Regime shift in marine ecosystems and implications for ... - CiteSeerX

Rev Fish Biol Fisheries (2009) 19:177–191 DOI 10.1007/s11160-008-9096-8

Regime shift in marine ecosystems and implications for fisheries management, a review Yan Jiao

Received: 29 May 2008 / Accepted: 12 September 2008 / Published online: 19 October 2008 Ó Springer Science+Business Media B.V. 2008

Abstract The concept of regime shift in marine ecosystems has arisen in recent years. This study reviews the origin, scientific meaning, driving forces of marine ecosystem regime shrifts. Driving forces include climate-ocean oscillation, fishing, introduced species, river flow, eutrophication, disease and pollution. Several controversial questions are discussed: whether regime shift is just a climate oscillation noise, the difficulties of separating driving forces, and whether the impacts of ecosystem regime shift are necessarily bad or good. The methods used to model regime shifts in ecosystems are reviewed. These methods include two types of models: general circulation model and historical description. The implications of regime shift in fisheries are considered, including examples of impacts, consequences of not considering regime shift, and difficulties in incorporating regime shift into the current stock assessment models. Possible future research needed to understand and model regime shifts is discussed, including strategies for fisheries management. Keywords Regime shift Marine ecosystem Fisheries Red noise General circulation model

Y. Jiao (&) Department of Fisheries and Wildlife Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0321, USA e-mail: [email protected]

Regime shift defined Until recently the word ‘‘regime’’ was not in the vocabulary of many fisheries biologists. Even today, scientists are not entirely sure how to define regimes. Regime in the dictionary has two meanings: a regular pattern of occurrence or action (as of seasonal rainfall); and the characteristic behavior or orderly procedure of a natural phenomenon or process (Merriam-Webster’s Collegiate Dictionary 1998). Isaacs (1976) introduced to fishery scientists the concept of a number of possible ‘locked-in persistent episodes’, or ‘regimes’, in climate, oceanography and biological systems (Steele 1996; Beamish et al. 1999, 2000). He suggested that these regimes are connected by a number of abrupt discontinuities. Regimes are large, linked climate-ocean ecosystems that shift over 10–30-year periods (Beamish and Mahnken 1999). A regime shift is a change in the mean of a data series and the shift is completed within several months to a year (Beamish et al. 2000). Ecosystems are dynamic due to chaotic disturbance inside and outside of the community. Though most evidence of shifts is provided by economic species, the term ‘‘regime shift’’ in ecosystems suggests that changes are causally connected and can be linked to other changes in non-commercial species, such as planktonic invertebrates. Climate changes alter abiotic environmental factors and impact biotic diversity and richness. Fluctuations or successions of the community can occur, as in

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the sardine/anchovy fluctuation in the eastern Pacific, zooplankton abundance changes, fish community changes in the northeast Pacific, and the complementary trend of fish and shellfish landings in the northwest Atlantic Ocean (Ebbesmeyer et al. 1991; Francis and Hare 1994; Mann and Drinkwater 1994). Over the last two decades, biological ‘‘output’’ variables have often covaried strongly and for prolonged periods with variables that affect and reflect the physical dynamics of the ocean. These time series correlations suggest a close association between marine populations and ‘‘ocean climate’’ (Mackas and de Young 2001). After Ebbesmeyer et al. (1991) showed a consistent climate-related abrupt shift around 1975–1979, based on the study of 40 environmental variables from the Pacific and the Americas, another four regime shifts were identified in 1890, 1925, 1947, and 1989 (Ebbesmeyer et al. 1991; Beamish et al. 1999; Watanabe and Nitta 1999; Mantua 2001). Many climate indices were determined to have undergone a regime shift in 1977 contributing to ecosystem regime shifts world wide, including SO (Southern Oscillation Index), PNA (Pacific North American Index), ALPI (Aleutian Low Pressure Index), NPI (North Pacific Index), Ninõ 3, Ninõ 1 ? 2, Ninõ 3 ? 4, Ninõ 4, WP (west Pacific Index), PDO (Pacific Decadal Oscillation), PCI (Pacific Circulation Index), AO (Arctic Oscillation), NAO (North Atlantic Oscillation), ACI (Atmospheric Circulation Index for the Atlantic basin), and TNI (Trans-Ninõ Index). At the same time, a number of studies found synchronous changes in the environment and biological abundance. Catch and abundance pattern in fish populations were used as a mirror of climate-ocean regime shifts (Hare and Francis 1995; Clark et al. 1999; Anderson 2000; McFarlane et al. 2000; Reid et al. 2001). The potential implications for fisheries management from the regional impacts of environmental changes have been studied for many years. Though overfishing was deemed to be responsible for the shifts, there was historical evidence (Soutar and Isaacs 1974; Cushing 1982; Francis and Hare 1994) that supports the idea that these shifts occurred before there was heavy fishing (Steele 1996). Evidence therefore supports the assumption of regime shifts, indicating that environmental factors are the

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Rev Fish Biol Fisheries (2009) 19:177–191

underlying mechanism, along with fishing effort (Steele 1996). Fish populations are influenced by many factors of their natural environments during all phases of their life cycles, but especially in their early life stages. Subtle changes in key environmental variables, such as temperature, salinity, wind, ocean currents, and upwelling, can sharply alter the abundance, distribution, and availability of fish populations, either directly or by affecting prey or predator populations. Human activities can also affect the sustainability of fish populations through the application of a variety of different management schemes by using new technologies, each of which could have a beneficial or adverse consequence for the state of the fishery (Glantz 1992). Such changes affect production (especially recruitment) directly, and cause severe confusion in management systems when assessment scientists cannot distinguish between climatic and harvesting impacts. It was not until the ‘‘regime shift’’ concept was used (Francis and Hare 1994; Hare and Francis 1995; Mantua et al. 1997) that it became a popular topic in ecosystem, fishery and climate-ecosystem research (Steele 1996, 1998). PDOs (Pacific Decadal Oscillation) of 1977 and 1989 were regarded as regime shifts through intervention and cross correlation analyses. There were substantial changes during these two periods in Climate Index and biological factors (fish and plankton abundance, Fromentin and Planque 1996; Hare and Mantua 2000; Mantua and Hare 2002). However, David Pierce (Scripps Institute of Oceanography) and Matthew Newman (NOAA– CIRES Climate Diagnostics Center) drew the same conclusion that the PDO during the last 50 years were caused by slow variation in the chaotic evolution of tropical ENSO episodes, with some amplification of the decadal components by the long, slow responses of the extratropical Pacific Ocean to the year-to-year ENSO variations (Dettinger 2001). PDO is speculated to originate in the tropics, and is not a climatic oscillation independent from the extratropical North Pacific (Pierce 2001; Newman et al. 2003). Decadal variations of the Pacific climate are artifacts of slow but essentially random variations of the global climate. Pierce and Newman termed this ‘‘red-noise climate’’, contrary to the concept of climate regime shift (Pierce 2001; Newman et al. 2003).


Marine ecosystem regime shift Examples of regime shifts in ecosystems In the northeast Pacific, large fluctuations of California sardine (Sardinops sagax caerulea) and anchovies (Engraulis mordax) have been observed over the past 50 years, similar to their major recoveries and subsequent collapses over the last 2000 years. The current recovery is not unlike those of the past in magnitude (Baumgartner et al. 1992). At the same time, Pacific salmon from Alaska (Oncorhynchus spp.) showed nearly synchronous changes across different species as well as across a large portion of their spatial range in 1976/1977 and the late 1940s/ early 1950s (Francis and Hare 1994; Mantua et al. 1997; Mantua and Hare 2002). Sablefish (Anoplopoma fimbria) exhibited decadal-scale patterns in the relative success of year classes (King and McFarlane 2000). Pacific halibut (Hippoglossus stenolepis) experienced large periods of increase and decrease over the last 100 years (McCaughran 1996; Clark et al. 1999). The great increase of halibut in 1977– 1978 was regarded as part of the same regime shift related to climate change (Clark et al. 1999). Alaska king crab (Paralithodes camtschatica), Tanner crab (Chionoecetes bairdi), and pink shrimp (Pandalus borealis), important crustaceans in the Gulf of Alaska, have experienced severe changes in their distributions and abundance during the past 45 years. This could be due to climatic change in the ocean, which may cause low recruitment or high natural mortality (i.e., negative regimes) (Haaker et al. 1998; Fu and Quinn 2000), overfishing, or a combination of these factors. During periods of warming, the crustaceans decline, while finfish such as cod, pollock, and flatfish increase in numbers. Zooplankton production was found to have large and highly significant interannual and interdecadal fluctuations during summer in the subarctic North Pacific. Between the late 1950s–early 1960s and the 1980s, there were significant increases in zooplankton biomass, regarded as characteristic of those climatedriven regime shifts in ecosystems (Fromentin and Planque 1996). Although there is no recent evidence from phytoplankton in the Northeast Pacific, there is evidence from Hawaiian waters (Haigh et al. 2001), which showed a regime shift in 1976. The entire biotic shift from plankton and shellfish to finfish

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corresponded with the timing of the climatic shift shown as abrupt changes of air temperature and pressure, and water temperature and salinity. Another important example of ecosystem regime shift is in the northwest Atlantic Ocean. Northern cod (Gadus morhua) experienced large fluctuations spatially and temporally. Northern cod began to increase sharply about 1960, but after a peak in 1968, landings declined steadily for a decade, rose briefly through the 1980s, and recently plunged to the lowest level in this century (Shelton et al. 1996). Other fishes, such as haddock (Melanogrammus aeglefinus), red hake (Urophychis chuss), and capelin (Mallotus villosus), and invertebrates such as short-finned squid (Illex illecebrosus), Atlantic lobster (Homarus americamus), and sea scallops (Placopecten magellanicus), also experienced large variations in landings (Koslow et al. 1987; Rowell and Trites 1985; Williamson 1992; Hoffmann and Powell 1998; Post et al. 1999), yet showed synchronous changes over large geographic areas. The southeast Australian ecosystem also experienced impacts from climate changes, which were somewhat different. Recent studies show that climate is linked to changes in some of the fish stocks and reefs in this area. Catches of Tasmanian rock lobster (Jasus edwardsii), abalone species, and a variety of finfishes have shown correlations with climate variation in local wind fields, sea surface temperatures and surface productivity. Orange roughy (Hoplostethus atlanticus) showed substantial responses to climate variability at 600–1,500 m depth (CVAP 1998). Reef bleaching is severe under El Ninõ conditions. The time series data strongly suggest two influences: variation in the strength and annual persistence of regional winds; and a long-term southward shift in the subtropical ridge, which is assumed to affect rainfall, nutrient regeneration, and primary productivity. It is unlikely to affect winds, which have shifted poleward approximately 300 km in the past 70 years (CVAP 1998). Possible driving forces for marine ecosystem regime shifts Driving force 1: climate-ocean decadal oscillations Climate changes has been regarded as one of the major driving forces of marine ecosystem regime shift (Benson and Trites 2002; Beamish et al. 2004;

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Lees et al. 2006). Some scientists prefer that the term marine ecosystem regime shifts be strictly used when linked to climatic changes (Beamish et al. 2004). The major reason to link ecosystem regime shift to climate changes is because of the consistent time scales between ecosystem regime shifts and climate regime shifts, as shown in many examples. As discussed by Beamish and Mahnken (1999), regime shifts are changes in state over 10–30 years, and regimes are large, linked climate-ocean ecosystems. Climate changes at decadal scales, from natural or anthropogenic causes, are likely to produce or enhance regime shifts. Decadal regime shifts occur over spatial scales of thousands of kilometers (Table 1, Fig. 1). They are mainly influenced by physical processes of gyres (e.g., Sargasso Sea), continental upwelling (e.g., Peru Current), water mass boundaries (e.g., Antarctic convergence), and climatic-oceanic oscillations (e.g., ENSO, AO, NAO, and PDO independently or in combination). Biological responses to the regime shifts occur in regional communities and ecosystems. Marine ecosystems are changeable, given such a changeable environment as the ocean. Table 1 (from Steele 1998) demonstrates that the similarity in time scales between the physical and biological processes in the ocean indicates that marine ecosystems are closely coupled to the physical dynamics. Figure 1 illustrates the major mechanisms that induce changes in biota. Physical effects include turbulence, vertical mixing, tidal mixing and run-off on a scale of less than 1 km; fronts, upwelling and tides, tidal mixing and internal waves on scales of 1–1,000 km; gyres, large currents, global climate oscillations etc. on scales of thousands of meters; and thermohaline circulation on scales of millions of meters. Changes in ocean physics at decadal scales can be a significant factor influencing regime shifts

on the shelf and in inshore waters (Hoffmann and Powell 1998). El Ninõ is a disruption of the ocean-atmosphere system in the tropical Pacific having important consequences for weather around the globe. During El Ninõ, the trade winds relax in the central and western Pacific leading to a depression of the thermocline in the eastern Pacific, and an elevation of the thermocline in the west. This reduces the efficiency of upwelling to cool the surface and cuts off the supply of nutrient rich thermocline water to the euphotic zone in the eastern Pacific coast. The result is a rise in sea surface temperature and a drastic decline in primary production, adversely affecting higher trophic levels of the marine food chain, including commercial fisheries in the region. In spite of the destructive nature of El Ninõ, some marine creatures benefit from the disturbance, such as purple snails (Plicopurpura pansa), octopuses (Octopus sp.) and shrimp stocks of the eastern Pacific coast (Forrester 1997; Arntz 1986). El Ninõ and La Nina greatly impact the fisheries and ecosystems of coastal Peru and Chile. The Australian and Indian Ocean, north Pacific and some other extra tropic ecosystems are also greatly influenced over decadal scales. The PDO (Hare 1996; Mantua and Hare 2002) has been described as a long-lived El Ninõ-like pattern of Pacific climate variability. Major changes in northeast Pacific marine ecosystems have been correlated with phase changes in the PDO. Warm eras enhanced coastal biological productivity along the western North America shelf, while cold PDO eras have the opposite impact on marine ecosystem productivity. PDO influences the biomass, growth, distribution, and migration of plankton, invertebrates, and fishes (Hare et al. 1999; Hare and Mantua 2000).

Table 1 A schematic description gives the time scales of change for processes related to ecosystem variability (Steele 1998) Process

Time scale (years) 1

3

Stock recruitment El Ninõ prediction

Stressed (overfished)

Physics Models

Single species

Management

TAC

Biology

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10

30 /Regime shifts?

North Atlantic oscillation

100 Natural Thermo-haline circulation

Multispecies

Community/ecosystem

Mesh size

Effort control


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Fig. 1 Schematic showing the dominant space and time scales in the ocean for (a) physical motions and (b) biological population. In (b) the overlapping boxes to the left represent typical size ranges (on the x axis) and typical times for population doubling (on the y axis) for each type of organism. The boxes to the right present typical space scales for each organism during its lifetime. Figure from Hoffmann and Powell (1998)

NAO is a large-scale oscillation in atmospheric conditions between the subtropical high located near the Azores and the sub-polar low near Iceland. Changes in the mass and pressure fields lead to variability in the strength and pathway of storm systems crossing the Atlantic from the US East coast to Europe. The NAO is most noticeable during the winter season (November–April) with maximum amplitude and persistence in the Atlantic sector. A positive NAO index results in warm and wet winters in Europe and cold and dry winters in northern Canada and Greenland, whereas negative NAO conditions bring moist air into the Mediterranean

and cold air to northern Europe, and milder winters to Greenland (Hurrell 1995). Communities and ecosystems in north Atlantic are greatly influenced by it (Drinkwater 2002). The Arctic Oscillation has wide effects in the Northern Hemisphere. Acceleration of a counterclockwise spinning ring of air around the polar region could be responsible for warmer winters in Scandinavia and Siberia, thinning of the stratospheric ozone layer, and significant changes in surface winds that might have contributed to Arctic ice thinning (Stricherz 1999). Recent research shows that the NAO is linked to the AO (Kerr 2000). The trend in the Arctic

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Oscillation has been reproduced in climate models involving simulated increased concentrations of greenhouse gases (Fyfe et al. 1999). Driving force 2: fishing For the mechanisms of regime shifts in ecosystems, most of the science focuses on climate-related changes. There are also many other reasons that may cause or maintain large marine ecosystem changes. Fishing is regarded as another important driving force of marine ecosystem regime shrifts (Reid et al. 2000; Jackson et al. 2001; Daskalov 2002; Harvey et al. 2003). Overfishing is especially severe on species of high economic value, such as the top predators (Christensen et al. 2003). A deteriorating environment combined with overfishing may result in fishing down below the level of minimum population biomass. This kind of deterioration in a dynamic ecosystem is hard to recover from, as indicated by Pacific sardine around California, and Atlantic cod around Newfoundland. Recent anomalies in the periodicities of stock fluctuations were likely due to intense fishing in an unfavorable climate regime (Steele 1998; Hilborn et al. 2003; Lees et al. 2006). Fishing pressure in the North Atlantic has been regarded as the cause that obscures or distorts natural multi-annual abundance swings (Bakun 2004). Marine food webs have also changed in the last half-century. From the trophic level trends in major fishery areas (Pauly et al. 1998), a synchronous but opposite trend was observed, which implied community changes due to decadal regime shifts. At the same time a common trend of trophic level decline was observed, implicating worldwide overfishing. Driving force 3: introduced species Introduced species have long been recognized as a source that can greatly change marine ecosystems (Bakun 2004). Introduced species such as mollusks, Spartina sea grasses and fishes have been observed to cause large marine ecosystem changes (Thompson 1991). One famous example of great marine ecosystem change resulting from an introduced species is Potamocorbula amurensis in northern San Francisco Bay. Potamocorbula amurensis is a benthic suspension-feeding bivalve native to Asian Pacific waters. It

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was accidentally introduced in San Francisco Bay in 1986, and dispersed quickly throughout the estuary with dramatic ecological consequences. Field and laboratory evidence suggested that this species was capable of consuming most of the phytoplankton produced in northern San Francisco Bay (Colern and Alpine 1991). Benthic and pelagic communities have been altered. As with many changes due to introduced species in freshwater, estuarine, and marine ecosystems, it is hard for the ecosystem to reestablish or recover. Driving force 4: river flow River flow has been a major driving force for coastal ecosystem changes (Royer 1979; Mahe 1991; Chen et al. 2000). In the Yellow Sea and Bohai Sea drainages, terrestrial run-off has changed greatly in the last 50 years because of climate and humaninduced reasons. The biological community in this area has also changed greatly, and succession of dominant species has been obvious among different periods. Some coastal species, such as large icefish (Protosalanx hyalocranius) and estuarine tapertail anchovy (Coilia ectenes), which prefer low salinity habitat, have disappeared (Chen et al. 2000). Nutrient runoff has eutrophified the coastal waters, changing the ecosystem greatly. Driving force 5: other forces that can cause or enforce large marine ecosystem changes Other forces that are observed to cause or enhance large marine ecosystem changes include pollution, disease and eutrophication (Bakun 2004; Lees et al. 2006). The disease-driven collapse of the herbivorous urchin Diadema antillarum is one important reason that the Jamaica coral reef community converted from a coral habitat to a seaweed habitat (Hughes 1994). Coastal eutrophication can cause extensive hypoxia and anoxia, such as the 1987 summer of the Long Island Sound, New York. The complicated biological relationships among fisheries and other aquatic biota and their physiological responses to environmental change are not well understood. As a result, clear understanding of their impacts upon regime shifts is a problem. Multiple regimes have been observed in some marine ecosystems (Zhang et al. 2000; Overland et al. 2006; Steele


2004). Regime shift patterns in different ecosystems are still not well understood and hard to predict through climatic-oceanic oscillation models (Zhang et al. 2000; Pierce 2001).

Controversy or questions about the concept of regime shift Regime shift and red noise? Regime shift is a concept of decadal or centennial scale. In such a short time it is hard to determine whether a shift has occurred or not over longer periods. The term red noise and red-shifted noise have been coined for noise with positive autocorrelation, which results in a dominance of low frequencies in the spectral decomposition of the time series (Rudnick and Davis 2003; Bakun 2004; Overland et al. 2006). Generally, regimes occur on decadal time scales (Beamish et al. 2000). Statistical time series analyses have generally compared 20– 60 years of time series data, and regarded the shifts among them as a regime shift. Although Francis and Hare (1994) extracted white noise while analyzing intervention, most of the works on climate fishery analysis did not consider the chaos from red noise. Many climatologists concluded that the recent climate and fishery changes are basically a random fluctuation with red noise inside, that is, red-noise climate and red noise ecosystem dynamics (e.g., Pierce 2001; Rudnick and Davis 2003). Detailed statistical analyses on red noise in climate models are provided by Sardeshmukh et al. (2001), Torrence and Compo (1998), and Overland et al. (2006). An alternative is to discard long-term time series analyses, and only consider regime changes over decadal time scales. Hare and Mantua (2000) introduced the concept of abrupt changes at low frequency, which helped to avoid the dispute about red noise. How to separate impacts from different driving forces in ecosystem regime shifts? Although there is historical evidence of regime shifts, recent periodicities of stock fluctuations have shown differences in amplitude and cycle (e.g., North Pacific sardine and anchovy, Peru anchovy, Atlantic cod) due to heavy fishing. There are also other factors that

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could induce ecosystem changes, including introduced species, commercial fishing, and pollution. These mechanisms have been recognized, and much work has been done to prevent over-fishing and pollution and to protect ecosystems (Bakun 2004; Lees et al. 2006). While attempting to quantify these impacts on ecosystems in assessment and management, it is hard to differentiate among them (Reid et al. 2000). More work is needed to enhance the study of biological and physical processes in marine ecosystems to best understand how to accurately quantify them. Marine ecosystem regime shift, good or bad? Positive effects such as longer growing seasons, lower natural winter mortality, and faster growth rates of species in high latitude may be offset by negative factors such as an abrupt changing climate that alters established reproductive patterns, migration routes, and ecosystem relationships. Although marine systems are much more responsive to decadal scale alternations in their physical environment, they are also much more adaptable to those alternations. Regime shifts in fish communities can have major economic consequences without being obvious ecological disasters (Steele 1998): succession and replacement are observed. There are obvious declines in the landings of highly economic species, but there are also increases in other fishes and bivalves, such as the replacement of cod, haddock, and flounder by dogfish, skate, and mackerel on Georges Bank off New England (Fogarty and Murawski 1998), and the replacement of small yellow croaker (Larimichthys polyactis), large yellow croaker (Larimichthys crocea), and left eye flatfish (Paralichthys olivaceus) by herring (Ilisha elongata) and noodlefish (Salangidae) in the Yellow and Bohai Sea (Chen et al. 2000). The new marine community is as diverse and ecologically acceptable as the previous one, and in some places, more diversified, though the economic value may be lower. Some natural or human induced factors cause switches in fish communities over time scales of a few decades. Coastal domain areas are easily impacted by human activities through addition of nutrients, drainage, land development, and by fish and shellfish farming. ‘‘Restoration’’ of a marine ecosystem is basically impossible (Steele 1998), as it is difficult to

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determine the pristine, pre-disturbance state. Continental shelves are the regions where approximately 85% of fish landings originate, and where most questions arise, including environmental changes on fisheries and fishing impacts on marine environments. In the deep ocean there is large interannual variability in physical processes with strong indications of longer-term trends in both biomass and species composition. Changes in major current systems alter the basic productivity and also have significant consequences for marine communities in the ocean (Mann and Lazier 1996).

Models in simulating and predicting ecosystem regime shift Because of the wide influence of regime shift in ecosystems, modeling this phenomenon becomes important in marine ecosystem study. Our ability to manage marine ecosystems will increase with better modeling quality. There are two basic kinds of models in simulation and predicting regime shifts: historical descriptive models and general circulation models (GCMs). The first type uses analogies, descriptively or statistically approaching the issue of scale through the interpretation of patterns in time and space. The second method simulates the general circulations in climate-ocean or climate-ocean-biotic systems to describe the climate and ecosystem dynamics. Time series methods of intervention analysis (Francis and Hare 1994; Hare and Francis 1995; Mantua et al. 1997; Zhang et al. 1997), cross correlation analysis (Francis and Hare 1994), Auto Regressive Integrated Moving Average (ARIMA) (Francis and Hare 1994; Hare and Francis 1995; Mantua et al. 1997; Zhang et al. 1997), Cumulative Summation (CuSum) (Murdoch 1979; Ebbesmeyer et al. 1991; Hare and Mantua 2000) and red noise models (Pierce 2001; Rudnick and Davis 2003; Overland et al. 2006) have been used in the research of historical descriptive models. Correlation associations have a disturbing tendency to break down part way through long time series (Mackas and de Young 2001). Atmospheric GCMs have been used to address how the regional climate associated with monsoon rainfall and circulation anomalies responds to imposed SST anomalies. Difficulties exist: for example,

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17 atmospheric modeling groups in the Monsoon Numerical Experimentation Group (MONEG) of the world Climate Research Programme ran a 90-day integration using the observed global SST fields for 1987 and 1988 as boundary forcing and atmospheric fields as initial conditions. The results were not ideal and suggested that the Indian Ocean region is difficult to model. The physical mechanisms that caused the PDO are not currently known (Mantua and Hare 2002). Despite the difficulties of developing GCMs, several studies have appeared in recent years (Werner et al. 2001; de Young et al. 2004). No matter if one adheres to the regime shift concept that there are relatively rapid changes in community structure, or accepts only the probability of auto-correlation around decadal time scales, it is evident that predictions based on uncorrelated, white noise, recruitment will degrade rapidly. Presently, with a short history of ecosystem and climate data, it is effective to regard this kind of change in ecosystems as a process whereby the ecosystem changes from one stable state to another stable state because of human-induced or natural causes. Considering new types of recruitment and growth patterns in making assessments is necessary where old patterns fail (Steele 1996). It is important to note that forecasting by analogy is not an attempt to assess the direct effects of a climate change on the biological aspects of living marine resources: correlation does not imply causality. The physical-biological mechanisms that drive the observed correlations need to be understood in order to identify a stable predictive scheme and therefore be effective in a time of changing background climate. Recent works have sought the key mechanism or link between climate and oceanic ecosystem output. Polovina et al. (1995) concluded from their research that changes in mixed layer depths occurred on decadal and basin scales and could be a key mechanism linking variation in the atmosphere and oceanic ecosystems. Climate warming was regarded as the key mechanism that caused the decline of zooplankton in the California Current (Roemmich and McGowan 1995). Higher surface temperature increased the thickness of the discontinuity layer, which resulted in less lifting of the thermocline by wind-driven upwelling, and subsequently reduced the inorganic nutrient supply for zooplankton. It is


therefore important to explore mechanisms in specific regional marine ecosystems in order to specify the specific mechanism in that region. The regime shift concept also implies that different regimes have an inherent stability, so that significant forcing is required to flip the system to an alternative state (Steele and Henderson 1984). Regime shift models usually have only two states, which are usually not the real systems because environmental changes or human forcing may result in unintended consequences. Many kinds of fish have disappeared from marine ecosystems, and may never come back, and lagged effects have appeared for some species. As a result, some ecosystems have shown more than two regimes during dynamic time series (Zhang et al. 2000; Bakun 2004). The production of fish biomass in the oceans is governed by interactions among numerous physical, chemical, and biological processes. It is hard to define an effective GCM to be used for climaterelated impact analyses. The key physical mechanisms of the ocean that impact biological factors are not sufficiently studied. Many ocean systems or domains can provide more production, such as upwelling systems for pelagic fishes, phytoplankton, and zooplankton, downwelling domains for bottom fishes, mixing of runoff and ocean sea water, mixing of warm and cold currents, and advection (Ware and McFarlane 1989; Ware and Thomson 1991; Roemmich and McGowan 1995; Wickett 1967). Analyses are qualitative and not included in the ecosystem models and fishery assessment. At present, the spatial resolution of GCMs is too coarse to be useful for reliable and credible regional impact assessment. When computers become more powerful and our understanding of the physical process increases, we will rely more and more on the dynamic forecasts of GCMs. They have the tremendous advantage of working forward from the actual present observed conditions, thus avoiding the problem of statistically averaging over a number of events that differ in important details. Models in simulating and predicting regime shifts may fail because of co-impacts from natural and human-induced reasons. Rigler (1982) concluded that an inability of empirical science to provide reliable predictions or forecasts of future states of both ecological and connected weather systems has led to a general resistance to historical science. He also said

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that this is not the fault of the method: rather, it is because ‘‘long-term abundance of species in systems subject to anthropogenic or other changes is not predictable.’’ From Table 1 (Steele 1998), we can see that annual and decadal perspectives are not alternatives, but are complementary. Assimilating different types of research (single species, multispecies, and ecosystem) and assessment approaches into a single scientific program or management system is important for future work. This helps to improve understanding, assessment and management of our marine resources under the situation of regime shifts.

Impact of regime shifts on fisheries and difficulties in integrating regime shifts into fisheries science Climate changes affect marine ecology through alteration of nutrient cycles, primary production, fish growth, recruitment, and community changes. Fisheries certainly are affected, as recruitment (Mann and Drinkwater 1994; Wada and Jacobson 1998), and growth (DeAngelis et al. 1991; Hutchings 1999; Clark et al. 1999) are heavily impacted. Changes in catches, dominant species, composition, replacement, and distribution are widely observed in world fisheries (Mann and Drinkwater 1994; Arntz 1986; Beamish et al. 1999). Use of climate information in fisheries and ecosystem models is increasing to identify methodological and theoretical gaps in current knowledge and explore the possibilities for resolving them. This information is also being used to qualify the ecological impact with a view toward incorporating climate information within operational decision making in fishery management. In addition, global collapses of fish stocks were likely affected not only by ocean climate changes and fishing, but were also influenced by many other factors, such as pollution (Wolfe 1985), introduced species, and other human activities. Most global commercial fisheries have been affected by decadal climate changes. Commercial fishing catches can provide a measure of the ecosystem changes. The causes of collapses of the California sardine and anchovy have been argued for decades, including overfishing, environmental fluctuation, or competition for food resources between sardine and anchovies. Analysis of fossil sardine time series has

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revealed that sardine production has fluctuated naturally (Soutar and Isaacs 1974); large, natural, prehistorical fluctuations were clearly unrelated to commercial fishing. Spectral analysis of the scaledeposition series off southern California from A.D. 270 through 1970 showed that sardines and anchovies both tend to vary over a period of approximately 60 years. It also revealed nine major recoveries and subsequent collapses of the sardine population over 1,700 years. The current recovery is not unlike those of the past in magnitude (Baumgartner et al. 1992). Through intervention analysis, the climate index of Kodiak Winter Air Temperature and North Pacific Index, and Alaska salmonid production showed nearly synchronous interventions across different species, as well as across a large part of the spatial range in 1979 and late 1940s/early 1950s. Mantua et al. (1997) analyzed climate indices in the North Pacific Ocean along with regional climate indices within salmonid habitats. Their work showed intervention in the time series data and strong correlation among them. Peruvian anchovy and Chilean sardine was one of the earliest fisheries realized by fishermen and scientists to have large fluctuations. This fishery is one of the most productive in the world. During the 1960s and early 1970s, catches in this area comprised nearly 20% of the world’s landings. Catch data are available from 1950 to the present. This time series showed that catch stayed low until 1958, then sharply increased in 1958–1962, when the annual catch reached 7,000,000 tonnes. Subsequently, it grew to 12,000,000 tonnes in 1970, suffered a major collapse in 1972, and then showed a slow and uneven recovery process. The 1972 collapse was considered to have been prompted by an El Ninõ event. There was also a synchronous change between fish catch and climatic conditions, which leads to the prospect of probabilistic catch forecasts of anchovy constructed on the basis of ENSO forecasts. However, climate variability is not a factor that can be linearly translated into absolute catch values. Historical, political, and perhaps circumstantial factors also play roles (Glantz 1992; Hilborn et al. 2003). Another important point concerning the impact from regime shifts is that most of the time we only pay attention to the negative impact, but responses of biological factors to climate changes are not always negative. Even in El Ninõ periods, when anchovy was

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reduced, sardine and jack mackerel increased (Glantz 1992). In the northeast Pacific marine ecosystem, warm PDO phases have favored high salmon production in Alaska and low salmon production off the west coast of California, Oregon, and Washington. Conversely, cool PDO eras have favored low salmon production in Alaska and relatively high salmon production for California, Oregon, and Washington (Hare 1996; Hare et al. 1999). The community/ ecosystem changes when the climate changes, but is still functional though the economic influence may be heavy (Steele 1996). The goal of fishery management is to predict next year’s fish biomass through models, which may include backward Virtual Population Analysis (VPA), and forward recruitment, growth, and mortality predictions. For management purposes, production theory, yield-per-recruitment and spawner-recruitment models are widely used to conduct best policy searches (Quinn and Deriso 1999). No matter which kind of model is used, historical data are needed. After a population dynamic model is established, data are fitted to the model, different management options are tested to predict future biomass and to get the best management limit. The classical population dynamic models usually assume that the environment is an averaged constant, because it is hard to account for dynamic patterns and intervening variables; these issues have been ignored in stock assessment using either VPA analysis or forward abundance prediction. Production models ignore year to year changes in abundance. Finally, yield per recruitment models unrealistically assume that recruitment is constant from year to year (Hilborn and Walters 1992; Quinn and Deriso 1999). Climate can have substantial impacts on various stages in the life cycle of many species, and can alter primary production, triggering changes that propagate through the trophic web. Relevant impacts can occur from very local to basin-wide spatial scales, and on timescales ranging from hourly to interdecadal. Recruitment changes triggered by environmental changes are widely recognized, such as the recruitment of Atlantic cod fisheries after 1989. At the same time changes in growth, natural mortality, distribution and immigration are also observed in many fisheries (Drinkwater 2002). Regime shifts are therefore quite contrary to the common assumption of stable state, around which


there lies a normal distribution of variation, in fisheries science. Conventional statistical analyses, which assume linearity and normality in a set of observations, are inadequate in understanding underlying mechanisms that link climate, ocean and biotic systems across possible regimes. It is therefore necessary to have comprehensive knowledge of both short-term and long-term effects, including climate changes, regime shifts, changes in fish stocks and feedback of biological systems to physical events (Beamish et al. 1999). As for ecosystem models discussed in the section of modeling marine ecosystem regime shrift, there are also two approaches for incorporating environmental data into stock assessment. One is to add environmental parameters into classical stock assessment models directly (e.g., Jacobson and MacCall 1995). Another is to build biological-physical process models (Bakun 1996; Haigh et al. 2001; Wiebe et al. 2001). The first approach is practical, based on the present knowledge and data available. However, there are problems: correlation does not mean causality; correlation associations are not always consistent along time series; and climate variability cannot be linearly translated into absolute catch value because of this non-consistency. The second approach involves marine ecosystem models. It is hard to define an effective GCM to be used for climate-related impact analyses, and it is hard to separate the impacts from fishing and climate changes. At the same time, in the present situation of low-resolution climate data, GCMs are insufficient for assessment of fish population processes at regional scales. Research needs to incorporate regime shifts in modeling fish dynamics, although there are some studies that incorporate environmental factors into classical stock assessment directly, or use GCMs to predict population biomass (Werner et al. 1996, 1997, 2001). As it is difficult to find key environmental factors attributed to the regime shift, an option is to use classical models but different parameters to simulate different regimes. This requires accurate forecasting of decadal climate change. Until GCM or a well performing correlation model can be developed, an alternative is to discard long term time series statistical analysis and only consider regime shifts over decadal time scales. By doing this the concept of ecosystem regime shift is useful from a management standpoint even if the climate regime shifts cannot be

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accurately predicted (Beamish et al. 2004). Precautionary reference points with a risk assessment that fully consider uncertainty of the ecosystem variability should improve future management (Jiao et al. 2005).

Potential future research More work is needed to improve our understanding of regime shift and its impacts on ecosystems and implications for fishery management. At the same time it is important to reconsider the regime shifts from macroscopic and microcosmic viewpoints. The essence of ecosystem sensitivity and adaptability requires extension of research on biological-physical processes in regional fisheries. The terms stability, sustainability, and restoration are widely used in ecosystem and fisheries research. The concept of the marine protected area is also a popular term in ecology and fisheries (Pauly et al. 2002). In some situations, it can be helpful in keeping/ increasing biodiversity and/or productivity to provide disturbances to the ecosystem (Lessard et al. 2005). From an economic viewpoint, it is also practical to induce disturbance to an ecosystem to get the maximum benefit without destroying the ecosystem. Fish landings are correlated with climate indices (SST, inflow, salinity, pressure), but are also correlated with predator abundance, water current, and biomass of phytoplankton and zooplankton. Which is the key mechanism: insufficient plankton as food, Ekman influences on larval fishes, changed temperature and salinity, other physical/chemical circumstances, or co-impacts? Finding the answers should be of high priority in current and future research as classical population assessment models are updated to incorporate climate impact and GCMs. Traditionally, we have focused on yearly recruitment to individual fish stocks. The possibility of fishing down marine food webs requires comparison of changes in community structure over time scales of a few years in highly over-fished regions to longer historical scales in future work (Steele 1998). It is time to consider questions from an ecosystem viewpoint. Present management failures call for deeper understanding of fishing effects in the long term, such as the ability of stocks to survive a number of years of adverse environmental conditions.

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Recent research has related the fisheries phenomena of strong and weak recruitment/productivity to the noise in nature. Red noise has been used in the explanation of regime shift (Pierce 2001). The classification of noise by spectral density is given ‘‘color’’ terminology, extending the concept of red noise. White noise means a constant spectral density and temporally uncorrelated noise signal. For colored noise, the spectral density changes with changing frequency, and the noise signal is auto-correlated (Halley 1996; Petchey 2000). Noises in nature may not be white, but colored in many cases (Halley 1996; Caswell and Cohen 1995; Vasseur and Yodzis 2004). Colored noises have more serious implications for endangered species and species with low population size (Halley and Kunin 1999; Morales 1999; Schwager et al. 2006). Before we are able to simulate the processes using GCMs, classical stock assessment model with colored noises may lead to better inferences about the population size and the fishery status (Schwager et al. 2006; Jiao et al. 2008). More work is required on research techniques, such as climate forecast systems. Detailed and highresolution data are needed for both GCMs and classical stock assessment. Satellite information is an effective tool in climate forecasting and biological-physical analysis, which provides far superior spatial coverage and in situ information. Forecasts of El Ninõ clearly have direct value for fisheries in the productive regions of western South America. More accurate prediction of El Ninõ will also be very valuable for countries outside of the tropics, such as Japan, Canada and the United States. It can also help fisheries managers to distinguish between changes in populations due to anthropogenic factors and changes from natural conditions. The application of satellite information will be widely needed in both climate and ecosystem research. Understanding the linkages between physical environmental and biological factors requires input from a variety of disciplines, coordinated research programs, and considerable cooperation at national and international levels. The works previously done on ecosystem regime shift are at ocean basin scales, e.g., Mantua et al. (1997) and Zhang et al. (2000). These large changes in ecosystems are world-wide, as indicated by the catch trajectories of some sympatric world fisheries. Catch trajectories of many fisheries from west and east Pacific, Atlantic and Australia waters show

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regime patterns. At the same time, given the controversy over independence between PDO and NAO, it seems that climate oscillation may be world-wide. PDO may be the result of joint action on the North Pacific by El Ninõ and AO. NAO may express AO in the North Atlantic. World ecosystems are all influenced by oscillations of El Ninõ, AO, and Antarctic Oscillation. Every basin scale and ocean scale ecosystem, because of their distinctive geographic characteristics, may show delayed significant to inconspicuous ecological changes at similar or different time scales. From this point of view, it is time to consider climate GCMs as world climaticoceanic oscillations, not limited to basin and ocean scales. Ecosystem models will benefit from accurate climate oscillation predictions, but at present, consideration of the key mechanisms that influence the regional ecosystems is required.

Conclusion Climate-ocean impacts have traditionally been ignored in stock assessment, but are now known to be important. Stock assessments and stewardship of fisheries now require a distinction between fishing effects and climate-driven effects. For now it is not an issue of which approach is most appropriate, but rather that there is a requirement to both provide annual assessments and improve understanding of natural processes. More effective strategies for fisheries will be provided. With the development of science and technology, the mechanisms of climatedriven effects will be clearer and GCMs will be more accurate (de Young et al. 2004). Acknowledgements I would like to thank Dr. Yong Chen of the University of Maine, Dr. Joe Wroblewski, Dr. David Schneider and Dr. Paul Snelgrove of Memorial University of Newfoundland who provide valuable advice. This research was supported by grants from Virginia Polytechnic Institute and State University, and the USDA Cooperative State Research, Education and Extension Service, Hatch Project #0210510, to Y. Jiao.

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