of this kind can, in princi-ple, be easily associated with participatoryecological monitoring projects or citizen-sci-ence initiatives, provided that they areposted on the internet (see Andrianandra-sana et al. [2005] on wetland monitoring in

Madagascar and Leach and Scoones [2006] on participatory geographic information system citizen-maps fohydrological monitoring).

One primary difficulty, however, lies in designing monitoring systems that are able to scan the internet continuously for predefined ecological events and changes thamight signal emerging ecological vulnerabilities, and subsequently integrating that information with existing, offcial monitoring data. Although the realization of such system is far into the future, innovative uses of wecrawlers (software programs or automated scripts thabrowse the World Wide Web in a methodical, automatemanner) are likely to provide an important complemento conventional monitoring in the present. The casstudy we highlight of the live reef fish trade is a cleaexample of the problems inherent in relying on officiadata alone, and one where a creative application of internet-based information could provide a valuable resourc(see Panel 1).

The potential of web crawlers is illustrated by the succes

of the Global Public Health Intelligence Networ(GPHIN), an early disease detection system developed bHealth Canada for the World Health Organization(WHO). GPHIN gathers information about unusual disease events by monitoring internet-based global medisources, such as news wires, web sites, local online newspapers, and public health e-mail information services, in eighlanguages, with non-English articles filtered through translation engine. The system retrieves approximatel20003000 news items per day; roughly 30% are rejected aduplicative or irrelevant, but the remainder are sorted bGPHIN analysts and posted on GPHINs secure websit(Weir and Mykhalovskiy 2006).

The ability to trawl extensively for various signals, thwide diversity of information sources, and the ability tidentify alarming early-warning signs seem give the systemthe flexibility and speed needed to detect unexpecte

Panel 1. Web crawlers and the live fish trade

Globalized markets have become important drivers for fisheries systems, dri-

ving rapid development, overexploitation, and collapse of local fisheries,

before effective management can be established (Berkes et al. 2006).The live

reef fish trade (LRFT) supplying seafood to restaurants in Asia is a good exam-

ple. This fishery has been characterized by a boom-and-bust pattern of

sequential exploitation of reefs and nations, and serial depletion of the most

valuable species (Scales et al. 2006).

Although some Pacific Ocean nations have recognized the threat of LRFTand have started to take precautionary actions, coordinated by the Secretariat

of the Pacific Community (Sadovy et al. 2003), many in other areas, such as the

Caribbean and the Western Indian Ocean, have not, and lack of data on the

status of many small-scale reef fisheries has also been a severe impediment to

action. Socioeconomic and ecological signals, provided by web crawlers, could

potentially improve early detection of nations and regions at risk of being hit

by the next sequential wave of LRFT. Examples of the types of signals that

could be used include trade advertisements, availability of products by area,

prices, number of suppliers, observations by non-state entities, such as envi-

ronmental organizations, and newsletters.

8/14/2019 Galaz et al 2009

4/8

V Galaz et al. Web crawlers and ecological monitoring


disease outbreaks. For example, GPHIN currently capturesthe first hints of about 40% of the 200250 outbreaks subse-quently investigated and verified by WHO each year.GPHIN was also one of the first systems to obtain non-offi-cial reports of a suspected influenza outbreak in mainlandChina in 2002, which, 3 months later, was identified byWHO as severe acute respiratory syndrome (SARS; Fidler2005; Weir and Mykhalovskiy 2006).

Web crawlers and ecosystems

Abrupt losses of ecosystem services are obviously difficultto forecast with certainty, mainly because they result frommultiple changes at different scales (Clark et al. 2001).However, research on coupled social and ecological sys-tems over the past decade has identified several changesthat may provide early warnings of potential damage toecosystem services. For example, an abrupt transition froma coral-dominated reef to an algae-dominated one may be

preceded by declining abundance of large herbivorous fish(Nystrm et al. 2000); a rapid transition from a clear to ahighly turbid and eutrophic state in a lake may be precededby increased fertilizer use on nearby farms (Gordon et al.2008); and heavy investment in specific fishing gear andtechnical equipment may precede the loss of certain keyspecies in marine fisheries (Berkes et al. 2006). Figure 1uses the example of coral reef ecosystems to illustrate

diverse sources of internet-based information on both dri-vers and ecosystem responses, to monitor and forewarn ofpending ecological shifts. Nonetheless, the collection andpresentation of signals need to be supplemented by expertanalysis, knowledge management approaches (seeMcDermott [1999] for more information), and an under-standing of local ecological and social conditions. Onlythen will we be able to obtain robust estimates of possibleimpacts and to evaluate the possible countermeasures oradaptation strategies we might use in response (Crowl et al.2008).

Figure 1. Examples of drivers and impact signals regarding a coral reef socialecological system that, in principle, could be detectedby a web crawler. Driver signals are key social, ecological, and economic factors that risk leading to loss of ecosystem services.

Impact signals are changes that may indicate pending loss of ecosystem services. Note that the list of signals is not exhaustive. Basedon Nystrm et al. (2000); Berkes et al. (2006); Scales et al. (2006); and McCook et al. (2007). The analysis of these signals isnot necessarily carried out by a single entity or individual, but rather may include, for example, academia, UN agencies, NGOs,

government and citizen scientists, and military and diplomatic agencies.

CourtesyofB

CronaandRK

autsky/Azote

Driver signals Data type

Drivers of system change

Foreign investment

G

lobal

Development aidinvestment

Regional

Subsidies forexploitation

Coastal developmentand constructionL

ocal

Online news media (eg newspapers, radio and television,and online newsletters)

Biogs (eg dialogues conducted through dive clubs, interestgroups, NGOs, electronic mailing lists, purchase requests)

Coral reef

Data type Impact signals

Governmentfisheries stats

Globa

l

Tradevolumes,

trophic

levels,

sizegrades

RegionalDisease

outbreak

Local

Coral cover, fishcommunity composition

Published (online) reports and documents fromgovernment agencies, UN agencies, OECD, and similar

Online accessible databases (eg trade statistics, landingstatistics)

8/14/2019 Galaz et al 2009

5/8



Analysis and response are not necessarily organizedaround a single ie national government entity. Onthe contrary, both might occur as a result of collabora-tions between, for example, state agencies and otherexpert analysts in the form of non-governmental organi-zations, private companies, universities, and the generalpublic. If the outputs are more widely available, analysisand response could even be the result of autonomousactions, assumed by independent organizations andindividuals.

Warnings that come too late

There are important differences between monitoring forloss of ES and disease outbreaks. Web-crawler-basedearly-warning systems for disease epidemics rely on theidentification of discrete events (Weir and Mykhalovskiy2006), rather than on monitoring underlying social, eco-nomic, or ecological changes. However, discrete eventscan, in principle, be used as early warnings of approach-ing abrupt shifts in ecological systems. Given the poten-tial for irreversible loss of ES, early warnings are impor-tant in allowing the introduction of management

responses before the ES are lost. Here, we suggesthree potential ways to using web crawlers tforewarn of ecological shifts.

First, web crawlers can collect information onthe drivers of ecosystem change, and not just othe resulting impacts. For example, if emerginmarkets for high-value species are known to bsocioeconomic drivers that lead to the overexploitation and collapse of a fishery (see Panel 1web crawlers can be designed to collect information on rapid changes in prices, landings, oinvestments in particular regions (Figure 2Meyerson and Reaser (2003), for instancereport on a web crawler developed by the UDepartment of Agricultures Animal and PlanHealth Inspection Service to search for, anreport on, sales of prohibited organisms over thinternet, in an attempt to address the threat oinvasive alien species.

Second, future early-warning systems ca

make use of the recent insight that variancwithin ecosystems can increase in response tstress. For example, the variability of fish populations has been shown to increase iresponse to exploitation (Hsieh et al. 2006Carpenter and Brock (2006) argue that variance within complex ecological systems generally increases in advance of catastrophishifts. Although web crawlers harvest information on discrete events, rather than providing the time series needed to formally analyzvariance patterns, increases in variance arvery likely to result in an increased frequenc

of what is perceived as unusual eventswhich may make their way into local newspapersblogs, or electronic mailing lists (Figure 3a)

Nonetheless, the realization that increased variancindicates a pending ecological shift is a recent onebased on ecological modeling (compare with Obornet al. 2005; van Nes and Scheffer 2007). Thus, whethethis approach is possible with web-crawler-based monitoring systems needs to be explored further.

Finally, a more clear-cut approach is one that buildon the fact that ecological shifts at small scales oftenprecede similar shifts in other locations or, more serously, larger-scale systemic changes. Examples includ

outbreaks of invasive alien species (Meyerson anReaser 2003), or the way in which resilience of ecosystems, such as forest reserves and coral reefs, is thoughto be dependent on surrounding refuge areas, which caaid the recovery from small-scale shifts through, foexample, the movement of species and supply of larva(Nystrm et al. 2000; Bengtsson et al. 2003; see Figur3b). Therefore, repeated small-scale shifts may not onllead to a cumulative loss of spatial resilience, but canalso provide early indications of large-scale systemilosses of ES (Figure 3b).

Figure 2. Ecological information is often accessible in several languages anddiverse settings on the internet. (a) This screen shot from a Chinese foodmarket web page illustrates the type of information that can be retrieved. (b)Information about marine species for sale in the market, together withinformation about the highest, lowest, and average price. The last column

provides price statistics for the chosen species. (c) A news section, whichincludes changes in access to specific marine species. First news item reads:According to an integrated investigation of the coastal zone, both Chinese

prawn and little yellow croaker have returned in the Bohai Sea, while thesixth news item reads: Big stocks of little yellow croaker have re-emergedafter 30 years in the Yellow Sea.

(b)

(c)

(a)

8/14/2019 Galaz et al 2009

6/8

V Galaz et al. Web crawlers and ecological monitoring

Data management and lack of societal response

Despite the exciting possibility of using web crawlers forecological monitoring and in early-warning systems, werecognize that crucial challenges need to be addressedbefore these systems can contribute to the detection (andpossible avoidance) of abrupt ecosystem changes. Thereis still a need to integrate, verify, and manage ecologicaland socioeconomic data. Data integration, expert analy-sis, and knowledge management have proven to be themain obstacles to ecological monitoring (Carpenter et al.2006), even among well-defined monitoring systems indeveloped countries. For example, communicable diseasesurveillance in the European Union (Amato-Gauci and

Ammon 2008) and invasive species monitoring in theUS (Meyerson and Reaser 2003; Crowl et al. 2008) illus-trate the difficulties posed by fragmented or otherwiseinsufficient social and ecological data, and the continu-ing risk of creating information junkyards ie increas-ingly large collections of data with little or no practicalvalue (McDermott 1999) instead of robust ecologicalmonitoring systems. Any web-crawler-based monitoringsystem would therefore require the support of a coupledknowledge management and expert judgment system.

Early warnings are never a guarantee of timely and appro-priate remedial responses. The need for prompt responses tooutbreaks of Ebola hemorrhagic fever and avian influenza

(H5N1), for example, has gained increased social and polit-ical support over the past few years, spurring the develop-ment of new, international regulations and response opera-tions. This is facilitated by a relatively strong internationalorganization for human health, with an international man-date the WHO (Fidler 2005). This development is instark contrast with global environmental governance,which suffers from implementation deficits, serious coordi-nation failures, and inadequate funding (Biermann 2002).Responses to infectious disease (eg isolation, vaccination,medical care) are also likely to be simpler and less politically

contentious than the responses to approaching ecological

shifts (eg fishing restrictions, restrictions on agriculturalactivity, implementation of deforestation legislation).

The challenges of data integration and the current lackof governmental response to ecological change shouldnot be underestimated. They do not, however, precludethe need to explore innovative solutions to bridge the gapbetween poor monitoring, and the rapid rate ofsocialecological change, with potentially serious reper-cussions for human well-being. The use of web crawlersshould be explored further, in an attempt to prepare forthe ecological challenges of an uncertain future.

Call for comments

The authors invite readers to discuss and comment onthis article at http://resilienceinnovation.blogspot.com

Acknowledgements

This work was supported by the Stockholm ResilienceCentre, and by grants from the Foundation for StrategicEnvironmental Research (Mistra). We thank F Westleyof the University of Waterloo and several other col-leagues for inspirational discussions on this subject.Assistance in translation and with the web search wasprovided by G Han, Stockholm Environment Institute.

ReferencesAmato-Gauci A and Ammon A. 2008. The surveillance of com-

municable diseases in the European Union a long-term strat-egy. Eurosurveillance 13: 26.

Andrianandrasana HT, Randriamahefasoa J, Durbin J, et al. 2005.Participatory ecological monitoring of the Alaotra wetlands inMadagascar. Biodivers Conserv 14: 275774.

Bengtsson J, Angelstam P, Elmqvist T, et al. 2003. Reserves,resilience and dynamic landscapes.Ambio 32: 38996.

Berkes F, Hughes TP, Steneck RS, et al. 2006. Globalization, rovingbandits, and marine resources. Science 311: 155758.


Time series

Distributions

Probabilitiesfor unusual events

(b)

Healthy local ecosystemLocal ecosystem in degraded stateFlows from refuge areas to assist recovery

Figure 3. (a) Increasing variance of a key system variable (eg fish abundance or nutrient concentration) (top). As varianceincreases, the probability distribution changes (bottom), which could imply more frequent observations of unusual events (beyondhorizontal dotted lines). (b) Ecological shifts at smaller scales can provide warnings of impending changes to large-scale systems.System 1: recovery from disturbances is assisted by multiple sources of ecological memory. System 2: higher frequency of localshifts, which increases the risk of the system moving into a large-scale phase shift. System 3: majority of sites are degraded, makingrecovery of both local sites and the large-scale system unlikely. Adapted from Nystrm et al. (2008).

(a)

System 1 System 2 System 3

8/14/2019 Galaz et al 2009

7/8


Biermann F. 2002. Green global governance the case for a WorldEnvironment Organisation.New Econ 9: 8286.

Carpenter SR and Brock WA. 2006. Rising variance: a leadingindicator of ecological transition. Ecol Lett 9: 31118.

Carpenter SR, DeFries R, Dietz T, et al. 2006. MillenniumEcosystem Assessment: research needs. Science 314: 25758.

Clark JS, Carpenter SR, Barber M, et al. 2001. Ecological forecasts:an emerging imperative. Science 293: 65760.

Crowl TA, Crist TO, Parmenter RR, et al. 2008. The spread ofinvasive species and infectious disease as drivers of ecosystemchange. Front Ecol Environ 6: 23846.

Danielsen F, Mendoza MM, Alviola P, et al. 2003. Biodiversitymonitoring in developing countries: what are we trying toachieve? Oryx 37: 40709.

Fidler DP. 2005. SARS, governance and the globalization of dis-ease. New York, NY: Palgrave McMillan.

Gordon L, Peterson GD, and Bennett EM. 2008. Agricultural mod-ifications of hydrological flows create ecological surprises.Trends Ecol Evol 23: 21119.

Gunderson LH. 2003. Adaptive dancing: interactions betweensocial resilience and ecological crises. In: Berkes F, Colding J,and Folke C (Eds). Navigating socialecological systems: build-ing resilience for complexity and change. Cambridge, UK:Cambridge University Press.

Hsieh CH, Reiss CS, Hunter JR, et al. 2006. Fishing elevates vari-ability in the abundance of exploited species. Nature 443:85962.

Hoegh-Guldberg O. 1999. Climate change, coral bleaching andthe future of the worlds coral reefs. Mar Freshwater Res 50:83966.

IER (Information Economy Report). 2005. Information economyreport. New York, NY and Geneva, Switzerland: UNConference on Trade and Development.

Leach M and Scoones I. 2006. The slow race: making technologywork for the poor. London, UK: Demos.

Levitt JN (Ed). 2002. Conservation in the internet age: threats andopportunities. Washington, DC: Island Press.

MA (Millennium Ecosystem Assessment). 2005. Ecosystems andhuman well-being. Washington, DC: Island Press.

McCook LJ, Folke C, Hughes TP, et al. 2007. Ecological resilience,

climate change and the Great Barrier Reef. In: Johnson JE andMarshall PA (Eds). Climate change and the Great Barrier Reef.

Townsville, Australia: Great Barrier Reef Marine ParAuthority.

McDermott R. 1999. Why information technology inspired bucannot deliver knowledge management. In: Lesser ELFontaine MA, and Slusher JA (Eds). Knowledge and communities. Boston, MA: Butterworth-Heinemann.

Meyerson LA and Reaser JK. 2003. Bioinvasions, bioterrorism, anbiosecurity. Front Ecol Environ 1: 30714.

Nystrm M, Folke C, and Moberg F. 2000. Coral reef disturbancand resilience in a human-dominated environment. Trends EcEvol 15: 41317.

Nystrm M, Graham NAJ, Lokrantz J, and Norstrm A. 200Capturing the cornerstones of coral reef resilience: linking theory to practice. Coral Reefs. 27: 795809.

Oborny B, Meszna G, and Szab G. 2005. Dynamics of populations on the verge of extinction. Oikos 109: 29196.

Sadovy YJ, Donaldson TJ, Graham TR, et al. 2003. While stocklast: the live reef food fish trade. Manila, Philippines: AsiaDevelopment Bank.

Scales H, Balmford A, Liu M, et al. 2006. Keeping bandits at bayScience 313: 61213.

Scheffer M, Carpenter SR, Foley JA, et al. 2001. Catastrophic shiftin ecosystems.Nature 413: 59196.

Schneider SH and Root TL. 1995. Ecological implications of cl

mate change will include surprises. Biodivers Conserv 5110919.UNEP (UN Environment Programme). 2007. Global environmen

outlook GEO4. Nairobi, Kenya: UN EnvironmenProgramme.

van Nes E and Scheffer M. 2007. Slow recovery from perturbationas a generic indicator of a nearby catastrophic shift. Am N169: 73847.

Walters CJ. 2007. Is adaptive management helping to solve fisheries problems?Ambio 36: 30407.

Weir L and Mykhalovskiy E. 2006. The geopolitics of global publihealth surveillance in the twenty-first century. In: Bashford A(Ed). Medicine at the border disease, globalization and security, 1850 to the present. New York, NY: Palgrave McMillan.

Wilkinson CR. 1999. The 19971998 mass bleaching evenaround the world. Compilation of internet reports, global cor

reef monitoring network. Townsville, Australia: AustraliaInstitute of Marine Science.


8/14/2019 Galaz et al 2009

8/8


WebPanel 1. Selected excerpts from the Coral List from early 1998

Accessible at www.coral.noaa.gov/lists/archives.shtml.Senders e-mail addresses have been removed,and region added by article authors.

PanamaWed, 1 Oct 1997 14:49:26Significant coral bleaching was observed on 17 September 1997 at Uva Island in the Gulf of Chiriqui, Pacific Panama.All zooxanthellatescleractinian coral species were affected, at all depths (no corals present >20 m). The most severely bleached (completely white)colonies still had extended polyps and no signs of algal overgrowth, suggesting the event occurred relatively recently. Most colonies ofthe hydrocoralMillepora intricata (the only common species of the genus remaining after the 198283 ENSO) were already dead andcovered with a thin algal film, suggesting they may have bleached earlier than the scleractinians.

GalapagosThu, 5 Jan 1998 21:11:06As of Dec 1830, bleaching was observed first hand in Galapagos.Roughly 20% of polyps of roughly 80% of the coral I saw was bleachednear the top (mostly a brown lumpy coral, I dont know the name, anyone?) although I was only able to visit Santa Cruz, Bartolome,Santa Fe, and Espanola; NOT the islands typically known for large coral assemblages (Devils Crown, Isabella).

GalapagosSat, 21 Jan 1998 12:32:09FYI, a NOAA Press Release:EL NINO CAUSING CORAL BLEACHING IN GALAPAGOS,NOAA ANNOUNCES

El Nios extremely warm waters in the Pacific Ocean have caused coral bleaching in the waters around the Galapagos Islands, theCommerce Departments National Oceanic and Atmospheric Administration announced today.

HawaiiTue, 3 Mar 1998 20:46:37The coral reef here is a bloody disaster.What isnt dead is bleached so white from loss of algae that I think much of it will starve beforeit comes good.The sea temp reached 33C at 15 meters depth at four mile reef last month.We are getting South easters, now bringingin cooler water but it is still very hot.This is unprecedented. No one can remember anything like this happening before.

Western SamoaThu, 5 Mar 1998 13:20:18A survey at Palolo Deep (a National Marine Park near Apia,Western Samoa) on 28 February revealed severe coral bleaching. Between60 to 70% of all staghornAcropora on the reef top was bleached.This has occurred with amazing rapidity (over a period of 56 days).In deeper water, all seemed well.

V Galaz et al. Supplemental information

galaz et al 2009

Documents