Andrew J. Constable THE CONVENTION AND ITS CONSERVATION OBJECTIVES

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1 BULLETIN OF MARINE SCIENCE, 74(3): , 2004 MOTE SYMPOSIUM INVITED PAPER MANAGING FISHERIES EFFECTS ON MARINE FOOD WEBS IN ANTARCTICA: TRADE-OFFS AMONG HARVEST STRATEGIES, MONITORING, AND ASSESSMENT IN ACHIEVING CONSERVATION OBJECTIVES Andrew J. Constable ABSTRACT Harvesting of marine living resources in the Southern Ocean is managed by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR), which is widely known for its ecosystem approach to managing fisheries and inclusion of food-web maintenance in the conservation objectives. CCAMLR has developed a precautionary approach to account for predators when setting catch limits for krill, the most important prey species in the Antarctic marine ecosystem. It has also established an ecosystem monitoring program to help detect effects of prey harvesting on predators. CCAMLRʼs approach is intended to permit both recovery of overexploited species and ecologically sustainable development of fisheries without causing damage to the Antarctic marine ecosystem. The trade-offs are between the harvest strategies (catches) and the ability to obtain sufficient information and knowledge for management of fisheries in a way that achieves the conservation objectives. This paper presents general principles of an ecosystem-based approach to fisheries using the CCAMLR approaches as examples. It discusses the precautionary and ecosystem approaches of CCAMLR, the types of objectives being considered for predators, and the general approaches and monitoring programs that could achieve ecosystem/food web conservation objectives. Like many large-scale marine ecosystems, that of the Antarctic has a checkered history of overexploitation of marine species, but unlike other systems, the Southern Ocean does not suffer multiple other human impacts such as pollution from urban and coastal sources and land-use practices. The politics and decision making are therefore potentially primarily oriented toward management of the fisheries. As a consequence, this region provides an opportunity for determining the simplest requirements for ecologically sustainable management practices for fisheries and, if the attempt is successful, could be used as a guide for establishing such practices elsewhere in the world. Here, I discuss the ecosystem-based approaches being developed for Antarctic fisheries on the basis of the objectives of the Convention on the Conservation of Antarctic Marine Living Resources. These approaches are intended to permit both recovery of overexploited species and ecologically sustainable development of fisheries without causing damage to the Antarctic marine ecosystem. I proceed on the premise that ecosystem-based management encompasses the principles listed in Table 1. The trade-offs in the management of fisheries in this region are between the catches and harvest strategies of the fisheries and the ability to obtain sufficient information and knowledge for management of the fisheries in a way that achieves the conservation objectives. Uncertainty in knowledge must be adequately taken into account in order that fisheries do not develop faster than does the ability to manage them. THE CONVENTION AND ITS CONSERVATION OBJECTIVES Harvesting of marine living resources in the Southern Ocean is managed by the Commission for the Conservation of Antarctic Marine Living Resources under the Convention Bulletin of Marine Science 2004 Rosenstiel School of Marine and Atmospheric Science of the University of Miami 583

2 584 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004 Table 1. Summary of characteristics of the ecosystem approach to managing fisheries. The ecosystem approach Manages people/activities Considers ecosystem effects Permits action despite incomplete knowledge Is robust in the face of uncertainties Makes explicit trade-offs of ecological, social, and economic risks Requires greater precaution than single species (use) management Keeps cost commensurate with value In contrast, it Does not manage ecosystems Is not ecosystem engineering Does not delay action until ecosystems are completely understood Is not based on a single comprehensive predictive model that can be used to make decisions Does not necessarily make the assumption that improved detail/complexity will result in diminished risk in all areas Should not lead managers to be less precautionary than does the precautionary approach Does not regard cost as immaterial on the Conservation of Antarctic Marine Living Resources. I will refer to the convention and the commission that administers it collectively as CCAMLR. The Antarctic Treaty Consultative Parties negotiated the convention to ensure the conservation of the marine ecosystem in advance of the expanding krill fishery; it came into force in CCAMLR was a major advance in the conservation and management of marine species in the Southern Ocean; not least it extended the coverage of the Antarctic Treaty System (the Antarctic Treaty covers the area south of 60 S) to encompass the whole of the Southern Ocean south of the Antarctic Convergence (Antarctic Polar Front; Everson, 1977). CCAMLR was considered, and remains by comparison to other international instruments, an innovative convention (Hofman, 1984), the first to take an ecosystem approach to managing fisheries and be recognized for developing a precautionary approach to setting catch limits (Constable et al., 2000). Its first primary objective is to ensure that stocks are maintained close to levels that ensure the greatest potential recruitment (birth and survival) of young fish to the population. This objective was based on conventional fisheries assessment practices and is used in part to guide the setting of catch limits. The second primary objective is conservation of the marine ecosystem. In this respect, the direct effects of fishing on by-catch species, including, among others, benthos, fishes, and seabirds, are important. The convention also requires the maintenance of ecological relationships between harvested, dependent and related populations, implying that the direct and indirect effects of fishing should not substantially alter the function of the marine ecosystem. It also includes an objective intended to allow the recovery of depleted species, such as seals and whales, and to avoid irreversible changes, i.e., changes that cannot be reversed within two to three decades. Finally, the convention includes rational use of marine living resources in the region within the context of conservation. THE ECOSYSTEM APPROACH TO MANAGING FISHERIES CCAMLR is widely known as the only international convention currently applying an ecosystem approach to managing fisheries. It owes this status largely to the development of stock assessments that endeavor to account for the needs of predators (the precautionary approach of CCAMLR) as well as the establishment of the CCAMLR Ecosystem Monitoring Program (CEMP, described by Agnew, 1997).

3 CONSTABLE: MANAGING EFFECTS OF FISHERIES ON FOOD WEBS 585 CEMP was initiated in 1986 to detect significant changes to the ecosystem, particularly in predators of krill, and to signal when such changes were the consequences of fishing. The CEMP is intended, in this way, to inform the commission when fishing affects species dependent on the target species. CEMP was deliberately restricted to monitoring a few selected predators and is established in only a few areas, and field work and data acquisition are carried out voluntarily by members of CCAMLR. The manner in which the data from CEMP will be used to form policy has yet to be resolved (Constable et al., 2000), although advances toward resolution have been made in recent years (de la Mare and Constable, 2000; Constable, 2002). Despite the apparent simplicity of the Antarctic marine ecosystem, the effects of a krill fishery on krill predators may be difficult to detect because of spatial and temporal variability in the dynamics of this ecosystem (Murphy et al., 1988; Constable and Nicol, 2002; Constable et al., 2003), potential shifts in predator diet depending on availability of prey (see, e.g., Agnew et al., 1998), and spatial differences in migration (see Constable et al., 2003, for review). Quantitative predictions of indirect effects of krill harvesting are currently difficult to formulate because only a few models address these issues (see Constable, 2002, for review). In the absence of such models, an important issue is how to manage for potential effects on predators of fishing for prey when little information is available on predicting how predators will respond to different levels of prey harvest. An additional complexity for managers is determining a reference image of the Antarctic marine ecosystem that can be used in formulating operational objectives, given that most of the information on which our ecosystem view is based has been gathered since Little is known, therefore, about how the system was structured before the decimation of seals over the last 200 yrs and of the great whales over the last 100 yrs, the substantial overharvesting of a number of fish species in the late 1960s and 1970s (Kock, 1992), and the reduction of sea-ice extent in the mid-20th century (de la Mare, 1997). Nevertheless, CCAMLR is in the process of formulating a full ecosystem-based management procedure for the krill fishery (Fig. 1; de la Mare, 1996, 1998; Constable, 2002). The development of an effective management procedure requires (i) operational objectives, (ii) methods for assessing the status of the system after the monitoring of indicators, and (iii) the decision rules for setting harvest controls based on the relative difference between the assessment and the objectives (see Fig. 1; de la Mare, 1986, 1987, 1996) while accounting for uncertainties. Developing a management procedure within CCAMLR is best illustrated in the development of the precautionary approach in CCAMLR (see Kock, 2000, for a full review) and methods used by the International Whaling Commission (see de la Mare, 1986, 1996; Cooke, 1999). The prospective development and evaluation of a management procedure by means of computer simulations provide the opportunity to evaluate explicitly the ecological and conservation implications of different harvest strategies and the potential problems that might arise in making decisions given the uncertainties in knowledge and assessments. To date, the development of management procedures has taken a single-species approach but has considered predators in the decision rules when the target species is considered an important prey species, such as Antarctic krill (see below). Little account has been taken of the responses of predators or other elements of the ecosystem to proposed harvest strategies. In particular, operational objectives for food webs and ecosystems in managed fisheries have not been well formulated, so implementing a suitably precautionary ecosystem approach to managing fisheries is difficult.

4 586 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004 Figure 1. The fishery control system with the ecosystem components included. Changes in catch limits or harvest controls are predetermined in the decision rules of the management procedure. Those rules compare the outcomes of the assessment with the operational objectives to determine what actions are required to ensure the objectives are met. OPERATIONAL OBJECTIVES FOR PREDATORS OF FISHED SPECIES Species that directly interact (as predators or prey) with the target and by-catch species of fisheries (here collectively termed fished species ; Fig. 2) are those most likely to exhibit an indirect response to fishing, particularly those with the strongest interactions with the fished species (Paine, 1980; Yodzis, 1994, 2000, 2001). The effects of fishing on dependent or related species are only important if fishing might alter the strengths of interactions among species, i.e., might change the magnitude and/or direction of effects between species. For example, theory suggests that predators of fished species would be competing with the fishery only if they are feeding substantially from the fished population and the fished population is insufficient to meet the needs of predators and support the fishery at the same time. In this case, competition would be evident if the productivity of the predators is reduced as a result of fishing, although before attribution of such evidence to competition, changes in the food web not necessarily caused by fishing could perhaps be considered. Reduced net productivity may be evident in a decrease of the biomass of the predator population (reduced growth or weight loss in individuals), reduced recruitment or increased mortality, and/or migration from the area. In addition, wider effects on the ecosystem might result if predators switch from preying on the fished species to preying on other species in the system. Complexities in the model arise if the life cycles of some dependent species include critical stages (such as breeding season for some land-based predators of krill) or if dependent species shift their foraging areas, for example seasonally. Constable (2001) discussed how conservation objectives can be derived for predators of fished species. Operational objectives based on reference points for ecologically related species (assemblages) but not directly affected by the fishing operation have been much more difficult to formulate than reference points for target species. Most attention

5 CONSTABLE: MANAGING EFFECTS OF FISHERIES ON FOOD WEBS 587 Figure 2. Schematic showing the primary food-web relationships between a fished species and other key species in the system (solid lines and plain symbols). The effects of the fishery on this ecosystem are shown with dashed lines and italicized symbols. Arrows indicate the direction of effect. Type of effect is indicated by the letters: C is a negative competitive effect on the species to which the arrow is pointing, P is a negative predatory effect, +? is a potential positive effect by the fishery on an inferior competitor of the fished species, TC+? is a potential positive effect arising from an apparent trophic cascade as a result of the fishery, and RC is potential resource competition between a fishery and predators of the fished species (following the schema of Fairweather, 1990, for biological interactions; after Constable, 2001). has been given to multispecies assemblages in which all species are exploited in some way (e.g., by May et al., 1979; Beddington and May 1982). Three types of objectives can be formulated: (i) maintaining biodiversity of the ecosystem, (ii) minimizing competition between fisheries and predators of target species, and (iii) maintaining productivity of predators of fished species. MAINTAINING BIODIVERSITY For ecological assemblages, attention seems to be focused on the maintenance of biodiversity and the potential consequences of biodiversity declines to the overall ecological function of those assemblages. In this case, field research is concentrated on identifying what gross changes in ecosystems occur as a result of human activities and theoretical models on understanding the implications of those changes. In most cases, studies focus on the extreme undesirable cases of change and the remedial action required to restore at least the main structural components the system, i.e., the focus is on conservation and restoration rather than prevention. These works identify species that require specific conservation measures because they are threatened with extinction. This approach may provide the last form of protection for a species, but in the ecosystem approach to fishing the very need for restorative actions would signal a failure in the management of those fisheries.

6 588 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004 MINIMIZING COMPETITION Two approaches to minimizing competition between fisheries and predators have been discussed in CCAMLR (reviewed by Constable, 2002). The first is to reduce the potential for interference with foraging of land-based predators by reducing overlap of predator foraging areas and fishing grounds. Although this step may reduce interference competition, it will not reduce consumption competition unless the predators feed on one population and the fishery exploits a different one. The second approach is to limit fishing to a level that does not alter the mean fitness of predators (Boyd, 2002) and will work only if maximum fitness of predators occurs well below the maximum availability of krill. The second approach provides an operational objective in general but may be harder to put into practice. For example, it will work only if the system is in equilibrium. As fitness will be density-dependent, the objective will have to be coupled with target levels for the magnitudes of the predator populations. Similarly, the field application of this approach would produce some years in which the total allowable catch is reduced to zero. MAINTAINING PRODUCTIVITY OF PREDATORS Three types of krill-fishery management procedures based on maintaining the productivity of predators that arises from the consumption of krill have been presented to the CCAMLR Scientific Committee. These are (1) maintenance of the median escapement from the fishery of the krill spawning stock at 75% of the median preexploitation level, (2) maintenance of abundance of predators at or above 50% of that prior to harvesting of the prey (Butterworth and Thomson, 1995; Thomson et al., 2000), and (3) maintenance of median annual predator productivity attributed to consumption of harvested species at or above 80% of its preexploitation level (Constable, 2001). These objectives recognize that fisheries affect marine food webs by removing production from the system. Objective 1 is a bottom-up target intended to provide for sufficient escapement of the fished species to provide for the requirements of the food web. In its current formulation, it provides a predictive approach to setting harvest controls. It is the current precautionary approach for krill. Objective 2 is a refined version of the first; it still draws on the precautionary approach but applies to individual predator species. Objective 3 is a top-down target intended to ensure that the productivity across all predators is maintained, but it does not specify how the individual species might respond. It is based on the direct linkage between fishing and predators of the fished species but recognizes that it may be difficult to manage the outcomes for individual predators, i.e., to develop a target status for the food web based on productivity arising from fished species (Constable, 2002). These objectives were developed with the acknowledgement that they are likely to need refining once the relationships between predators and prey become better understood and the requirements for maintaining food web function are better specified. THE PRECAUTIONARY APPROACH OF CCAMLR The precautionary approach of CCAMLR was developed in recognition of the uncertainties surrounding estimates of stock size, its relationship to preexploitation stock size, and the dependence of predictions of future status on the accuracy and precision of

7 CONSTABLE: MANAGING EFFECTS OF FISHERIES ON FOOD WEBS 589 Figure 3. Illustration of the uncertainties associated with stock assessments based on the dynamics of krill populations within plausible ranges of different population parameters (see Constable et al., 2003). (A) Two plausible stock trajectories with similar starting points but different rates of natural mortality (lines with cross marks, M = 0.55; plain lines, M = 0.59) and different recruitment time series (lower half of panel); trajectories in upper part of panel are for spawning biomass (dashed lines) and total biomass (solid lines); the diamond and circle show the respective estimates of total biomass used to estimate yield, and the diagonal and square crosses are the respective median preexploitation spawning biomasses, which are the reference levels. (B) 50 plausible trajectories. (C) Box plots showing the probability distributions for stock status over 20 yrs. the estimates of population demographic parameters and estimates of mortality arising from fishing. Figure 3 illustrates how these uncertainties are compounded to give many plausible scenarios of the time series of stock status. In the final analysis, the status of the stock in a given year, relative to preexploitation levels, cannot be indicated as a point estimate as in a survey but must be specified as a probability distribution that integrates across all the uncertainties. The precautionary approach, based on Article II of the convention, includes: (i) a reference point, agreed to be the median preexploitation spawning biomass, (ii) a target

8 590 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004 status, currently agreed to be the median spawning biomass at least one generation time after the start of fishing, and (iii) a threshold status, currently agreed to be 20% of the preexploitation median spawning biomass. All decisions are now in reference to the reference point, and the status of the stock is expected to vary naturally over time. The abundance of the stock in the year prior to exploitation is an inappropriate reference point because it does not take into account the status of the stock in that year relative to the median level. The ratio of the target status to the preexploitation median is set at 0.5 for species that are not considered to be substantial prey species in the food web. For important prey species, such as krill, this ratio is set at 0.75 to help ensure sufficient prey escape the fishery for consumption by predators. This target forms the basis of the predator criterion of the decision rule. If the spawning biomass of the fished species falls below the threshold status, then recruitment of new individuals to the stock will potentially be reduced substantially. This threshold forms the basis of the recruitment criterion of the decision rule. Decision rules have been formulated around these levels and take account of the probabilistic nature of the assessment process. Rational use has been interpreted to mean a constant long-term annual yield, so the aim of the assessment is to determine the longterm annual yield that will have a high likelihood of bringing the population to the target status and only a low likelihood of driving the population below the threshold status. DEVELOPING OPERATIONAL OBJECTIVES FOR FOOD WEBS A simplified food web based on the fished species at South Georgia (CCAMLR Statistical Subarea 48.3) is shown in Figure 4. In the case of the precautionary approach for krill, the predator criterion determines how much krill might need to escape the fishery to maintain predators such as seals, penguins, and whales. Maintaining the ecological relationships in the Antarctic ecosystem implies that the ecosystem should, by and large, be able to absorb the consequences of CCAMLR-regulated fishing without major changes in the strengths of natural interactions shown in Figure 3. Constable (2001) argues that a number of points are pertinent to determining a target status of the ecosystem. First, removal of fished species results in the removal of production from the system and therefore reduces the potential for production of higherorder predators and may therefore reduce populations of predators in the longer term. Second, the objectives of CCAMLR imply that biomass reductions of the magnitude considered appropriate in managing single-species fisheries, say to 50% of preexploitation levels, are likely to be inappropriate for such predators. Third, some predators of the fished species may need species-specific measures to assist recovery, e.g., the great whales. Given the predator criterion for determining catch limits of krill described above, a simple expectation would be that abundances of predators whose sole prey is krill would eventually be reduced by approximately 25%. This expectation would be met only if per capita productivity of krill and maximum per capita productivity of the predators remained unaltered at these new equilibria (see Mangel and Hofman, 1999). Only the productivity of predators attributed to krill consumption would be reduced by 25%, however, and most predators do not rely solely on krill. The consequences to the current abundance and overall productivity of predators and the structure of the food web generally are contingent on a number of factors (Constable

9 CONSTABLE: MANAGING EFFECTS OF FISHERIES ON FOOD WEBS 591 Figure 4. Structure of the food web around South Georgia Island in the Atlantic Ocean, including the fisheries for krill, Patagonian toothfish, and mackerel icefish. The dark grey boxes represent fished species, the light grey boxes predators of fished species, and the white boxes other types of prey, including mesopelagic (Mesopel.) and bathypelagic (Bathypel.) fish species and zooplankton (Zoopl.) (after Constable, 2001). 2001), including (i) the degree to which predators are obligate foragers on krill (i.e., abundance may not decline if the predator switches prey); (ii) the availability of other prey to replace the lost production of krill (i.e., if the predator switches prey, and the new prey species has used the surplus production left available by removal of the fished species, then no other alterations in the food web might arise); (iii) the degree to which a predator population can absorb the effects of a reduced food supply (i.e., a reduction in fished species might not cause a consequent reduction in reproductive success or increase in mortality because, prior to fishing, consumption exceeds the amount of food required to maintain critical population processes); (iv) whether an exploitable surplus of the fished species is present in the system; (v) the ability of a predator to compete for food with other predators and the fishery; and (vi) possible nonlinearity of the relationship between prey availability and predator production. In addition, the consequences to the food web generally will depend on the overall abundance of individual predators and their individual roles as consumers of and competitors with other species, which could lead to unexpected indirect feedbacks (positive and negative) to species of interest (Yodzis, 2000). The situation is made more complex when a number of prey species are being harvested. Combined, these factors potentially make the abundance and overall production (in number or biomass) of species relatively insensitive indicators of the effects of fishing on the ecosystem. Operational objectives for predators of fished species will need to encompass the general effect of lost production as well as ensuring that the lost production does not have an unacceptably large effect on any one predator, including the potential for flow-on effects in the food web. What, then, would be an operational objective for the ecosystem that encompassed the need to maintain ecological relationships and to allow for the recovery of some species? In the general case, the assumption is that the catch limits are derived with adequate con-

10 592 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004 fidence that the median annual production of predators arising from fished species will not be reduced by more than the expected reduction in median biomass of the fished species, although a higher target level may be necessary to produce the expected increased production of recovering species. Further, is some minimum level of production arising from fished species necessary to provide relative stability in or maintenance of ecological relationships and the food web? Constable (2001) combines these elements into an operational objective intended to maintain the production of predators arising from the consumption of fished species (an index, W) at or above some limit reference point. A subsidiary objective would be to ensure that the productivities of individual predator species are not disproportionately affected even though the overall objective is satisfied. The expected outcome of these objectives is that the contributions of different species to the food-web structure would remain largely unaltered by fishing and would therefore maintain ecological relationships in the system. This type of objective will result in attention to the primary interaction between fished species and their predators (Andrewartha and Birch, 1984) rather than examination of the consequences of secondary and other indirect interactions distant in the food web from the fished species. This approach recognizes the hierarchy of objectives related to the effects of fishing on the productivity of a system and the potential for changes to the food web. It can easily be made general for systems much more complex than the Antarctic and in which fisheries are already present. The important part of the assessment is to divide the system into a number of groups: (i) Fished species: All such species have presumably been assigned appropriate target levels or threshold reference points. They therefore form a contrived system in which managers could manipulate the abundance of each species in a variety of ways by varying the harvest strategy for each of the taxa. (ii) Obligate (dependent) predators of fished species: The effect of fishing on this group can be considered as a whole, i.e., as the effect of production lost from the system, or could be subdivided for exploration of the effects on individual species or groups of species. (iii) Prey of fished species and/or alternative prey of fished species: These taxa might assume greater importance in the diet of predators and/or might increase their productivity as a result of reductions in abundance of their predators and competitors. (iv) Predators of the nonfished prey species in the third group: The response of these predators would be difficult to predict without good knowledge of the function of the food web. AN ILLUSTRATIVE FOOD-WEB MODEL Constable (2001) developed a food-web model to illustrate this approach of evaluating the effects of lost production on predators of fished species and how this information could be combined to take account of the uncertainties in the dynamics of the food web. It is described in more detail by Constable (2001) but can be summarized here as a predator-prey-fisheries model in which the system is driven by the biomass of prey species. Each prey species is governed by a simple model of primary production; variations in the biomass of prey species are caused by a mortality rate (natural and fishing mortality) and stochastic variation in available primary production. Each of these parameters can be varied over time but is not influenced by the abundance of predators. A more

11 CONSTABLE: MANAGING EFFECTS OF FISHERIES ON FOOD WEBS 593 detailed population model for Antarctic krill, in which both recruitment and mortality are affected by variation in primary production and sea ice extent, is given by Constable et al. (2003). The fishery is modeled as using a constant annual catch. Each of the predators is modeled as an age-structured population with constant rate of natural mortality. Per capita recruitment is influenced by the annual carrying capacity, which is determined by the abundance of prey and moderated by the degree of competition with other predators. Competition is determined by the abundance of other predators weighted by the reliance on the same prey. The relationship between predators and prey is determined by relative effects rather than defining a functional feeding relationship. Biomass of predators is monitored by weight-at-age models. PREY DYNAMICS. The change in biomass is given by a density-dependent model, in which the biomass of the prey species and other competing prey species influence the per-unit-biomass recruitment to the population. The biomass, Bs s,y, of a prey species, s, at the beginning of a given year, y, is M F s, y s, y 1 s, y s s, y 1 Bs = Bs e + R (1) where M s is the natural mortality rate, F s,y 1 is the fishing mortality rate of the previous year required to yield the prescribed catch, and R' s,y is the recruitment biomass of the species in that year. Recruitment is Rs Bss, y 1Es, y 1; Es, y 1 > 0 Rs, y = 0 ; E s, y 1 0 (2) where R s is the maximum per-unit-biomass recruitment rate, and E s,y 1 is the densitydependent adjustment of the recruitment rate according to the status of the production environment and the magnitude of the prey populations relative to that status. It is estimated by E s, y 1 = 1 ns s = 1 c Ks Bs s, s s, y 1 s, y 1 (3) where c s,s' is the competition coefficients for each prey species and Ks s,y 1 is equivalent to the carrying capacity for the prey species in the given year. The competition coefficients are used to weight the biomass of all prey species for determination of the density-dependent adjustment to the per-unit-biomass recruitment; e.g., the subject prey species, s = s', would have c s' = 1. Other species will vary from 0 to 1. The state of the environment (carrying capacity) for the prey species, Ks s,y 1, varies each year. Its state is drawn at random from a log-normal distribution based on a specified mean, Ks s, and variance, σ 2, such that Ks

12 594 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, Ks = s, y Kss exp 1 η σ 2 Ks s (4) ( ), which is a normal distribution with zero 2 where η is drawn randomly from N 0;σ Ks s 2 mean and variance σ Kss. In this case, the carrying capacities of the prey species vary independently of one another. THE FISHERY. The fishing mortality of a target species in a given year, F s,y 1, depends on the population size of the target species and the magnitude of the constant annual yield, Y s. It is solved for by Newtonʼs method and the following function: ˆ s, y 1 s = M s + Fs, y 1 Y F B ( ( ) ) 1 M s + Fs, y 1 s, y 1 e If the stock is too small to support the catch level, then the value of F is constrained to 5 yr 1. PREDATOR MODEL. Each predator, p, is characterized by fully age-structured models with a plus class. In each year, the numbers, N p,a, at age a are advanced one year and discounted by natural mortality, M p,a, which is unrelated to the abundance of prey, such that (5) N p, a M p, a N p, a 1 e ; a < a = M p, a N N e ; a a ( + p, a 1 p, a ) = plus plus (6) Recruitment of age-0 individuals to the predator population is density dependent, such that the maximum per capita reproduction of individuals, r p, is moderated by the biomass of each of the predators, Bp p (number at age by weight at age, w p,a ), statistically weighted by the competition coefficient, C p,p', for each predator as described above for prey, and related to the abundance of prey available to the predator. The latter term is governed by the abundance of prey weighted by the selectivity, ps p,s, for that prey by the predator. It can also be adjusted by food value, pv p,s, if required. The degree of density dependence can be adjusted by means of the term, A p. The final recruitment is influenced by the natural mortality of new recruits in that first year, M p,0, and the number of mature adults in the population. Therefore, N p, 0 M aplus 0 p a p p > 0 a= amature p, rpe N, Ep ; Ep = 0 ; Ep p 0 (7) where

13 CONSTABLE: MANAGING EFFECTS OF FISHERIES ON FOOD WEBS 595 E p = 1 np p = 1 C Kp Bp p, p p p A p (8) and a plus Bpp = N p, awp, a a= 1 (9) ns Kp = ps pv Bs p, y p, s p, s s, y s= 1 (10) The parameter values used in this illustrative model are given in Table 2 and the results summarized in Figure 5 for three predators with different degrees of reliance on each of two prey species: Prey 1 fished and Prey 2 not fished. All species had density-dependent characteristics. Predator 1 was the focus of the model and depended most heavily on the fished prey species. The degrees of selectivity by Predator 1 on Prey 1 and 2 were 1.0 and 0.2 respectively. Predator 2 had equal selectivity on the two prey species, and Predator 3 depended mostly on Prey 2 (selectivities 0.2 and 1.0, respectively). Although the model can be used to simulate different life-history parameters, these model parameters are the same for all three predators. The only differences between predator species are the diet selectivity and the degree of competition between species. Predator 1 and Predator 2 are in greater competition with each other (0.5) than either is with Predator 3 (each 0.1), indicating the greater degree of overlap between those two species and the relative isolation of Predator 3 in the food web. The simulation is seeded with initial values for each of the five species and run for 500 yrs before a trial begins. The trial is run for 100 yrs; fishing begins in year 50. Fishing is characterized by a constant catch of 50 biomass units of Prey 1 each year. This approach is used because the Antarctic krill fishery is currently managed for a long-term constant annual yield (Constable et al., 2000). The yield was selected to deplete the stock in yrs, which is the critical period over which fishing should not be allowed to change the system irreversibly (Constable et al., 2000), so that various estimated parameters could be examined for their utility in precluding such an outcome. The fishery depletes Prey 1 to zero after 20 yrs (Fig. 5). The abundance of Prey 2 is three times that of Prey 1 in the absence of fishing, and its mean then increases over 10 yrs after the fishery begins as a result of the reduction in its competitor. As expected, the trajectory of Predator 3 is relatively insensitive to changes in the diet of Predator 1; it changes in direct response to changes in Prey 2. The time series of abundances for Predator 2 appears mostly influenced by that of Prey 2, although the decline of Prey 1 has slightly greater effect than it did on the trajectory of Predator 3. In the absence of information about Predator 3, however, Predator 2 could be construed to be unaffected by fishing. In this simple model, recruitment of new individuals is the only population parameter to vary each year as a result of changes in prey abundance, because all other parameters

14 596 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004 Table 2. Parameter values used in the illustrative food-web model described in the text (after Constable, 2001). Prey characteristics Prey 1 Prey 2 Annual carrying capacity of primary Mean 10,000 10,000 production CV Natural mortality (yr 1 ) Maximum per-unit-biomass recruitment Competition with the other prey species Predator characteristics Predator 1 Predator 2 Predator 3 Maximum per capita recruitment (age 0) Degree of density dependence in recruitment Age at maturity (yrs) Plus class Natural mortality (yr 1 ) Recruits Adults Body growth L [Length = L (1 exp( K.age))] K Length-to-weight conversion a (a.length b ) b Competition coefficients with Predator with Predator with Predator Selectivity of prey (Diet 1 given here) Prey Prey Food value of prey species Prey Prey are equal. Recruitment is therefore used as an index of annual production in this simulation. Per capita recruitment for each trial is shown in Figure 5C. Notably, Predators 2 and 3, and successive trials, differ little in per capita recruitment, indicating the relative insensitivity of this parameter, in this simulation, in species for which diet is mixed. In Figure 5D, for the illustrative model, W is compared to total production, both integrated over all predators. The results of this analysis indicated that a management procedure based on the per capita recruitment of Predator 1 may be equivalent to one based on W when Predator 1 depends mostly on the fished prey species. In this case, the time series of these parameters obviously declines after 10 yrs and before elimination of the fished prey, but a small change in the diet of Predator 1 that reduced its dependence on the fished prey could render per capita recruitment of Predator 1 relatively insensitive to the effects of fishing on Prey 1 until after its disappearance (Constable, 2001). The prey-switching scenario may be a realistic one and raises concerns about the utility of per capita recruitment as an index of food-web status. In addition, a population decline could result in an increase to preexploitation levels in per capita recruitment in the longer term, because of the interaction among numbers of predators, prey abundance, and per capita recruitment. Figure 5B,D shows that predators as a group may exhibit little or no overall change as a result of substantial overharvesting of prey species and that substantial declines in one or more predators as a result of prey harvesting may only be detected after irreversible

15 CONSTABLE: MANAGING EFFECTS OF FISHERIES ON FOOD WEBS 597 Figure 5. Trajectories of characteristics of a simulated food web of three predator species and two prey species over 100 yrs. (A) Biomass of Prey species 1 (solid line) and 2 (dashed line). Prey 1 is the fished species; 50 biomass units of it are removed each year if possible after Year 50. (B) Relative biomass of three predators with different feeding-selection patterns on Prey species 1 and Prey species 2 respectively: (Predator 1 (solid line), 1.0 and 0.2; Predator 2 (dashed line), 0.5 and 0.5; Predator 3 (dotted line), 0.2 and 1.0. (C) Per capita recruitment for each predator (lines as in panel B) and (D) productivity integrated for all predators with total production (dashed line) and production arising from consumption of the fished species, W (solid line) (summarized from Constable, 2001). change has occurred. Constable (2001) argues that parameters intended to reveal the effects on predators must be appropriately statistically weighted by the relative importance of fished species in predatorsʼ diets. This weighting is likely to yield more sensitive signals of the effects of fishing than are currently being used in monitoring programs. The initial evaluation presented here uses a one-way food-web model, in which the predators being monitored do not affect the mortality rate of the prey. This work must be extended to a full food-web model with two-way relationships, including functional feeding relationships between predators and prey. Nevertheless, the results illustrate the

16 598 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004 potential complexities involved in monitoring only a subset of the predator-prey system. MONITORING FOR EFFECTS OF FISHING IN LARGE-SCALE, DYNAMIC SYSTEMS The scales of interactions of different species with each other, with the environment, and with fisheries are an important issue in design of monitoring and assessment programs (see, e.g., Murphy et al., 1988). For fisheries, managers could monitor predators to determine whether harvesting affects their populations and to determine when harvest controls must be modified to conform to conservation objectives for predators. Such monitoring should take account of (i) the spatial and temporal relationships between predators and harvested species and (ii) the metapopulation structure and dynamics of the predators. The first will indicate how local consumption of harvested species on foraging grounds might be affected by harvesting. The second will reveal how any effect at the local scale might be manifested in the whole predator metapopulation, depending on the linkages between the local populations of that predator. In other words, the monitoring must reveal how a change in local consumption of harvested species leads to a change in one or more characteristics of the local population of predators (e.g., a landbased colony) at that time and then how such local effects might lead over time to an impact on the whole population through changes in survival, reproductive performance, or the movement of the predators (as offspring, immatures, or adults) among areas of local concentration. Importantly, it must determine what can be used to predict the timing and magnitude of population-wide and regional effects. The predators most accessible to monitoring are land-based species during their breeding season. An important question is whether accessible colonies of these predators are representative of the region, i.e., whether a change in one predator species at a given location at a given time represents the changes experienced by all species of predators at that time and whether that change is representative or indicative of the overall impact of the fishery over time on the populations of predators. Fishing is now commonly considered a large-scale experiment (Beddington and de la Mare, 1985; Ludwig et al., 1993; Mangel et al., 1996; Walters 1986). The principles for designing a monitoring program that tests adequately for the effects of an environmental perturbation, such as fishing, are well described in the literature (e.g., by Downes et al., 2002; Mangel et al., 1996). Two parts of the system can be manipulated for this purpose the location and timing of harvesting activities and the location and timing of monitoring. If the former cannot be controlled, then the monitoring program must have appropriate coverage across the whole of the harvesting unit and across all times to provide representative information on the effects of fishing. Otherwise, the effects of fishing may remain undetected. Ultimately the power of the program for management purposes will be dictated by the spatial and temporal relationships between the predator populations, the harvested prey population, the harvesting activities, and the monitoring activities, as well as the procedures that have been put in place for translation of the observed changes into management action.

17 CONSTABLE: MANAGING EFFECTS OF FISHERIES ON FOOD WEBS 599 SPATIAL CONFIGURATION OF MONITORING Regular monitoring of harvested species is conducted primarily at local scales (foraging areas) rather than over the range of a whole population and, at best, only annually. Similarly, regular monitoring of predators is primarily limited to the age groups of populations found breeding on land and is restricted to the breeding season. Local availability of a harvested prey species will be influenced by its population abundance and by the factors that influence the spatial patchiness of the population (e.g., for krill, Murphy et al., 1988; Miller and Hampton 1989; Hofmann et al., 1998). As a result, the density of a harvested species in a specified predator foraging area may or may not be directly correlated with population abundance. For predators, each colony is a single sampling replicate influenced by the local abundance of harvested prey species. Only monitoring of many replicate colonies randomly distributed throughout the range of the harvested prey population will provide representative estimates of the overall status of predators relative to the overall status of prey. The number of colonies needed will be dictated by the degree of homogeneity of prey density and the variability in measured parameters. Ideally, the sources of variation should be evaluated before full implementation of the monitoring program so that the number of monitoring sites chosen is sufficient for management purposes. Given the expense and difficulty of repeated sampling of the whole population of the harvested prey species, as well as the sampling of many predator colonies, satisfying the usual demands for representative sampling may not be possible. The resulting loss of power in the spatial design will have to be compensated for in the way the information is used in assessments encapsulated within a management procedure (see, e.g., Mapstone, 1995). MONITORING OVER TIME The main issue in design of a time series of sampling events is the magnitude of interannual variation that might arise (see Murphy et al., 1988, for review), including variation in abundance and/or productivity of harvested prey species; their availability on foraging grounds; feeding behavior of the predators, including prey switching (see, e.g., Agnew et al., 1998); and predator productivity (Constable, 2001). In addition, variation in the fishery might arise through varying dependence on particular fishing grounds coupled with varying relationships between fishing grounds and the predator foraging areas. Consequently, changes in predator performance from one year to the next may simply reflect this natural variation. A baseline period must therefore be established before, or at least early in, fishery development (de la Mare and Constable, 2000) so that changes in predator performance can be compared to this baseline as the fishery develops, revealing whether fishing for prey affects predators. The magnitude of change in predator performance that will trigger modification of harvest controls must also be determined. Careful attention to monitoring for trends in abundance of harvested species, such as long-term trends or cycles in abundance, may also be needed.

18 600 BULLETIN OF MARINE SCIENCE, VOL. 74, NO. 3, 2004 MONITORING TO DETECT EFFECTS OF FISHING A number of factors influence replicate observations of a predator parameter (indicator) at a given location (Constable, 2002; Table 3). For the purposes of detecting the effects of fishing, the value of the indicator is expected to correspond directly to the amount of the harvested species consumed by the predator. This value will be influenced by the degree to which the indicator is affected by such consumption (correlation with amount consumed) and the precision of the field method used to estimate the parameter. This correlation may also depend on the total quantity of food consumed and the composition of the diet, i.e., the quantity of other prey consumed. The amount consumed will be affected by the amount of harvested species available, the functional feeding relationship between the predator and availability of harvested prey species, and physical factors that might restrict foraging activities, such as the presence of sea ice in polar regions (see Constable et al., 2003). The local availability of a harvested prey species in a foraging area will be influenced by its regional abundance and by the factors that determine its patchiness, dynamics, and productivity, particularly in relation to the foraging area of interest. The regional abundance of a harvested species will be influenced by the general dynamics of the population and the influence of interannual variation in the physical environment, productivity, and natural mortality (Constable et al., 2003). Fishing for prey can affect a monitored predator in two ways. Fishing that takes place outside the foraging area affects the regional abundance of the harvested species (Butterworth and Thomson 1995; Thomson et al., 2000), producing a population-wide or global effect on prey (E a = a proportional change in overall density, Table 3). Fishing that occurs in the foraging area of the monitored predator is likely to produce a local effect, reduction of the availability of the harvested prey species (E f = proportional change in density, Table 3). This latter reduction would be expected to cause an observed change in the indicator in that season, as opposed to changes arising from the more distant largescale effects on the whole population of the harvested species, which might take some time to become manifest in the foraging area. These two effects large-scale (E a ) and small-scale (E f ) proportional reductions in harvested species density will potentially produce different signals in the monitoring program. If the fishery is random throughout the range of the harvested speciesʼ population, then E a and E f would differ little or not at all, and any predator colony could be monitored, but if harvesting is nonrandom, then clearly, E f will be greater in areas being harvested than in other areas that experience only the global effect of E a. In this case, it would be better to monitor predators in the areas in which fishing is occurring, because the effects in harvested areas will potentially be greater than those in nonharvested areas and more easily detected. Nonrandom harvesting is likely to provide more opportunities for discriminating between the effects of fishing and the effects of changes in other factors in the environment, because metapopulations of harvested species might cover large geographic areas. A randomly distributed fishery throughout the range of a krill population will affect all predator-monitoring sites equally and provide no means of studying the relative effects of fishing and the environment. This framework for local and population-wide effects provides a foundation for designing a monitoring program. If no areas or predators are to be disproportionately affected