- Email: [email protected]
Recent advances in the evaluation and implementation of harvest policies
Fisheries Research 94 (2008) 207–209
Contents lists available at ScienceDirect
Fisheries Research journal homepage: www.elsevier.com/locate/fishres
Foreword
Recent advances in the evaluation and implementation of harvest policies
The 16 papers that constitute this special issue make substantial contributions to the scholarship of harvest management, through review and synthesis of previous publications, simulation-based evaluation of management policies, including management procedures (MPs), or ways to estimate parameters used in control rules, and through presentation and discussion of important issues faced when harvest policies meet real fisheries. The assembled papers were derived from presentations at an American Fisheries Society symposium in 2007, earlier presentations at a Pacific Fishery Management Council workshop on harvest policy for the U.S. West Coast fisheries, and a widely distributed request for submissions. In this foreword, we (the guest editors for the special issue) seek commonalities among the contributions, emphasize what to us were interesting insights and trends, and suggest areas where additional work is needed. We do not attempt to summarize all the findings of the individual papers as readers can peruse the abstracts for that purpose. Instead, we emphasize connections among the papers and things for readers to think about as they consider the work on harvest policies in the special issue and evaluate other related work. Deroba and Bence reviewed existing literature that focused on the relative performance of different control rules. The most common single objective evaluated in studies that allowed comparison of control rules was maximization of yield, and a number of additional studies evaluated the related objective of maximizing profits. However, some studies also allowed comparison of control rules for risk of overexploitation or maintenance of biomass above a threshold, or the ability to maintain relatively constant catch, typically conditional on supporting a similar overall level of extraction. While a diversity of different performance measures were considered in the management policy evaluations presented in this issue (Cox and Kronlund; Dichmont et al.; Irwin et al.; Mapstone et al.; Nieland et al.; Punt et al.; Wilberg et al.), most fall into these same three general categories, and performance measures within a category were generally highly correlated. It is instructive that none of the policy evaluations were based on attempts to maximize a single utility function that combined multiple performance measures based on clearly articulated management objectives. We believe this is the reality of fishery management, where stakeholders need to see likely tradeoffs between factors they care about before a consensus can emerge on what tradeoff is an acceptable compromise. The days of simply managing to maximize yield or profits are gone, and considering tradeoffs among competing performance measures can be a challenge. However, the papers in this special issue suggest the task may be less daunting and of lower dimension than might be envisioned when lists of dozens of possi-
0165-7836/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.fishres.2008.09.029
ble performance measures are initially generated. Kolody et al. and Butterworth discussed the realities of working towards consensus, suggesting that MP evaluations can often produce agreement on an acceptable policy even when a priori fishery objectives are vaguely stated. Deroba and Bence reviewed only simple harvest policies whereby target fishing mortality could be expressed as a simple function of current estimated stock status. They argued that more complex approaches that attempted to optimize the sequence of catches are often impractical and unappealing to managers. In this special issue, several papers considered simple control rules of the type reviewed by Deroba and Bence (e.g., Cox and Kronlund; Dichmont et al.; Irwin et al.; Nieland et al.; Punt et al.; Wilberg et al.). Cox and Kronlund considered an empirical control rule that approximates a constant fishing rate policy, but with some smoothing of quotas. Dichmont et al. also considered more complex control rules, including one in which effort is set at each 2-year assessment–management cycle to the initial values for a longer time-series that is predicted to maximize economic yield. Intriguingly, this more complex rule performed well for a range of performance measures and produced less variable catches than simple rules conditioned to produce the same average spawning stock size. Although not noted by Deroba and Bence, one roadblock to optimizing complex rules is the apparent need to reach agreement a priori on what to optimize. Dichmont et al., however, showed how one might choose a quantity to optimize in defining a specific rule, but still consider multiple performance measures when the rule is evaluated. In addition to harvest policies that focus on regulating catch or effort, Mapstone et al. considered combinations of total fishery effort and area closures. Although most papers in this issue focused on catch and effort regulation, Mapstone et al. provided an example of area closure strategies that are of increasing interest to fishery managers. Most previous publications that allow comparison of control rules included very limited treatment of uncertainty (Deroba and Bence). Many assumed that a control rule could be developed assuming perfect knowledge of the underlying dynamic processes and that it could be applied with no uncertainty in stock assessment results or implementation. A number of past studies that appear to allow for uncertainty in stock assessment fixed the policy parameters for each control rule based on analysis assuming perfect information. We concur with Deroba and Bence that it is more appropriate to compare control rules allowing for uncertainty at each combination of policy parameters for each control rule. When this has been done for stock assessment uncertainty, the rela-
208
Foreword / Fisheries Research 94 (2008) 207–209
tive performance of different control rules changed, indicating that control rules and stock assessment uncertainty interact. Relatively little can be extracted from the literature about how other sources of uncertainty might influence the relative performance of control rules, but interactions between control rule performance and other sources of uncertainty certainly could not be ruled out. Punt et al. present results suggesting that control rule performance will interact with uncertainty about the steepness of the stock–recruitment relationship although Irwin et al. found that assessment error did not have much influence on the relative performance of control rules, a surprising result given the literature reviewed by Deroba and Bence. Irwin et al. speculated that this resulted from an interaction between assessment uncertainty and uncertainty about the stock–recruitment relationship. The results reviewed by Deroba and Bence, and presented by Punt et al. and Irwin et al., suggest that policy evaluations need to carefully consider and incorporate multiple sources of uncertainty. However, the observation of Punt et al. that many interactions between different uncertainties were small provides some comfort, as does the observation common to many of the papers in this issue that the relative performance of control rules was insensitive to a variety of uncertain assumptions, given that other uncertainties deemed most critical were incorporated or considered. The policy evaluations in the special issue generally incorporated a broader suite of uncertainties than were included in most of the studies reviewed by Deroba and Bence. These studies generally considered assessment and implementation error, and uncertainty in key parameters and relationships (especially the stock–recruitment relationship). Prior work and studies in the special issue allowed for process variation in recruitment, but several of the studies in the special issue also allowed for temporal variation in other processes such as growth (Irwin et al.; Wilberg et al.) and natural mortality (Nieland et al.). This inclusion of a wider range of uncertainties reflects the general trend in policy evaluations, most likely reflecting the increased awareness that accounting for uncertainty can influence how policies rank, and an increased capability (both computing and knowledge) for including such uncertainties. However, those attempting policy evaluations will continue to face the challenge of determining what uncertainties to include and in what way. There will always be limits to time, resources, and knowledge that preclude including all types of uncertainties in a fully realistic fashion. Thus some uncertainties are ignored or incorporated in a simplified fashion. For example, Punt et al., Irwin et al., Wilberg et al., and Nieland et al. all modeled assessment error as a simple autocorrelated process, whereas Cox and Kronlund and Dichmont et al. evaluated MPs that incorporated a detailed simulation of the stock assessment process (i.e., data were simulated at the time of each assessment, assessment models fit to the data, and control rules were applied based on the assessment results). Cox and Kronlund argued that incorporation of the actual stock assessment process is important given the complex nature of the assessment error time-series they observed. This is a topic deserving further study because when incorporated the simulated assessment process often dominates computing costs, and will generally limit the range of other factors that can be considered. More generally, Kolody et al. and the commentary by Butterworth provide different perspectives on the topic of deciding what critical uncertainties to include in an analysis. Kolody et al. argued that often fishery scientists will underestimate the magnitude of uncertainty, and what uncertainties get included in a MP evaluation is somewhat subjective. Butterworth suggested that the MP evaluation process is the best option available and questions whether chronic underestimation of uncertainty in such processes is a real problem. Kolody et al. suggested that there may be a small number of recurrent fishery problems that might be incorporated as
uncertainties in many MP evaluations, and called for a systematic review or meta-analysis on this topic. To this suggestion we note that the actual influence a “problem” has on the relative ranking of different policies needs to be considered when identifying them as problems. In this regard, there is much need for simulation studies addressing the general importance of different types of uncertainties in influencing the relative ranking of control rules and different policy parameters for a control rule, given the limited scope of past work (Deroba and Bence). Kolody et al. and Butterworth both suggested that a range of different control rules will allow for similar performance when the tradeoff among performance measures is considered. Kolody et al. frame this in terms of tuning policy parameters to obtain a particular result for one performance measure and then comparing results for other measures. Results presented by Cox and Kronlund and Dichmont et al. generally support this contention. On the other hand, both Punt et al. and Irwin et al. illustrate substantial differences among control rules when average fishing rates are high compared to the productivity of the population, and Dichmont et al. found marked differences amongst alternative MPs. The review by Deroba and Bence also showed that control rules can matter. While we agree with Kolody et al. and Butterworth that modest variations in control rules will often be less important than the objectives and performance measures used to judge outcomes, we caution that a conclusion that results are generally insensitive to the rules depends on the range of rules considered and how conservative management is. We note that most of the policy evaluations in the special issue considered a fairly narrow range of control rules. To the extent this reflects assumed knowledge about which control rules will work best, we suggest, based on the review of Deroba and Bence, that much remains unknown and consideration of a wider range of rules may be advisable. The policy evaluations in the special issue generally assumed that the needed stock status estimates and biomass reference points are available, and that the biomass reference points are well established. Haltuch et al. provided guidance both on what approach to use when estimating reference points and the likely uncertainty in resulting estimates. Brodziak et al. considered the case of rebuilding New England groundfish stocks, and given the absence of observations from periods of low fishing mortality presumed that the biomass reference point estimates were very uncertain. Brodziak et al. described an adaptive approach being used as part of the rebuilding plans for these stocks, an approach that allows for review and modification of rebuilding targets as additional information becomes available with respect to the reference points. We suspect that in general as new information becomes available, policy parameters will be updated (e.g., if additional information on natural mortality or stock–recruitment steepness is obtained). Most policy evaluations, however, treat the information on policy parameters as static, so one policy and associated parameters are used over lengthy time-horizons. We see some value of simulation testing of MPs that explicitly allow for policy parameters to be updated. This will become especially important if non-stationarity is allowed for in parameters that would influence relative policy performance, as it would be unrealistic to suppose that real assessment scientists and managers would be unable to detect all such changes or would not respond to them if detected. A number of papers in the special issue noted that model-based assessments and biomass reference point estimates are simply not available for many stocks (Cadrin and Pastoors; Dowling et al.; Prince et al.; Smith et al.). Cadrin and Pastoors emphasized the irony that management by biomass-based control rules was established at least partially to implement precautionary approaches to fishery management for U.S. federally managed marine fisheries and in the ICES system, yet it is commonly the case that stocks do not
Foreword / Fisheries Research 94 (2008) 207–209
have the information available to be managed in this way. Thus the stocks with the least information cannot be managed by the precautionary approach. Dowling et al. made a similar point for Australian fisheries. Cadrin and Pastoors, Dowling et al., and Smith et al. all advocated or described “tiered” approaches to the management of such fisheries, whereby the stock assessment process, the use of results in informing management actions, and the level of precaution is scaled to the amount of information that is available. Developing fisheries would only be able to expand in such datalimited systems if additional types of data and analysis became available. To our knowledge none of these kinds of management frameworks has been rigorously tested by simulation to verify that lower (more data poor) tiers are truly more conservative, although Smith et al. advocated such testing and indicated it had begun for Australia’s southeastern fisheries. We fully agree with the authors of these papers that fishery management frameworks need to be able to accommodate data-poor fisheries, and agree with Smith et al. that in principle such frameworks should be subjected to careful evaluation. At one level such evaluation could simply evaluate each management tier separately. More ambitiously, the process of transition from one tier to another could be considered. This appears to us to be an example of the situation described above whereby policy parameters are updated as additional information becomes available. While formal testing before implementation would be ideal, Smith et al. argued that a reasonable framework, and a flexible approach to improving it, is preferable to ad hoc management while one waits for the optimal framework to be derived. We support this approach given the difficulties of developing and implementing a MP testing framework, but caution that it should not lead to the MP testing process being delayed indefinitely. Both Mapstone et al. and Prince et al. described stock complexes or meta-populations, where it is simply not practical to conduct model-based assessments for each local subpopulation. Mapstone et al. considered management of the reef line fishery for common coral trout (Plectropomus leopardus) on the Great Barrier Reef, and described area-wide management of effort and percent area closed, with evaluation based on simulations of a meta-population. Prince et al. described a very different approach, whereby inexpensive and practical visual assessment methods are used to assess quickly what fraction of an abalone (Haliotis spp.) population is reaching reproductive maturity, and the use of this information in establishing management at the scale of an individual reef. These different solutions reflect the realities of different rates of mixing among the subpopulations and the practicality of individual reef assessment and management in the two systems. Both, however, illustrated that systematic management strategies can be developed in complex spatially structured populations, even when detailed model-based assessments are not practical at the smaller scale that resident local stocks interact with fisheries. Perhaps the most dominant theme across all the papers in this special issue is the importance of management-stakeholder
209
engagement during the development of harvest policies. Such engagement is described in the abstracts of Cox and Kronlund, Dowling et al., Irwin et al., Kolody et al., Mapstone et al., Prince et al., and Smith et al. There was virtually unanimous agreement among these authors that such engagement helps identify appropriate and acceptable policies or management strategies, performance measures to consider, and guides and improves model-development. Furthermore, involvement in the development of policies by a range of stakeholders was generally viewed as helping develop a common understanding and promoting later ability to agree on a policy. It was striking that even papers describing some difficulties in the implementation of harvest policies involving stakeholder interactions (Kolody et al.; Smith et al.) strongly advocated such early engagement. These papers did not merely applaud the involvement of stakeholders, they described processes by which this involvement occurred, and indicated what worked and what did not. Thus the papers in this special issue are a valuable source not just for technical information on policy evaluation, but also on practical issues related to developing policies that get implemented. Acknowledgements We are grateful to Antoinette van den Brakel, Journal Manager, and Alasdair McIntyre, Editor-In-Chief, for their assistance and guidance during the construction of this special issue. We are indebted to numerous reviewers whose recommendations led to substantially improved manuscripts for the special issue. We also thank the American Fisheries Society (AFS), the Marine Fisheries section of AFS, the American Institute of Fishery Research Biologists, the Pacific Fishery Management Council, and the U.S. National Marine Fisheries Service, for hosting or sponsoring previous symposia on harvest policies, which initiated many of the included papers. Guest Editors James R. Bence a,∗ Martin W. Dorn b Brian J. Irwin c André E. Punt d a Department of Fisheries and Wildlife, 13 Natural Resources Building, Michigan State University, East Lansing, MI 48824, USA b Alaska Fisheries Science Center, NMFS, NOAA 7600 Sand Point Way NE, Seattle, WA 98115, USA c Quantitative Fisheries Center, 153 Giltner Hall, Michigan State University, East Lansing, MI 48824, USA d School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195-5020, USA ∗ Corresponding
author. Tel.: +1 517 432 3812; fax: +1 517 432 1699. E-mail address: [email protected] (J.R. Bence)
Contents lists available at ScienceDirect
Fisheries Research journal homepage: www.elsevier.com/locate/fishres
Foreword
Recent advances in the evaluation and implementation of harvest policies
The 16 papers that constitute this special issue make substantial contributions to the scholarship of harvest management, through review and synthesis of previous publications, simulation-based evaluation of management policies, including management procedures (MPs), or ways to estimate parameters used in control rules, and through presentation and discussion of important issues faced when harvest policies meet real fisheries. The assembled papers were derived from presentations at an American Fisheries Society symposium in 2007, earlier presentations at a Pacific Fishery Management Council workshop on harvest policy for the U.S. West Coast fisheries, and a widely distributed request for submissions. In this foreword, we (the guest editors for the special issue) seek commonalities among the contributions, emphasize what to us were interesting insights and trends, and suggest areas where additional work is needed. We do not attempt to summarize all the findings of the individual papers as readers can peruse the abstracts for that purpose. Instead, we emphasize connections among the papers and things for readers to think about as they consider the work on harvest policies in the special issue and evaluate other related work. Deroba and Bence reviewed existing literature that focused on the relative performance of different control rules. The most common single objective evaluated in studies that allowed comparison of control rules was maximization of yield, and a number of additional studies evaluated the related objective of maximizing profits. However, some studies also allowed comparison of control rules for risk of overexploitation or maintenance of biomass above a threshold, or the ability to maintain relatively constant catch, typically conditional on supporting a similar overall level of extraction. While a diversity of different performance measures were considered in the management policy evaluations presented in this issue (Cox and Kronlund; Dichmont et al.; Irwin et al.; Mapstone et al.; Nieland et al.; Punt et al.; Wilberg et al.), most fall into these same three general categories, and performance measures within a category were generally highly correlated. It is instructive that none of the policy evaluations were based on attempts to maximize a single utility function that combined multiple performance measures based on clearly articulated management objectives. We believe this is the reality of fishery management, where stakeholders need to see likely tradeoffs between factors they care about before a consensus can emerge on what tradeoff is an acceptable compromise. The days of simply managing to maximize yield or profits are gone, and considering tradeoffs among competing performance measures can be a challenge. However, the papers in this special issue suggest the task may be less daunting and of lower dimension than might be envisioned when lists of dozens of possi-
0165-7836/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.fishres.2008.09.029
ble performance measures are initially generated. Kolody et al. and Butterworth discussed the realities of working towards consensus, suggesting that MP evaluations can often produce agreement on an acceptable policy even when a priori fishery objectives are vaguely stated. Deroba and Bence reviewed only simple harvest policies whereby target fishing mortality could be expressed as a simple function of current estimated stock status. They argued that more complex approaches that attempted to optimize the sequence of catches are often impractical and unappealing to managers. In this special issue, several papers considered simple control rules of the type reviewed by Deroba and Bence (e.g., Cox and Kronlund; Dichmont et al.; Irwin et al.; Nieland et al.; Punt et al.; Wilberg et al.). Cox and Kronlund considered an empirical control rule that approximates a constant fishing rate policy, but with some smoothing of quotas. Dichmont et al. also considered more complex control rules, including one in which effort is set at each 2-year assessment–management cycle to the initial values for a longer time-series that is predicted to maximize economic yield. Intriguingly, this more complex rule performed well for a range of performance measures and produced less variable catches than simple rules conditioned to produce the same average spawning stock size. Although not noted by Deroba and Bence, one roadblock to optimizing complex rules is the apparent need to reach agreement a priori on what to optimize. Dichmont et al., however, showed how one might choose a quantity to optimize in defining a specific rule, but still consider multiple performance measures when the rule is evaluated. In addition to harvest policies that focus on regulating catch or effort, Mapstone et al. considered combinations of total fishery effort and area closures. Although most papers in this issue focused on catch and effort regulation, Mapstone et al. provided an example of area closure strategies that are of increasing interest to fishery managers. Most previous publications that allow comparison of control rules included very limited treatment of uncertainty (Deroba and Bence). Many assumed that a control rule could be developed assuming perfect knowledge of the underlying dynamic processes and that it could be applied with no uncertainty in stock assessment results or implementation. A number of past studies that appear to allow for uncertainty in stock assessment fixed the policy parameters for each control rule based on analysis assuming perfect information. We concur with Deroba and Bence that it is more appropriate to compare control rules allowing for uncertainty at each combination of policy parameters for each control rule. When this has been done for stock assessment uncertainty, the rela-
208
Foreword / Fisheries Research 94 (2008) 207–209
tive performance of different control rules changed, indicating that control rules and stock assessment uncertainty interact. Relatively little can be extracted from the literature about how other sources of uncertainty might influence the relative performance of control rules, but interactions between control rule performance and other sources of uncertainty certainly could not be ruled out. Punt et al. present results suggesting that control rule performance will interact with uncertainty about the steepness of the stock–recruitment relationship although Irwin et al. found that assessment error did not have much influence on the relative performance of control rules, a surprising result given the literature reviewed by Deroba and Bence. Irwin et al. speculated that this resulted from an interaction between assessment uncertainty and uncertainty about the stock–recruitment relationship. The results reviewed by Deroba and Bence, and presented by Punt et al. and Irwin et al., suggest that policy evaluations need to carefully consider and incorporate multiple sources of uncertainty. However, the observation of Punt et al. that many interactions between different uncertainties were small provides some comfort, as does the observation common to many of the papers in this issue that the relative performance of control rules was insensitive to a variety of uncertain assumptions, given that other uncertainties deemed most critical were incorporated or considered. The policy evaluations in the special issue generally incorporated a broader suite of uncertainties than were included in most of the studies reviewed by Deroba and Bence. These studies generally considered assessment and implementation error, and uncertainty in key parameters and relationships (especially the stock–recruitment relationship). Prior work and studies in the special issue allowed for process variation in recruitment, but several of the studies in the special issue also allowed for temporal variation in other processes such as growth (Irwin et al.; Wilberg et al.) and natural mortality (Nieland et al.). This inclusion of a wider range of uncertainties reflects the general trend in policy evaluations, most likely reflecting the increased awareness that accounting for uncertainty can influence how policies rank, and an increased capability (both computing and knowledge) for including such uncertainties. However, those attempting policy evaluations will continue to face the challenge of determining what uncertainties to include and in what way. There will always be limits to time, resources, and knowledge that preclude including all types of uncertainties in a fully realistic fashion. Thus some uncertainties are ignored or incorporated in a simplified fashion. For example, Punt et al., Irwin et al., Wilberg et al., and Nieland et al. all modeled assessment error as a simple autocorrelated process, whereas Cox and Kronlund and Dichmont et al. evaluated MPs that incorporated a detailed simulation of the stock assessment process (i.e., data were simulated at the time of each assessment, assessment models fit to the data, and control rules were applied based on the assessment results). Cox and Kronlund argued that incorporation of the actual stock assessment process is important given the complex nature of the assessment error time-series they observed. This is a topic deserving further study because when incorporated the simulated assessment process often dominates computing costs, and will generally limit the range of other factors that can be considered. More generally, Kolody et al. and the commentary by Butterworth provide different perspectives on the topic of deciding what critical uncertainties to include in an analysis. Kolody et al. argued that often fishery scientists will underestimate the magnitude of uncertainty, and what uncertainties get included in a MP evaluation is somewhat subjective. Butterworth suggested that the MP evaluation process is the best option available and questions whether chronic underestimation of uncertainty in such processes is a real problem. Kolody et al. suggested that there may be a small number of recurrent fishery problems that might be incorporated as
uncertainties in many MP evaluations, and called for a systematic review or meta-analysis on this topic. To this suggestion we note that the actual influence a “problem” has on the relative ranking of different policies needs to be considered when identifying them as problems. In this regard, there is much need for simulation studies addressing the general importance of different types of uncertainties in influencing the relative ranking of control rules and different policy parameters for a control rule, given the limited scope of past work (Deroba and Bence). Kolody et al. and Butterworth both suggested that a range of different control rules will allow for similar performance when the tradeoff among performance measures is considered. Kolody et al. frame this in terms of tuning policy parameters to obtain a particular result for one performance measure and then comparing results for other measures. Results presented by Cox and Kronlund and Dichmont et al. generally support this contention. On the other hand, both Punt et al. and Irwin et al. illustrate substantial differences among control rules when average fishing rates are high compared to the productivity of the population, and Dichmont et al. found marked differences amongst alternative MPs. The review by Deroba and Bence also showed that control rules can matter. While we agree with Kolody et al. and Butterworth that modest variations in control rules will often be less important than the objectives and performance measures used to judge outcomes, we caution that a conclusion that results are generally insensitive to the rules depends on the range of rules considered and how conservative management is. We note that most of the policy evaluations in the special issue considered a fairly narrow range of control rules. To the extent this reflects assumed knowledge about which control rules will work best, we suggest, based on the review of Deroba and Bence, that much remains unknown and consideration of a wider range of rules may be advisable. The policy evaluations in the special issue generally assumed that the needed stock status estimates and biomass reference points are available, and that the biomass reference points are well established. Haltuch et al. provided guidance both on what approach to use when estimating reference points and the likely uncertainty in resulting estimates. Brodziak et al. considered the case of rebuilding New England groundfish stocks, and given the absence of observations from periods of low fishing mortality presumed that the biomass reference point estimates were very uncertain. Brodziak et al. described an adaptive approach being used as part of the rebuilding plans for these stocks, an approach that allows for review and modification of rebuilding targets as additional information becomes available with respect to the reference points. We suspect that in general as new information becomes available, policy parameters will be updated (e.g., if additional information on natural mortality or stock–recruitment steepness is obtained). Most policy evaluations, however, treat the information on policy parameters as static, so one policy and associated parameters are used over lengthy time-horizons. We see some value of simulation testing of MPs that explicitly allow for policy parameters to be updated. This will become especially important if non-stationarity is allowed for in parameters that would influence relative policy performance, as it would be unrealistic to suppose that real assessment scientists and managers would be unable to detect all such changes or would not respond to them if detected. A number of papers in the special issue noted that model-based assessments and biomass reference point estimates are simply not available for many stocks (Cadrin and Pastoors; Dowling et al.; Prince et al.; Smith et al.). Cadrin and Pastoors emphasized the irony that management by biomass-based control rules was established at least partially to implement precautionary approaches to fishery management for U.S. federally managed marine fisheries and in the ICES system, yet it is commonly the case that stocks do not
Foreword / Fisheries Research 94 (2008) 207–209
have the information available to be managed in this way. Thus the stocks with the least information cannot be managed by the precautionary approach. Dowling et al. made a similar point for Australian fisheries. Cadrin and Pastoors, Dowling et al., and Smith et al. all advocated or described “tiered” approaches to the management of such fisheries, whereby the stock assessment process, the use of results in informing management actions, and the level of precaution is scaled to the amount of information that is available. Developing fisheries would only be able to expand in such datalimited systems if additional types of data and analysis became available. To our knowledge none of these kinds of management frameworks has been rigorously tested by simulation to verify that lower (more data poor) tiers are truly more conservative, although Smith et al. advocated such testing and indicated it had begun for Australia’s southeastern fisheries. We fully agree with the authors of these papers that fishery management frameworks need to be able to accommodate data-poor fisheries, and agree with Smith et al. that in principle such frameworks should be subjected to careful evaluation. At one level such evaluation could simply evaluate each management tier separately. More ambitiously, the process of transition from one tier to another could be considered. This appears to us to be an example of the situation described above whereby policy parameters are updated as additional information becomes available. While formal testing before implementation would be ideal, Smith et al. argued that a reasonable framework, and a flexible approach to improving it, is preferable to ad hoc management while one waits for the optimal framework to be derived. We support this approach given the difficulties of developing and implementing a MP testing framework, but caution that it should not lead to the MP testing process being delayed indefinitely. Both Mapstone et al. and Prince et al. described stock complexes or meta-populations, where it is simply not practical to conduct model-based assessments for each local subpopulation. Mapstone et al. considered management of the reef line fishery for common coral trout (Plectropomus leopardus) on the Great Barrier Reef, and described area-wide management of effort and percent area closed, with evaluation based on simulations of a meta-population. Prince et al. described a very different approach, whereby inexpensive and practical visual assessment methods are used to assess quickly what fraction of an abalone (Haliotis spp.) population is reaching reproductive maturity, and the use of this information in establishing management at the scale of an individual reef. These different solutions reflect the realities of different rates of mixing among the subpopulations and the practicality of individual reef assessment and management in the two systems. Both, however, illustrated that systematic management strategies can be developed in complex spatially structured populations, even when detailed model-based assessments are not practical at the smaller scale that resident local stocks interact with fisheries. Perhaps the most dominant theme across all the papers in this special issue is the importance of management-stakeholder
209
engagement during the development of harvest policies. Such engagement is described in the abstracts of Cox and Kronlund, Dowling et al., Irwin et al., Kolody et al., Mapstone et al., Prince et al., and Smith et al. There was virtually unanimous agreement among these authors that such engagement helps identify appropriate and acceptable policies or management strategies, performance measures to consider, and guides and improves model-development. Furthermore, involvement in the development of policies by a range of stakeholders was generally viewed as helping develop a common understanding and promoting later ability to agree on a policy. It was striking that even papers describing some difficulties in the implementation of harvest policies involving stakeholder interactions (Kolody et al.; Smith et al.) strongly advocated such early engagement. These papers did not merely applaud the involvement of stakeholders, they described processes by which this involvement occurred, and indicated what worked and what did not. Thus the papers in this special issue are a valuable source not just for technical information on policy evaluation, but also on practical issues related to developing policies that get implemented. Acknowledgements We are grateful to Antoinette van den Brakel, Journal Manager, and Alasdair McIntyre, Editor-In-Chief, for their assistance and guidance during the construction of this special issue. We are indebted to numerous reviewers whose recommendations led to substantially improved manuscripts for the special issue. We also thank the American Fisheries Society (AFS), the Marine Fisheries section of AFS, the American Institute of Fishery Research Biologists, the Pacific Fishery Management Council, and the U.S. National Marine Fisheries Service, for hosting or sponsoring previous symposia on harvest policies, which initiated many of the included papers. Guest Editors James R. Bence a,∗ Martin W. Dorn b Brian J. Irwin c André E. Punt d a Department of Fisheries and Wildlife, 13 Natural Resources Building, Michigan State University, East Lansing, MI 48824, USA b Alaska Fisheries Science Center, NMFS, NOAA 7600 Sand Point Way NE, Seattle, WA 98115, USA c Quantitative Fisheries Center, 153 Giltner Hall, Michigan State University, East Lansing, MI 48824, USA d School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195-5020, USA ∗ Corresponding
author. Tel.: +1 517 432 3812; fax: +1 517 432 1699. E-mail address: [email protected] (J.R. Bence)