Use of Productivity and Susceptibility Indices 

to Determine Stock Vulnerability, with 

Example Applications to Six U.S. Fisheries 

W. S. Patrick, P. Spencer, O. Ormseth, J. Cope, J. Field, D. Kobayashi, 

T. Gedamke, E. Cortés, K. Bigelow, W. Overholtz, J. Link, and P. Lawson

U.S. Department of Commerce 

National Oceanic and Atmospheric Administration 

National Marine Fisheries Service 

NOAA Technical Memorandum NMFS-F/SPO-101 

October 2009
 
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Suggested citation: 

Patrick, W. S., P. Spencer, O. Ormseth, J. Cope, J. Field, D. Kobayashi, T. Gedamke, E. Cortés, 

K. Bigelow, W. Overholtz, J. Link, and P. Lawson. 2009. Use of productivity and susceptibility 

indices to determine stock vulnerability, with example applications to six U.S. fisheries. U.S. 

Dep. Commer., NOAA Tech. Memo. NMFS-F/SPO-101, 90 p. 

A copy of this report may be obtained from: 

Alaska Fisheries Science Center, F/AKC 

NMFS, NOAA 

760 Sand Point Way NE 

Seattle, WA 98115 

Or online at:

http://spo.nmfs.noaa.gov/tm/

ii 
iii

EXECUTIVE SUMMARY 

In response to congressional action, the U.S. National Oceanic and Atmospheric 

Administration’s National Marine Fisheries Service (NMFS) in 2009 revised the National 

Standard 1 (NS1) guidelines that govern federal fisheries management in the United 

States. The term “vulnerability” is referenced in sections of the NS1 guidelines that deal 

with: 1) differentiating between “fishery” and “ecosystem components” stocks, 

2) assembling and managing stock complexes, and 3) creating management control rules. 

NMFS created a Vulnerability Evaluation Work Group (VEWG) in January 2008 to 

provide a methodology for determining vulnerability. While quantitative modeling 

provides the most rigorous method for determining whether a stock is vulnerable to 

becoming overfished or is currently experiencing overfishing, insufficient data exist to 

perform such modeling for many of the stocks managed by NMFS. These relatively 

data-poor stocks highlight the need to develop a flexible semi-quantitative methodology 

that can be applied broadly to many fisheries and regions. The methodology developed 

and six example applications to U.S. fisheries are contained in this document. 

The vulnerability of a stock to becoming overfished is defined in the NS1 

guidelines as a function of its productivity (“the capacity of the stock to produce MSY 

and to recover if the population is depleted”) and its susceptibility to the fishery (“the 

potential for the stock to be impacted by the fishery, which includes direct captures, as 

well as indirect impacts to the fishery”). Upon review of several risk assessment 

methods, the Productivity and Susceptibility Assessment (PSA) was chosen as the best 

approach for determining the vulnerability of data-poor stocks. The PSA evaluates an 

array of productivity and susceptibility attributes for a stock, from which index scores for 

productivity and susceptibility are computed and graphically displayed. The PSA 

methodology described in this document scores attributes on a three-point scale (i.e., 1 = 

low, 2 = moderate, 3 = high). The weighted average of each factor’s attribute scores is 

plotted in an x-y scatter plot and the vulnerability score of the stock is calculated by 

measuring the Euclidean distance of the datum point from the origin of the plot. Stocks 

that receive a low productivity score and a high susceptibility score are considered to be 

the most vulnerable, while stocks with a high productivity score and low susceptibility 

score are considered to be the least vulnerable. 

The PSA methodology contains several modifications to previously published 

examples, including: 1) expanding the number of attributes scored from 13 to 22 to 

consider both direct and indirect impacts; 2) redefining the attribute scoring bins to align 

with life history characteristics of fish species found in U.S. waters; 3) developing an 

attribute weighting system that allows users to customize the analysis for a particular 

fishery; 4) developing a data quality index based on five tiers of data quality, ranging 

from best data to no data, to provide an estimate of information uncertainty; and 5) 

developing a protocol for addressing stocks captured by different sectors of a fishery 

(e.g., different gear types, different regions). 

The PSA was applied to six U.S. fisheries, containing 162 stocks that exhibited 

varying degrees of productivity, susceptibility, and data quality. The PSA was capable of 

broadly distinguishing between stocks based on fishing pressure, as stocks that were 

known to be overfished or undergoing overfishing in the past had significantly higher 

vulnerability scores (P = 0.002) than other stocks, and post hoc analysis of four potential 
iv

candidates for ecosystem component stocks had some of lowest vulnerability scores. 

However, the vulnerability of non-target stocks was not significantly different from target 

stocks for three of the example applications (Hawaii longline-tuna sector, Hawaii 

longline-swordfish sector, and Atlantic shark complex), highlighting the need to carefully 

examine non-target stocks when determining ecosystem component stocks. Thresholds 

for low, moderate, and high vulnerability that could be used to distinguish ecosystem 

component stocks will likely depend on the nature of the fishery to which the PSA is 

applied. It is recommended that the fishery management councils and their associated 

scientific and statistical committees jointly determine these thresholds to aid in their 

decision making process. 

The degree of consistency within the productivity and susceptibility scores was 

determined from correlations of a particular attribute to its overall productivity or 

susceptibility score (after removal of the attribute being evaluated). High correlation 

scores were observed for the majority (i.e., 20 of 22 attributes) of the productivity and 

susceptibility attributes, indicating a high degree of consistency with the productivity and 

susceptibility attributes. 

The PSA developed for this report considers missing data as an endpoint in a continuum 

of data quality. Data availability in the example applications was generally high for the 

majority of the attributes examined, averaging 88% and ranging from 30% to 100%. 

Data quality is a consideration in interpreting the vulnerability scores, and it is 

recommended that managers employ the precautionary approach when evaluating a PSA 

with limited or poor data. Resources for conducting a vulnerability analysis can be found 

at http://www.nmfs.noaa.gov/msa2007/catchlimits.htm/vulnerability. 
v

TABLE OF CONTENTS 

EXECUTIVE SUMMARY iii 

1.0 INTRODUCTION 1 

2.0 NEED FOR ASSESSING VULNERABILITY 1 

2.1 Differentiating Between Fishery and Ecosystem 

Component Stocks 1 

2.2 Assembling and Managing Stock Complexes 2 

2.3 Modifying Control Rules 3 

3.0 DETERMINING VULNERABILITY 3 

3.1 The Productivity and Susceptibility Analysis (PSA) 4 

4.0 THE VULNERABILITY INDEX 5 

4.1 Identifying Productivity and Susceptibility Attributes 5 

4.2 Defining Attribute Scores and Weights 5 

4.3 Productivity Attributes 6 

4.4 Susceptibility Attributes 9 

 4.4.1 Catchability 9 

 4.4.2 Management 11 

4.5 Data Quality Index 12 

4.6 Different Sectors and Gear Types 13 

5.0 EXAMPLE APPLICATIONS 13 

5.1 Northeast Groundfish Multi-species Fishery 13 

5.2 Highly Migratory Atlantic Shark Complexes 14 

5.3 California Nearshore Groundfish Finfish Assemblage 17 

5.4 California Current Coastal Pelagic Species 18 

5.5 Skates (Rajidae) of the Bering Sea and Aleutian Islands 

Management Area 20 

5.6 Hawaii-based Longline Fishery: A Comparison of the 

Tuna and Swordfish Sectors 21 

6.0 SYNTHESIS AND DISCUSSION 23 

6.1 Range of Vulnerability Scores 23 

6.2 Relationship of Vulnerability to Fishing Pressure 24 

6.3 Comparisons Between Target and Non-target Stocks 24 

6.4 Data Availability and Data Quality 25 

6.5 Degree of Consistency within Productivity and Susceptibility 

 Scores 25 

6.6 Correlations to Other Risk Analysis 25 

6.7 Cluster Analysis for Determining Stock Complex Groupings 26 

7.0 CONCLUSIONS 26 

8.0 ACKNOWLEDGEMENTS 27 

9.0 LITERATURE CITED 29 

TABLES AND FIGURES 39 

APPENDICES 65 

vii

LIST OF TABLES 

Table 1. Productivity and susceptibility attributes and rankings. 41 

Table 2. Productivity attribute thresholds based on the empirical 

relationships between tmax, M, k, and tmat (noted as “Modeling”), as well as 

a survey of stocks landed by U.S. fisheries representing all six regional 

management areas (n = 141; noted as “US Fisheries”). 43 

Table 3. The susceptibility scoring thresholds for desirability/value of a 

Stock. 44 

Table 4. The five tiers of data quality used when evaluating the productivity 

and susceptibility of an individual stock. 45 

Table 5. Data for example applications including identification numbers, 

common and scientific names, productivity, susceptibility and vulnerability and 

data quality scores. ID numbers are used to note stocks in summary x-y plots that 

include multiple fisheries, while group IDs are used in x-y plots for a particular 

fishery. 46 

Table 6. Non-parametric statistical analysis of targeted versus non-targeted 

species among productivity (VEP), susceptibility (VES), and vulnerability 

(VE) scores. 50 

Table 7. Summary of the productivity and susceptibility scoring frequencies 

and correlations to its overall factor/category score. Correlations were based 

on stock attributes scores (1 – 3) compared to a modified categorical score for 

 the stock, which did not included the related attribute score. 51 

Table 8. Regression and correlation analysis of our vulnerability analysis 

compared to (A) fuzzy logic vulnerability assessment (FishBase.org source) 

and (B) AFS’ vulnerability scores (Musick 1999). 52 

ix

LIST OF FIGURES 

Figure 1. An example of the productivity and susceptibility x-y plot. This plot 

has been modified slightly from Stobutzki et al. (2001b) by reversing the 

productivity scale to begin with 3 (high productivity) instead of 1 (low 

productivity). 53 

Figure 2. Overall distribution of productivity and susceptibility x-y plot for 

the 166 stocks evaluated in this study, as well as the associated data quality 

of each datum point (see Table 5 for reference IDs). 54 

Figure 3. Overall distribution of data quality scores for the productivity and 

susceptibility factors, noting the number of attributes used for each stock 

(see Table 5 for reference IDs). 55 

Figure 4. Northeast Groundfish Multispecies Fishery productivity and 

susceptibility x-y plot (see Table 5 for reference group numbers). 56

Figure 5. Highly Migratory Atlantic Shark Complex productivity and 

susceptibility x-y plot (see Table 5 for reference group numbers). 57 

Figure 6. California Nearshore Groundfish Finfish Assemblage productivity 

and susceptibility x-y plot (see Table 5 for reference group numbers). 58 

Figure 7. California Current Coastal Pelagic Species productivity and 

susceptibility x-y plot (see Table 5 for reference group numbers). 59 

Figure 8. Skates of the Bering Sea and Aleutian Islands Management Area 

productivity and susceptibility x-y plot (see Table 5 for reference group 

numbers). 60

Figure 9. Hawaii-based Tuna Longline Fishery productivity and 

susceptibility x-y plot (see Table 5 for reference group numbers). 61 

Figure 10. Hawaii-based Swordfish Longline Fishery productivity and 

susceptibility x-y plot (see Table 5 for reference group numbers). 62 

Figure 11. Differences in productivity observed in a subset of forty stocks 

from the West Coast Groundfish Fishery Management Plan, including 

nearshore (black) and shelf (grey) species. 63 
x

Figure 12. A subset of the stocks from the example applications (n = 54) for 

which the status (either overfished or undergoing overfishing) could be 

determined between the years of 2000 and 2008. The dashed line references 

the minimum vulnerability scores observed among the 162 stocks evaluated in 

the example applications. 64 
xi

LIST OF APPENDICES 

Appendix A. The list of marine fish stocks that were considered to be 

representative of U.S. fisheries, and used to help define scoring bins for the 

following productivity attributes: maximum age, maximum size, growth 

coefficient, natural mortality, and age at maturity. 67 

Appendix B. Scoring of productivity and susceptibility attributes 

for the example applications. 71 

Appendix C. Data quality plots for the example applications. 79 

Appendix D. A subset of the stocks from the example applications for 

which status determinations could be made between the years of 2000 and 2008. 89 

1

1.0 INTRODUCTION 

In 1976, the Fishery Conservation and Management Act (FCA), later renamed the 

Magnuson-Stevens Act, was signed into law to implement the management of living 

marine resources (Public Law 109-479). The Act has since been renamed and amended 

several times (National Research Council 1994, Darcy and Matlock 1999), most recently 

through the 2006 Magnuson-Stevens Fishery Conservation and Management 

Reauthorization Act (MSRA). The MSRA added, among other things, new requirements 

for fishery management councils to set annual catch limits (ACLs) and establish 

accountability measures (AMs) for each of its managed fisheries to ensure that 

overfishing (i.e., F > FMSY) does not occur (Public Law 94-265). 

To assist the eight regional fishery management councils in implementing the new 

ACL and AM requirements, the U.S. National Oceanic and Atmospheric 

Administration’s National Marine Fisheries Service (NMFS) revised its National 

Standard 1 (NS1) guidelines, which provides guidance on how conservation and 

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

the optimum yield from each fishery (see 74 FR 3178, January 16, 2009). Because the 

guidelines are written for a general audience, greater technical detail has often been 

needed to further explain how certain aspects of the MSA should be implemented 

(Restrepo and Powers 1999). For example, in the NS1 guidelines, the “vulnerability” of 

fish stocks is referenced as one of the bases for: 1) differentiating between stocks that are 

“in the fishery” versus those that are “ecosystem components,” 2) defining stock 

complexes, and 3) creating a buffer between target and limit fishing mortality reference 

points. While the NS1 guidelines define the term “vulnerability,” during the scoping 

period NMFS received several public comments requesting that they further describe 

how the vulnerability of a stock should be evaluated, especially for stocks for which 

biological or fishery data are limited (termed “data-poor” stocks). In response, a 

Vulnerability Evaluation Work Group (VEWG) was established to develop a 

methodology for determining the vulnerability of data-poor stocks managed under a 

fishery management plan (FMP). The objective of this report is to explain the 

methodology developed for determining vulnerability and present six example 

applications to U.S. fisheries. We begin by reviewing the need for assessing vulnerability 

for the three tasks identified above. 

 

2.0 NEED FOR ASSESSING VULNERABILITY 

2.1 Differentiating Between Fishery and Ecosystem Component Stocks 

The NS1 guidelines recommend that ACLs and AMs are needed for all federally 

managed fisheries, unless they have been explicitly exempted by the MSRA (i.e., stocks 

managed according to international agreement, or a fish with a life cycle of less than 1 

year). NMFS defines a “fishery” as one or more stocks that can be treated as a unit for 

purposes of conservation and management and can be identified on the basis of 

geographical, scientific, technical, recreational, and economic characteristics; and any 

fishing for such stocks (see MSA § 3(13)). Given the broad definition of “fishery,” 

managers have had considerable discretion in defining the “fishery” in their FMPs (73 FR 
2

32527, June 9, 2008). Some FMPs may include only one stock (e.g., Mid-Atlantic 

Council – Bluefish) while others include hundreds of species (e.g., Western Pacific 

Council – Coral Reef Ecosystem). The latter is an example of a fishery management 

council including all species within their management area into the FMP in order to 

monitor the impacts of the fishery on other parts of the ecosystem. Because the 

requirements for assigning ACLs and AMs were meant to be applied to only those stocks 

and stock complexes considered to be “in the fishery”, NMFS suggests that species added 

to an FMP for data collection or ecosystem considerations could be exempted from ACL 

and AMs requirements and classified as “ecosystem components” (see NS1 Guidelines § 

600.310(d)). 

In general, stocks “in the fishery” include target stocks (those that are directly 

pursued by commercial fisheries) and non-target stocks (fish species that are not targeted 

but are caught incidentally in target fisheries). Stocks may be managed as single species 

or in stock complexes. All stocks “in the fishery” are generally retained for sale or 

personal use and/or are vulnerable to overfishing, being overfished, or could become so 

in the future based on the best available information. As a default, NMFS declares that 

all stocks and stock complexes currently listed in FMPs are considered “in the fishery” 

and are required to have status determination criteria (SDC) and related reference points 

(see NS1 Guidelines § 600.310). Because ecosystem component stocks are a type of 

non-target stock not generally retained for sale or personal use, occasional retention of 

the species is not in and of itself a reason to classify the stock as “in the fishery”. In 

addition, ecosystem component stocks must not be subject to overfishing, becoming 

overfished, or likely to become so in the future based on the best available information, in 

the absence of conservation and management measures. While these NS1 definitions are 

useful, they lack technical details on how to determine whether a non-targeted stock is 

likely to become subject to overfishing or become overfished in the future. Instead, the 

NS1 guidelines refer generally to this likelihood as the “vulnerability” of a stock, noting 

that stocks in an FMP should be monitored regularly to determine whether their 

vulnerability has changed. 

2.2 Assembling and Managing Stock Complexes 

 Stocks with similar geographic distributions and life histories are sometimes 

grouped into stock complexes by managers. Stocks may be grouped into complexes for 

various reasons. For example, complexes may include stocks in a multispecies fishery in 

which it is difficult to harvest or target species independently (e.g., the U.S. West Coast 

multispecies trawl fishery for the Dover sole Microstomus pacificus – thornyhead 

Sebastolobus spp. – sablefish Anoplopoma fimbria complex); stocks with insufficient 

data to make a status determination (e.g., undergoing overfishing, overfished); or stocks 

that are not reliably identified by fishermen (e.g., the blackspotted rockfish, Sebastes 

melanostictus, looks very similar to the rougheye rockfish, S. aleutianus). 

 The NS1 guidelines recommend that the vulnerability of stocks be considered 

when establishing or reorganizing stock complexes or when evaluating whether a 

particular stock should be included in an existing complex. Currently, the status of many 

stock complexes is monitored using indicator stock(s), which have sufficient data 

available to define their status determination criteria and to set an ACL (see 
3

§ 600.310(d)). However, if the indicator stock is less vulnerable than other stocks in the 

complex, those other stocks could be undergoing overfishing or be overfished while the 

indicator stock is not (Shertzer and Williams 2008). Therefore, the NS1 guidelines 

recommend that if individual stocks within a complex have a wide range of 

vulnerabilities, the stock complex should either be divided into smaller complexes with 

similar vulnerabilities, or an indicator stock should be chosen to represent the more 

vulnerable stocks within the complex. If data are insufficient to take these actions, then 

the stock complex should be managed more conservatively. 

2.3 Modifying Control Rules

 Restrepo and Powers (1999) define a control rule as “a variable over which 

management has some direct control as a function of some other variable related to the 

stock.” Within the NS1 guidelines, control rules are used to determine how fishing 

mortality rate (F) or catch (total weight or number of fish) should change as a function of 

spawning biomass of the stock or stock complex. The NS1 guidelines also state that the 

acceptable biological catch (ABC) and annual catch target (ACT) control rules should 

take into account scientific and management uncertainty, as well as other pertinent 

information (e.g., potential consequences of overfishing). In general, control rules are 

policies to help fishery managers, in consultation with fisheries scientists, establish 

fishing limits based on the best available scientific information. Control rules should be 

designed so that management actions become more conservative as biomass estimates, or 

other proxies, for a stock or stock complex decline and as science and management 

uncertainty increases (see § 600.310(f)) 

Within the NS1 Guidelines limit and target hierarchy (e.g., OFL ≥ ABC ≥ ACL ≥

ACT), the ABC control rule defines the buffer between the overfishing limit (OFL) and 

ABC. The OFL is the annual amount of catch that corresponds to FMSY or its proxy (the 

fishing rate that results in maximum sustainable yield) applied to the current abundance 

of the stock, and is considered a maximum limit to catch. The ABC is set below the OFL 

to take into account the scientific uncertainty in the estimation of OFL, as well as other 

information that may be useful for determining the buffer (e.g., vulnerability to 

overfishing). Similarly, the ACT control rule is used as an AM to define the buffer 

between the ACL and ACT, and is intended to account for management or 

implementation uncertainty. A stock that is found to be particularly vulnerable to the 

effects of overfishing might be given a larger buffer between either the OFL and the ABC 

or the ACL and the ACT (but not in both control rules, so as not to “double count” and 

provide unduly cautious management advice). For additional information regarding the 

ABC and ACT control rules see § 600.310(f). 

3.0 DETERMINING VULNERABILITY 

The vulnerability of a stock to becoming overfished is defined in this report as the 

potential for the productivity of the stock to be diminished by direct and indirect fishing 

pressure. Vulnerability is expected to differ among stocks based on the life history 

characteristics and susceptibility to the fishery. This definition follows from Stobutzki et 

al. (2001b), and includes the two key elements of 1) stock productivity (a function of the 
4

stock’s life-history characteristics); and 2) stock susceptibility, or the degree to which the 

fishery can negatively impact the stock. This definition differs from that often used in 

evaluation of species at risk of extinction, where the concern is the likelihood of 

recovering from a diminished abundance and the focus is placed upon the productivity of 

the stock (Musick 1999). In our case, a stock with a low level of productivity would not 

be considered vulnerable to fishing unless there was also some susceptibility of the stock 

to the fishery. The interaction between the productivity of a species and its susceptibility 

to the fishery has a long history in fisheries science (Beverton and Holt 1957, Adams 

1980, Jennings et al. 1998, Reynolds et al. 2001, Dulvy et al. 2004). 

 Several risk assessment methods were reviewed to determine which approach 

would be flexible and broadly applicable across fisheries and regions, and was best suited 

for the NS1 guidelines use of the term vulnerability. The methods reviewed generally 

involved semi-quantitative analyses because the data necessary for fully quantitative 

analyses are not available for many fisheries (Dulvy et al. 2003). Previous examples of 

semi-quantitative risk assessments have addressed the fishery impacts on bycatch species 

(Jennings et al. 1999, Milton 2001, Stobutzki et al. 2001b), extinction risk (Musick 1999, 

Roberts and Hawkins 1999, Dulvy and Reynolds 2002, Cheung et al. 2005, Patrick and 

Damon-Randall 2008), and ecosystem viability (Jennings et al. 1999, Fletcher 2005, 

Fletcher et al. 2005, Astles et al. 2006). A modified version of the Productivity and 

Susceptibility Assessment (PSA) was selected as the best approach for examining the 

vulnerability of stocks due to its history of use in other fisheries (Milton 2001; Stobutzki 

et al. 2001a, 2001b; Environment Australia 2002; Gribble et al. 2004; Webb and Hobday 

2004; Braccini et al. 2006; Griffiths et al. 2006; Zhou and Griffiths 2008) and 

recommendations by several organizations and work groups as a reasonable approach for 

determining risk (Hobday et al. 2004, 2007; Smith et al. 2007; Rosenberg et al. 2007). 

3.1 The Productivity and Susceptibility Analysis (PSA) 

The PSA was originally developed to classify differences in bycatch sustainability 

in the Australian prawn fishery (Milton 2001, Stobutzki et al. 2001b) by evaluating the 

productivity of a stock and its susceptibility to the fishery. Stobutzki et al. (2001b) define 

“productivity” as the capacity of a species to recover once the population is depleted (i.e., 

resilience) and “susceptibility” as the likelihood or propensity of species to capture and 

mortality from the fishery. 

In the original form of the PSA, values for the two factors productivity (p) and 

susceptibility (s) of a stock were determined by providing a score ranging from 1 to 3 for 

a standardized set of attributes related to each factor. When data were lacking, scores 

could be based on similar taxa or given the highest vulnerability score as a precautionary 

approach. The individual attribute scores were then averaged for each factor and 

graphically displayed on an x-y scatter plot (Fig. 1). The overall vulnerability score (v) 

of a stock was calculated as the Euclidean distance from the origin of the x-y scatter plot 

(i.e., 3.0, 1.0) and the datum point (note the x-axis scale is reversed): 

 ( )( ) 2 2

spv −+−= 13 [1] 
5

Stocks that received a low productivity score and a high susceptibility score were 

considered to be the most vulnerable to overfishing, while stocks with a high productivity 

score and low susceptibility score were considered to be the least vulnerable. 

The PSA was later modified in 2004 by the Australian Ecological Risk 

Assessment (AERA) team (Hobday et al. 2004), who expanded the structure of the PSA 

to include habitat and community components so the tool could be used to assess the 

vulnerability of an ecosystem. In 2007, the AERA also modified the susceptibility score 

to be the product rather than the average of the susceptibility attributes (Hobday et al. 

2007). Revisions to the PSA were also suggested in Lenfest expert working group 

reports on setting annual catch limits for U.S. fisheries (Rosenberg et al. 2007) and 

determining the risk of over-exploitation for data-poor pelagic Atlantic sharks 

(Simpfendorfer et al. 2008). In Section 4.0 we review how we adapted previous 

applications of PSAs for this report, including descriptions of the productivity and 

susceptibility attributes and the methodology for defining attribute scores and assessing 

data quality. 

4.0 THE VULNERABILITY INDEX 

4.1 Identifying Productivity and Susceptibility Attributes 

Originally, the Stobutzki et al. (2001b) and Milton (2001) analyses were limited 

to 13 attributes (7 susceptibility and 6 productivity attributes). Using partial correlations, 

Stobutzki et al. (2001b) found no redundancy in the 13 attributes. Hobday et al. (2004) 

and Rosenberg et al. (2007) expanded to 75 the number of attributes that could be 

considered for scoring, none of which had previously been examined for redundancy. 

Development of the PSA utilized in this report began with examination of the 

attributes developed by Hobday et al. (2004). This list of attributes was reduced to 35 

after removal of attributes perceived as redundant or pertaining more to risk analyses for 

fishing impacts on habitat quality or overall ecosystem health. The remaining attributes 

were evaluated in a two-phase process. In phase one, the VEWG members provided 

individual scores (i.e., “yes”, “no”, or “maybe”) to determine whether each attribute was: 

1) scientifically valid for calculating productivity or susceptibility of a stock, 2) useful at 

different scales (i.e., stocks of various sizes and spatial distributions), and 3) capable of 

being calculated for most fisheries (i.e., data availability). Attributes receiving a majority 

of “yes” scores for all three factors were retained. In phase 2, attributes receiving mixed 

scores, as well as new attributes that had not been previously identified, were evaluated in 

a group discussion. Through this process, 18 (9 productivity, 9 susceptibility) of the 35 

attributes were selected and four new attributes were added, including: 1) recruitment 

pattern, 2) management strategy, 3) fishing rate relative to natural mortality, and 4) 

desirability/value of the fishery. Overall, 22 attributes (10 productivity, 12 susceptibility) 

were selected for the analysis. 

4.2 Defining Attribute Scores and Weights 

The original analyses performed by Milton (2001) and Stobutzki et al. (2001b) 

defined the criteria for which a score of 1, 2, or 3 should be given to a productivity or 
6

susceptibility attribute. For instance, the attribute scoring bins for the maximum size of a 

species were defined by Stobutzki et al. (2001b) by dividing the length of the largest 

species examined in their study by 3, thereby dividing the scoring bins into equal thirds. 

The PSA developed for this report also scores the productivity and susceptibility 

attributes on a scale of 1 to 3, although an intermediate score (e.g., 1.5 or 2.5) can be used 

when data span two categories. Descriptions of the productivity and susceptibility 

attributes and explanations of the scoring criteria are given in the following two sections. 

Not all of the productivity and susceptibility attributes listed in Table 1 will be 

equally useful for determining the vulnerability of a stock. Previous versions of the PSA 

utilized an attribute weighting scheme in which higher weights were applied to the more 

important attributes (Stobutzki et al. 2001b, Hobday et al. 2004, Rosenberg et al. 2007). 

We recommend a default weight of 2 for the productivity and susceptibility attributes, 

where attribute weights can be adjusted within a scale from 0 to 4 to customize the 

analysis for each fishery. However, we do not recommend adjusting the weighting 

among stocks within any given fishery, as inconsistent weights for individual stocks 

within a PSA analysis can cause problems with transparency and interpretation of the 

results and analysis. In determining the proper weighting of each attribute, users should 

consider the relevance of the attribute for describing productivity or susceptibility rather 

than the availability of data for that attribute (e.g., data-poor attributes should not 

automatically receive low weightings). In some rare cases, it is also anticipated that some 

attributes will receive a weighting of zero, removing them from the analysis, because the 

attribute has no relation to the fishery and its stocks. 

The scoring criteria should ideally be based on clear rules and leave as few 

attributes as possible up to subjective interpretation (Lichtensten and Newman 1967, 

Janis 1983, Von Winterfeldt and Edwards 1986, Bell et al. 1988). However, not all of the 

selected attributes translate into quantitative definitions for the scoring criteria, a situation 

also seen by Stobutzki et al. (2002). To reduce scoring bias, all weighting and attribute 

scores should be determined using a collaborative process (e.g., the Delphi method – 

Okoli and Pawlowski 2004, Landeta 2006), rather than being scored by one or two 

individuals (Janis 1983, Von Winterfeldt and Edwards, 1986, Bell et al. 1988). 

4.3 Productivity Attributes 

“Productivity” is defined as the capacity of the stock to recover once the 

population is depleted (Stobutzki et al. 2001b). This largely reflects the life-history 

characteristics of the stock. While there is some redundancy among the productivity 

attributes, the inclusion of multiple life history traits allows a more comprehensive 

assessment of productivity. Many of these attributes are based on the Musick (1999) 

qualitative extinction risk assessment and the PSA of Stobutzki et al. (2001b). However, 

the scoring thresholds have been modified in many cases to better suit the distribution of 

life history characteristics observed in U.S. fish stocks (Table 2). 

Information on maximum length, maximum age, age at maturity, natural 

mortality, and von Bertalanffy growth coefficient were available from 140+ stocks 

considered to be representative of U.S. fisheries (Appendix A). For these attributes, 

analysis of variance (ANOVA) was used to define attribute scoring thresholds that 

produced significantly different bins of data. In order to ensure consistency in these 
7

attributes, the scoring thresholds from the analysis of variance were also compared to 

published relationships among maximum age and natural mortality (Alverson and Carney 

1975, Hoenig 1983), von Bertalanffy growth coefficient (Froese and Binohlan 2000), and 

age at maturity (Froese and Binohlan 2000). We have defined 10 productivity attributes: 

Population growth (r): This is the intrinsic rate of population growth or maximum 

population growth that would be expected to occur in a population under natural 

conditions (i.e., no fishing), and thus directly reflects stock productivity. The scoring 

definitions were taken from Musick (1999), who stated that r should take precedence 

over other productivity attributes (e.g., given a weighting of 4) as it combines many of 

the other attributes defined below. 

Maximum age (tmax): Maximum age is a direct indication of the natural mortality 

rate (M), where low levels of M are negatively correlated with high maximum ages 

(Hoenig 1983). The scoring definitions were based on the ANOVA applied to the 

observed fish stocks considered to be representative of U.S. fisheries (Appendix A). The 

tmax for a majority of these fish ranges between 10 to 30 years. 

Maximum size (Lmax): Maximum size is also correlated with productivity, with 

large fish tending to have lower levels of productivity (Roberts and Hawkins 1999), 

though this relationship tends to degrade at higher taxonomic levels. The scoring 

definitions were based on the ANOVA applied to the observed fish stocks considered to 

be representative of U.S. fisheries (Appendix A). The Lmax for a majority of these fish 

ranges between 60 to 150 cm TL. 

Growth coefficient (k): The von Bertalanffy growth coefficient measures how 

rapidly a fish reaches its maximum size, where long-lived, low-productivity stocks tend 

to have low values of k (Froese and Binohlan 2000). The attribute scoring definitions 

based upon the ANOVA applied to the fish stocks considered to be representative of U.S. 

fisheries was 0.15 to 0.25. This is roughly consistent with the values obtained from 

Froese and Binohlan’s (2000) empirical relationship k = 3/ tmax of 0.1 to 0.3, based upon 

tmax values of 10 and 30. 

Natural mortality (M): Natural mortality rate directly reflects population 

productivity, as stocks with high rates of natural mortality will require high levels of 

production in order to maintain population levels. Several methods for estimating M rely 

upon the negative relationship between M and tmax, including Hoenig’s (1983) regression 

based upon empirical data, the quantile method that depends upon exponential mortality 

rates (Hoenig 1983), and Alverson and Carney’s (1975) relationship between mortality, 

growth, and tmax. The attribute scoring thresholds from the ANOVA applied to the fish 

stocks considered to be representative of U.S. fisheries was 0.2 to 0.4, and were roughly 

consistent with those produced from Hoenig’s (1983) empirical regression of 0.14 to 0.4, 

based on tmax values of 10 and 30.

Fecundity: Fecundity (i.e., the number of eggs produced by a female for a given 

spawning event or period) varies with size and age of the spawner, so we followed 

Musick’s (1999) recommendation that fecundity should be measured at the age of first 

maturity. As Musick (1999) noted, low values of fecundity imply low population 

productivity but high values of fecundity do not necessarily imply high population 

productivity; thus, this attribute may be more useful at the lower fecundity values. The 

scoring definitions were taken from Musick (1999), which range between fecundities of 

1,000 and 100,000. 
8

Breeding strategy: The breeding strategy of a stock provides an indication of the 

level of mortality that might be expected for the offspring in the first stages of life. To 

estimate offspring mortality, we used Winemiller’s (1989) index of parental investment. 

The index ranges in score from 0 to 14 and is composed of: 1) the placement of larvae or 

zygotes (i.e., in nest or into water column; score ranges from 0 to 2); 2) the length of time 

of parental protection of zygotes or larvae (score ranges from 0 to 4); and 3) the length of 

gestation period or nutritional contribution (score ranges from 0 to 8). To translate 

Winemiller’s index into our 1-3 ranking system, we examined King and McFarlane’s 

(2003) parental investment scores for 42 North Pacific stocks. These 42 stocks covered a 

wide range of life-histories and habitats, including 10 surface pelagic, 3 mid-water 

pelagic, 3 deep-water pelagic, 18 near-shore benthic, and 9 offshore benthic stocks. 

Thirty-one percent of the stocks had a Winemiller score of zero, and 40% had a 

Winemiller score of 4 or higher, so 0 and 4 were used as the breakpoints between our 

ranking categories. 

 Recruitment pattern: Stocks with sporadic and infrequent recruitment success 

often are long-lived and thus might be expected to have lower levels of productivity 

(Musick 1999). This attribute is intended as a coarse index to distinguish stocks with 

sporadic recruitment patterns and high frequency of year-class failures from those with 

relatively steady recruitment. Thus, the frequency of year-class success (defined as 

exceeding a recruitment level associated with year-class failure) was used for this 

attribute. Because this attribute was viewed as a course index, the VEWG chose 10% 

and 75% as the breakpoints between our ranking categories so that scores of 1 and 3 

identified relatively extreme differences in recruitment patterns. 

Age at maturity (tmat): Age at maturity tends to be positively related with 

maximum age (tmax), as long-lived, lower productivity stocks will have higher ages at 

maturity relative to short-lived stocks. The attribute scoring definitions based upon the 

ANOVA applied to the fish stocks considered to be representative of U.S. fisheries was 2 

to 4 years. This range is lower than that observed from Froese and Binohlan’s (2000) 

empirical relationship between tmat and tmax, which was 3 to 9 based upon values of tmax of 

10 and 30. However, the Froese and Binohlan (2000) used data from many fish stocks 

around the world, which may not be representative of U.S. stocks. For the PSA, the 

thresholds obtained from the ANOVA applied to stocks considered representative of U.S. 

fisheries were used. 

Mean trophic level: The position of a stock within the larger fish community can 

be used to infer stock productivity, with lower-trophic-level stocks generally being more 

productive than higher-trophic-level stocks. The trophic level of a stock can be computed 

as a function of the trophic levels of the organisms in its diet. For this attribute, stocks 

with trophic levels higher than 3.5 were categorized as low productivity stocks and stocks 

with trophic levels less than 2.5 were categorized as high-productivity stocks, with 

moderate productivity stocks falling between these bounds. These attribute threshold 

roughly categorize piscivores to higher trophic levels, omnivores to intermediate trophic 

levels, and planktivores to lower trophic levels (Pauly et al. 1998). 

 
9

4.4 Susceptibility Attributes

Susceptibility is defined as the potential for a stock to be impacted by a fishery. 

Previous applications have focused on the catchability and mortality of stocks, and 

addressed other attributes such as management effectiveness and effects of fishing gear 

on habitat quality in subsequent analyses (Hobday et al. 2007). Our susceptibility index 

includes all these attributes in an effort to make the results of analysis more transparent 

and understandable. However, since these attributes address different aspects of 

susceptibility, we have differentiated the catchability and management attributes as subcategories under the susceptibility factor. 

Similar to AERA's susceptibility attributes (Hobday et al. 2007), catchability 

attributes provide information on the likelihood of a stock’s capture by a particular 

fishery, given the stock’s range, habitat preferences, and behavioral responses and/or 

morphological characteristics that may affect its susceptibility to the fishing gear 

deployed in that fishery. Management attributes consider how the fishery is managed: 

fisheries with conservative management measures in place that effectively control the 

catch in the fishery are less likely to have overfishing occurring. For some of these 

attributes, criteria are somewhat general in order to accommodate the wide range of 

fisheries and systems. We defined 12 susceptibility (7 catchability and 5 management) 

attributes. 

4.4.1 Catchability 

Areal overlap: This attribute pertains to the extent of geographic overlap between 

the known distribution of a stock and the distribution of the fishery. Greater overlap 

implies greater susceptibility, as some degree of geographical overlap is necessary for a 

fishery to impact a stock. The simplest approach is to determine, either qualitatively or 

quantitatively, the proportion of the spatial distribution of a given fishery that overlaps 

that of the stock, based on known geographical distributions of both. If data regarding 

spatial distributions are lacking, inferences on areal overlap may be made from 

knowledge of depth distributions of the fishery and the stock. For example, an upper 

bound estimate of areal overlap may be made from knowledge of the portion of fishing 

effort that occurs in the areas which encompass the depths occupied by a species. 

Geographic concentration: Geographical concentration is the extent to which the 

stock is concentrated into small areas. The rationale for including this attribute is that a 

stock with a relatively even distribution across its range may be less susceptible than a 

highly aggregated stock. For some species, a useful measure of this attribute is the 

minimum estimate of the proportion of area occupied by a certain percentage of the stock 

(Swain and Sinclair 1994), which can be computed in cases where survey data exist. 

First, the cumulative frequency of the survey catch per unit effort (CPUE) is computed as 

∑∑

∑∑

= =

= = = h

i

n

j

ij

i

i

h

i

n

j

ij

i

i

i

i

y

n

A

cIy

n

A

cF

1 1

1 1

)(

( 100) where 

⎭

⎬

⎫

⎩

⎨

⎧ ≤ = otherwise

cyif cI ij

,0

,1 ( , ) [2]
10

h is the number of strata, yij is the CPUE of tow j in stratum i, and ni and Ai are the 

number of tows and area, respectively, for stratum i. Equation 2 is used to compute the 

CPUE cz associated with a particular percentile z of the species CPUE data. The 

cumulative area associated with a particular density level c is then estimated as 

∑∑= =

=

h

i

n

j i

i

i

cI

n

A cG

1 1

( where ) )(

⎭

⎬

⎫

⎩

⎨

⎧ ≤ = otherwise

cyif cI ij

,0

,1 ( , ) [3]

and the minimum area corresponding to the 100 - z percentile is obtained by subtracting 

G(cz) from the total survey area AT. For example, the area covered by 95% of the stock 

(D95) is computed as 

 ( ) 95 05 = T − cGAD . [4] 

The area covered by 95% of the concentration is then divided by AT to get the proportion 

of the survey area occupied by the stock. 

 For many stocks, this index gives a general index of areal coverage that relates 

well to geographic concentration. However, some stocks can cover a small area even 

though the stocks were not concentrated in a small number of locations (i.e., a “patchy” 

stock that is distributed over the survey area). Thus, some refinements to the index may 

be necessary to characterize geographic concentration in these cases. 

Vertical overlap: Similar to geographical overlap, this attribute concerns the 

position of the stock within the water column (i.e., demersal or pelagic) relative to the 

fishing gear. Information on the depth at which gear is deployed (e.g., depth range of 

hooks for a pelagic longline fishery) and the depth preference of the species (e.g., 

obtained from archival tagging or other sources) can be used to estimate the degree of 

vertical overlap between fishing gear and a stock. 

Seasonal migrations: Seasonal migrations either to or from the fishery area (i.e. 

spawning or feeding migrations) could affect the overlap between the stock and the 

fishery. This attribute also pertains to cases where the location of the fishery changes 

seasonally, which may be relevant for stocks captured as bycatch. 

Schooling, aggregation, and other behaviors: This attribute encompasses 

behavioral responses of both individual fish and the stock in response to fishing. 

Individual responses may include, for example, herding or gear avoidance behavior that 

would affect catchability. An example of a population-level response is a reduction in 

the area of stock distribution with reduction in population size, potentially leading to 

increases in catchability (MacCall 1990). 

Morphology affecting capture: This attribute pertains to the ability of the fishing 

gear to capture fish based on their morphological characteristics (e.g., body shape, spiny 

versus soft rayed fins). Because gear selectivity varies with size and age, this measure 

should be based on the age or size classes most representative of the entire stock. 

Desirability/value of the fishery: This attribute assumes that highly valued fish 

stocks are more susceptible to overfishing or becoming overfished by recreational or 

commercial fishermen due to increased effort. To identify the value of the fish, we 

suggest using the price per pound or annual landing value for commercial stocks (using 

the higher of the two values) or the retention rates for recreational fisheries (Table 3). 

Commercial landings and recreational retention rates can be found at: 

www.st.nmfs.noaa.gov/st1/commercial/landings/annual_landings.html
11

and 

www.st.nmfs.noaa.gov/st1/recreational/queries/index.html

4.4.2 Management 

Management strategy: The susceptibility of a stock to overfishing may largely 

depend on the effectiveness of fishery management procedures used to control catch 

(Sethi et al. 2005, Rosenberg et al. 2007, Shertzer et al. 2008, Dankel et al. 2008). Stocks 

that are managed using catch limits for which the fishery can be closed before the catch 

limit is exceeded (i.e., in-season or proactive accountability measures) are considered to 

have a low susceptibility to overfishing. However, stocks that do not have specified 

catch limits or accountability measures are highly susceptible to overfishing if their 

abundance trends are not monitored. Stocks that are managed using catch limits and 

reactive accountability measures (e.g., catch levels are not determined until after the 

fishing season) are considered to be moderately susceptible to overfishing or becoming 

overfished. 

Fishing mortality rate (relative to M): This criterion is applicable to stocks where 

estimates of both fishing mortality rates (F) and (M) are available. Because sustainable 

fisheries management typically involves conserving the reproductive potential of a stock, 

it is recommended that the average F on mature fish be used where possible as opposed 

to the fully selected or “peak” F. We base our thresholds on the conservative rule of 

thumb that the M should be an upper limit of F (Thompson 1993; Restrepo et al. 1998), 

and thus F/M should not exceed 1. For this attribute, we define intermediate F/M values 

as those between 0.5 and 1.0; values above 1.0 or below 0.5 are defined as high and low 

susceptibility, respectively. 

Biomass of Spawners: Analogous to fishing mortality rate, the extent to which 

fishing has depleted the biomass of a stock relative to expected unfished levels offers 

information on realized susceptibility. One way to measure this is to compare the current 

stock biomass against an estimate of B0 (the estimated biomass with no fishing). If B0 is 

not available, one could compare the current stock size against the maximum observed 

from a time series of population size estimates (e.g., from a research survey). If a time 

series is used, it should be of adequate length (e.g., > 5 years). Note that the maximum 

observed survey estimate may not correspond to the true maximum biomass for stocks 

with substantial observation errors in survey biomass estimates. Additionally, stocks 

may decline in abundance from environmental factors not related to susceptibility to the 

fishery, so this should be considered in evaluating depletion estimates. Notwithstanding 

these issues, which can be addressed with the data quality score described below, some 

measure of current stock abundance was viewed as a useful attribute. 

Survival after capture and release: Fish survival after capture and release varies by 

species, region, and gear type or even market conditions, and thus can affect the 

susceptibility of the stock. When data are lacking, the VEWG suggest using NMFS' 

forthcoming National Bycatch Report to estimate bycatch mortality. The report will 

provide comprehensive estimates of bycatch of fish, marine mammals, and non-marine 

mammal protected resources in major U.S. commercial fisheries, and should allow users 

to develop a proxy based on similar fisheries. 
12

Fishery impact on habitat: A fishery may have an indirect effect on a species via 

adverse impacts on habitat. Defining these effects is the focus of environmental impact 

statements or essential fish habitat evaluations that have been conducted by NMFS, and 

this work can be used to evaluate this attribute. Thus, the impacts on habitat may be 

categorized with respect to whether adverse impacts on habitat are minimal, temporary, 

or mitigated. 

4.5 Data Quality Index 

The uncertainty associated with data-poor stocks can lead to errors in risk 

assessment (Astles et al. 2006, Peterman 1990, Scandol 2003). As a precautionary 

measure, ecological risk assessments have often provided higher-level risk scores when 

data are missing in an attempt to avoid incorrectly identifying a high-risk stock as a lowrisk (Milton 2001, Stobutzki et al. 2001b, Astles et al. 2006). While this approach can be 

viewed as precautionary, it also confounds the issues of data quality with risk assessment. 

For example, under this approach a data-poor stock may receive a high-risk evaluation 

either from an abundance of missing data or from the risk assessment of the available 

data, with the result that the risk scores may be inflated (see Hobday et al. 2004). In 

contrast, we considered missing data within the larger context of data quality, and report 

the overall quality of data as a separate value. 

A data quality index was developed that provides an estimate of uncertainty for 

individual vulnerability scores based on five tiers ranging from best data or high belief in 

the score to no data or little belief in the score (Table 4). The data quality score is 

computed for the productivity and susceptibility scores as a weighted average of the data 

quality scores for the individual attributes, and denotes the overall quality of the data or 

belief in the score rather than the actual type of data used in the analysis. For example, a 

data quality score of 3 (related to limited data), could be derived from data equally 

divided among scores of “1, best data” and “5, no data.” It is important to highlight the 

data quality associated with each vulnerability score when plotting the data on an x-y 

scatter plot (Fig. 2). Similar to Webb and Hobday (2004), we suggest dividing the data 

quality scores into three groupings (low > 3.5; moderate 2.0 to 3.5; and high < 2.0) for 

display purposes. We also recommend that the data quality scores be: 1) plotted as a 

separate graph noting how many attributes were used in the analysis (Fig. 3; Appendices 

1- 6) and 2) listed in a table to provide decision makers with more information on the 

scores, such as mean score, range, mode, variance, etc. In the case of missing data for an 

attribute (data quality score of 5), this attribute would not be used in the computation of 

the vulnerability score but would be reflected in the computation of overall data quality. 

Thus, a stock with missing data for many attributes would have a low overall data quality 

score. 

Data quality scores can be used to reflect the extent to which historical data on 

productivity and susceptibility pertains to current conditions. Productivity and 

abundance of marine stocks often show low-frequency trends or “regime shifts” that 

reflect environmental variability (Spencer and Collie 1997, Hare and Mantua 2000), and 

erroneous estimates of productivity could occur if historical data that do not reflect 

current conditions are utilized. A lack of recent data reflecting current environmental 

conditions can be reflected in the data quality score. For stocks with relatively short 
13

generation times it is important to conduct the PSA analysis frequently to monitor 

environmental-driven changes in stock status and productivity. 

4.6 Different Sectors and Gear Types 

As noted earlier, the PSA was first developed to evaluate the sustainability of bycatch 

species in the Australian commercial prawn fishery, which consists of a single sector 

(i.e., trawl fishery), and subsequent applications to other fisheries have also consisted of 

single sectors. However, PSA scores may vary between sectors of a single fishery (e.g., 

gear sectors, commercial versus recreational sectors), or between multiple fisheries that 

harvest a single stock. For example, the susceptibility score for “survival after capture 

and release” may differ greatly between trawl and gill net gears. Similarly, the “degree 

of habitat disturbance” would vary greatly depending on the habitat type and gear used to 

capture a species (e.g., bottom trawl versus rod and reel). In these cases, each sector of a 

fishery or each fishery should have its own vulnerability evaluation performed to 

determine which stocks in that sector or fishery are most vulnerable. An overarching 

vulnerability evaluation score should be calculated for each stock listed in an FMP using 

a weighting system based on the sectors landings over some predetermined time frame 

(i.e., based on average landings). 

5.0 EXAMPLE APPLICATIONS 

To demonstrate the utility of the vulnerability evaluation, we evaluated six U.S. 

fisheries that had varying degrees of productivity, susceptibility, and data quality (see 

Appendices 2 – 10). These example applications show that there can be considerable 

variation in vulnerability within currently grouped complexes, and between sectors (see 

Northeast Groundfish and Pacific longline studies). Please note, however, this report 

should not be considered the “official” vulnerability analysis for the six fisheries we 

examined. The fishery management councils and their scientific and statistical 

committees, or NMFS scientists in the case of highly migratory species, who are charged 

with managing these fisheries should perform their own vulnerability analysis or modify 

ours to meet their data quality standards. 

5.1 Northeast Groundfish Multi-species Fishery 

Within the NMFS Northeast Region, 19 groundfish stocks are assessed as a group 

on a 5-year planning horizon by the Groundfish Assessment Review Meeting (GARM) 

committee for the New England Fishery Management Council (NEFMC). The GARM 

stocks include gadoids (e.g., Atlantic cod Gadus morhua, haddock Melanogrammus 

aeglefinus, white hake Urophycis tenuis, several flatfish (e.g. yellowtail flounder 

Limanda ferruginea, witch flounder Glyptocephalus cynoglossus, American plaice 

Hippoglossoides platessoides, and winter flounder Pseudopleuronectes americanus), and 

related demersal stocks, which are overall valued at about U.S. $75 million (NMFS 

2008). Previously, the entire complex was overfished during the International 

Commission on of the Northwest Atlantic Fisheries (ICNAF) era (1960s to1970s) and 

also after extension of the exclusive economic zones in 1976. More recently this 
14

complex has been managed by the NEFMC under the Multispecies Groundfish FMP. 

The fishery is currently managed with area closures, mesh-size regulations, and effort 

reduction procedures (days at sea), and is almost entirely prosecuted with bottom trawl 

gear, with small amounts of landings by gill nets and longlines. The fleet fishes mostly 

on Georges Bank, but also has a significant component in the Gulf of Maine and in the 

Southern New England region. Several stocks in the complex have recovered (e.g., 

Georges Bank haddock, redfish Sebastes marinus), but many are still chronically 

overfished (e.g. southern New England yellowtail flounder, Georges Bank cod). 

Data quality for the entire group is relatively high, with long-term time series of 

catch and research vessel survey data available; however, information for windowpane 

flounder Scophthalmus aquosus, ocean pout Zoarces americanus, and Atlantic halibut 

Hippoglossus hippoglossus is not quite as good as for the other members of the group. 

Life history information for most of the stocks is relatively complete, many are assessed 

with fairly detailed analytical stock assessment models, and new research on movements, 

morphometrics, bycatch, and improved survey techniques is ongoing. 

These stocks range from relatively low (e.g., ocean pout) to high productivity 

(e.g., Georges Bank haddock) in their life histories, and some are more susceptible than 

others to overfishing (e.g., halibut, white hake), habitat disturbances (e.g., winter 

flounder), and gear interactions (e.g., Gulf of Maine and Georges Bank cod). We note 

that the spread across GARM stocks is smaller than that in other fisheries (see below) due 

to their similarities in life history and targeting fishing gear. Overall, these GARM stocks 

clustered into two groups based on differences in productivity (Fig. 4). The first cluster of 

stocks contains cod, haddock, and most of the flatfish, etc. The second cluster contains 

redfish, white hake, and halibut, and is somewhat more vulnerable to overfishing because 

the life histories of these stocks suggest they are generally less productive. 

5.2 Highly Migratory Atlantic Shark Complexes 

 Atlantic shark species are divided into four management groups under the current 

Highly Migratory Species (HMS) FMP: 1) large coastal, 2) small coastal, 3) pelagic, and 

4) prohibited. The four groups were designed to facilitate management, but do not 

necessarily reflect the exact habitat preferences or life histories of the component species. 

In general, large coastal sharks are large sharks characterized by slow growth rates, low 

fecundity, late age at maturation, and long lifespan. These species generally utilize 

estuaries and nearshore waters during at least part of their life cycle, but also occur in and 

sometimes beyond waters of the continental shelf. Typical large coastal sharks are 

blacktip shark Carcharhinus limbatus, sandbar shark C. plumbeus, bull shark C. leucas, 

tiger shark Galeocerdo cuvier, and hammerhead shark Sphyrna zygaena. By contrast, 

small coastal sharks reach a smaller size, tend to grow and mature more rapidly and have 

shorter lifespans, and are generally restricted to more coastal waters. Atlantic sharpnose 

shark Rhizoprionodon terraenovae and bonnethead shark S. tiburo exemplify a “typical” 

small coastal shark. Pelagic sharks are large, with life history characteristics generally 

intermediate to those of the two other groups, which range widely in the upper reaches of 

the ocean and undertake extensive, sometimes transoceanic, migrations. Typical pelagic 

sharks are blue shark Prionace glauca, shortfin mako shark Isurus oxyrinchus, and 

thresher sharks Alopias spp. Prohibited species are a mixture of species once included in 
15

the other management groups and having coastal, pelagic, and coastal-pelagic habitat 

preferences. They include some charismatic species, such as the white shark 

Caracharodon carcharias, whale shark Rhincodon typus, and basking shark Cetorhinus 

maximus, and three species that have been proposed for listing under the U.S. 

Endangered Species Act (dusky shark Carcharhinus obscurus, night shark C. signatus, 

and sand tiger shark Carcharias taurus). Prohibited species tend to be large and rare, and 

have life history characteristics that make them particularly vulnerable to overfishing. In 

some cases, however, they were included in this group to err on the side of caution 

because of a complete absence of biological data on the species (e.g., Caribbean 

sharpnose shark Rhizoprionodon porosus, smalltail shark Carcharhinus porosus, and 

Atlantic angel shark Squatina dumeril). As a group, sharks exhibit low productivity (as 

compared to teleosts, for example), mainly owing to their reduced reproductive rates. We 

included 37 species of sharks in our analysis (Table 5). 

 Although shark production is relatively low compared to other marine resources, 

U.S. commercial and recreational shark fisheries are likely to account for more than U.S. 

$100 million annually, with the global shark fin trade alone being valued at close to U.S. 

$400 million (Clarke 2003). In addition to direct consumption and production of shark 

products, net benefits in the shark fishery are also derived from the existence value of 

sharks for non-consumptive user groups (Davis et al. 1997, Cardenas-Torres et al. 2007, 

Rowat and Engelhardt 2007). While there are bottom longline and drift gillnet fisheries 

that target sharks in the United States, sharks are also caught incidentally as bycatch in a 

variety of fisheries (e.g., gill net, pelagic longline and trawl fisheries), with the magnitude 

of this bycatch being poorly known in general. The commercial fishery is a limited 

access fishery with incidental retention limits, observer and reporting requirements, and a 

ban on finning. Sharks are also commonly caught in U.S. recreational fisheries, 

including private boats, charterboats, and headboats. Recreational regulations allow 

retention of one shark per vessel per trip, with a 4.5 ft (1.4 m) fork length minimum size 

requirement, and an additional allowance of one Atlantic sharpnose shark and one 

bonnethead shark per person per trip with no minimum size. In general, the U.S. Atlantic 

shark fishery is primarily a southeastern fishery extending from Virginia to Texas, 

although sharks are also landed in the states north of Virginia. All sharks fall under the 

jurisdiction of NMFS’ Highly Migratory Species Division. 

Both the quality and quantity of available biological and fishery data vary by 

species of sharks. While relatively good information is available for the most important 

species in the fisheries, basic biological information is lacking for the less common 

species. Analytical stock assessments are thus available for only a few species: sandbar 

and blacktip sharks (large coastal); Atlantic sharpnose shark, bonnethead shark, 

blacknose shark Carcharhinus acronotus, and finetooth shark C. isodon (small coastal); 

blue and shortfin mako shark (pelagic); and dusky shark (prohibited). 

The information used to score the productivity attributes was derived from a 

dedicated shark life history database maintained by NMFS (citations available upon 

request). The information used to score the susceptibility attributes was derived from 

various sources. The area overlap and geographic concentration attributes were scored 

using information from IUCN species distribution maps (pelagic shark species), HMS 

essential fish habitat maps (large and small coastal sharks), ICCAT effort distribution 

maps (pelagic sharks), Coastal Fishery Logbook effort maps (large coastal sharks), and 
16

shrimp trawl effort distribution (small coastal sharks). For vertical overlap, we used 

mostly unpublished information from archival tags and published papers (a variety of 

species, mostly pelagic); for morphology affecting capture, we used data on size of 

animals caught in various scientific observer programs (U.S. Pelagic Longline Observer 

Program for pelagic sharks, Bottom Longline Observer Program for large coastal sharks, 

Shrimp Trawl Observer Program for small coastal sharks); for survival after capture and 

release, the data also came from the three observer programs referenced above. There 

was consistently no information for several attributes (recruitment pattern, seasonal 

migrations, and schooling/aggregation behavior). Information for F relative to M and 

SSB was only available for those species for which stock assessments have been 

conducted. 

The susceptibility aspect refers to the main fishery affecting each group: pelagic 

longline fishery for tunas and tuna-like species (pelagic sharks), bottom longline directed 

shark fishery (large coastal sharks), and bottom trawl shrimp fishery (small coastal 

sharks). Weights for each attribute were assigned by discussion and consensus between 

the two assessment scientists involved in the evaluation. For the productivity attributes, 

both scientists felt that the intrinsic rate of increase (r) was the most valuable quantitative 

measure of productivity and was assigned the highest weight of 4. Measured fecundity

and estimated natural mortality were also viewed as important indicators and were 

assigned a weight of 3. The recruitment attribute, on the other hand, was assigned a 

weight of zero because it was felt it was not a good indicator for productivity of sharks as 

currently defined. For the susceptibility attributes, it was felt that the overlap between the 

distribution of the species and the fisheries (areal overlap and vertical overlap) and the 

probability of survival after capture and release were the most important attributes and 

were assigned a weight of 4. The remaining susceptibility attributes were given a default 

weighting of 2. 

 The productivity scores clearly separated the highly productive Atlantic sharpnose 

and bonnethead (small coastal) and the Caribbean sharpnose shark (prohibited, but note 

the low data quality) from the other species in the analysis (Table 5; Fig. 5). The 

remaining species were grouped toward the lower end of the productivity scale, with 

scores ranging from 1.0 to 1.35. Within this grouping, the relatively higher productivity 

of species such as the tiger and nurse (large coastal) and blue (pelagic) sharks were 

reflected in the scoring; however, the overall scoring showed little contrast for over 50% 

of the stocks analyzed (22 of the 37 stocks had weighted productivity scores of 1.1 or 

less). While this level of detail may be appropriate for intertaxonomic comparisons, it 

would not be adequate for a PSA applied to sharks only for which the use of a continuous 

score, such as the intrinsic rate of increase (r) provides much more contrast (Cortés et al. 

2008, Simpfendorfer et al. 2008). The susceptibility scores show more overall contrast 

than the productivity scores, with a range of 1.4 to 2.9. A number of ecologically 

different species have similar susceptibilities to the main fisheries, while several less 

common species (e.g., sixgill, sharpnose sevengill, bigeye, sandtiger, and whale shark) 

show decreased susceptibility (but also note the lower data quality). It is interesting to 

note that 12 of the 14 species with the lowest susceptibility scores fall into the prohibited 

FMP group. 

 
17

5.3 California Nearshore Groundfish Finfish Assemblage 

The California nearshore finfish assemblage is a complex of 19 nearshore species, 

with a unique history of landings comprising a mix of heavy recreational and lucrative 

commercial fisheries. Most of the species in this fishery are rockfishes (family 

Scorpaenidae, with most of these of the genus Sebastes), but there are also two greenlings 

(family Hexagrammidae), one prickleback (family Stichaeidae), and one wrasse (family 

Labridae) (Table 5). The species are typically associated with nearshore rocky reef or 

kelp forest communities, and have a range of life histories. Most are relatively longlived, slow-growing, and either live-bearing (Sebastes) or egg-guarding (cabezon 

Scorpaenichthys marmoratus, greenlings); there is also one protogynous hermaphrodite 

(California sheephead Semicossyphus pulcher). By virtue of their life history 

characteristics and accessibility to a wide range of fishing types, most have been shown 

or are perceived to be vulnerable to overexploitation in the absence of effective 

management regimes (Gunderson et al. 2008). Although the total landings by volume 

tend to be small (only 224 tons landed commercially in California waters in 2006), many 

of the premium/live-fish fishery targets are highly lucrative, with ex-vessel values of up 

to U.S. $10 per pound (and net revenues of U.S. $2.2 million in 2006). Through the 

1990s, as commercial landings in the major offshore fisheries sectors decreased, the livefish fishery harvest began to represent a greater proportion of landings and revenue in 

California. For example, between 1989 and 1992 the nearshore, live-fish trap fishery 

developed in response to demand in high-end restaurants, increasing from 2 to 27 boats 

that landed over 52,000 lbs of live fish (Palmer-Zwahlen et al. 1993). Recreational 

fisheries consist largely of commercial passenger fishing vessels (CPFVs), an important 

activity in many coastal communities for which the economic contribution can be 

comparable to the landed value of the commercial catch. Private boats access, pier and 

jetty fishing, and spearfishing also contribute to the high recreational effort targeted at 

these species. 

Most of the nearshore species are considered to be relatively data-limited, with 

relatively modest research done on their life history and little or no fishery-independent 

survey data available for monitoring trends in abundance. Only 5 of the 16 species 

managed by the Pacific Fishery Management Council (gopher rockfish Sebastes 

carnatus, black rockfish Sebastes melanops, blue rockfish Sebastes mystinus, cabezon 

and California scorpionfish Scorpaena guttata) have formally adopted stock assessments 

that included part or all of their California populations. An assessment also exists for 

sheephead, although the results have not been directly applied in management. An 

assessment for kelp greenling also exists for the Oregon population. Most of these 

assessments have been considered to have moderate to poor data availability, and a 

majority of the remaining nearshore species have even less available data for potential 

assessments, such that alternative means of monitoring of stock status and evaluating the 

vulnerability to overexploitation are key management priorities. 

The average productivity and susceptibility scores for each of the 19 nearshore 

species are shown in Figure 6. These scores were produced using the default weighting 

of 2, as all attributes were viewed to be equally applicable. Susceptibility scores are 

similar for all species (average range between 2.0 and 2.4), with only the California 

scorpionfish scoring below 2. Considering the productivity axis, two primary clusters 
18

can be distinguished: one of relatively deeper-living, larger, and longer-lived rockfishes 

(though grass rockfish Sebastes rastrelliger is one of the shallowest-living of the species 

considered), and the other mainly smaller, shorter-lived species with varying reproductive 

life histories. Combining the two axes, there is a loose but noticeable negative linear 

relationship between productivity and susceptibility. Of the species considered, brown 

rockfish S. auriculatus, blue rockfish, China rockfish S. nebulosus, copper rockfish S. 

caurinus, and quillback rockfish S. maliger appear to be the most vulnerable, based on 

their relatively lower productivity and greater susceptibility; black rockfish, olive 

rockfish S. serranoides, and grass rockfish are also ranked as among the more vulnerable 

species. Interestingly, all of the most vulnerable species are Sebastes, consistent with the 

perceived higher vulnerability of the slower-growing and longer-lived members of this 

genus relative to most other groundfish. Given that these are among the more valuable 

commercial targets, but are characterized by long lifespans and slow growth rates, these 

results are consistent with expectations (Table 5). 

5.4 California Current Coastal Pelagic Species 

The coastal pelagic species fisheries management plan (CPS FMP) on the U.S. 

West Coast includes four species of schooling pelagic fishes (Pacific sardine Sardinops 

sagax, northern anchovy Engraulis mordax, Pacific mackerel Scomber japonicus, and 

jack mackerel Trachurus symmetricus), market squid Doryteuthis opalescens, and more 

recently two species of euphausiids declared prohibited due to their important role as 

forage. Euphausiids are not included in this assessment as the lack of any historical 

fisheries in the California Current, and the recent ban on future fisheries, gives us no 

ability to evaluate susceptibility. However, several additional coastal pelagic species, 

currently not managed under the CPS FMP, exhibit similar life history characteristics and 

trophic roles as the five above. Consequently, we considered Pacific herring Clupea 

pallasi, Pacific bonito Sarda chiliensis, and Pacific saury Cololabis saira as well. All of 

these species are characterized by rapid growth, relatively short lifespans, and significant 

short- and long-term variability in abundance, productivity, and distribution. These 

species also represent key energy pathways from planktonic communities to highertrophic-level predators such as salmon, tunas, groundfish, sharks, seabirds, and marine 

mammals. Commercial fisheries for these stocks are typically high-volume and, despite 

moderate ex-vessel values, they are among the most economically significant fisheries in 

the California Current. Many of these species are also targeted by fisheries in both 

Mexico and Canada; however, there are no formal international management agreements 

in place for these partially shared resources. 

The Pacific sardine fishery was the largest in the United States throughout the 

first half of the 20th century, with annual landings greater than 700,000 tons during its 

peak. Although the notorious collapse of the sardine stock in the 1950s led to several 

decades of low abundance and landings, the current stock biomass and fishery are again 

among the largest on the U.S. West Coast. The northern anchovy fishery was of 

considerable economic significance throughout the 1970s and early 1980s, but biomass 

levels have been relatively low since the early 1980s and the current fishery is negligible. 

Although not taxonomically related, both Pacific and jack mackerel are larger, have 

greater longevity (particularly jack mackerel), and are higher-trophic-level components of 
19

this assemblage that have variously been important in the CPS fisheries in the California 

Current. The market squid is a very short-lived and highly variable stock that has been a 

significant target of commercial fisheries for over 100 years and is frequently the largest 

(by volume) fishery in California waters. For the three species not in the CPS FMP, 

Pacific herring is a state-managed species of considerable economic importance in 

California and modest importance in the Pacific Northwest. Pacific bonito is a larger 

piscivorous species rarely found north of Point Conception that is an occasional 

commercial target and a fairly important recreational target. Pacific saury is a pelagic 

species of little commercial importance in the California Current but of considerable 

economic importance in the western Pacific. 

These species are typical of the coastal pelagic community of upwelling 

ecosystems, which collectively account for as much as one-third of total global marine 

fish landings. The population dynamics of all of these species can be characterized as 

highly dynamic in space and time, with tremendous interannual and interdecadal 

variability in abundance, productivity and distribution. Although the mechanisms behind 

these fluctuations remain largely unknown, this variability is widely held to be a 

consequence of the dynamic nature of oceanographic features in coastal upwelling 

ecosystems over both interannual and interdecadal time scales (Bakun 1996, MacCall 

1996, Schwartzlose et al. 1999). The current management regime for the federally 

managed CPS species includes threshold biomass levels because of the considerable 

importance of these species as forage to higher-trophic-level predators. Additionally, 

Pacific sardine are managed using a climate-based harvest control rule, in recognition of 

the significance of climate factors in driving productivity and abundance (PFMC 1998). 

The productivity and vulnerability scores developed for these eight species are 

shown in Figure 7. All are estimated to range between moderate and high productivity 

(with the caveat that they routinely undergo extensive periods in which productivity 

declines to very low levels), with the species having the fastest growth rates and shortest 

lifespans (market squid, Pacific saury and northern anchovy) among the highest in their 

collective productivity scores. The relatively longer-lived Pacific and jack mackerel are 

characterized by lower productivity. The generally above-average susceptibility scores 

for these species are in part a consequence of relatively high susceptibility due to 

schooling behavior and the hyperstability of catch rates (Hilborn and Walters 1992). 

Higher scores for California market squid and Pacific herring reflect their current 

relatively high exploitation rates in fisheries that target spawning aggregations. In 

contrast, Pacific saury and jack mackerel are generally widespread, located in offshore 

waters, and effectively unexploited in the California Current, even though both stocks 

now may be at low levels of abundance due to climate factors. 

Despite their rapid growth and relatively high natural mortality rates, high 

interannual and interdecadal recruitment variability tempers the higher productivity 

scores for all species. Such variability is significant with respect to assessing the 

vulnerability of these stocks to overexploitation, as the failure to recognize climate-driven 

changes in the productivity of coastal pelagics was a key factor behind the notorious 

collapse of the California sardine fishery in the 1950s and 1960s and of the largest 

historic fishery on the planet – Peruvian anchoveta Engraulis ringens. Between 1971 and 

1973, anchoveta landings fell from over 12 million tons per year to less than 2 million 

(Schwartzlose et al. 1999). Although this fishery also has recovered to the point where it 
20

is again the largest fishery by volume in the world’s oceans, there is general agreement 

that coastal pelagic populations are highly vulnerable to overexploitation in the absence 

of effective monitoring and management systems. 

 

5.5 Skates (Rajidae) of the Bering Sea and Aleutian Islands Management Area 

The Bering Sea and Aleutian Islands (BSAI) FMP contains 13 species of skate 

(Rajidae) that are incidentally caught by the commercial fisheries in this management 

area off Alaska. Although not targeted, these skate species are caught in substantial 

amounts by bottom trawl and longline vessels pursuing other species and are valued at 

U.S. $2 million (2006 NMFS commercial landing statistics). They are managed by the 

North Pacific Fishery Management Council as part of its “Other Species” group, which 

also contains sharks, squid, octopus, and sculpins (Ormseth and Matta 2007). An 

aggregate catch limit is established annually for this entire group. 

 Skates in the BSAI vary in size and other life history traits, as well as in 

abundance and distribution. The BSAI consists of three main regions: the eastern Bering 

Sea (EBS) shelf, which is quite broad; the EBS slope; and the Aleutian Islands (AI). The 

EBS shelf contains the vast majority of the skate biomass in the BSAI but has relatively 

low species diversity of skates, with the Alaska skate (Bathyraja parmifera) dominating 

the biomass. The skate communities of the EBS slope and the AI are much more diverse, 

and species are distributed unequally among the three areas. Because Alaska skate 

dominate the shelf, where fishing activity is strongest, they are the main species caught in 

commercial fisheries. Data quality is greatest for this species (Table 5). 

Within our analysis, all attributes were weighted equally with the exception of 

recruitment pattern. Based on skate life histories and information available for B. 

parmifera, we concluded that skates have low recruitment variability and that year 

classes tend to be small but of consistent size. Because it was unclear how this pattern 

might affect productivity, particularly as the criteria are based on the frequency of 

successful year classes, we decided to reduce the weight to 1. Extensive life history data 

were available for only a subset of the species (Alaska skate, Aleutian skate B. aleutica, 

Bering skate B. interrupta, big skate Raja binoculata, and longnose skate R. rhina). For 

the remaining species, maximum size was the only attribute for which we had 

information. Other life history attributes for these species were assigned based on results 

for the better-known species, and were assigned a data quality score of 3. 

During the scoring process, we identified some attributes that warranted further 

explanation, including breeding strategy, management strategy, areal overlap, and 

geographic concentration. We used Winemiller’s (1989) index to estimate breeding 

strategy, but modified it somewhat for skates. When Winemiller mentions “parental 

protection of zygotes or larvae,” it seems as if he has teleosts in mind and perhaps is 

thinking of nest-guarding behavior. For skates, there is no nest-guarding protection as 

such, but the spawners do produce a tough egg case that helps ward off predation for up 

to several years. We evaluated this as providing lengthy protection to the offspring, and 

gave a score of 4 for the “parental protection” portion of the index. 

Regarding the attribute management strategy, all skate species received a score of 

1 for this attribute, because a catch limit is set for the BSAI skate complex and catch is 

monitored weekly throughout the fishing season. However, because skate catch limits are 
21

managed in aggregate with a larger “Other Species” group, and identification of rarer 

skates can be problematic, there is more potential for inadvertent overfishing of skates 

than indicated by the attribute score. A data quality score of 2 was assigned to reflect this 

inconsistency. 

We quantified areal overlap by examining the percentage of the stock distribution 

(based on survey data) that occurs within the depths of the trawl fishery. First, we 

examined the observed trawling effort (in minutes) by depth from the North Pacific 

Fishery Observer Program and noted that nearly all of the trawling effort occurs at depths 

less than 300 m. Next, we quantified the proportion of the total CPUE data, per year, that 

exists at depths shallower than 300 m. For each skate species in each year, we produced 

the cumulative distribution of CPUE as a function of depth, which gives the proportion of 

the sum of the CPUE data that occurs shallower than a given depth. From this 

distribution, we were able to identify the proportion of the CPUE data that occurred 

shallower than 300 m, which we took as the maximum percentage areal overall with the 

fishery. Note that the actual overlap may be less because of spatial and/or temporal 

mismatches between the distributions of the fishery and the stock, so this is a 

conservative estimate of areal overlap. This technique was applied to the NMFS Aleutian 

Islands trawl survey and the NMFS eastern Bering Sea slope trawl survey; the eastern 

Bering Sea shelf survey occurs at depths less than 200 m, so all CPUE from this survey 

would be less than 300 m. 

Lastly, we quantified geographic concentration as the area covered by 95% of the 

stock relative to the area covered by the survey using the method of Swain and Sinclair 

(1994) described in the Section 4.4.1. 

Overall, all attributes received a score less than 2.0 for BSAI skates, because the 

species were considered highly susceptible to becoming overfished and their productivity 

was relatively low compared to other U.S. fish stocks. Many of the skate species were 

clustered close together in the PSA plot (Fig. 8). This result is likely due to three factors: 

1) most skates share similar life histories that tend toward low productivity; 2) BSAI 

skate species show similar susceptibility to trawl and longline fishing gear; and 3) similar 

attribute scores were assigned to many of the data-poor species. One species (longnose 

skate) stood out from the rest as a result of lower productivity, which in turn resulted 

from its larger size and longevity. Of the remaining 12 species, four (Aleutian skate, 

Bering skate, big skate, and butterfly skate Bathyraja mariposa) showed reduced 

susceptibility relative to the others. This resulted from differences in spatial distribution 

that reduced their susceptibility to fisheries. Data quality was highest for Alaska skate 

and lowest for the eight species for which life-history data were mostly unavailable. The 

lowest data quality score was 3: we had enough data to produce a score for each attribute. 

The results of this PSA suggest that skates in the BSAI are vulnerable to fishing activity 

and should be carefully managed to reduce the likelihood of overfishing. 

5.6 Hawaii-based Longline Fishery: A Comparison of the Tuna and Swordfish 

Sectors 

The Hawaii-based longline fishery is a year-round pelagic fishery operating out of 

Hawaii that targets a range of pelagic finfish species with hook and line gear for the fresh 

fish market, and is comprised of approximately 125 active fishing vessels in a limited 
22

entry program (WPRFMC 2007). This is the largest commercial fishery in Hawaii in 

both landings (21.6 million pounds for 2006) and revenue (U.S. $54.4 million ex-vessel 

revenue for 2006). The Hawaii-based longline fishery began in 1917 using tuna fishing 

methods imported from Japan. The fishery underwent a substantial expansion from 1987 

to 1993 due to the introduction of swordfish (Xiphias gladius) fishing methods using 

shallow set fishing gear (Boggs and Ito 1993). As this sector of the fishery became more 

heavily regulated (due primarily to interactions with sea turtles in the late 1990s) shallow 

set fishing effort decreased substantially with a corresponding expansion of the tuna 

sector of the fishery, which primarily targeted bigeye tuna (Thunnus obesus) with deep 

set gear. These two sectors of the fishery continue through present time; fishermen can 

either set shallow gear to target swordfish in the higher latitudes, or set deep gear to 

target tuna primarily in lower latitudes (Bigelow et al. 2006). Tunas are the largest 

component of the overall catch (59% for 2006), with bigeye tuna alone comprising 40% 

of the total longline landings for 2006. Billfish are the second largest component of the 

overall catch (22% for 2006), with swordfish alone composing 10% of the total longline 

landings for 2006. Both sectors are tightly regulated to reduce conflicts with recreational 

fishermen, to reduce protected species interactions, and to minimize risks of overfishing. 

The Western Pacific Regional Fishery Management Council (WPRFMC) pelagics 

FMP includes 28 stocks or assemblages as pelagic management unit species (PMUS). 

This PSA considered 33 stocks, since two assemblages (oilfishes and pomfrets) were 

disaggregated to individual species (Table 5). These PMUS taxa can be aggregated into 

four general categories of tunas, billfishes, other bony fishes, and sharks (Table 5). A 

wide variety of data sources were examined to extract pertinent biological and fishery 

information for this PSA case study, and included published and unpublished scientific 

findings, webpage summaries, personal communications, and NMFS research finding 

from longline observer data, auction data, logbook data, and State of Hawaii commercial 

catch data (citations available upon request). The dual nature of the fishery necessitated 

that two separate PSA results be prepared – one for the shallow set swordfish fishery 

sector and one for the deep set tuna fishery sector. Productivity attributes were identical 

for the two PSA applications; however, susceptibility values can vary substantially 

between the sectors for the same species due to differences in the geographic fishing 

areas, seasonal patterns of fishing effort, vertical positioning of the fishing gear in the 

water column, and bycatch survival. Other gear-related issues involved in targeting of 

particular species are also important and are incorporated in the sector-specific 

susceptibility scorings. 

PSA scorings for the two fishery sectors are shown in Table 5 and Figures 9 and 10. 

Generally, all stocks fell into the region characterized as moderate to low productivity 

and moderate to low susceptibility. Sharks and others were among the lower productivity 

stocks, while tunas and billfishes tended to be among the higher productivity stocks, 

when examined as broader taxonomic groupings (Table 5). Interestingly, it was observed 

that the productivity scores for blue shark, bigeye thresher shark Alopias superciliosus , 

longfin mako shark Isurus paucus, oceanic whitetip Carcharhinus longimanus , silky 

Carcharhinus falciformis, and the common thresher shark Alopias vulpinus differed from 

those recorded in the Highly Migratory Atlantic Shark Complexes case study (Table 5, 

Appendix B). These differences are likely related to intraspecific variations in life 

history patterns (Cope 2006), and the use of different weightings in the vulnerability 
23

analysis. Sharks and billfishes were among the lower susceptibility stocks, while tunas 

and others were among the higher susceptibility stocks. The swordfish sector exhibited 

an overall slightly reduced susceptibility when compared to the tuna sector, probably due 

to the higher level of targeting in this sector of the fishery. In fact, only five stocks had a 

higher susceptibility in the swordfish sector than the tuna sector (Figs. 9 and 10). 

Therefore, ~85% (28 out of 33) of the stocks analyzed here had an equal or higher 

susceptibility in the tuna sector than the swordfish sector of the longline fishery. Further 

analysis is needed to fully understand the roles of spatiotemporal patterns of fishing gear 

deployment, gear specificity, catchability, and the biology of the individual stocks. 

6.0 SYNTHESIS AND DISCUSSION 

6.1 Range of Vulnerability Scores 

The managed stocks evaluated in this report represent both targeted (n = 71; 44%) 

and non-targeted species (n = 91; 56%) that were included in FMPs to prevent 

overfishing and rebuild overfished stocks (see MSA §§ 303(a)(1)(A) & 303(b)(1)(A)). 

The stocks generally displayed vulnerability scores greater than 1.0 or, when plotted, are 

above the 2.0 isopleth (the distance should be measured from the origin, which in this 

case is 3,1; see Table 5 and Fig. 2). The only exception to this observation was the 

Pacific saury, which received a susceptibility score of 1.91, a productivity score of 2.70 

and a vulnerability score of 0.96. 

Within any particular example application, the range of productivity and 

selectivity values can be restricted depending upon the characteristics of the species of 

interest. For example, the species in the Atlantic shark complex showed a wide range of 

susceptibility values, but 34 of the 37 species had productivity values between 1.0 and 

1.5. Similarly, the 13 BSAI skate species had productivity scores between 1.0 and 1.5, 

and susceptibility scores between 1.5 and 2.0. In contrast, the species in the Hawaii 

longline fishery (both the tuna and swordfish sectors) showed an expanded range of 

productivity and susceptibility scores. The restricted range in some of the example 

applications may reflect the species chosen for these examples, and it is possible that a 

more expanded range would be observed if the PSA was applied to all species in a FMP. 

For example, BSAI skates are managed within the BSAI groundfish FMP which includes 

a range of life-history types, including gadids and flatfish, and the productivity and 

susceptibility scores for these species would likely show some contrast from those 

obtained for skates. 

A restricted range of scores from a PSA might motivate some to modify the 

attribute definitions to produce greater contrast. However, it is important to recognize 

that the overall goal of the PSA is to estimate vulnerability relative to an overall standard 

appropriate for the range of federally managed species. Thus, a lack of contrast in 

vulnerability scores may simply reflect a limited breadth of species diversity. For 

example, examination of a subset of approximately 40 stocks in the U.S. West Cost 

Groundfish FMP indicates that none have a maximum age less than 10 years, and nearly 

60% have a maximum age over 30 years (Fig. 11). Similarly, over 80% of these stocks 

have natural mortality rates estimated to be less than 0.20, and half have a von 

Bertalanffy growth coefficient of less than 0.15. A similar lumping of values takes place 
24

for other attributes, including age at 50% maturity. Thus, it may be advantageous in 

some cases to redefine the attribute score definitions in order to increase the contrast 

within a given region or FMP, while recognizing that the vulnerability scores for that 

particular fishery no longer represent the risk of overfishing based on the original scoring 

criteria. Analyses that use modified attribute scoring definitions should be clearly labeled 

to avoid confusion with PSAs based on the scoring bins indentified in the report. 

6.2 Relationship of Vulnerability to Fishing Pressure

 In order to evaluate the effect of fishing pressure on vulnerability, we examined a 

subset (n = 50) of the example application stocks for which status determination criteria 

were available to determine if the stock had been overfished or undergone overfishing 

between the years of 2000 – 2008 (Fig. 12; Appendix D). Kruskall-Wallis tests indicated 

that there were significant differences in susceptibility (P = 0.001) and vulnerability (P = 

0.002) scores between stocks that had been overfished or undergone overfishing in the 

past (i.e., New England Groundfish and Atlantic Shark Complexes) and those that had 

not. However, productivity scores were not found to be significantly different (P = 

0.891). Stocks that had been overfished or undergone overfishing in the past generally 

had susceptibility scores greater than 2.3 and vulnerability scores greater than 1.8. 

To further examine the effect of fishing pressure on PSA results, we evaluated 

four lightly fished non-target species in the South Atlantic-Gulf of Mexico 

snapper/grouper bottom longline fishery that were considered potential ecosystem 

component species (i.e., low vulnerability to overfishing/overfished) based on their 

average landings (< 5 metric tons/year) and price/pound (< U.S. $1.00) (Table 5; Fig. 2; 

Appendix Fig. C8). Three of the four non-target species received vulnerability scores 

less than 1.0, but the other stock (sand tilefish) received a vulnerability score of 1.1 due 

to its moderate productivity (2.1) and susceptibility (1.9). However, several other stocks 

that would not be considered ecosystem stocks had similar vulnerability scores. Though 

based on limited data, these post hoc results involving overfished and potential ecosystem 

component stocks indicate that although the PSA is capable of identifying low-, 

moderate-, and highly vulnerable stocks, a fixed threshold for delineating between low 

and highly vulnerable stocks in all situations was not observed. 

Determination of appropriate thresholds for low-, moderate-, and highly 

vulnerable stocks will likely reflect upon the nature of each particular fishery and the 

management action to which it will apply. In some cases, a fishery management council 

may prefer to use the results of the PSA in a qualitative manner to inform management 

decisions rather than as a basis for specifying rigid decision rules. When thresholds are 

desired, we recommend that fishery management councils and their associated SSCs 

jointly determine appropriate thresholds on a fishery-by fishery basis. 

6.3 Comparisons Between Target and Non-target Stocks 

Comparisons of productivity and susceptibility between target and non-target 

stocks can be made in the Hawaii longline (tuna sector), Hawaii longline (swordfish 

sector), and the Atlantic shark complex (Table 5 notes which stocks were considered 

targets and non-targets). Kruskall-Wallis tests revealed that the productivity scores were 
25

significantly different between the target and non-target stocks in each of the two sectors 

of the Hawaii longline fishery (P = 0.026) (Figs. 9 and 10; Table 6), whereas the 

susceptibility scores were significantly different (P = 0.000) in the Atlantic shark 

complex (Fig. 5; Table 6). None of these cases showed significant differences in both 

axes, and no significant differences were observed in vulnerability. These results suggest 

that non-target stocks can be as vulnerable to overfishing as the target stocks of a fishery, 

and reinforce the need to carefully examine the vulnerability of non-target stocks when 

making management decisions. 

 

6.4 Data Availability and Data Quality 

Application of a PSA to data-poor stocks will very likely reveal missing data for 

one or more attributes. From our example applications, data availability was relatively 

high for the majority of the attributes evaluated, averaging 88% and ranging from 30% to 

100% in scoring frequency (Table 7; Fig. 3). However, the quality of this data was 

considered moderate (i.e., medium data quality scores 2 to 3), with an exception of the 

Northeast Multi-species Groundfish fishery (Table 5, Fig. 3). The high degree of data 

quality for these targeted stocks reflects the relatively long time series of fishery and 

survey data. In general, a relationship between susceptibility and data quality is intuitive 

in that valuable stocks are likely the most susceptible due to targeting, and the priority 

placed upon the collection of data for valuable target fisheries. It is recommended that 

the data quality of vulnerability scores be considered in the decision-making process, and 

that the precautionary approach is employed if vulnerability scores were made with 

limited or poor data. 

6.5 Degree of Consistency within Productivity and Susceptibility Scores 

The degree of consistency within the productivity and susceptibility scores was 

determined from correlations of a particular attribute to its overall productivity or 

susceptibility score (after removal of the attribute being evaluated). In this analysis, 

susceptibility attributes related to management were separated from other susceptibility 

attributes. All but two of the attributes had relatively high correlation coefficients, 

averaging 0.43 and ranging from -0.21 to 0.80 (Table 7). The correlation coefficients for 

recruitment pattern (-0.21) and seasonal migration (0.06) were unusually low and could 

reflect the narrow range of observed recruitment patterns or seasonal migrations, as is 

evident from each attribute being scored 90% of time as a moderate risk. The restricted 

range observed for these attributes could also reflect the definition of scoring bins that 

were used. While these attributes were not informative for the majority of the stocks we 

examined here, it is anticipated that in some fisheries these attributes may prove to be 

more useful. As previously noted, in these cases the attribute weight can be adjusted to 

reflect its utility. 

6.6 Correlations to Other Risk Analysis 

The productivity scores obtained from our PSA analysis generally correspond to 

Musick’s (1999) extinction risk analysis and vulnerability analysis of Cheung et al. 
26

(2005), which is integrated into the FishBase database (www.fishbase.org). In contrast to 

the PSA analysis which evaluates vulnerability to overfishing, these approaches aim to 

evaluate the risk of extinction as a function of stock productivity, trends in abundance, 

and life-history characteristics. As expected, scores from Musik (1999) and Cheung et al. 

(2005) were highly correlated with our productivity scores and not correlated with our 

susceptibility scores (Table 8; Figs. 13 and 14). Since vulnerability scores are dependent 

on productivity and susceptibility scores, correlations between our PSA vulnerability 

score and the other risk analyses were moderate (Table 8; Figs. 13 and 14). 

6.7 Cluster Analysis for Determining Stock Complex Groupings 

 The NS1 guidelines emphasize that when stock complexes are created to manage 

data-poor stocks, the stocks should be sufficiently similar in geographic distribution, life 

history, and vulnerabilities such that the impact of management actions on the stocks 

within the complex is similar (see § 600.310 (d)(8)). The NS1 guidelines also state that 

the vulnerability of stocks should be evaluated when determining if a particular stock 

complex should be established or reorganized, or if a particular stock should be included 

in a complex. To help determine the appropriate grouping of vulnerable stocks, it is 

recommended that a hierarchical cluster or discriminant function analysis be conducted. 

 

7.0 CONCLUSIONS 

While there are many qualitative risk analyses currently used by fisheries 

scientists and managers, a PSA is a particularly useful methodology for determining 

vulnerability because it evaluates both the productivity of the stock and its susceptibility 

to the fishery. Several modifications to previously published PSAs were developed to 

better evaluate U.S. fisheries and incorporate the principles described in the NS1 

guidelines. The output from this relatively simple and straightforward tool provides SSC 

and Council members an index of how vulnerable their managed stocks are to becoming 

overfished. It also provides guidance to help determine the needed strength of 

conservation measures and the degree of precaution to apply in management measures. 

The vulnerability of a stock should be considered when determining: 1) which stocks are 

fishery and ecosystem component stocks; 2) the appropriate grouping of data-poor stocks 

into stock complexes; and 3) appropriate buffers in either the ABC or ACT control rules. 

Our analyses indicate that the PSA is generally capable of distinguishing the 

vulnerability of stocks that experience differing levels of fishing pressure, although fixed 

thresholds separating low, medium, and high vulnerability stocks were not observed. 

Due to differences in data quality and the manner in which FMPs were developed, it is 

recommended that fishery management councils and their SSCs determine thresholds 

between low, medium, and high vulnerability stocks on a fishery-by-fishery basis. 

Similar to Shertzer and Williams (2008), our example applications showed that 

current stock complexes exhibit a wide range of vulnerabilities (e.g., pomfrets and 

sharks). Therefore, the SSCs and Councils should consider reorganizing complexes that 

exhibit a wide range of vulnerabilities, or at least consider choosing an indicator stock 

that represents the more vulnerable stock(s) within the complex. If an indicator stock is 

found to be less vulnerable than other members of the complex, management measures 
27

need to be more conservative so that the more vulnerable members of the complex are 

not at risk from the fishery (see § 600.310(d)(9)). Lastly, it is recommended that SSC or 

Council members consider using information on vulnerability to adjust the buffer either 

between OFL and ABC, or ACL and ACT, but not both in order to avoid “doublecounting” of the vulnerability information. 

8.0 ACKNOWLEDGEMENTS 

We would like to thank M. Key for her assistance in evaluating the vulnerability 

the California Nearshore Groundfish and Coastal Pelagic fisheries. We also thank the 

internal reviewers who provided helpful editorial comments including: S. Branstetter, K. 

Brewster-Geisz, D. DeMaster, J. Ferdinand, B. Harman, B. Karp, A. Katekaru, J. 

Kimmel, A. MacCall, J. Makaiau, J. McGovern, R. Methot, M. Nelson, C. Patrick, F. 

Pfielger, P. Steele, A. Strelcheck, G. Tromble, and J. Wilson. 

29

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39

TABLES AND FIGURES 

41

Table 1. Productivity and susceptibility attributes and rankings.

Productivity attribute High (3) Moderate (2) Low (1)

r > 0.5 0.16 - 0.5 < 0.16

Maximum age < 10 years 10 - 30 years > 30 years

Maximum size < 60 cm 60 - 150 cm > 150 cm

von Bertalanffy growth 

coefficient (k ) > 0.25 0.15 - 0.25 < 0.15

Estimated natural mortality > 0.40 0.20 - 0.40 < 0.20

Measured fecundity > 10e4 10e2 - 10e3 < 10e2

Breeding strategy 0 Between 1 and 3 ≥ 4

Recruitment pattern

Highly frequent recruitment success 

(> 75% of year classes are 

successful)

Moderately frequent recruitment 

success (between 10% and 75% of 

year classes are successful)

Infrequent recruitment success (< 

10% of year classes are successful)

Age at maturity < 2 year 2 - 4 years > 4 years

Mean trophic level < 2.5 Between 2.5 and 3.5 > 3.5

Ranking
42

Table 1 (continued). 

Susceptibility attribute Low (1) Moderate (2) High (3)

Areal overlap < 25% of stock occurs in the area 

fished

Between 25% and 50% of the stock 

occurs in the area fished

> 50% of stock occurs in the area 

fished

Geographic concentration Stock is distributed in > 50% of its 

total range

Stock is distributed in 25% to 50% of 

its total range

Stock is distributed in < 25% of its 

total range

Vertical overlap < 25% of stock occurs in the depths 

fished

Between 25% and 50% of the stock 

occurs in the depths fished

> 50% of stock occurs in the depths 

fished

Seasonal migrations Seasonal migrations decrease 

overlap with the fishery 

Seasonal migrations do not 

substantially affect the overlap with 

the fishery

Seasonal migrations increase 

overlap with the fishery

Schooling/Aggregation and 

other behavioral responses

Behavioral responses decrease the 

catchability of the gear 

Behavioral responses do not 

substantially affect the catchability of 

the gear 

Behavioral responses increase the 

catchability of the gear (i.e., 

hyperstability of CPUE with 

schooling behavior)

Morphology affecting capture Species shows low selectivity to the 

fishing gear. 

Species shows moderate selectivity 

to the fishing gear. 

Species shows high selectivity to the 

fishing gear. 

Desirability/Value of the 

fishery

Stock is not highly valued or desired 

by the fishery (< $1/lb; < $500K/yr 

landed; < 33% retention)

Stock is moderately valued or 

desired by the fishery ($1 - $2.25/lb; 

$500k - $10,000K/yr landed; 33-66% 

retention)

Stock is highly valued or desired by 

the fishery (> $2.25/lb; > 

$10,000K/yr landed; > 66% 

retention)

Management strategy

Targeted stocks have catch limits 

and proactive accountability 

measures; non-target stocks are 

closely monitored.

Targeted stocks have catch limits 

and reactive accountability 

measures

Targeted stocks do not have catch 

limits or accountability measures; 

non-target stocks are not closely 

monitored.

Fishing rate relative to M < 0.5 0.5 - 1.0 > 1

Biomass of spawners (SSB) 

or other proxies

B is > 40% of B0 (or maximum 

observed from time series of 

biomass estimates)

B is between 25% and 40% of B0 

(or maximum observed from time 

series of biomass estimates)

B is < 25% of B0 (or maximum 

observed from time series of 

biomass estimates)

Survival after capture and 

release Probability of survival > 67% Probability of survival between 33% and 67% Probability of survival < 33%

Fishery impact to EFH or 

habitat in general for nontargets

Adverse effects absent, minimal or 

temporary

Adverse effects more than minimal 

or temporary but are mitigated

Adverse effects more than minimal 

or temporary and are not mitigated

Ranking
43

Table 2. Productivity attribute thresholds based on the empirical relationships between 

tmax, M, k, and tmat (noted as “Modeling”), as well as a survey of stocks landed by U.S. 

fisheries representing all six regional management areas (n = 141; noted as “US 

Fisheries”).

Attribute Source Low Moderate High

Modeling < 0.10 0.10 - 0.30 > 0.30

U.S. fisheries < 0.15 0.15 - 0.25 > 0.25

Threshold < 0.15 0.15 - 0.25 > 0.25

Modeling < 0.14 0.14 - 0.40 > 0.40

U.S. fisheries < 0.20 0.20 - 0.40 > 0.40

Threshold < 0.20 0.20 - 0.40 > 0.40

Modeling > 30 10 - 30 < 10

U.S. fisheries > 30 10 - 30 < 10

Threshold > 30 10 - 30 < 10

Modeling > 9 3 - 9 < 3

U.S. fisheries > 4 2 - 4 < 2

Threshold > 4 2 - 4 < 2

Modeling - - -

U.S. fisheries > 150 60 - 150 < 60

Threshold > 150 60 - 150 < 60

t mat 

(yrs)

L max 

(cm)

Productivity

K

M 

(M /yr)

t max 

(yrs)
44

Table 3. The susceptibility scoring thresholds for desirability/value of a stock. 

Sector Measure Low (1) Moderate (2) High (3)

$/lb < $1.00 $1.00 - $2.25 > $2.25

Annual landings value < $500,000 $500,000 - 

$10,000,000 > $10,000,000

Recreational % Retention < 33% 34 - 66% > 66%

Susceptibility score

Commercial
45

Table 4. The five tiers of data quality used when evaluating the productivity and 

susceptibility of an individual stock. 

Data quality score Description Example

1 (Best data) Information is based on collected data for the 

stock and area of interest that is established and substantial.

Data rich stock assessment, 

published literature that uses multiple 

methods, etc.

2

 (Adequate data) Information with limited coverage and 

corroboration, or for some other reason deemed not as 

reliable as Tier 1 data

Limited temporal or spatial data, 

relatively old information, etc

3

 (Limited data) Estimates with high variation and limited 

confidence and may be based on similar taxa or life history 

strategy.

Similar genus or family, etc.

4

 (Very limited data) Expert opinion or based on general 

literature review from wide range of species, or outside of 

region

General data – not referenced

5 (No data) No information to base score on – not included in 

the PSA, but included in the DQI score.
46

Table 5. Data for example applications including identification numbers, common and scientific names, product

and vulnerability and data quality scores. ID numbers are used to note stocks in summary x-y plots that include m

while group IDs are used in x-y plots for a particular fishery. 

ID Group ID Fishery Stock Scientific name Productivity Susceptibility Vulnerability

No. of

productivity 

attributes 

scored

Prod

data 

1 1 Sixgill shark* Hexanchus griseus 1.1 1.4 2.0 9 

2 2 Sharpnose sevengill shark* Heptranchias perlo 1.1 1.4 1.9 4 3 3 Bigeye sandtiger shark* Odontaspis noronhai 1.1 1.6 2.0 9 

4 4 Whale shark* Rhincodon typus 1.3 1.7 1.9 9 5 5 Caribbean sharpnose shark* Rhizoprionodon porosus 1.8 1.6 1.4 9 

6 6 Angel shark* Squatina dumeril 1.3 1.6 1.8 9 

7 7 White shark* Carcharodon carcharias 1.1 1.7 2.1 9 8 8 Basking shark* Cetorhinus maximus 1.0 1.8 2.1 9 

9 9 Sandtiger shark* Carcharias taurus 1.1 1.8 2.0 9 

10 10 Blue shark* Prionace glauca 1.3 1.9 1.9 9 

11 11 Smalltail shark* Carcharhinus porosus 1.3 1.8 1.9 9 12 12 Nurse shark Ginglymostoma cirratum 1.3 1.8 1.9 9 

13 13 Galapagos shark* Carcharhinus galapagensis 1.2 1.9 2.0 9 14 14 Dusky shark* Carcharhinus perezi 1.0 2.1 2.2 9 15 15 Porbeagle* Lamna nasus 1.0 2.1 2.3 9 

16 16 Common thresher shark* Alopias vulpinus 1.1 2.3 2.3 9 

17 17 Oceanic whitetip shark* Carcharhinus longimanus 1.1 2.2 2.3 9 

18 18 Blacknose shark Carcharhinus acronotus 1.3 2.3 2.2 9 19 19 Lemon shark Negaprion brevirostris 1.0 2.2 2.3 9 

20 20 Shortfin mako shark* Isurus oxyrinchus 1.0 2.3 2.4 9 

21 21 Longfin mako shark* Isurus paucis 1.1 2.3 2.3 9 

22 22 Tiger shark Galeocerdo cuvier 1.4 2.3 2.1 9 

23 23 Smooth hammerhead shark Sphyrna zygaena 1.1 2.3 2.3 9 24 24 Caribbean reef shark* Carcharhinus perezi 1.0 2.4 2.4 8 

25 25 Blacktip shark Carcharhinus limbatus 1.2 2.4 2.3 9 

26 26 Scalloped hammerhead shark Sphyrna lewini 1.0 2.4 2.4 9 

27 27 Sandbar shark Carcharhinus plumbeus 1.0 2.4 2.4 9 

28 28 Bigeye thresher shark* Alopias superciliosus 1.1 2.4 2.4 9 29 29 Finetooth shark Carcharhinus isodon 1.3 2.5 2.2 9 

30 30 Night shark* Carcharhinus signatus 1.1 2.5 2.4 9 

31 31 Bignose shark* Carcharhinus altimus 1.1 2.5 2.4 9 32 32 Bonnethead shark Sphyrna tiburo 1.7 2.5 2.0 9 

33 33 Spinner shark Carcharhinus brevipinna 1.2 2.6 2.4 9 

34 34 Bull shark Carcharhinus leucas 1.1 2.6 2.4 9 

35 35 Great hammerhead shark Sphyrna mokarran 1.0 2.5 2.5 9 36 36 Atlantic sharpnose shark Rhizoprionodon terraenovae 1.8 2.6 2.0 9 

37 37 Silky shark Carcharhinus falciformis 1.1 2.7 2.6 9 

38 1 Alaska skate* Bathyraja parmifera 1.4 1.9 1.8 10 

39 2 Aleutian skate* Bathyraja aleutica 1.3 1.6 1.8 9 

40 3 Commander skate* Bathyraja lindbergi 1.4 1.8 1.8 9 

41 4 Whiteblotched skate* Bathyraja maculata 1.4 1.8 1.8 9 

42 5 Whitebrow skate* Bathyraja minispinosa 1.4 1.8 1.8 9 

43 6 Roughtail skate* Bathyraja trachura 1.4 1.8 1.8 9 

44 7 Bering skate* Bathyraja interrupta 1.4 1.6 1.7 9 

45 8 Mud skate* Bathyraja taranetzi 1.4 1.8 1.8 9 

46 9 Roughshoulder skate* Amblyraja badia 1.4 1.7 1.8 9 

47 10 Big skate* Raja binoculata 1.3 1.6 1.8 9 

48 11 Longnose skate* Raja rhina 1.3 1.6 1.8 9 

49 12 Butterfly skate* Bathyraja mariposa 1.4 1.6 1.7 9 

50 13 Deepsea skate* Bathyraja abyssicola 1.4 1.8 1.8 9 

BSAI Skate ComplexesAtlantic Shark Complex
47

Table 5. (continued). 

ID Group ID Fishery Stock Scientific name Productivity Susceptibility Vulnerability

No. of

productivity 

attributes 

scored

Prod

data 

51 1 California sheephead Semicossyphus pulcher 1.9 2.2 1.7 10 

52 2 Cabezon Scorpaenichthys marmoratus 2.0 2.2 1.6 10 53 3 Kelp greenling Hexigrammos decagrammus 2.0 2.1 1.4 10 

54 4 Rock greenling Hexigrammos lagocephalus 2.0 2.1 1.5 10 55 5 California scorpionfish Scorpaena guttata 2.0 1.8 1.3 10 

56 6 Monkyface prickelback Cebidichthys violaceus 1.8 2.0 1.6 10 

57 7 Black rockfish Sebastes melanops 1.4 2.2 2.0 10 

58 8 Black-and-yellow rockfish Sebastes chrysomelas 1.9 2.3 1.7 10 59 9 Blue rockfish Sebastes mystinus 1.5 2.3 2.0 10 60 10 Brown rockfish Sebastes auriculatus 1.7 2.4 1.9 10 

61 11 Calico rockfish* Sebastes dallii 1.8 2.0 1.5 10 

62 12 China rockfish Sebastes nebulosus 1.6 2.5 2.0 10 

63 13 Copper rockfish Sebastes caurinus 1.3 2.3 2.2 10 64 14 Gopher rockfish Sebastes carnatus 2.0 2.2 1.6 10 

65 15 Grass rockfish Sebastes rastrelliger 1.6 2.2 1.8 10 

66 16 Kelp rockfish Sebastes atrovirens 1.9 2.0 1.5 10 

67 17 Olive rockfish Sebastes serranoides 1.5 2.2 2.0 10 

68 18 Quillback rockfish Sebastes maliger 1.3 2.4 2.3 10 

69 19 Treefish rockfish Sebastes serriceps 1.9 2.3 1.7 10 

70 1 Pacific sardine Sardinops sagax 2.5 2.1 1.2 10 

71 2 Northern anchovy Engraulis mordax 2.8 2.1 1.2 10 72 3 Pacific mackerel Scomber japonicus 2.2 2.2 1.5 10 

73 4 Jack mackerel Trachurus symmetricus 2.1 1.9 1.3 10 

74 5 Market squid Doryteuthis opalescens 2.6 2.3 1.4 10 75 6 Pacific herring Clupea pallasii 2.4 2.5 1.6 10 

76 7 Pacific bonito Sarda chiliensis 2.5 2.1 1.3 10 77 8 Pacific saury Cololabis saira 2.7 1.9 1.0 10 

78 1 Gulf of Maine cod Gadus morhua 2.3 2.5 1.7 10 

79 2 Georges Bank cod Gadus morhua 2.3 2.6 1.7 10 80 3 Gulf of Maine haddock Melanogrammus aeglefinus 2.0 2.4 1.7 10 

81 4 Georges Bank haddock Melanogrammus aeglefinus 2.0 2.5 1.8 10 82 5 Redfish Sebastes marinus 2.5 2.3 1.4 10 83 6 Pollock Pollachius virens 2.3 2.4 1.5 10 84 7 Cape Cod/Gulf of Maine yellowtail flounder Limanda ferruginea 2.1 2.6 1.8 10 

85 8 Georges Bank yellowtail flounder Limanda ferruginea 2.1 2.5 1.8 10 

86 9 Southern New England yellowtail flounder Limanda ferruginea 2.1 2.6 1.8 10 

87 10 American plaice Hippoglossoides platessoides 2.2 2.3 1.5 10 

88 11 Witch flounder Glyptocephalus cynoglossus 2.2 2.5 1.7 10 89 12 Gulf of Maine Winter flounder Pseudopleuronectes americanus 2.0 2.5 1.8 10 90 13 Georges Bank Winter flounder Pseudopleuronectes americanus 2.0 2.5 1.8 10 91 14 Southern New England/Mid-Atlantic winter flounder Pseudopleuronectes americanus 2.0 2.5 1.8 10 92 15 Gulf of Maine/Georges Bank windowpane Scophthalmus aquosus 2.0 2.2 1.6 10 93 16 Southern New EnglandMid-Atlantic windowpane Scophthalmus aquosus 2.0 2.2 1.6 10 

94 17 Ocean pout Zoarces americanus 2.5 2.3 1.4 10 95 18 White hake Urophycis tenuis 2.5 2.4 1.5 10 96 19 Atlantic halibut Hippoglossus hippoglossus 2.6 2.6 1.6 10 

97 1 Albacore Thunnus alalunga 1.9 2.0 1.5 10 98 2 Bigeye tuna Thunnus obesus 1.9 2.1 1.5 10 99 3 Black marlin* Makaira mazara 1.8 1.8 1.5 10 

100 4 Bullet tuna Auxis rochei rochei 2.3 1.8 1.0 10 

HA Pelagic Longline - Swordfish

NE Groundfish

CA Current Pelagics

CA Nearshore Groundfish
48

Table 5. (continued). 

ID Group ID Fishery Stock Scientific name Productivity Susceptibility Vulnerability

No. of

productivity 

attributes 

scored

Prod

data 

101 5 Pacific pomfret* Brama japonica 2.3 1.6 1.0 9 

102 6 Blue shark* Prionace glauca 1.5 1.7 1.7 10 103 7 Bigeye thresher shark* Alopias superciliosus 1.4 1.7 1.8 10 

104 8 Blue marlin* Makaira nigricans 1.8 1.8 1.4 10 

105 9 Dolphin fish (mahi mahi)* Coryphaena hippurus 2.3 1.9 1.1 10 

106 10 Brilliant pomfret* Eumegistus illustris 1.7 2.1 1.7 4 

107 11 Kawakawa* Euthynnus affinis 2.3 1.7 1.0 10 

108 12 Spotted moonfish* Lampris guttatus 1.5 2.0 1.8 6 

109 13 Longfin mako shark* Isurus paucus 1.4 1.5 1.7 10 

110 14 Salmon shark* Lamna ditropis 1.2 1.9 2.0 8 

111 15 Striped marlin* Tetrapturus audax 2.0 2.0 1.4 10 

112 16 Oilfish* Ruvettus pretiosus 2.0 1.8 1.2 10 

113 17 Northern bluefin tuna* Thunnus orientalis 1.7 2.2 1.7 10 114 18 Roudi escolar* Promethichthys prometheus 2.1 1.7 1.1 10 

115 19 Pelagic thresher shark* Alopias pelagicus 1.5 1.5 1.6 10 

116 20 Sailfish* Istiophorus platypterus 1.9 1.8 1.3 10 

117 21 Skipjack tuna Katsuwonus pelamis 2.4 1.9 1.0 10 

118 22 Shortfin mako shark* Isurus oxyrinchus 1.4 1.5 1.6 10 

119 23 Shortbill spearfish* Tetrapturus angustirostris 2.2 1.8 1.2 10 

120 24 Broad billed swordfish Xiphias gladius 1.8 1.7 1.3 10 121 25 Flathead pomfret* Taractichthys asper 1.7 1.5 1.3 4 

122 26 Dagger pomfret* Taractichthys rubescens 1.5 1.7 1.7 4 

123 27 Sickle pomfret* Taractichthys steindachneri 1.8 2.1 1.6 5 

124 28 Wahoo* Acanthocybium solandri 2.3 2.0 1.2 10 

125 29 Yellowfin tuna Thunnus albacares 2.3 1.9 1.2 10 126 30 Oceanic whitetip shark* Carcharhinus longimanus 1.3 1.4 1.7 10 

127 31 Silky shark* Carcharhinus falciformis 1.3 1.5 1.7 10 

128 32 Common thresher shark* Alopias vulpinus 1.7 1.7 1.5 3 

129 33 Escolar* Lepidocybium flavobrunneum 2.0 1.8 1.3 10 

130 1 Albacore Thunnus alalunga 1.9 2.1 1.6 10 131 2 Bigeye tuna Thunnus obesus 1.9 2.1 1.6 10 

132 3 Black Marlin* Makaira mazara 1.8 2.0 1.5 10 133 4 Bullet tuna Auxis rochei rochei 2.3 1.8 1.0 10 

134 5 Pacific pomfret* Brama japonica 2.2 1.9 1.2 9 

135 6 Blue Shark* Prionace glauca 1.5 1.6 1.6 10 

136 7 Bigeye thresher shark* Alopias superciliosus 1.4 1.5 1.7 10 

137 8 Blue marlin* Makaira nigricans 1.8 1.9 1.5 10 

138 9 Dolphin fish (mahi mahi)* Coryphaena hippurus 2.3 1.9 1.2 10 

139 10 Brilliant pomfret* Eumegistus illustris 1.7 2.3 1.8 4 

140 11 Kawakawa* Euthynnus affinis 2.3 1.7 1.0 10 

141 12 Spotted moonfish* Lampris guttatus 1.5 2.2 1.9 6 

142 13 Longfin mako shark* Isurus paucus 1.4 1.9 1.8 10 

143 14 Salmon shark* Lamna ditropis 1.2 1.7 1.9 8 

144 15 Striped marlin* Tetrapturus audax 2.0 1.8 1.3 10 

145 16 Oilfish* Ruvettus pretiosus 2.0 1.8 1.3 10 

146 17 Northern bluefin tuna* Thunnus orientalis 1.7 2.0 1.7 10 

147 18 Roudi escolar* Promethichthys prometheus 2.1 1.9 1.3 10 

148 19 Pelagic thresher* Alopias pelagicus 1.5 1.9 1.7 10 

149 20 Sailfish* Istiophorus platypterus 1.9 1.9 1.4 10 

150 21 Skipjack tuna Katsuwonus pelamis 2.4 2.0 1.2 10 

HA Pelagic Longline - tuna

HA Pelagic Longline - Swordfish
49

Table 5. (continued). 

ID Group ID Fishery Stock Scientific name Productivity Susceptibility Vulnerability

No. of

productivity 

attributes 

scored

Prod

data 

151 22 Shortfinned mako shark* Isurus oxyrinchus 1.4 1.9 1.8 10 

152 23 Short bill spearfish* Tetrapturus angustirostris 2.2 1.8 1.1 10 

153 24 Broad billed swordfish* Xiphias gladius 1.8 1.6 1.3 10 154 25 Flathead pomfret* Taractichthys asper 1.7 1.6 1.4 4 

155 26 Dagger pomfret* Taractichthys rubescens 1.5 1.7 1.7 4 

156 27 Sickle pomfret* Taractichthys steindachneri 1.8 2.2 1.6 5 

157 28 Wahoo* Acanthocybium solandri 2.3 2.1 1.3 10 

158 29 Yellowfin tuna Thunnus albacares 2.3 2.0 1.2 10 159 30 Oceanic whitetip shark* Carcharhinus longimanus 1.3 1.6 1.8 10 

160 31 Silky shark* Carcharhinus falciformis 1.3 1.7 1.8 10 

161 32 Common thresher shark * Alopias vulpinus 1.7 1.9 1.6 3 

162 33 Escolar* Lepidocybium flavobrunneum 2.0 1.8 1.3 10 

163 1 Sand tilefish* Malacanthus plumieri 2.1 1.5 1.1 10 

164 2 Rock sea bass* Centropristis philadelphica 2.7 1.7 0.7 10 

165 3 Margate* Haemulon album 2.4 1.8 1.0 10 

166 4 Bar jack* Caranx ruber 2.1 1.4 0.9 10 

* Non-target stocks

HA Pelagic Longline - tuna

South Atlantic and Gulf of Mexico Longline
50

Table 6. Non-parametric statistical analysis of targeted versus non-targeted species among productivity (VEP), s

and vulnerability (VE) scores. 

Fishery Number Productivity Susceptibility Vulnerability

Hawaii longline - Tuna sector 33 0.026 0.373 0.072

Hawaii longline - Swordfish sector 33 0.026 0.153 0.058

Atlantic shark complexes 37 0.150 0.000 0.380

Combined 103 0.752 0.000 0.160

Kruskall-Wallis P values
51

Table 7. Summary of the productivity and susceptibility scoring frequencies and 

correlations to its overall factor/category score. Correlations were based on stock 

attributes scores (1 – 3) compared to a modified categorical score for the stock, which did 

not included the related attribute score. 

No.scored

Frequency 

scored

Pearson 

correlation 

coefficient P value

Productivity

r 128 96% 0.596 0.000

maximum age 126 95% 0.674 0.000

Maximum size 128 96% 0.592 0.000

von Bertalanffy growth coefficient (k ) 129 97% 0.656 0.000

Estimated natural mortality 127 95% 0.785 0.000

Measured fecundity 126 95% 0.509 0.000

Breeding strategy 133 100% 0.568 0.000

Recruitment pattern 84 63% -0.211 0.054

Age at maturity 125 94% 0.802 0.000

Mean trophic level 132 99% 0.439 0.000

Susceptibility

Management

Management strategy 133 100% 0.154 0.077

Fishing rate relative to M 79 59% 0.510 0.000

Biomass of spawners (SSB) or other 

proxies 78 59% 0.389 0.000

Survival after capture and release 126 95% 0.201 0.024

Fishery impact to EFH or habitat in 

general for non-targets 133 100% 0.286 0.001

Catchability

Areal overlap 123 92% 0.333 0.000

Geographic concentration 133 100% 0.345 0.000

Vertical overlap 133 100% 0.772 0.000

Seasonal migrations 49 37% 0.058 0.692

Schooling/Aggregation and other 

behavioral responses 87 65% 0.340 0.001

Morphology affecting capture 132 99% 0.319 0.000

Desirability/Value of the fishery 133 100% 0.504 0.000

Category
52

Table 8. Regression and correlation analysis of our vulnerability analysis compared to (A) fuzzy logic vulnerabi(FishBase.org source) and (B) AFS’ vulnerability scores (Musick 1999). 

(A)

Example application Number

y

vs. FishBase

p y

vs. FishBase

y

vs. FishBase

y

vs. FishBase

p y

vs. FishBase

y

vs. FishBase

California coastal pelagics 8 0.505 0.012 0.103 -0.709 -0.110 0.313

Hawaii longline - Tuna sector 33 0.398 0.014 0.356 -0.631 -0.117 0.599

Hawaii longline - Swordfish sector 33 0.398 0.043 0.343 -0.631 -0.208 0.586

Northeast groundfish 19 0.512 0.013 0.440 -0.716 0.114 0.665

Atlantic shark complexes 37 0.353 0.015 0.093 -0.594 -0.121 0.302

California nearshore groundfish 19 0.445 0.398 0.559 -0.667 0.631 0.742

Bering Sea/Aleutian Islands skates 13 0.137 0.001 0.010 -0.035 0.307 0.307

Combined 162 0.459 0.028 0.234 -0.674 -0.163 -0.484

(B)

Example application Number

y

vs. AFS

p y

vs. AFS

y

vs. AFS

y

vs. AFS

p y

vs. AFS

y

vs. AFS

Hawaii longline - Tuna sector 33 0.815 0.145 0.682 0.903 0.319 -0.827

Hawaii longline - Swordfish sector 33 0.815 0.102 0.682 0.903 0.380 -0.826

Northeast groundfish 19 0.756 0.023 0.425 -0.279 -0.220 0.103

Atlantic shark complexes 37 0.848 0.003 0.439 -0.040 0.120 0.105

California nearshore groundfish 19 0.468 0.234 0.468 0.642 -0.296 -0.568

Bering Sea/Aleutian Islands skates 13 NA NA 0.000 -0.072 -0.196 -0.196

Combined 154 0.494 0.000 0.354 0.737 -0.005 -0.596

Coefficent of determination (R2)

Coefficent of determination (R2)

Pearson's correlation coefficentPearson's correlation coefficent
53

Figure 1. An example of the productivity and susceptibility x-y plot. This plot has been 

modified slightly from Stobutzki et al. (2001b) by reversing the productivity scale to 

begin with 3 (high productivity) instead of 1 (low productivity). 
54

Figure 2. Overall distribution of productivity and susceptibility x-y plot for the 166 

stocks evaluated in this study, as well as the associated data quality of each datum point 

(see Table 5 for reference IDs). 
55

Figure 3. Overall distribution of data quality scores for the productivity and 

susceptibility factors, noting the number of attributes used for each stock (see Table 5 for 

reference IDs). 
56

Figure 4. Northeast Groundfish Multispecies Fishery productivity and susceptibility x-y 

plot (see Table 5 for reference group numbers).
57

Figure 5. Highly Migratory Atlantic Shark Complex productivity and susceptibility x-y 

plot (see Table 5 for reference group numbers). 
58

Figure 6. California Nearshore Groundfish Finfish Assemblage productivity and 

susceptibility x-y plot (see Table 5 for reference group numbers). 
59

Figure 7. California Current Coastal Pelagic Species productivity and susceptibility x-y 

plot (see Table 5 for reference group numbers). 
60

Figure 8. Skates of the Bering Sea and Aleutian Islands Management Area productivity 

and susceptibility x-y plot (see Table 5 for reference group numbers).
61

Figure 9. Hawaii-based Tuna Longline Fishery productivity and susceptibility x-y plot 

(see Table 5 for reference group numbers). 
62

Figure 10. Hawaii-based Swordfish Longline Fishery productivity and susceptibility x-y 

plot (see Table 5 for reference group numbers). 
63

Figure 11. Differences in productivity observed in a subset of forty stocks from the West 

Coast Groundfish Fishery Management Plan, including nearshore (black) and shelf (grey) 

species. 

von-Bertelanffy growth coefficient (K)

0

2

4

6

8

0 0.03 0.06 0.09 0.12 0.15 0.18 0.21 0.24 0.27 0.3

WC_NSG

WC_gfish

Natural Mortality

0

2

4

6

8

0.03 0.06 0.09 0.12 0.15 0.18 0.21 0.24 0.27 0.3

WC_NSG

WC_gfish

Maximum age

0

2

4

6

8

10

0 10 20 30 40 50 60 70 80 90 100

WC_NSG

WC_gfish
64

Figure 12. A subset of the stocks from the example applications (n = 50) for which the 

status (either overfished or undergoing overfishing) could be determined between the 

years of 2000 and 2008. The dashed line references the minimum vulnerability scores 

observed among the 162 stocks evaluated in the example applications. 
65

APPENDICES 

67

Appendix A. The list of marine fish stocks that were considered to be representative of 

U.S. fisheries, and used to help define scoring bins for the following productivity 

attributes: maximum age, maximum size, growth coefficient, natural mortality, and age at 

maturity. 

Number Family name Genus species Common name

1 Acanthuridae Acanthurus bahianus Ocean surgeonfish

2 Alopiidae Alopias superciliosus Bigeye thresher shark

3 Anguillidae Anguilla rostrata American eel

4 Anoplopomatidae Anoplopoma fimbria Sablefish

5 Balistidae Balistes ventula Queen triggerfish

6 Bramidae Brama japonica Pacific pomfret

7 Bramidae Eumegistus illustris Brillant pomfret

8 Bramidae Taractes asper Flathead/Rough pomfret

9 Bramidae Taractichthys steindachneri Sickle Pomfret/Monchong

10 Carangidae Caranx crysos Blue runner

11 Carangidae Seriolda lalandi Amberjack

12 Carangidae Seriola zonata Banded rudderfish

13 Carangidae Trachinotus carolinus Florida pompano

14 Carcharhinidae Prionace glauca Blue shark

15 Carcharhinidae Carcharhinus longimanus Oceanic whitetip shark

16 Carcharhinidae Carcharhinus falciformis Silky shark

17 Cheatodontidae Chaetodon ocellatus Spotfin butterflyfish

18 Clupeidae Sardinops sagax Pacific sardine

19 Clupeidae Clupea harengus harengus Atlantic herring

20 Clupeidae Alosa sapidissima American shad

21 Clupeidae Alosa aestivalis Blueback herring

22 Coryphaenaidea Coryphaena hippurus Mahi mahi/Dolphin fish

23 Cottidae Scorpaenichthys marmoratus Cabezon

24 Engraludae Engraulis mordax Northern anchovy

25 Ephippidae Cheatodipterus faber Atlantic spadefish

26 Gadidae Gadus morhua Atlantic cod

27 Gadidae Melanogrammus aeglefinus Haddock

28 Gadidae Pollachius virens Pollock

29 Gadidae Theragra chalcogramma Walleye Pollock

30 Gempylidae Ruvettus pretiosus Oilfish

31 Gempylidae Promethichthys prometheus Roudi escolar

32 Haemulidae Haemulon plumierii White grunt

33 Haemulidae Anisotremus surinamensis Margate

34 Hexagrammidae Hexagrammos decagrammus Kelp greenling

35 Hexagrammidae Hexagrammos lagocephalus Rock greenling

36 Hexagrammidae Ophiodon elongatus Lingcod

37 Holocentridae Holocentrus rufus Longspine squirrelfish

38 Istophoridae Makaira indica Black marlin

39 Istophoridae Makaira nigricans Blue marlin

40 Istophoridae Istiophorus platypterus Sailfish

41 Istophoridae Tetrapturus angustirostris Short bill spearfish

42 Labridae Semicossyphus pulcher California sheephead

43 Labridae Bodianus rufus Spanish hogfish

44 Lamnidae Isurus paucus Longfin mako shark

45 Lamnidae Lamna ditropis Salmon shark

46 Lamnidae Isurus oxyrinchus Shortfin mako shark

47 Lampridae Lampris guttatus Spotted moonfish

48 Loliginidae Loligo opalescens Market squid

49 Lophiidae Lophius americanus Pacific squid

50 Lutjanidae Lutjanus campechanus Red snapper
68

Appendix A (continued). 

Number Family name Genus species Common name

51 Lutjanidae Rhomboplites aurorubens Vermillion snapper

52 Lutjanidae Ocyurus chrysurus Yellowtail snapper

53 Lutjanidae Lutjanus analis Mutton snapper

54 Lutjanidae Lutjanus synagris Lane snapper

55 Lutjanidae Lutjanus apodus Schoolmaster

56 Lutjanidae Pristipomodies filamentosus Opakapaka/Pink snapper

57 Lutjanidae Etelis cornuscans Onaga/Flame snapper

58 Lutjanidae Etelis carbunculus Ehu/Ruby snapper

59 Lutjanidae Aprion virescens Uku/Grey snapper

60 Malacanthidae Lopholatilus chamaeleonticeps Golden tilefish

61 Megalopidae Megalops atlanticus Tarpon

62 Merluccidae Merluccius productus Pacific whiting

63 Moronidae Morone saxatilis Striped bass

64 Mugilidae Mugil cephalus Striped mullet

65 Mullidae Mullodichthys martinicus Yellow goatfish

66 Osmeridae Osmerus mordax mordax Rainbow smelt

67 Phycidae Urophycis tenuis White hake

68 Pleuronectidae Limanda ferruginea Yellowtail flounder

69 Pleuronectidae Hippoglossoides platessoides American plaice

70 Pleuronectidae Glyptocephalus cynoglossus Witch flounder

71 Pleuronectidae Pseudopleuronectes americanus Winter flounder

72 Pleuronectidae Scophthalmus aquosus Windowpane flounder

73 Pleuronectidae Paralichthys dentatus Summer flounder

74 Pleuronectidae Hippoglossus hippoglossus Halibut

75 Pleuronectidae Atheresthes stomias Arrowtooth flounder

76 Pleuronectidae Microstomus pacificus Dover sole

77 Pleuronectidae Eopsetta jordani Petrale sole

78 Pleuronectidae Platichthys stellatus Starry flounder

79 Polyprionidae Polyprion americanus Wreckfish

80 Pomacanthidae Holacanthus ciliaris Queen angelfish

81 Pomacanthidae Pomacanthus arcuatus Gray angelfish

82 Pomatomidae Pomatomos saltatrix Bluefish

83 Rachycentridae Rachycentron canadum Cobia

84 Rajiidae Bathyraja parmifera Alaska skate

85 Rajiidae Bathyraja aleutica Aleutian skate

86 Rajiidae Bathyraja interrupta Bering skate

87 Rajiidae Raja eglanteria Clearnose skate

88 Rajiidae Dipturus laevis Barndoor skate

89 Salmonidae Salmo salar Atlantic salmon

90 Salmonidae Oncorhynchus tshawytscha Chinook salmon

91 Salmonidae Oncorhynchus keta Chum salmon

92 Scaridae Scarus guacamaia Rainbow parrotfish

93 Scaridae Scarus coelestinus Midnight parrotfish

94 Scaridae Sparisoma viride Stoplight parrotfish

95 Scieanidae Sciaenops ocellatus Red drum

96 Scieanidae Micropogonias undulatus Atlantic croaker

97 Scieanidae Leiostomus xanthurus Spot

98 Scorpaenidae Scorpaena guttata California scorpionfish

99 Scorpaenidae Sebastes maliger Quillback rockfish

100 Scorpaenidae Sebastes caurinus Copper rockfish
69

Appendix A (continued). 

Number Family name Genus species Common name

101 Scorpaenidae Sebastes mystinus Blue rockfish

102 Scorpaenidae Sebastes melanops Black rockfish

103 Scorpaenidae Sebastes carnatus Gopher rockfish

104 Scorpaenidae Sebastes atrovirens Kelp rockfish

105 Scorpaenidae Sebastes viviparus Redfish

106 Scorpaenidae Sebastes flavidus Yelloweye rockfish

107 Scorpaenidae Sebastes paucispinis Bocaccio

108 Scorpaenidae Sebastes crameri Darkblotched rockfish

109 Scorpaenidae Sebastes jordani Shortbelly rockfish

110 Scorpaenidae Sebastes goodei Chilipepper rockfish

111 Scorpaenidae Sebastes levis Cowcod

112 Scorpaenidae Sebastolobus altivelis Longspine thornyhead

113 Scorpaenidae Sebastes alutus Pacific ocean perch

114 Scrombridae Scomber japonicus Pacific mackerel

115 Scrombridae Trachurus symmetricus Jack mackerel

116 Scrombridae Thunnus thynnus Northern bluefin tuna

117 Scrombridae Katsuwonus pelamis Skipjack tuna

118 Scrombridae Acanthocybium solandri Wahoo

119 Scrombridae Thunnus albacares Yellowfin tuna

120 Scrombridae Scomberomorus cavalla King mackerel

121 Scrombridae Thunnus alalunga Albacore

122 Scrombridae Thunnus obesus Bigeye tuna

123 Scrombridae Auxis rochei rochei Bullet tuna

124 Scrombridae Euthynnus affinis Eastern little/Mackerel tuna

125 Serranidae Centropristis striata Black sea bass

126 Serranidae Cephalophols cruentata Graysby

127 Serranidae Epinephelus itajara Jewfish

128 Serranidae Epinephelus striatus Nassau grouper

129 Serranidae Epinephelus adscensionis Rock hind

130 Serranidae Epinephelus quernus Hapuupuu/Hawaiian grouper

131 Serranidae Mycteroperca veneosa Yellowfin grouper

132 Sparidae Stenotomus chrysops Scup

133 Sparidae Lagodon rhomboides Pinfish

134 Sparidae Diplodus holbrookii Spottail pinfish

135 Sparidae Archosargus probatocephalus Sheepshead

136 Sparidae Pagrus pagrus Red porgy/Common seabream

137 Sparidae Calamus bajondao Jolthead porgy

138 Stichaenidae Cebidichthys violaceus Monkyface prickleback

139 Stromateidae Pepriuls triacanthus Butterfish

140 Xiphiidae Xiphias gladius Broad billed swordfish

141 Zoarcidae Gymnelus viridis Ocean pout

71

Appendix B. 

Scoring of productivity and susceptibility attributes for the example applications 

73

Table B1. Scoring of the productivity attributes for the example applications. 

Fishery Stock r Maximum age

Maximum 

size

von 

Bertalanffy 

growth 

coefficient 

(k )

Estimated 

natural 

mortality

Measured 

fecundity

Breeding 

strategy

Recruitment 

pattern

Age at 

maturity

Mean trophic 

level

Shortfin mako 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Blue shark 2.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Common thresher 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Porbeagle 1.0 1.5 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Oceanic whitetip 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Bigeye thresher 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Longfin mako 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Sixgill shark 1.0 1.0 1.0 1.5 1.0 1.0 1.0 1.0 1.0

Sharpnose sevengill shark 1.5 1.0 1.0 1.0

Sandbar shark 1.0 1.5 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Blacktip shark 1.0 2.0 1.0 1.5 1.0 1.0 1.0 1.0 1.0

Spinner shark 1.0 2.0 1.0 1.0 1.5 1.0 1.0 1.0 1.0

Silky shark 1.0 2.0 1.0 1.5 1.0 1.0 1.0 1.0 1.0

Bull shark 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Tiger shark 2.0 2.0 1.0 1.5 1.0 1.0 1.0 1.0 1.0

Nurse shark 2.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0

Lemon shark 1.0 1.5 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Scalloped hammerhead 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Great hammerhead 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Smooth hammerhead 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Dusky shark 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Caribbean reef shark 1.0 1.5 1.0 1.0 1.0 1.0 1.0 1.0

Night shark 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Bignose shark 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Galapagos shark 1.0 2.0 1.0 1.5 1.0 1.0 1.0 1.0 1.0

Sandtiger shark 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Bigeye sandtiger shark 1.0 1.5 1.0 1.0 1.0 1.0 1.0 1.0 1.0

White shark 1.0 1.5 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Basking shark 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0

Whale shark 1.0 1.5 1.0 1.0 1.0 2.0 1.0 1.0 2.0

Atlantic sharpnose shark 2.0 2.5 2.0 3.0 2.0 1.0 1.0 2.0 1.0

Bonnethead shark 2.0 2.5 1.5 3.0 1.5 1.0 1.0 2.0 1.0

Blacknose shark 1.0 2.0 1.0 2.0 1.0 1.0 1.0 2.0 1.0

Finetooth shark 1.0 2.5 1.5 2.0 1.5 1.0 1.0 1.0 1.0

Angel shark 1.0 2.0 1.5 2.0 1.5 1.0 1.0 1.0 1.0

Smalltail shark 1.0 2.5 1.5 1.0 1.5 1.0 1.0 1.0 1.0

Caribbean sharpnose shark 2.0 2.0 2.0 3.0 2.0 1.0 1.0 2.0 1.0

Alaska skate 2.0 2.0 2.0 1.0 1.0 1.0 1.0 3.0 1.0 1.0

Aleutian skate 2.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0

Commander skate 2.0 2.0 2.0 1.0 1.5 1.0 1.0 1.0 1.0

Whiteblotched skate 2.0 2.0 2.0 1.0 1.5 1.0 1.0 1.0 1.0

Whitebrow skate 2.0 2.0 2.0 1.0 1.5 1.0 1.0 1.0 1.0

Roughtail skate 2.0 2.0 2.0 1.0 1.5 1.0 1.0 1.0 1.0

Bering skate 2.0 2.0 2.0 1.0 2.0 1.0 1.0 1.0 1.0

Mud skate 2.0 2.0 2.0 1.0 1.5 1.0 1.0 1.0 1.0

Roughshoulder skate 2.0 2.0 2.0 1.0 1.5 1.0 1.0 1.0 1.0

Big skate 2.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0

Longnose skate 2.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Butterfly skate 2.0 2.0 2.0 1.0 1.5 1.0 1.0 1.0 1.0

Deepsea skate 2.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0

California sheephead 2.0 2.0 2.0 1.0 2.0 1.0 3.0 2.0 1.0 2.0

Cabezon 2.0 2.0 2.0 2.0 2.0 2.0 1.0 2.0 2.0 1.0

Kelp greenling 2.0 2.0 3.0 2.0 2.5 2.0 1.0 1.0 2.0 2.0

Rock greenling 2.0 2.0 3.0 2.0 2.5 2.0 1.0 1.0 2.0 2.0

California scorpionfish 2.0 2.0 3.0 1.0 1.0 2.0 2.0 2.0 2.5 2.0

Monkyface prickelback 2.0 2.0 2.0 1.0 1.0 2.0 2.0 2.0 2.0 1.0

Black rockfish 1.0 1.0 2.0 1.0 1.0 1.0 1.0 2.0 1.0 2.0

Black-and-yellow rockfish 1.0 2.0 3.0 2.0 2.0 1.0 1.0 2.0 2.0 2.0

Blue rockfish 1.0 1.0 3.0 1.5 1.0 1.0 1.0 2.0 1.0 2.0

Brown rockfish 1.0 1.0 2.0 2.0 2.0 1.0 1.0 2.0 2.0 1.0

Calico 1.0 2.0 3.0 1.0 2.0 2.0 1.0 2.0 2.0 2.0

China rockfish 1.0 1.0 3.0 2.0 1.0 1.0 1.0 2.0 1.0 2.0

Copper rockfish 1.0 1.0 1.0 1.5 1.0 1.0 1.0 2.0 1.0 2.0

Gopher rockfish 1.0 2.0 3.0 2.5 2.0 1.0 1.0 2.0 2.0 2.0

Grass rockfish 1.0 2.0 2.0 1.0 1.0 1.0 1.0 2.0 2.0 2.0

Kelp rockfish 1.0 2.0 3.0 3.0 2.0 1.0 1.0 2.0 1.5 2.0

Olive rockfish 1.0 1.0 2.0 1.5 1.0 1.0 1.0 2.0 1.5 2.0

Quillback rockfish 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 2.0

Treefish rockfish 1.0 2.0 3.0 2.0 2.0 1.0 1.0 2.0 2.0 2.0

Pacific sardine 2.0 2.5 3.0 3.0 2.5 3.0 2.5 1.5 2.5 2.5

Northern anchovy 2.5 3.0 3.0 3.0 3.0 3.0 3.0 2.0 3.0 2.5

Pacific mackerel 1.5 2.0 2.5 3.0 3.0 3.0 2.5 1.0 2.0 1.5

Jack mackerel 2.0 2.0 2.0 1.0 2.0 3.0 3.0 1.0 3.0 2.0

Atlantic shark complexes

California coastal pelagics

Bering Sea/Aleutian Islands 

skate complex

California nearshore 

groundfish
74

Table B1 (continued). 

Fishery Stock r

Maximum 

Age

Maximum 

Size

von 

Bertalanffy 

Growth 

Coefficient (k)

Estimated 

Natural 

Mortality

Measured 

Fecundity

Breeding 

Strategy

Recruitment 

Pattern

Age at 

Maturity

Mean Trophic 

Level

Market squid 3.0 3.0 3.0 3.0 3.0 2.5 2.5 1.0 3.0 2.0

Pacific herring 2.0 2.0 2.0 3.0 3.0 3.0 3.0 2.0 2.0 2.0

Pacific bonito 2.0 3.0 2.0 3.0 3.0 3.0 3.0 2.0 3.0 1.0

Pacific saury 2.0 3.0 3.0 3.0 3.0 3.0 3.0 2.0 3.0 2.0

Albacore 2.0 2.0 2.0 2.0 2.0 3.0 1.0 2.0 2.0 1.0

Bigeye tuna 2.0 3.0 1.0 2.0 2.0 3.0 1.0 2.0 2.0 1.0

Black marlin 3.0 3.0 1.0 1.0 1.0 3.0 1.0 2.0 2.0 1.0

Bullet tuna 3.0 3.0 2.0 3.0 3.0 3.0 1.0 2.0 2.0 1.0

Pacific pomfret 3.0 3.0 2.0 2.0 3.0 1.0 2.0 3.0 1.0

Blue shark 2.0 2.0 1.0 2.0 2.0 1.0 1.0 2.0 1.0 1.0

Bigeye thresher shark 2.0 2.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 1.0

Blue marlin 3.0 2.0 1.0 2.0 1.0 3.0 1.0 2.0 2.0 1.0

Dolphin fish (mahi mahi) 3.0 3.0 1.0 3.0 3.0 3.0 1.0 2.0 3.0 1.0

Brilliant pomfret 2.0 1.0 2.0 2.0

Kawakawa 3.0 3.0 2.0 3.0 3.0 3.0 1.0 2.0 2.0 1.0

Spotted moonfish 2.0 1.0 2.0 1.0 2.0 1.0

Longfin mako shark 2.0 2.0 1.0 2.0 1.0 1.0 1.0 2.0 1.0 1.0

Salmon shark 2.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0

Striped marlin 1.0 3.0 1.0 3.0 3.0 3.0 1.0 2.0 2.0 1.0

Oilfish 3.0 2.0 1.0 3.0 3.0 3.0 1.0 2.0 1.0 1.0

Northern bluefin tuna 2.0 2.0 1.0 1.0 2.0 3.0 1.0 2.0 2.0 1.0

Roudi escolar 3.0 2.0 3.0 2.0 3.0 3.0 1.0 2.0 1.0 1.0

Pelagic thresher shark 3.0 2.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 1.0

Sailfish 3.0 2.0 1.0 1.0 3.0 3.0 1.0 2.0 2.0 1.0

Skipjack tuna 3.0 3.0 2.0 3.0 3.0 3.0 1.0 2.0 3.0 1.0

Shortfin mako shark 2.0 2.0 1.0 2.0 1.0 1.0 1.0 2.0 1.0 1.0

Short bill spearfish 3.0 3.0 1.0 3.0 3.0 3.0 1.0 2.0 2.0 1.0

Broadbill swordfish 2.0 2.0 1.0 3.0 2.0 3.0 1.0 2.0 1.0 1.0

Flatheat pomfret 2.0 1.0 2.0 2.0

Dagger pomfret 2.0 1.0 2.0 1.0

Sickle pomfret 3.0 2.0 1.0 2.0 1.0

Wahoo 3.0 3.0 2.0 2.0 3.0 3.0 1.0 2.0 3.0 1.0

Yellowfin tuna 3.0 3.0 2.0 3.0 3.0 3.0 1.0 2.0 2.0 1.0

Oceanic whitetip shark 2.0 2.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0

Silky shark 2.0 2.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0

Common thresher shark 2.0 1.0 2.0

Escolar 3.0 2.0 2.0 2.0 3.0 3.0 1.0 2.0 1.0 1.0

GM cod 2.0 3.0 3.0 2.0 2.0 1.0 3.0 2.0 2.0 3.0

GB cod 2.0 3.0 3.0 2.0 2.0 1.0 3.0 2.0 2.0 3.0

GM haddock 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0

GB haddock 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0

Redfish 3.0 3.0 2.0 3.0 3.0 1.0 3.0 2.0 3.0 2.0

Pollock 2.0 3.0 3.0 2.0 2.0 1.0 3.0 2.0 2.0 3.0

CC-GM yellowtail flounder 2.0 3.0 2.0 2.0 2.0 1.0 3.0 2.0 2.0 2.0

GB yellowtail flounder 2.0 3.0 2.0 2.0 2.0 1.0 3.0 2.0 2.0 2.0

SNE yellowtail flounder 2.0 3.0 2.0 2.0 2.0 1.0 3.0 2.0 2.0 2.0

American plaice 2.0 3.0 2.0 2.0 2.0 1.0 3.0 2.0 3.0 2.0

Witch flounder 2.0 3.0 2.0 2.0 2.0 1.0 3.0 2.0 3.0 2.0

GM winter flounder 2.0 2.0 2.0 2.0 2.0 1.0 3.0 2.0 2.0 2.0

GB winter flounder 2.0 2.0 2.0 2.0 2.0 1.0 3.0 2.0 2.0 2.0

SNE-MA winter flouder 2.0 2.0 2.0 2.0 2.0 1.0 3.0 2.0 2.0 2.0

GM-GB windowpane 2.0 2.0 2.0 2.0 2.0 1.0 3.0 2.0 2.0 2.0

SNE-MA windowpane 2.0 2.0 2.0 2.0 2.0 1.0 3.0 2.0 2.0 2.0

Ocean pout 2.0 3.0 3.0 2.0 2.0 2.0 3.0 3.0 2.5 2.0

White hake 3.0 3.0 3.0 2.0 2.0 1.0 3.0 2.0 3.0 3.0

Halibut 3.0 3.0 3.0 2.0 3.0 1.0 3.0 2.0 3.0 3.0

Sand tilefish 3.0 2.0 2.0 1.5 2.0 3.0 3.0 2.0 1.0 1.0

Bar jack 3.0 2.0 2.5 1.5 2.0 3.0 3.0 2.0 1.5 1.0

Rock sea bass 3.0 3.0 3.0 3.0 3.0 2.5 3.0 2.0 2.5 2.0

Margate 3.0 2.5 2.5 2.0 2.0 3.0 3.0 2.0 2.0 2.0

Weights

 Atlantic sharks - 4, 2, 1, 2, 3, 3, 2, 0, 2

 Bering Sea/Aluetian Islands Skates - 2, 2, 2, 2, 2, 2, 2, 1, 2, 2

 California nearshore groundfish - 2, 2, 2, 2, 2, 2, 2, 2, 2, 2

 California coastal pelagics - 2, 2, 2, 2, 2, 2, 2, 2, 2, 2

 Hawaii longline - both sectors - 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 

 Northeast groundfish - 2, 2, 2, 2, 2, 2, 2, 2, 2, 2

 South Atlantic and Gulf of Mexico snapper-grouper bottom longline - 4, 2, 2, 2, 2, 2, 2, 2, 3, 2

South Atlantic and Gulf of 

Mexico bottom longline 

fishery

California coastal pelagics

Hawaii longline fishery - both 

sectors

Northeast groundfish
75

Table B2. Scoring of the susceptibility attributes for the example applications. 

Fishery Stock

Management 

strategy Areal overlap

Geographic 

concentration

Vertical 

overlap

Fishing rate 

relative to M

Biomass of 

spawners 

(SSB) or other 

proxies

Seasonal 

migrations

Schooling/ 

Aggregation 

and other 

behavioral responses

Morphology 

affecting 

capture

Survival after 

capture and 

release

Desirability/ Value of the 

fishery

Fishery impact 

to EFH or 

habitat in 

general for nontargets

Shortfin mako 2.0 2.0 1.0 3.0 2.0 2.0 3.0 3.0 3.0 1.0

Blue shark 2.0 2.0 1.0 3.0 1.0 1.0 2.0 3.0 1.0 3.0 1.0

Common thresher 2.0 3.0 1.0 3.0 3.0 2.0 1.0 1.0

Porbeagle 2.0 2.0 2.0 3.0 1.0 3.0 3.0 1.0 3.0 1.0

Oceanic whitetip 2.0 2.0 1.0 3.0 3.0 2.0 3.0 1.0

Bigeye thresher 1.0 3.0 2.0 3.0 3.0 2.0 3.0 1.0

Longfin mako 1.0 3.0 1.0 3.0 3.0 2.0 3.0 1.0

Sixgill shark 1.0 3.0 1.0 1.0 1.0 1.0 1.0 1.0

Sharpnose sevengill shark 1.0 3.0 1.0 1.0 1.0 1.0 1.0

Sandbar shark 1.0 3.0 1.0 3.0 2.0 2.0 3.0 3.0 3.0 3.0 1.0

Blacktip shark 2.0 3.0 1.0 3.0 2.0 1.0 3.0 3.0 3.0 3.0 1.0

Spinner shark 2.0 3.0 1.0 3.0 3.0 3.0 3.0 1.0

Silky shark 2.0 3.0 3.0 3.0 3.0 3.0 3.0 1.0

Bull shark 2.0 3.0 1.0 3.0 3.0 3.0 3.0 1.0

Tiger shark 2.0 2.0 2.0 3.0 3.0 2.0 3.0 1.0

Nurse shark 2.0 2.0 1.0 2.0 3.0 1.0 3.0 1.0

Lemon shark 2.0 2.0 1.0 2.0 3.0 3.0 3.0 1.0

Scalloped hammerhead 2.0 3.0 1.0 3.0 2.0 1.0 3.0 3.0 3.0 3.0 1.0

Great hammerhead 2.0 3.0 1.0 3.0 3.0 3.0 3.0 1.0

Smooth hammerhead 2.0 1.0 2.0 3.0 3.0 3.0 3.0 1.0

Dusky shark 1.0 3.0 1.0 3.0 1.0 1.0 3.0 2.0 3.0 1.0

Caribbean reef shark 1.0 1.0 3.0 3.0 3.0 3.0 3.0 3.0 1.0

Night shark 1.0 3.0 1.0 3.0 3.0 3.0 3.0 1.0

Bignose shark 1.0 3.0 1.0 3.0 3.0 3.0 3.0 1.0

Galapagos shark 1.0 1.0 1.0 3.0 3.0 3.0 1.0

Sandtiger shark 1.0 3.0 1.0 2.0 3.0 1.0 1.0 3.0 1.0

Bigeye sandtiger shark 1.0 1.0 3.0 1.0 3.0 1.0 3.0 1.0

White shark 1.0 1.0 1.0 2.0 3.0 3.0 1.0

Basking shark 1.0 1.0 1.0 3.0 3.0 1.0 3.0 1.0

Whale shark 1.0 1.0 1.0 3.0 1.0 3.0 1.0

Atlantic sharpnose shark 2.0 3.0 1.0 3.0 3.0 3.0 3.0 3.0 3.0 1.0

Bonnethead shark 2.0 3.0 1.0 3.0 2.0 3.0 3.0 3.0 3.0 1.0

Blacknose shark 2.0 3.0 1.0 3.0 1.0 1.0 3.0 3.0 3.0 1.0

Finetooth shark 2.0 3.0 1.0 3.0 3.0 3.0 1.0 3.0 3.0 1.0

Angel shark 1.0 3.0 1.0 1.0 1.0 3.0 1.0

Smalltail shark 1.0 1.0 3.0 3.0 1.0 3.0 1.0

Caribbean sharpnose shark 1.0 1.0 1.0 3.0 1.0 3.0 1.0

Alaska skate 1.0 1.0 3.0 1.0 3.0 3.0 1.0 2.0 1.0 3.0

Aleutian skate 1.0 1.0 1.0 1.0 2.0 3.0 1.0 2.0 1.0 3.0

Commander skate 1.0 3.0 1.0 1.0 2.0 3.0 1.0 2.0 1.0 3.0

Whiteblotched skate 1.0 3.0 1.0 1.0 2.0 3.0 1.0 2.0 1.0 3.0

Whitebrow skate 1.0 3.0 1.0 1.0 2.0 3.0 1.0 2.0 1.0 3.0

Roughtail skate 1.0 3.0 1.0 1.0 2.0 3.0 1.0 2.0 1.0 3.0

Bering skate 1.0 1.0 1.0 1.0 2.0 3.0 1.0 2.0 1.0 3.0

Mud skate 1.0 3.0 1.0 1.0 2.0 3.0 1.0 2.0 1.0 3.0

Roughshoulder skate 1.0 1.0 1.0 2.0 3.0 1.0 2.0 1.0 3.0

Big skate 1.0 1.0 1.0 1.0 2.0 3.0 1.0 2.0 1.0 3.0

Longnose skate 1.0 1.0 1.0 2.0 3.0 1.0 2.0 1.0 3.0

Butterfly skate 1.0 1.0 1.0 1.0 2.0 3.0 1.0 2.0 1.0 3.0

Deepsea skate 1.0 3.0 1.0 1.0 2.0 3.0 1.0 2.0 1.0 3.0

Atlantic shark complexes

Bering Sea/Aleutian Island Skate 

Complex
76

Table B2 (continued). 

Fishery Stock

Management 

strategy Areal overlap

Geographic 

concentration

Vertical 

overlap

Fishing rate 

relative to M

Biomass of 

spawners 

(SSB) or other 

proxies

Seasonal 

migrations

Schooling/ 

Aggregation 

and other 

behavioral responses

Morphology 

affecting 

capture

Survival after 

capture and 

release

Desirability/ Value of the 

fishery

Fishery impact 

to EFH or 

habitat in 

general for nontargets

California sheephead 2.0 3.0 1.0 3.0 2.0 3.0 2.0 3.0 3.0 1.0 2.0 2.0

Cabezon 1.0 3.0 3.0 3.0 2.0 2.0 2.0 3.0 3.0 1.0 2.0 2.0

Kelp greenling 1.0 3.0 3.0 3.0 1.0 1.0 2.0 2.0 3.0 1.0 2.5 2.0

Rock greenling 2.0 3.0 3.0 3.0 1.0 1.0 2.0 2.0 3.0 1.0 2.0 2.0

California scorpionfish 1.0 3.0 3.0 3.0 1.0 1.0 2.0 2.0 2.0 1.0 1.5 1.0

Monkyface prickelback 2.0 3.0 3.0 3.0 2.0 1.0 2.0 1.0 3.0 1.0 1.5 2.0

Black rockfish 1.0 3.0 3.0 3.0 2.0 1.0 2.0 3.0 3.0 2.0 1.0 2.0

Black-and-yellow rockfish 2.0 3.0 3.0 3.0 1.0 1.0 2.0 3.0 3.0 2.0 2.5 2.0

Blue rockfish 1.0 3.0 3.0 3.0 2.0 2.0 2.0 3.0 3.0 2.5 1.0 2.0

Brown rockfish 2.0 3.0 3.0 3.0 2.0 1.0 2.0 3.0 3.0 2.0 2.5 2.0

Calico 2.0 3.0 3.0 3.0 1.0 1.0 2.0 2.0 1.0 3.0 1.0 2.0

China rockfish 2.0 3.0 3.0 3.0 2.0 1.0 2.0 3.0 3.0 2.5 2.5 2.0

Copper rockfish 2.0 3.0 3.0 3.0 2.0 1.0 2.0 3.0 3.0 2.5 2.0 2.0

Gopher rockfish 1.0 3.0 3.0 3.0 1.0 1.0 2.0 3.0 3.0 2.0 2.5 2.0

Grass rockfish 2.0 3.0 3.0 3.0 2.0 1.0 2.0 1.0 3.0 1.0 3.0 2.0

Kelp rockfish 2.0 3.0 3.0 3.0 1.0 1.0 2.0 1.0 2.0 1.5 2.5 2.0

Olive rockfish 2.0 3.0 3.0 3.0 2.0 1.0 2.0 3.0 3.0 2.0 1.0 2.0

Quillback rockfish 2.0 3.0 3.0 3.0 2.0 1.0 2.0 3.0 3.0 2.5 2.0 2.0

Treefish rockfish 2.0 3.0 3.0 3.0 1.0 1.0 2.0 3.0 3.0 2.0 3.0 2.0

Pacific sardine 1.0 2.0 3.0 1.5 2.0 2.0 3.0 3.0 3.0 2.0 1.0

Northern anchovy 2.0 2.0 3.0 1.0 2.0 2.0 3.0 3.0 3.0 1.0 1.0

Pacific mackerel 1.0 2.0 3.0 2.0 2.0 2.0 3.0 3.0 3.0 2.0 1.0

Jack mackerel 2.0 2.0 2.0 1.0 1.0 2.0 3.0 3.0 3.0 1.0 1.0

Market squid 2.0 2.0 3.0 2.5 2.0 3.0 3.0 3.0 2.0 2.0 1.0

Pacific herring 2.0 2.0 3.0 2.5 3.0 3.0 3.0 3.0 3.0 2.0 1.0

Pacific bonito 2.0 2.0 3.0 2.0 2.0 1.0 3.0 3.0 3.0 2.0 1.0

Pacific saury 2.0 2.0 2.0 1.0 2.0 2.0 3.0 2.0 3.0 1.0 1.0

Albacore 1.0 1.0 1.0 2.0 3.0 1.0 2.0 3.0 3.0 2.0 1.0

Bigeye tuna 1.0 1.0 1.0 2.0 3.0 2.0 2.0 3.0 3.0 3.0 1.0

Black marlin 1.0 1.0 1.0 2.0 2.0 2.0 3.0 1.0 1.0

Bullet tuna 2.0 1.0 1.0 2.0 2.0 1.0 3.0 1.0 1.0

Pacific pomfret 2.0 1.0 1.0 2.0 2.0 3.0 2.0 1.0 1.0

Blue shark 1.0 1.0 1.0 3.0 3.0 1.0 2.0 3.0 1.0 1.0 1.0

Bigeye thresher shark 2.0 1.0 1.0 2.0 2.0 3.0 2.0 1.0 1.0

Blue marlin 1.0 1.0 1.0 2.0 2.0 1.0 2.0 3.0 3.0 1.0 1.0

Dolphin fish (mahi mahi) 1.0 1.0 1.0 2.0 2.0 3.0 3.0 2.0 1.0

Brilliant pomfret 2.0 1.0 3.0 2.0 2.0 3.0 3.0 2.0 1.0

Kawakawa 2.0 1.0 1.0 2.0 2.0 1.0 3.0 1.0 1.0

Spotted moonfish 1.0 1.0 1.0 2.0 2.0 3.0 3.0 2.0 1.0

Longfin mako shark 2.0 1.0 1.0 2.0 2.0 3.0 1.0 1.0 1.0

Salmon shark 2.0 1.0 1.0 2.0 2.0 3.0 3.0 1.0 1.0

Striped marlin 1.0 1.0 1.0 2.0 3.0 2.0 3.0 3.0 2.0 1.0

Oilfish 2.0 1.0 1.0 2.0 2.0 3.0 2.0 2.0 1.0

Northern bluefin tuna 1.0 1.0 1.0 3.0 3.0 2.0 2.0 3.0 3.0 2.0 1.0

Roudi escolar 2.0 1.0 1.0 2.0 2.0 3.0 2.0 1.0 1.0

Pelagic thresher shark 2.0 1.0 1.0 1.0 2.0 3.0 2.0 1.0 1.0

Sailfish 1.0 1.0 1.0 2.0 2.0 3.0 3.0 1.0 1.0

Skipjack tuna 1.0 1.0 1.0 2.0 1.0 1.0 2.0 3.0 3.0 2.0 1.0

Shortfin mako shark 2.0 1.0 1.0 2.0 2.0 3.0 1.0 1.0 1.0

Short bill spearfish 1.0 1.0 1.0 2.0 2.0 3.0 3.0 1.0 1.0

California coastal pelagics

Hawaii longline fishery - 

Swordfish sector

California nearshore groundifsh
77

Table B2 (continued). 

Fishery Stock

Management 

strategy Areal overlap

Geographic 

concentration

Vertical 

overlap

Fishing rate 

relative to M

Biomass of 

spawners 

(SSB) or other 

proxies

Seasonal 

migrations

Schooling/ 

Aggregation 

and other 

behavioral responses

Morphology 

affecting 

capture

Survival after 

capture and 

release

Desirability/ 

Value of the 

fishery

Fishery impact 

to EFH or 

habitat in 

general for nontargets

Broadbill swordfish 1.0 1.0 1.0 2.0 1.0 1.0 2.0 3.0 3.0 1.0 1.0

Flatheat pomfret 2.0 1.0 1.0 2.0 2.0 3.0 1.0 1.0 1.0

Dagger pomfret 2.0 1.0 1.0 2.0 2.0 3.0 2.0 1.0 1.0

Sickle pomfret 2.0 1.0 1.0 3.0 2.0 3.0 3.0 2.0 1.0

Wahoo 1.0 1.0 1.0 2.0 2.0 3.0 3.0 2.0 1.0

Yellowfin tuna 1.0 1.0 1.0 2.0 2.0 1.0 2.0 3.0 3.0 2.0 1.0

Oceanic whitetip shark 1.0 1.0 1.0 2.0 2.0 3.0 1.0 1.0 1.0

Silky shark 2.0 1.0 1.0 2.0 2.0 3.0 1.0 1.0 1.0

Common thresher shark 2.0 1.0 1.0 2.0 2.0 3.0 2.0 1.0 1.0

Escolar 2.0 1.0 1.0 2.0 2.0 3.0 2.0 1.5 1.0

Albacore 1.0 1.0 1.0 3.0 3.0 1.0 2.0 3.0 3.0 2.0 1.0

Bigeye tuna 1.0 1.0 1.0 2.0 3.0 2.0 2.0 3.0 3.0 3.0 1.0

Black marlin 1.0 1.0 1.0 3.0 2.0 2.0 3.0 1.0 1.0

Bullet tuna 2.0 1.0 1.0 2.0 2.0 1.0 3.0 1.0 1.0

Pacific pomfret 2.0 1.0 1.0 3.0 2.0 3.0 2.0 1.0 1.0

Blue shark 1.0 1.0 1.0 3.0 3.0 1.0 2.0 3.0 1.0 1.0 1.0

Bigeye thresher shark 2.0 1.0 1.0 2.0 2.0 3.0 1.0 1.0 1.0

Blue marlin 1.0 1.0 1.0 3.0 2.0 1.0 2.0 3.0 3.0 1.0 1.0

Dolphin fish (mahi mahi) 1.0 1.0 1.0 2.0 2.0 3.0 3.0 2.0 1.0

Brilliant pomfret 2.0 1.0 3.0 3.0 2.0 3.0 3.0 2.0 1.0

Kawakawa 2.0 1.0 1.0 2.0 2.0 1.0 3.0 1.0 1.0

Spotted moonfish 1.0 1.0 1.0 3.0 2.0 3.0 3.0 2.0 1.0

Longfin mako shark 2.0 1.0 1.0 3.0 2.0 3.0 2.0 1.0 1.0

Salmon shark 2.0 1.0 1.0 2.0 2.0 3.0 2.0 1.0 1.0

Striped marlin 1.0 1.0 1.0 3.0 3.0 2.0 3.0 1.0 2.0 1.0

Oilfish 2.0 1.0 1.0 1.0 2.0 3.0 3.0 2.0 1.0

Northern bluefin tuna 1.0 1.0 1.0 2.0 3.0 2.0 2.0 3.0 3.0 2.0 1.0

Roudi escolar 2.0 1.0 1.0 3.0 2.0 3.0 2.0 1.0 1.0

Pelagic thresher shark 2.0 1.0 1.0 3.0 2.0 3.0 2.0 1.0 1.0

Sailfish 1.0 1.0 1.0 2.0 2.0 3.0 3.0 1.0 1.0

Skipjack tuna 1.0 1.0 1.0 3.0 1.0 1.0 2.0 3.0 3.0 2.0 1.0

Shortfin mako shark 2.0 1.0 1.0 3.0 2.0 3.0 2.0 1.0 1.0

Short bill spearfish 1.0 1.0 1.0 2.0 2.0 3.0 3.0 1.0 1.0

Broadbill swordfish 1.0 1.0 1.0 1.0 1.0 1.0 2.0 3.0 3.0 1.0 1.0

Flatheat pomfret 2.0 1.0 1.0 3.0 2.0 3.0 1.0 1.0 1.0

Dagger pomfret 2.0 1.0 1.0 3.0 2.0 3.0 1.0 1.0 1.0

Sickle pomfret 2.0 1.0 1.0 3.0 2.0 3.0 3.0 2.0 1.0

Wahoo 1.0 1.0 1.0 3.0 2.0 3.0 3.0 2.0 1.0

Yellowfin tuna 1.0 1.0 1.0 3.0 2.0 1.0 2.0 3.0 3.0 2.0 1.0

Oceanic whitetip shark 1.0 1.0 1.0 3.0 2.0 3.0 1.0 1.0 1.0

Silky shark 2.0 1.0 1.0 3.0 2.0 3.0 1.0 1.0 1.0

Common thresher shark 2.0 1.0 1.0 3.0 2.0 3.0 2.0 1.0 1.0

Escolar 2.0 1.0 1.0 2.0 2.0 3.0 2.5 1.5 1.0

GM cod 2.0 3.0 2.0 3.0 3.0 2.0 2.0 2.0 2.0 3.0 3.0 3.0

GB cod 2.0 3.0 2.0 3.0 3.0 3.0 2.0 2.0 2.0 3.0 3.0 3.0

GM haddock 2.0 3.0 2.0 3.0 2.0 2.0 2.0 2.0 2.0 3.0 3.0 3.0

GB haddock 2.0 3.0 2.0 3.0 2.0 3.0 2.0 2.0 2.0 3.0 3.0 3.0

Redfish 2.0 3.0 2.0 3.0 2.0 1.5 2.0 2.0 2.0 3.0 2.0 3.0

Pollock 2.0 3.0 2.0 3.0 2.0 2.0 2.0 2.0 2.0 3.0 2.0 3.0

CC-GM yellowtail flounder 2.0 3.0 2.0 3.0 3.0 3.0 2.0 2.0 2.0 3.0 3.0 3.0

Hawaii longline fishery - 

Swordfish sector

Northeast groundfish

Hawaii longline fishery - Tuna 

sector
78

Table B2 (continued). 

Fishery Stock

Management 

strategy Areal overlap

Geographic 

concentration

Vertical 

overlap

Fishing rate 

relative to M

Biomass of 

spawners 

(SSB) or other 

proxies

Seasonal 

migrations

Schooling/ 

Aggregation 

and other 

behavioral responses

Morphology 

affecting 

capture

Survival after 

capture and 

release

Desirability/ Value of the 

fishery

Fishery impact 

to EFH or 

habitat in 

general for nontargets

GB yellowtail flounder 2.0 3.0 2.0 3.0 3.0 2.0 2.0 2.0 2.0 3.0 3.0 3.0

SNE yellowtail flounder 2.0 3.0 2.0 3.0 3.0 3.0 2.0 2.0 2.0 3.0 3.0 3.0

American plaice 2.0 3.0 2.0 3.0 2.0 1.0 2.0 2.0 2.0 3.0 2.0 3.0

Witch flounder 2.0 3.0 2.0 3.0 3.0 2.0 2.0 2.0 2.0 3.0 3.0 3.0

GM winter flounder 2.0 3.0 2.0 3.0 3.0 2.0 2.0 2.0 2.0 3.0 3.0 3.0

GB winter flounder 2.0 3.0 2.0 3.0 3.0 2.0 2.0 2.0 2.0 3.0 3.0 3.0

SNE-MA winter flouder 2.0 3.0 2.0 3.0 3.0 2.0 2.0 2.0 2.0 3.0 3.0 3.0

GM-GB windowpane 2.0 3.0 2.0 3.0 2.0 2.0 2.0 2.0 2.0 3.0 1.0 3.0

SNE-MA windowpane 2.0 3.0 2.0 3.0 2.0 2.0 2.0 2.0 2.0 3.0 1.0 3.0

Ocean pout 2.0 3.0 2.0 3.0 2.0 2.0 2.0 2.0 2.0 3.0 1.0 3.0

White hake 2.0 3.0 2.0 3.0 3.0 2.0 2.0 2.0 2.0 3.0 2.0 3.0

Halibut 2.0 3.0 2.0 3.0 3.0 3.0 2.0 2.0 2.0 3.0 3.0 3.0

Sand tilefish 3.0 1.0 1.0 1.0 2.0 2.0 2.5 1.0 1.5

Bar jack 3.0 1.0 1.0 1.0 2.0 1.5 2.5 1.0 1.5

Rock sea bass 3.0 1.0 1.0 1.0 2.0 2.0 3.0 1.0 1.5

Margate 3.0 1.0 1.0 2.0 2.0 2.0 3.0 1.0 1.5

Weights Atlantic sharks - 2, 4, 2, 4, 3, 3, 2, 2, 3, 4, 2, 2 Bering Sea and Aluetian Islands skates - 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 California nearshore groundfish - 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 California coastal pelagics - 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 Hawaii longline - both sectors - 1, 2, 1, 3, 1, 1, 0, 2, 1, 3, 2, 1 Northeast groundfish - 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 South Atlantic and Gulf of Mexico snapper-grouper bottom longline - 2, 4, 2, 3, 2, 2, 1, 2, 2, 2, 2, 1

Northeast groundfish

South Atlantic and Gulf of 

Mexico snapper-grouper bottom 

longline
79

Appendix C. 

Data quality plots for the example applications. 

81

Figure C1. Data quality plot for the Northeast Groundfish Multispecies Fishery. 
82

Figure C2. Data quality plot for the Highly Migratory Atlantic Shark Complex. 
83

Figure C3. Data quality plot for the California Nearshore Groundfish Finfish Assemblage. 
84

Figure C4. Data quality plot for California Current Coastal Pelagic Species. 
85

Figure C5. Data quality plot for the Skates of the Bering Sea and Aleutian Islands Management 

Area. 
86

Figure C6. Data quality plot for the Hawaii-based Tuna Longline Fishery. 
87

Figure C7. Data quality plot for the Hawaii-based Swordfish Longline Fishery. 
88

Figure C8. Data quality plot for the four non-target species captured in the South Atlantic/Gulf 

of Mexico Snapper-Grouper Bottom Longline Fishery. 
89

Appendix D. A subset of the stocks from the example applications for which status 

determinations could be made between the years of 2000 and 2008. 

ID Fishery Stock Productivity Susceptibility Vulnerability Overfishing Overfished

14 Dusky shark 1.04 2.06 2.23 Y Y

15 Porbeagle 1.04 2.14 2.27 N Y

18 Blacknose shark 1.28 2.29 2.15 Y Y

25 Blacktip shark 1.16 2.43 2.33 N N

27 Sandbar shark 1.04 2.42 2.42 Y Y

29 Finetooth shark 1.31 2.47 2.24 Y N 

32 Bonnethead shark 1.71 2.55 2.01 N N

36 Atlantic sharpnose shark 1.84 2.63 2.00 N N

52 Cabezon 2.03 2.24 1.57 N

53 Kelp greenling 2.03 2.07 1.45 N N

55 California scorpionfish 2.03 1.79 1.25 N

57 Black rockfish 1.41 2.20 1.99 Y N

64 Gopher rockfish 1.98 2.24 1.61 N

70 Pacific sardine 2.48 2.09 1.21 N N

71 Northern anchovy 2.77 2.13 1.15 N

72 Pacific mackerel 2.16 2.20 1.47 N N

73 Jack mackerel 2.07 1.91 1.30 N

78 Gulf of Maine cod 2.26 2.53 1.70 Y Y

79 Georges Bank cod 2.33 2.58 1.71 Y Y

80 Gulf of Maine haddock 2.01 2.44 1.75 N Y

81 Georges Bank haddock 1.98 2.49 1.80 N Y

82 Redfish 2.50 2.32 1.42 N N

83 Pollock 2.28 2.36 1.54 N N

84 Cape Cod/Gulf of Maine yellowtail flounder 2.11 2.56 1.79 Y Y

85 Georges Bank yellowtail flounder 2.13 2.54 1.76 Y Y

86 Southern New England yellowtail flounder 2.10 2.58 1.82 Y Y

87 American plaice 2.23 2.26 1.48 N Y

88 Witch flounder 2.18 2.46 1.67 N N

89 Gulf of Maine winter flounder 1.97 2.50 1.82 Y N

90 Georges Bank winter flounder 2.05 2.49 1.77 Y N

91 Southern New England/Mid-Atlantic winter flounder 1.96 2.47 1.80 Y Y

92 Gulf of Maine/Georges Bank windowpane 1.98 2.24 1.60 N N

93 Southern New EnglandMid-Atlantic windowpane 2.02 2.24 1.58 N Y

94 Ocean pout 2.49 2.29 1.39 N Y

95 White hake 2.52 2.37 1.45 Y Y

96 Atlantic halibut 2.63 2.61 1.65 Y

97 Albacore 1.92 1.99 1.46 N N

98 Bigeye tuna 1.95 2.10 1.52 Y N

102 Blue shark 1.51 1.71 1.65 N N

104 Blue marlin 1.77 1.77 1.45 N N

117 Skipjack tuna 2.41 1.85 1.04 N N

120 Broad billed swordfish 1.84 1.68 1.35 N N

125 Yellowfin tuna 2.29 1.94 1.18 Y N

2000 - 2008 stock status

Hawaii pelagic longline - 

Swordfish

CA Current Pelagics

CA Nearshore Groundfish

NE Groundfish
90

Appendix D (continued). 

ID Fishery Stock Productivity Susceptibility Vulnerability Overfishing Overfished

130 Albacore 1.91 2.14 1.57 N N

131 Bigeye tuna 1.85 2.08 1.58 Y N

135 Blue shark 1.49 1.64 1.64 N N

137 Blue marlin 1.77 1.93 1.54 N N

150 Skipjack tuna 2.44 2.04 1.18 N N

153 Broad billed swordfish 1.81 1.58 1.33 N N

158 Yellowfin tuna 2.31 2.01 1.23 Y N

Hawaii pelagic longline - Tuna

2000 - 2008 stock status