- Email: [email protected]
Regime shifts in the ocean: reconciling observations and theory
Progress in Oceanography Progress in Oceanography 60 (2004) 135–141 www.elsevier.com/locate/pocean
Regime shifts in the ocean: reconciling observations and theory John H. Steele
*
Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
Abstract The discussions in the Villefranche Workshop covered a wide range of issues. The term regime shift was originally confined to spatial or temporal correspondences between climatic indices and population abundance. The body of evidence for physical–biological coupling has certainly generated a much better appreciation of the natural decadal scale variability in marine systems. It is difficult, however, to deduce from these time series, the mechanisms or trophic pathways that produce the correspondence. Ideally, we would need experimental manipulations such as those used in small lakes, to unravel the causal connections. Since this is impossible in the open sea, we must use comparisons between systems subject to different types of perturbation or stress. We focused at the Workshop on the effects of overfishing in different marine regimes. The consequences of large scale changes in community structure imposed by excessive fishing give valuable case studies. Coral reefs, rocky shores, freshwater and terrestrial ecosystems provide other examples. The possible existence of similar processes across such diverse systems raises corresponding questions about common ecological principles. The adaptive benefits of maximizing resilience (defined as minimizing the largest eigenvalue of the perturbed system) were considered. The corollary of this assumption is that, at the limits of adaptation, there will be switching between communities, providing a potential ground for a broad definition of regime shifts. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: Regime shifts; Resilience; Alternative stable states
Contents
*
1.
Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
136
2.
Observations and analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
136
3.
Theories and models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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4.
Management issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Tel.: +1-508-289-2220; fax: +1-508-457-2184. E-mail address: [email protected] (J.H. Steele).
0079-6611/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.pocean.2004.02.004
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J.H. Steele / Progress in Oceanography 60 (2004) 135–141
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
139
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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1. Background The contributions in this Special Issue are the proceedings of a Workshop in honor of Dr George D. Grice III hosted at the Laboratoire Oceanographique, Villefranche, 16–19 April, 2003. The concept of regime shifts has two distinct origins; one from observations in the ocean, the other from theoretical ecology. The former focussed initially on physical and biological changes in the North Pacific Ocean between 1976 and 1980 when the physical and, especially, the biological variables showed marked and rapid changes from one ‘‘regime’’ to another (Wooster & Zhang, 2004). This concept is now being applied to many other marine communities and to other types of forcing (Alheit & Niquen, 2004; Cury & Shannon, 2004). At the same time, mathematical ecologists were developing relatively simple models that produced sudden switches between distinct equilibrium conditions resulting from gradual changes in particular components of the communities (May, 1977). The initial application was to spruce budworm outbreaks (Ludwig, Jones, & Holling, 1978) but similar models have been used for plankton and fisheries (Collie & DeLong, 1999; Spencer & Collie, 1996; Steele & Henderson, 1981, 1984). These two developments provide the basis for considering a significant revision of our description of how marine ecosystems respond to external forcing, particularly at longer time scales. In place of the idea of a relatively fixed baseline of ‘‘pristine’’ ecosystems, we have the concept of systems that can, naturally, occupy alternative coherent configurations as the external world changes. The challenge, then, is to define how such systems respond to anthropogenic change in climate, fisheries or eutrophication. Further, are there similarities with responses in freshwater and terrestrial systems? Specific questions that formed the starting point for the workshop in Villefranche were: (1) How far do the data support the basic concept of ‘‘punctuated equilibrium’’, of relatively rapid switches between relatively constant, albeit noisy, steady states? Do we see other dynamics, such as cycles? Or is stochastic variability dominant at all scales? (2) Do these ecological switches require proportionate patterns in the external forcing; or, as theory suggests, can they occur as the result of gradual longer term changes (represented by auto-correlated time series and often referred to as ‘‘red noise’’)? (3) As well as ‘‘bottom-up’’ physical trends, can the shifts be caused by forcing at the top trophic levels – especially changes in fish populations – as theory indicates? (4) How should one attempt to integrate highly non-linear theory using a few variables, with observations of community transitions involving large numbers of species? (5) How can we benefit from the similarities between marine and terrestrial aspects of these concepts? (6) And what is the relevance of these concepts to management issues?
2. Observations and analyses It is generally accepted that time series of ocean physics are auto-correlated at daily to decadal and longer scales, that is, they are characterized by ‘‘red noise’’ (Wunsch, 1981). Similar relations are seen in ecological data (Pimm & Redfearn, 1988). So it is not surprising that we see patterns in these series. Our perceptions of these patterns tend to take two forms, ‘‘cycles’’ and ‘‘shifts’’. Cushing (1982) described the
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Russell Cycle of herring, pilchard and mackerel in the English Channel, but the relatively abrupt changes in these species could as easily be called shifts. More recently, Chavez, Ryan, Lluch-Cota, and Niquen (2003) use both formulations to interpret changes in upwelling systems. The emergence of shifts as a favored description arose from the statistical analysis of North Pacific data (see Bakun, in press; Wooster & Zhang, 2004). In the years 1976–1978 there was a marked geographic shift in atmospheric pressure distribution, the Pacific Decadal Oscillation, with corresponding changes not only in Sea Surface Temperature but also in zooplankton (Brodeur & Ware, 1992). These data, plus trends in fish stocks – particularly salmonids – were used by Ebbesmeyer, Cayan, McLain, Nichols, Peterson, and Redwood (1991) for a statistical analysis that produced a composite picture with a clearly defined shift between earlier and later quasi-steady states. Later, Hare and Mantua (2000) used the same methods on 100 time series that revealed another shift around 1989 when the physical data did not show any abrupt change. The same statistical methods have been applied to North Sea data (Beaugrand & Ibanez, 2003) and show the same punctuated equilibrium. There are, however, questions about the statistical techniques used. Rudnick and Davis (2003) have shown that these methods, when applied to randomly generated red noise time series generate punctuated equilibrium. They conclude that ‘‘detection of a shift by this procedure is not evidence of non-linear processes leading to bi-stable behavior or any other meaningful regime shift’’. Solow and Beet (in press) propose a formal statistical approach to identify an abrupt shift between locally stable states. They apply the method to some North Sea data and do not detect such a shift. Thus, the statistical techniques used so far may not be sufficient by themselves to determine unequivocally a regime shift. The 1977–1978 events in the North Pacific certainly suggest some relatively rapid and large scale changes. But a major feature of the data are the geographic shifts in location of atmospheric pressure, SST, zooplankton and, possibly, salmonid abundance (see Mantua, 2004). Further, Beaugrand (2004) indicates that the North Sea shift – and the Russell Cycle – may be merely reflections of a larger ocean-wide northerly trend in the North Atlantic (Edwards, Reid, & Planque, 2001). Lastly, as Cushing (1982) and Rothschild (1986) have pointed out, changes in abundance of sardines and anchovy are always associated with major geographic redistribution. The descriptions of these data (Alheit & Niquen, 2004; Chavez et al., 2003; deYoung, Harris, Alheit, Beaugrand, Mantua, & Shannon, 2004; Klyashtorin, 1998) all emphasize the coherence at multi-decadal time scales between changing patterns in physical and biological distributions. The focus has been on open ocean and upwelling ecosystems. There is however another body of evidence derived from continental shelf ecosystems and, especially, from demersal fish communities at higher latitudes. The context here is the extreme over-fishing of gadoid stocks particularly in the North Atlantic. The best known examples concern cod in the North Sea (Cook, Sinclair, & Stefansson, 1997), around Newfoundland (Myers & Worm, 2003) and on Georges Bank (Collie & DeLong, 1999). In each case, after cod stocks had been fished to commercial extinction; fishing effort was set to very low or zero levels, but the stocks have not recovered. Similar if less extreme patterns hold for haddock and some other gadoids. But also, in each area, there appears to be partial if not complete replacement by other species or trophic groups: flatfish and then shellfish in Newfoundland; elasmobranchs and herring on Georges Bank, small pelagics including Gadus esmarkii in the North Sea. Bakun (in press) raises the question of whether these marked and persistent changes in community structure on continental shelves should be put under the rubric of regime shifts developed for open ocean systems. The primary forcing in the shelf systems is top–down, although climatic change is often invoked as a contributing factor. The common feature of both the open ocean, pelagic systems and the demersal shelf communities is the relatively rapid change between quite distinct and long lasting community structures. The comparison raises interesting questions. Are there common underlying mechanisms that determine these patterns (Cury et al., 2000)? Do the data sets for shelf and open ocean provide tests for a single theory? These questions can be raised more generally, not only for coastal marine systems such as coral reefs (Knowlton, 1992; McManus, Lambert, Kesner-Reyes, Vergara, & Ablan, 2000) or kelp forests (Estes & Duggins, 1995),
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but also for freshwater and terrestrial systems (Ludwig et al., 1978; Scheffer, Carpenter, Foley, Folke, & Walker, 2001; Scheffer & van Nes, 2004; Steele, 1985; Stenseth, Bjornstad, & Sattoh, 1996). These comparisons necessarily raise theoretical issues about the generality and applicability of ecological concepts.
3. Theories and models Long term observations in the ocean are essentially time series of contemporaneous variations in several physical, chemical or biotic variables. Correlations between physical and biotic time series are a regular feature; but these relations change or break down nearly as frequently (Reid, Planque, & Edwards, 1998; Skud, 1982; Solow, 2002). Thus models need to go beyond the correlations and speculate about possible non-linear processes linking ecosystems to external forcing. There are three levels of complexity that are often used as descriptors of these relations between forcing and response: (1) nearly linear; (2) non-linear but single valued; (3) with multiple stable states. These alternatives are well described and illustrated in Collie, Richardson, and Steele (2004), Scheffer et al. (2001), and Scheffer and van Nes (2004). The underlying issue is whether these three categories represent distinct and separate theories that apply to different ecosystems; for example freshwater lakes or the open ocean pelagic realm. Or whether the range described by (1), (2) and (3) can be represented as different states of the same general theory or model. This is central to the argument about possible uses of the term regime shift. But more broadly it is also critical to the question of the applicability of ecological concepts across marine, freshwater and terrestrial systems. Thus, the same model could apply even where the slow forcing was very different; tree growth for the spruce/budworm system, nutrient enrichment in lakes, ocean climate in the sea. The simulation models used to generate threshold responses (Ludwig et al., 1978; May, 1977; Rinaldi & Scheffer, 2000; Spencer & Collie, 1996; Steele & Henderson, 1984) all show the triple-valued region that gives alternative stable states occupying a small fraction of parameter space. So the system response is very dependent on the parameter values. Away from the triple-valued region the response to variable forcing lies between (1) and (2). Thus the most parsimonious hypothesis or null model would assume that the observed responses (1, 2, 3) represent different regions of parameter space. More complicated models involving two or more variables can have oscillatory bifurcations (Edwards & Brindley, 1996; Murray, 1989) where the correlation between forcing and response breaks down. A similar conclusion can be reached with simple finite difference (discrete time) models. As May (1974) showed, these have complex dynamics – from monotonic response through cycles to chaos. But, as Hassell, Lawton, and May (1976) observed, natural insect populations appear to ‘‘prefer’’ to aggregate in the parameter space with monotonic responses to disturbance. This theme is developed by Stenseth et al. (1996). From this point of view, the emergence of threshold or chaotic responses is the result of a change in parameters rather than in formulation. This approach then raises the question of how populations or communities ‘‘choose’’ certain parameter values. From Hassell et al. (1976), one might infer that populations minimize return time after perturbation. Simulations of food chain adaptation using genetic algorithms (Cropp & Gabric, 2002) suggest the hypothesis that systems ‘‘evolve the most stable states available within the constraints of their environment’’. The simple food chain model used by Cropp and Gabric does not include any competition, thus, by definition, there cannot be switching. The model of the pelagic microbial food web by Laws, Falkowski, Smith, Ducklow, and McCarthy (2000) has alternative pathways through large and small plankton. The simulation maximizes resilience to determine which pathway is dominant for a range of sea temperature and primary production. The result is that the system can be expected to be close to equilibrium; and at equilibrium tends to either large or small plankton with, respectively, small or large fractions of limiting nutrients recycled. The output (see Fig. 4 in Laws, 2004) shows a cliff separating the two regimes. This result might be thought to be a more complicated example of competitive exclusion.
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The hypothesis that the planktonic food web is always near equilibrium was tested by Vezina and Pahlow (2003). He found that equilibrium values calculated with an inverse model (Vezina & Platt, 1988) were close to those derived from a dynamic simulation of the same web through a spring outburst and a summer minimum. Thus within a food web subject to variable external forcing, we might expect the system to have dominant components that track the forcing. The system stays near the equilibrium values until trends or red noise in the forcing move it outside the limits of adaptability or competitiveness. Then another suite of species becomes dominant. On this basis, switching between different communities in response to trends in forcing (temperature, productivity or fishing effort) would seem a reasonable hypothesis. Given the hysteresis associated with the longer lived components, clearly defined shifts may not be observed (Spencer & Collie, 1997a). Also, oscillatory regions in parameter space occur in many plankton models (Edwards & Brindley, 1996) and these would complicate time series.
4. Management issues The three ‘‘regimes’’ outlined earlier and fully described in Collie et al. (2004) and Scheffer and van Nes (2004), provide a basis for examining management options. In this context, the primary issue is whether a system is inside or out of the triple valued region of parameter space. So far there is no convincing evidence that changes in ocean climate such as El Nino, Pacific Decadal Oscillation or the North Atlantic Oscillation induce bi-stable modes in marine ecosystems (Alheit & Niquen, 2004; deYoung et al., 2004). The evidence for such bi-stable modes induced by over-fishing is circumstantial (Collie et al., 2004),and depends mainly on the lack of recovery of fish stocks such as North Atlantic cod and haddock. However the lack of recovery of over-fished stocks in the northern Benguela system (Cury & Shannon, 2004) indicates comparable features in pelagic systems. A secondary inference – or hypothesis – from these speculations would concern the conditions under which any system would be in the triple-valued region of parameter space. The conjecture that systems adapt or evolve to be well away from such regions would imply that evidence for such behavior is also evidence for major changes in parameters or functional relations not usually subject to perturbation. Eutrophication in lakes or extreme over-fishing in the sea could fall in this category. Whether or not large scale and large amplitude climate change would qualify is a relevant question. It may be that spatial shifts of geographic boundaries of stocks or communities would be subject to such hysteresis. All the evidence (Beamish, Benson, Sweeting, & Neville, 2004) illustrates the potentially severe management consequences of ecosystems with alternative stable states. Thus, although the basis for the existence of alternative states may be circumstantial, a precautionary approach would require that this explanation of the observations be given precedence. This is one reason for the focus on this aspect in our discussions. Acknowledgements This is Woods Hole Oceanographic Institution Contribution No. 11031. Partial support was provided by NSF/OCE 00642/02. References Alheit, J., & Niquen, M. (2004). Regime shifts in the Humboldt current ecosystem. Progress in Oceanography, doi:10.1016/ j.pocean.2004.02.006. Bakun, A. (in press). Regime shifts. In K. Brink, & A. R. Robinson (Eds.), The seas (Vol. 14). Coasts.
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Beamish, R. J., Benson, A. J., Sweeting, R. M., & Neville, C. M. (2004). Regimes and the history the major fisheries of Canada’s west coast. Progress in Oceanography, doi:10.1016/j.pocean.2004.02.009. Beaugrand, G. (2004). The North Sea regime shift: Evidence, causes, mechanism and consequences. Progress in Oceanography, doi:10.1016/j.pocean.2004.02.018. Beaugrand, G., & Ibanez, F. (2003). Monitoring marine planktonic systems (2): Long term changes in North Sea calanoid copepods in relation to hydro-meteorological variability. Limnology and Oceanography. Brodeur, R. D., & Ware, D. M. (1992). Long term variability in zooplankton biomass in the subarctic Pacific Ocean. Fisheries Oceanography, 1, 32–39. Chavez, F. P., Ryan, J., Lluch-Cota, S. E., & Niquen, M. C. (2003). From anchovies to sardines and back: Multi-decadal change in the Pacific Ocean. Science, 299, 217–221. Collie, J. S., & DeLong, A. K. (1999). Multispecies interactions in the Georges Bank fish community. In Alaska sea grant college program, ecosystem approaches for fisheries management (pp. 187–210). Alaska: Sea Grant Publication AK-SG-99-01. Collie, J. S., Richardson, K., & Steele, J. H. (2004). Regime shifts: Can ecological theory illuminate the mechanisms? Progress in Oceanography, doi:10.1016/j.pocean.2004.02.013. Cook, R. M., Sinclair, A., & Stefansson, G. (1997). Potential collapse of North Sea cod stocks. Nature, 385, 521–522. Cropp, R., & Gabric, A. (2002). Ecosystem adaptation: Do ecosystems maximize resilience? Ecology, 83, 2019–2026. Cury, P., Bakun, A., Crawford, J. M., Jarre, A., Quisimnones, R. A., Shannon, L. J., & Verheye, H. M. (2000). Small pelagics in upwelling systems: Patterns of interaction and structural changes in ‘‘wasp-waist’’ ecosystems. ICES Journal of Marine Science, 57, 603–618. Cury, P., & Shannon, L. (2004). Regime shifts in the Benguela ecosystem: Facts, theories and hypotheses. Progress in Oceanography, doi:10.1016/j.pocean.2004.02.007. Cushing, D. H. (1982). Climate and fisheries. London: Academic Press, 373 pp. deYoung, B., Harris, R., Alheit, J., Beaugrand, G., Mantua, N., & Shannon, L. (2004). Detecting regime shifts in the ocean: Data considerations. Progress in Oceanography, doi:10.1016/j.pocean.2004.02.017. Ebbesmeyer, C. C., Cayan, D. R., McLain, D. R., Nichols, F. H., Peterson, D. H., & Redwood, K. T. (1991). 1976 step in the Pacific climate: Forty environmental changes between 1968–1975 and 1977–1984. In J. L. Betancourt, & V. L. Tharp (Eds.), Proceedings of the seventh annual climate workshop (Vol. 26, pp. 115–126). California Department of Water Resources, Technical Report. Edwards, A. M., & Brindley, J. (1996). Oscillatory behaviour in a three-component plankton population model. Dynamics and Stability of Systems, 11, 347–370. Edwards, M., Reid, P., & Planque, B. (2001). Long-term and regional variability of phytoplankton biomass in the Northeast Atlantic (1960–1995). ICES Journal of Marine Science, 58, 39–49. Estes, J. A., & Duggins, D. O. (1995). Sea otters and kelp forest in Alaska: Generality and variation in a community ecological paradigm. Ecological Monographs, 65, 75–100. Hare, S. R., & Mantua, N. J. (2000). Empirical evidence for North Pacific regime shifts in 1977 and 1989. Progress in Oceanography, 47, 103–145. Hassell, M. P., Lawton, J. H., & May, R. M. (1976). Patterns of dynamical behaviour in single-species populations. Journal of Animal Ecology, 45, 471–486. Klyashtorin, L. B. (1998). Long-term climate change and main commercial fish production in the Atlantic and Pacific. Fisheries Research, 37, 115–125. Knowlton, N. (1992). Thresholds and multiple stable states in coral reef community dynamics. American Zoologist, 32, 674–682. Laws, E. A. (2004). Export flux and stability as regulators of community composition in pelagic marine communities: Implications for regime shifts. Progress in Oceanography, doi:10.1016/j.pocean.2004.02.015. Laws, E. A., Falkowski, P. G., Smith, W. O., Ducklow, H., & McCarthy, J. J. (2000). Temperature effects on export production in the open ocean. Global Biogeochemical Cycles, 14, 1231–1246. Ludwig, D., Jones, D., & Holling, C. S. (1978). Qualitative analysis of insect outbreak systems: The spruce budworm and the forest. Journal of Animal Ecology, 47, 315–332. Mantua, N. (2004). Methods for detecting regime shifts in large marine ecosystems: A review with approaches applied to North Pacific data. Progress in Oceanography, doi:10.1016/j.pocean.2004.02.016. May, R. M. (1974). Biological populations with non-overlapping generations: Stable points, stable cycles and chaos. Science, 186, 645– 647. May, R. M. (1977). Thresholds and breakpoints in ecosystems with a multiplicity of stable states. Nature, 269, 471–477. McManus, J. W., Lambert, A. B. M., Kesner-Reyes, K. N., Vergara, S. G., & Ablan, M. C. (2000). Coral reef fishing and coral-algal phase shifts: Implications for global reef status. ICES Journal of Marine Science, 57, 572–578. Murray, J. D. (1989). Mathematical biology. Berlin: Springer-Verlag. Myers, R. A., & Worm, B. (2003). Rapid worldwide depletion of predatory fish communities. Nature, 423, 280–283. Pimm, S. L., & Redfearn, A. (1988). The variability of population densities. Nature, 334, 613–614.
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Reid, P. C., Planque, B., & Edwards, M. (1998). Is observed variability in the long-term results of the CPR survey a response to climate change? Fisheries Oceanography, 7(3/4), 282–288. Rinaldi, S., & Scheffer, M. (2000). Geometric analysis of ecological models with slow and fast processes. Ecosystems, 3, 507–521. Rothschild, B. J. (1986). Dynamics of marine fish populations. Cambridge, MA: Harvard University Press. Rudnick, D. L., & Davis, R. E. (2003). Red noise and regime shifts. Deep-sea Research, 50, 691–699. Scheffer, M., Carpenter, J., Foley, J. A., Folke, C., & Walker, B. (2001). Catastrophic shifts in ecosystems. Nature, 413, 591–596. Scheffer, M., & van Nes, E. H. (2004). Mechanisms for regime shifts: Can we use lakes as microcosms for oceans? Progress in Oceanography, doi:10.1016/j.pocean.2004.02.008. Skud, B. E. (1982). Dominance in fishes: The relation between environment and abundance. Science, 216, 144–149. Solow, A. R. (2002). Fisheries recruitment and the North Atlantic oscillation. Fisheries Research, 54, 295–297. Solow, A. R., & Beet, A. R. (in press). A test for a regime shift. Fisheries Oceanography. Spencer, P. D., & Collie, J. S. (1996). A simple predator–prey model of exploited marine fish populations incorporating alternative prey. ICES Journal of Marine Science, 53, 615–628. Spencer, P. D., & Collie, J. S. (1997a). Patterns of population variability in marine fish stocks. Fisheries Oceanography, 6, 188–204. Steele, J. H. (1985). A comparison of terrestrial and marine ecological systems. Nature, 313, 355–358. Steele, J. H., & Henderson, E. W. (1981). A simple plankton model. American Naturalist, 117, 676–691. Steele, J. H., & Henderson, E. W. (1984). Modeling long-term fluctuations in fish stocks. Science, 224, 985–987. Stenseth, N. C., Bjornstad, O. N., & Sattoh, T. (1996). A gradient from stable to cyclic populations of Clethrionomys rufocanus in Hokkaido, Japan. Proceedings Royal Society London, Series B, 263, 1117–1126. Vezina, A. F., & Pahlow, M. (2003). Reconstruction of ecosystem flows using inverse methods. Journal of Marine Systems, 40/41, 55– 77. Vezina, A. R., & Platt, T. (1988). Food web dynamics in the ocean I. Best estimates of flow networks using inverse methods. Marine Ecology – Progress Series, 42, 269–287. Wooster, W. S., & Zhang, C. I. (2004). Regime shifts in the North Pacific: Early indications of the 1976–1977 event. Progress in Oceanography, doi:10.1016/j.pocean.2004.02.005. Wunsch, C. (1981). Low frequency variability of the sea. In B. A. Warren & C. Wunsch (Eds.), Evolution of physical oceanography (pp. 362–375). MIT Press.
Regime shifts in the ocean: reconciling observations and theory John H. Steele
*
Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA
Abstract The discussions in the Villefranche Workshop covered a wide range of issues. The term regime shift was originally confined to spatial or temporal correspondences between climatic indices and population abundance. The body of evidence for physical–biological coupling has certainly generated a much better appreciation of the natural decadal scale variability in marine systems. It is difficult, however, to deduce from these time series, the mechanisms or trophic pathways that produce the correspondence. Ideally, we would need experimental manipulations such as those used in small lakes, to unravel the causal connections. Since this is impossible in the open sea, we must use comparisons between systems subject to different types of perturbation or stress. We focused at the Workshop on the effects of overfishing in different marine regimes. The consequences of large scale changes in community structure imposed by excessive fishing give valuable case studies. Coral reefs, rocky shores, freshwater and terrestrial ecosystems provide other examples. The possible existence of similar processes across such diverse systems raises corresponding questions about common ecological principles. The adaptive benefits of maximizing resilience (defined as minimizing the largest eigenvalue of the perturbed system) were considered. The corollary of this assumption is that, at the limits of adaptation, there will be switching between communities, providing a potential ground for a broad definition of regime shifts. Ó 2004 Elsevier Ltd. All rights reserved. Keywords: Regime shifts; Resilience; Alternative stable states
Contents
*
1.
Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
136
2.
Observations and analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
136
3.
Theories and models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
138
4.
Management issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
139
Tel.: +1-508-289-2220; fax: +1-508-457-2184. E-mail address: [email protected] (J.H. Steele).
0079-6611/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.pocean.2004.02.004
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J.H. Steele / Progress in Oceanography 60 (2004) 135–141
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
139
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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1. Background The contributions in this Special Issue are the proceedings of a Workshop in honor of Dr George D. Grice III hosted at the Laboratoire Oceanographique, Villefranche, 16–19 April, 2003. The concept of regime shifts has two distinct origins; one from observations in the ocean, the other from theoretical ecology. The former focussed initially on physical and biological changes in the North Pacific Ocean between 1976 and 1980 when the physical and, especially, the biological variables showed marked and rapid changes from one ‘‘regime’’ to another (Wooster & Zhang, 2004). This concept is now being applied to many other marine communities and to other types of forcing (Alheit & Niquen, 2004; Cury & Shannon, 2004). At the same time, mathematical ecologists were developing relatively simple models that produced sudden switches between distinct equilibrium conditions resulting from gradual changes in particular components of the communities (May, 1977). The initial application was to spruce budworm outbreaks (Ludwig, Jones, & Holling, 1978) but similar models have been used for plankton and fisheries (Collie & DeLong, 1999; Spencer & Collie, 1996; Steele & Henderson, 1981, 1984). These two developments provide the basis for considering a significant revision of our description of how marine ecosystems respond to external forcing, particularly at longer time scales. In place of the idea of a relatively fixed baseline of ‘‘pristine’’ ecosystems, we have the concept of systems that can, naturally, occupy alternative coherent configurations as the external world changes. The challenge, then, is to define how such systems respond to anthropogenic change in climate, fisheries or eutrophication. Further, are there similarities with responses in freshwater and terrestrial systems? Specific questions that formed the starting point for the workshop in Villefranche were: (1) How far do the data support the basic concept of ‘‘punctuated equilibrium’’, of relatively rapid switches between relatively constant, albeit noisy, steady states? Do we see other dynamics, such as cycles? Or is stochastic variability dominant at all scales? (2) Do these ecological switches require proportionate patterns in the external forcing; or, as theory suggests, can they occur as the result of gradual longer term changes (represented by auto-correlated time series and often referred to as ‘‘red noise’’)? (3) As well as ‘‘bottom-up’’ physical trends, can the shifts be caused by forcing at the top trophic levels – especially changes in fish populations – as theory indicates? (4) How should one attempt to integrate highly non-linear theory using a few variables, with observations of community transitions involving large numbers of species? (5) How can we benefit from the similarities between marine and terrestrial aspects of these concepts? (6) And what is the relevance of these concepts to management issues?
2. Observations and analyses It is generally accepted that time series of ocean physics are auto-correlated at daily to decadal and longer scales, that is, they are characterized by ‘‘red noise’’ (Wunsch, 1981). Similar relations are seen in ecological data (Pimm & Redfearn, 1988). So it is not surprising that we see patterns in these series. Our perceptions of these patterns tend to take two forms, ‘‘cycles’’ and ‘‘shifts’’. Cushing (1982) described the
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Russell Cycle of herring, pilchard and mackerel in the English Channel, but the relatively abrupt changes in these species could as easily be called shifts. More recently, Chavez, Ryan, Lluch-Cota, and Niquen (2003) use both formulations to interpret changes in upwelling systems. The emergence of shifts as a favored description arose from the statistical analysis of North Pacific data (see Bakun, in press; Wooster & Zhang, 2004). In the years 1976–1978 there was a marked geographic shift in atmospheric pressure distribution, the Pacific Decadal Oscillation, with corresponding changes not only in Sea Surface Temperature but also in zooplankton (Brodeur & Ware, 1992). These data, plus trends in fish stocks – particularly salmonids – were used by Ebbesmeyer, Cayan, McLain, Nichols, Peterson, and Redwood (1991) for a statistical analysis that produced a composite picture with a clearly defined shift between earlier and later quasi-steady states. Later, Hare and Mantua (2000) used the same methods on 100 time series that revealed another shift around 1989 when the physical data did not show any abrupt change. The same statistical methods have been applied to North Sea data (Beaugrand & Ibanez, 2003) and show the same punctuated equilibrium. There are, however, questions about the statistical techniques used. Rudnick and Davis (2003) have shown that these methods, when applied to randomly generated red noise time series generate punctuated equilibrium. They conclude that ‘‘detection of a shift by this procedure is not evidence of non-linear processes leading to bi-stable behavior or any other meaningful regime shift’’. Solow and Beet (in press) propose a formal statistical approach to identify an abrupt shift between locally stable states. They apply the method to some North Sea data and do not detect such a shift. Thus, the statistical techniques used so far may not be sufficient by themselves to determine unequivocally a regime shift. The 1977–1978 events in the North Pacific certainly suggest some relatively rapid and large scale changes. But a major feature of the data are the geographic shifts in location of atmospheric pressure, SST, zooplankton and, possibly, salmonid abundance (see Mantua, 2004). Further, Beaugrand (2004) indicates that the North Sea shift – and the Russell Cycle – may be merely reflections of a larger ocean-wide northerly trend in the North Atlantic (Edwards, Reid, & Planque, 2001). Lastly, as Cushing (1982) and Rothschild (1986) have pointed out, changes in abundance of sardines and anchovy are always associated with major geographic redistribution. The descriptions of these data (Alheit & Niquen, 2004; Chavez et al., 2003; deYoung, Harris, Alheit, Beaugrand, Mantua, & Shannon, 2004; Klyashtorin, 1998) all emphasize the coherence at multi-decadal time scales between changing patterns in physical and biological distributions. The focus has been on open ocean and upwelling ecosystems. There is however another body of evidence derived from continental shelf ecosystems and, especially, from demersal fish communities at higher latitudes. The context here is the extreme over-fishing of gadoid stocks particularly in the North Atlantic. The best known examples concern cod in the North Sea (Cook, Sinclair, & Stefansson, 1997), around Newfoundland (Myers & Worm, 2003) and on Georges Bank (Collie & DeLong, 1999). In each case, after cod stocks had been fished to commercial extinction; fishing effort was set to very low or zero levels, but the stocks have not recovered. Similar if less extreme patterns hold for haddock and some other gadoids. But also, in each area, there appears to be partial if not complete replacement by other species or trophic groups: flatfish and then shellfish in Newfoundland; elasmobranchs and herring on Georges Bank, small pelagics including Gadus esmarkii in the North Sea. Bakun (in press) raises the question of whether these marked and persistent changes in community structure on continental shelves should be put under the rubric of regime shifts developed for open ocean systems. The primary forcing in the shelf systems is top–down, although climatic change is often invoked as a contributing factor. The common feature of both the open ocean, pelagic systems and the demersal shelf communities is the relatively rapid change between quite distinct and long lasting community structures. The comparison raises interesting questions. Are there common underlying mechanisms that determine these patterns (Cury et al., 2000)? Do the data sets for shelf and open ocean provide tests for a single theory? These questions can be raised more generally, not only for coastal marine systems such as coral reefs (Knowlton, 1992; McManus, Lambert, Kesner-Reyes, Vergara, & Ablan, 2000) or kelp forests (Estes & Duggins, 1995),
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but also for freshwater and terrestrial systems (Ludwig et al., 1978; Scheffer, Carpenter, Foley, Folke, & Walker, 2001; Scheffer & van Nes, 2004; Steele, 1985; Stenseth, Bjornstad, & Sattoh, 1996). These comparisons necessarily raise theoretical issues about the generality and applicability of ecological concepts.
3. Theories and models Long term observations in the ocean are essentially time series of contemporaneous variations in several physical, chemical or biotic variables. Correlations between physical and biotic time series are a regular feature; but these relations change or break down nearly as frequently (Reid, Planque, & Edwards, 1998; Skud, 1982; Solow, 2002). Thus models need to go beyond the correlations and speculate about possible non-linear processes linking ecosystems to external forcing. There are three levels of complexity that are often used as descriptors of these relations between forcing and response: (1) nearly linear; (2) non-linear but single valued; (3) with multiple stable states. These alternatives are well described and illustrated in Collie, Richardson, and Steele (2004), Scheffer et al. (2001), and Scheffer and van Nes (2004). The underlying issue is whether these three categories represent distinct and separate theories that apply to different ecosystems; for example freshwater lakes or the open ocean pelagic realm. Or whether the range described by (1), (2) and (3) can be represented as different states of the same general theory or model. This is central to the argument about possible uses of the term regime shift. But more broadly it is also critical to the question of the applicability of ecological concepts across marine, freshwater and terrestrial systems. Thus, the same model could apply even where the slow forcing was very different; tree growth for the spruce/budworm system, nutrient enrichment in lakes, ocean climate in the sea. The simulation models used to generate threshold responses (Ludwig et al., 1978; May, 1977; Rinaldi & Scheffer, 2000; Spencer & Collie, 1996; Steele & Henderson, 1984) all show the triple-valued region that gives alternative stable states occupying a small fraction of parameter space. So the system response is very dependent on the parameter values. Away from the triple-valued region the response to variable forcing lies between (1) and (2). Thus the most parsimonious hypothesis or null model would assume that the observed responses (1, 2, 3) represent different regions of parameter space. More complicated models involving two or more variables can have oscillatory bifurcations (Edwards & Brindley, 1996; Murray, 1989) where the correlation between forcing and response breaks down. A similar conclusion can be reached with simple finite difference (discrete time) models. As May (1974) showed, these have complex dynamics – from monotonic response through cycles to chaos. But, as Hassell, Lawton, and May (1976) observed, natural insect populations appear to ‘‘prefer’’ to aggregate in the parameter space with monotonic responses to disturbance. This theme is developed by Stenseth et al. (1996). From this point of view, the emergence of threshold or chaotic responses is the result of a change in parameters rather than in formulation. This approach then raises the question of how populations or communities ‘‘choose’’ certain parameter values. From Hassell et al. (1976), one might infer that populations minimize return time after perturbation. Simulations of food chain adaptation using genetic algorithms (Cropp & Gabric, 2002) suggest the hypothesis that systems ‘‘evolve the most stable states available within the constraints of their environment’’. The simple food chain model used by Cropp and Gabric does not include any competition, thus, by definition, there cannot be switching. The model of the pelagic microbial food web by Laws, Falkowski, Smith, Ducklow, and McCarthy (2000) has alternative pathways through large and small plankton. The simulation maximizes resilience to determine which pathway is dominant for a range of sea temperature and primary production. The result is that the system can be expected to be close to equilibrium; and at equilibrium tends to either large or small plankton with, respectively, small or large fractions of limiting nutrients recycled. The output (see Fig. 4 in Laws, 2004) shows a cliff separating the two regimes. This result might be thought to be a more complicated example of competitive exclusion.
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The hypothesis that the planktonic food web is always near equilibrium was tested by Vezina and Pahlow (2003). He found that equilibrium values calculated with an inverse model (Vezina & Platt, 1988) were close to those derived from a dynamic simulation of the same web through a spring outburst and a summer minimum. Thus within a food web subject to variable external forcing, we might expect the system to have dominant components that track the forcing. The system stays near the equilibrium values until trends or red noise in the forcing move it outside the limits of adaptability or competitiveness. Then another suite of species becomes dominant. On this basis, switching between different communities in response to trends in forcing (temperature, productivity or fishing effort) would seem a reasonable hypothesis. Given the hysteresis associated with the longer lived components, clearly defined shifts may not be observed (Spencer & Collie, 1997a). Also, oscillatory regions in parameter space occur in many plankton models (Edwards & Brindley, 1996) and these would complicate time series.
4. Management issues The three ‘‘regimes’’ outlined earlier and fully described in Collie et al. (2004) and Scheffer and van Nes (2004), provide a basis for examining management options. In this context, the primary issue is whether a system is inside or out of the triple valued region of parameter space. So far there is no convincing evidence that changes in ocean climate such as El Nino, Pacific Decadal Oscillation or the North Atlantic Oscillation induce bi-stable modes in marine ecosystems (Alheit & Niquen, 2004; deYoung et al., 2004). The evidence for such bi-stable modes induced by over-fishing is circumstantial (Collie et al., 2004),and depends mainly on the lack of recovery of fish stocks such as North Atlantic cod and haddock. However the lack of recovery of over-fished stocks in the northern Benguela system (Cury & Shannon, 2004) indicates comparable features in pelagic systems. A secondary inference – or hypothesis – from these speculations would concern the conditions under which any system would be in the triple-valued region of parameter space. The conjecture that systems adapt or evolve to be well away from such regions would imply that evidence for such behavior is also evidence for major changes in parameters or functional relations not usually subject to perturbation. Eutrophication in lakes or extreme over-fishing in the sea could fall in this category. Whether or not large scale and large amplitude climate change would qualify is a relevant question. It may be that spatial shifts of geographic boundaries of stocks or communities would be subject to such hysteresis. All the evidence (Beamish, Benson, Sweeting, & Neville, 2004) illustrates the potentially severe management consequences of ecosystems with alternative stable states. Thus, although the basis for the existence of alternative states may be circumstantial, a precautionary approach would require that this explanation of the observations be given precedence. This is one reason for the focus on this aspect in our discussions. Acknowledgements This is Woods Hole Oceanographic Institution Contribution No. 11031. Partial support was provided by NSF/OCE 00642/02. References Alheit, J., & Niquen, M. (2004). Regime shifts in the Humboldt current ecosystem. Progress in Oceanography, doi:10.1016/ j.pocean.2004.02.006. Bakun, A. (in press). Regime shifts. In K. Brink, & A. R. Robinson (Eds.), The seas (Vol. 14). Coasts.
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