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Eutrophication, fisheries, and consumer-resource dynamics in marine pelagic ecosystems

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Abstract Anthropogenic nutrient enrichment and fishing influence marine ecosystems worldwide by altering resource availability and food-web structure. Meta-analyses of 47 marine mesocosm experiments manipulating nutrients and consumers, and of time
  was measured at 37°C with phosphatidylcholine hy-droperoxide at 3 mM GSH according to ( 16 ). Controlsamples were treated identically but with 5 mM2-mercaptoethanol.18. Other examples of “gene sharing” or “moonlightingproteins,” respectively, are reviewed by J. Piatigorsky, Prog. Ret. Eye Res. 17 , 145 (1998); C. J. Jefferey, Trends Biol. Sci. 24 , 8 (1999).19. M. Maiorino et al ., Biol. Chem. Hoppe Seyler  376 , 651(1995); F. Ursini et al ., Methods Enzymol. 252 , 38(1995).20. F. Bauche´, M.-H. Fouchard, B. Je´gou, FEBS Lett. 349 ,392 (1994); R. Shalgi, J. Seligman, N. S. Kosower, Biol.Reprod. 40 , 1037 (1989); J. Seligman, N. S. Kosower,R. Shalgi, ibid. 46 , 301 (1992); H. M. Fisher and R. J.Aitken, J. Exp. Zool. 277 , 390 (1997).21. F. Weitzel and A. Wendel, J. Biol. Chem. 268 , 6288(1993); R. Brigelius-Flohe´, B. Friedrichs, S. Maurer, M.Schultz, R. Streicher, Biochem. J. 328 , 199 (1997);P. A. Sandstrom, J. Murray, T. M. Folks, A. M. Diamond, Free Radical Biol. Med. 24 , 1485 (1998).22. In other gels, mitochondrial glutathione S-transferasesubunit Yb-2 (accession number 121719) and endo-thelin converting enzyme (NCBI accession number 1706564) could be identified by MALDI-TOF or pep-tide sequencing.23. Supported by the German Ministry of Education,Science and Technology, the Italian Ministry of Uni-versity and Scientific Research, National ResearchCouncil, Italy, and the BIOMED 2 program of theEuropean Community.2 April 1999; accepted 29 July 1999 Eutrophication, Fisheries, andConsumer-Resource Dynamicsin Marine Pelagic Ecosystems Fiorenza Micheli* Anthropogenic nutrient enrichment and fishing influence marine ecosystemsworldwide by altering resource availability and food-web structure. Meta-analyses of 47 marine mesocosm experiments manipulating nutrients andconsumers, and of time series data of nutrients, plankton, and fishes from 20natural marine systems, revealed that nutrients generally enhance phytoplank-ton biomass and carnivores depress herbivore biomass. However, resource andconsumer effects attenuate through marine pelagic food webs, resulting in aweak coupling between phytoplankton and herbivores. Despite substantialphysical and biological variability in marine pelagic ecosystems, alterations of resource availability and consumers result in general patterns of communitychange. Increased nutrient loadings and fisheries ex- ploitation are major human perturbations tomarine ecosystems worldwide ( 1 ). Alterationof resource availability represents a “bottom-up” perturbation of marine ecosystems, where-as removal of consumer biomass throughfishing represents a “top-down” disturbance.An understanding of how bottom-up and top-down processes influence the dynamics of marine communities is necessary for effec-tive management of marine ecosystems in theface of environmental variability and multi- ple human impacts. However, it is difficult todetermine the effects of resource availabilityand food-web interactions in open (pelagic),highly variable marine systems; most propo-sitions are based on anecdotal evidence fromcatastrophic events such as El Nin˜o years ( 2 ),fishery collapses ( 3 ), and the introduction of exotic species ( 4 ). To determine how marine pelagic ecosystems respond to variation inthe quantity of resources and consumers, Iconducted meta-analyses of data from a va-riety of experimental and natural systems and examined whether changes in the abundanceof consumers (pelagic zooplanktivorous fish)cascade down marine food webs to affectlower trophic levels, and whether changes innutrient availability and primary productivitycascade up marine food webs to affect higher trophic levels.To address these questions, I assembled data from experimental manipulations con-ducted in marine mesocosms and from long-term monitoring of open marine ecosystems.Experiments conducted in mesocosms elimi-nate open-system dynamics but representcontrolled alterations of nutrient availabilityand food-web structure. In contrast, long-term monitoring of open marine systems doc-uments patterns at realistic spatial and tem- poral scales. The first data set comprised  phytoplankton and mesozooplankton (mostlyherbivorous copepod crustaceans larger than150 to 300 ␮ m) data from marine mesocosmexperiments where nutrient availability wasmanipulated by adding N compounds, or where food-web structure was manipulated  by adding or removing zooplanktivorous fishor invertebrates ( 5 ). The second data set con-sisted of time series (7 to 45 years) of Navailability (measured as the annual loadingor as the averageNconcentrationduringwinter months), primary productivity, and the bio-mass of phytoplankton, mesozooplankton,and pelagic zooplanktivorous fish for 20 openmarine ecosystems ( 6  ).For the mesocosm experiments, I quanti-fied responses of phytoplankton and mesozoo- plankton to nutrient and food-web manipula-tions by using the natural logarithm of theratio between the mean value of the variablein mesocosms with carnivores (zooplanktivo-rous fish or invertebrates) or nutrients (inor-ganic N compounds) added and in unmanip-ulated, control mesocosms ( 7  ). Zooplankti-vores caused significant decreases in meso-zooplankton biomass, both in mesocosmswith no N added (Fig. 1A) and in mesocosmsenriched with N (Fig. 1B). Zooplanktivorescaused an increase in phytoplankton biomass, but this trend was statistically significant onlyin systems that were also enriched with N(Fig. 1, A and B). Nitrogen addition caused similar and significant increases in phyto- plankton biomass in mesocosms containingtwo (phytoplankton and zooplankton; Fig. 1C)or three trophic levels (phytoplankton, zoo- plankton, and zooplanktivores; Fig. 1D). Un-der either food-web configuration, nutrientaddition did not affect mesozooplankton bio-mass (Fig. 1, C and D). The effects of themanipulations were not significantly correlat-ed with either experiment duration or meso-cosm size in zooplanktivore-manipulation ex- periments ( 8 ), and the effects were only weak-ly correlated with duration but not with size innutrient-manipulation experiments ( 9 ). There-fore, these results are unlikely to be biased bythe short duration or small mesocosm sizesused in most experiments.For the 20 open marine ecosystems, Iexamined the cross-correlation between timeseries of nutrients, productivity, and biomassof different trophic levels using Spearmanrank correlation ( 10 ) . Theoretical models ex- ploring the relations among resource avail-ability, food-web structure, and biomass of different trophic levels predict patterns of  biomass accrual along productivity gradientsat equilibrium, that is, after transient effectshave disappeared ( 11, 12 ). Because seasonalevents such as upwelling and sudden increas-es in fish density from immigration or springreproduction are transient effects, I used year-ly values of productivity and biomass toapproximate equilibrium conditions. Year-to-year fluctuations in mesozooplankton bio-mass were negatively correlated with zoo- planktivorous fish ( r  ϭ Ϫ 0.22; 95% confi-dence limits ϭ Ϫ 0.31 and  Ϫ 0.12; N  ϭ 19),indicating that fish predation may controlmesozooplankton biomass. In contrast, thecorrelation between mesozooplankton and  National Center for Ecological Analysis and Synthesis,Santa Barbara, CA 93101, USA.*Present address: Dipartimento di Scienze dell’Uomoe dell’Ambiente, Universita’ di Pisa, 56126 Pisa, Italy.E-mail: f.micheli@trident.nettuno.it R E P O R T S 27 AUGUST 1999 VOL 285 SCIENCE www.sciencemag.org 1396   phytoplankton was not significant ( r  ϭϪ 0.07; 95% confidence limits ϭ Ϫ 0.15 and 0.01; N  ϭ 19). This result may indicate thatmesozooplankton does not control phyto- plankton biomass, although a nonsignificantcorrelation could arise through mechanismsother than uncoupling between trophic levels. Negative correlations between zooplanktivo-rous fish and mesozooplankton and betweenmesozooplankton and phytoplankton werefound in six systems, but they were not sta-tistically significant (significance level ␣ ϭ 0.05) except for the correlation betweenzooplanktivores and mesozooplankton in onesystem, the subarctic Pacific ( 13 ). Thus, in pelagic marine ecosystems alterations of con-sumer abundance can cascade down food webs to affect phytoplankton biomass, butthis effect is uncommon. Similarly, effects of changes in N availability and primary pro-ductivity rarely cascade upward to affect bio-mass of marine pelagic consumers. In gener-al, N availability and primary productivitywere positively correlated with phytoplank-ton biomass (Fig. 2). Correlations of nutrientsand productivity with mesozooplankton and zooplanktivore biomass were not significantand showed no overall trend (Fig. 2). Posi-tive, although nonsignificant, correlations be-tween primary productivity and biomass of all trophic levels were found only in twosystems ( 14 ).Meta-analyses of data from mesocosm ex- periments and natural marine ecosystems in-dicated that pelagic marine food webs arecharacterized by bottom-up control of prima-ry producers (phytoplankton) through N avail-ability and top-down control of herbivores(mesozooplankton) through predation by car-nivores (zooplanktivorous fish). Both analy-ses indicated a weak coupling between pri-mary producers and herbivores. Zooplankti-vores tend to decrease mesozooplanktonabundance, but the mesozooplankton com-monly has no effect on the phytoplankton(Fig. 1). Conversely, increased N availabilityenhances primary producers but does not en-hance the mesozooplankton (Figs. 1 and 2).In general, the effects of consumer-resourceinteractions do not cascade upward or down-ward through marine pelagic food webs.The effects of carnivores (zooplanktivo-rous fish) on herbivores (mesozooplankton)and of nutrients on plants (phytoplankton),and the loose coupling between herbivoresand plants, are pervasive. These patterns wereobserved at vastly different spatial (meso-cosms to open ocean systems) and temporalscales (days to decades) and are similar tothose found in syntheses of data from fresh-water systems ( 15 ). The generality of these patterns indicates that similar mechanismsmay underlie the dynamics of closed (fresh-water) and open (marine) aquatic systems.Open, highly variable systems such as marine pelagic ecosystems may be regulated by local biological interactions similar to those occur-ring within naturally closed lake ecosystemsor experimentally enclosed marine and fresh-water systems.There are at least three biological mecha-nisms that might account for the observed weak coupling between primary producersand herbivores. First, coupling between tro- phic levels may be dampened by speciesinteractions within the zooplankton; interfer-ence among zooplankton species may limittheir population growth and hinder their top-down effects on the phytoplankton ( 12 ). Thetrophic level abstraction used in many theo-retical and empirical studies ignores the com- plexity of species interactions and thus mayinadequately describe real food webs. Sec-ond, the efficiency of the transfer of primary productivity to higher trophic levels and theimpact of herbivores on primary producersmay depend on food quality, particularly the proportion of edible and inedible algae withinthe phytoplankton ( 16  ). Increased proportionsof inedible algae frequently accompany in-creased productivity caused by anthropogenicnutrient enrichment ( 17  ). Finally, in open ma-rine systems, advection or loss of nutrientsand individuals from the focal system maydampen effects of local biological interac-tions and lead to an uncoupling between ad- jacent trophic levels ( 18 ). These mechanismsmight act jointly to weaken primary producer-herbivore coupling in marine pelagic food webs.These results have implications for man-agement of marine ecosystems. First, thegenerality of a weak coupling of N loadingand phytoplankton productivity with higher trophic levels (Figs. 1 and 2) implies thatanthropogenic nutrient loading to coastalwaters is unlikely to result in increased fish biomass, regardless of local physical and  biological conditions and of the magnitudeof nutrient enrichment. Phytoplankton pro-duction resulting from increased nutrientloading may be recycled within the plank-ton by microorganisms ( 19 ) or be lost from pelagic marine food webs when detritussettles to the ocean floor ( 20 ). Second,fluctuations in stocks of planktivorous pe-lagic fishes commonly affect zooplanktoncommunities but rarely cascade throughmarine pelagic food webs to affect phyto- plankton biomass. Thus, pelagic fisheriesare expected to influence other ecosystemcomponents, not directly targeted by thefishery, by affecting zooplankton biomassand food availability for other carnivores.However, it is unlikely that manipulationsof marine food webs similar to those pro- posed for lakes ( 21 ) could be effective incontrolling the response of primary produc-ers to nutrient enrichment in coastal waters.Improved understanding of consumer-re-source dynamics is critical both to predictthe consequences of multiple anthropogenic perturbations to aquatic ecosystems and to de-velop sustainable management practices. References and Notes 1. P. M. Vitousek, H. A. Mooney, J. Lubchenco, J. M.Melillo, Science 277 , 494 (1997); L. W. Botsford, J. C.Castilla, C. H. Peterson, ibid. , p. 509.2. R. T. Barber and F. B. Chavez, ibid. 222 , 1203 (1983);P. Lehodey et al ., Nature 389 , 715 (1997).3. G. Murphy, in Fish Population Dynamics, J. A. Gullard,Ed. (Wiley, Chichester, UK, 1977), pp. 283–308; M. J.Fogarty and S. A. Murawski, Ecol. Appl . 8 , S6 (1998). Fig. 1. Responses of phytoplankton and meso-zooplankton to the addition of ( A and B ) zoo-planktivorous fish or invertebrates and ( C and D ) inorganic N compounds to mesocosms con-taining pelagic marine communities. (A) Zoo-planktivore addition was the only manipulationconducted in these experiments; (B) in additionto manipulating zooplanktivores, nutrients wereadded in identical amounts to both control andzooplanktivore mesocosms; (C) both controland nutrient-enriched mesocosms containedonly phytoplankton and zooplankton; (D) bothcontrol and nutrient-enriched mesocosms con-tained phytoplankton, zooplankton, and zoo-planktivorous fish or invertebrates. Means areaverages of the log-transformed ratios of themean treatment biomass divided by the meanin the controls, weighted by sampling variances.Bars are 95% confidence intervals. The number of experiments used to calculate each averagelog response ratio is indicated to the right of each mean. Fig. 2. Correlation of ( A ) annual N availability(winter concentrations or loadings of inorganicN) and ( B ) mean annual primary productivitywith (i) phytoplankton, (ii) mesozooplankton,and (iii) zooplanktivorous fish biomass in ma-rine pelagic food webs. Means are averages of Spearman rank correlations between time se-ries, weighted by sampling variances. Bars are95% confidence intervals. The number of cor-relation coefficients averaged is indicated near each mean. R E P O R T S www.sciencemag.org SCIENCE VOL 285 27 AUGUST 1999 1397  4. A. E. Alpine and J. E. Cloern, Limnol. Oceanogr. 37 ,946 (1992); Y. P. Zaitsev, Fish. Oceanogr  . 1 , 180(1992).5. Only experiments conducted in marine or estuarinewaters [salinity 4 to 35 practical salinity units (psu)]and including both treatment and control mesocosmswere included. Experiments ranged from 4 to 365days and were conducted in containers ranging from3 ϫ 10 6 to 1.3 ϫ 10 6 liters in volume. Of the 47experiments included in the analyses, 14 were unrep-licated and 33 used two to four replicate mesocosms.The nutrients added were nitrite, nitrate, or ammo-nia, alone or in combination with phosphate andsilica. Zooplanktivores were various species of mysidshrimp, coelenterates, chaetognates, or planktivorousfish. When time series of experimental results weregiven, I averaged the data over the whole duration of the experiments for meta-analysis. Data were ex-tracted from tables or digitized from figures reportedin the following papers: P. C. Abreu et al ., Estuaries 17 ,575 (1994); J. G. Baretta-Bekker, B. Riemann, J. W.Baretta, E. Koch-Rasmussen, Mar. Ecol. Prog. Ser. 106 , 187 (1994); D. L. Breitburg et al ., Limnol. Ocean-ogr. 44 , 837 (1999); E. E. Deason and T. J. Smayda  , J.Plankton Res . 4 , 219 (1982); P. H. Doering et al ., Mar.Ecol. Prog. Ser. 52 , 287 (1989); R. S. Fulton, J. Exp.Mar. Biol. Ecol . 72 , 67 (1983); Oecologia 62 , 97(1984); J. Plankton Res . 6 , 399 (1984); E. Graneli andK. Sundback, J. Exp. Mar. Biol. Ecol . 85 , 253 (1985); E.Graneli et al. , J. Plankton Res . 15 , 213 (1993); R. P.Harris et al ., in Marine Mesocosms, G. D. Grice andM. R. Reeve, Eds. (Springer-Verlag, New York, 1982),pp. 353–388; M. Hein and B. Riemann, J. Exp. Mar.Biol. Ecol . 188 , 167 (1995); A. S. Heiskanen, T. Tam-minen, K. Gundersen, Mar. Ecol. Prog. Ser. 145 , 195(1996); S. J. Horsted, T. G. Nielsen, B. Riemann, J.Pock-Steen, P. K. Bjørnsen, ibid. 48 , 217 (1988); A. Jacobsen, J. K. Egge, B. R. Heimdal, J. Exp. Mar. Biol.Ecol . 187 , 239 (1995); K. Kivi, H. Kuosa, S. Tanskanen, Mar. Ecol. Prog. Ser. 136 , 59 (1996); J. Kuiper, U. H.Brockmann, H. van het Groenewoud, G. Hoornsman,K. D. Hammer, ibid. 14 , 9 (1983); P. Kuuppo-Leinikki et al ., ibid. 107 , 89 (1994); P. Kuuppo, R. Autio, H.Kuosa, O. Seta¨la¨, S. Tanskanen, East. Coast. Shelf Sci. 46 , 65 (1998); P. Olsson, E. Graneli, P. Carlsson, P.Abreu, J. Exp. Mar. Biol. Ecol . 158 , 249 (1992); H. W.Paerl, J. Rudek, M. A. Mallin, Mar. Biol. 107 , 247(1990); J. L. Pinckney, H. W. Paerl, E. Haugen, P. A.Tester, Mar. Ecol. Prog. Ser. , in press; B. Riemann,T. G. Nielsen, S. J. Horsted, P. K. Bjørnsen, J. Pock-Steen, ibid. 48 , 205 (1988); B. Riemann et al ., ibid. 65 ,159 (1990); S. Schulz, G. Bruel, A. Irmisch, Limno-logica 20 , 89 (1990); N. C. Sonntag and T. R. Parsons,  J. Plankton Res . 1 , 85 (1979); A. Uitto, S. Kaitala, H.Kuosa, R. Pajuniemi, Aqua Fenn. 25 , 23 (1995).6. The time series data sets consisted of yearly or summer averages of nutrients, productivity, or bio-mass. Time series ranged from 7 to 45 years and hadbeen gathered between 1948 and 1994 in 16 coastalareas from the Baltic Sea (nine areas: Arkona Sea,Great Belt, Bornholm Sea, Gotland Sea, ArchipelagoSea, Gulf of Riga, Kattegat, Mecklenburg Bay, andOresund), the North Sea (four areas: Skagerrak, Ger-man Bight, Southern Bight, and NorthumberlandCoast), the English Channel (off Plymouth, UK), themiddle Adriatic Sea, and the Gulf of Thailand and four offshore areas from the Peruvian and the Californiaupwelling systems, the Gulf of Alaska (ocean stationP), and the subarctic Pacific (south of the AleutianIslands). All systems are subject to intense humandisturbance through fishing and anthropogenic nutri-ent loadings to the coastal systems. Data were ex-tracted from tables or digitized from figures pub-lished in the following papers and reports: L. Anders-son and L. Rydberg, East. Coast. Shelf Sci  . 26 , 559(1988); M. C. Austen et al. , J. Mar. Biol. Assoc. UK  71 ,179 (1991); G. T. Boalch, D. S. Harbour, E. I. Butler, ibid. 58 , 943 (1978); E. Bonsdorff, E. M. Blomqvist, J.Mattila, A. Norkko, Oceanol. Acta 20 , 319 (1997);R. D. Brodeur  et al. , Calif. Coop. Ocean. Fish. Investig . Rep . 37 , 80 (1996); R. Milla´n-Nu´n˜ez, S. Alvarez-Bor-rego, C. C. Trees  , ibid  ., p. 241; A. Corten, Neth. J. SeaRes . 25 , 227 (1990); D. H. Cushing, ICES J. Mar. Sci  . 52 , 611 (1995); R. R. Dickson, P. M. Kelly, J. M.Colebrook, W. S. Wooster, D. H. Cushing, J. PlanktonRes . 10 , 151 (1988); Food and Agriculture Organiza-tion of the United Nations, Gen. Fish. Counc. Medi-terr. No. 63 (1990); Baltic Marine Environment Pro-tection Commission (Helsinki Commission), Baltic Sea Environmental Proceedings No. 35B (1990); W.Hickel, J. Berg, K. Treutner, ICES Mar. Sci. Symp . 195 ,249 (1992); “Reports of the ICES Advisory Committeeon Fishery Management,” ICES ( Int. Counc. Explor. Sea ) Coop. Res. Rep. No. 196 (1993); J. Jakobsson, ICES Mar. Sci. Symp . 195 , 291 (1992); K. Kononen, H.Theede, W. Schramm, Kiel. Meeresforsch . 6 , 281(1988); P. Muck, in The Peruvian Upwelling System:Dynamics and Interactions , D. Pauly et al ., Eds.(ICLARM, Manila, Philippines, 1989); E. Ojaveer, Ed .,Ecosystem of the Gulf of Riga Between 1920 and 1990 (Estonian Academic Publisher, Tallinn, 1995); S.Schulz, W. Kaiser, G. Breuel, Int. Rev. Gesamt Hydro-biol . 76 , 351 (1991); K. Sherman and L. M. Alexander,Eds., Variability and Management of Large MarineEcosystems (AAAS, Washington, DC, 1985), pp. 33–54; A. Shiomoto, K. Tadokoro, K. Nagasawa, Y. Ishida, Mar. Ecol. Prog. Ser. 150 , 75 (1997); G. Sinovcic andV. Alegria-Hernandez, Oceanol. Acta 20 , 201 (1997);P. E. Smith and R. W. Eppley, Limnol. Oceanogr. 27 , 1(1982); A. J. Southward and G. T. Boalch, in Aspects of Long-Term Changes in the Ecosystem of the WesternEnglish Channel in Relation to Fish Populations, T.Wyatt and M. G. Larraneta, Eds. (Instituto Investiga-ciones Marinas, Vigo, Spain, 1988), pp. 415–447; S.Suvapepun, Mar. Pollut. Bull . 23 , 213 (1991); M.Viitasalo, thesis, Finnish Institute of Marine Research,Helsinki, 1994; D. M. Ware and G. A. McFarlane, Can. Spec. Publ. Fish. Aquat. Sci. No. 121 (1995), p. 509; D.Woehrling and G. Le Fe`vre-Lehoe¨rff, Oceanol. Acta 21 , 113 (1998).7. L. V. Hedges and I. Olkin, Statistical Methods for Meta-Analysis (Academic Press, Orlando, FL, 1985); J.Gurevitch and L. V. Hedges, in Design and Analysis of Ecological Experiments , S. Scheiner and J. Gurevitch,Eds. (Chapman & Hall, New York, 1993); C. W. Os-enberg, O. Sarnelle, S. Cooper, Am. Nat. 150 , 798(1997). Averages of the mean response ratios acrossall studies, weighted by the sampling variance, areconsidered significantly different from zero (that is,there is a significant effect of experimental treat-ment) when the 95% confidence limits around themean do not overlap zero.8. Log of the response ratio versus duration of experi-ments (in days), r  ϭ Ϫ 0.21, P Ͼ 0.10, N ϭ 18(without nutrients added), and r  ϭϪ 0.38, P Ͼ 0.10, N ϭ 17 (with nutrients added) for phytoplankton; r  ϭ 0.29, P Ͼ 0.10, N ϭ 13 (without nutrients added),and r  ϭ 0.07, P Ͼ 0.10, N ϭ 10 (with nutrientsadded) for mesozooplankton. Log of the responseratio versus mesocosm volume (in liters), r  ϭϪ 0.20, P Ͼ 0.10, N ϭ 18 (without nutrients added), and r  ϭϪ 0.30, P Ͼ 0.10, N ϭ 17 (with nutrients added) for phytoplankton; r  ϭ 0.38, P Ͼ 0.10, N ϭ 13 (withoutnutrients added), and r  ϭϪ 0.001, P Ͼ 0.10, N ϭ 10(with nutrients added) for mesozooplankton.9. Log of the response ratio versus duration of experi-ments (in days), r  ϭ 0.23, P ϭ 0.09, N ϭ 54 (withoutzooplanktivores), and r  ϭ 0.067, P ϭ 0.01, N ϭ 14(with zooplanktivores) for phytoplankton; r  ϭ 0.57, P ϭ 0.08, N ϭ 10 (without zooplanktivores), and r  ϭ 0.08, P Ͼ 0.10, N ϭ 10 (with zooplanktivores) for mesozooplankton. Log of the response ratio versusmesocosm volume (in liters), r  ϭϪ 0.12, P Ͼ 0.10, N ϭ 54 (without zooplanktivores), and r  ϭϪ 0.35, P Ͼ 0.10, N ϭ 14 (with zooplanktivores) for phytoplank-ton; r  ϭ Ϫ 0.09, P Ͼ 0.10, N ϭ 10 (without zoo-planktivores), and r  ϭ –0.14, P Ͼ 0.10, N ϭ 10 (withzooplanktivores) for mesozooplankton. For all analy-ses, qualitatively similar results were obtained whenshort- and long-duration experiments were excluded.10. I combined correlation coefficients ( r  ) using standardmeta-analytical techniques described by W. R. Shad-ish and C. K. Haddock [in The Handbook of Research Synthesis, H. Cooper and L. V. Hedges, Eds. (RusselSage Foundation, New York, 1994), pp. 261–281].Each coefficient was obtained from correlation be-tween 7 to 45 pairs of data points. Because of temporal autocorrelation within time series, the as-sumption of independence between years is violatedand cross-correlation estimates may be biased. Biasdue to the autocorrelation within each data serieswas corrected by adjusting the degrees of freedom of the cross-correlation with the formula proposed byM. S. Bartlett [ J. Res. Stat. Soc. Suppl . 8 , 24 (1946)].11. L. Oksanen et al. , Am. Nat. 118 , 240 (1981).12. E. McCauley, W. W. Murdoch, S. Watson, ibid  . 134 ,288 (1988); G. G. Mittelbach, C. W. Osenberg, M. A.Leibold, in Size Structured Populations , B. Ebenmanand L. Persson, Eds. (Springer-Verlag, Berlin, Germa-ny, 1988), pp. 217–235; R. Arditi and L. R. Ginzburg, J.Theor. Biol . 139 , 311 (1989); G. A. Polis and R. D.Holt, Trends Ecol. Evol . 7 , 151 (1992); G. A. Polis andD. R. Strong, Am. Nat. 147 , 813 (1996); K. S. McCann,A. Hastings, D. R. Strong, Proc. R. Soc. London Ser. B 265 , 205 (1998).13. Zooplanktivores versus mesozooplankton: subarcticPacific, r  ϭϪ 0.81, P Ͻ 0.01, N ϭ 10; Gotland Sea, r  ϭϪ 0.01, P Ͼ 0.10, N ϭ 12; Arkona Sea, r  ϭϪ 0.23, P Ͼ 0.10, N ϭ 12; middle Adriatic Sea, r  ϭϪ 0.03, P Ͼ 0.10, N ϭ 9.3; German Bight, r  ϭϪ 0.51, P Ͼ 0.10, N ϭ 10.2; Gulf of Alaska, r  ϭϪ 0.09, P Ͼ 0.10, N ϭ 23.6. Mesozooplankton versus phytoplankton: sub-arctic Pacific, r  ϭϪ 0.57, P ϭ 0.09, N ϭ 10; GotlandSea, r  ϭϪ 0.47, P Ͼ 0.10, N ϭ 12; Arkona Sea, r  ϭϪ 0.01, P Ͼ 0.10, N ϭ 12; middle Adriatic Sea, r  ϭϪ 0.34, P Ͼ 0.10, N ϭ 8.6; German Bight, r  ϭϪ 0.28, P Ͼ 0.10, N ϭ 19.5; Gulf of Alaska, r  ϭϪ 0.26, P Ͼ 0.10, N ϭ 19.14. Primary productivity versus phytoplankton: Kattegat, r  ϭ 0.42, P Ͼ 0.10, N ϭ 10; Middle Adriatic Sea, r  ϭ 0.38, P Ͼ 0.10, N ϭ 6.3; Primary productivity versusmesozooplankton: Kattegat, r  ϭ 0.2, P Ͼ 0.10, N ϭ 5.3; Middle Adriatic Sea, r  ϭ 0.41, P Ͼ 0.10, N ϭ 9.4.Primary productivity versus zooplanktivores: Katte-gat, r  ϭ 0.19, P Ͼ 0.10, N ϭ 7.6; Middle Adriatic Sea, r  ϭ 0.55, P Ͼ 0.10, N ϭ 5.4.15. D. J. McQueen, Freshw. Biol . 23 , 613 (1990); M. T.Brett and C. R. Goldman, Proc. Natl. Acad. Sci. U.S.A . 93 , 7723 (1996); M. T. Brett and C. R. Goldman,  Science 275 , 384 (1997).16. M. Leibold, Am. Nat. 134 , 922 (1989); J. P. Grover, ibid. 145 , 746 (1995); M. T. Brett and D. C. Mueller-Navarra, Freshw. Biol . 38 , 483 (1997).17. H. W. Paerl, Limnol. Oceanogr. 33 , 823 (1988); E.McCauley, J. A. Downing, S. Watson, Can. J. Fish. Aquat. Sci  . 46 , 1171 (1989).18. G. A. Polis, W. B. Anderson, R. D. Holt, Annu. Rev.Ecol. Syst  . 28 , 289 (1997); G. R. Huxel and K. S.McCann, Am. Nat. 152 , 460 (1998).19. L. R. Pomeroy, Biol. Sci. 24 , 242 (1974); T. Fenchel,  Annu. Rev. Ecol. Syst  . 19 , 19 (1988).20. G. T. Rowe, in Coastal Upwelling, F. A. Richards, Ed.(American Geophysical Union, Washington, DC,1981); L. Legendre, J. Plankton Res . 12 , 681 (1990); P.Wassmann and M. Barnes, Oceanogr. Mar. Biol. Annu. Rev  . 29 , 87 (1991).21. J. Shapiro, V. Lamarra, M. Lynch, in Proceedings of a Symposium on Water Quality Management ThroughBiological Control , P. L. Brezonik and J. L. Fox, Eds.(Univ. of Florida, Gainesville, 1975), pp. 85–96; R. D.Gulati et al ., Eds., Biomanipulation—Tool for Water Management  (Kluwer, Dordrecht, The Netherlands,1990); S. R. Carpenter and J. F. Kitchell, Limnol.Oceanogr. 37 , 208 (1992).22. I thank P. Amarasekare, J. Bascompte, L. Benedetti-Cecchi, O. Bjørnstad, D. Breitburg, M. Brett, S. Car-penter, K. Cottingham, G. Englund, B. Kendall, J.Kitchell, H. Lenihan, K. McCann, E. McCauley, G.Mittelbach, W. Murdoch, C. Parmesan, C. H. Peterson, J. Pinckney, O. Sarnelle, D. Schindler, A. Sih, and twoanonymous reviewers for helpful comments and S.Glaholt for helping in assembling the data used inthese analyses. This study was conducted at theNational Center for Ecological Analysis and Synthe-sis, a Center funded by NSF (grant DEB-94-21535),the University of California–Santa Barbara, the Cali-fornia Resources Agency, and the California Environ-mental Protection Agency.23 March 1999; accepted 7 July 1999 R E P O R T S 27 AUGUST 1999 VOL 285 SCIENCE www.sciencemag.org 1398
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