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10 Health Applications of Remote Sensing and Climate Modeling Paul R. Epstein Remote satellite sensing of the oceans, land masses, ice cover, and the atmo- sphere has been used for understanding biogeochemical cycles and their relation- ships to biotic activity. An important insight emerging from research on climate and ecosystems is that climatic changes and variations alter the ecology of dis- ease organisms that attack human beings and their food supplies. Remotely sensed data being used to monitor climatic phenomena and improve understanding and forecasting of climate are proving useful for forecasting the spread of disease and offer great potential for the development of health early warning systems. Re- mote sensing can aid in the monitoring of disease carriers (vectors), breeding sites, and animal reservoirs in both marine and terrestrial ecosystems. Integrated into geographic information systems, it can advance disease surveillance, as well as aid in the development of timely, environmentally sound public health interventions. This chapter examines five applications of remote sensing for disease sur- veillance: (1) monitoring coastal algal blooms and toxic phytoplankton to sup- port early warning systems for paralytic shellfish poisoning and cholera; (2) monitoring terrestrial habitats to identify and control mosquito and rodent disease vectors; (3) building models of climate variability that can be used to predict conditions conducive to disease outbreaks; (4) using climate-change models to project potential disease distribution; and (5) detecting tropospheric temperatures to help understand physical and biological changes, particularly the spread of disease, at high altitudes. 197

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198 HEALTH APPLICATIONS OF REMOTE SENSING AND CLIMATE MODELING MONITORING COASTAL ALGAL BLOOMS Remote sensing data from the Coastal Zone Color Scanner (CZCS) and the Sea-viewing Wide-Field-of-view Sensor (SeaWiFS), as well as measurements of sea-surface temperatures using the Advanced Very High Resolution Radiometer (AVHRR), have been used to assess phytoplankton blooms (Feldman et al., 1984; Brock et al., 1991; Brock and McClain, 1992) and primary productivity (Aiken et al., 1992~. In one application, the AVHRR instrument, which senses red and infrared wavelengths, provides measures of sea-surface temperatures, which are correlated with the appearance of algal blooms. In Woods Hole, Massachusetts, AVHRR images are used in real time to detect and follow spring plumes from Massachusetts rivers flowing to the coast. Based on these images, boats are sent out for targeted sampling to detect blooms of Alexandrium tamarense, a toxic phytoplankton responsible for paralytic shellfish poisoning. When A. tamarense is found, shellfish beds in the area are closed to harvesting, thus preventing outbreaks of the disease (D. Anderson, Woods Hole Oceano- graphic Institution, personal communication, 1993~. Similar remote sensing technology can provide early warning of cholera outbreaks. Since the 1960s, researchers in Bangladesh have associated outbreaks of cholera with seasonal coastal algal blooms (Cockburn and Cassanos, 1960~. Recently, Colwell and associates have used fluorescent antibody probes to iden- tify a viable, nonculturable "dormant" form of Vibrio cholerae, which attaches to a wide range of marine life (Colwell et al., 1985) and reemerges to an infectious state along with algal blooms. Algal blooms are grazed by zooplankton that, over days to weeks, are capable of concentrating V. cholerae bacteria to infectious doses in their egg sacs. Zooplankton may be consumed directly in drinking water (as in Bangladesh), or be passed up the marine food chain (through shellfish filter feeders or finfish) and thus be introduced into human populations indirectly. Consequently, early detection of phytoplankton blooms and targeted sam- pling for cholera bacteria can constitute a cholera early warning system. Early detection in the marine environment can allow for timely institution of public health measures that include temporary bans on shellfish and finfish consump- tion, use of new oral recombinant immunizations, increased chlorination of wa- ter, and preparation of medical treatment facilities. Early treatment can reduce the case fatality rate from 50 to 1 percent. We are currently in the Seventh World Pandemic of cholera (E1 Tor strain). This pandemic began in the Bay of Bengal in the 1960s, arrived in Africa in the 1970s, and infected Latin America in the 1990s. In 1992, a new strain of cholera emerged, called V. cholerae 0139 Bengal. There is no cross-immunity between the older and newly emerged forms (i.e., previous infection with E1 Tor does not protect against 0139 Bengal). While currently confined to the Bay of Bengal, this new organism could find its way in bilge or ballast water, or with a traveler,

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PAUL R. EPSTEIN 199 to other parts of the world. Thus a cholera early warning system could help anticipate and reduce the impact of an Eighth World Pandemic of cholera. MONITORING TERRESTRIAL HABITATS OF DISEASE VECTORS Remote sensing has been used to delineate the habitats of vectors bearing diseases such as African sleeping sickness (Epstein, Rogers, and Slooff, 1993; Rogers and Packer, 1993) and malaria (Dister et al., 1993) so that controls can be instituted. The potential of the approach can be illustrated with the example of Eastern Equine Encephalitis (FEE), a disease transferred to humans by the bite of Aedes Texans mosquitoes. EKE most often affects children, and even in small outbreaks the consequences are grave and are terrifying for communities af- fected. Up to half of those infected may die, and half of the survivors are left with permanent neurological complications. Knowing where temporary pools of standing water are, when they will ap- pear, and perhaps how long they will last is necessary so that environmentally appropriate actions can be taken to control populations of EEE-infected mosqui- toes. Early use of Bti (Bacillus thuringiensis var. israelensis), a nontoxic, inex- pensive larvicide, is the alternative to widespread spraying of the adulticide malathion. Maturation of larvae to adults occurs in about 7 days, so accurate information on standing pools of water within 2 days after a rain will allow time for dip sampling to test for the presence of vectors in pools and subsequent application of Bti (Epstein, Rogers and Slooff, 1993~. The best approach for mapping standing water dependably involves the ac- quisition of remotely sensed images. These and other data layers can then be used together for analysis in an appropriate geographic information system. Real- time information following summer rains can be obtained from oblique-angled synthetic aperture radar (SAR), which can penetrate vegetative and cloud cover (Imhoff and McCandless, 1988; Imhoff and Gesch, 1990) to distinguish smooth water surfaces, thus helping to focus dip sampling and the application of larvi- cidal treatment in a timely fashion. Imagery from Landsat, with 30-m spatial resolution and coverage every 16 days (at best), or from Systeme pour ['Observation de la Terre (SPOT), which has the advantage of relatively more on-demand coverage and 20- and 10-m spatial resolution, will be helpful in developing a series of baseline maps identifying areas at risk for infection. SAR may be most appropriate for providing real-time accurate estimates of the locations of standing water. Aircraft-collected SAR data could be acquired and processed at an appropriate scale and processed for use in a timely fashion.

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200 HEALTH APPLICATIONS OF REMOTE SENSING AND CLIMATE MODELING MODELING CLIMATE VARIABILITY AND DISEASE OUTBREAKS During the warm phase of the E1 Nino/Southern Oscillation (ENSO), spe- cific areas of the globe are consistently affected by drought, whereas others experience excessive precipitation. While Southeast Brazil has rain, for example, Northeast Brazil has intensified drought. The climatic effects of ENS O are stronger (more consistent) in some areas; Southern Africa repeatedly experiences drought during an E1 Nino. All tropical oceans warm in relation to the ENSO pattern, and evaporation from the Atlantic can cause floods in a warmer Central Europe. Tracking ENS O events in relation to epidemics is a key to identifying the impacts of climate variability and weather on disease patterns. Associations in themselves are not proof of causation, but a preponderance of globally distributed evidence and a plausible mechanism (extremes of precipitation and temperature) lend credence to a strong role for climate in disease distribution. E1 Nino warm events are associated with upsurges of cholera in Bangladesh (R.B. Sack, The Johns Hopkins University School of Public Health, personal communication); typhoid, shigellosis, and hepatitis after flooding in South America (Cabello,1991~; viral encephalitides (Murray Valley and epidemic poly- arthritis from Ross River virus) in Australia (Nicholls, 1993~; and Eastern Equine Encephalitis in Massachusetts (Edman et al., 1993~. Other ENSO-related out- breaks of disease include malaria worldwide (Bouma et al., 1994a), in Pakistan (Bouma et al., 1994b), and in Venezuela (Bouma and Dye, 1997~; malaria and dengue ("breakbone") fever upsurges in Costa Rica (Kassutto and Epstein, un- published data); dengue fever in Northeast Brazil (unpublished data); epidemic malaria in the Indian subcontinent, 1874-1945 (M.J. Bouma, London School of Hygiene and Tropical Medicine, personal communication); and agricultural ro- dent infestations in Zimbabwe (1973-1983, 1994) (Epstein and Chikwenhere, 1994~. There is also a direct relationship between monsoons, which are biennially related to ENSO, and the spread of the brown plant hopper (Nilaparvata lugans) rice pest in Southeast Asia (Walker, 1994~. Interannual climate variability such as ENSO may be related to upsurges of soil-borne organisms as well. From the 428 reported cases of San Joaquim Valley fever (due to the fungus Coccidioidomycosis immitis) in the 1980s, 1200 cases occurred in 1991, and over 4000 in the E1 Nino years of 1992 and 1993 (Centers for Disease Control and Prevention, 1994~. An earthquake and a pro- longed drought followed by torrential rains are considered to have been contribu- tory factors. Additionally, disease events across taxa appear to cluster during ENSO warm-phase years. Disease events along the U.S. Atlantic coast during 1987, an ENS O warm-phase year heralding the warmest year of this century to that time, included extensive Caribbean coral bleaching; a large Florida sea grass die-off;

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PAUL R. EPSTEIN 201 transfer of the agent of neurological shellfish poisoning (Gymnodinium breve) from the Gulf of Mexico to North Carolina; a large die-off of sea mammals in New England and the North Sea; the emergence of amnesic shellfish poisoning in Prince Edward Island (caused by a newly discovered diatom toxin, domoic acid, and later appearing worldwide) (Epstein, Ford, and Colwell, 1993~; and an out- break of spruce budworms in the Northeast Canadian balsam forests. ENSO warm events are also correlated with new appearances of harmful algal blooms in Asia (Hallaegraeff, 1993) and along the U.S. Atlantic coast (M. Altalo, Scripps Institution of Oceanography, personal communication). This evidence of relationships between ENS O and disease outbreaks sug- gests that predictions of ENS O can be used in health early warning systems. Dynamic atmospheric-oceanic coupled general circulation models that depend in part on remote sensing of sea-surface temperatures provide predictions of ENSO and of its climatic effects. These models and their predictions are based on analysis of geographic patterns of climate since 1877 (Kaladis and Diaz, 1989; Glantz et al., 1991) and are improving in their ability to make regional predic- tions of climatic events. As the models increase in predictive skill, they will become increasingly useful for predicting climatic conditions conducive to dis- ease outbreaks. USING CLIMATE CHANGE MODELS TO PROJECT DISEASE DISTRIBUTION Animals and plants have clear thresholds for viability, as well as temperature and humidity ranges in which they mature, replicate, and thrive (Gill, 1920a, b; Burgos, 1990; Burgos et al., 1994; Dobson and Carper, 1993~. Shifts in tempera- ture isotherms in latitude and altitude with climate change could thus have pro- found impacts on ecotones (the geographical dividing lines between ecosystems), on biota, and in particular on the distribution of pests and pathogens. Several models using remote sensing, geographic information systems, and general circulation climate models have been used to project for particular areas of the world how conditions conducive to vector-borne diseases may change with global warming scenarios. The affected diseases include malaria, schistosomia- sis (Martens et al., 1994; Matsuoka et al., 1994), and dengue fever (rocks et al., 1995~. Plate 10-1 (facing page 183) shows the output of one such model for malaria. UNDERSTANDING THE SPREAD OF DISEASE AT HIGH ALTITUDES Recent reports indicate that malaria and dengue fever are appearing at higher altitudes than at any time during this century. In addition, plants have been observed to be moving to higher altitudes on 30 Alpine peaks, in the U.S. Sierra

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202 HEALTH APPLICATIONS OF REMOTE SENSING AND CLIMATE MODELING Nevada, in Alaska, and in New Zealand. Moreover, summit glaciers are retreat- ing on many continents, and ice caps show evidence of accelerated warming during this century (Thompson et al., 1995~. An initial examination of data from the Microwave Sounding Unit (MSU) and infrared sensors (Susskind et al., 1997) suggests that in E1 Nino years, warm- ing in the upper atmosphere may exceed warming occurring on the earth's sur- face. There are several possible contributing factors to explain these observa- tions. One is the increased relative heat absorption of carbon in the upper troposphere, because it is cooler at higher altitudes. Second, increased tropo- spheric water evaporation due to deep oceanic warming (Southwest Pacific, At- lantic, and Indian Oceans) (Parilla et al., 1994; Thwaites, 1994), exaggerated during E1 Nino years (Graham, 1995), may augment greenhouse warming and increase high, heat-trapping clouds. Third, sulfur-enriched lower clouds may also increase with increased atmospheric water vapor, and may reflect and absorb solar energy and cool the earth's surface with rain. Such analyses can help in developing causal models that link predictable seasonal-to-interannual climate changes, such as ENS O and global warming pro- cesses, to their effects on the ecology of disease organisms. This sort of under- standing will be valuable to public health officials in affected areas, such as major high-altitude tropical cities, in forecasting the potential for epidemics and taking appropriate action. SUMMARY AND CONCLUSION The costs of not understanding present climate instability and likely changes in climate due to human activities may be enormous. Disease outbreaks cause disability and mortality, and the impacts can ripple through societies and econo- mies. In 1995, the dengue outbreak cost Central American nations $7.5 million in control efforts alone; Peruvian fisheries lost $750 million in seafood exports during the 1991 cholera epidemic; and international airline and hotel industries lost an estimated $2 billion from the Indian plague in 1994. The global resur- gence of malaria, dengue fever, and cholera and the emergence of relatively new diseases such as Ebola can impact development, trade and tourism, agri- culture, and livestock. Remote sensing alone or integrated into geographical information systems and general circulation models has multiple applications for understanding bio- logical processes, and in particular, disease phenomena. Health early warning systems that can identify climate conditions conducive to outbreaks and disease clusters are becoming feasible enabling early, environmentally sound public health interventions. For vector-borne diseases, such interventions include, among others, immunizations, where appropriate; source reduction, such as neigh- borhood cleanups and the clearing of breeding sites; selective applications of pesticides or the nontoxic Bti; and community distribution of bednets.

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PAUL R. EPSTEIN 203 A cholera early warning system that uses remote sensing to detect coastal algal blooms and target surveillance has immediate relevance to protecting popu- lations. Public health responses based on these data include increased surveil- lance, preparation of oral rehydration treatment centers, increased chlorination of water supplies, and temporary closure of shellfish beds and fishing grounds. Additionally, remote sensing can be used in projecting future potential dis- ease distribution due to climate change. It can also play a central role in a multidisciplinary exploration of current physical and biological changes occur- nng at high altitudes, thus providing policy makers with important information on climate trends and their impacts. REFERENCES Aiken, J., G.F. Moore, and P.M. Holligan 1992 Remote sensing of oceanic biology in relation to global climate change, Journal of Phy- cology 28:579-590. Bouma, M.J., and C. Dye 1997 Cycles of malaria associated with E1 Nino in Venezuela. Journal of the American Medi- cal Association 278:1772-1774. Bouma, M.J., H.E. Sondorp, and J.H. van der Kaay 1994a Health and climate change. Lancet 343:302. 1994b Climate change and periodic epidemic malaria. Lancet 343:1440. Brock, J.C., C.R. McClain, M.E. Luthur, and W.W. Hay 1991 The phytoplankton blooms in the Northwestern Arabian Seas during the Southwest mon- soon of 1979. Journal of Geophysical Research 96(C 11)20:613-622. Brock, J.C., and C.R. McClain 1992 Interannual variability in phytoplankton blooms observed in the Northwestern Arabian Sea during the Southwest monsoon. Journal of Geophysical Research 97:733-375. Burgos, J.J. 1990 Analogias agroclimatologicas utiles pare la adaptacion al posible cambio climatico global de America del Sur. Revista Geofisica 32:79-95. Burgos, J.J., S.I. Curto de Casas, R.U. Carcavallo, and G.I. Galindez 1994 Global climate change in the distribution of some pathogenic complexes. Entomologia y Vectores 1:69-82. Cabello, F. 1991 Una visita a un antiguo paradigma en Chile: Deterioro economico y social y epidemias. Interciencia 16:176-181. Centers for Disease Control and Prevention 1994 Update: Coccidioidomycosis California, 1991-1993. MMWR43:421-423. Cockburn, T.A., and J.G. Cassanos 1960 Epidemiology of epidemic cholera. Public Health Reports 75:791. Colwell, R.R., P.R. Brayton, D.J.Grimes, S.A.Ruszak, H. Hug, and L.M. Palmer 1985 Viable but non-culturable Vibrio cholerae and related pathogens in the environment: Implications for release of genetically engineered microorganisms, Biotechnology 3:817- 820.

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204 HEALTH APPLICATIONS OF REMOTE SENSING AND CLIMATE MODELING Dister, S., L. Beck, B. Wood, R. Falco, and D. Fish 1993 The use of remote sensing technologies in a landscape approach to the study of Lyme disease transmission risk. Pp. 1149-1155 in Proceedings of GIS '93: Seventh Annual Symposium in Geographic Information Systems in Forestry, Environmental and Natural Resource Management. Dobson, A., and R. Carper 1993 Biodiversity. Lancet 342:1096-1099. Edman, J.D., R. Timperi, and D. Werner 1993 Epidemiology of Eastern Equine Encephalitis in Massachusetts. Journal of the Florida Mosquito Control Association 64:84-96. Epstein, P.R., and G.P. Chikwenhere 1994 Biodiversity questions (letter). Science 265:1510-1511. Epstein, P.R., T.E. Ford, and R.R. Colwell 1993 Marine ecosystems. Lancet 342: 1216-1219. Epstein, P.R., D.J. Rogers, and R. Slooff 1993 Satellite imaging and vector-borne disease. Lancet 341:1404-1406. Feldman, G., D. Clark, and D. Halpern 1984 Satellite color observations of the phytoplankton distribution in the Eastern Equatorial Pacific during the 1982-1983 E1 Nino. Science 226:1069-1071. Focks, D.A., E. Daniels, D.G. Haile, and L.E. Keesling 1995 A simulation model of the epidemiology of urban dengue fever: Literature analysis, model development, preliminary validation, and samples of simulation results. American Journal of Tropical Medicine and Hygiene 53:489-506. Gill, C.A. 1920a The relationship between malaria and rainfall. Indian Journal of Medical Research 37:618-632. 1920b The role of meteorology and malaria. Indian Journal of Medical Research 8:633-693. Glantz, M.H., R.W. Katz, and N. Nicholls 1991 Teleconnections Linking Worldwide Climate Anomalies. Scientific Basis and Societal Impact. New York: Cambridge University Press. Graham, N.E. 1995 Simulation of recent global temperature trends. Science 267:666-671. Hallaegraeff, G.M. 1993 A review of harmful algal blooms and their apparent global increase. Phycologia 32:79- 99. Imhoff, M.L., and D.B. Gesch 1990 The derivation of a sub-canopy digital terrain model of a flooded forest using synthetic aperture radar. Photogrammetric Engineering and Remote Sensing (11 June):1157-1162. Imhoff, M.L., and S.W. McCandless 1988 Flood boundary delineation through clouds and vegetation using L-band space-borne radar: A potential new tool for disease vector control programs. Acta Astranautica 17(9): 1003-1007. Kaladis, G.N., and H.F. Diaz 1989 Global climatic anomalies associated with extremes in the southern oscillation. Journal of Climate 2:1069-1090. Martens, W.J.M., J. Rotmans, and L.W. Niessen 1994 Climate Change and Malaria Risk: An Integrated Modelling Approach. GLOBE Report Series No. 3 RIVM Report No. 461502003. Bilthoven, The Netherlands: Global Dynam- ics and Sustainable Development Programme.

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PAUL R. EPSTEIN 205 Martens, W.J.M., T.H. Jeffen, and D.A. Focks 1997 Sensitivity of malaria, schistosomiasis, and dengue to global warming. Climatic Change 35: 145- 156. Matsuoka, Y., and K. Kai 1994 An estimation of climatic change effects on malaria. Journal of Global Environment Engineering 1:1-15. Nicholls, N. 1993 E1 Nino-Southern Oscillation and vector-borne disease. Lancet 342:1284-1285. Parrilla, G., A. Lavin, H. Bryden, M. Garcia, R. Millard 1994 Rising temperatures in the sub-tropical North Atlantic Ocean over the past 35 years. Nature 369:48-51. Rogers, D.J., and M.J. Packer 1993 Vector-borne diseases, models, and global change. Lancet 342:1282-1284. Susskind, J., P. Piraino, L. Rokke, L. Iredell, and A. Mehta 1997 Characteristics of the TOVS Pathfinder Path A data set. Bulletin of the American Meteo- rological Society 78:1449-1472. Thompson, L.G., E. Mosley-Thompson, M.E. Davis, et al. 1995 Late glacial stage and Holocene tropical ice core records from Huascaran, Peru. Science 269:46-50. Thwaites, T. 1994 Are the antipodes in hot water? New Scientist 12(November):21. Walker, B.H. 1994 Landscape to regional-scale responses of terrestrial ecosystems to global change. Ambio 23:67-73. ADDITIONAL READINGS Anon 1994 Indian Ocean may have E1 Nino of its own. EOS Transactions, American Geophysical Union. 75(December 13):585-586. Bengtsson, L., U. Schlese, E. Roeckner, M. Latif, T.P. Barnett, and N. Graham 1993 A two-tiered approach to long-range climate forecasting. Science 261:1026-1029. Bindoff, N.L., and J.A. Church 1992 Warming of the water column in the southwest Pacific. Nature 357:59-62. Broecker, W.S. 1987 Unpleasant surprises in the greenhouse? Nature 328:123-126. Chang, P., J.I. Link, and L.I. Hong 1997 A decadal climate variation in the tropical Atlantic Ocean from thermodynamic air-sea interactions. Nature 385:516-518. Dansgaard, W., S.J. Johnson, H.B. Clausen, et. al. 1993 Evidence for general instability of past climate from a 250-yr ice-core record. Nature 364:218-220. Darzi, M., J.K. Firestone, G. and C.R. McClain 1991 Current efforts regarding the SEAPAK oceanographic analysis system. Pp. 109-115 in Proceedings of 7th Conference on Interactive and Informative Processing Systems for Meteorology, Hydrology, and Oceanography. Boston, Mass.: American Meteorological Society. Easterling, D.R., B. Horton, P. D. Jones, T.C. Peterson, T.R. Karl, D.E. Parker, M.J. Salinger, V. Razuvayev, N. Plummer, P. Jamason, and C.K. Folland 1997 Maximum and minimum temperature trends for the globe. Science 277:363-367.

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206 HEALTH APPLICATIONS OF REMOTE SENSING AND CLIMATE MODELING Epstein, P.R. 1993 Algal blooms in the spread and persistence of cholera. BioSystems 31:209-221. Epstein, P.R., O.C. Pena, and J.B. Racedo 1995 Climate and disease in Colombia. Lancet 346:1243-1244. Feldman, G., et. al. 1989 Ocean color: Availability of the global data set, EOS Transactions, American Geophys cat Union 70 (23):634-635, 645-641. Gordon, H.R., D.K. Clark, J.W. Brown, O.B. Brown, R.H. Evans, and W.W. Bronkow 1983 Phytoplankton pigment concentration in the Middle Atlantic Bight: Comparison of ship determinations and CZCS estimates, Applied Optics 22 (1):20-36. Graham, N.E. 1995 Simulation of recent temperature trends. Science 267:666-671. Greenland Ice-Core Project (GRIP) 1993 Climate instability during the last interglacial period recorded in the GRIP ice core. Na- ture 364:203-207. Holligan, P.M., M. Viollier, D.S. Harbour, P. Kamus, and N. Champaque, Philippe 1983 Satellite and ship studies of coccolithophore production along our continental shelf edge. Nature 304(28):339-342. Holliday, D.V. 1993 Applications of advanced acoustic technology in large marine ecosystem studies. Pp. 301-319 in Large Marine Ecosystems: Stress, Mitigation, and Sustainability, K. Sherman, L.M. Alexander, B. D. Gold, eds. Washington, D.C.: AAAS Press. Karl, T.R., P.D. Jones, R.W. Knight, G. Kukla, N. Plummer, V. Razuvayev, K.P. Gallo, J. Lindsay, R.J. Charlson, and T.C. Peterson 1993 A new perspective on recent global warming: Asymmetric trends of daily maximum and minimum temperature. Bulletin of the American Meteorological Society 74: 1007-1023. Karl, T.R., R.W. Knight, D.R. Easterling, and R.G. Quayle 1995a Trends in U.S. climate during the twentieth century. Consequences 1:3-12. Karl, T.R., R.W. Knight, and N. Plummer 1995b Trends in high-frequency climate variability in the twentieth century. Nature 377:217- 220. Karl, T.R., N. Nicholls, and J. Gregory 1997 The coming climate. Scientific American (May):78-83. Kiladis, G.N., and H.F. Diaz 1989 Global climatic anomalies associated with extremes in the Southern Oscillation. Journal of Climate 2:1069. Loevinsohn, M. 1994 Climatic warming and increased malaria incidence in Rwanda. Lancet 343:714-718. Manabe, S., and R.J. Stouffer 1993 Century-scale effects of increased atmospheric CO2 on the ocean-atmosphere system. Nature 364:215-218. Martens, W.J.M. 1995 Modelling the Effect of Global Warming on the Prevalence of Schistosomiasis. Report No. 461502010. Bilthoven, The Netherlands: National Institute of Public Health and the Environment (RIVM). Martin, P.H., and M.G. Lefebvre 1995 Malaria and climate: Sensitivity of malaria potential transmission to climate. Ambio 24:200-207. Meehl, G.A., and W.M. Washington 1993 South Asian summer monsoon variability in a model with double atmospheric carbon dioxide concentration. Science 260:1101-1104.

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PAUL R. EPSTEIN 207 Mayewski, P.A., L.D. Meeker, S. Whitlow, et al. 1993 The atmosphere during the Younger Dryas. Science 261:195-197. Parrilla, G., A. Lavin, H. Bryden, M. Garcia, and R. Millard 1994 Rising temperatures in the sub-tropical North Atlantic Ocean over the past 35 years. Nature 369:48-51. Pearce, F. 1997 Southern oceans hold key to climate. New Scientist 5(April):21. Powers, D.A. 1993 Application of molecular techniques to large marine ecosystems. Pp. 320-352 in: Large Marine Ecosystems: Stress, Mitigation, and Sustainability, K. Sherman, L.M. Alexander, B. D. Gold, eds., Washington, D.C.: AAAS Press. Regaldo, A. 1995 Listen up! The world's oceans may be starting to warm. Science 268: 1436-1437. Travis, J. 1994 Taking a bottom-to sky "slice" of the Arctic Ocean. Science 266:1947-1948. Trenberth, K.E., and T.J. Hoar 1996 The 1990-1995 E1 Nino-Southern Oscillation event: Longest on record. Geophysical Research Letters 23:57-60. Tziperman, E. 1997 Inherently unstable climate behavior due to weak thermohaline ocean circulation. Nature 386:592-595. Yoder, J.A., and G. Garcia-Moliner 1994 Application of satellite remote sensing and optical buoys/moorings to LME studies. Ambio 23:353-358. Zebiak, S.E., and M.A. Cane 1991 Natural climate variability in a coupled model. In Greenhouse Gas-Induced Climatic Change: A Critical Appraisal of Simulations and Observations, M.E. Schlesinger, ed. Amsterdam, The Netherlands: Elsevier Science Publishers BV.

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