NOAA Technical Memorandum NMFS-AFSC-154
 

Economic Analysis of Protection 

of Essential Fish Habitat 

in Alaskan Fisheries: 

An Analysis of Research Needs 

by 

D. S. Holland

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration 

National Marine Fisheries Service 

Alaska Fisheries Science Center 

August 2005 
NOAA Technical Memorandum NMFS 

The National Marine Fisheries Service's Alaska Fisheries Science Center 

uses the NOAA Technical Memorandum series to issue informal scientific and 

technical publications when complete formal review and editorial processing 

are not appropriate or feasible. Documents within this series reflect sound 

professional work and may be referenced in the formal scientific and technical 

literature. 

The NMFS-AFSC Technical Memorandum series of the Alaska Fisheries 

Science Center continues the NMFS-F/NWC series established in 1970 by the 

Northwest Fisheries Center. The NMFS-NWFSC series is currently used by 

the Northwest Fisheries Science Center. 

This document should be cited as follows: 

Holland, D. S. 2005. Economic analysis of protection of essential fish 

habitat in Alaskan fisheries: An analysis of research needs. U.S. Dep. 

Commer., NOAA Tech. Memo. NMFS-AFSC-154, 39 p. 

Reference in this document to trade names does not imply endorsement by 

the National Marine Fisheries Service, NOAA. 
NOAA Technical Memorandum NMFS-AFSC-154
 

Economic Analysis of Protection 

of Essential Fish Habitat 

in Alaskan Fisheries: 

An Analysis of Research Needs 

by 

D. S. Holland

Alaska Fisheries Science Center 

7600 Sand Point Way N.E. 

Seattle, WA 98115 

www.afsc.noaa.gov 

Current address: 

Gulf of Maine Research Institute 

350 Commercial Street 

Portland, ME 04101 

U.S. DEPARTMENT OF COMMERCE

Carlos M. Gutierrez, Secretary 

National Oceanic and Atmospheric Administration 

Vice Admiral Conrad C. Lautenbacher, Jr., U.S. Navy (ret.), Under Secretary and Administrator 

National Marine Fisheries Service 

William T. Hogarth, Assistant Administrator for Fisheries 

August 2005 
This document is available to the public through: 

National Technical Information Service 

U.S. Department of Commerce 

5285 Port Royal Road 

Springfield, VA 22161 

www.ntis.gov 
Notice to Users of this Document 

This document is being made available in .PDF format for the convenience of users; however, the 

accuracy and correctness of the document can only be certified as was presented in the original hard 

copy format. 

Abstract 

This report identifies the types of information necessary to evaluate the economic impacts of 

Essential Fish Habitat (EFH) protection and the gaps in the existing knowledge and research 

methodologies necessary for this evaluation. A discussion of the basic elements of a benefit-cost 

analysis or a cost-effectiveness analysis for essential fish habitat protective measures (EFHPM) is 

used to identify the types of benefits, both use and nonuse benefits, which must be quantified and 

monetized if they are to be used in a formal benefit-cost analysis. There is also a discussion of the 

potential costs of EFHPM and how they might be measured. This discussion identified large gaps 

in knowledge and in valuation methodologies. On the basis of the relative importance of these 

knowledge gaps and the feasibility of filling them with new research, a number of potentially 

productive areas of research are identified. This research would improve our ability to more 

optimally balance habitat protection against costly constraints on fishing. The research areas 

identified include random utility modeling of location choice, bioeconomic modeling to estimate 

fishery benefits of habitat protection, non-market valuation of habitat protection programs 

perhaps paired with research on benefit transfer techniques, and market based systems to costeffectively achieve specified habitat protection objectives. 

iii

Contents

Abstract.........................................................................................................iii 

Introduction ......................................................................................................................................1 

Background on Essential Fish Habitat .............................................................................................2 

Economic Analysis of EFH Protection ............................................................................................5 

Benefit-cost Analysis....................................................................................................................5 

Cost-effectiveness Analysis........................................................................................................10 

EFH, Risk and the Precautionary Principle ................................................................................10 

Economic Research Topics Relevant for Evaluation 

 of EFHPM in the Federal Fisheries off Alaska ............................................................................13 

Estimating Costs of EFH Protection ...........................................................................................14 

Estimating Fishery Benefits of EFH Protection..........................................................................18 

Nonfishery Benefits and Benefit Transfer ..................................................................................23 

Market-based Instruments and Property Rights Approaches to EFH Protection........................24 

Conclusions and Recommendations...............................................................................................28 

Citations..........................................................................................................................................29 

Appendix: Fishery Impacts of Marine Reserves and Marine Protected Areas ..............................35 

v

Introduction

The objectives of this report are to identify the types of information necessary to evaluate the 

economic impacts of Essential Fish Habitat (EFH) protection, identify gaps in the knowledge and 

research methodologies necessary for this evaluation, and discuss research topics relating to the 

economic evaluation of EFH protection. The report makes recommendations of projects that are 

likely to be useful and feasible given informational and budget constraints. 

The report also reviews the economic literature that pertains to EFH and provides a discussion of 

the reasons for designating EFH and the issues associated with evaluating the economic impacts 

of EFH protection. Although there are a variety of non-fishing actions that may affect habitat, 

such as nutrient releases, recreation, shipping, introduction of exotic species and wetland 

conversion, the focus of this report is on EFH protection measures targeted at modifying fishing 

practices. This includes spatial controls on fishing, gear restrictions, and other measures that 

regulate the activities of fishers with the intent of providing protection to EFH. 

The rest of the report is organized into four major sections. The first section contains a general 

discussion of EFH policy, legislation and regulations. This section discusses the motivation and 

intent of the legislation and the general issues associated with economic evaluation of fishery 

management measures that might be implemented to protect EFH, henceforth referred to as EFH 

protection measures (EFHPM). Here it is considered that EFHPM encompasses a broad range of 

possible measures that may be employed to protect fishery habitat and the ecosystem of which 

fisheries are a part. These include but are not limited to marine reserves, defined as areas where 

no extractive activities are allowed, marine protected areas (MPAs) that put spatial restrictions on

certain activities such as fishing for particular species or using particular gears, and general 

restrictions on use of particular fishing practices and gears. They may also include economic 

incentive or property rights based strategies designed to afford protection to EFH. The second 

section provides a discussion of the general approaches and methodologies that might be applied 

to evaluate the economic impacts of EFHPM. These include benefit-cost analysis, costeffectiveness analysis and economic impact analysis. We also include a brief discussion of issues 

associated with risk and uncertainty. This section also identifies and categorizes the potential 

benefits and costs of EFHPM and briefly discusses the methodologies appropriate for evaluating 

them. Section 4 continues with a discussion of specific methodologies for evaluating certain 

categories of costs and benefits of EFHPM and reviews some applications of these 

methodologies. Section 4 also proposes a number of potentially useful research efforts related to 

economic evaluation of EFHPM. The final section provides a summary of findings and 

recommendations. An appendix reviewing literature on marine reserves and MPAs is included at 

the end of the report. While not all of this literature is directly applicable to EFHPM, it is likely 

that EFHPM will rely heavily on these types of measures and this literature is instructive since 

the arguments for and against EFHPM are likely to bring forth many of the same arguments 

brought to bear in the debate over marine reserves and MPAs. 
Background on Essential Fish Habitat 

In 1996, the U.S. Congress added new habitat conservation provisions to the Magnuson-Stevens 

Fishery Conservation and Management Act (MSFCMA). These changes came in response to 

concerns that habitat loss was threatening the viability of many of the Nation’s fisheries. The 

findings section of the MSFCMA states “One of the greatest long-term threats to the viability of 

commercial and recreational fisheries is the continuing loss of marine, estuarine, and other 

aquatic habitats. Habitat considerations should receive increased attention for the conservation 

and management of fishery resources in the United States.” 

The MSFCMA directs the National Marine Fisheries Service (NMFS) and the eight regional 

fishery management councils, under authority of the Secretary of Commerce to identify and 

describe EFH in each fishery management plan, minimize to the extent practical the adverse 

effects of fishing on EFH, and identify other actions to encourage the conservation and 

enhancement of EFH. Section 303(a)(7) of the MSFMCA requires that fishery management 

councils, to the extent practical, shall minimize adverse effects on essential fish habitat (EFH) 

caused by fishing. In addition, 50 CFR, part 600.815 (a)(3) requires councils to prevent, mitigate, 

or minimize adverse effects from fishing, to the extent practical, if there is any evidence 

indicating that a particular fishing practice is having an identifiable adverse effect on EFH. 

Section 600.815 (a)(4) permits a range of management options to protect EFH including fishing 

equipment restrictions, time and area closures, and harvest limits. 

Essential Fish Habitat is defined as “those waters and substrate necessary to fish for spawning, 

breeding, feeding, or growth to maturity (16 U.S.C. 1802(10)). “Waters” include all aquatic areas 

and associated physical, chemical, and biological properties that are used by fish. Areas 

historically important to fish may also be included. “Substrate” includes sediment, hard bottom,

structures underlying the waters, and associated biological communities. The NMFS defines 

“necessary” to mean the habitat required to support a sustainable fishery and the managed 

species’ contribution to a healthy ecosystem. 

The EFH provisions of the MSFCMA address a relatively broad concept of habitat centered on its 

role in supporting a healthy marine ecosystem. Rosenberg et al. (2000) discuss the intent of EFH 

legislation as it relates to ecosystem health. They note that EFH completes the foundation for an 

ecosystem approach to marine fisheries that is vital to the efforts of NOAA/NMFS to improve 

the health of fish stocks, ensure the long-term sustainability of fisheries and fishing communities, 

and develop a more comprehensive approach to fisheries management. This interrelationship 

between EFH and marine ecosystems is discussed in some detail in the final rule on EFH 

promulgated by NMFS (2002) which states: 

Councils should strive to understand the ecological roles (e.g., prey, competitors, trophic 

links within food webs, nutrient transfer between ecosystems, etc.) played by managed 

species within their ecosystems. They should protect, conserve, and enhance adequate 

quantities of EFH to support a fish population that is capable of fulfilling all of those other 

contributions that the managed species makes to maintaining a healthy ecosystem as well 

as supporting a sustainable fishery......Ecological relationships among species and 

2
between the species and their habitat require, where possible, that an ecosystem approach 

be used in determining the EFH of a managed species or species assemblage. The extent 

of the EFH should be based on the judgment of the Secretary and the appropriate 

Council(s) regarding the quantity and quality of habitat that is necessary to maintain a 

sustainable fishery and the managed species' contribution to a healthy 

ecosystem......Healthy ecosystem means an ecosystem where ecological productive 

capacity is maintained, diversity of the flora and fauna is preserved, and the ecosystem 

retains the ability to regulate itself. Such an ecosystem should be similar to comparable, 

undisturbed ecosystems with regard to standing crop, productivity, nutrient dynamics, 

trophic structure, species richness, stability, resilience, contamination levels, and the 

frequency of diseased organisms. 

Rosenberg et al. (2000) argue that to achieve the goals of the congressional EFH mandate, the 

regional fishery management councils and NMFS must consider a greater diversity of fisherymanagement tools, including MPAs, marine zoning, and marine reserves. They note, however, 

that NMFS and the councils should learn how to minimize threats without imposing undue 

hardships on recreational and commercial fisheries (e.g., by modifying fishing practices or gears 

rather than banning fishing). They state that “the legal mandates and the agency’s precautionary 

approach to resource management mean we must proceed without delay with reasonable 

conservation measures for fish habitat.” 

Rosenberg et al. (2000) note, however, that “for habitat conservation and enhancement to 

contribute fully to fishery management, we must continue to sharpen our policymaking and to 

improve the scientific basis for decisions. It is not sufficient simply to provide general protection 

measures because adverse impacts to our nation’s fisheries may occur.” The language of the 

MSFCMA and the EFH rules promulgated by NMFS suggest that EFH protection is required to 

the extent that the lack of this protection puts the sustainability of the fishery at risk, but does not 

explicitly require managers to attempt to design EFH protections to optimize either fishery 

productivity or fishery profits. Councils are instructed to “consider the nature and extent of the 

adverse effect on EFH and the long and short-term costs and benefits of potential management 

measures to EFH, associated fisheries, and the nation, consistent with National Standard 7” (50 

CFR §600.815). Councils are not, however, required to perform a formal benefit-cost analysis. 

Nevertheless, the protection of EFH must be considered within the context of the rest of the 

MSFCMA, which requires consistency with the several national standards. From an economic 

standpoint, the most important standard is National Standard 1 which states that “Conservation 

and management measures shall prevent overfishing while achieving, on a continuing basis, the 

optimum yield from each fishery for the United States fishing industry.” “Optimum”, with respect 

to the yield from a fishery, is defined in the law as the amount of fish which: 

 (A) will provide the greatest overall benefit to the Nation, particularly with respect to food 

production and recreational opportunities, and taking into account the protection of marine 

ecosystems; 

(B) is prescribed as such on the basis of the maximum sustainable yield from the fishery, as 

reduced by any relevant economic, social, or ecological factor; and 

3
(C) in the case of an overfished fishery, provides for rebuilding to a level consistent with 

producing the maximum sustainable yield in such fishery. 

In order to be in compliance with the MSFCMA, actions taken to protect EFH must consider the 

corresponding benefits and costs of these actions. Unfortunately, the determination of the net 

economic benefits from conserving and protecting EFH is likely to be extremely complicated. In 

the first place there may be considerable uncertainty as to what effect protective actions will 

actually have on habitat and, in the case of habitat rehabilitation, how long recovery will take. 

Even if the physical impacts on habitat are perfectly understood, valuing those impacts remains 

difficult. It is not generally adequate or appropriate to directly value the features of habitat. 

Although protection of specific components of the habitat (e.g., marine plants and animals such 

as corals and sponges) may generate non-use values which might be estimated with non-market 

valuation techniques, these benefits are arguably not the primary focus of the EFH provisions of 

the MSFCMA1

. The benefits from habitat protection in the Alaska EEZ are likely to be primarily 

indirect benefits resulting from increases in the productivity of fisheries, reduction of variability 

in production, or reduction of risk of fishery depletion or collapse. 

To the extent that EFH protections eventually result in increased catches (or prevent decreases in 

catches) they may increase both consumer surplus and producer surplus. The effect on consumer 

surplus will depend on both changes in landings and the elasticity of demand, and may depend on 

changes in product mix that could results from EFHPM. The effects on producer surplus are even 

less clear since EFHPM are likely to affect both revenues and costs. Assuming relatively elastic 

demand, changes in revenues might be closely correlated with changes in total catches. However, 

factors other than total catch may influence prices either positively or negatively. For example, 

changes in the average sizes of fish might occur, and variation in price-based on fish size is 

common. For fisheries where roe or milt contributes a significant proportion of value, EFH 

measures that affect the proportion of catch taken around spawning will clearly have an impact on 

total revenues. Even if EFH protections increase revenues in the long run, short-run impacts on 

revenues are quite likely to be negative. In fisheries constrained by limits on Total Allowable 

Catches (TAC), the EFH protections may have no impact on total catches unless TACs are 

reduced, but for other fisheries where TACs are not constraining, EFH protections may lead to 

reduced catches if they increase harvest costs. There are a number of species in the North Pacific 

for which TACs are not constraining, so the possibility that EFH protections might reduce 

catches is real. To the extent that EFH protections close areas to fishing or restrict certain fishing 

practices and gears, they are likely to increase harvest costs. These increases may be offset in the 

long run if EFH increases the biomass of fish stocks thereby increasing fish availability. This is 

more likely for fisheries that had been overfished or where habitat damage was having substantial 

impacts on fishery productivity. But even where increases in productivity occur they will not 

necessarily compensate for increased costs. 

The discussion above makes clear that the economic impacts of measures designed to protect 

EFH are likely to be complex and highly uncertain. A full understanding of these impacts 

requires knowledge of the effects of fishing practices and gears on marine habitat as well as an 

understanding of the degree to which and the speed at which habitat will recover from previous 

damage. The linkages between habitat quality and fishery productivity and stability must also be 

 1

 Other legislation, the Endangered Species Act in particular, may require protection of particular animals or plants

but generally does not require a balancing of the costs and benefits of doing so. 

4
known. Next, changes in fishery productivity as well as qualitative changes in fish catches must 

be translated into changes in revenues and consumer surplus. The effects of regulatory measures 

on harvest costs must be also factored in. Finally, it is important to consider the relative timing of

cost and benefit streams. Benefits from EFH protection are likely to be realized only after several 

years while increases in costs and decreases in catches may be immediate. Thus the discount rate 

that is used to compare costs or benefits incurred at different times together with predictions of 

when those costs and benefits will be incurred can affect the benefit-cost analysis dramatically. 

Resolving uncertainty in the time path of outcomes may be as important as understanding 

equilibrium2

 outcomes. 

Economic Analysis of EFH Protection

King et al. (2002) provide a good general discussion of issues associated with economic analysis 

of marine habitat degradation. They discuss the three analytical tools most widely used by 

economists: 1) Benefit-Cost Analysis – a tool that provides a quantitative method for comparing 

the economic benefits and costs associated with a specific project.; 2) Cost-effectiveness Analysis 

– a technique used to rank the project alternatives that achieve a similar or predetermined 

outcome; and 3) Economic Impact Analysis – an approach that allows one to assess how the 

direct impact of expenditures of a project will ripple through the local economy and affect 

regional employment and income. In the discussion below we focus primarily on various aspects 

of first two of these general methodologies and on the tools and analytical techniques required to 

determine the net economic effect of EFHPM on the value of fisheries. EFH policies are likely to 

have varying economic impacts on regions and communities that will be of interest to

policymakers, but the standard tools used for economic impact analysis should be applicable to 

evaluation of EFH without substantial modification once the direct impact on fisheries is 

understood3

. Cost-benefit analysis and cost-effectiveness analyses of EFH policies, in contrast, 

are likely to require new purpose-built analytical tools such as models that can predict the 

biological and economic effects of spatial management measures (e.g., area closures and spatial 

gear restrictions). It is these tools that we focus on in this report. 

Benefit-cost Analysis

An economic evaluation of regulatory actions to protect EFH should go beyond the identification 

of various benefits and costs that may result from their implementation. As Farrow (1996) notes 

in his discussion of benefit-cost analysis for marine reserves, “what is important is the process 

that draws all impacts together in a summary analysis and the delivery of a bottom line result.” 

To do this, benefits and costs must generally be quantified and expressed in monetary terms so 

that they can be compared. We can not compare, in a benefit-cost framework, “biological 

benefits” to “economic costs” without a common metric such as money. 

 2

 Not least because marine fisheries and the ecosystems that support them are constantly subject to a variety of 

exogenous shocks so that they are never really in equilibrium. 3 That is not to say that the models (e.g,. IMPLAN) used for this analysis could not be improved to give 

more accurate predictions of impacts on communities, but that work is ongoing and not specifically related 

to EFH protection.

5
In general, a benefit-cost analysis should attempt to determine whether the proposed management 

measure provides greater net benefits than some alternative policy. The basis for comparison 

might be the status quo or might include alternative policies designed to achieve similar 

objectives. Quantification and valuation of costs and benefits of EFHPM such as area closures are

likely to be difficult and imprecise due to uncertainty about the direct and indirect impacts of a 

closure and of what would happen in the absence of one. However, the difficulty of this task 

should not be used as an excuse to avoid it completely. 

A complete economic evaluation of an EFHPM should also explore the probable distribution of 

benefits and costs. Even if a particular measure does have positive net benefits overall, it is likely 

to disadvantage some individuals and groups. Compensation of losers in some form may be 

necessary or advisable to ensure the acceptance and success of the measure. Measures perceived 

as unfairly imposed are likely to require higher compliance costs and may generate political 

action that will block their implementation or lead to their failure or repeal. 

EFH protections are likely to generate costs immediately while benefits may not be realized for 

some time. There is an extensive literature on marine reserves and MPAs (see Appendix) that 

supports this contention. The delay in benefits versus costs should be explicitly considered when 

undertaking a benefit-cost analysis. The standard procedure is to compare the present value of all 

cost and benefit streams, which requires discounting future costs and benefits by the appropriate 

discount rate. There is often disagreement about what the appropriate discount rate should be, and 

there is no standard rate used for public policy evaluation. Harte et al. (2000) state that “no valid 

argument exists for zero or very low discount rates” and recommend real discount rates of 

between 4% and 15% as appropriate for applied fishery analyses. Their report notes that “the 

predominant consensus in the literature supports the use of discount rates near inflation-adjusted 

market interest rates (e.g., rates between 4% and 12%).” A discount rate of 7% has been used in 

evaluating Federal fisheries policies in some cases (NEFMC 1996). 

It is not sufficient to simply identify or even quantify the types of costs and benefits that may 

result from implementation of EFH protections, but it is useful to categorize these cost and 

benefits. This will help to facilitate and organise the appropriate scientific and economic analysis 

for a specific proposal. It is likely that any benefit-cost analysis of EFHPM will have to make use 

of a number of separate valuations of different impacts, positive and negative, and then compare 

these.4

 These valuations may require data that is not currently available and development of new 

valuation methodologies tailored for application to Alaskan EFHPM. 

Although EFH is geared toward ensuring the productivity and sustainability of fisheries, the 

debate over the merits of EFH protections is often polarized in terms of extractive and nonextractive uses. It is generally argued that appropriately designed measures such as marine 

reserves or MPAs will generate net benefits to non-extractive users,5

 while it is the perception of

many extractive users of the marine environment (e.g., commercial and recreational fishermen) 

that these measures will result in a net loss of benefits to them. The two groups need not be 

mutually exclusive, but categorizing costs and benefits in this way facilitates economic analysis. 

 4

 Economic impacts, even expectations of long-run impacts, may be revealed in values of fishing rights for fisheries

with marketable fishing rights. However, even in these cases, benefits to non-extractive users will not be revealed. 5

 This in no way suggests that a poorly designed reserve might not result in a net loss of non-extractive value. 

6
Non-extractive Benefits and Costs -- Non-extractive benefits associated with EFH may include 

both use values and non-use values. Non-use values are benefits derived without actually 

experiencing something physically or visually (e.g., someone may derive value in knowing there 

are wilderness areas even though they have no desire to visit them). Protecting marine habitat 

may generate non-use values if there are features of these areas including plants or animals that 

might be lost or disturbed in the absence of protections. The primary method used to estimate 

non-use values is contingent valuation which uses survey techniques to elicit the value a sample 

of individuals places on the resource being evaluated. These values are then extrapolated to a 

broader group to determine the welfare effects of changes in the quantity or quality of the 

resource. There is considerable debate regarding the accuracy of these methods and which 

methods provide better estimates, but these valuations have been used in public policy and in the 

courts. A Blue Ribbon panel of economics and survey research experts chaired by Kenneth 

Arrow and Robert Solow concluded that, if properly conducted under strict guidelines, contingent 

valuation “can convey useful and reliable information that can produce estimates reliable enough 

to be the starting point of a judicial process of damage assessment” (Arrow et al. 1993). It is 

important to keep in mind that the non- use benefits associated with EFHPM may relate more to 

changing the probabilities of extinction of known or unknown species or subspecies rather than 

losses of particular numbers of plants and animals. 

EFHPM may also generate non-extractive use values associated with a viewing or recreational 

experience of some sort (e.g., the value of a diving experience inside a marine reserve or a whale 

watching cruise). Although there may be no observable market transactions for amenity values 

that reveal their net value, a variety of methods can be used to estimate these benefits including 

contingent valuation and revealed preference techniques that infer value from observing choices 

and actions rather than relying on respondents to provide their own valuation. The regulatory 

impact review (RIR) suggests that non-extractive use benefits are not likely to be significantly 

affected by the EFHPM being considered by the North Pacific Fishery Management Council 

(NPFMC), so costly quantification of these impacts may not be justified.

There may also be costs to non-extractive users. Sanchirico et al. (2002) note that non-extractive 

activities such as diving and snorkeling or anchoring of pleasure boats may cause damage to 

habitat reducing some of the non use benefits a marine reserve was meant to provide. The 

development associated with tourism attracted by a reserve can lead to environmental degradation 

that may reverse initial conservation benefits. A marine reserve that excludes fishing from a large 

area may also result in the disappearance of traditional fishing communities which local residents, 

tourists and the general public may perceive as a loss of value. Again it appears unlikely that 

these costs will be significant for the EFHPM being considered for Alaskan fisheries.

Extractive Benefits and Costs -- EFHPM could result in at least short-term losses to both 

commercial and recreational fishers displaced from their traditional fishing areas. However, EFH 

protection need not exclude all fishing as long as the impact of a particular activity on habitat is 

minimal. Consequently, they may have little effect on recreational fisheries. To the extent that

EFHPM do displace recreational fisheries, they may result in a loss of value to recreational 

fishers. It is also possible that EFHPM could impact recreational fishers negatively if they 

displace commercial fishing effort into recreational fishing areas. The RIR or EFH does not 

7
explicitly address this issue. Presumably this is because impacts of proposed EFHPM on 

recreational fisheries are not expected to be large.

The EFHPM are quite likely to have significant negative economic impacts on commercial 

fisheries, at least in the short-run. This is an increasing concern if area closures in both inshore 

and offshore areas are imposed. An extensive review of empirical studies of marine reserves 

noted that “there are no well-documented examples where marine fishery sanctuaries have been 

shown to provide and maintain net economic benefits for previously existing fisheries” (Ward et 

al. 2001). Marine fishery sanctuaries are not the only EFHPM available, but they are one of the 

primary tools being considered. 

The benefits from commercial fishing can be divided into two primary categories, consumer 

surplus and producer surplus. Consumer surplus is the aggregate difference between what 

consumers would have been willing to pay for the seafood they consumed and the price they 

actually paid. Essentially this is the net benefit that consumers received from purchasing seafood 

rather than spending that money on the next best thing. We are not aware of any studies on the 

level of consumer surplus associated with Alaskan fisheries, and it is difficult to say how 

substantial it may be. The RIR was unable to quantify the effects of EFHPM on consumer 

surplus. It is probably reasonable to say that there may be some decrease in consumer surplus if 

catches decrease. Quantification of demand and consumer surplus for Alaskan fisheries would be 

useful for a variety of public policy analyses, but is probably of secondary importance for benefitcost analysis of EFH measures. Before a more accurate understanding of the effects of EFH 

protection on catches is achieved, there is limited value in determining how those changes would 

affect consumer surplus. 

Of more direct concern is the impact of EFHPM on producer surplus from commercial fisheries. 

Producer surplus generated by fisheries is the economic profit attributable to exploitation of the 

fisheries. It can be defined quite simply as the difference between revenues and the opportunity 

cost of inputs used in generating those revenues. This includes the costs of inputs such as fuel and 

labor as well as the opportunity cost of capital. The EFHPM could impact the producer surplus 

from commercial fisheries in a variety of ways. The most obvious impact is the effect on total 

catch and associated revenues. It is important to keep in mind that the percentage decrease in 

producer surplus is likely to be greater than the decrease in revenues. The slimmer the margin is 

between revenues and costs the greater will be the ratio of the drop in producer surplus relative to 

the drop in revenues. 

As we discuss in the Appendix, the modeling literature on marine reserves and fishery area 

closures suggests that they may very well decrease catches in well managed fisheries and are 

unlikely to increase them. In general, a decrease (increase) in catch will result in a decrease 

(increase) in revenues and producer surplus. The EFHPM may, however, increase or preserve the 

productivity of fisheries and consequently have a more positive impact on catches than the 

literature on marine reserves suggests (see Appendix).

Reductions in revenues due to reduced catches may be partly offset if decreased supply leads to 

increased prices. This of course depends on the nature of demand just as consumer surplus does. 

Prices may also be affected by changes in the quality of product, and these changes might be 

8
either positive or negative. For example, closures of spawning areas may reduce harvests of roe 

or milt which could greatly decrease the value of a given quantity of fish for some species. An 

area closure that prevents targeting of larger size fish and results in a smaller average individual 

size might lead to a decline in average price. The opposite could also occur in the long run as a 

result of large fish spilling over from a marine reserve or may result directly from closing areas 

with high concentrations of juveniles. However, if there are substantial benefits in selection of

larger fish, this might be achieved more efficiently in other ways such as changes in codend mesh 

size (Holland 2003). 

The EFHPM are likely to have their greatest impacts on the cost side of producer surplus. Since 

they constrain choices, they can be expected to increase the cost of taking a given amount of fish. 

In the short run, this is an obvious outcome. Fishermen are presumably fishing in the location 

using the fishing practices they find most profitable. Location preference could be because the 

fish caught there are more valuable (e.g., they are larger or the proximity to market allows them

to be delivered in better condition). Alternatively, the location preference might be due to safety 

reasons or to the expectation of higher catch rates there than at alternative sites. In the latter case, 

reducing catch rates results in a higher cost per unit of catch. Any of these reasons suggest a loss 

in producer surplus following exclusion from the preferred fishing areas. The same argument can 

be applied to restrictions on particular fishing methods or gears. 

The costs of area closures, particularly areas closed to all fishing, may actually compound over 

time. If the same level of total catch is maintained after an area is closed the displacement of

effort from the closed area to other areas may result in crowding and gear conflicts as well as 

local depletion. Over time, if the closure is effective at protecting a resident fish population, the 

density of fish inside it will build up relative to the density outside. Obviously, catch rates would 

be higher and unit costs lower, if fishing were concentrated where fish are concentrated but this 

will not be possible if those areas are closed to fishing. 

A final category of costs to consider is management and enforcements costs. Monitoring and 

enforcement of EFHPM may significantly increase costs both for the management agencies and 

for fishing vessels. For example, some of the alternatives being considered for providing 

increased protection to EFH in the Alaskan fisheries are expected to require expanded use of 

electronic vessel monitoring systems (VMS) which will require both additional expenditures on 

government infrastructure and personnel, and expenditures by fishers on equipment and 

operational costs of the VMS (Tetra Tech et al. 2003). 

Both the costs and benefits resulting from EFHPM are in fact streams of costs and benefits over 

time. The cost and benefits streams are unlikely to be constant or simultaneous. For example, a 

marine reserve is likely to increase fishing costs immediately while any gains resulting from the 

emigration of large fish from a reserve are likely to be several years down the road. Nonextractive benefits such a fish viewing will also only be fully realized after many years. An 

appropriate economic evaluation of and EFHPM must take the timing of costs and benefits into 

account by transforming cost and benefits streams into present value terms so that they can be 

compared. 

9
Cost-effectiveness Analysis

It may be easier to quantify the costs of EFHPM than the benefits. If actions to protect EFH are 

taken without a clear understanding of benefits, cost-effectiveness analysis rather than benefitcost analysis may be the appropriate evaluative approach. Cost-effectiveness analysis simply 

attempts to find the policy that will achieve a desired outcome at the lowest cost. For example if a 

decision has been made to close 20% of a certain type of habitat to trawling but there is little 

knowledge of the relative benefits of closing one part versus another, the appropriate question is 

which 20% can be closed at the least cost. 

Cost-effectiveness analysis is likely to be less onerous than a full benefit-cost analysis primarily 

because only certain impacts need be valued, and the focus is likely to be on more certain and 

shorter term impacts. The specific types of costs that might be considered in such an analysis 

were discussed above so we do not go through them again here. As with benefit-cost analysis it 

will be appropriate to discount costs that fall in the future relative to immediate ones.

EFH, Risk and the Precautionary Principle 

Uncertainty and risk associated with irreversible events is a dominant issue in policy arguments 

by natural scientists advocating policies for marine management and habitat protection. Auster 

(2001) advocates a hierarchical approach to marine resource management. He suggests making 

use of MPAs and marine reserves when limited information is available on impacts of human 

activities and potential impacts are severe and difficult to reverse. The hierarchy he proposes is 

preventive approach – corrective approach – precautionary approach – precautionary principle. 

Thresholds based on the relative size of the footprint (percentage of total area fished) trigger 

precautionary closures unless enough information is available for preventive and corrective 

measures. Preventive measures are to be used when spatial patterns of resource use, habitat, and 

biological diversity are understood. They include spatially explicit gear restrictions or effort 

limitations. Corrective measures adjust boundaries of preventive measures when new information 

becomes available (this is not really mitigation but adaptation). Precautionary approaches are 

implemented when we know little about linkages between habitat and exploited populations 

particularly for sensitive or long-lived species. This approach calls for marine reserves targeted at 

specific habitat types or species. Finally the precautionary principle is used when “we know the 

least about the system and the potential for irreversible damage is high or very high such that it is 

not possible to predict that actions will not irreversibly damage habitats or threaten the extinction 

of species.” Here a network of marine reserves of representative habitats might be called for. 

Auster (2001) suggests that this approach (with the precautionary approach or principle in the 

form of area closures) be invoked when thresholds based on the fishery footprint are crossed. He 

argues that this provides incentives to industry to help collect more detailed spatially explicit 

information. Of course it could also motivate strategic reporting. 

Many economists also advocate conservative, precautionary approaches to deal with risk and 

uncertainty. As King et al. (2002) explain, “evaluating risk is an inherent component of economic 

analysis and is useful even when evidence regarding biological impacts is weak. The unique role 

of risk in economic analysis has some potentially powerful applications in habitat protection 

because it can be used to demonstrate that over and above any direct project costs that are being 

10
measured; there are additional costs due to the fact that a project may also be expected to increase 

society’s risks.” King et al. (2002), in this comment, are referring to benefit-cost analysis of 

development projects that may have negative impacts on EFH, but the rationale applies to 

continuation of fishing practices that may be impacting fishery habitat and consequently posing 

some risk to the future productivity of the fishery or some other valuable components of the 

marine ecosystem. If the habitat delivers a stream of benefits with a present value of $100,000 

and there is a 20% chance that those benefits will be lost if a particular fishing practice is allowed 

to continue, the expected value of the gross benefits from restricting the fishing practice would be 

$20,000. However if the public is risk-averse regarding that loss, it might require more 

compensation to allow this practice to continue and leave welfare unchanged. These types of 

considerations are particularly cogent when the potential impacts are irreversible or where 

recovery might take a very long time. 

The regulatory impact review prepared for the EFH Environmental Impact Statement (EIS)(Tetra 

Tech et al. 2003) provides some discussion of this issue motivating the need for precautionary 

management on the basis of what they term “opportunity reservation value.” 

Opportunity reservation value is defined here to mean a societal value distinct from

traditional option value, the latter being an individually held form of future use value. In 

this instance, the value being defined may be regarded as a collective hedge against 

irreversible loss of some highly valuable goods or service, flowing from EFH, that has not 

yet been recognized. That is, ecosystems such as those that comprise EFH are enormously 

complex and, as yet, not well understood. EFH may provide some future consumptive use 

benefit that is not currently used or even identified. For example, minimizing the adverse 

effects of fishing practices on EFH may preserve a species of plant or animal or an 

ecological process that, in the future, may prove to have irreplaceable, tangible value to 

the world’s population. Such examples already exist. Specifically, marine sponges have 

yielded valuable medicinal compounds for use in anti-malaria and HIV infection 

suppression drugs. At present, it is not known whether or how many of these potentially 

valuable species or functions exist and, therefore, it is not possible to place a monetary 

value on their future use. Retention of the option to exploit these public assets in the 

future clearly has some reservation value, and argues for a precautionary management

approach (i.e., erring on the side of preserving these assets). 

While the concept of a risk premium associated with potential but uncertain losses of marine 

habitat or its components is intuitively appealing, determining its existence and magnitude is 

likely to be problematic. The appropriate risk premium can depend on risk aversion which may 

be positive or negative depending on the nature of gains and losses. One source of this risk 

premium might be option value, a concept introduced by Weisbrod (1964). This is essentially 

equivalent to the concept of “opportunity reservation value” discussed above. Freeman (1993) 

explains that originally option value was seen to arise when an individual was uncertain as to 

whether he would demand a good in the future and was faced with uncertainty about its 

availability. “If option price is defined as the maximum sum the individual would be willing to 

pay to preserve the option to visit a site before his own demand uncertainty is resolved, then the 

excess of option price over expected consumer surplus can be called option value.” Bishop (1982) 

suggested an alternative concept of option value where the individual’s demand is certain, but 

11
supply is not. Bishop (1982) showed that option value is positive for a project that eliminates 

uncertainty in supply. However, Freeman (1993) explains that formal analysis of this concept 

uncovered subtleties and complications that showed that option value could be either positive or 

negative even for risk averse individuals. Freeman (1993) is quite skeptical of the concept of

option value and states that: 

“we can now see that option value is really just the algebraic difference between the 

expected values of two different points on a willingness-to-pay locus. Specifically, it is 

the algebraic difference between the expected value of consumer surplus and the stateindependent willingness to pay (option price). Since these two points represent alternative 

ways of measuring the same welfare change, the difference between their expected values 

cannot be a separate component of value. Furthermore, option value cannot be measured 

separately; it can only be calculated if we have enough information on preferences to 

calculate both option price and expected surplus. And finally, as I have shown in this 

chapter, neither of these points on the willingness-to-pay locus has any particular claim as 

a superior welfare measure. I think it is time to expunge option value from the list of 

possible benefits associated with environmental protection.” 

Another source of a risk premium referred to as quasi-option value was proposed by Arrow and 

Fisher (1974). Quasi-option value describes the welfare gain from delaying a decision when there 

is uncertainty about the payoffs of alternative choices that may be resolved over time, and when 

at least one of those choices involves an irreversible commitment of resources. Arrow and Fisher 

(1974) showed that quasi-option value is not dependent on risk aversion. Freeman (1993) notes 

however “that it is not difficult to imagine situations where the relevant information to guide 

future decisions can be gained only be undertaking now at least some development” in which 

case quasi-option value of development, rather than forgoing development, could be positive. 

Freeman goes on to argue: 

“whether quasi-option value exists or whether it is positive or negative for preservation 

depends on the nature of uncertainty, the opportunities for gaining information, and the 

structure of the decision problem. Quasi-option value is not a component of the values 

individuals attach to resource changes. Even if individuals’ utility functions were known, 

quasi-option value could not be estimated separately and added into a benefit-cost 

calculation. Quasi-option value is a benefit of adopting better decision-making 

procedures. Its magnitude can only be revealed by comparing two strategies where one of 

the strategies involves optimal sequential decision making to take advantage of 

information obtained by delaying irreversible resource commitments. The decision maker 

who knows how to use an optimal sequential decision-making strategy has no reason to 

calculate quasi-option value. The calculation would be redundant since the best decision is 

already known.” 

Another important concept relating to risk and uncertainty is the Safe Minimum Standard (SMS) 

(Ciriacy-Wantrup 1952). In the case of an activity with some positive probability of causing an 

irreversible impact of unknown value it may not be possible to determine the optimal level of the 

activity to balance against the expected value of the loss. An alternative is to set a safe minimum 

standard believed sufficient to ensure the loss is not incurred and not allow that standard to be 

12
breached unless the costs of doing so are catastrophic. Ciriacy-Wantrup and Phillips (1970, p. 28) 

explain “here the objective is not to maximize a definite quantitative net gain but to choose 

premium payments and losses in such a way that maximum possible losses are minimized.” This 

concept is intuitively appealing to many, but it has been found to be somewhat flawed from a 

logical/philosophical perspective. Ready and Bishop (1991) show with a simple and clear 

exposition that a two-person minimax game against nature cannot provide a firm theoretical 

foundation for the SMS approach of endangered species preservation. The problem is laid out as 

follows. Preservation of a species by choosing SMS involves forgoing development benefits. 

There is an unknown probability of a disease in the future which might be cured by a species lost 

if development proceeds. In an insurance game, the occurrence of the disease is uncertain but the 

cure is definite if the species still exists. In the lottery game, there is only a finite probability that 

the disease will be cured if development doesn’t proceed and the species is preserved. The 

maximum loss in the insurance game occurs if development proceeds and the disease happens, so 

SMS is the dominant strategy to minimize the maximum loss. But for the lottery game the 

maximum loss is not developing and not having the cure for the disease anyway when it comes. 

The dominant strategy is develop and hope. To the extent that the lottery game is a better 

description of reality, it brings the logic of the SMS into question. 

Despite its flaws, the SMS approach is more or less that adopted by the Endangered Species Act 

(ESA). To the extent that particular fishing practices or other marine activities create a significant

risk of extinction of a particular species the ESA would be the pertinent legislation, but reliance 

on a safe minimum standard approach is less defensible for general EFH protections oriented at 

ecosystem protection or protection of biodiversity where there are not specific species or unique 

objects at risk. 

The concept of a precautionary approach to marine resource management is much in vogue, but 

operationalizing the concept is problematic. An analysis of the arguments over risk premiums, 

option value and safe minimum standards suggests that a precautionary approach to EFH 

management is not necessarily welfare increasing. Actions to protect habitat that are costly and 

have highly uncertain payoffs should themselves be considered cautiously. 

Economic Research Topics Relevant for Evaluation 

of EFHPM in the Federal Fisheries off Alaska

The discussions of the EFH EIS and RIR and the economic analysis of EFH protection above 

highlight a number of areas where data and research methodologies are inadequate. Many of 

these areas, such as estimates of operating costs of fishing vessels and improvements to economic 

impact analysis tools such as IMPLAN, are needed for a wide variety of regulatory analyses but 

need not be tailored for use in economic analysis of EFHPM. The Alaska Fisheries Science 

Center, the NMFS Alaska Regional Office, and the NPFMC have active programs in these areas. 

There are a number of research topics specifically related to economic analysis of EFHPM for 

which we believe new or increased research efforts are likely to be worthwhile. These include 

random utility modeling of location choice, bioeconomic modeling to estimate fishery benefits of 

habitat protection, non-market valuation of habitat protection programs, and market-based 

systems to cost-effectively achieve specified habitat protection objectives. The following sections 

discuss each of these topics in turn. The essential elements of the research and some of the 

13
relevant literature on theory and previous applications are reviewed. It is outside the scope of this 

paper to provide a detailed research proposal for each of these areas, but potentially useful 

research is discussed in general terms.

Estimating Costs of EFH Protection

Analysis of the costs of EFHPM is likely to focus on the value of activities displaced or restricted. A 

first order analysis might simply look at the revenues or preferably the net profits associated with the 

activities that are being excluded. This approach has been applied for the RIR for the Alaska EFH

EIS (Tetra Tech 2003) and was also applied by NMFS and New England Fishery Management 

Council staff to estimate the costs of EFH closures in the Northeast (NMFS, NEFSC 2003). As the 

authors of the Northeast EFH analysis note, this approach may tend to overestimate costs: “All other

things being equal, this “no displacement” assumption is likely to result in an upper bound 

estimate of revenue impact since vessels would attempt to maintain fishing incomes by 

redirecting effort to alternative fishing locations.” 

In most cases, it is unrealistic to assume that restriction of particular activities will not result in some 

redirection of that activity unless there is a simultaneous reduction in total allowable catch or effort. 

Restrictions on fishing location choices or practices can be expected to initially reduce the 

profitability of fishers, but the net losses will depend on the value of the alternative choices still 

available. Net losses can be estimated by comparing the revenues or preferably the profits that that 

displaced fishers can expect to make from their next most valuable option relative to the restricted 

option. However determining which option would be the most profitable for an individual and what 

the change in profits would be is not straightforward. Even if the profitability of other areas is

observable (i.e., information on catch rates and revenues in other areas is available), it may not be

fair to assume that a displaced fisher can expect to generate profits equal to observed average profits 

in other areas. There may be learning costs associated with moving to a new area and fishers may 

have insufficient information to choose the most profitable alternatives. Also, if a substantial amount 

of effort is displaced, it may be unrealistic to assume that it can be moved to other areas without 

negative effects on catch rates in those areas. 

Therefore it may be important to use a more sophisticated approach to modeling effort displacement

and estimating the economic losses associated with that displacement. To understand longer run 

results it may be necessary to integrate fleet dynamics models into dynamic bioeconomic models. 

There is a growing literature on modeling location choice and entry-exit decisions in commercial 

fisheries (e.g., Bockstael and Opaluch 1983, Curtis and Hicks 2000, Dupont 1993, Eales and Wilen

1986, Hicks et al. 2004, Holland and Sutinen 1999 and 2000, Mistiaen and Strand 2000, Smith 

2000, Smith and Wilen 2003). These modeling techniques can be used to predict where effort will

be displaced which may be useful in modeling expected impacts on catches and costs. The models

can also be used to directly estimate the welfare losses to fisheries associated with eliminating 

specific fishing options. 

These analyses use discrete choice random utility models to model fishing decisions. Individuals are 

assumed to choose among several discrete alternatives (e.g., fisheries and/or areas). We observe

which alternative they choose and assume that the alternative chosen is the one which generates the

14
highest expected utility for them. The probability that a particular alternative k is chosen can be 

expressed as: 

 , (1)

where U represents an indirect utility function for choice k for a specific individual. Assuming U 

can be divided into a systematic component, V, and a random component, ε, and that utility is a 

function of alternative specific characteristics, q, and individual specific characteristics, s that may 

differ across individuals n, the discrete choice probability can be written: 

≠ ] (2)

Equation 2 from observed choices leads to predicted choice probabilities at the individual and 

aggregate level. Some behavioral assumptions are required. Typically, choices are assumed to be 

independent and mutually exclusive such that the ratio of the probabilities of choosing any one 

alternative over another are not affected by the attributes of other alternatives in the choice set6

. In

most applications to date, the random components of the indirect utility functions, ε, are assumed to 

be independently and identically distributed (iid) with the Gumbel (type I extreme value) 

distribution. Given these assumptions the parameters of the utility function can be estimated with 

the multinomial logit (MNL) model. Combining the arguments qk and sn of the utility function into a 

vector xk with parameter vector Beta, β, the MNL choice probabilities can be expressed as: 

P k P U U k C k k k k C k ( ) max , , ' '= ≥ ' ∀ ∈ ≠ ⎡

⎣⎢ ⎤

∈ ⎦⎥

P k P[V q s q s V q s q s k C k k k k n k n k k n k n i ( ) ( , ) ( , ) ( , ) ( , ) , ' = + ε ε ≥ ' ' + ' ∀ ∈ 

P k V q s

V q s

x

x

k k n

k k n

k C

k

k

k C

( ) exp ( , )

exp ( , )

exp '

exp ' = =

∈ ∈

∑ ∑

β

β (3) 

Bockstael and Opaluch (1983) appear to have been the first to use this approach to model fishing 

decisions in a commercial fishery. They use a random utility formulation based on individual profit 

and wealth maximization to model individual fisher’s decisions to enter or exit the groundfish 

fishery or participate in other fisheries. The decision was assumed to occur on an annual basis. They

found that, “while fishermen demonstrate a bias toward remaining in the same fishery, sufficient

incentives, in terms of changes in expected returns and risk, are shown to elicit a response.” 

Dupont (1993) extended the methodology of Opaluch and Bockstael to a model of location choice in 

the British Columbia salmon fishery and found that fishers switch areas in response to higher

expected profits and wealth but, as other models have shown, demonstrate bias toward remaining in 

the same area. 

Eales and Wilen (1986) used the individual utility framework to model daily location choice 

decisions of vessels in the California pink shrimp fishery. They utilize a nested logit model that 

predicts first the choice between large zones and then the choice of areas within a zone. The nested 

logit model is a generalization of the multinomial logit model that accommodates cases where the 

 6

 Increased computing power has also made other estimation techniques feasible such as multinomial probit and 

random parameter logit models. These models can be less restrictive in their assumptions. 

15
random component for groups of alternatives may be correlated, a violation of the iid assumption of 

the multinomial logit model. In this case, it was assumed that there are unobserved characteristics 

common to areas within a given zone. As with the studies discussed above, Eales and Wilen’s 

results indicate that information derived from the prior day’s catches (by the fleet, not by individual

fisher) were a good proxy for individual expectations about relative profitability of various fishing 

areas the following day. 

Holland and Sutinen (1999 and 2000) extend this discrete choice modeling approach to

accommodate fishery and location choice on a trip-by-trip basis in a multispecies, multi-area 

fishery. The resulting individual level behavioral models are used to predict aggregate temporal and 

spatial fishing effort distribution for the overall trawler fleet. Effort distribution predictions can be

updated as the characteristics and size of the fleet and the biological and regulatory status of the 

fishery change. Like Eales and Wilen, they utilize a nested model structure assuming a hierarchy of 

decisions; first a fishery and zone choice (e.g., groundfish on Georges Bank or squid in southern 

New England), and then an area choice within the larger zone. Fishers are assumed to make a 

decision between one of 41 discrete fishery-area alternatives prior to leaving port. Each fishing

alternative has an expected utility based on a vector of explanatory variables and an error term. 

Holland and Sutinen specify a two-level model where individuals first choose a fishery/zone

combination, j, at the highest level of the model and an area, k, within the larger zone at the lowest

level. The choice probability of the multinomial logit model is now redefined as the conditional 

probability of area k in fishery/zone j, k|j : 

P k j

x

x

x

J

k j

k j

k Cj

k j

j

( | )

exp '

exp '

exp '

exp

|

|

| = =

∈

∑

β

β

β , (4)

where j

and is known as the inclusive value for fishery/zone J and represents the composite utility of the 

choices within branch j. 

The probability of choosing a particular fishery/zone j is: 

 J Log x j k

k C j

= ∈

∑ exp ' β |

P j

z J

z J

j j j

j j j

j C

( )

exp( ' )

exp( ' ) = +

∈

∑

γ σ

γ σ , (5)

where σ is the inclusive value parameter, zj is a vector of variables that differ across fishery and 

zone, but not across area alternatives, and γj the estimated parameter vector or the variable vector zj. 

The unconditional probability of choosing a given alternative is the product of the probability of

choosing the fishery/zone, and the conditional probability of choosing the area given the zone and 

fishery choice, P(j) × P(k|j). Commercially available software is used to estimate the parameters of 

this model simultaneously using full information maximum likelihood methods. 

16
Smith and Wilen (2003) used daily logbook and landings data to estimate a location choice model 

for divers in a commercial sea urchin fishery in California. They utilized a nested logit 

specification to model first a decision to dive or not dive and a location choice decision

contingent on the decision to dive that day. The utility of not diving captures the utility of leisure, 

work opportunities outside of fishing, and the value of avoiding exposure to unsafe diving 

conditions. Theirs is the first analysis to model discrete participation and location choices jointly

in a commercial fishery.

Curtis and Hicks (2000) and Hicks, Kirkley and Strand (2004) use location choice models to 

estimate welfare losses associated with area closures. This is accomplished via numerical 

methods, by calculating equivalent variation (EV) for a closure of certain areas in the choice set 

 , (6)

where the maximum expected utility for a choice occasion, , is equal to 

V (W E( ) ,Var( ) ) V (W E( ) EV,Var( ) ) 0 0 0 0 1 0 0 0 + π π = + π + π

V (W E( ) ,Var( ) ) i 0 0 0 + π π

{ [ ]}i

j j j

0 E max U(W + E(π ) + EV,Var(π )) + ε ,∀j∈S . Notice that EMAX is taken over the original 

set of fishing alternatives S to yield V0 and taken over only those sites remaining open S1 to 

yield V1. Therefore, EV is the amount of money necessary after the closure of some sites to hold

utility at a level as if the closures never happened. The expectations operator of the maximum 

expected utility function is the researcher’s expectation taken over εa. Hicks et al. (2004) use an 

iterative approach to calculate EV since some of the explanatory variables in the utility function 

(namely aggregate effort in each area) change when effort is redistributed after a closure: “In 

order to model the welfare effects and choice probabilities, we conduct an iterative analysis. We

first, calculate each vessel’s choice probabilities for all sites that remain open after the EFH 

designation. Using these choice probabilities (which are greater than zero for only those sites 

remaining open after the EFH designation) to allocate effort across fishing grounds, we 

recalculate the variables fleet and fleet-squared to update the previous thirty day’s activity, so that 

the choice model properly accounts for the closure effects. This process is repeated until choice 

probabilities stabilize.” 

The AFSC is currently funding work on location choice modeling of vessels fishing in the 

walleye pollock fishery in the Bering Sea and Western Gulf of Alaska. Layton et al. (2003) have 

developed a variant of the random utility modeling approaches described above. They formulate 

the location choice model directly in terms of expected profits and allow actual observed catch to 

differ from expected catch. This leads to a tractable discrete/continuous model where the discrete 

portion relates to the choice of fishing location, and the actual catch constitutes the continuous 

portion. This work is in progress and results will be supplied directly to the AFSC so there is not 

a more extensive discussion in this report. The initial results from this work appear promising 

however, and, in principle, the methodology could be applied to other fleets and areas that may be 

affected by EFHPM. Layton, Hanie and Huppert do not use this model to estimate welfare 

effects, but measures of equivalent variation could be presumably derived from the model in a 

similar manner to Curtis and Hicks (2000) and Hicks et al. ( 2004). 

A potential limitation of the specific approach taken by Layton at al. (2003) relates to the data 

used to support the analysis. Their analysis is applied to a fleet of pollock vessels with complete 

17
fishery observer coverage. The vessels most likely to be impacted by EFHPM are bottom trawlers 

fishing cod, flounders, rockfish, etc. Many of these vessels are only partially covered by 

observers. It may be necessary to adapt the approach to use of logbook or fish ticket data which 

may require changing the temporal and/or spatial resolution of the model. This is not a trivial 

matter as it affects both the reliability of results and the degree to which specific area closures can 

be evaluated. 

Whether or not subsequent modeling of other fleets requires changes in data sources and 

resolution of the choices modeled, the amount of effort required should not be underestimated. 

Location choice applications tend to require a great deal of time for data preparation. Designing 

the specific of the model such as the appropriate resolution of the choice modeled and the 

formulation of the explanatory variables can also be quite time consuming. It will generally be 

advisable to interview at least some of the decision makers (presumably skippers) to develop a 

better understanding of how they make their location choice decisions (Holland and Sutinen 

2000). 

Estimating Fishery Benefits of EFH Protection

Evaluating the economic benefits of EFH protection is a challenging task on which limited 

progress has been made to date. There have been a large number of empirical studies that have 

documented the ability of fishery area closures, marine reserves and MPAs to protect fish 

populations within their boundaries (see Appendix), but much less focus on their effects on 

habitat. There have also been a number of recent studies on the effects of fishing on marine 

habitat (NRC 2002). These studies have documented significant damage to habitat from certain 

fishing gears. From these studies of area closures and impacts of fishing, we can conclude that 

measures such as gear restrictions, MPAs, and marine reserves are likely to be effective at 

protecting marine habitat to the extent that threats from non-fishery sources are not important. 

Protection is likely to be extended to physical features and sessile organisms as well as some

species that exhibit limited movement and are afforded protection by a closure. However, success 

at linking these biological outcomes to changes in fishery productivity and consequent extractive 

benefits has been quite limited to date. 

Estimating the fishery benefits of habitat protection has and will likely continue to rely heavily on

modeling. A modeling approach is necessary because it is generally not possible to directly 

observe the effects of habitat protection on the productivity of the fishery. It is sometimes 

possible to observe how individual fish utilize habitat to escape predators or for reproductive 

purposes, but relating changes in habitat to changes in productivity of the fish stock must rely on 

a statistical analysis of changes in fishery biomass and catches or on some type of analytical 

model or numerical simulation. Statistical approaches must isolate the impacts of habitat 

protection from those of other factors such as regulatory, environmental, and market changes that 

affect natural productivity or human activity. Because of the amount of natural variation in 

fishery productivity, the lack of accuracy of observations of fish stock size and growth, and the 

long time frames over which habitat impacts are likely to be realized, statistical approaches to 

evaluating benefits of habitat protection have met with very limited success to date. 

18
An alternative is to incorporate mechanisms for habitat quality to influence natural mortality, 

growth, or recruitment into analytical models or numerical simulations. These models allow us to 

test the effects on fishery productivity of specific assumptions about linkages between habitat 

quality, fish mortality, and growth. In the end, the reliability of the results is dependent on the 

veracity of the assumptions, but sensitivity of the results to uncertainty about the assumptions can 

be measured. Knowler (2002) provides a good review of bioeconomic models that incorporate 

environmental influences on fisheries. He reviews both empirical, statistically based applications,

and theoretical models. Several of these papers are discussed below. 

Analytical Models -- Knowler begins by reviewing the basic static equilibrium fishery model 

(i.e., Gordon-Schaeffer) and discusses some of the papers that have extended that model to 

incorporate environmental effects. He then develops the dynamic fishery model with optimal 

control theory. He apparently finds little in the literature in the way of dynamic analytical fishery 

models, but reviews a general optimal control model of resource use by Freeman (1991) that 

allows the growth of the resource stock to be influenced by a variable representing environmental 

quality. This model does not provide a great deal of enlightenment other than to show that 

optimal time paths of resource use are dependent on environmental quality. 

Strand et al.(unpublished) also pursue an analytical modeling approach based on optimal control 

theory. The authors develop a dynamic analytical model of a fishery where natural mortality and 

growth may be functions of habitat which is in turn affected negatively by fishing. This 

somewhat general biological model is incorporated in a less general dynamic economic model 

(notably no interaction, either biological or economic, between areas is specified – it would have 

complicated the first order conditions from the Hamiltonian considerably). They use this model 

simply to point out that optimal habitat protection will be a function of the effects it has on 

harvesting costs and on stock productivity. In the concluding section the authors note that 

operationalizing this type of model for an empirical analysis is not possible at this point. Their 

statement regards EFH in the Mid-Atlantic, but is equally true for almost all fisheries. “It is 

unlikely that this type of model will be useful for the decisions of the Mid-Atlantic Fishery 

Management Council anytime in the near future. Although some of the economic requirements of 

using it for certain species are available, the biological effects of the gear in different areas are 

largely unknown. Although the model helps one think through the problem and organize how the 

factors interact with one another, it needs the biological information to be implemented.” 

Statistical Approaches Based on Reduced Form Models -- Quantitative empirical studies by 

economists on the value EFH to fisheries can be divided into two groups. Early studies, and the 

majority of studies done, are statistical estimations of reduced form fishery models. Barbier 

(2000) reviews a number of these econometrically estimated bioeconomic models that include 

habitat fishery linkages. Although different applications are based on varying assumptions about 

the way in which habitat affects fishery productivity, the equations estimated are similar and are 

subject to similar problems. 

The reduced form specified by Lynne et al (1981) is: 

t

2

0 1 1 2 1 3 1 ( ) ( ) H E t t t t t = + β β β ln M − − + E ln M + β H + ε − , (7)

19
where H is harvest, E is observed effort and M is a measure of habitat quantity. Despite the 

inclusion of lagged harvest and the fact that current capacity is represented by lagged marsh area, 

the basis for the model is essentially static. The reduced form assumes that the fishery is in 

bioeconomic equilibrium and does not include the lagged catch term. The lagged harvest term is 

an ad hoc addition to account for the fact that “stock adjustment may require several periods.” 

Lynne et al. (1981) use this model to estimate the effect of salt marsh losses on the gross value of 

a blue crab fishery. Carrying capacity is assumed to be a function of acreage of marsh 

(specifically the natural log of salt marsh area). The authors find a statistically significant 

relationship between catch and marsh and, in a separate model, find that marsh interacted with 

effort shows a nonindependent linkage between effort and habitat effects. Kahn and Kemp (1985) 

use a similar methodology to estimate marginal damage function from destroying submerged 

aquatic vegetation (SAV). Although the coefficient on SAV is only marginally significant, the 

authors go on to derive a marginal damage function that takes into account losses to both 

consumer and producer surplus. 

The reduced form of an explicit intertemporal formulation where the habitat affects the growth 

rate of the stock over time is specified by Barbier and Strand (1998) as: 

2

2 2

1 2 ( ) * * ( ) * q H qEK M E E Ln Mlag E

r = − = β β + , (8)

This formulation assumes an open access fishery with zero profits (e.g., total costs are equal gross 

revenue). The fishery is assumed to always be in bioeconomic equilibrium although that 

equilibrium is shifting over time. Barbier and Strand (1998) use this model to evaluate the role of 

mangroves on gross revenues from a shrimp fishery in Campeche Mexico. They find a rather 

small effect of mangrove loss on the fishery, and that over exploitation was the main cause of 

welfare losses. Barbier et al. (2002) use a similar approach to estimates the welfare losses to 

inshore artisanal fisheries in Thailand from conversion of mangrove swamps for shrimp farming.

They get significant results and estimate losses of consumer surplus between $11,000 and 

$408,000 per acre. The range is due to varying assumptions about elasticity of demand about 

which they have no information. They ignore producer surplus which is deemed to be zero as this 

is a classic open access fishery. A primary contribution of the paper is to illustrate how the 

institutional setting of open access and demand elasticity combine to affect the level of welfare 

losses associated with mangrove conversion. 

Other applications using variants of this general approach include Loomis (1988), which looks at 

effects of logging on value of salmon and steelhead fishing, and Swallow (1990) which looks at 

an indirect effect on a shrimp fishery of wetland conversion that reduces salinity in an estuary and 

consequently the productivity of the shrimp fishery. Swallow shows that ignoring effects on the 

fishery results in too fast and too much development. Interestingly, both of these studies give

relatively low values for stopping the activity that is harming the fishery. 

A primary difficulty with the approaches described above, aside from having a good measure of 

habitat loss to use in the estimation, is that the change in habitat is likely to be unidirectional and 

is likely to be correlated with other variables that might be the cause of reduced catches. These 

might be natural environmental fluctuations or manmade. Increasing fishing effort or maintaining 

20
too high a level of catch in the face of declining fish stocks is an obvious one. The correlation of 

these explanatory variables can make it impossible to identify the extent to which individual 

variable influenced the outcomes, particularly if the fish stock has been on a one-way trip 

downward. 

Dynamic Bioeconomic Simulation Models -- Knowler concludes, and we concur that, although 

analytical models and simple reduced form bioeconomic models provide some insights, it may be 

profitable to begin to incorporate more realistic assumptions about biological, ecological, and 

economic processes into more complex dynamic models. Presumably this will require numerical 

simulation modeling as these models quickly become intractable to analytical manipulation. 

A number of simulation modeling applications, both by economists and natural scientists, that 

evaluate the effects of habitat protection on fishery productivity are reviewed. Most of these 

applications (e.g., Mangel 2000, Sanchirico 2004) are essentially theoretical models that sidestep 

issues associated with estimating the functional relationships between habitat protection, habitat 

quality and growth or reproduction of the fish stock. They posit a stylized fishery model, where 

fishery growth is a function of biomass as well as habitat, and explore the impacts of closing 

some areas to fishing, thereby protecting habitat. Others, such as Lindholm et al. (2001), are 

based on actual fisheries but are essentially a numerical story telling device incorporating 

arbitrary assumptions and parameters about habitat functionality. Lindholm assumes that natural 

mortality of juvenile cod is lower in areas with structural complexity in substrate and that 

complexity and reduced mortality is only maintained if the area is closed to fishing. Lindholm et 

al. (2001) further assume density dependent mortality and movement. They go on to show, as is 

rather obvious from their assumptions, that if you close areas with complex substrate you will 

reduce juvenile mortality and increase recruitment. They argue that areas must be large to reduce 

the effect of density dependent mortality and movement, and that the closures should be in the 

complex habitat as opposed to other areas -- for obvious reasons. The authors infer that the 

increased recruitment will increase fishery harvests, but they don’t model the fishery itself or 

account for the possibility that adult fish may tend to aggregate in closed areas where they can not 

be caught. 

These theoretical and quasi-theoretical simulation approaches can be useful in clarifying the 

implications of some of the assumptions about how habitat may affect fishery productivity, but 

they tend to show that specific assumptions about functional forms and the magnitude of 

parameters (such as the effect of habitat quality on growth or recruitment) matter a great deal. 

Examples of studies that have attempted to estimate these functional relationships and incorporate 

them into empirically based simulation models are few. Knowler et al. (2003) is probably the best 

example of this approach. They statistically estimate the effect of habitat on the stock-recruitment 

function and introduce this into a dynamic simulation model of the fishery. The authors use a 

cross-sectional time series approach to isolate the effect of habitat loss on recruitment to salmon 

fisheries. This is possible because there are multiple discrete spawning populations and time 

series of returns and data on habitat loss. They estimated a linear relationship between the 

instantaneous average annual change in recruitment for each stream () and the "habitat concerns 

index" (HCI). These results are incorporated into a modified Beverton-Holt stock-recruitment 

function to model the production of coho smolts (SM) as a function of spawner escapement (Xt-2 - 

ht-2) and habitat quality (Q). They then incorporate these results into a dynamic bioeconomic 

21
model to calculate the benefits (in terms of the value that increased salmon catches add to 

producer and consumer surplus). They use this model to estimate the marginal value of

protecting salmon habitat which can be compared to the cost of doing so. They also discuss the 

additional value of ecosystem services that habitat protection might provide. 

Another example is Rodwell et al. (2002) which models habitat quality effects of area closures 

and effort management in a reef fishery in Kenya. The study uses a dynamic model but looks at 

equilibrium conditions. Though it is not completely clear, they appear to try to fit the model and 

its parameters with data from a fishery near Mombassa, Kenya. The main novel aspect of this 

model is to posit that both natural mortality and movement are related to habitat quality. Habitat 

quality is assumed to improve over time in the reserve and reduce natural mortality as well as, in 

some versions of the model, to influence migration and transfer of recruits between the reserve 

and open area. The study does not compare different reserves sizes; rather it looks at the effect of 

alternative assumptions about habitat effects with various exploitation rates while maintaining the 

current reserve size. In their sensitivity analysis they do compare results to those with no closure. 

They find that the increases in equilibrium catch and biomass with the habitat effect on natural 

mortality are greater when there is higher exploitation. If habitat quality also increases relative 

movement into the reserve, the increases in catch from habitat effects are less. The results are not 

very general and the model specification is quite complex and ad hoc. Unfortunately, habitat 

parameters are really just guesses (though some sensitivity analysis is done). They fall short of 

explaining the conditions under which habitat effects of a closure result in increased catches. The 

results from the sensitivity analysis show maximum equilibrium catches around 30% higher with 

the reserve than without it but requiring an exploitation rate of 0.4 instead of 0.3. This occurs 

when there is no habitat effect on movement – it only affects natural mortality. Very slight 

increases still occur with a moderate effect of habitat on movement. Otherwise highest catches 

are achieved with an exploitation rate of 0.3 and no closure. 

Useful Modeling Research for the Alaska EEZ -- The reduced form statistical approaches to 

evaluating EFHPM do not appear to be particularly useful or applicable to EFHPM for Alaska

fisheries for several reasons. It is not clear that there is a good measurable proxy for habitat 

quantity or quality and observed productivity of fisheries appears to be more influenced by 

climate cycles than by habitat effects. Furthermore, total catches have been dictated by 

regulations and are probably not closely related either to surplus production or to a bioeconomic 

open access equilibrium. The basic methodology used by Knowler (2002) would seem to be 

appropriate for offshore fishery analysis (that is modeling marginal increase in optimal catch and 

profit as a function of increased recruitment which is in turn a function of habitat). Unfortunately, 

applying a similar methodology to offshore fisheries such as pollock, cod or rockfish looks 

daunting if not impossible. Estimating the habitat parameter for recruitment for the offshore fish 

is likely to be problematic. The cross-sectional approach used for Pacific salmon was innovative, 

but is probably not feasible for offshore fish stocks since stock and recruitment estimates are for 

large stocks. Straight time series analysis is likely to be confounded by non-habitat factors (e.g.,

climate cycles and such). Furthermore there isn't good time series (or even current) data on 

benthic habitat quality. On top of all that, the stock recruitment functions for the stocks in 

question are likely to be very noisy and not well estimated. Still it may be useful to build these 

types of models to test the ramifications of reasonable assumptions about functional relationships 

between habitat protection and fishery productivity and stability. Even if they do not generate 

22
believable estimates of benefits, these models can be useful for identifying critical parameters

that justify increased empirical research. 

Nonfishery Benefits and Benefit Transfer

While the primary motivation for EFH protection is presumably to increase productivity of 

fisheries and/or ensure their sustainability, there may also be other benefits that accrue as a result 

of conserving non-commercial species and habitat independent of their role in supporting 

commercial or recreational fisheries. Estimating the economic value of conserving habitat for its 

own sake (e.g., existence value) requires valuation using stated preference techniques (e.g., 

contingent valuation studies). These valuation approaches have been widely used and are finding 

increasing acceptance in policymaking and the courts. There is a large and expanding literature 

on these methods which is not practical to review here. However, as the RIR for the NPFMC 

EFH EIS (Tetra Tech et al. 2003) notes, there are no existing valuation studies that provide 

estimates of the existence value of EFH. Furthermore any valuation of the impacts of EFHPM on 

existence value would need to value the marginal gain or loss in habitat which may have little to 

do with the total existence value of a certain type or area of habitat. It may well be that the value 

of marginal losses of habitat relates to changes in probability of losing particular species or risks 

of causing an ecosystem to collapse. These increased risks may be small for initial habitat losses 

but might increase as more areas are lost. 

Designing appropriate valuation studies will be a challenging task. Due to the lack of information 

about the relationship between policies and outcomes, these studies may need to focus on 

valuation of habitat protection programs and their objectives rather than valuing habitat itself or 

even damages to it. This was the approach taken in a contingent valuation of a protection program 

for Steller sea lions in Alaska by Giraud et al. (2002). They asked respondents to value a 

protection program for sea lions that would increase costs for the fishing industry as well as 

increase their taxes. The respondents were informed that the causes of sea lion population decline 

are uncertain and that the protection program would not ensure their survival but was designed to 

increase the probability that the population would survive. The respondents were asked whether 

they would vote yes on a ballot measure for such a program given that it would result in a 

specified increase in their taxes. A similar approach might be appropriate for valuation of a 

habitat protection program. However, the challenge is to effectively communicate the expected 

impacts of the program to elicit the value individuals actually place on those outcomes. This is 

tricky since the specific outcomes themselves are not well understood and the public is not likely 

to be familiar with the particular organisms or physical features that may be affected. Essentially 

they are being asked to value something they know virtually nothing about solely on the basis of 

information provided by individuals conducting the survey. 

Nonmarket valuation studies are likely to be very expensive to carry out correctly and the results 

may only be applicable to the specific case. Benefit transfer techniques that allow results from

one study to be extrapolated to another area can reduce the cost of valuation, but require that the 

characteristics of the locations and user groups are highly similar. Boyd and Waigner (2003) 

describe how benefit transfer, and a related technique they refer to as an indicator approach, can 

be used to estimate the value of a site using information from a valuation study done in another 

location. 

23
Benefit transfer methods essentially take the benefits estimated at a well-studied reference site 

and relate those benefits to the benefits likely to be found at a site of interest for preservation, 

mitigation, or exchange. The “transfer” of the benefits is made a function of differences in the 

reference site and site of interest. For example, if an exhaustive analysis of recreational 

benefits at the study site found a benefit of $10 million, the value of the new site may be more 

or less, or close to the study site, depending on observed differences in the sites. 

The indicator approach suggested by Boyd and Waigner (2003) is similar if less formal than 

standard benefit transfer techniques. 

First, indicators can be statistically evaluated in order to establish a more scientific 

justification for their use as proxies for monetized benefit estimates. In principle, statistical

analysis can determine the success of specific indicators as predictors of a known set of 

ecosystem benefits. What is needed is meta-analysis, taking established benefit estimates 

from existing studies, and testing the degree to which indicators are predictive of those benefit 

estimates across a range of studies and locations. The results of such an analysis would 

validate or reject individual indicators’ predictive value, on average, across different study 

areas. Indicators can then be used as inputs to so-called benefit transfer studies. 

Boyd and Waigner’s research relates to terrestrial areas, but presumably similar techniques would 

be applicable in the marine environment. We are unaware of any nonmarket valuation studies of 

areas closely enough related to habitat in Alaskan fisheries to be used in a benefit transfer 

exercise, but in designing any future valuation of marine habitat for Alaska fisheries, the ability to 

use these values effectively for benefit transfer should be considered. This may be best 

accomplished by linking the valuation to indicators of habitat value for different types of habitat. 

However, it should be noted that the accuracy of benefit transfer techniques may be low. A 

Monte Carlo study by Chattopadhyay (2003) finds that, “even under extremely favorable transfer 

conditions, quality of transferred benefits is poor.” 

Market-based Instruments and Property Rights Approaches to EFH Protection

The focus of the discussion so far has been on how to estimate the benefits and costs of command 

and control style approaches to habitat protection such as area closures and gear restrictions. 

Market-based environmental management approaches that rely on economic incentives or 

property rights might significantly decrease the costs and increase the effectiveness of an EFH 

protection program. Savings would be due to added flexibility that would allow damage reduction 

or mitigation measures to be implemented by those with the lowest cost of doing so. 

Market-based environmental management tools are often divided into tax-based and quantitybased systems. Tax based systems levy a tax on undesirable activities or subsidize desirable ones 

in order to achieve environmental objectives. In principle, a tax on a negative activity set equal to 

the marginal social cost of damage associated with that activity should result in a reduction of 

that activity to an optimal level. Generally this will not eliminate the activity since the marginal 

cost of avoiding damage often rises as the level of activity is reduced and the marginal benefits 

24
may also decline. Subsidies for abating damaging activities can, in principle, achieve similar 

results to taxes. In either case, the individual begins to internalize the external cost of the damage 

associated with the activity. 

Implementing a system of taxes or subsidies for protection of EFH might be possible but presents 

some serious difficulties. An efficient tax or subsidy system would require estimates of the 

marginal social cost of damage associated with particular fishing activities. As discussed above, 

determining the economic value of habitat and the marginal social cost of damage is problematic. 

The difficulties go beyond simply estimating the value of protecting a particular area of habitat. 

The marginal social cost of habitat damage in a particular location is likely to change over time. 

Fishing activity in an area may reduce the value of the habitat that remains there which will 

reduce the marginal social cost of further activity in that area. For example, it may be that most of 

the damage to an area might be incurred after a few passes of a dredge. Subsequent fishing in that 

area may inflict little additional damage. If the area is left unfished for a while and begins to 

regenerates, the value of the habitat will begin to increase again. Thus, an efficient tax system

would require adjusting the tax according to the current state of the habitat in each location. 

Quantity-based systems, such as cap-and-trade systems, set an upper limit on a measurable 

activity or output and create a market to allocate rights efficiently. They can achieve a costeffective outcome though overall economic efficiency is dependent on setting the total quantity 

allocated at the optimal level. Cap-and-trade systems have been implemented for pollution 

emissions, water use and in fisheries in the form of individual transferable rights to catch or 

effort. Rights to shares of the activity or output are often allocated to existing users on the basis of

historical use, but trading amongst users eventually leads to a more efficient distribution of use 

rights. There may be restrictions on trading if the location of the activity matters. Other

restrictions on trading or concentration of ownership may be imposed to restrict market power or 

achieve social objectives. Rights may be allocated or held by individuals, companies, or 

cooperatives. For fisheries, these property right systems have been introduced to limit overexploitation and to end the race-to-fish which dissipates resource rents by increasing the costs of 

fishing and often by reducing the value of the fish landed. They do not generally internalize other 

costs associated with fishing such as damage to EFH. However cap-and-trade systems for 

environmental bads such as volatile organic chemicals have proved effective at achieving 

environmental objectives cost-effectively. In some cases it may be useful to more carefully define 

property rights for fishery exploitation to address these incidental outcomes of exercising catch 

rights. This may involve bundling, attenuation, or division of resource use rights (Edwards 2003). 

Defining catch rights to better protect habitat might take the form of attenuating the exercise of

those rights by proscribing certain fishing methods or fishing in specific areas that results in 

excessive damage to EFH. Alternatively, assuming that impacts can be monitored and measured, 

bundling of rights might allow holders of catch rights to inflict a level of habitat impact or 

bycatch proportional to their catch rights. Neither approach can be expected to create incentives 

for habitat stewardship or result in an optimal degree or spatial distribution of avoidance of 

habitat damage. Edwards (2003) suggests that “Introducing habitat attributes to the bundle of 

property rights requires the right to exclude others from your territory” He suggests that territorial 

use rights (TURFs) might accomplish this. However, TURFs present both political difficulties 

and practical difficulties. Multiple species are likely to be found in and move through any given 

25
location. It is probably not possible to give an individual entity exclusive right to all fishing in an 

area big enough to enclose all the species fished there. Holland (2004) suggests that spatial 

fishery catch rights in the form of spatial Individual Transferable Quotas (ITQ) might facilitate 

habitat protection. These catch rights would not give exclusive rights to all fishing in a particular

area, but the catch rights themselves would specify where they could be exercised. This might 

allow Coasean bargaining approaches to habitat protection similar to land covenants. For example 

right holders in certain areas might agree to easements on their fishing rights precluding the use 

of certain gears. This may still require contracting with a number of rights holders, but it would 

not be possible for rights holders in other areas to move in to the covenanted areas and use the 

restricted gears. It is not clear, however, who would contract with fishers for these easements and 

who would pay for them since the benefits are essentially a public good. 

An alternative to attenuation or bundling of marine resource use rights is to create a separate 

property right associated directly with habitat impacts. Experience in Australia with property 

rights for irrigation water has demonstrated the value of separating the property right for 

removals of water from a river or groundwater aquifer from negative outcomes such as salinity 

loading associated with its use (Young and McColl 2002, 2003). The authors note that negative 

externalities associated with water use can vary with how and where the water is used. A 

separation of the rights for the negative impacts of water use from the quantity of water used will 

tend to lead to a shift of use to users with lesser external impacts per unit of water use. Thus, a 

given level of abatement of those impacts can be achieved at lower cost than either reducing total 

water use or specifying consistent best management practices for all users. A similar approach 

might be taken with habitat impacts of fishing. A correctly designed approach would lead to a 

distribution of rights that would equate marginal costs of avoiding habitat damage across fishers 

thereby minimizing the cost of achieving a given level of habitat protection. 

Developing, monitoring and enforcing a system of individual property rights for habitat impacts 

in Alaskan fisheries is likely to be difficult and costly. It may be more practical and less costly to 

monitor the impacts associated with a group of fishers than to monitor individual impacts. If so, it 

might be possible to devolve responsibility for achieving habitat related outcomes to a 

cooperative of fishers. The cooperative would be responsible for achieving specific outcomes or 

face some penalty such as fines, taxes, or a limiting of catch rights. This approach may allow 

more flexibility in how habitat impacts are avoided which could reduce the cost of achieving 

those outcomes. 

Cooperatives in Alaskan fisheries have had some success at managing external effects of fishing 

manifested in the form of bycatch. The “Sea State” system used by the bottom trawl fleet in the 

Gulf of Alaska and the Bering Sea facilitates a cooperative approach to minimizing the cost of 

caps on bycatch of halibut, salmon, and crab. The members of this cooperative share spatially 

explicit information about bycatch rates and agree to avoid fishing in areas with high bycatch 

rates. This allows the fleet to take more or all of TACs that might otherwise be constrained by 

fishery closures. However, cooperative approaches such as Sea State that rely on voluntary 

restraint by members are vulnerable to free rider behavior that limit their effectiveness. Sea State 

was no exception to this and its effectiveness was limited by a lack of participation by some of 

the fleet (Holland and Ginter 2001). Cooperatives such as the Pollock Conservation Coop with 

more formal legal contracts that clearly define the rights and responsibilities of individual 

26
members may be more effective at achieving outcomes. Such cooperatives effectively create 

individual rights but leave the definition, monitoring, and enforcement of those rights to the 

membership of the cooperative rather than government. 

Economists working on market-based tools to control nonpoint pollution have explored a variety 

of contracts that might be useful for facilitating a cooperative approach to habitat protection. The 

primary characteristic of these contracts is that they impose a collective penalty or withhold a 

collective subsidy if a pollution abatement target is not met. The share of the penalty that the 

individual pays or the conditional subsidy that they receive must be higher than their individual 

marginal cost of abatement. Early proposals of this type of system (Meran and Schwalbe 1987, 

Segerson 1988) required individually tailored contracts with each participant in the system. The 

informational requirements were likely to be prohibitively costly unless abatement costs are 

homogeneous across the group. Bystrom and Bromley (1988) propose a system that allows for 

identical contracts or a single contract with the group even when the group is heterogeneous. The 

system requires full participation of all parties affecting the ambient standard being monitored 

and trading of and side payments for abatement activities. It effectively creates a “virtual” 

tradable permit system that should allocate abatement activities such that marginal abatement 

costs are equated across participants. Bystrom and Bromley develop a theoretical exposition of 

this system with an enforceable contract achieved by a subgame perfect Nash equilibrium. 

Economists at Ohio State University are undertaking some interesting work that will test real

world applications of these approaches. Pushkarskaya (2003) and Taylor (2003) explore 

variations on these group contacting systems for nonpoint pollution using an experimental 

economics approach. Randall (2003) describes this work which was undertaken by two doctoral 

candidates working under him: 

“Pushkarskya’s contract solves a 2-stage generalized agency problem. The regulator offers to 

buy pollution reduction credits from a contracting team of NP sources. The adverse selection 

problem is solved via a bidding process that selects the least-cost team of NP abaters. 

Individual abatement targets are assigned and enforced within the team. The incentivecompatibility of this contract can be proven only if it is assumed that team members know 

each other’s costs. While this clearly assumes greater knowledge than exists, it is reasonable 

to assume that NP sources in a sub-watershed (neighbors farming in a natural amphitheater) 

have better information about each other’s costs than the regulator. Taylor’s contract relaxes 

the assumption regarding team members’ knowledge of others’ costs. He, too, solves a 2-

stage generalized agency problem. The regulator solves the adverse selection through an 

abatement procurement auction. NP sources bid individual abatement quantity and associated 

price; the contracting team is formed from the lowest cost bidders. Moral hazard is solved by 

an “all-or-nothing” team contract – all are paid if the aggregate NP abatement target is met, 

but none are paid in the event of a shortfall. 

Experimental work on these contract forms is ongoing and moving toward actual application as 

Randall (2003) explains. Pushkarskaya is exploring the performance of her contract in a 

structured series of experiments in a laboratory setting and soon she will begin to experiment with

farmers using a mobile laboratory. The research team has completed a series of focus groups 

using Taylor’s contract with farmers, in a setting that explicitly acknowledges the uncertainty of 

27
abatement performance. One interesting finding is that farmers tend consistently to worry more 

about unfavorable weather than about shirking within the group, as sources of possible failure to 

meet the group abatement commitment. As a result, risk averse individuals tend to exceed their 

contracted abatement responsibilities to ensure the target is not missed by chance. The final step 

in research currently planned will be a demonstration in a watershed in Ohio, where farmers have 

agreed to use Section 319 funds (money that typically is used to subsidize implementation of Best 

Management Practices) for payments to farmers as specified by Taylor’s contract. 

Experimental work of this type might be quite useful for studying cooperative contracts for EFH 

protection. This research would also be highly relevant for bycatch reduction programs and in 

general for cooperative approaches to fishery resource management 

Conclusions and Recommendations

The MSFCMA has recognized the importance of EFH for maintaining healthy fisheries and 

requires fishery managers to take actions to minimize the impacts of fishing on EFH to the extent 

practicable. The requirement for practicability means that the benefits of EFHPM must be 

balanced against their costs. However, the information required to design an economically 

optimal set of EFHPM is probably not available for any fishery anywhere in the world and clearly 

is not for Alaskan fisheries. Valuation and even methods for valuation of benefits and of costs of 

EFHPM are inadequate. 

In this report, there is a discussion of the basic elements of a benefit-cost analysis or a costeffectiveness analysis for EFHPM. A number of different types of benefits, both use and nonuse 

benefits, which must be quantified and monetized if they are to be used in a formal benefit-cost

analysis have been identified. There is also a discussion of the potential costs of EFHPM and how 

they might be measured. This discussion identified large gaps in knowledge and in valuation 

methodologies. On the basis of the relative importance of these knowledge gaps and the 

feasibility of filling them with new research, a number of potentially productive areas of research 

are indentified. This research would improve our ability to more optimally balance habitat 

protection against costly constraints on fishing. The research areas identified include random

utility modeling of location choice, bioeconomic modeling to estimate fishery benefits of habitat 

protection, non-market valuation of habitat protection programs perhaps paired with research on 

benefit transfer techniques, and market based systems to cost-effectively achieve specified habitat 

protection objectives. 

In conclusion, it will be some time before it is possible to quantify the benefits of EFHPM. Yet it 

will probably be necessary to ensure some level of habitat protection without fully understanding 

the benefits of doing so. Consequently there is a need to develop cost-effective ways of achieving 

a given level of habitat protection. This should go beyond developing the tools to quantify the 

relative costs of different sets of EFHPM. What may be most useful is to develop systems that 

utilize economic incentives and property rights to facilitate desired outcomes. Well designed 

incentive systems could substantially reduce the cost of achieving these outcomes. 

28
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34
Appendix: Fishery Impacts of Marine Reserves and Marine Protected Areas 

Marine reserves (which are defined here as areas closed to all extractive activities) and Marine 

Protected Areas (MPA) (which may restrict some but not all extractive activities in an area) are 

specifically mentioned in the final rule on EFH as a means of affording protection to EFH. 

Various types of MPAs are the primary EFHPM being considered by the NPFMC. There is a 

large and growing literature that focuses expressly on the impacts of marine reserves and MPAs 

on fisheries. Although most of this literature does not directly focus on habitat protection and the 

feedback of habitat quality on fishery production and stability, it is worthwhile to review it here 

because many of the general arguments made for or against use of marine reserves and MPAs are 

likely to be brought forward when considering closures designed specifically for EFH protection. 

In most cases, the proposed fishery benefits from marine reserves come from constraining 

overfishing rather than protection of fishery habitat per se, but these arguments have relevance 

even in cases where other management measures such as TACs are effective at constraining 

catch. If, as is often argued, closing even a fairly large proportion of a fishery will increase or at 

least will not decrease fishery yields, even when catches are already constrained to optimal levels, 

benefits from habitat protection may be realized without sacrifice of fishery yields. If habitat 

protection then feeds back to increase the potential productivity of the fish stock, yields may be 

increased. On the other hand if closing a proportion of the fishery reduces the surplus production 

that can safely be taken, EFH closures may result in fishery losses unless improved habitat 

quality increases the productivity of the fish stock. As the discussion below shows, the answer to 

this question is hotly debated and far from resolved. What is arguably more clear is that marine 

reserves and MPAs are likely to increase harvest costs which must be weighed against potential 

increases in catches. 

The literature provides substantial evidence from both empirical and modeling studies that marine 

reserves can contribute to protection of marine species and habitat within their boundaries. In 

some cases this can yield benefits including amenity values associated with enhanced viewing 

opportunities inside reserves and existence value associated with preservation of threatened 

species or, more generally, of biodiversity. It has also been shown that, under certain

circumstances, marine reserves can increase the benefits derived from marine fisheries. However, 

no studies have shown that benefits will accrue to fisheries where catches are already constrained 

to desired levels using output controls as is the case for Alaskan fisheries. A number of studies 

also identify costs associated with the creation of marine reserves and note that the distribution of 

costs and benefits is likely to be uneven in most cases. Costs are generally borne by excluded 

extractive users while benefits accrue to non-extractive users of the reserve and what it protects. 

The most general conclusion that can be distilled from the plethora of empirical and modeling 

studies on marine reserves and MPAs is that their impacts, both positive and negative, will vary 

greatly depending on design and on the physical, biological and socioeconomic characteristics of 

the environment in which they are imposed. In addition, the biological effects of a given marine 

35
reserve or MPA will vary across species, and the economic impacts will vary across individuals 

and groups. This suggests that a generic evaluation of the benefits and costs of marine reserves or 

MPAs is unlikely to provide general conclusions regarding their net value. It also suggests that 

any specific marine reserve or MPA may or may not be in the public interest, and that an 

evaluation of costs and benefits should be undertaken for each specific proposal. 

Empirical Studies of Marine Reserves 

The literature on marine reserves includes a large number of empirical studies that explore the 

impacts of closures on fish populations inside the closures but relatively few that document the 

effects on catches outside them. Though they have been used in many fisheries the impacts of 

marine reserves are not well understood. A study of time and area closures in over fifty fisheries 

in eleven countries produced little clear evidence of improved resource conservation (OECD 

1997). 

Nearly all of the empirical studies that document increased catches after creation or elimination 

of a marine reserve are isolated examples of fisheries with very different physical and socioeconomic characteristics to any in Alaska. A recent comprehensive study of marine fishery 

reserves worldwide (Ward et al. 2001) concludes “there appear to be few well documented 

examples of fisheries that have been shown to benefit from the introduction of reserves. The 

experiences often cited in support of reserves are limited to either the recovery of stocks from a 

highly depleted state, using temporary closures of various forms, or involve mainly subsistencescale tropical reef fisheries. Experiences in neither of these categories can be related directly to 

the world’s commercial capture fisheries, and there is little documented evidence that in a well 

managed fishery, no-take reserves offer additional advantages to a fishery over and above those 

offered by better classical management techniques.” 

Modelling Studies Marine Reserves Impacts on Fisheries 

As Holland (2002) notes, there are serious limitations to our ability to answer these questions 

with empirical research. Doing so rigorously would require a number of replications over long 

periods with comparisons to controls, and even then might only provide conclusions valid for 

very specific sets of circumstances. The full impacts of reserves can take many years to be 

realized and will be confounded by environmental and regulatory changes. Modelling studies 

provide an alternative approach to evaluate basic questions about how reserves of various designs 

in various environments might affect fisheries. 

Several published modeling studies of marine reserves and closed area for fisheries suggest that a 

correctly sized marine reserve may increase yields in fisheries that are subject to growth or 

recruitment overfishing, but that little if any yield increases can be achieved in fisheries where 

effort is already at the level that produces maximum sustainable yield or maximum yield per 

recruit (e.g., Beverton and Holt 1957, Guénette and Pitcher 1999, Hannesson 1998 and 2002, 

Hastings and Botsford 1999, Holland and Brazee 1996, Nowlis and Roberts 1999, Polacheck 

1990, Rodwell et al. 2002, Sanchirico and Wilen 1998, 1999 and 2002a, Smith and Wilen 2003). 

36
In these models increased yields are achieved through two possible mechanisms. Fishing 

mortality and growth overfishing are reduced by decreasing the efficiency of effort. This is 

achieved by closing a proportion of fishable area which, over time, leads to an increase in the 

density of the stock inside the marine reserve. Catch per unit effort is reduced relative to what it 

would be if the fish stock or fishing effort was evenly distributed spatially or concentrated where 

fish are concentrated. If fishing mortality was above the maximum yield per recruit level prior to 

implementation of the marine reserve, this may lead to increased yield per recruit, but only if 

mature fish eventually leave the protection of the marine reserve and are caught. Modelling 

studies have shown that this requires high rates of migration out of the reserve (Polacheck 1990, 

Holland and Brazee 1996). 

Models that incorporate a stock-recruitment function explore the possibility of increasing yields 

through a second mechanism. If the marine reserve protects a resident stock of fish it may allow 

an older, more fecund spawning stock to develop, potentially increasing recruitment. However 

these benefits are likely to be small unless the spawning stock is at low levels since recruitment 

per spawner is generally considered to be a decreasing function of total spawning stock. 

Most of the models cited above compare only equilibrium catches with and without reserves. 

However, as Holland and Brazee (1996) show, there will generally be an initial drop in catches 

and it may be many years before catches return to or exceed pre-reserve levels. Thus even, if 

equilibrium catches are higher post-reserve, the present value of the stream of revenues over time 

might be lower with a reserve than without it. Holland and Brazee (1996) also show that 

equilibrium catches with a reserve are unlikely to exceed catches without one if the fishing 

mortality is already set at the level that yields the highest catch in the absence of a reserve. 

Nearly all of the models and empirical studies of marine reserves have focused on open access 

fisheries or fisheries managed with input controls. While these studies have shown that marine 

reserves can lead to increased catches in heavily exploited fisheries, they have failed to 

demonstrate that reserves can increase either catches or profits for fisheries that are optimally 

managed (with either input or output controls). Furthermore marine reserves do nothing to correct 

the incentives that lead to dissipation of resource rents and may actually lead to increases in 

fishing capacity (Hannesson 1998). 

Only one modeling study has examined the effects of spatial controls on efforts in an optimally 

managed fishery. This study by Sanchirico and Wilen (2002b) shows that the distribution of 

effort might not be optimal in a fishery managed with Individual Transferable Quotas (ITQ) or 

with a spatially homogeneous landings tax. Optimal effort distribution might be achieved with a 

spatially specific landings tax or with spatially specific ITQs. However, it suggests that marine 

reserves, which are equivalent to an infinite landings tax for that area, are generally not optimal 

policies in terms of economic efficiency. 

It is important to keep in mind, that establishing a marine reserve is only one method of reducing 

fishing mortality and probably not the most efficient or effective method in most cases. Marine 

reserves may be the only option in some developing countries where resources to control effort or 

catches directly are not available. However, in Alaska, TACs provide an effective means of 

controlling fishing. 

37
Although models shed some light on how marine reserves might affect fisheries, the generality of 

results from existing models is limited by the uniqueness of different marine systems. Comparing 

different marine reserve models, one can conclude that results are highly sensitive to the specifics

of the biological and socio-economic systems embodied in the implicit and explicit assumptions 

of the models. Results from explicitly spatial models of marine reserves demonstrate that the 

location and shape of the marine reserve in the context of the larger fishery system is likely to be 

just as important as its relative size in determining outcomes (Guénette et al. 2000, Holland 2000, 

Walters and Bonfil 1999). This is particularly true when the fishery system includes multiple, 

spatially heterogeneous species and user groups. Holland (2000) shows that even when a marine 

reserve increases the productivity of fish stocks directly impacted by it, other biologically 

separate fisheries and the incomes of fishermen dependent on them may fall as a result of the 

displaced effort those fisheries absorb. The benefits and costs of a marine reserve must be 

addressed in the context of the larger system. 

Most of the modeling of marine reserves has utilized single species models. Even in fisheries 

where it is possible to tightly control the level of fishing effort there may be a limited ability to 

manipulate the relative levels of fishing mortality on different species that are caught together. 

Consequently, any level of fishing effort may be too high for some species and too low for others. 

Fisheries controlled by total allowable catches (TACs) for individual species may be subject to 

the same problem leading to regulatory discards for species with lower TACs relative to their 

catchability. If there are consistent heterogeneous patterns in the distribution of different species 

it may be possible to tune the relative fishing mortality of species using spatial controls on effort 

(either marine reserves or seasonal closures). Holland (2003) explored this possibility with a 

linear advection/diffusion model of three intermingling groundfish stocks (cod, haddock and 

yellowtail flounder). The model suggests that potential equilibrium revenue gains from using area 

closures to manipulate the distribution of effort are quite limited. This was partly the case because 

redistributions of effort away from areas with the highest concentrations of cod increases the total 

cost of achieving a given level of combined revenues from the three stocks. Controls on nominal 

effort and appropriate mesh size are shown to be the most important management measures. 

There are alternative and more efficient means to control the relative catch rates of different 

species and stocks. For example, individual quota systems provide strong financial incentives to 

do just this. Quota values of species which are constraining catch of other species will tend to rise 

reducing the net benefits accruing to individuals who catch them. This provides incentives to 

individual fishermen to alter the species mix of catch of their catch by changing fishing locations 

or other aspects of their fishing strategy. 

One argument for including marine reserves as part of an optimal fishery management system is 

their potential utility as a hedge against management failures. However, the evidence that marine 

reserves are an effective tool for this purpose is equivocal. One of the most cited papers 

supporting the use of marine reserves as a hedge against management failures is Lauck et al. 

(1998); however this paper makes a number of strong and unrealistic assumptions that bring into 

question the generality of the results. This paper shows that, under some very specific conditions, 

a marine reserve combined with catch controls can achieve a given level of fish stock protection 

(modeled as the probability the fish stock is maintained above 60% of virgin biomass over a 40 

year time horizon) with a higher average catch than a strategy that relies solely on catch controls. 

38
The model assumes that catch varies randomly around a mean equal to the TAC. A key 

assumption is that, after each fishing season, the stock redistributes itself evenly over the fishable 

area and the reserve. This assumption is equivalent to assuming a zero diffusion rate during the 

fishing seasons and an infinite diffusion rate between seasons. This seems quite unrealistic and is 

key to the results of the model. Although the authors do not develop the point, they show that a 

greater level of total effort is required to achieve a given level of catch with a reserve in place. 

This is consistent with other models and suggests that gains in revenues are likely to be offset by 

increased harvesting cost. 

It should also be noted that poorly designed marine reserves have the potential to increase risks in 

some cases. By displacing fishing effort they may increase pressure in certain areas critical to 

particular species. They may also serve to concentrate spawning stock biomass spatially. The 

changes in spatial distribution of adult animals may affect average spawning success and may 

alter the survivorship of larvae and juveniles by effectively changing the average oceanographic 

conditions and the predation they face at various pre-recruit stages. Sinclair (1988) argues that 

the spatial distribution of spawners and ocean conditions that impact retention of eggs and larvae 

may be much more important than resource limitations or predation in determining recruitment. 

Larson and Julian (1999) point out that if there is stochastic spatial variation in the sources of 

successful recruits, spatial concentration of the spawning stock may increase the variance of the 

stock recruitment relationship and thereby increase the risk of fishery collapse. Under these 

circumstances, having a number of substocks of spawners distributed spatially is acting as a 

hedge against spawning failures of other substocks. The value of this portfolio may be reduced by 

concentrating most of the total spawning stock inside the closed area. 

There can be little disagreement that stock assessments are often highly uncertain and that actual 

catches will often diverge from TACs. This indeed suggests the need for precautionary 

approaches to management. But, while marine reserves may play some role in applying the 

precautionary approach in some cases, they too are an imperfect means of compensating for 

management failures and only one tool among many. Many prominent fisheries scientists argue 

that classical management tools, augmented with modern risk management procedures, can 

overcome the fisheries-management problems experienced in the past (Rosenberg et al. 1993, 

Mace 1996).

Conclusions

A conceptual exploration of the impact of reserves and a review of the literature suggest that few 

general conclusions can be drawn regarding the net value of marine reserves and MPAs. It seems 

clear that the total net benefits of a marine reserve or an MPA, including both extractive and nonextractive uses, could be either positive or negative depending on its design and location and the 

degree to which the area closure or system of closures complements or conflicts with other 

marine management methods. 

Both the benefits of a well-designed system of reserves and the costs of a poorly designed one 

could be substantial. Due to the importance of this issue and the potentially large magnitude of 

costs and benefits that may result, a serious effort should be undertaken to assess the costs and 

benefits of specific marine reserves when they are proposed. 

39
RECENT TECHNICAL MEMORANDUMS


Copies of this and other NOAA Technical Memorandums are available from the 

National Technical Information Service, 5285 Port Royal Road, Springfield, VA 22167 

(web site: www.ntis.gov). Paper and microfiche copies vary in price. 

AFSC153 FRITZ, L. W., and C. STINCHCOMB. 2005. Aerial, ship, and land-based surveys of Steller sea lions 

(Eumetopias jubatus) in the western stock in Alaska, June and July 2003 and 2004, 56 p. NTIS number 

PB2005-108287. 

152 AMES, R. T., G. H. WILLIAMS, and S. M. FITZGERALD. 2005. Using digital video monitoring systems 

in fisheries: Application for monitoring compliance of seabird avoidance devices and seabird mortality in 

Pacific halibut longline fisheries, 93 p. NTIS No. PB2005-107312. 

151 TESTA, J. W. (editor). 2005. Fur seal investigations, 2002-2003, 72 p. NTIS number pending. 

150 GULLAND, F. M. D., H. PÉREZ-CORTÉS M., J. URBÁN R., L. ROJAS-BRACHO, G. YLITALO, J. WEIR, 

S. A. NORMAN, M. M. MUTO, D. J. RUGH, C. KREUDER, and T. ROWLES. 2005. Eastern North Pacific 

gray whale (Eschrichtius robustus) unusual mortality event, 1999-2000: A compilation, 33 p. NTIS number 

pending. 

149 RUGH, D. J., K. E. W. SHELDEN, C. L. SIMS, B. A. MAHONEY, B. K. SMITH, L. K. LITZKY, and R. C. 

HOBBS. 2005. Aerial surveys of belugas in Cook Inlet, Alaska, June 2001, 2002, 2003, and 2004, 71 p. 

NTIS No. PB2005-103921. 

148 EILER, J. H., T. R. SPENCER, J. J. PELLA, M. M. MASUDA, and R. R. HOLDER. 2004. Distribution and 

movement patterns of chinook salmon returning to the Yukon River basin in 2000-2002, 99 p. NTIS No. 

PB2005-100707. 

147 MCELDERRY, H., J. SCHRADER, D. MCCULLOUGH, J. ILLINGWORTH, S. FITZGERALD, and S. 

DAVIS. 2004. Electronic monitoring of seabird interactions with trawl third-wire cables on trawl vessels ­

a pilot study, 39 p. NTIS No. PB2005-100243. 

146 WING, B. L., and D. R. BARNARD. 2004. A field guide to Alaskan corals, 67 p. NTIS No. PB2005-100429. 

145 RUGH, D. J., B. A. MAHONEY, and B. K. SMITH. 2004. Aerial surveys of beulga whales in Cook Inlet, 

Alaska, between June 2001 and June 2002, 26 p. NTIS No. PB2004-106479. 

144 ANGLISS, R. A., and K. L. LODGE. 2004. Alaska marine mammal stock assessments, 2003, 224 p. 

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143 ZENGER, H. H. JR. 2004. Data report: 2002 Aleutian Islands bottom trawl survey, 247 p. NTIS No. 

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142 STEVENSON, D. E. 2004. Identification of skates, sculpins, and smelts by observers in North Pacific 

groundfish fisheries (2002-2003), 67 p. NTIS No. PB2004-105817. 

141 HOFF, G. R., and L. L. BRITT. 2003. The 2002 eastern Bering Sea upper continental slope survey of 

groundfish and invertebrate resources, 261 p. NTIS No. PB2004-101668. 

140 STONE, R. P., and M. M. MASUDA. 2003. Characteristics of benthic sediments from areas open and 

closed to bottom trawling in the Gulf of Alaska., 40 p. + Appendices (111 p.). NTIS No. PB2004-100650 

139 JOHNSON, S. W., M. L. MURPHY, D. J. CSEPP, P. M. HARRIS, and J. F. THEDINGA. 2003. A survey 

of fish assemblages in eelgrass and kelp habitats of southeastern Alaska, 39 p. NTIS No. PB2004­

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