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Figure 6.16. The overcapacity ratio, defined as the actual capacity of the fishery divided by the estimated long-run sustainable capacity, of various historical fisheries (data from Clark 1990) and of the USA as a whole in 2002 (D. Fluharty, personal communication).

Ecology Population Size

on Bayesian methods in stock assessment and fishery management. Good starting points include Thompson (1992), McAllister et a/. (1994), Ellison (1996), Kinas (1996), McAllister (1996), McAllister and lanelli (1997), McAllister and Kirkwood (1998a, b, 1999), Meyer and Millar (1999a, b), Patterson (1999), Wade (2000), Adkison and Zhenming (2001), Hammond and O'Brien (2001), Harley and Myers (2001), McAllister et a/. (2001), Dorn (2002), Millar (2002), Rivot et a/. (2001), and Chen et a/. (2003). Liermann and Hilborn (1997) apply Bayesian methods to the analysis of depensation in the stock-recruitment relationship.

More about salmon

There is so much more that I would like to tell you about salmon that it could be another book. These are remarkable fish because of their diadromy (migration between freshwater and marine environments; see McDowall (1988)) and semelparity (in general, just a single reproductive event, the major exception being Atlantic salmon Salmo salar L.). If one extends from the salmon to the salmonids (thus including, for example, trout and charr), iteroparity is much more common. Salmonids are fished both commercially and recreationally. Because of their interest as sport fish, salmon are described in a number of well written books for the lay person; some of my favorites are Ade (1989), Stolz and Schnell (1991), Behnke (1992, 2002), Watson (1993, 1999), and Greer (1995), Although not about salmon, John McPhee's book (McPhee 2002) about shad, which are also diadromous, is a great joy. If you can find Malloch (1994), which is a reprint of a 1909 publication, it is well worth reading. For more academic treatments on Atlantic salmon consult Mills (1989), and on Pacific salmon consult Groot and Margolis (1991), Pearcy (1992) and Groot et al.

(1995). Some of the most interesting questions involving salmon are those relating to the diversity of life histories and early maturation (usually males) in freshwater before migration to the ocean. Fisher et al. (1991) discuss the integration of fishery and water resource management.

Models with process uncertainty and observation noise

Taking into account process uncertainty and observation noise is a difficult task, somewhat beyond the goal of this chapter. Schnute (1993) lays out some of the questions via clear and simple examples, but the methodology of solution rapidly becomes difficult. Entry points for methods and for some applications include Reed and Simons

(1996), Patterson (1998), Patterson et al. (2001), de Valpine and Hastings (2002), Hinrichsen (2002), Kehler et al. (2002), Tang and Wang (2002), Cooper et al. (2003), Lindley (2003), and Mesnil (2003). As Sinclair et al. (2002a) note, size-selective mortality, density and temperature form a tangled bank when one tries to understand length at age, which as we have discussed is fundamental for age-structured management models. Millar and Meyer (2000) and Schnute and Kronlund (2002) use an explicit Bayesian approach for this problem.

Marine reserves

There is a growing literature on both theoretical (Apollorio 1994, Guenette et al. 1998, Horwood et al. 1998, Guenette and Pitcher 1999, Pezzey et al. 2000, Soh et al. 2000, Lindholm et al. 2001, Council 2001, Acosta 2002, Apostolaki et al. 2002, Brooks 2002, and

Lockwood et a/. 2002) and empirical (McClanahan and Kauna-Arara 1996, Edgar and Barrett 1999, Jennings 2000, Mosquera et a/. 2000, Paddack and Estes 2000, Sanchez Lizaso et a/. 2000, Cote et a/. 2001, Halpern and Warner 2002, Fanshawe et a/. 2003, and Shears and Babcock 2003) aspects of marine reserves. As we have discussed, one of the important issues with marine reserves is economic (associated with the suite of questions concerning foregone catch, the effects of reserves on yield and displaced fishing effort). There is less literature on this question (Farrow and Sumaila 2002, Rudd et a/. 2003) but the topic is important (Sanchirico et a/. 2003).

Fishing as an agent of selection

We have ignored the evolutionary response of stocks to fishing, but there is a growing literature that fishing acts as a clear agent of selection and that responses can be rapid (Cardinale and Modin 1999, Cardinale and Arrhenius 2000, Law 2000, Hutchings 2000, 2001, Sadovy 2001, and Conover and Munch 2002).

Ecosystem-based approaches to fishery management

We have focussed on single species models because understanding them is essential if one wants to move forward to models based on community or ecosystem concepts, as we surely must (Sherman and Alexander 1989, Sherman et a/. 1990, 1991, 1993, Richards and Maguire 1998, Murawski 2000, Pitcher 2000, Link et a/. 2002, Pitcher et a/. 2002, Sinclair et a/. 2002b, and Christensen et a/. 2003). In the future, fishery management will likely take an ecosystem-based approach (Pikitch et a/. 2004). We are accumulating some theoretical and empirical (Gislason 1994, Daskalov 2002) knowledge about ecosystem effects of fishing and ecosystem approaches to management. Furthermore, management based on an ecosystem approach explicitly recognizes the role of climate in the production of fish (for examples, see Healey (1990), Mullan (1993), Bakun (1996), Klyashtorin (1998), Kuikka et a/. (1999), Fiksen and Slotte (2002), Swansburg et a/. (2002), and Williams (2003)).

In the late 1990s, I served on the Ecosystem Advisory Panel which sent to Congress Ecosystem-based Fishery Management. A Report to Congress by the Ecosystem Advisory Pane/ (available, among other places at my website: http://www.soe.ucsc.edu/~msmangel/ and http://www.soe.ucsc.edu/~msmangel/eprints_topical.htm). An executive summary of our conclusions is as follows.

Basic ecosystem characteristics and operating principles

(1) Prediction

• Prediction is limited. Uncertainty and indeterminacy are fundamental characteristics of the dynamics of complex adaptive systems. It is not possible to predict the behaviors of these systems with absolute certainty, regardless of the amount of scientific effort invested. We can, however, find the boundaries of expected behavior and improve our understanding of the underlying dynamics. Thus, ecosystems are not totally predictable, but they are not totally unpredictable either. There are limits to their predictability.

(2) Resilience

• Thresholds are real. Ecosystems are finite and exhaustible. But, they usually have a high buffering capacity and are fairly resilient to stress. Often, as we begin to apply a stress to an ecosystem, its structure and behavior may at first not change noticeably. Only after a critical threshold is passed does the system begin to deteriorate rapidly. Since there is little change initially in behavior with increasing stress, these thresholds are very difficult to predict before they are reached. The nonlinear dynamics which cause this kind of behavior are a basic characteristic of ecosystems.

• Changes can be irreversible. When an ecosystem is radically altered, it may never return to its original condition, even after the stress is removed. This phenomenon (called hysteresis) is common in many complex adaptive systems.

• Diversity is important to ecosystem functioning. The diversity of components at the individual, species, and landscapes scales strongly affects ecosystem behavior. Although the overall productivity of ecosystems may not change significantly when particular species are added or removed, their resilience may be affected.

(3) Space and time linkages

• Multiple scales interact. Ecosystems cannot be understood from the perspective of a single time, space, or complexity scale. At minimum, both the next larger scale and the next lower scale of interest must be considered when effects of perturbations are analyzed.

• Components are strongly linked. The components within ecosystems are linked by flows of materials, energy, and information in complex patterns. The impacts of disrupting these patterns are highly variable and poorly understood.

• Ecosystem boundaries are open. Ecosystems are thermodynamically open, far from equilibrium systems, and cannot be adequately understood without knowledge of their boundary conditions, energy flows, and internal cycling of nutrients and other materials. Environmental variability can alter spatial boundaries and energy inputs to ecosystems.

• Ecosystems change with time. Ecosystems change with time in response to natural and anthropogenic influences. Different components of ecosystems change at different rates and can influence the overall structure of the ecosystem itself. The human component of ecosystems (especially technology and institutions) changes rapidly, far outstripping the capacity for change of other components of the ecosystem.

Social goals

• Apply a precautionary approach. Because predictability is limited and we now live in a world where humans are an important component of almost all ecosystems, it is reasonable to assume that human activities will impact ecosystems at several scales. We should reverse the current burden of proof and presume that adverse impacts will occur, unless and until it can be shown otherwise.

• Purchase ''insurance''. To guard against uncertain adverse impacts we should purchase ''insurance'' of various kinds, ranging from the physical insurance of marine protected areas to the financial insurance of environmental bonds.

• Make local goals compatible with global objectives. Changing human behavior is most easily accomplished by changing the local incentives which individuals face to be consistent with broader social goals. The lack of consistency between local incentives and global goals is the root cause of many ''social traps,'' including those in fisheries management. Changing incentives is complex and must be accomplished in culturally appropriate ways. For example, in western market economies, changing market incentives may be appropriate, but this will not generally be the case for other cultures.

• Promote participation, fairness and equity in policy and management. Policies that are developed and implemented with the full participation and consideration of all stakeholders, including the interests of future generations, are more likely to be - and to be perceived as - fair and equitable.

• Treat actions as experiments. Management actions and policies are analogous to experiments and should be based upon hypotheses about the ecosystem response. This requires close monitoring of results to determine to what extent the hypotheses hold.

Risk analysis

Francis and Shotton (1997) offer a general review of the notions of risk in fishery management; Pielke and Conant (2003) another view based on ''best-practices''; Hutchings et al. (1997) discuss the role of science and government control of information. One must deeply understand uncertainty and its implications, at a variety of levels, to proceed with effective risk analyses. The work of Dovers and colleagues (Dovers and Handmer 1995; Dovers et a/. 1996) is a good starting point. Other explicit treatments of risk analysis in fisheries include McAllister and Peterman (1992a, b), Punt and Hilborn (1997), MacCall (1998), Robb and Peterman (1998), Schnute et a/. (2000), van Oostenbrugge et a/. (2001), Jonzen et a/. (2002) and Myers et a/. (2002).

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